Search results for: Artificial Bee Colony algorithm

Authors: Gus Calderon, Richard McCreight, Tammy Schwartz

Abstract:

Forensic analysis of community wildfire destruction in California has shown that reducing or removing flammable vegetation in proximity to buildings and structures is one of the most important wildfire defenses available to homeowners. State laws specify the requirements for homeowners to create and maintain defensible space around all structures. Unfortunately, this decades-long effort had limited success due to noncompliance and minimal enforcement. As a result, vulnerable communities continue to experience escalating human and economic costs along the wildland-urban interface (WUI). Quantifying vegetative fuels at both the community and parcel scale requires detailed imaging from an aircraft with remote sensing technology to reduce uncertainty. FireWatch has been delivering high spatial resolution (5” ground sample distance) wildfire hazard maps annually to the community of Rancho Santa Fe, CA, since 2019. FireWatch uses a multispectral imaging system mounted onboard an aircraft to create georeferenced orthomosaics and spectral vegetation index maps. Using proprietary algorithms, the vegetation type, condition, and proximity to structures are determined for 1,851 properties in the community. Secondary data processing combines object-based classification of vegetative fuels, assisted by machine learning, to prioritize mitigation strategies within the community. The remote sensing data for the 10 sq. mi. community is divided into parcels and sent to all homeowners in the form of defensible space maps and reports. Follow-up aerial surveys are performed annually using repeat station imaging of fixed GPS locations to address changes in defensible space, vegetation fuel cover, and condition over time. These maps and reports have increased wildfire awareness and mitigation efforts from 40% to over 85% among homeowners in Rancho Santa Fe. To assist homeowners fighting increasing insurance premiums and non-renewals, FireWatch has partnered with Black Swan Analytics, LLC, to leverage the multispectral imagery and increase homeowners’ understanding of wildfire risk drivers. For this study, a subsample of 100 parcels was selected to gain a comprehensive understanding of wildfire risk and the elements which can be mitigated. Geospatial data from FireWatch’s defensible space maps was combined with Black Swan’s patented approach using 39 other risk characteristics into a 4score Report. The 4score Report helps property owners understand risk sources and potential mitigation opportunities by assessing four categories of risk: Fuel sources, ignition sources, susceptibility to loss, and hazards to fire protection efforts (FISH). This study has shown that susceptibility to loss is the category residents and property owners must focus their efforts. The 4score Report also provides a tool to measure the impact of homeowner actions on risk levels over time. Resiliency is the only solution to breaking the cycle of community wildfire destruction and it starts with high-quality data and education.

Keywords: defensible space, geospatial data, multispectral imaging, Rancho Santa Fe, susceptibility to loss, wildfire risk.

Procedia PDF Downloads 77

Authors: Sneha Shankar, Orlando Buendia, Will Evans

Abstract:

Alpha-1 antitrypsin deficiency (AATD) is a rare, genetic, and multisystemic condition. Underdiagnosis is common, leading to chronic pulmonary and hepatic complications, increased resource utilization, and additional costs to the healthcare system. Currently, there is limited evidence of the direct medical costs of AATD diagnosis in the UK. This study explores the economic impact of AATD patients during the 3 years before diagnosis and to identify the major cost drivers using primary and secondary care electronic health record (EHR) data. The 3 years before diagnosis time period was chosen based on the ability of our tool to identify patients earlier. The AATD algorithm was created using published disease criteria and applied to 148 known AATD patients’ EHR found in a primary care database of 936,148 patients (413,674 Biobank and 501,188 in a single primary care locality). Among 148 patients, 9 patients were flagged earlier by the tool and, on average, could save 3 (1-6) years per patient. We analysed 101 of the 148 AATD patients’ primary care journey and 20 patients’ Hospital Episode Statistics (HES) data, all of whom had at least 3 years of clinical history in their records before diagnosis. The codes related to laboratory tests, clinical visits, referrals, hospitalization days, day case, and inpatient admissions attributable to AATD were examined in this 3-year period before diagnosis. The average cost per patient was calculated, and the direct medical costs were modelled based on the mean prevalence of 100 AATD patients in a 500,000 population. A deterministic sensitivity analysis (DSA) of 20% was performed to determine the major cost drivers. Cost data was obtained from the NHS National tariff 2020/21, National Schedule of NHS Costs 2018/19, PSSRU 2018/19, and private care tariff. The total direct medical cost of one hundred AATD patients three years before diagnosis in primary and secondary care in the UK was £3,556,489, with an average direct cost per patient of £35,565. A vast majority of this total direct cost (95%) was associated with inpatient admissions (£3,378,229). The DSA determined that the costs associated with tier-2 laboratory tests and inpatient admissions were the greatest contributors to direct costs in primary and secondary care, respectively. This retrospective study shows the role of EHRs in calculating direct medical costs and the potential benefit of new technologies for the early identification of patients with AATD to reduce the economic burden in primary and secondary care in the UK.

Keywords: alpha-1 antitrypsin deficiency, costs, digital health, early diagnosis

Procedia PDF Downloads 140

Authors: Nareshkumar Harale, B. B. Meshram

Abstract:

The continued exponential growth of successful cyber intrusions against today’s businesses has made it abundantly clear that traditional perimeter security measures are no longer adequate and effective. We evolved the network trust architecture from trust-untrust to Zero-Trust, With Zero Trust, essential security capabilities are deployed in a way that provides policy enforcement and protection for all users, devices, applications, data resources, and the communications traffic between them, regardless of their location. Information exchange over the Internet, in spite of inclusion of advanced security controls, is always under innovative, inventive and prone to cyberattacks. TCP/IP protocol stack, the adapted standard for communication over network, suffers from inherent design vulnerabilities such as communication and session management protocols, routing protocols and security protocols are the major cause of major attacks. With the explosion of cyber security threats, such as viruses, worms, rootkits, malwares, Denial of Service attacks, accomplishing efficient and effective intrusion detection and prevention is become crucial and challenging too. In this paper, we propose a design and analysis model for next generation network intrusion detection and protection system as part of layered security strategy. The proposed system design provides intrusion detection for wide range of attacks with layered architecture and framework. The proposed network intrusion classification framework deals with cyberattacks on standard TCP/IP protocol, routing protocols and security protocols. It thereby forms the basis for detection of attack classes and applies signature based matching for known cyberattacks and data mining based machine learning approaches for unknown cyberattacks. Our proposed implemented software can effectively detect attacks even when malicious connections are hidden within normal events. The unsupervised learning algorithm applied to network audit data trails results in unknown intrusion detection. Association rule mining algorithms generate new rules from collected audit trail data resulting in increased intrusion prevention though integrated firewall systems. Intrusion response mechanisms can be initiated in real-time thereby minimizing the impact of network intrusions. Finally, we have shown that our approach can be validated and how the analysis results can be used for detecting and protection from the new network anomalies.

Keywords: network intrusion detection, network intrusion prevention, association rule mining, system analysis and design

Procedia PDF Downloads 200

Authors: Esmael Makarian, Ayub Elyasi, Fatemeh Saberi, Olusegun Stanley Tomomewo

Abstract:

Rock mechanical investigation is one of the most crucial activities in underground operations, especially in surveys related to hydrocarbon exploration and production, geothermal reservoirs, energy storage, mining, and geotechnics. There is a wide range of traditional methods for driving, collecting, and analyzing rock mechanics data. However, these approaches may not be suitable or work perfectly in some situations, such as fractured zones. Cutting-edge technologies have been provided to solve and optimize the mentioned issues. Internet of Things (IoT), Edge, and Cloud Computing technologies (ECt & CCt, respectively) are among the most widely used and new artificial intelligence methods employed for geomechanical studies. IoT devices act as sensors and cameras for real-time monitoring and mechanical-geological data collection of rocks, such as temperature, movement, pressure, or stress levels. Structural integrity, especially for cap rocks within hydrocarbon systems, and rock mass behavior assessment, to further activities such as enhanced oil recovery (EOR) and underground gas storage (UGS), or to improve safety risk management (SRM) and potential hazards identification (P.H.I), are other benefits from IoT technologies. EC techniques can process, aggregate, and analyze data immediately collected by IoT on a real-time scale, providing detailed insights into the behavior of rocks in various situations (e.g., stress, temperature, and pressure), establishing patterns quickly, and detecting trends. Therefore, this state-of-the-art and useful technology can adopt autonomous systems in rock mechanical surveys, such as drilling and production (in hydrocarbon wells) or excavation (in mining and geotechnics industries). Besides, ECt allows all rock-related operations to be controlled remotely and enables operators to apply changes or make adjustments. It must be mentioned that this feature is very important in environmental goals. More often than not, rock mechanical studies consist of different data, such as laboratory tests, field operations, and indirect information like seismic or well-logging data. CCt provides a useful platform for storing and managing a great deal of volume and different information, which can be very useful in fractured zones. Additionally, CCt supplies powerful tools for predicting, modeling, and simulating rock mechanical information, especially in fractured zones within vast areas. Also, it is a suitable source for sharing extensive information on rock mechanics, such as the direction and size of fractures in a large oil field or mine. The comprehensive review findings demonstrate that digital transformation through integrated IoT, Edge, and Cloud solutions is revolutionizing traditional rock mechanical investigation. These advanced technologies have empowered real-time monitoring, predictive analysis, and data-driven decision-making, culminating in noteworthy enhancements in safety, efficiency, and sustainability. Therefore, by employing IoT, CCt, and ECt, underground operations have experienced a significant boost, allowing for timely and informed actions using real-time data insights. The successful implementation of IoT, CCt, and ECt has led to optimized and safer operations, optimized processes, and environmentally conscious approaches in underground geological endeavors.

