Search results for: industrial wireless network (IWN)
6994 Artificial Neural Network Reconstruction of Proton Exchange Membrane Fuel Cell Output Profile under Transient Operation
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Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient
Procedia PDF Downloads 1706993 Study of the Behavior and Similarities of Flow and Level Transmitters in the Industries
Authors: Valentina Alessandra Carvalho do Vale, Elmo Thiago Lins Cöuras Ford
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In view of the requirements of the current industrial processes, the instrumentation plays a critical role. In this context, this work aims to raise some the operating characteristics of the level and flow transmitters, in addition to observing their similarities and possible limitations for certain configurations.Keywords: flow, level, instrumentation, configurations of meters, method of choice of the meters, instrumentation in the industrial processes
Procedia PDF Downloads 5796992 An Ensemble-based Method for Vehicle Color Recognition
Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi
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The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network
Procedia PDF Downloads 856991 Online Social Network Vital to Hospitality and Tourism Marketing and Management
Authors: Nureni Asafe Yekini, Olawale Nasiru Lawal, Bola Dada, Gabriel Adeyemi Okunlola
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This study is focused on the strengths and challenges associated with using the online social network as a rapidly evolving medium in marketing tourism services and businesses among the youths in Nigeria. The paper examines the Nigerian tourists’ attitude, mainly towards three aspects: application of Internet for travel and tourism; usage of online social networks in sharing travel and tourism experiences; and trust in electronic-media for marketing tourism businesses and services. The aim of this research is to determine the level of application of internet tools in marketing tourism businesses and services in Nigeria. This study reports an empirical analysis based on data obtained from a survey among 1004 Nigerian tourists. The outcome confirms the research hypothesis and points to crucial importance of introducing online social network site for marketing tourism businesses and services in Nigeria, and increasing the awareness for Nigeria as a tourist destination. Moreover, the paper strongly recommends the use of online social network as a tool for marketing tourism businesses and services, and the need for identifying effective framework for application of ICT tools in marketing tourism businesses and services in Nigeria at large.Keywords: tourism business, internet, online social networks, tourism services, ICT
Procedia PDF Downloads 3576990 Methaheuristic Bat Algorithm in Training of Feed-Forward Neural Network for Stock Price Prediction
Authors: Marjan Golmaryami, Marzieh Behzadi
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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange
Procedia PDF Downloads 5496989 A Custom Convolutional Neural Network with Hue, Saturation, Value Color for Malaria Classification
Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi
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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images
Procedia PDF Downloads 906988 Understanding Health Behavior Using Social Network Analysis
Authors: Namrata Mishra
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Health of a person plays a vital role in the collective health of his community and hence the well-being of the society as a whole. But, in today’s fast paced technology driven world, health issues are increasingly being associated with human behaviors – their lifestyle. Social networks have tremendous impact on the health behavior of individuals. Many researchers have used social network analysis to understand human behavior that implicates their social and economic environments. It would be interesting to use a similar analysis to understand human behaviors that have health implications. This paper focuses on concepts of those behavioural analyses that have health implications using social networks analysis and provides possible algorithmic approaches. The results of these approaches can be used by the governing authorities for rolling out health plans, benefits and take preventive measures, while the pharmaceutical companies can target specific markets, helping health insurance companies to better model their insurance plans.Keywords: breadth first search, directed graph, health behaviors, social network analysis
Procedia PDF Downloads 4736987 Partial M-Sequence Code Families Applied in Spectral Amplitude Coding Fiber-Optic Code-Division Multiple-Access Networks
Authors: Shin-Pin Tseng
