Search results for: transportation network

Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

Procedia PDF Downloads 140

Authors: Eslam Mohammed Abdelkader, Mohamed Marzouk, Tarek Zayed

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Infrastructure systems are crucial to every aspect of life on Earth. Existing Infrastructure is subjected to degradation while the demands are growing for a better infrastructure system in response to the high standards of safety, health, population growth, and environmental protection. Bridges play a crucial role in urban transportation networks. Moreover, they are subjected to high level of deterioration because of the variable traffic loading, extreme weather conditions, cycles of freeze and thaw, etc. The development of Bridge Management Systems (BMSs) has become a fundamental imperative nowadays especially in the large transportation networks due to the huge variance between the need for maintenance actions, and the available funds to perform such actions. Deterioration models represent a very important aspect for the effective use of BMSs. This paper presents a probabilistic time-based model that is capable of predicting the condition ratings of the concrete bridge decks along its service life. The deterioration process of the concrete bridge decks is modeled using semi-Markov process. One of the main challenges of the Markov Chain Decision Process (MCDP) is the construction of the transition probability matrix. Yet, the proposed model overcomes this issue by modeling the sojourn times based on some probability density functions. The sojourn times of each condition state are fitted to probability density functions based on some goodness of fit tests such as Kolmogorov-Smirnov test, Anderson Darling, and chi-squared test. The parameters of the probability density functions are obtained using maximum likelihood estimation (MLE). The condition ratings obtained from the Ministry of Transportation in Quebec (MTQ) are utilized as a database to construct the deterioration model. Finally, a comparison is conducted between the Markov Chain and semi-Markov chain to select the most feasible prediction model.

Keywords: bridge management system, bridge decks, deterioration model, Semi-Markov chain, sojourn times, maximum likelihood estimation

Procedia PDF Downloads 203

Authors: Jinan Fiaidhi, Sabah Mohammed

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Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network.

Keywords: thick data analytics, siamese neural network, triplet-loss model, few shot learning

Procedia PDF Downloads 103

Authors: M. Nasri Nasrabadi, A. A. Hassani

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Karoon is one of the greatest and longest rivers of Iran, which because of the existence of numerous industrial, agricultural centers and drinking usage, has a strategic situation in the west and southwest parts of Iran, and the optimal monitoring of its water quality is an essential and indispensable national issue. Due to financial constraints, water quality monitoring network design is an efficient way to manage water quality. The most crucial part is to find appropriate locations for monitoring stations. Considering the objectives of water usage, we evaluate existing water quality sampling stations of this river. There are several methods for assessment of existing monitoring stations such as Sanders method, multiple criteria decision making and dynamic programming approach (DPA) which DPA opted in this study. The results showed that due to the drinking water quality index out of 20 existing monitoring stations, nine stations should be retained on the river, that include of Gorgor-Band-Ghir of A zone, Dez-Band-Ghir of B zone, Teir, Pole Panjom and Zargan of C zone, Darkhoein, Hafar, Chobade, and Sabonsazi of D zone. In additional, stations of Dez river have the best conditions.

Keywords: DPA, karoon river, network monitoring, water quality, sampling site

Procedia PDF Downloads 371

Authors: Imen Bouazzi

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The IEEE-802.15.4 standard utilizes the CSMA-CA mechanism to control nodes access to the shared wireless communication medium. It is becoming the popular choice for various applications of surveillance and control used in wireless sensor network (WSN). The benefit of this standard is evaluated regarding of the packet loss probability who depends on the configuration of IEEE 802.15.4 MAC parameters and the traffic load. Our exigency is to evaluate the effects of various configurable MAC parameters on the performance of beaconless IEEE 802.15.4 networks under different traffic loads, static values of IEEE 802.15.4 MAC parameters (macMinBE, macMaxCSMABackoffs, and macMaxFrame Retries) will be evaluated. To performance analysis, we use ns-2[2] network simulator.