Keywords: rock mechanical studies, internet of things, edge computing, cloud computing, underground surveys, geological operations

Procedia PDF Downloads 26

Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

Abstract:

Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

Procedia PDF Downloads 94

Authors: M. Reza Hashemi, Chris Small, Scott Hayward

Abstract:

The Northeast Coast of the US faces damaging effects of coastal flooding and winds due to Atlantic tropical and extratropical storms each year. Historically, several large storm events have produced substantial levels of damage to the region; most notably of which were the Great Atlantic Hurricane of 1938, Hurricane Carol, Hurricane Bob, and recently Hurricane Sandy (2012). The objective of this study was to develop an integrated modeling system that could be used as a forecasting/hindcasting tool to evaluate and communicate the risk coastal communities face from these coastal storms. This modeling system utilizes the ADvanced CIRCulation (ADCIRC) model for storm surge predictions and the Simulating Waves Nearshore (SWAN) model for the wave environment. These models were coupled, passing information to each other and computing over the same unstructured domain, allowing for the most accurate representation of the physical storm processes. The coupled SWAN-ADCIRC model was validated and has been set up to perform real-time forecast simulations (as well as hindcast). Modeled storm parameters were then passed to a coastal risk assessment tool. This tool, which is generic and universally applicable, generates spatial structural damage estimate maps on an individual structure basis for an area of interest. The required inputs for the coastal risk model included a detailed information about the individual structures, inundation levels, and wave heights for the selected region. Additionally, calculation of wind damage to structures was incorporated. The integrated coastal risk assessment system was then tested and applied to Charlestown, a small vulnerable coastal town along the southern shore of Rhode Island. The modeling system was applied to Hurricane Sandy and a synthetic storm. In both storm cases, effect of natural dunes on coastal risk was investigated. The resulting damage maps for the area (Charlestown) clearly showed that the dune eroded scenarios affected more structures, and increased the estimated damage. The system was also tested in forecast mode for a large Nor’Easters: Stella (March 2017). The results showed a good performance of the coupled model in forecast mode when compared to observations. Finally, a nearshore model XBeach was then nested within this regional grid (ADCIRC-SWAN) to simulate nearshore sediment transport processes and coastal erosion. Hurricane Irene (2011) was used to validate XBeach, on the basis of a unique beach profile dataset at the region. XBeach showed a relatively good performance, being able to estimate eroded volumes along the beach transects with a mean error of 16%. The validated model was then used to analyze the effectiveness of several erosion mitigation methods that were recommended in a recent study of coastal erosion in New England: beach nourishment, coastal bank (engineered core), and submerged breakwater as well as artificial surfing reef. It was shown that beach nourishment and coastal banks perform better to mitigate shoreline retreat and coastal erosion.

Keywords: ADCIRC, coastal flooding, storm surge, coastal risk assessment, living shorelines

Procedia PDF Downloads 82

Authors: Fouzia Arif, Arif S. Mohammad, Hifsa Altaf, Lubna Raees

Abstract:

Introduction: The term "disability", refers to any condition that impedes the completion of daily tasks using traditional methods. In developing countries like Pakistan, disable population is usually excluded from the mainstream. In squatter settlements the situation is more critical. Sultanabad is one of the squatter settlements of Karachi. Purpose of case study is to improve the health of disabled children’s, and create awareness among the parents and community. Through a household visit, Shiraz, a young disabled boy of 15.5 years old was identified. Her mother articulated that her son was living normally and happily with his parents two years back. When he was 13 years old and student of class 8th, both his legs were traumatized in a Railway Train Accident while playing cricket. He got both femoral shaft fractured severely. He was taken to Jinnah Post Graduate Medical Centre (JPMC) where his left leg was amputated at above knee level and right leg was opened & fixed by reduction internally, luckily bone healed moderately with the passage of time. Methods: In Squatter settlements of Karachi Sultanabad, a survey was conducted in two sectors. Disability screening questionnaire was developed, collaboration with community through household visits, outreach sessions 23cases of disabled were identified who were socialized through sports, Musical program and get-together was organized with stockholder for creating awareness among community and parent’s. Collaboration was established with different NGOs, Government, stakeholders and community support for establishment of Physiotherapy Center. During home visit it was identified that Shiraz was on bed since last 1 year, his family could not afforded cost of physiotherapist and medical consultation due to poverty. Parents counseling was done mentioning that Shiraz needed to take treatment. After motivation his parents agreed for treatment. He was consulted by an orthopedic surgeon in AKUH, Who referred to DMC University of Health Science for rehabilitation service. There he was assessed and referred for Community Based Physiotherapy Centre Sultanabad. Physiotherapist visited home along with Coordinator for Special children and assessed him regularly, planned Physiotherapy treatment for abdominal, high muscles strutting exercise foot muscles strengthening exercise, knee mobilization weight bearing from partial to full weight gradually, also strengthen exercise were given for residual limb as the boy was dependent on it. He was also provided by an artificial leg and training was done. Result: Shiraz is now fully mobile, he can walk independently even out of home, functional ability progress improved and dependency factors reduced. It was difficult but not impossible. We all have sympathy but if we have empathy then we can rehabilitate the community in a better way. His parents are very happy and also the community is surprised to see him in such better condition. Conclusion: Combined efforts of physiotherapist, Coordinator of special children, community and parents made a drastic change in Shiraz’s case by continuously motivating him for better outcome. He is going to school regularly without support. Since he belongs to a poor family he faces financial constraints for education and clinical follow ups regularly.

Keywords: femoral shaft fracture, trauma, orthopedic surgeon, physiotherapy treatment

Procedia PDF Downloads 214

Authors: Aitor Bilbao, Dragos Axinte, John Billingham

Abstract:

The inverse problem in Energy Beam (EB) Processes consists of defining the control parameters, in particular the 2D beam path (position and orientation of the beam as a function of time), to arrive at a prescribed solution (freeform surface). This inverse problem is well understood for conventional machining, because the cutting tool geometry is well defined and the material removal is a time independent process. In contrast, EB machining is achieved through the local interaction of a beam of particular characteristics (e.g. energy distribution), which leads to a surface-dependent removal rate. Furthermore, EB machining is a time-dependent process in which not only the beam varies with the dwell time, but any acceleration/deceleration of the machine/beam delivery system, when performing raster paths will influence the actual geometry of the surface to be generated. Two different EB processes, Abrasive Water Machining (AWJM) and Pulsed Laser Ablation (PLA), are studied. Even though they are considered as independent different technologies, both can be described as time-dependent processes. AWJM can be considered as a continuous process and the etched material depends on the feed speed of the jet at each instant during the process. On the other hand, PLA processes are usually defined as discrete systems and the total removed material is calculated by the summation of the different pulses shot during the process. The overlapping of these shots depends on the feed speed and the frequency between two consecutive shots. However, if the feed speed is sufficiently slow compared with the frequency, then consecutive shots are close enough and the behaviour can be similar to a continuous process. Using this approximation a generic continuous model can be described for both processes. The inverse problem is usually solved for this kind of process by simply controlling dwell time in proportion to the required depth of milling at each single pixel on the surface using a linear model of the process. However, this approach does not always lead to the good solution since linear models are only valid when shallow surfaces are etched. The solution of the inverse problem is improved by using a discrete adjoint optimization algorithm. Moreover, the calculation of the Jacobian matrix consumes less computation time than finite difference approaches. The influence of the dynamics of the machine on the actual movement of the jet is also important and should be taken into account. When the parameters of the controller are not known or cannot be changed, a simple approximation is used for the choice of the slope of a step profile. Several experimental tests are performed for both technologies to show the usefulness of this approach.