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Nowadays, numerous spectral amplitude coding (SAC) fiber-optic code-division-multiple-access (FO-CDMA) techniques were appealing due to their capable of providing moderate security and relieving the effects of multiuser interference (MUI). Nonetheless, the performance of the previous network is degraded due to fixed in-phase cross-correlation (IPCC) value. Based on the above problems, a new SAC FO-CDMA network using partial M-sequence (PMS) code is presented in this study. Because the proposed PMS code is originated from M-sequence code, the system using the PMS code could effectively suppress the effects of MUI. In addition, two-code keying (TCK) scheme can applied in the proposed SAC FO-CDMA network and enhance the whole network performance. According to the consideration of system flexibility, simple optical encoders/decoders (codecs) using fiber Bragg gratings (FBGs) were also developed. First, we constructed a diagram of the SAC FO-CDMA network, including (N/2-1) optical transmitters, (N/2-1) optical receivers, and one N×N star coupler for broadcasting transmitted optical signals to arrive at the input port of each optical receiver. Note that the parameter N for the PMS code was the code length. In addition, the proposed SAC network was using superluminescent diodes (SLDs) as light sources, which then can save a lot of system cost compared with the other FO-CDMA methods. For the design of each optical transmitter, it is composed of an SLD, one optical switch, and two optical encoders according to assigned PMS codewords. On the other hand, each optical receivers includes a 1 × 2 splitter, two optical decoders, and one balanced photodiode for mitigating the effect of MUI. In order to simplify the next analysis, the some assumptions were used. First, the unipolarized SLD has flat power spectral density (PSD). Second, the received optical power at the input port of each optical receiver is the same. Third, all photodiodes in the proposed network have the same electrical properties. Fourth, transmitting '1' and '0' has an equal probability. Subsequently, by taking the factors of phase‐induced intensity noise (PIIN) and thermal noise, the corresponding performance was displayed and compared with the performance of the previous SAC FO-CDMA networks. From the numerical result, it shows that the proposed network improved about 25% performance than that using other codes at BER=10-9. This is because the effect of PIIN was effectively mitigated and the received power was enhanced by two times. As a result, the SAC FO-CDMA network using PMS codes has an opportunity to apply in applications of the next-generation optical network.Keywords: spectral amplitude coding, SAC, fiber-optic code-division multiple-access, FO-CDMA, partial M-sequence, PMS code, fiber Bragg grating, FBG
Procedia PDF Downloads 3856986 Forecasting the Temperature at a Weather Station Using Deep Neural Networks
Authors: Debneil Saha Roy
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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast horizon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron
Procedia PDF Downloads 1786985 Effect of Social Network Ties on Virtual Organization Success: Mediate Role of Knowledge Sharing Behaviors: An Empirical Study in Tourism Sector Firms in Jordan
Authors: Raed Hanandeh
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This empirical study examines how knowledge sharing behaviors mediate the effect Technology-driven strategy on virtual organization success in Jordanian tourism sector firms. The results reveal that Social network ties are positively related to web knowledge seeking, web knowledge contributing and interactive system, but negatively related to accidental knowledge leakage. Furthermore, all types of knowledge sharing behavior are positively related to virtual organization success. Data collected from 23 firms. The total number of questionnaires mailed, 250 questionnaires were delivered. 214 were considered valid out of 241 Responses were received. The findings provide evidence that knowledge sharing behavior play a mediating role between Social network ties and virtual organization success and show that, web knowledge seeking, web knowledge contributing and interactive system playing an important impact on virtual organization success through knowledge sharing behaviors.Keywords: social network ties, virtual organization success, knowledge sharing behaviors, web knowledge
Procedia PDF Downloads 2746984 Environmental Limits of Using Newly Developed Progressive Polymer Protection and Repair Systems
Authors: Jana Hodna, Bozena Vacenovska, Vit Petranek
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The paper is focused on the identification of limiting environmental factors of individual industrial floors on which newly developed polymer protection and repair systems with the use of secondary raw materials will be used. These mainly include floors with extreme stresses and special requirements for materials used. In relation to the environment of a particular industrial floor, it is necessary to ensure, for example, chemical stability, resistance to higher temperatures, resistance to higher mechanical stress, etc. for developed materials, which is reflected in the demands for the developed material systems. The paper describes individual environments and, in relation to them, also requirements for individual components of the developed materials and for the developed materials as a whole.Keywords: limits, environment, polymer, industrial floors, recycling, secondary raw material, protective system
Procedia PDF Downloads 2716983 Can the Intervention of SCAMPER Bring about Changes of Neural Activation While Taking Creativity Tasks?
