Search results for: network lifetime

Authors: Marina Stramarkou, Sofia Papadaki, Maria Kaloupi, Ioannis Batzakas

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Biofouling is a complex problem of the aquaculture industry, as it reduces the efficiency of the equipment and causes significant losses of cultured organisms. Nowadays, the current antifouling methods are proved to be labor intensive, have limited lifetime and use toxic substances that result in fish mortality. Several species of marine algae produce a wide variety of biogenic compounds with antibacterial and antifouling properties, which are effective in the prevention and control of biofouling and can be incorporated in antifouling coatings. In the present work, Fucus spiralis, a species of macro algae, and Chlorella vulgaris, a well-known species of microalgae, were used for the isolation and recovery of bioactive compounds, belonging to groups of fatty acids, lipopeptides and amides. The recovery of the compounds was achieved through the application of the ultrasound- assisted extraction, an environmentally friendly method, using green, non-toxic solvents. Moreover, the coating of the antifouling agents was done by innovative encapsulation and coating methods, such as electro-hydrodynamic process. For the encapsulation of the bioactive compounds natural matrices were used, such as polysaccharides and proteins. Water extracts that were incorporated in protein matrices were considered the most efficient antifouling coating.

Keywords: algae, electrospinning, fatty acids, ultrasound-assisted extraction

Procedia PDF Downloads 334

Authors: Ridoy Das, Myriam Neaimeh, Yue Wang, Ghanim Putrus

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Electric vehicles are a popular transportation medium renowned for potential environmental benefits. However, large and uncontrolled charging volumes can impact distribution networks negatively. Smart charging is widely recognized as an efficient solution to achieve both improved renewable energy integration and grid relief. Nevertheless, different decision-makers may pursue diverse and conflicting objectives. In this context, this paper proposes a multi-objective optimization framework to control electric vehicle charging to achieve both energy cost reduction and peak shaving. A weighted-sum method is developed due to its intuitiveness and efficiency. Monte Carlo simulations are implemented to investigate the impact of uncertain electric vehicle driving patterns and provide decision-makers with a robust outcome in terms of prospective cost and network loading. The results demonstrate that there is a conflict between energy cost efficiency and peak shaving, with the decision-makers needing to make a collaborative decision.

Keywords: electric vehicles, multi-objective optimization, uncertainty, mixed integer linear programming

Procedia PDF Downloads 173

Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

Procedia PDF Downloads 102

Authors: Reena Murali, David Peter S.

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The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies shows that up regulation of mRNA cause serious diseases like Cancer. So designing effective siRNA with good knockdown effects play an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (ΔG), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.

Keywords: artificial neural network, double stranded RNA, RNA interference, short interfering RNA

Procedia PDF Downloads 518

Authors: Tharntip Tawnie Chutikulrungsee, Oliver Kisalay Burmeister, Maumita Bhattacharya, Dragana Calic

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Sharing on social network sites (SNSs) has rapidly emerged as a new social norm and has become a global phenomenon. Billions of users reveal not only their own information (self disclosure) but also information about others (other-generated disclosure), resulting in a risk and a serious threat to either personal or informational privacy. Self-disclosure (SD) has been extensively researched in the literature, particularly regarding control of individual and existing privacy management. However, far too little attention has been paid to other-generated disclosure (OGD), especially by insiders. OGD has a strong influence on self-presentation, self-image, and electronic word of mouth (eWOM). Moreover, OGD is more credible and less likely manipulated than SD, but lacks privacy control and legal protection to some extent. This article examines OGD in depth, ranging from motivation to both online and offline impacts, based upon lived experiences from both ‘the disclosed’ and ‘the discloser’. Using purposive sampling, this phenomenological study involves an online survey and in-depth interviews. The findings report the influence of peer disclosure as well as users’ strategies to mitigate privacy issues. This article also calls attention to the challenge of OGD privacy and inadequacies in the law related to privacy protection in the digital domain.