Keywords: abrasive waterjet machining, energy beam processes, inverse problem, pulsed laser ablation

Procedia PDF Downloads 256

Authors: Kristina Pflug, Markus Busch

Abstract:

Being able to predict polymer properties and processing behavior based on the applied operating reaction conditions in one of the key challenges in modern polymer reaction engineering. Especially, for cost-intensive processes such as the high-pressure polymerization of low-density polyethylene (LDPE) with high safety-requirements, the need for simulation-based process optimization and product design is high. A multi-scale modelling approach was set-up and validated via a series of high-pressure mini-plant autoclave reactor experiments. The approach starts with the numerical modelling of the complex reaction network of the LDPE polymerization taking into consideration the actual reaction conditions. While this gives average product properties, the complex polymeric microstructure including random short- and long-chain branching is calculated via a hybrid Monte Carlo-approach. Finally, the processing behavior of LDPE -its melt flow behavior- is determined in dependence of the previously determined polymeric microstructure using the branch on branch algorithm for randomly branched polymer systems. All three steps of the multi-scale modelling approach can be independently validated against analytical data. A triple-detector GPC containing an IR, viscosimetry and multi-angle light scattering detector is applied. It serves to determine molecular weight distributions as well as chain-length dependent short- and long-chain branching frequencies. 13C-NMR measurements give average branching frequencies, and rheological measurements in shear and extension serve to characterize the polymeric flow behavior. The accordance of experimental and modelled results was found to be extraordinary, especially taking into consideration that the applied multi-scale modelling approach does not contain parameter fitting of the data. This validates the suggested approach and proves its universality at the same time. In the next step, the modelling approach can be applied to other reactor types, such as tubular reactors or industrial scale. Moreover, sensitivity analysis for systematically varying process conditions is easily feasible. The developed multi-scale modelling approach finally gives the opportunity to predict and design LDPE processing behavior simply based on process conditions such as feed streams and inlet temperatures and pressures.

Keywords: low-density polyethylene, multi-scale modelling, polymer properties, reaction engineering, rheology

Procedia PDF Downloads 105

Authors: Amani Tahat, Serdal Kirmizialtin

Abstract:

Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

Procedia PDF Downloads 197

Authors: Alexander Vandesompele, Joni Dambre

Abstract:

The diversity in dendritic arborization, as first illustrated by Santiago Ramon y Cajal, has always suggested a role for dendrites in the functionality of neurons. In the past decades, thanks to new recording techniques and optical stimulation methods, it has become clear that dendrites are not merely passive electrical components. They are observed to integrate inputs in a non-linear fashion and actively participate in computations. Regardless, in simulations of neural networks dendritic structure and functionality are often overlooked. Especially in a machine learning context, when designing artificial neural networks, point neuron models such as the leaky-integrate-and-fire (LIF) model are dominant. These models mimic the integration of inputs at the neuron soma, and ignore the existence of dendrites. In this work, the LIF point neuron model is extended with a simple form of dendritic computation. This gives the LIF neuron increased capacity to discriminate spatiotemporal input sequences, a dendritic functionality as observed in another study. Simulations of the spiking neurons are performed using the Bindsnet framework. In the common LIF model, incoming synapses are independent. Here, we introduce a dependency between incoming synapses such that the post-synaptic impact of a spike is not only determined by the weight of the synapse, but also by the activity of other synapses. This is a form of short term plasticity where synapses are potentiated or depressed by the preceding activity of neighbouring synapses. This is a straightforward way to prevent inputs from simply summing linearly at the soma. To implement this, each pair of synapses on a neuron is assigned a variable,representing the synaptic relation. This variable determines the magnitude ofthe short term plasticity. These variables can be chosen randomly or, more interestingly, can be learned using a form of Hebbian learning. We use Spike-Time-Dependent-Plasticity (STDP), commonly used to learn synaptic strength magnitudes. If all neurons in a layer receive the same input, they tend to learn the same through STDP. Adding inhibitory connections between the neurons creates a winner-take-all (WTA) network. This causes the different neurons to learn different input sequences. To illustrate the impact of the proposed dendritic mechanism, even without learning, we attach five input neurons to two output neurons. One output neuron isa regular LIF neuron, the other output neuron is a LIF neuron with dendritic relationships. Then, the five input neurons are allowed to fire in a particular order. The membrane potentials are reset and subsequently the five input neurons are fired in the reversed order. As the regular LIF neuron linearly integrates its inputs at the soma, the membrane potential response to both sequences is similar in magnitude. In the other output neuron, due to the dendritic mechanism, the membrane potential response is different for both sequences. Hence, the dendritic mechanism improves the neuron’s capacity for discriminating spa-tiotemporal sequences. Dendritic computations improve LIF neurons even if the relationships between synapses are established randomly. Ideally however, a learning rule is used to improve the dendritic relationships based on input data. It is possible to learn synaptic strength with STDP, to make a neuron more sensitive to its input. Similarly, it is possible to learn dendritic relationships with STDP, to make the neuron more sensitive to spatiotemporal input sequences. Feeding structured data to a WTA network with dendritic computation leads to a significantly higher number of discriminated input patterns. Without the dendritic computation, output neurons are less specific and may, for instance, be activated by a sequence in reverse order.

Keywords: dendritic computation, spiking neural networks, point neuron model

Procedia PDF Downloads 100

Authors: Chiyoung Kim, Jaegeol Yim

Abstract:

Bus service is often either main or the only public transportation available in cities. In metropolitan areas, both subways and buses are available whereas in the medium sized cities buses are usually the only type of public transportation available. Bus Information Systems (BIS) provide current locations of running buses, efficient routes to travel from one place to another, points of interests around a given bus stop, a series of bus stops consisting of a given bus route, and so on to users. Thanks to BIS, people do not have to waste time at a bus stop waiting for a bus because BIS provides exact information on bus arrival times at a given bus stop. Therefore, BIS does a lot to promote the use of buses contributing to pollution reduction and saving natural resources. BIS implementation costs a huge amount of budget as it requires a lot of special equipment such as road side equipment, automatic vehicle identification and location systems, trunked radio systems, and so on. Consequently, medium and small sized cities with a low budget cannot afford to install BIS even though people in these cities need BIS service more desperately than people in metropolitan areas. It is possible to provide BIS service at virtually no cost under the assumption that everybody carries a smartphone and there is at least one person with a smartphone in a running bus who is willing to reveal his/her location details while he/she is sitting in a bus. This assumption is usually true in the real world. The smartphone penetration rate is greater than 100% in the developed countries and there is no reason for a bus driver to refuse to reveal his/her location details while driving. We have developed a mobile app that periodically reads values of sensors including GPS and sends GPS data to the server when the bus stops or when the elapsed time from the last send attempt is greater than a threshold. This app detects the bus stop state by investigating the sensor values. The server that receives GPS data from this app has also been developed. Under the assumption that the current locations of all running buses collected by the mobile app are recorded in a database, we have also developed a web site that provides all kinds of information that most BISs provide to users through the Internet. The development environment is: OS: Windows 7 64bit, IDE: Eclipse Luna 4.4.1, Spring IDE 3.7.0, Database: MySQL 5.1.7, Web Server: Apache Tomcat 7.0, Programming Language: Java 1.7.0_79. Given a start and a destination bus stop, it finds a shortest path from the start to the destination using the Dijkstra algorithm. Then, it finds a convenient route considering number of transits. For the user interface, we use the Google map. Template classes that are used by the Controller, DAO, Service and Utils classes include BUS, BusStop, BusListInfo, BusStopOrder, RouteResult, WalkingDist, Location, and so on. We are now integrating the mobile app system and the web app system.