Authors: Yu-Chu Yeh, WeiChin Hsu, Chih-Yen Chang
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Substitution, combination, modification, putting to other uses, elimination, and rearrangement (SCAMPER) has been regarded as an effective technique that provides a structured way to help people to produce creative ideas and solutions. Although some neuroscience studies regarding creativity training have been conducted, no study has focused on SCAMPER. This study therefore aimed at examining whether the learning of SCAMPER through video tutorials would result in alternations of neural activation. Thirty college students were randomly assigned to the experimental group or the control group. The experimental group was requested to watch SCAMPER videos, whereas the control group was asked to watch natural-scene videos which were regarded as neutral stimulating materials. Each participant was brain scanned in a Functional magnetic resonance imaging (fMRI) machine while undertaking a creativity test before and after watching the videos. Furthermore, a two-way ANOVA was used to analyze the interaction between groups (the experimental group; the control group) and tasks (C task; M task; X task). The results revealed that the left precuneus significantly activated in the interaction of groups and tasks, as well as in the main effect of group. Furthermore, compared with the control group, the experimental group had greater activation in the default mode network (left precuneus and left inferior parietal cortex) and the motor network (left postcentral gyrus and left supplementary area). The findings suggest that the SCAMPER training may facilitate creativity through the stimulation of the default mode network and the motor network.Keywords: creativity, default mode network, neural activation, SCAMPER
Procedia PDF Downloads 1016982 Design of an Improved Distributed Framework for Intrusion Detection System Based on Artificial Immune System and Neural Network
Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar
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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network
Procedia PDF Downloads 1096981 Gender Effects in EEG-Based Functional Brain Networks
Authors: Mahdi Jalili
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Functional connectivity in the human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks – whose nodes are brain regions and edges correspond to functional links between them – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using correlation analysis and were further binarized to obtain binary functional networks. Global and local efficiency measures – as graph theory metrics– were computed for the extracted networks. We found that male brains have a significantly greater global efficiency (i.e., global communicability of the network) across all frequency bands for a wide range of cost values in both hemispheres. Furthermore, for a range of cost values, female brains showed significantly greater right-hemispheric local efficiency (i.e., local connectivity) than male brains.Keywords: EEG, brain, functional networks, network science, graph theory
Procedia PDF Downloads 4446980 Finite Volume Method in Loop Network in Hydraulic Transient
Authors: Hossain Samani, Mohammad Ehteram
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In this paper, we consider finite volume method (FVM) in water hammer. We will simulate these techniques on a looped network with complex boundary conditions. After comparing methods, we see the FVM method as the best method. We compare the results of FVM with experimental data. Finite volume using staggered grid is applied for solving water hammer equations.Keywords: hydraulic transient, water hammer, interpolation, non-liner interpolation
Procedia PDF Downloads 3506979 Identification and Optimisation of South Africa's Basic Access Road Network
Authors: Diogo Prosdocimi, Don Ross, Matthew Townshend
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Road authorities are mandated within limited budgets to both deliver improved access to basic services and facilitate economic growth. This responsibility is further complicated if maintenance backlogs and funding shortfalls exist, as evident in many countries including South Africa. These conditions require authorities to make difficult prioritisation decisions, with the effect that Road Asset Management Systems with a one-dimensional focus on traffic volumes may overlook the maintenance of low-volume roads that provide isolated communities with vital access to basic services. Given these challenges, this paper overlays the full South African road network with geo-referenced information for population, primary and secondary schools, and healthcare facilities to identify the network of connective roads between communities and basic service centres. This connective network is then rationalised according to the Gross Value Added and number of jobs per mesozone, administrative and functional road classifications, speed limit, and road length, location, and name to estimate the Basic Access Road Network. A two-step floating catchment area (2SFCA) method, capturing a weighted assessment of drive-time to service centres and the ratio of people within a catchment area to teachers and healthcare workers, is subsequently applied to generate a Multivariate Road Index. This Index is used to assign higher maintenance priority to roads within the Basic Access Road Network that provide more people with better access to services. The relatively limited incidence of Basic Access Roads indicates that authorities could maintain the entire estimated network without exhausting the available road budget before practical economic considerations get any purchase. Despite this fact, a final case study modelling exercise is performed for the Namakwa District Municipality to demonstrate the extent to which optimal relocation of schools and healthcare facilities could minimise the Basic Access Road Network and thereby release budget for investment in roads that best promote GDP growth.Keywords: basic access roads, multivariate road index, road prioritisation, two-step floating catchment area method
