Search results for: graph neural network

Authors: Shan-Jen Cheng, Te-Jen Chang, Kuang-Hsiung Tan, Shou-Ling Kuo

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Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.

Keywords: PEMFC, neural network, nonlinear modeling, NNARX

Procedia PDF Downloads 365

Authors: Yu Song, Yuefei Jin

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Urban public transportation can be integrated when there is an efficient connection between urban rail lines, however, there are currently no effective or quick solutions being investigated for this connection. This paper analyzes the space-time distribution and travel demand of passenger connection travel based on taxi track data and data from the road network, excavates potential bus connection stations based on potential connection demand data, and introduces the link growth probability model in the complex network to solve the basic connection bus lines in order to ascertain the direction of the bus lines that are the most connected given the demand characteristics. Then, a tree view exhaustive approach based on constraints is suggested based on graph theory, which can hasten the convergence of findings while doing chain calculations. This study uses WEI QU NAN Station, the Xi'an Metro Line 2 terminal station in Shaanxi Province, as an illustration, to evaluate the model's and the solution method's efficacy. According to the findings, 153 prospective stations have been dug up in total, the feeder bus network for the entire line has been laid out, and the best route adjustment strategy has been found.

Keywords: feeder bus, route optimization, link growth probability, the graph theory

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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 373

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: 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: Redouane Tlemsani, Redouane, Belkacem Kouninef, Abdelkader Benyettou

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In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables).

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, networks

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Authors: Van Trieu, Shouhuai Xu, Yusheng Feng

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Tracking attack trajectories can be difficult, with limited information about the nature of the attack. Even more difficult as attack information is collected by Intrusion Detection Systems (IDSs) due to the current IDSs having some limitations in identifying malicious and anomalous traffic. Moreover, IDSs only point out the suspicious events but do not show how the events relate to each other or which event possibly cause the other event to happen. Because of this, it is important to investigate new methods capable of performing the tracking of attack trajectories task quickly with less attack information and dependency on IDSs, in order to prioritize actions during incident responses. This paper proposes a two-level graph causality framework for tracking attack trajectories in internet networks by leveraging observable malicious behaviors to detect what is the most probable attack events that can cause another event to occur in the system. Technically, given the time series of malicious events, the framework extracts events with useful features, such as attack time and port number, to apply to the conditional independent tests to detect the relationship between attack events. Using the academic datasets collected by IDSs, experimental results show that the framework can quickly detect the causal pairs that offer meaningful insights into the nature of the internet network, given only reasonable restrictions on network size and structure. Without the framework’s guidance, these insights would not be able to discover by the existing tools, such as IDSs. It would cost expert human analysts a significant time if possible. The computational results from the proposed two-level graph network model reveal the obvious pattern and trends. In fact, more than 85% of causal pairs have the average time difference between the causal and effect events in both computed and observed data within 5 minutes. This result can be used as a preventive measure against future attacks. Although the forecast may be short, from 0.24 seconds to 5 minutes, it is long enough to be used to design a prevention protocol to block those attacks.

Keywords: causality, multilevel graph, cyber-attacks, prediction

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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

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Authors: Danilo López, Edwin Rivas, Leyla López

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This paper presents the development of an algorithm that predicts the arrival of a secondary user (SU) to a base station (BS) in a cognitive network based on infrastructure, requesting a Best Effort (BE) or Real Time (RT) type of service with a determined bandwidth (BW) implementing neural networks. The algorithm dynamically uses a neural network construction technique using the geometric pyramid topology and trains a Multilayer Perceptron Neural Networks (MLPNN) based on the historical arrival of an SU to estimate future applications. This will allow efficiently managing the information in the BS, since it precedes the arrival of the SUs in the stage of selection of the best channel in CRN. As a result, the software application determines the probability of arrival at a future time point and calculates the performance metrics to measure the effectiveness of the predictions made.