Keywords: bus information system, GPS, mobile app, web site

Procedia PDF Downloads 187

Authors: Abhisek Chakrabarty, Subhraprakash Mandal

Abstract:

Flash flood is one of the catastrophic natural hazards in the mountainous region of India. The recent flood in the Mandakini River in Kedarnath (14-17th June, 2013) is a classic example of flash floods that devastated Uttarakhand by killing thousands of people.The disaster was an integrated effect of high intensityrainfall, sudden breach of Chorabari Lake and very steep topography. Every year in Himalayan Region flash flood occur due to intense rainfall over a short period of time, cloud burst, glacial lake outburst and collapse of artificial check dam that cause high flow of river water. In Sikkim-Derjeeling Himalaya one of the probable flash flood occurrence zone is Teesta Watershed. The Teesta River is a right tributary of the Brahmaputra with draining mountain area of approximately 8600 Sq. km. It originates in the Pauhunri massif (7127 m). The total length of the mountain section of the river amounts to 182 km. The Teesta is characterized by a complex hydrological regime. The river is fed not only by precipitation, but also by melting glaciers and snow as well as groundwater. The present study describes an attempt to model surface runoff in upper Teesta basin, which is directly related to catastrophic flood events, by creating a system based on GIS technology. The main object was to construct a direct unit hydrograph for an excess rainfall by estimating the stream flow response at the outlet of a watershed. Specifically, the methodology was based on the creation of a spatial database in GIS environment and on data editing. Moreover, rainfall time-series data collected from Indian Meteorological Department and they were processed in order to calculate flow time and the runoff volume. Apart from the meteorological data, background data such as topography, drainage network, land cover and geological data were also collected. Clipping the watershed from the entire area and the streamline generation for Teesta watershed were done and cross-sectional profiles plotted across the river at various locations from Aster DEM data using the ERDAS IMAGINE 9.0 and Arc GIS 10.0 software. The analysis of different hydraulic model to detect flash flood probability ware done using HEC-RAS, Flow-2D, HEC-HMS Software, which were of great importance in order to achieve the final result. With an input rainfall intensity above 400 mm per day for three days the flood runoff simulation models shows outbursts of lakes and check dam individually or in combination with run-off causing severe damage to the downstream settlements. Model output shows that 313 Sq. km area were found to be most vulnerable to flash flood includes Melli, Jourthang, Chungthang, and Lachung and 655sq. km. as moderately vulnerable includes Rangpo,Yathang, Dambung,Bardang, Singtam, Teesta Bazarand Thangu Valley. The model was validated by inserting the rain fall data of a flood event took place in August 1968, and 78% of the actual area flooded reflected in the output of the model. Lastly preventive and curative measures were suggested to reduce the losses by probable flash flood event.

Keywords: flash flood, GIS, runoff, simulation model, Teesta river basin

Procedia PDF Downloads 276

Authors: Bahar Salavati Vie Le Sage, Ismaïl Ben Hariz, Flavien Viguier, Sirine Noura Kahil, Audrey Jacquin, Maxime Convert

Abstract:

The ballast layer plays a key role in railroad maintenance and the geometry of the track structure. Ballast also holds the track in place as the trains roll over it. Track ballast is packed between the sleepers and on the sides of railway tracks. An imbalance in ballast volume on the tracks can lead to safety issues as well as a quick degradation of the overall quality of the railway segment. If there is a lack of ballast in the track bed during the summer, there is a risk that the rails will expand and buckle slightly due to the high temperatures. Furthermore, the knowledge of the ballast quantities that will be excavated during renewal works is important for efficient ballast management. The volume of excavated ballast per meter of track can be calculated based on excavation depth, excavation width, volume of track skeleton (sleeper and rail) and sleeper spacing. Since 2012, SNCF has been collecting 3D points cloud data covering its entire railway network by using 3D laser scanning technology (LiDAR). This vast amount of data represents a modelization of the entire railway infrastructure, allowing to conduct various simulations for maintenance purposes. This paper aims to present an automated method for ballast volume estimation based on the processing of LiDAR data. The estimation of abnormal volumes in ballast on the tracks is performed by analyzing the cross-section of the track. Further, since the amount of ballast required varies depending on the track configuration, the knowledge of the ballast profile is required. Prior to track rehabilitation, excess ballast is often present in the ballast shoulders. Based on 3D laser scans, a Digital Terrain Model (DTM) was generated and automatic extraction of the ballast profiles from this data is carried out. The surplus in ballast is then estimated by performing a comparison between this ballast profile obtained empirically, and a geometric modelization of the theoretical ballast profile thresholds as dictated by maintenance standards. Ideally, this excess should be removed prior to renewal works and recycled to optimize the output of the ballast renewal machine. Based on these parameters, an application has been developed to allow the automatic measurement of ballast profiles. We evaluated the method on a 108 kilometers segment of railroad LiDAR scans, and the results show that the proposed algorithm detects ballast surplus that amounts to values close to the total quantities of spoil ballast excavated.

Keywords: ballast, railroad, LiDAR , cloud point, track ballast, 3D point

Procedia PDF Downloads 71

Authors: Jerome Joshi

Abstract:

The paper presents the modelling of financial markets using the Stochastic Pi Calculus model. The Stochastic Pi Calculus model is mainly used for biological applications; however, the feature of this model promotes its use in financial markets, more prominently in high frequency trading. The trading system can be broadly classified into exchange, market makers or intermediary traders and fundamental traders. The exchange is where the action of the trade is executed, and the two types of traders act as market participants in the exchange. High frequency trading, with its complex networks and numerous market participants (intermediary and fundamental traders) poses a difficulty while modelling. It involves the participants to seek the advantage of complex trading algorithms and high execution speeds to carry out large volumes of trades. To earn profits from each trade, the trader must be at the top of the order book quite frequently by executing or processing multiple trades simultaneously. This would require highly automated systems as well as the right sentiment to outperform other traders. However, always being at the top of the book is also not best for the trader, since it was the reason for the outbreak of the ‘Hot – Potato Effect,’ which in turn demands for a better and more efficient model. The characteristics of the model should be such that it should be flexible and have diverse applications. Therefore, a model which has its application in a similar field characterized by such difficulty should be chosen. It should also be flexible in its simulation so that it can be further extended and adapted for future research as well as be equipped with certain tools so that it can be perfectly used in the field of finance. In this case, the Stochastic Pi Calculus model seems to be an ideal fit for financial applications, owing to its expertise in the field of biology. It is an extension of the original Pi Calculus model and acts as a solution and an alternative to the previously flawed algorithm, provided the application of this model is further extended. This model would focus on solving the problem which led to the ‘Flash Crash’ which is the ‘Hot –Potato Effect.’ The model consists of small sub-systems, which can be integrated to form a large system. It is designed in way such that the behavior of ‘noise traders’ is considered as a random process or noise in the system. While modelling, to get a better understanding of the problem, a broader picture is taken into consideration with the trader, the system, and the market participants. The paper goes on to explain trading in exchanges, types of traders, high frequency trading, ‘Flash Crash,’ ‘Hot-Potato Effect,’ evaluation of orders and time delay in further detail. For the future, there is a need to focus on the calibration of the module so that they would interact perfectly with other modules. This model, with its application extended, would provide a basis for researchers for further research in the field of finance and computing.

Keywords: concurrent computing, high frequency trading, financial markets, stochastic pi calculus

Procedia PDF Downloads 52

Authors: Ugo Galvanetto, Pirto G. Pavan, Mirco Zaccariotto

Abstract:

The discipline of Computer-integrated surgery (CIS) will provide equipment able to improve the efficiency of healthcare systems and, which is more important, clinical results. Surgeons and machines will cooperate in new ways that will extend surgeons’ ability to train, plan and carry out surgery. Patient specific CIS of the brain requires several steps: 1 - Fast generation of brain models. Based on image recognition of MR images and equipped with artificial intelligence, image recognition techniques should differentiate among all brain tissues and segment them. After that, automatic mesh generation should create the mathematical model of the brain in which the various tissues (white matter, grey matter, cerebrospinal fluid …) are clearly located in the correct positions. 2 – Reliable and fast simulation of the surgical process. Computational mechanics will be the crucial aspect of the entire procedure. New algorithms will be used to simulate the mechanical behaviour of cutting through cerebral tissues. 3 – Real time provision of visual and haptic feedback A sophisticated human-machine interface based on ergonomics and psychology will provide the feedback to the surgeon. The present work will address in particular point 2. Modelling the cutting of soft tissue in a structure as complex as the human brain is an extremely challenging problem in computational mechanics. The finite element method (FEM), that accurately represents complex geometries and accounts for material and geometrical nonlinearities, is the most used computational tool to simulate the mechanical response of soft tissues. However, the main drawback of FEM lies in the mechanics theory on which it is based, classical continuum Mechanics, which assumes matter is a continuum with no discontinuity. FEM must resort to complex tools such as pre-defined cohesive zones, external phase-field variables, and demanding remeshing techniques to include discontinuities. However, all approaches to equip FEM computational methods with the capability to describe material separation, such as interface elements with cohesive zone models, X-FEM, element erosion, phase-field, have some drawbacks that make them unsuitable for surgery simulation. Interface elements require a-priori knowledge of crack paths. The use of XFEM in 3D is cumbersome. Element erosion does not conserve mass. The Phase Field approach adopts a diffusive crack model instead of describing true tissue separation typical of surgical procedures. Modelling discontinuities, so difficult when using computational approaches based on classical continuum Mechanics, is instead easy for novel computational methods based on Peridynamics (PD). PD is a non-local theory of mechanics formulated with no use of spatial derivatives. Its governing equations are valid at points or surfaces of discontinuity, and it is, therefore especially suited to describe crack propagation and fragmentation problems. Moreover, PD does not require any criterium to decide the direction of crack propagation or the conditions for crack branching or coalescence; in the PD-based computational methods, cracks develop spontaneously in the way which is the most convenient from an energy point of view. Therefore, in PD computational methods, crack propagation in 3D is as easy as it is in 2D, with a remarkable advantage with respect to all other computational techniques.