Keywords: WSN, packet loss, CSMA/CA, IEEE-802.15.4

Procedia PDF Downloads 333

Authors: Omar M. Elmabrouk

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The cutting tool, in the high-speed machining process, is consistently dealing with high localized stress at the tool tip, tip temperature exceeds 800°C and the chip slides along the rake face. These conditions are affecting the tool wear, the cutting tool performances, the quality of the produced parts and the tool life. Therefore, a thin film coating on the cutting tool should be considered to improve the tool surface properties while maintaining its bulks properties. One of the general coating processes in applying thin film for hard coating purpose is PVD magnetron sputtering. In this paper, the prediction of the effects of PVD magnetron sputtering coating process parameters, sputter power in the range of (4.81-7.19 kW), bias voltage in the range of (50.00-300.00 Volts) and substrate temperature in the range of (281.08-600.00 °C), were studied using artificial neural network (ANN). The results were compared with previously published results using RSM model. It was found that the ANN is more accurate in prediction of tool hardness, and hence, it will not only improve the tool life of the tool but also significantly enhances the efficiency of the machining processes.

Keywords: artificial neural network, hardness, prediction, titanium aluminium nitrate coating

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Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane

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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.

Keywords: ASD, artificial neural network, kinect, stereotypical motor movements

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Authors: Umar Hayatu Sidik

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Application of liquid base petroleum fuel (petrol and diesel) for transportation fuel causes emissions of greenhouse gases (GHGs), while natural gas (NG) reduces the emissions of greenhouse gases (GHGs). At present, compression and liquefaction are the most matured technology used for transportation system. For transportation use, compression requires high pressure (200–300 bar) while liquefaction is impractical. A relatively low pressure of 30-40 bar is achievable by adsorbed natural gas (ANG) to store nearly compressed natural gas (CNG). In this study, adsorbents for high-pressure adsorption of methane (CH4) was prepared from coconut shells and polyetheretherketone (PEEK) using potassium hydroxide (KOH) and microwave-assisted activation. Design expert software version 7.1.6 was used for optimization and prediction of preparation conditions of the adsorbents for CH₄ adsorption. Effects of microwave power, activation time and quantity of PEEK on the adsorbents performance toward CH₄ adsorption was investigated. The adsorbents were characterized by Fourier transform infrared spectroscopy (FTIR), thermogravimetric (TG) and derivative thermogravimetric (DTG) and scanning electron microscopy (SEM). The ideal CH4 adsorption capacities of adsorbents were determined using volumetric method at pressures of 5, 17, and 35 bar at an ambient temperature and 5 oC respectively. Isotherm and kinetics models were used to validate the experimental results. The optimum preparation conditions were found to be 15 wt% amount of PEEK, 3 minutes activation time and 300 W microwave power. The highest CH4 uptake of 9.7045 mmol CH4 adsorbed/g adsorbent was recorded by M33P15 (300 W of microwave power, 3 min activation time and 15 wt% amount of PEEK) among the sorbents at an ambient temperature and 35 bar. The CH4 equilibrium data is well correlated with Sips, Toth, Freundlich and Langmuir. Isotherms revealed that the Sips isotherm has the best fit, while the kinetics studies revealed that the pseudo-second-order kinetic model best describes the adsorption process. In all scenarios studied, a decrease in temperature led to an increase in adsorption of both gases. The adsorbent (M33P15) maintained its stability even after seven adsorption/desorption cycles. The findings revealed the potential of coconut shell-PEEK as CH₄ adsorbents.

Keywords: adsorption, desorption, activated carbon, coconut shells, polyetheretherketone

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Authors: Gabriella Fardhiyanti, Victor Wee

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Air connectivity is the crucial factors to boost a region economics growth. It will open the accessibility to support regional competitiveness and helps to achieve ASEAN (Association of Southeast Asian Nations) integration in term of economic integration, business investment, promote intra-regional trade, and creates the sense of belongingness among ASEAN people in the region. An increasing number of air connectivity and transportation will be benefiting the region because air transportation is a vital hub for ASEAN. The aim of this paper is to address the importance of air connectivity in promoting ASEAN Integration, by focusing on the ASEAN vision for a more integrated region. The assessment uses based on the Netscan Air connectivity model based on the flight destination and airport connectivity index, further analysis present that air connectivity significantly influence ASEAN tourism sector. Follow by the implications of open skies policy for the liberation of the aviation industry and the growth of low cost-carriers (LCCs) in the region. This paper provides recommendation and strategy for overcoming the challenges faced by ASEAN to boost ASEAN tourism integration successfully. The findings can assist in guiding policy and industry stakeholders in the future decision relating to air liberalization and more integrated system in the region.