Procedia PDF Downloads 2326978 Comparison of Spiral Circular Coil and Helical Coil Structures for Wireless Power Transfer System
Authors: Zhang Kehan, Du Luona
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Wireless power transfer (WPT) systems have been widely investigated for advantages of convenience and safety compared to traditional plug-in charging systems. The research contents include impedance matching, circuit topology, transfer distance et al. for improving the efficiency of WPT system, which is a decisive factor in the practical application. What is more, coil structures such as spiral circular coil and helical coil with variable distance between two turns also have indispensable effects on the efficiency of WPT systems. This paper compares the efficiency of WPT systems utilizing spiral or helical coil with variable distance between two turns, and experimental results show that efficiency of spiral circular coil with an optimum distance between two turns is the highest. According to efficiency formula of resonant WPT system with series-series topology, we introduce M²/R₋₁ to measure the efficiency of spiral circular coil and helical coil WPT system. If the distance between two turns s is too close, proximity effect theory shows that the induced current in the conductor, caused by a variable flux created by the current flows in the skin of vicinity conductor, is the opposite direction of source current and has assignable impart on coil resistance. Thus in two coil structures, s affects coil resistance. At the same time, when the distance between primary and secondary coils is not variable, s can also make the influence on M to some degrees. The aforementioned study proves that s plays an indispensable role in changing M²/R₋₁ and then can be adjusted to find the optimum value with which WPT system achieves the highest efficiency. In actual application situations of WPT systems especially in underwater vehicles, miniaturization is one vital issue in designing WPT system structures. Limited by system size, the largest external radius of spiral circular coil is 100 mm, and the largest height of helical coil is 40 mm. In other words, the turn of coil N changes with s. In spiral circular and helical structures, the distance between each two turns in secondary coil is set as a constant value 1 mm to guarantee that the R2 is not variable. Based on the analysis above, we set up spiral circular coil and helical coil model using COMSOL to analyze the value of M²/R₋₁ when the distance between each two turns in primary coil sp varies from 0 mm to 10 mm. In the two structure models, the distance between primary and secondary coils is 50 mm and wire diameter is chosen as 1.5 mm. The turn of coil in secondary coil are 27 in helical coil model and 20 in spiral circular coil model. The best value of s in helical coil structure and spiral circular coil structure are 1 mm and 2 mm respectively, in which the value of M²/R₋₁ is the largest. It is obviously to select spiral circular coil as the first choice to design the WPT system for that the value of M²/R₋₁ in spiral circular coil is larger than that in helical coil under the same condition.Keywords: distance between two turns, helical coil, spiral circular coil, wireless power transfer
Procedia PDF Downloads 3496977 Organization Structure of Towns and Villages System in County Area Based on Fractal Theory and Gravity Model: A Case Study of Suning, Hebei Province, China
Authors: Liuhui Zhu, Peng Zeng
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With the rapid development in China, the urbanization has entered the transformation and promotion stage, and its direction of development has shifted to overall regional synergy. China has a large number of towns and villages, with comparative small scale and scattered distribution, which always support and provide resources to cities leading to urban-rural opposition, so it is difficult to achieve common development in a single town or village. In this context, the regional development should focus more on towns and villages to form a synergetic system, joining the regional association with cities. Thus, the paper raises the question about how to effectively organize towns and villages system to regulate the resource allocation and improve the comprehensive value of the regional area. To answer the question, it is necessary to find a suitable research unit and analysis of its present situation of towns and villages system for optimal development. By combing relevant researches and theoretical models, the county is the most basic administrative unit in China, which can directly guide and regulate the development of towns and villages, so the paper takes county as the research unit. Following the theoretical concept of ‘three structures and one network’, the paper concludes the research framework to analyse the present situation of towns and villages system, including scale structure, functional structure, spatial structure, and organization network. The analytical methods refer to the fractal theory and gravity model, using statistics and spatial data. The scale structure analyzes rank-size dimensions and uses the principal component method to calculate the comprehensive scale of towns and villages. The functional structure analyzes the