Keywords: computational mechanics, peridynamics, finite element, biomechanics

Procedia PDF Downloads 50

Authors: Angela U. Makolo

Abstract:

Protein-coding and Non-coding regions determine the biology of a sequenced transcriptome. Research advances have shown that Non-coding regions are important in disease progression and clinical diagnosis. Existing bioinformatics tools have been targeted towards Protein-coding regions alone. Therefore, there are challenges associated with gaining biological insights from transcriptome sequence data. These tools are also limited to computationally intensive sequence alignment, which is inadequate and less accurate to identify both Protein-coding and Non-coding regions. Alignment-free techniques can overcome the limitation of identifying both regions. Therefore, this study was designed to develop an efficient sequence alignment-free model for identifying both Protein-coding and Non-coding regions in sequenced transcriptomes. Feature grouping and randomization procedures were applied to the input transcriptomes (37,503 data points). Successive iterations were carried out to compute the gradient vector that converged the developed Protein-coding and Non-coding Region Identifier (PNRI) model to the approximate coefficient vector. The logistic regression algorithm was used with a sigmoid activation function. A parameter vector was estimated for every sample in 37,503 data points in a bid to reduce the generalization error and cost. Maximum Likelihood Estimation (MLE) was used for parameter estimation by taking the log-likelihood of six features and combining them into a summation function. Dynamic thresholding was used to classify the Protein-coding and Non-coding regions, and the Receiver Operating Characteristic (ROC) curve was determined. The generalization performance of PNRI was determined in terms of F1 score, accuracy, sensitivity, and specificity. The average generalization performance of PNRI was determined using a benchmark of multi-species organisms. The generalization error for identifying Protein-coding and Non-coding regions decreased from 0.514 to 0.508 and to 0.378, respectively, after three iterations. The cost (difference between the predicted and the actual outcome) also decreased from 1.446 to 0.842 and to 0.718, respectively, for the first, second and third iterations. The iterations terminated at the 390th epoch, having an error of 0.036 and a cost of 0.316. The computed elements of the parameter vector that maximized the objective function were 0.043, 0.519, 0.715, 0.878, 1.157, and 2.575. The PNRI gave an ROC of 0.97, indicating an improved predictive ability. The PNRI identified both Protein-coding and Non-coding regions with an F1 score of 0.970, accuracy (0.969), sensitivity (0.966), and specificity of 0.973. Using 13 non-human multi-species model organisms, the average generalization performance of the traditional method was 74.4%, while that of the developed model was 85.2%, thereby making the developed model better in the identification of Protein-coding and Non-coding regions in transcriptomes. The developed Protein-coding and Non-coding region identifier model efficiently identified the Protein-coding and Non-coding transcriptomic regions. It could be used in genome annotation and in the analysis of transcriptomes.

Keywords: sequence alignment-free model, dynamic thresholding classification, input randomization, genome annotation

Procedia PDF Downloads 31

Authors: Sabina Żołędowska, Tadeusz Ossowski, Robert Bogdanowicz, Jacek Ryl, Paweł Rostkowski, Michał Kruczkowski, Michał Sobaszek, Zofia Cebula, Grzegorz Skowierzak, Paweł Jakóbczyk, Lilit Hovhannisyan, Paweł Ślepski, Iwona Kaczmarczyk, Mattia Pierpaoli, Bartłomiej Dec, Dawid Nidzworski

Abstract:

The circular economy of water presents a pressing environmental challenge in our society. Water contains various harmful substances, such as drugs, antibiotics, hormones, and dioxides, which can pose silent threats. Water pollution has severe consequences for aquatic ecosystems. It disrupts the balance of ecosystems by harming aquatic plants, animals, and microorganisms. Water pollution poses significant risks to human health. Exposure to toxic chemicals through contaminated water can have long-term health effects, such as cancer, developmental disorders, and hormonal imbalances. However, effective remediation systems can be implemented to remove these contaminants using electrocatalytic processes, which offer an environmentally friendly alternative to other treatment methods, and one of them is the innovative iCLARE system. The project's primary focus revolves around a few main topics: Reactor design and construction, selection of a specific type of reticulated vitreous carbon foams (RVC), analytical studies of harmful contaminants parameters and AI implementation. This high-performance electrochemical reactor will be build based on a novel type of electrode material. The proposed approach utilizes the application of reticulated vitreous carbon foams (RVC) with deposited modified metal oxides (MMO) and diamond thin films. The following setup is characterized by high surface area development and satisfactory mechanical and electrochemical properties, designed for high electrocatalytic process efficiency. The consortium validated electrode modification methods that are the base of the iCLARE product and established the procedures for the detection of chemicals detection: - deposition of metal oxides WO3 and V2O5-deposition of boron-doped diamond/nanowalls structures by CVD process. The chosen electrodes (porous Ferroterm electrodes) were stress tested for various parameters that might occur inside the iCLARE machine–corosis, the long-term structure of the electrode surface during electrochemical processes, and energetic efficacy using cyclic polarization and electrochemical impedance spectroscopy (before and after electrolysis) and dynamic electrochemical impedance spectroscopy (DEIS). This tool allows real-time monitoring of the changes at the electrode/electrolyte interphase. On the other hand, the toxicity of iCLARE chemicals and products of electrolysis are evaluated before and after the treatment using MARA examination (IBMM) and HPLC-MS-MS (NILU), giving us information about the harmfulness of using electrode material and the efficiency of iClare system in the disposal of pollutants. Implementation of data into the system that uses artificial intelligence and the possibility of practical application is in progress (SensDx).

Keywords: waste water treatement, RVC, electrocatalysis, paracetamol

Procedia PDF Downloads 50

Authors: Junior Akunzi

Abstract:

In the matter of patient treatment planning quality assurance in 3D conformational therapy (3D-CRT) and volumetric arc therapy (VMAT or RapidArc), the independent monitor unit verification calculation (MUVC) is an indispensable part of the process. Concerning 3D-CRT treatment planning, the MUVC can be performed manually applying the standard ESTRO formalism. However, due to the complex shape and the amount of beams in advanced treatment planning technic such as RapidArc, the manual independent MUVC is inadequate. Therefore, commercially available software such as RadCalc can be used to perform the MUVC in complex treatment planning been. Indeed, RadCalc (version 6.3 LifeLine Inc.) uses a simplified Clarkson algorithm to compute the dose contribution for individual RapidArc fields to the isocenter. The purpose of this project is the validation of RadCalc in 3D-CRT and RapidArc for treatment planning dosimetry quality assurance at Antoine Lacassagne center (Nice, France). Firstly, the interfaces between RadCalc and our treatment planning systems (TPS) Isogray (version 4.2) and Eclipse (version13.6) were checked for data transfer accuracy. Secondly, we created test plans in both Isogray and Eclipse featuring open fields, wedges fields, and irregular MLC fields. These test plans were transferred from TPSs according to the radiotherapy protocol of DICOM RT to RadCalc and the linac via Mosaiq (version 2.5). Measurements were performed in water phantom using a PTW cylindrical semiflex ionisation chamber (0.3 cm³, 31010) and compared with the TPSs and RadCalc calculation. Finally, 30 3D-CRT plans and 40 RapidArc plans created with patients CT scan were recalculated using the CT scan of a solid PMMA water equivalent phantom for 3D-CRT and the Octavius II phantom (PTW) CT scan for RapidArc. Next, we measure the doses delivered into these phantoms for each plan with a 0.3 cm³ PTW 31010 cylindrical semiflex ionisation chamber (3D-CRT) and 0.015 cm³ PTW PinPoint ionisation chamber (Rapidarc). For our test plans, good agreements were found between calculation (RadCalc and TPSs) and measurement (mean: 1.3%; standard deviation: ± 0.8%). Regarding the patient plans, the measured doses were compared to the calculation in RadCalc and in our TPSs. Moreover, RadCalc calculations were compared to Isogray and Eclispse ones. Agreements better than (2.8%; ± 1.2%) were found between RadCalc and TPSs. As for the comparison between calculation and measurement the agreement for all of our plans was better than (2.3%; ± 1.1%). The independent MU verification calculation software RadCal has been validated for clinical use and for both 3D-CRT and RapidArc techniques. The perspective of this project includes the validation of RadCal for the Tomotherapy machine installed at centre Antoine Lacassagne.