Keywords: facebook, online privacy, other-generated disclosure, social networks sites (SNSs)

Procedia PDF Downloads 240

Authors: Hanieh Tarbiat Khosrowshahi, Mojtaba Shakeri

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Wireless Sensor Networks (WSNs) consist of a set of sensor nodes with limited capability. WSNs may suffer from multiple node failures when they are exposed to harsh environments such as military zones or disaster locations and lose connectivity by getting partitioned into disjoint segments. Relay nodes (RNs) are alternatively introduced to restore connectivity. They cost more than sensors as they benefit from mobility, more power and more transmission range, enforcing a minimum number of them to be used. This paper addresses the problem of RN placement in a multiple disjoint network by developing a genetic algorithm (GA). The problem is reintroduced as the Steiner tree problem (which is known to be an NP-hard problem) by the aim of finding the minimum number of Steiner points where RNs are to be placed for restoring connectivity. An upper bound to the number of RNs is first computed to set up the length of initial chromosomes. The GA algorithm then iteratively reduces the number of RNs and determines their location at the same time. Experimental results indicate that the proposed GA is capable of establishing network connectivity using a reasonable number of RNs compared to the best existing work.

Keywords: connectivity restoration, genetic algorithms, multiple-node failure, relay nodes, wireless sensor networks

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Authors: Omer Cahana, Ofer Levi, Maya Herman

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

Procedia PDF Downloads 82

Authors: Kidus Dawit Gedamu

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Addis Ababa City Road Authority is responsible for managing and setting performance evaluation of the city’s road network using the International Roughness Index (IRI). This helps the authority to conduct pavement condition assessments of asphalt roads each year to determine the health status or Level of service (LOS) of the roadway network and plan program improvements such as maintenance, resurfacing and rehabilitation. For a lower IRI limit economical and acceptable maintenance strategy may be selected among a number of maintenance alternatives. The Highway Development and Management (HDM-4) tool can do such measures to help decide which option is the best by evaluating the economic and structural conditions. This paper specifically addresses flexible pavement, including two principal arterial streets under the administration of the Addis Ababa City Roads Authority. The roads include the road from Megenagna Interchange to Ayat Square and from Ayat Square to Tafo RA. First, it was assessed the procedures followed by the city's road authority to develop the appropriate road maintenance strategies. Questionnaire surveys and interviews are used to collect information from the city's road maintenance departments. Second, the project analysis was performed for functional and economic comparison of different maintenance alternatives using HDM-4.

Keywords: appropriate maintenance strategy, cost stream, road deterioration, maintenance alternative

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Authors: Evangelos G. Ilias, Panagiotis G. Ilias, Vasileios T. Popotas

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The United Kingdom has one of the oldest railway networks in the world dating back to 1825 when the world’s first passenger railway was opened. The network has some 40,000 bridges of various construction types using a wide range of materials including masonry, steel, cast iron, wrought iron, concrete and timber. It is commonly accepted that the successful operation of the network is vital for the economy of the United Kingdom, consequently the cost effective maintenance of the existing infrastructure is a high priority to maintain the operability of the network, prevent deterioration and to extend the life of the assets. Every bridge on the railway network is required to be assessed every eighteen years and a structured approach to assessments is adopted with three main types of progressively more detailed assessments used. These assessment types include Level 0 (standardized spreadsheet assessment tools), Level 1 (analytical hand calculations) and Level 2 (generally finite element analyses). There is a degree of conservatism in the first two types of assessment dictated to some extent by the relevant standards which can lead to some structures not achieving the required load rating. In these situations, a Level 2 Assessment is often carried out using finite element analysis to uncover ‘latent strength’ and improve the load rating. If successful, the more sophisticated analysis can save on costly strengthening or replacement works and avoid disruption to the operational railway. This paper presents the ‘strengthening by assessment’ achieved by Level 2 analyses. The use of more accurate analysis assumptions and the implementation of non-linear modelling and functions (material, geometric and support) to better understand buckling modes and the structural behaviour of historic construction details that are not specifically covered by assessment codes are outlined. Metallic bridges which are susceptible to loss of section size through corrosion have largest scope for improvement by the Level 2 Assessment methodology. Three case studies are presented, demonstrating the effectiveness of the sophisticated Level 2 Assessment methodology using finite element analysis against the conservative approaches employed for Level 0 and Level 1 Assessments. One rail overbridge and two rail underbridges that did not achieve the required load rating by means of a Level 1 Assessment due to the inadequate restraint provided by U-Frame action are examined and the increase in assessed capacity given by the Level 2 Assessment is outlined.