Keywords: air connectivity, ASEAN integration, low-cost carries, NetScan connectivity model, open skies policy

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Authors: Benkouiten Imene, Chabli Ouerdia, Boutoutaou Hamid, Kadri Nesrine, Bouledroua Omar

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Algeria is actively enhancing gas productivity by augmenting the supply flow. However, this effort has led to increased internal pressure, posing a potential risk to the pipeline's integrity, particularly in the presence of corrosion defects. Sonatrach relies on a vast network of pipelines spanning 24,000 kilometers for the transportation of gas and oil. The aging of these pipelines raises the likelihood of corrosion both internally and externally, heightening the risk of ruptures. To address this issue, a comprehensive inspection is imperative, utilizing specialized scraping tools. These advanced tools furnish a detailed assessment of all pipeline defects. It is essential to recalculate the pressure parameters to safeguard the corroded pipeline's integrity while ensuring the continuity of production. In this context, Sonatrach employs symbolic pressure limit calculations, such as ASME B31G (2009) and the modified ASME B31G (2012). The aim of this study is to perform a comparative analysis of various limit pressure calculation methods documented in the literature, namely DNV RP F-101, SHELL, P-CORRC, NETTO, and CSA Z662. This comparative assessment will be based on a dataset comprising 329 burst tests published in the literature. Ultimately, we intend to introduce a novel approach grounded in the finite element method, employing ANSYS software.

Keywords: pipeline burst pressure, burst test, corrosion defect, corroded pipeline, finite element method

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Authors: Fatai Olakunle Ogundele

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Road construction is increasingly common in today's world as human development expands and people increasingly rely on cars for transportation on a daily basis. The construction of a large network of roads has dramatically altered the landscape and impacted well-being in a number of deleterious ways. In addition, the road can also shift population demographics and be a source of pollution into the environment. Road construction activities normally result in changes in alteration of the soil's physical properties through soil compaction on the road itself and on adjacent areas and chemical and biological properties, among other effects. Understanding roadside soil properties that are influenced by road construction activities can serve as a basis for formulating conservation-based management strategies. Therefore, this study examined the effects of road construction on soil properties and soil organic carbon along Lagos Badagry Expressway, Lagos, Nigeria. The study adopted purposive sampling techniques and 40 soil samples were collected at a depth of 0 – 30cm from each of the identified road intersections and infrastructures using a soil auger. The soil samples collected were taken to the laboratory for soil properties and carbon stock analysis using standard methods. Both descriptive and inferential statistical techniques were applied to analyze the data obtained. The results revealed that soil compaction inhibits ecological succession on roadsides in that increased compaction suppresses plant growth as well as causes changes in soil quality.

Keywords: highway, soil properties, organic carbon, road construction, land degradation

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Authors: Sepideh Jabbari Behnam, Shadabeh Gashtasbi Iraei, Elnaz Mohsenin, MohammadAli Aghajani

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Urban neighborhoods, as important urban forming cells, play a key role in creating urban texture and integrated form. Nowadays, most of neighborhood divisions are based on urban management systems but without considering social issues and the other aspects of urban life. This can cause problems such as providing inappropriate services for city dwellers, the loss of local identity and etc. In this regard for regenerating of such neighborhoods, it is essential to locate neighborhood centers with appropriate access and services for all residents. The main objective of this article is reaching to the location of neighborhood centers in a way that, most of issues relating to the physical features (such as the form of access network and texture permeability and etc.) and other qualities such as land uses, densities and social and economic features can be done simultaneously. This paper attempts to use methods of spatial analysis in order to surveying spatial structure and space syntax of urban textures and Urban Network Analysis Systems. This can be done by one of GIS toolbars which is named UNA (Urban Network Analysis) with the use of its five functions (include: Reach, Betweenness, Gravity, Closeness, Straightness).These functions were written according to space syntax theory and offer its relating output. This paper tries to locate and evaluate the optimal location of neighborhood centers in order to create local centers. This is done through weighing of each of these functions and taking into account of spatial features.