Keywords: 3D conformational radiotherapy, intensity modulated radiotherapy, monitor unit calculation, dosimetry quality assurance

Procedia PDF Downloads 188

Authors: Jie Liu, Jinqu Cheng, Qiyuan Peng, Yong Yin

Abstract:

Urban rail transit (URT) plays a significant role in dealing with traffic congestion and environmental problems in cities. However, equipment failure and obstruction of links often lead to URT links’ capacities loss in daily operation. It affects the reliability and transport service quality of URT network seriously. In order to measure the influence of links’ capacities loss on reliability and transport service quality of URT network, passengers are divided into three categories in case of links’ capacities loss. Passengers in category 1 are less affected by the loss of links’ capacities. Their travel is reliable since their travel quality is not significantly reduced. Passengers in category 2 are affected by the loss of links’ capacities heavily. Their travel is not reliable since their travel quality is reduced seriously. However, passengers in category 2 still can travel on URT. Passengers in category 3 can not travel on URT because their travel paths’ passenger flow exceeds capacities. Their travel is not reliable. Thus, the proportion of passengers in category 1 whose travel is reliable is defined as reliability indicator of URT network. The transport service quality of URT network is related to passengers’ travel time, passengers’ transfer times and whether seats are available to passengers. The generalized travel cost is a comprehensive reflection of travel time, transfer times and travel comfort. Therefore, passengers’ average generalized travel cost is used as transport service quality indicator of URT network. The impact of links’ capacities loss on transport service quality of URT network is measured with passengers’ relative average generalized travel cost with and without links’ capacities loss. The proportion of the passengers affected by links and betweenness of links are used to determine the important links in URT network. The stochastic user equilibrium distribution model based on the improved logit model is used to determine passengers’ categories and calculate passengers’ generalized travel cost in case of links’ capacities loss, which is solved with method of successive weighted averages algorithm. The reliability and transport service quality indicators of URT network are calculated with the solution result. Taking Wuhan Metro as a case, the reliability and transport service quality of Wuhan metro network is measured with indicators and method proposed in this paper. The result shows that using the proportion of the passengers affected by links can identify important links effectively which have great influence on reliability and transport service quality of URT network; The important links are mostly connected to transfer stations and the passenger flow of important links is high; With the increase of number of failure links and the proportion of capacity loss, the reliability of the network keeps decreasing, the proportion of passengers in category 3 keeps increasing and the proportion of passengers in category 2 increases at first and then decreases; When the number of failure links and the proportion of capacity loss increased to a certain level, the decline of transport service quality is weakened.

Keywords: urban rail transit network, reliability, transport service quality, links’ capacities loss, important links

Procedia PDF Downloads 107

Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

Abstract:

In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

Procedia PDF Downloads 68

Authors: Mohammad Zamani, Ramin Mansouri

Abstract:

Spillways are one of the most important hydraulic structures of dams that provide the stability of the dam and downstream areas at the time of flood. A circular vertical spillway with various inlet forms is very effective when there is not enough space for the other spillway. Hydraulic flow in a vertical circular spillway is divided into three groups: free, orifice, and under pressure (submerged). In this research, the hydraulic flow characteristics of a Circular Vertical Spillway are investigated with the CFD model. Two-dimensional unsteady RANS equations were solved numerically using Finite Volume Method. The PISO scheme was applied for the velocity-pressure coupling. The mostly used two-equation turbulence models, k-ε and k-ω, were chosen to model Reynolds shear stress term. The power law scheme was used for the discretization of momentum, k, ε, and ω equations. The VOF method (geometrically reconstruction algorithm) was adopted for interface simulation. In this study, three types of computational grids (coarse, intermediate, and fine) were used to discriminate the simulation environment. In order to simulate the flow, the k-ε (Standard, RNG, Realizable) and k-ω (standard and SST) models were used. Also, in order to find the best wall function, two types, standard wall, and non-equilibrium wall function, were investigated. The laminar model did not produce satisfactory flow depth and velocity along the Morning-Glory spillway. The results of the most commonly used two-equation turbulence models (k-ε and k-ω) were identical. Furthermore, the standard wall function produced better results compared to the non-equilibrium wall function. Thus, for other simulations, the standard k-ε with the standard wall function was preferred. The comparison criterion in this study is also the trajectory profile of jet water. The results show that the fine computational grid, the input speed condition for the flow input boundary, and the output pressure for the boundaries that are in contact with the air provide the best possible results. Also, the standard wall function is chosen for the effect of the wall function, and the turbulent model k-ε (Standard) has the most consistent results with experimental results. When the jet gets closer to the end of the basin, the computational results increase with the numerical results of their differences. The mesh with 10602 nodes, turbulent model k-ε standard and the standard wall function, provide the best results for modeling the flow in a vertical circular Spillway. There was a good agreement between numerical and experimental results in the upper and lower nappe profiles. In the study of water level over crest and discharge, in low water levels, the results of numerical modeling are good agreement with the experimental, but with the increasing water level, the difference between the numerical and experimental discharge is more. In the study of the flow coefficient, by decreasing in P/R ratio, the difference between the numerical and experimental result increases.

Keywords: circular vertical, spillway, numerical model, boundary conditions

Procedia PDF Downloads 53

Authors: Andrew D. Henshaw, James M. Austin

Abstract:

Introduction: Human trafficking is an increasingly serious transnational criminal enterprise and social security issue. Despite ongoing efforts to mitigate the phenomenon and a significant expansion of security scrutiny over past decades, it is not receding. This is true for many nations in Southeast Asia, widely recognized as the global hub for trafficked persons, including men, women, and children. Clearly, human trafficking is difficult to address because there are numerous drivers, causes, and motivators for it to persist, such as non-military and non-traditional security challenges, i.e., climate change, global warming displacement, and natural disasters. These make displaced persons and refugees particularly vulnerable. The issue is so large conservative estimates put a dollar value at around $150 billion-plus per year (Niethammer, 2020) spanning sexual slavery and exploitation, forced labor, construction, mining and in conflict roles, and forced marriages of girls and women. Coupled with corruption throughout military, police, and civil authorities around the world, and the active hands of powerful transnational criminal organizations, it is likely that such figures are grossly underestimated as human trafficking is misreported, under-detected, and deliberately obfuscated to protect those profiting from it. For example, the 2022 UN report on human trafficking shows a 56% reduction in convictions in that year alone (UNODC, 2022). Our Approach: To better understand this, our research utilizes a bespoke methodology. Applying a JAM (Juxtaposition Assessment Matrix), which we previously developed to detect flows of dark money around the globe (Henshaw, A & Austin, J, 2021), we now focus on the human trafficking paradigm. Indeed, utilizing a JAM methodology has identified key indicators of human trafficking not previously explored in depth. Being a set of structured analytical techniques that provide panoramic interpretations of the subject matter, this iteration of the JAM further incorporates behavioral and driver indicators, including the employment of Open-Source Artificial Intelligence (OS-AI) across multiple collection points. The extracted behavioral data was then applied to identify non-traditional indicators as they contribute to human trafficking. Furthermore, as the JAM OS-AI analyses data from the inverted position, i.e., the viewpoint of the traffickers, it examines the behavioral and physical traits required to succeed. This transposed examination of the requirements of success delivers potential leverage points for exploitation in the fight against human trafficking in a new and novel way. Findings: Our approach identified new innovative datasets that have previously been overlooked or, at best, undervalued. For example, the JAM OS-AI approach identified critical 'dark agent' lynchpins within human trafficking that are difficult to detect and harder to connect to actors and agents within a network. Our preliminary data suggests this is in part due to the fact that ‘dark agents’ in extant research have been difficult to detect and potentially much harder to directly connect to the actors and organizations in human trafficking networks. Our research demonstrates that using new investigative techniques such as OS-AI-aided JAM introduces a powerful toolset to increase understanding of human trafficking and transnational crime and illuminate networks that, to date, avoid global law enforcement scrutiny.