Keywords: assessment, bridges, buckling, finite element analysis, non-linear modelling, strengthening

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Authors: Vaclav Mentl, P. Zlabek, J. Volak

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New methods of mechanical testing were developed recently that are based on making use of miniature test specimens (e.g. Small Punch Test). The most important advantage of these method is the nearly non-destructive withdrawal of test material and small size of test specimen what is interesting in cases of remaining lifetime assessment when a sufficient volume of the representative material cannot be withdrawn of the component in question. In opposite, the most important disadvantage of such methods stems from the necessity to correlate test results with the results of standardised test procedures and to build up a database of material data in service. The correlations among the miniature test specimen data and the results of standardised tests are necessary. The paper describes the results of fatigue tests performed on miniature tests specimens in comparison with traditional fatigue tests for several steels applied in power producing industry. Special miniature test specimens fixtures were designed and manufactured for the purposes of fatigue testing at the Zwick/Roell 10HPF5100 testing machine. The miniature test specimens were produced of the traditional test specimens. Seven different steels were fatigue loaded (R = 0.1) at room temperature.

Keywords: mechanical properties, miniature test specimens, correlations, small punch test, micro-tensile test, mini-charpy impact test

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Authors: Sihem Chaibeddra, Fatoum Kharchi

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This study deals with the characterization of stabilized earth in the field of construction from the behavior under changes in conservation conditions that may occur during the lifetime of the material, namely, the exposure to high humidity and temperature variations. These two parameters are involved increasingly, because of climate changes that are confronting earth-based constructions to conditions for which they were not originally designed. These exposure conditions may affect the long-term behavior of the material and the entire structure. A cement treatment was adopted for stabilizing the earth with dosages ranging from 4, 6, 8 to 10%. The influence of addition percentage was analyzed in this context based on laboratory tests measuring the evolution of compressive strength, rate of absorption and shrinkage, and finally thermal conductivity. It was shown that the behaviour was dependent on the ambient conditions which influence the action of the binder. Temperate cure has proved beneficial for the material as the cement content increased. Moisture has less affected the compressive strength with increasing the cement content. The absorption was reduced with the increase of cement dosage. Regarding the variation of shrinkage, cement assays have presented an optimum value beyond which the addition of further quantities was less advantageous. The thermal conductivity on the other hand, increased with increasing cement content, which decreased the insulating properties of the material.

Keywords: behavior, characterization, construction, earth, stabilization

Procedia PDF Downloads 232

Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

Procedia PDF Downloads 150

Authors: Ian O'Loughlin

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Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.

Keywords: artificial reasoning, human memory, machine learning, neural networks

Procedia PDF Downloads 260

Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

Procedia PDF Downloads 180

Authors: Shola Haruna Adeosun, F. Adebiyi Ajoke, Odedeji Adeoye

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Video billboard advertising by networks and brand switching was conceived out of inquisition at the huge billboard advertising expenditures made by the three major GSM network operators in Nigeria. The study was anchored on Lagos State Metropolis with a current census population over 1,000,000. From this population, a purposive sample of 400 was adopted, and the questionnaire designed for the survey was carefully allocated to members of this ample in the five geographical zones of the city so that each rung of the society was well represented. The data obtained were analyzed using tables and simple percentages. The results obtained showed that subscribers of these networks were hardly influenced by the video billboard advertisements. They overwhelmingly showed that rather than the slogans of the GSM networks carried on the video billboards, it was the incentives to subscribers as well as the promotional strategies of these organizations that moved them to switch from one network to another. These switching lasted only as long as the incentives and promotions were in effect. The results of the study also seemed to rekindle the age-old debate on media effects, by the unyielding schools of the theory of ‘all-powerful media’, ‘the limited effects media’, ‘the controlled effects media’ and ‘the negotiated media influence’.

Keywords: evaluation, impact, video billboard, marketing, services

Procedia PDF Downloads 241

Authors: Kaining Zhang, Lang Chen, Danyang Liu, Jianying Lu, Kun Yang, Junying Wu

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In order to solve the problem of a large amount of simulation and limited simulation scale in the first-principle molecular dynamics simulation of energetic material detonation reaction, we established an artificial intelligence model for analyzing and predicting the detonation reaction mechanism of CL-20 based on the first-principle molecular dynamics simulation of the multiscale shock technique (MSST). We employed principal component analysis to identify the dominant charge features governing molecular reactions. We adopted the K-means clustering algorithm to cluster the reaction paths and screen out the key reactions. We introduced the neural network algorithm to construct the mapping relationship between the charge characteristics of the molecular structure and the key reaction characteristics so as to establish a calculation method for predicting detonation reactions based on the charge characteristics of CL-20 and realize the rapid analysis of the reaction mechanism of energetic materials.