Keywords: evaluate optimal location, Local centers, location of neighborhood centers, Spatial analysis, Urban network

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Authors: Panagiotis Gkekas, Christos Sougles, Dionysios Kehagias, Dimitrios Tzovaras

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This paper presents the architecture of the “Intelligent cooperative integrated system for road safety and road infrastructure maintenance towards 2020” (ODOS2020) advanced infrastructure, which implements a number of cooperative ITS applications based on Internet of Things and Infrastructure-to-Vehicle (V2I) technologies with the purpose to enhance the active road safety level of vehicles through the provision of a fully automated V2I environment. The primary objective of the ODOS2020 project is to contribute to increased road safety but also to the optimization of time for maintenance of road infrastructure. The integrated technological solution presented in this paper addresses all types of vehicles and requires minimum vehicle equipment. Thus, the ODOS2020 comprises a low-cost solution, which is one of its main benefits. The system architecture includes an integrated notification system to transmit personalized information on road, traffic, and environmental conditions, in order for the drivers to receive real-time and reliable alerts concerning upcoming critical situations. The latter include potential dangers on the road, such as obstacles or road works ahead, extreme environmental conditions, etc., but also informative messages, such as information on upcoming tolls and their charging policies. At the core of the system architecture lies an integrated sensorial network embedded in special road infrastructures (strips) that constantly collect and transmit wirelessly information about passing vehicles’ identification, type, speed, moving direction and other traffic information in combination with environmental conditions and road wear monitoring and predictive maintenance data. Data collected from sensors is transmitted by roadside infrastructure, which supports a variety of communication technologies such as ITS-G5 (IEEE-802.11p) wireless network and Internet connectivity through cellular networks (3G, LTE). All information could be forwarded to both vehicles and Traffic Management Centers (TMC) operators, either directly through the ITS-G5 network, or to smart devices with Internet connectivity, through cloud-based services. Therefore, through its functionality, the system could send personalized notifications/information/warnings and recommendations for upcoming events to both road users and TMC operators. In the course of the ODOS2020 project pilot operation has been conducted to allow drivers of both C-ITS equipped and non-equipped vehicles to experience the provided added value services. For non-equipped vehicles, the provided information is transmitted to a smartphone application. Finally, the ODOS2020 system and infrastructure is appropriate for installation on both urban, rural, and highway environments. The paper presents the various parts of the system architecture and concludes by outlining the various challenges that had to be overcome during its design, development, and deployment in a real operational environment. Acknowledgments: Work presented in this paper was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation (call RESEARCH–CREATE–INNOVATE) under contract no. Τ1EDK-03081 (project ODOS2020).

Keywords: infrastructure to vehicle, intelligent transportation systems, internet of things, road safety

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Authors: A. Andreasyan, C. Connors

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The Elliptic Curve Digital Signature algorithm-based X509v3 certificates are becoming more popular due to their short public and private key sizes. Moreover, these certificates can be stored in Internet of Things (IoT) devices, with limited resources, using less memory and transmitted in network security protocols, such as Internet Key Exchange (IKE), Transport Layer Security (TLS) and Secure Shell (SSH) with less bandwidth. The proposed method gives another advantage, in that it increases the performance of the above-mentioned protocols in terms of key exchange by saving one scalar multiplication operation.

Keywords: cryptography, elliptic curve digital signature algorithm, key exchange, network security protocol

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.

Keywords: speed model, artificial neural network, arterial, mixed traffic

Procedia PDF Downloads 380

Authors: Margareth Guimaraes Lima, Marilisa Berti A. Barros, Deborah Carvalho Malta