functional types and industrial development of towns and villages. The spatial structure analyzes the aggregation dimension, network dimension, and correlation dimension of spatial elements to represent the overall spatial relationships. In terms of organization network, from the perspective of entity and ono-entity, the paper analyzes the transportation network and gravitational network. Based on the present situation analysis, the optimization strategies are proposed in order to achieve a synergetic relationship between towns and villages in the county area. The paper uses Suning county in the Beijing-Tianjin-Hebei region as a case study to apply the research framework and methods and then proposes the optimization orientations. The analysis results indicate that: (1) The Suning county is lack of medium-scale towns to transfer effect from towns to villages. (2) The distribution of gravitational centers is uneven, and the effect of gravity is limited only for nearby towns and villages. The gravitational network is not complete, leading to economic activities scattered and isolated. (3) The overall development of towns and villages system is immature, staying at ‘single heart and multi-core’ stage, and some specific optimization strategies are proposed. This study provides a regional view for the development of towns and villages and concludes the research framework and methods of towns and villages system for forming an effective synergetic relationship between them, contributing to organize resources and stimulate endogenous motivation, and form counter magnets to join the urban-rural integration.Keywords: towns and villages system, organization structure, county area, fractal theory, gravity model
Procedia PDF Downloads 1386976 Exploring the Applications of Neural Networks in the Adaptive Learning Environment
Authors: Baladitya Swaika, Rahul Khatry
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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.Keywords: computer adaptive tests, item response theory, machine learning, neural networks
Procedia PDF Downloads 1766975 Hysteresis Modeling in Iron-Dominated Magnets Based on a Deep Neural Network Approach
Authors: Maria Amodeo, Pasquale Arpaia, Marco Buzio, Vincenzo Di Capua, Francesco Donnarumma
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Different deep neural network architectures have been compared and tested to predict magnetic hysteresis in the context of pulsed electromagnets for experimental physics applications. Modelling quasi-static or dynamic major and especially minor hysteresis loops is one of the most challenging topics for computational magnetism. Recent attempts at mathematical prediction in this context using Preisach models could not attain better than percent-level accuracy. Hence, this work explores neural network approaches and shows that the architecture that best fits the measured magnetic field behaviour, including the effects of hysteresis and eddy currents, is the nonlinear autoregressive exogenous neural network (NARX) model. This architecture aims to achieve a relative RMSE of the order of a few 100 ppm for complex magnetic field cycling, including arbitrary sequences of pseudo-random high field and low field cycles. The NARX-based architecture is compared with the state-of-the-art, showing better performance than the classical operator-based and differential models, and is tested on a reference quadrupole magnetic lens used for CERN particle beams, chosen as a case study. The training and test datasets are a representative example of real-world magnet operation; this makes the good result obtained very promising for future applications in this context.Keywords: deep neural network, magnetic modelling, measurement and empirical software engineering, NARX
Procedia PDF Downloads 1316974 An Efficient Algorithm for Global Alignment of Protein-Protein Interaction Networks
Authors: Duc Dong Do, Ngoc Ha Tran, Thanh Hai Dang, Cao Cuong Dang, Xuan Huan Hoang
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Global aligning two protein-protein interaction networks is an essentially important task in bioinformatics/computational biology field of study. It is a challenging and widely studied research topic in recent years. Accurately aligned networks allow us to identify functional modules of proteins and/ororthologous proteins from which unknown functions of a protein can be inferred. We here introduce a novel efficient heuristic global network alignment algorithm called FASTAn, including two phases: the first to construct an initial alignment and the second to improve such alignment by exerting a local optimization repeated procedure. The experimental results demonstrated that FASTAn outperformed the state-of-the-art global network alignment algorithm namely SPINAL in terms of both commonly used objective scores and the run-time.Keywords: FASTAn, Heuristic algorithm, biological network alignment, protein-protein interaction networks
Procedia PDF Downloads 6066973 Load Forecasting Using Neural Network Integrated with Economic Dispatch Problem
Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq
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High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch
Procedia PDF Downloads 1906972 Scheduling Tasks in Embedded Systems Based on NoC Architecture
Authors: D. Dorota