Keywords: human trafficking, open-source intelligence, transnational crime, human security, international human rights, intelligence analysis, JAM OS-AI, Dark Money

Procedia PDF Downloads 50

Authors: Jing Jin

Abstract:

The incorporation of artificial intelligence (AI) and machine learning (ML) in aircraft manufacturing and aerospace supply chains leads to the generation of a substantial amount of data among various tiers of suppliers and OEMs. Identifying the high-quality information challenges decision-makers. The application of AI/ML models necessitates access to 'high-quality' information to yield desired outputs. However, the process of information sharing introduces complexities, including distortion through various communication channels and biases introduced by both human and AI entities. This phenomenon significantly influences the quality of information, impacting decision-makers engaged in configuring supply chain systems. Traditionally, distorted information is categorized as 'low-quality'; however, this study challenges this perception, positing that distorted information, contributing to stakeholder goals, can be deemed high-quality within supply chains. The main aim of this study is to identify and evaluate the dimensions of information quality crucial to the UK aerospace supply chain. Guided by a central research question, "What information quality dimensions are considered when defining information quality in the UK aerospace supply chain?" the study delves into the intricate dynamics of information quality in the aerospace industry. Additionally, the research explores the nuanced impact of information distortion on stakeholders' decision-making processes, addressing the question, "How does the information distortion phenomenon influence stakeholders’ decisions regarding information quality in the UK aerospace supply chain system?" This study employs deductive methodologies rooted in positivism, utilizing a cross-sectional approach and a mono-quantitative method -a questionnaire survey. Data is systematically collected from diverse tiers of supply chain stakeholders, encompassing end-customers, OEMs, Tier 0.5, Tier 1, and Tier 2 suppliers. Employing robust statistical data analysis methods, including mean values, mode values, standard deviation, one-way analysis of variance (ANOVA), and Pearson’s correlation analysis, the study interprets and extracts meaningful insights from the gathered data. Initial analyses challenge conventional notions, revealing that information distortion positively influences the definition of information quality, disrupting the established perception of distorted information as inherently low-quality. Further exploration through correlation analysis unveils the varied perspectives of different stakeholder tiers on the impact of information distortion on specific information quality dimensions. For instance, Tier 2 suppliers demonstrate strong positive correlations between information distortion and dimensions like access security, accuracy, interpretability, and timeliness. Conversely, Tier 1 suppliers emphasise strong negative influences on the security of accessing information and negligible impact on information timeliness. Tier 0.5 suppliers showcase very strong positive correlations with dimensions like conciseness and completeness, while OEMs exhibit limited interest in considering information distortion within the supply chain. Introducing social network analysis (SNA) provides a structural understanding of the relationships between information distortion and quality dimensions. The moderately high density of ‘information distortion-by-information quality’ underscores the interconnected nature of these factors. In conclusion, this study offers a nuanced exploration of information quality dimensions in the UK aerospace supply chain, highlighting the significance of individual perspectives across different tiers. The positive influence of information distortion challenges prevailing assumptions, fostering a more nuanced understanding of information's role in the Industry 4.0 landscape.

Keywords: information distortion, information quality, supply chain configuration, UK aerospace industry

Procedia PDF Downloads 25

Authors: John D. Sanderson

Abstract:

The popularity of American cinema worldwide has contributed to the development of sociolects related to specific film genres in other cultural contexts by means of screen translation, in many cases eluding norms of usage in the target language, a process whose result has come to be known as 'dubbese'. A consequence for the reception in countries where local audiovisual fiction consumption is far lower than American imported productions is that this linguistic construct is preferred, even though it differs from common everyday speech. The iconography of film genres such as science-fiction, western or sword-and-sandal films, for instance, generates linguistic expectations in international audiences who will accept more easily the sociolects assimilated by the continuous reception of American productions, even if the themes, locations, characters, etc., portrayed on screen may belong in origin to other cultures. And the non-normative language (e.g., calques, semantic loans) used in the preferred mode of linguistic transfer, whether it is translation for dubbing or subtitling, has diachronically evolved in many cases into a status of canonized sociolect, not only accepted but also required, by foreign audiences of American films. However, a remarkable step forward is taken when this typology of artificial linguistic constructs starts being used creatively by nationals of these target cultural contexts. In the case of Spain, the success of American sitcoms such as Friends in the 1990s led Spanish television scriptwriters to include in national productions lexical and syntactical indirect borrowings (Anglicisms not formally identifiable as such because they include elements from their own language) in order to target audiences of the former. However, this commercial strategy had already taken place decades earlier when Spain became a favored location for the shooting of foreign films in the early 1960s. The international popularity of the then newly developed sub-genre known as Spaghetti-Western encouraged Spanish investors to produce their own movies, and local scriptwriters made use of the dubbese developed nationally since the advent of sound in film instead of using normative language. As a result, direct Anglicisms, as well as lexical and syntactical borrowings made up the creative writing of these Spanish productions, which also became commercially successful. Interestingly enough, some of these films were even marketed in English-speaking countries as original westerns (some of the names of actors and directors were anglified to that purpose) dubbed into English. The analysis of these 'back translations' will also foreground some semantic distortions that arose in the process. In order to perform the research on these issues, a wide corpus of American films has been used, which chronologically range from Stagecoach (John Ford, 1939) to Django Unchained (Quentin Tarantino, 2012), together with a shorter corpus of Spanish films produced during the golden age of Spaghetti Westerns, from una tumba para el sheriff (Mario Caiano; in English lone and angry man, William Hawkins) to tu fosa será la exacta, amigo (Juan Bosch, 1972; in English my horse, my gun, your widow, John Wood). The methodology of analysis and the conclusions reached could be applied to other genres and other cultural contexts.

Keywords: dubbing, film genre, screen translation, sociolect

Procedia PDF Downloads 137

Authors: Lucas Barbosa Da Silva, Jun Okamoto Jr.

Abstract:

Precision Agriculture has long being benefited from crop fields’ aerial imagery. This important tool has allowed identifying patterns in crop fields, generating useful information to the production management. Reflectance intensity data in different ranges from the electromagnetic spectrum may indicate presence or absence of nutrients in the soil of an area. Different relations between the different light bands may generate even more detailed information. The knowledge of the nutrients content in the soil or in the crop during its growth is a valuable asset to the farmer that seeks to optimize its yield. However, small farmers in Brazil often lack the resources to access this kind information, and, even when they do, it is not presented in a comprehensive and/or objective way. So, the challenges of implementing this technology ranges from the sampling of the imagery, using aerial platforms, building of a mosaic with the images to cover the entire crop field, extracting the reflectance information from it and analyzing its relationship with the parameters of interest, to the display of the results in a manner that the farmer may take the necessary decisions more objectively. In this work, it’s proposed an analysis of soil nutrient contents based on image processing of satellite imagery and comparing its outtakes with commercial laboratory’s chemical analysis. Also, sources of satellite imagery are compared, to assess the feasibility of using Google Earth data in this application, and the impacts of doing so, versus the application of imagery from satellites like Landsat-8 and Pleiades. Furthermore, an algorithm for building mosaics is implemented using Google Earth imagery and finally, the possibility of using unmanned aerial vehicles is analyzed. From the data obtained, some soil parameters are estimated, namely, the content of Potassium, Phosphorus, Boron, Manganese, among others. The suitability of Google Earth Imagery for this application is verified within a reasonable margin, when compared to Pleiades Satellite imagery and to the current commercial model. It is also verified that the mosaic construction method has little or no influence on the estimation results. Variability maps are created over the covered area and the impacts of the image resolution and sample time frame are discussed, allowing easy assessments of the results. The final results show that easy and cheaper remote sensing and analysis methods are possible and feasible alternatives for the small farmer, with little access to technological and/or financial resources, to make more accurate decisions about soil nutrient management.

Keywords: remote sensing, precision agriculture, mosaic, soil, nutrient content, satellite imagery, aerial imagery

Procedia PDF Downloads 145

Authors: Asim Abbasi, Muhammad Sufyan, Iqra, Hafiza Javaria Ashraf

Abstract:

The share of vector-borne diseases (VBDs) in the global burden of infectious diseases is almost 17%. The advent of new drugs and latest research in medical science helped mankind to compete with these lethal diseases but still diseases transmitted by different mosquito species, including filariasis, malaria, viral encephalitis and dengue are serious threats for people living in disease endemic areas. Injudicious and repeated use of pesticides posed selection pressure on mosquitoes leading to development of resistance. Hence biological control agents are under serious consideration of scientific community to be used in vector control programmes. Fish have a history of predating immature stages of different aquatic insects including mosquitoes. The noteworthy examples in Africa and Asia includes, Aphanius discolour and a fish in the Panchax group. Moreover, common mosquito fish, Gambusia affinis predates mostly on temporary water mosquitoes like anopheline as compared to permanent water breeders like culicines. Mosquitoes belonging to genus Toxorhynchites have a worldwide distribution and are mostly associated with the predation of other mosquito larvae habituating with them in natural and artificial water containers. These species are harmless to humans as their adults do not suck human blood but feeds on floral nectar. However, their activity is mostly temperature dependent as Toxorhynchites brevipalpis consume 359 Aedes aegypti larvae at 30-32 ºC in contrast to 154 larvae at 20-26 ºC. Although many bacterial species were isolated from mosquito cadavers but those belonging to genus Bacillus are found highly pathogenic against them. The successful species of this genus include Bacillus thuringiensis and Bacillus sphaericus. The prime targets of B. thuringiensis are mostly the immatures of genus Aedes, Culex, Anopheles and Psorophora while B. sphaericus is specifically toxic against species of Culex, Psorophora and Culiseta. The entomopathogenic nematodes belonging to family, mermithidae are also pathogenic to different mosquito species. Eighty different species of mosquitoes including Anopheles, Aedes and Culex proved to be highly vulnerable to the attack of two mermithid species, Romanomermis culicivorax and R. iyengari. Cytoplasmic polyhedrosis virus was the first described pathogenic virus, isolated from the cadavers of mosquito specie, Culex tarsalis. Other viruses which are pathogenic to culicine includes, iridoviruses, cytopolyhedrosis viruses, entomopoxviruses and parvoviruses. Protozoa species belonging to division microsporidia are the common pathogenic protozoans in mosquito populations which kill their host by the chronic effects of parasitism. Moreover, due to their wide prevalence in anopheline mosquitoes and transversal and horizontal transmission from infected to healthy host, microsporidia of the genera Nosema and Amblyospora have received much attention in various mosquito control programmes. Fungal based mycopesticides are used in biological control of insect pests with 47 species reported virulent against different stages of mosquitoes. These include both aquatic fungi i.e. species of Coelomomyces, Lagenidium giganteum and Culicinomyces clavosporus, and the terrestrial fungi Metarhizium anisopliae and Beauveria bassiana. Hence, it was concluded that the integrated use of all these biological control agents can be a healthy contribution in mosquito control programmes and become a dire need of the time to avoid repeated use of pesticides.

Keywords: entomopathogenic nematodes, protozoa, Toxorhynchites, vector-borne

Procedia PDF Downloads 238

Authors: Jamuna Loganath

Abstract:

Education is the key to the development of any individual’s personality. Today’s students will be tomorrow’s citizens of the global society. The education of the student is the edifice on which his/her future will be built. Schools therefore should provide an all-round development of students so as to foster a healthy society. The behaviors and the attitude of the students in school play an essential role for the success of the education process. Frequent reports of misbehaviors such as clowning, harassing classmates, verbal insults are becoming common in schools today. If this issue is left unattended, it may develop a negative attitude and increase the delinquent behavior. So, the need of the hour is to find a solution to this problem. To solve this issue, it is important to monitor the students’ behaviors in school and give necessary feedback and mentor them to develop a positive attitude and help them to become a successful grownup. Nevertheless, measuring students’ behavior and attitude is extremely challenging. None of the present technology has proven to be effective in this measurement process because actions, reactions, interactions, response of the students are rarely used in the course of the data due to complexity. The purpose of this proposal is to recommend an effective supervising system after carrying out a feasibility study by measuring the behavior of the Students. This can be achieved by equipping schools with CCTV cameras. These CCTV cameras installed in various schools of the world capture the facial expressions and interactions of the students inside and outside their classroom. The real time raw videos captured from the CCTV can be uploaded to the cloud with the help of a network. The video feeds get scooped into various nodes in the same rack or on the different racks in the same cluster in Hadoop HDFS. The video feeds are converted into small frames and analyzed using various Pattern recognition algorithms and MapReduce algorithm. Then, the video frames are compared with the bench marking database (good behavior). When misbehavior is detected, an alert message can be sent to the counseling department which helps them in mentoring the students. This will help in improving the effectiveness of the education process. As Video feeds come from multiple geographical areas (schools from different parts of the world), BIG DATA helps in real time analysis as it analyzes computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. It also analyzes data that can’t be analyzed by traditional software applications such as RDBMS, OODBMS. It has also proven successful in handling human reactions with ease. Therefore, BIG DATA could certainly play a vital role in handling this issue. Thus, effectiveness of the education process can be enhanced with the help of video analytics using the latest BIG DATA technology.

Keywords: big data, cloud, CCTV, education process

Procedia PDF Downloads 218

Authors: Michela Quadrini

Abstract:

Chord diagrams occur in mathematics, from the study of RNA to knot theory. They are widely used in theory of knots and links for studying the finite type invariants, whereas in molecular biology one important motivation to study chord diagrams is to deal with the problem of RNA structure prediction. An RNA molecule is a linear polymer, referred to as the backbone, that consists of four types of nucleotides. Each nucleotide is represented by a point, whereas each chord of the diagram stands for one interaction for Watson-Crick base pairs between two nonconsecutive nucleotides. A chord diagram is an oriented circle with a set of n pairs of distinct points, considered up to orientation preserving diffeomorphisms of the circle. A linear chord diagram (LCD) is a special kind of graph obtained cutting the oriented circle of a chord diagram. It consists of a line segment, called its backbone, to which are attached a number of chords with distinct endpoints. There is a natural fattening on any linear chord diagram; the backbone lies on the real axis, while all the chords are in the upper half-plane. Each linear chord diagram has a natural genus of its associated surface. To each chord diagram and linear chord diagram, it is possible to associate the intersection graph. It consists of a graph whose vertices correspond to the chords of the diagram, whereas the chord intersections are represented by a connection between the vertices. Such intersection graph carries a lot of information about the diagram. Our goal is to define an LCD equivalence class in terms of identity of intersection graphs, from which many chord diagram invariants depend. For studying these invariants, we introduce a new representation of Linear Chord Diagrams based on a set of appropriate topological operators that permits to model LCD in terms of the relations among chords. Such set is composed of: crossing, nesting, and concatenations. The crossing operator is able to generate the whole space of linear chord diagrams, and a multiple context free grammar able to uniquely generate each LDC starting from a linear chord diagram adding a chord for each production of the grammar is defined. In other words, it allows to associate a unique algebraic term to each linear chord diagram, while the remaining operators allow to rewrite the term throughout a set of appropriate rewriting rules. Such rules define an LCD equivalence class in terms of the identity of intersection graphs. Starting from a modelled RNA molecule and the linear chord, some authors proposed a topological classification and folding. Our LCD equivalence class could contribute to the RNA folding problem leading to the definition of an algorithm that calculates the free energy of the molecule more accurately respect to the existing ones. Such LCD equivalence class could be useful to obtain a more accurate estimate of link between the crossing number and the topological genus and to study the relation among other invariants.

Keywords: chord diagrams, linear chord diagram, equivalence class, topological language

Procedia PDF Downloads 174

Authors: Rafael do Amaral Teodoro, Leandro Augusto da Silva

Abstract:

Originated from graphite, graphene is a two-dimensional (2D) material that promises to revolutionize technology in many different areas, such as energy, telecommunications, civil construction, aviation, textile, and medicine. This is possible because its structure, formed by carbon bonds, provides desirable optical, thermal, and mechanical characteristics that are interesting to multiple areas of the market. Thus, several research and development centers are studying different manufacturing methods and material applications of graphene, which are often compromised by the scarcity of more agile and accurate methodologies to characterize the material – that is to determine its composition, shape, size, and the number of layers and crystals. To engage in this search, this study proposes a computational methodology that applies deep learning to identify graphene oxide crystals in order to characterize samples by crystal sizes. To achieve this, a fully convolutional neural network called U-net has been trained to segment SEM graphene oxide images. The segmentation generated by the U-net is fine-tuned with a standard deviation technique by classes, which allows crystals to be distinguished with different labels through an object delimitation algorithm. As a next step, the characteristics of the position, area, perimeter, and lateral measures of each detected crystal are extracted from the images. This information generates a database with the dimensions of the crystals that compose the samples. Finally, graphs are automatically created showing the frequency distributions by area size and perimeter of the crystals. This methodological process resulted in a high capacity of segmentation of graphene oxide crystals, presenting accuracy and F-score equal to 95% and 94%, respectively, over the test set. Such performance demonstrates a high generalization capacity of the method in crystal segmentation, since its performance considers significant changes in image extraction quality. The measurement of non-overlapping crystals presented an average error of 6% for the different measurement metrics, thus suggesting that the model provides a high-performance measurement for non-overlapping segmentations. For overlapping crystals, however, a limitation of the model was identified. To overcome this limitation, it is important to ensure that the samples to be analyzed are properly prepared. This will minimize crystal overlap in the SEM image acquisition and guarantee a lower error in the measurements without greater efforts for data handling. All in all, the method developed is a time optimizer with a high measurement value, considering that it is capable of measuring hundreds of graphene oxide crystals in seconds, saving weeks of manual work.

Keywords: characterization, graphene oxide, nanomaterials, U-net, deep learning

Procedia PDF Downloads 135