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The relationship between physical activity (PA) and depression has been investigated, in both, observational and clinical studies: PA can integrate the treatments for depression; the physical inactivity (PI) may contribute to increase depression symptoms; and on the other hand, emotional problems can decrease PA. The main of this study was analyze the association among leisure and transportation PI and time watching television (TV) according to depression (minor and major), evaluated with the Patient Health Questionnaire (PHQ-9). The association was also analyzed by gender. This is a cross-sectional study. Data were obtained from the National Health Survey 2013 (PNS), performed with representative sample of the Brazilian adult population, in 2013. The PNS collected information from 60,202 individuals, aged 18 years or more. The independent variable were: leisure time physical inactivity (LTPI), considering inactive or insufficiently actives (categories were linked for analyzes), those who do not performed a minimum of 150 or 74 minutes of moderate or vigorous LTPA, respectively, by week; transportation physical inactivity (TPI), individuals who did not reached 150 minutes, by week, travelling by bicycle or on foot to work or other activities; daily time watching TV > 5 hours. The principal independent variable was depression, identified by PHQ-9. Individuals were classified with major depression, with > 5 symptoms, more than seven days, but one of the symptoms was “depressive mood” or “lack of interest or pleasure”. The others had minor depression. The variables used to adjustment were gender, age, schooling and chronic disease. The prevalence of LTPI, TPI and TV time were estimated according to depression, and differences were tested with Chi-Square test. Adjusted prevalence ratios were estimated using multiple Poisson regression models. The analyzes also had stratification by gender. Mean age of the studied population was 42.9 years old (CI95%:42.6-43.2) and 52.9% were women. 77.5% and 68.1% were inactive or insufficiently active in leisure and transportation, respectively and 13.3% spent time watching TV 5 > hours. 6% and 4.1% of the Brazilian population were diagnosed with minor or major depression. LTPI prevalence was 5% and 9% higher among individuals with minor and major depression, respectively, comparing with no depression. The prevalence of TPI was 7% higher in those with major depression. Considering larger time watching TV, the prevalence was 45% and 74% higher among those with minor and major depression, respectively. Analyzing by gender, the associations were greater in men than women and TPI was note be associated, in women. The study detected the higher prevalence of leisure time physical inactivity and, especially, time spent watching TV, among individuals with major and minor depression, after to adjust for a number of potential confounding factors. TPI was only associated with major disorders and among men. Considering the cross-sectional design of the research, these associations can point out the importance of the mental problems control of the population to increase PA and decrease the sedentary lifestyle; on the other hand, the study highlight the need of interventions by encouraging people with depression, to practice PA, even to transportation.

Keywords: depression, physical activity, PHQ-9, sedentary lifestyle

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Authors: Abdulkader Helwan

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Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.

Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation

Procedia PDF Downloads 527

Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

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Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: Tatsuya Otoshi, Masayuki Murata

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With the development and diversification of applications on the Internet, applications that require high responsiveness, such as video streaming, are becoming mainstream. Application responsiveness is not only a matter of communication delay but also a matter of time required to grasp changes in network conditions. The tradeoff between accuracy and measurement time is a challenge in network control. We people make countless decisions all the time, and our decisions seem to resolve tradeoffs between time and accuracy. When making decisions, people are known to make appropriate choices based on relatively small samples. Although there have been various studies on models of human decision-making, a model that integrates various cognitive biases, called ”quantum decision-making,” has recently attracted much attention. However, the modeling of small samples has not been examined much so far. In this paper, we extend the model of quantum decision-making to model decision-making with a small sample. In the proposed model, the state is updated by value-based probability amplitude amplification. By analytically obtaining a lower bound on the number of samples required for decision-making, we show that decision-making with a small number of samples is feasible.

Keywords: quantum decision making, small sample, MPEG-DASH, Grover's algorithm

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Authors: Jaishree Ranganathan, MuthuPriya Shanmugakani Velsamy, Shamika Kulkarni, Angelina Tzacheva

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Emotions play an important role in everyday life. An-alyzing these emotions or feelings from social media platforms like Twitter, Facebook, blogs, and forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to benefit the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to benefit the end user. In this paper, we propose automatic classification of emotions in Twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion that helps to provide actionable suggestion.

Keywords: emotion mining, twitter, recurrent neural network, gated recurrent unit, actionable pattern mining

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Authors: Kavian Khosravinia, Mohd Khair Hassan, Ribhan Zafira Abdul Rahman, Syed Abdul Rahman Al-Haddad

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The CAN (controller area network) bus is introduced as a multi-master, message broadcast system. The messages sent on the CAN are used to communicate state information, referred as a signal between different ECUs, which provides data consistency in every node of the system. OBD-II Dongles that are based on request and response method is the wide-spread solution for extracting sensor data from cars among researchers. Unfortunately, most of the past researches do not consider resolution and quantity of their input data extracted through OBD-II technology. The maximum feasible scan rate is only 9 queries per second which provide 8 data points per second with using ELM327 as well-known OBD-II dongle. This study aims to develop and design a programmable, and latency-sensitive vehicle data acquisition system that improves the modularity and flexibility to extract exact, trustworthy, and fresh car sensor data with higher frequency rates. Furthermore, the researcher must break apart, thoroughly inspect, and observe the internal network of the vehicle, which may cause severe damages to the expensive ECUs of the vehicle due to intrinsic vulnerabilities of the CAN bus during initial research. Desired sensors data were collected from various vehicles utilizing Raspberry Pi3 as computing and processing unit with using OBD (request-response) and direct CAN method at the same time. Two types of data were collected for this study. The first, CAN bus frame data that illustrates data collected for each line of hex data sent from an ECU and the second type is the OBD data that represents some limited data that is requested from ECU under standard condition. The proposed system is reconfigurable, human-readable and multi-task telematics device that can be fitted into any vehicle with minimum effort and minimum time lag in the data extraction process. The standard operational procedure experimental vehicle network test bench is developed and can be used for future vehicle network testing experiment.