Keywords: energetic material detonation reaction, first-principle molecular dynamics simulation of multiscale shock technique, neural network, CL-20

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Authors: Ting Kai Chia, Ruifeng Yan, Feifei Bai, Tapan Saha

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This research presents a real-world power system oscillation incident in 2022 originated by a hybrid solar photovoltaic (PV) and wind renewable energy farm with a rated capacity of approximately 300MW in Australia. The voltage and reactive power outputs recorded at the point of common coupling (PCC) oscillated at a sub-synchronous frequency region, which sustained for approximately five hours in the network. The reactive power oscillation gradually increased over time and reached a recorded maximum of approximately 250MVar peak-to-peak (from inductive to capacitive). The network service provider was not able to quickly identify the location of the oscillation source because the issue was widespread across the network. After the incident, the original equipment manufacturer (OEM) concluded that the oscillation problem was caused by the incorrect setting recovery of the hybrid power plant controller (HPPC) in the voltage and reactive power control loop after a loss of communication event. The voltage controller normally outputs a reactive (Q) reference value to the Q controller which controls the Q dispatch setpoint of PV and wind plants in the hybrid farm. Meanwhile, a feed-forward (FF) configuration is used to bypass the Q controller in case there is a loss of communication. Further study found that the FF control mode was still engaged when communication was re-established, which ultimately resulted in the oscillation event. However, there was no detailed explanation of why the FF control mode can cause instability in the hybrid farm. Also, there was no duplication of the event in the simulation to analyze the root cause of the oscillation. Therefore, this research aims to model and replicate the oscillation event in a simulation environment and investigate the underlying behavior of the HPPC and the consequent oscillation mechanism during the incident. The outcome of this research will provide significant benefits to the safe operation of large-scale renewable energy generators and power networks.

Keywords: PV, oscillation, modelling, wind

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Authors: Omer Cahana, Maya Herman, Ofer Levi

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Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.

Keywords: MRI, deep learning, variational network, computer vision, compress sensing

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Authors: Farhan Beg, Raymond Moberly

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The Indian subcontinent is facing a massive challenge with regards to energy security in member countries, to provide reliable electricity to facilitate development across various sectors of the economy and consequently achieve the developmental targets. The instability of the current precarious situation is observable in the frequent system failures and blackouts. The deployment of interconnected electricity ‘Supergrid’ designed to carry huge quanta of power across the Indian sub-continent is proposed in this paper. Besides enabling energy security in the subcontinent, it will also provide a platform for Renewable Energy Sources (RES) integration. This paper assesses the need and conditions for a Supergrid deployment and consequently proposes a meshed topology based on Voltage Source High Voltage Direct Current (VSC-HVDC) converters for the Supergrid modeling. Various control schemes for the control of voltage and power are utilized for the regulation of the network parameters. A 3 terminal Multi Terminal Direct Current (MTDC) network is used for the simulations.

Keywords: super grid, wind and solar energy, high voltage direct current, electricity management, load flow analysis

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Authors: Ching Louis Liu, Edmund Kazmierczak, Tim Miller

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Safety analysis for multi-agent systems is complicated by the, potentially nonlinear, interactions between agents. This paper proposes a method for analyzing the safety of multi-agent systems by explicitly focusing on interactions and the accident data of systems that are similar in structure and function to the system being analyzed. The method creates a Bayesian network using the accident data from similar systems. A feature of our method is that the events in accident data are labeled with HAZOP guide words. Our method uses an Ontology to abstract away from the details of a multi-agent implementation. Using the ontology, our methods then constructs an “Interaction Map,” a graphical representation of the patterns of interactions between agents and other artifacts. Interaction maps combined with statistical data from accidents and the HAZOP classifications of events can be converted into a Bayesian Network. Bayesian networks allow designers to explore “what it” scenarios and make design trade-offs that maintain safety. We show how to use the Bayesian networks, and the interaction maps to improve multi-agent system designs.