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This paper presents a method to generate and schedule task in the architecture of embedded systems based on the simulated annealing. This method takes into account the attribute of divisibility of tasks. A proposal represents the process in the form of trees. Despite the fact that the architecture of Network-on-Chip (NoC) is an interesting alternative to a bus architecture based on multi-processors systems, it requires a lot of work that ensures the optimization of communication. This paper proposes an effective approach to generate dedicated NoC topology solving communication problems. Network NoC is generated taking into account the energy consumption and resource issues. Ultimately generated is minimal, dedicated NoC topology. The proposed solution is assumed to be a simple router design and the minimum number of lines.Keywords: Network-on-Chip, NoC-based embedded systems, scheduling task in embedded systems, simulated annealing
Procedia PDF Downloads 3786971 Multi-Stream Graph Attention Network for Recommendation with Knowledge Graph
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In recent years, Graph neural network has been widely used in knowledge graph recommendation. The existing recommendation methods based on graph neural network extract information from knowledge graph through entity and relation, which may not be efficient in the way of information extraction. In order to better propose useful entity information for the current recommendation task in the knowledge graph, we propose an end-to-end Neural network Model based on multi-stream graph attentional Mechanism (MSGAT), which can effectively integrate the knowledge graph into the recommendation system by evaluating the importance of entities from both users and items. Specifically, we use the attention mechanism from the user's perspective to distil the domain nodes information of the predicted item in the knowledge graph, to enhance the user's information on items, and generate the feature representation of the predicted item. Due to user history, click items can reflect the user's interest distribution, we propose a multi-stream attention mechanism, based on the user's preference for entities and relationships, and the similarity between items to be predicted and entities, aggregate user history click item's neighborhood entity information in the knowledge graph and generate the user's feature representation. We evaluate our model on three real recommendation datasets: Movielens-1M (ML-1M), LFM-1B 2015 (LFM-1B), and Amazon-Book (AZ-book). Experimental results show that compared with the most advanced models, our proposed model can better capture the entity information in the knowledge graph, which proves the validity and accuracy of the model.Keywords: graph attention network, knowledge graph, recommendation, information propagation
Procedia PDF Downloads 1196970 Catalytic Wet Air Oxidation as a Pretreatment Option for Biodegradability Enhancement of Industrial Effluent
Authors: Sushma Yadav, Anil K. Saroha
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Complex industrial effluent generated from chemical industry is contaminated with toxic and hazardous organic compounds and not amenable to direct biological treatment. To effectively remove many toxic organic pollutants has made it evident that new, compact and more efficient systems are needed. Catalytic Wet Air Oxidation (CWAO) is a promising treatment technology for the abatement of organic pollutants in wastewater. A lot of information is available on using CWAO for the treatment of synthetic solution containing single organic pollutant. But the real industrial effluents containing multi-component mixture of organic compounds were less studied. The main objective of this study is to use the CWAO process for converting the organics into compounds more amenable to biological treatment; complete oxidation may be too expensive. Therefore efforts were made in the present study to explore the potential of alumina based Platinum (Pt) catalyst for the treatment of industrial organic raffinate containing toxic constituents like ammoniacal nitrogen, pyridine etc. The catalysts were prepared by incipient wetness impregnation method and characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX) and BET (Brunauer, Emmett, and Teller) surface area. CWAO experiments were performed at atmospheric pressure and (30 °C - 70 °C) temperature conditions and the results were evaluated in terms of COD removal efficiency. The biodegradability test was performed by BOD/COD ratio for checking the toxicity of the industrial wastewater as well as for the treated water. The BOD/COD ratio of treated water was significantly increased and signified that the toxicity of the organics was decreased while the biodegradability was increased, indicating the more amenability towards biological treatment.Keywords: alumina based pt catalyst, BOD/COD ratio, catalytic wet air oxidation, COD removal efficiency, industrial organic raffinate
Procedia PDF Downloads 3066969 Developing a Cloud Intelligence-Based Energy Management Architecture Facilitated with Embedded Edge Analytics for Energy Conservation in Demand-Side Management
Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li
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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification
Procedia PDF Downloads 2516968 Multivariate Analysis on Water Quality Attributes Using Master-Slave Neural Network Model
Authors: A. Clementking, C. Jothi Venkateswaran
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Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources.Keywords: master-slave back propagation neural network model(MSBPNNM), water quality analysis, multivariate analysis, environmental mining
Procedia PDF Downloads 4786967 Distributed Coordination of Connected and Automated Vehicles at Multiple Interconnected Intersections
Authors: Zhiyuan Du, Baisravan Hom Chaudhuri, Pierluigi Pisu