Keywords: CAN bus, OBD-II, vehicle data acquisition, connected cars, telemetry, Raspberry Pi3

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Authors: Nalinee B. Suryawanshi, Vinay M. Bhandari, Laxmi Gayatri Sorokhaibam, Vivek V. Ranade

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Deep desulphurization of transportation fuels is a major environmental concern all over the world and recently prescribed norms for the sulphur content require below 10 ppm sulphur concentrations in fuels such as diesel and gasoline. The existing technologies largely based on catalytic processes such as hydrodesulphurization, oxidation require newer catalysts and demand high cost of deep desulphurization whereas adsorption based processes have limitations due to lower capacity of sulphur removal. The present work is an attempt to provide alternatives for the existing methodologies using a newer non-catalytic process based on hydrodynamic cavitation. The developed process requires appropriate combining of organic and aqueous phases under ambient conditions and passing through a cavitating device such as orifice, venturi or vortex diode. The implosion of vapour cavities formed in the cavitating device generates (in-situ) oxidizing species which react with the sulphur moiety resulting in the removal of sulphur from the organic phase. In this work, orifice was used as a cavitating device and deep desulphurization was demonstrated for removal of thiophene as a model sulphur compound from synthetic fuel of n-octane, toluene and n-octanol. The effect of concentration of sulphur (up to 300 ppm), nature of organic phase and effect of pressure drop (0.5 to 10 bar) was discussed. A very high removal of sulphur content of more than 90% was demonstrated. The process is easy to operate, essentially works at ambient conditions and the ratio of aqueous to organic phase can be easily adjusted to maximise sulphur removal. Experimental studies were also carried out using commercial diesel as a solvent and the results substantiate similar high sulphur removal. A comparison of the two cavitating devices- one with a linear flow and one using vortex flow for effecting pressure drop and cavitation indicates similar trends in terms of sulphur removal behaviour. The developed process is expected to provide an attractive environmental engineering alternative for deep desulphurization of transportation fuels.

Keywords: cavitation, petroleum, separation, sulphur removal

Procedia PDF Downloads 375

Authors: Leah Wright, Trevor Townsend

Abstract:

Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand.

Keywords: Cloud theory, decision-making, mode choice models, travel behaviour, uncertainty

Procedia PDF Downloads 375

Authors: A. Hadadi, M. Abdollahi, A. Dustmohammadi

Abstract:

The growing use of DGs in distribution networks provide many advantages and also cause new problems which should be anticipated and be solved with appropriate solutions. One of the problems is transient voltage drop and short circuit in the electrical network, in the presence of distributed generation - which can lead to instability. The appearance of the short circuit will cause loss of generator synchronism, even though if it would be able to recover synchronizing mode after removing faulty generator, it will be stable. In order to increase system reliability and generator lifetime, some strategies should be planned to apply even in some situations which a fault prevent generators from separation. In this paper, one fault current limiter is installed due to prevent DGs separation from the grid when fault occurs. Furthermore, an innovative objective function is applied to determine the impedance optimal amount of fault current limiter in order to improve transient stability of distributed generation. Fault current limiter can prevent generator rotor's sudden acceleration after fault occurrence and thereby improve the network transient stability by reducing the current flow in a fast and effective manner. In fact, by applying created impedance by fault current limiter when a short circuit happens on the path of current injection DG to the fault location, the critical fault clearing time improve remarkably. Therefore, protective relay has more time to clear fault and isolate the fault zone without any instability. Finally, different transient scenarios of connection plan sustainability of small scale synchronous generators to the distribution network are presented.