Keywords: multi-agent system, safety analysis, safety model, integration map

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Authors: Xiao Zhou, Jianlin Cheng

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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

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Authors: Christi Conde, Claudia Macias, Bianca Sornillo

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The effects of having a child with Autism Spectrum Disorder (ASD) extends to the family specifically, to the typically developing siblings. Provided that Filipino values involve close family-ties and family-centeredness, this study is interested in exploring the experiences of Filipino adolescents as a sibling of those diagnosed with ASD. A total of eleven (11) Filipino individuals, 3 males and 8 females, ages 11-24 years old, participated in the study – 6 of them were interviewed while the rest partook in a ginabayang talakayan (a variation of a focus group discussion). The data were analyzed using thematic analysis. Results showed 5 major themes: (1) the individual has mixed emotions and perceptions towards sibling, (2) the individual experiences differential treatment from parents, (3) the individual has responsibilities towards sibling, (4) the individual experiences personal growth, and (5) the individual is adjusting to the unfavorable effects of having sibling with ASD. Another emerging theme is an interplay between acceptance of one’s sibling, and one’s emotions and perceptions. It was also observed that there were more positive changes than negative within the individual. Having a lifetime responsibility towards sibling was also evident. Differences across ages involve the depth of awareness of the sibling’s condition and its implications. Acknowledgement of future responsibilities was evident regardless of age.

Keywords: adolescents, emotions, experiences, perceptions, qualitative research, siblings with ASD

Procedia PDF Downloads 342

Authors: Varun Singh, Mainak Bandyopadhyay, Maharana Pratap Singh

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The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS) and traffic assignment problem (in transportation planning). Well known applications of evolutionary methods like Genetic Algorithms (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO) have come up to solve complex optimization problems to overcome the shortcomings of existing shortest path analysis methods. It has been reported by various researchers that PSO performs better than other evolutionary optimization algorithms in terms of success rate and solution quality. Further Geographic Information Systems (GIS) have emerged as key information systems for geospatial data analysis and visualization. This research paper is focused towards the application of PSO for solving the shortest path problem between multiple points of interest (POI) based on spatial data of Allahabad City and traffic speed data collected using GPS. Geovisualization of results of analysis is carried out in GIS.

Keywords: particle swarm optimization, GIS, traffic data, outliers

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Authors: Ailing Yang, Penghan Song, Aihua Cai

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The nonlinear maneuvering target tracking problem is mainly a state estimation problem when the target motion model is uncertain. Traditional solutions include Kalman filtering based on Bayesian filtering framework and extended Kalman filtering. However, these methods need prior knowledge such as kinematics model and state system distribution, and their performance is poor in state estimation of nonprior complex dynamic systems. Therefore, in view of the problems existing in traditional algorithms, a convolution LSTM target state estimation (SAConvLSTM-SE) algorithm based on Self-Attention memory (SAM) is proposed to learn the historical motion state of the target and the error distribution information measured at the current time. The measured track point data of airborne radar are processed into data sets. After supervised training, the data-driven deep neural network based on SAConvLSTM can directly obtain the target state at the next moment. Through experiments on two different maneuvering targets, we find that the network has stronger robustness and better tracking accuracy than the existing tracking methods.

Keywords: maneuvering target, state estimation, Kalman filter, LSTM, self-attention

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Authors: X. F. Sun, X. D. Wen, L. J. Liu, C. C. Wu, Y. H. Jia

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The field reversed closed magnetic field configuration plasmoid has a potential for large thrust and high power propulsion missions such as deep space exploration due to its high plasma density and larger azimuthal current, which will be a most competitive program for the next generation electric propulsion technology. Moreover, without the electrodes, it also has a long lifetime. Thus, the research on this electric propulsion technology is quite necessary. The plasmoid will be formatted and accelerated by applying a rotation magnetic field (RMF) method. And, the essence of this technology lies on the generation of the azimuthal electron currents driven by RMF. Therefore, the effect of RMF current on the plasmoid acceleration efficiency is a concerned problem. In the paper, the influences of the penetration process of RMF in plasma, the relations of frequency and amplitude of input RF power with current strength and the RMF antenna configuration on the plasmoid acceleration efficiency will be given by a two-fluid numerical simulation method. The results show that the radio-frequency and input power have remarkable influence on the formation and acceleration of plasmoid. These results will provide useful advice for the development, and optimized designing of field reversed configuration plasmoid thruster.