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In connected vehicle systems where wireless communication is available among the involved vehicles and intersection controllers, it is possible to design an intersection coordination strategy that leads the connected and automated vehicles (CAVs) travel through the road intersections without the conventional traffic light control. In this paper, we present a distributed coordination strategy for the CAVs at multiple interconnected intersections that aims at improving system fuel efficiency and system mobility. We present a distributed control solution where in the higher level, the intersection controllers calculate the road desired average velocity and optimally assign reference velocities of each vehicle. In the lower level, every vehicle is considered to use model predictive control (MPC) to track their reference velocity obtained from the higher level controller. The proposed method has been implemented on a simulation-based case with two-interconnected intersection network. Additionally, the effects of mixed vehicle types on the coordination strategy has been explored. Simulation results indicate the improvement on vehicle fuel efficiency and traffic mobility of the proposed method.Keywords: connected vehicles, automated vehicles, intersection coordination systems, multiple interconnected intersections, model predictive control
Procedia PDF Downloads 3576966 Rain Gauges Network Optimization in Southern Peninsular Malaysia
Authors: Mohd Khairul Bazli Mohd Aziz, Fadhilah Yusof, Zulkifli Yusop, Zalina Mohd Daud, Mohammad Afif Kasno
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Recent developed rainfall network design techniques have been discussed and compared by many researchers worldwide due to the demand of acquiring higher levels of accuracy from collected data. In many studies, rain-gauge networks are designed to provide good estimation for areal rainfall and for flood modelling and prediction. In a certain study, even using lumped models for flood forecasting, a proper gauge network can significantly improve the results. Therefore existing rainfall network in Johor must be optimized and redesigned in order to meet the required level of accuracy preset by rainfall data users. The well-known geostatistics method (variance-reduction method) that is combined with simulated annealing was used as an algorithm of optimization in this study to obtain the optimal number and locations of the rain gauges. Rain gauge network structure is not only dependent on the station density; station location also plays an important role in determining whether information is acquired accurately. The existing network of 84 rain gauges in Johor is optimized and redesigned by using rainfall, humidity, solar radiation, temperature and wind speed data during monsoon season (November – February) for the period of 1975 – 2008. Three different semivariogram models which are Spherical, Gaussian and Exponential were used and their performances were also compared in this study. Cross validation technique was applied to compute the errors and the result showed that exponential model is the best semivariogram. It was found that the proposed method was satisfied by a network of 64 rain gauges with the minimum estimated variance and 20 of the existing ones were removed and relocated. An existing network may consist of redundant stations that may make little or no contribution to the network performance for providing quality data. Therefore, two different cases were considered in this study. The first case considered the removed stations that were optimally relocated into new locations to investigate their influence in the calculated estimated variance and the second case explored the possibility to relocate all 84 existing stations into new locations to determine the optimal position. The relocations of the stations in both cases have shown that the new optimal locations have managed to reduce the estimated variance and it has proven that locations played an important role in determining the optimal network.Keywords: geostatistics, simulated annealing, semivariogram, optimization
Procedia PDF Downloads 3046965 Survey Based Data Security Evaluation in Pakistan Financial Institutions against Malicious Attacks
Authors: Naveed Ghani, Samreen Javed
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In today’s heterogeneous network environment, there is a growing demand for distrust clients to jointly execute secure network to prevent from malicious attacks as the defining task of propagating malicious code is to locate new targets to attack. Residual risk is always there no matter what solutions are implemented or whet so ever security methodology or standards being adapted. Security is the first and crucial phase in the field of Computer Science. The main aim of the Computer Security is gathering of information with secure network. No one need wonder what all that malware is trying to do: It's trying to steal money through data theft, bank transfers, stolen passwords, or swiped identities. From there, with the help of our survey we learn about the importance of white listing, antimalware programs, security patches, log files, honey pots, and more used in banks for financial data protection but there’s also a need of implementing the IPV6 tunneling with Crypto data transformation according to the requirements of new technology to prevent the organization from new Malware attacks and crafting of its own messages and sending them to the target. In this paper the writer has given the idea of implementing IPV6 Tunneling Secessions on private data transmission from financial organizations whose secrecy needed to be safeguarded.Keywords: network worms, malware infection propagating malicious code, virus, security, VPN
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