Keywords: critical clearing time, fault current limiter, synchronous generator, transient stability, transient states

Procedia PDF Downloads 191

Authors: John Segars

Abstract:

Built out of a real-world need to have better, more detailed, asset and infrastructure documentation, this project will lay out the case for using the database functionality of ArcGIS as a tool to track and maintain infrastructure location, status, maintenance and serviceability. Workflows and processes will be presented and detailed which may be applied to an organizations’ infrastructure needs that might allow them to make use of the robust tools which surround the ArcGIS platform. The end result is a value-added information system framework with a geographic component e.g., the spatial location of various I.T. assets, a detailed set of records which not only documents location but also captures the maintenance history for assets along with photographs and documentation of these various assets as attachments to the numerous feature class items. In addition to the asset location and documentation benefits, the staff will be able to log into the devices and pull SNMP (Simple Network Management Protocol) based query information from within the user interface. The entire collection of information may be displayed in ArcGIS, via a JavaScript based web application or via queries to the back-end database. The project is applicable to all organizations which maintain an IT infrastructure but specifically targets post-secondary educational institutions where access to ESRI resources is generally already available in house.

Keywords: ESRI, GIS, infrastructure, network documentation, PostgreSQL

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

Procedia PDF Downloads 356

Authors: Elvan Ender Altay, Zeynep Pirselimoglu Batman, Murat Zencirkiran

Abstract:

Ancient cities are, according to United Nations Educational, Scientific and Cultural Organization (UNESCO), landscape areas designed and created by people, at the same time naturally developing and constantly changing sustainable cultural landscapes. Ancient cities are the urban settlements where we can see the reflection of public lifestyle existed thousands of years ago. The conceptual and spatial traces in ancient cities, are crucial for examining the city history and its preservation. This study is intended to demonstrate the impacts of human life and physical environment on the cultural landscape. This research aims to protect and maintain cultural continuity of the ancient cities in Bursa which contain archeological and historical elements and could not majorly reach to the day because of not being protected and to show importance of landscape architecture to ensure this protection. In this context, ancient cities in Bursa were researched and a total of 7 ancient cities were identified. These ancient cities are; Apollonia, Lopadion, Nicaea, Myrleia, Cius, Daskyleion and Basilinopolis. In the next stage, the spatial performances of ancient cities were assessed by weighted criteria method. The highest score is the Nicaea Ancient City. Considering current situation of the ancient cities in Bursa, it is seen that most of them could not survive until our day due to lack of interest in these areas. As a result, according to the findings, it is a priority to create a protective band with green areas around the archaeological sites, thus adapting to nearby areas and emphasizing culture. In addition, proposals have been made to provide a transportation network that does not harm the ancient cities and the cultural landscape.

Keywords: ancient cities, Bursa, landscape, spatial performance

Procedia PDF Downloads 191

Authors: Masoud Hassanzade Novin, Bahloul Salmani

Abstract:

The process of the translation is not merely the linguistic aspects. It is also considered in the cultural framework of both the source and target text cultures. The translation process and translated texts are confronted the new aspect in 20th century which is considered mostly in the patronage framework and ideological grillwork of the target language. To have these factors scrutinized in the process of the translation both micro-element factors and macro-element factors can be taken into consideration. For the purpose of this study through a qualitative type of research based on critical discourse analysis approach, the case study of the novel “1984” written by George Orwell was chosen as the corpus of the study to have the contrastive analysis by its Persian translated texts. Results of the study revealed some distortions embedded in the target texts which were overshadowed by ideological aspect and patronage network. The outcomes of the manipulated terms were different in various categories which revealed the manipulation aspects in the texts translated.

Keywords: critical discourse analysis, ideology, patronage network, translated texts

Procedia PDF Downloads 315

Authors: Simiao Ren, En Wei

Abstract:

Drowning is a significant safety issue worldwide, and a robust computer vision-based alert system can easily prevent such tragedies in swimming pools. However, due to domain shift caused by the visual gap (potentially due to lighting, indoor scene change, pool floor color etc.) between the training swimming pool and the test swimming pool, the robustness of such algorithms has been questionable. The annotation cost for labeling each new swimming pool is too expensive for mass adoption of such a technique. To address this issue, we propose a domain-aware data augmentation pipeline based on Gaussian Poisson Generative Adversarial Network (GP-GAN). Combined with YOLOv8, we demonstrate that such a domain adaptation technique can significantly improve the model performance (from 0.24 mAP to 0.82 mAP) on new test scenes. As the augmentation method only require background imagery from the new domain (no annotation needed), we believe this is a promising, practical route for preventing swimming pool drowning.

Keywords: computer vision, deep learning, YOLOv8, detection, swimming pool, drowning, domain adaptation, generative adversarial network, GAN, GP-GAN

Procedia PDF Downloads 85