Keywords: rotation magnetic field, current driven, plasma penetration, electric propulsion

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Authors: O. Abdelkhalek, A. Naimi, M. Rami, M. N. Tandjaoui, A. Kechich

Abstract:

This paper deals with the active and reactive power flow analysis inside the unified power quality conditioner (UPQC) during several cases. The UPQC is a combination of shunt and series active power filter (APF). It is one of the best solutions towards the mitigation of voltage sags and swells problems on distribution network. This analysis can provide the helpful information to well understanding the interaction between the series filter, the shunt filter, the DC bus link and electrical network. The mathematical analysis is based on active and reactive power flow through the shunt and series active power filter. Wherein series APF can absorb or deliver the active power to mitigate a swell or sage voltage where in the both cases it absorbs a small reactive power quantity whereas the shunt active power absorbs or releases the active power for stabilizing the storage capacitor’s voltage as well as the power factor correction. The voltage sag and voltage swell are usually interpreted through the DC bus voltage curves. These two phenomena are introduced in this paper with a new interpretation based on the active and reactive power flow analysis inside the UPQC. For simplifying this study, a linear load is supposed in this digital simulation. The simulation results are carried out to confirm the analysis done.

Keywords: UPQC, Power flow analysis, shunt filter, series filter.

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Authors: Vethathirri Ramanujam Srinivasan, S. M. Shiva Nagendra

Abstract:

Air quality management in urban areas is a serious concern in both developed and developing countries. In this regard, more number of air quality monitoring stations are planned to mitigate air pollution in urban areas. In India, Central Pollution Control Board has set up 574 air quality monitoring stations across the country and proposed to set up another 500 stations in the next few years. The number of monitoring stations for each city has been decided based on population data. The setting up of ambient air quality monitoring stations and their operation and maintenance are highly expensive. Therefore, there is a need to optimize monitoring networks for air quality management. The present paper discusses the various methods such as Indian Standards (IS) method, US EPA method and European Union (EU) method to arrive at the minimum number of air quality monitoring stations. In addition, optimization of rain-gauge method and Inverse Distance Weighted (IDW) method using Geographical Information System (GIS) are also explored in the present work for the design of air quality network in Chennai city. In summary, additionally 18 stations are required for Chennai city, and the potential monitoring locations with their corresponding land use patterns are ranked and identified from the 1km x 1km sized grids.

Keywords: air quality monitoring network, inverse distance weighted method, population based method, spatial variation

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Authors: Lu Zhao, Nadir Farhi, Zoi Christoforou, Nadia Haddadou

Abstract:

The safety test of deploying intelligent connected vehicles (ICVs) on the road network is a critical challenge. Road traffic network simulation can be used to test the functionality of ICVs, which is not only time-saving and less energy-consuming but also can create scenarios with car collisions. However, the relationship between different human driver behaviors and the car-collision occurrences has been not understood clearly; meanwhile, the procedure of car-collisions generation in the traffic numerical simulators is not fully integrated. In this paper, we propose an approach to identify specific driver profiles from real driven data; then, we replicate them in numerical traffic simulations with the purpose of generating inter-vehicular collisions. We proposed three profiles: (i) 'aggressive': short time-headway, (ii) 'inattentive': long reaction time, and (iii) 'normal' with intermediate values of reaction time and time-headway. These three driver profiles are extracted from the NGSIM dataset and simulated using the intelligent driver model (IDM), with an extension of reaction time. At last, the generation of inter-vehicular collisions is performed by varying the percentages of different profiles.

Keywords: vehicular collisions, human driving behavior, traffic modeling, car-following models, microscopic traffic simulation

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

Abstract:

In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

Procedia PDF Downloads 244

Authors: Mukesh Ranjan

Abstract:

Applying probit model on the micro data of NSS 71st round on education for understanding the access to education post the passage of Right to Education act,2009 in India. The empirical analysis shows that at all India level the mean age of enrollment in school is 5.5 years and drop-out age is around 14 years (or studied up to class 7) and around 60 percent females never get enrolled in any school in their lifetime. Nearly 20 percent children in Bihar never seen school and surprisingly, the relatively developed states like Gujarat, Maharashtra, Karnataka, Kerala and Tamil Nadu have more than one-third of the children and half of the children in Andhra Pradesh, West Bengal and Orissa as educationally wasted. The relative contribution in educational wastage is maximum by Bengal (10 %) while UP contributed a maximum of 30 % in educational non-enrollment in the country. Educational wastage is more likely to increase with age. Marriage is a resistive factor in getting education. Muslims are educationally more deprived than Hindus. Larger family and rich household are less likely to be educationally deprived. Major reasons for drop-out until 9 years were lack of interest in education and financial constraint; between 10-12 years, lack of interest and unable to cope up with studies and post 12 years financial constraint, marriage and other household reasons.

Keywords: probit model, educational wastage, educational non-enrollment, educational deprivation

Procedia PDF Downloads 294