Keywords: cognitive radio, base station, best effort, MLPNN, prediction, real time

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Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

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In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

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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

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Authors: Linh Nguyen Tung, Anh Truong Viet, Nghien Nguyen Ba, Chuong Trinh Trong

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This paper proposes a reconfiguration methodology based on a continuous genetic algorithm (CGA) and particle swarm optimization (PSO) for minimizing active power loss and minimizing voltage deviation. Both algorithms are adapted using graph theory to generate feasible individuals, and the modified crossover is used for continuous variable of CGA. To demonstrate the performance and effectiveness of the proposed methods, a comparative analysis of CGA with PSO for network reconfiguration, on 33-node and 119-bus radial distribution system is presented. The simulation results have shown that both CGA and PSO can be used in the distribution network reconfiguration and CGA outperformed PSO with significant success rate in finding optimal distribution network configuration.

Keywords: distribution network reconfiguration, particle swarm optimization, continuous genetic algorithm, power loss reduction, voltage deviation

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Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Hiba Hamdi, Thomas Corpetti, Laure Roupioz, Xavier Briottet

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Air temperatures are generally higher in cities than in their rural surroundings. The overheating of cities is a direct consequence of increasing urbanization, characterized by the artificial filling of soils, the release of anthropogenic heat, and the complexity of urban geometry. This phenomenon, referred to as urban heat island (UHI), is more prevalent during heat waves, which have increased in frequency and intensity in recent years. In the context of global warming and urban population growth, helping urban planners implement UHI mitigation and adaptation strategies is critical. In practice, the study of UHI requires air temperature information at the street canyon level, which is difficult to obtain. Many urban air temperature simulation models have been proposed (mostly based on physics or statistics), all of which require a variety of input parameters related to urban morphology, land use, material properties, or meteorological conditions. In this paper, we build and evaluate a neural network model based on Urban Weather Generator (UWG) model simulations and data from meteorological stations that simulate air temperature over Toulouse, France, on days favourable to UHI.

Keywords: air temperature, neural network model, urban heat island, urban weather generator

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Authors: Sanjeev Kumar Sharma, Arnab Mondal, Ranjit Kumar Upadhyay

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Excitable cells often produce different oscillatory activities that help us to understand the transmitting and processing of signals in the neural system. We consider a FitzHugh-Rinzel (FH-R) model and studied the different dynamics of the model by considering the parameter c as the predominant parameter. The model exhibits different types of neuronal responses such as regular spiking, mixed-mode bursting oscillations (MMBOs), elliptic bursting, etc. Based on the bifurcation diagram, we consider the three regimes (MMBOs, elliptic bursting, and quiescent state). An analytical treatment for the occurrence of the supercritical Hopf bifurcation is studied. Further, we extend our study to a network of a hundred neurons by considering the bi-directional synaptic coupling between them. In this article, we investigate the alternation of spiking propagation and bursting phenomena of an uncoupled and coupled FH-R neurons. We explore that the complete graph of heterogenous desynchronized neurons can exhibit different types of bursting oscillations for certain coupling strength. For higher coupling strength, all the neurons in the network show complete synchronization.

Keywords: excitable neuron model, spiking-bursting, stability and bifurcation, synchronization networks

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Sopheak Sorn, Kwok Yip Szeto

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Reliability is one of the important measures of how well the system meets its design objective, and mathematically is the probability that a complex system will perform satisfactorily. When the system is described by a network of N components (nodes) and their L connection (links), the reliability of the system becomes a network design problem that is an NP-hard combinatorial optimization problem. In this paper, we address the network design problem for a random point set’s pattern in two dimensions. We make use of a Voronoi construction with each cell containing exactly one point in the point pattern and compute the reliability of the Voronoi’s dual, i.e. the Delaunay graph. We further investigate the communicability of the Delaunay network. We find that there is a positive correlation and a negative correlation between the homogeneity of a Delaunay's degree distribution with its reliability and its communicability respectively. Based on the correlations, we alter the communicability and the reliability by performing random edge flips, which preserve the number of links and nodes in the network but can increase the communicability in a Delaunay network at the cost of its reliability. This transformation is later used to optimize a Delaunay network with the optimum geometric mean between communicability and reliability. We also discuss the importance of the edge flips in the evolution of real soap froth in two dimensions.

Keywords: Communicability, Delaunay triangulation, Edge Flip, Reliability, Two dimensional network, Voronio

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Authors: Rihab Ben Lamine, Raoudha Ben Jemaa, Ikram Amous Ben Amor

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This paper proposes a graph planning technique for semantic adaptable Web Services composition. First, we use an ontology based context model for extending Web Services descriptions with information about the most suitable context for its use. Then, we transform the composition problem into a semantic context aware graph planning problem to build the optimal service composition based on user's context. The construction of the planning graph is based on semantic context aware Web Service discovery that allows for each step to add most suitable Web Services in terms of semantic compatibility between the services parameters and their context similarity with the user's context. In the backward search step, semantic and contextual similarity scores are used to find best composed Web Services list. Finally, in the ranking step, a score is calculated for each best solution and a set of ranked solutions is returned to the user.

Keywords: semantic web service, web service composition, adaptation, context, graph planning

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Authors: M. K. Wijayarathna, S. M. Buddika Harshanath

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Monitoring student engagement has become a crucial part of the educational process and a reliable indicator of the capacity to retain information. As online learning classrooms are now more common these days, students' attention levels have become increasingly important, making it more difficult to check each student's concentration level in an online classroom setting. To profile student attention to various gradients of engagement, a study is a plan to conduct using machine learning models. Using a convolutional neural network, the findings and confidence score of the high accuracy model are obtained. In this research, convolutional neural networks are using to help discover essential emotions that are critical in defining various levels of participation. Students' attention levels were shown to be influenced by emotions such as calm, enjoyment, surprise, and fear. An improved virtual learning system was created as a result of these data, which allowed teachers to focus their support and advise on those students who needed it. Student participation has formed as a crucial component of the learning technique and a consistent predictor of a student's capacity to retain material in the classroom. Convolutional neural networks have a plan to implement the platform. As a preliminary step, a video of the pupil would be taken. In the end, researchers used a convolutional neural network utilizing the Keras toolkit to take pictures of the recordings. Two convolutional neural network methods are planned to use to determine the pupils' attention level. Finally, those predicted student attention level results plan to display on the graphical user interface of the System.

Keywords: HTML5, JavaScript, Python flask framework, AI, graphical user

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Authors: Feng Wang, Shencheng Ni, Shuying Han, Shanhong You

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With the development of optical communication, optical performance monitoring (OPM) has received more and more attentions. Since optical signal-to-noise ratio (OSNR) is directly related to bit error rate (BER), it is one of the important parameters in optical networks. Recently, artificial neural network (ANN) has been greatly developed. ANN has strong learning and generalization ability. In this paper, a method of OSNR monitoring based on delay-tap sampling (DTS) and ANN has been proposed. DTS technique is used to extract the eigenvalues of the signal. Then, the eigenvalues are input into the ANN to realize the OSNR monitoring. The experiments of 10 Gb/s non-return-to-zero (NRZ) on–off keying (OOK), 20 Gb/s pulse amplitude modulation (PAM4) and 20 Gb/s return-to-zero (RZ) differential phase-shift keying (DPSK) systems are demonstrated for the OSNR monitoring based on the proposed method. The experimental results show that the range of OSNR monitoring is from 15 to 30 dB and the root-mean-square errors (RMSEs) for 10 Gb/s NRZ-OOK, 20 Gb/s PAM4 and 20 Gb/s RZ-DPSK systems are 0.36 dB, 0.45 dB and 0.48 dB respectively. The impact of chromatic dispersion (CD) on the accuracy of OSNR monitoring is also investigated in the three experimental systems mentioned above.

Keywords: artificial neural network (ANN), chromatic dispersion (CD), delay-tap sampling (DTS), optical signal-to-noise ratio (OSNR)

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Sergey Ermolin, Olga Ermolin

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A Deep Convolution Neural Network with Triplet Loss is used to identify duplicate images in real-estate advertisements in the presence of image artifacts such as watermarking, cropping, hue/brightness adjustment, and others. The effects of batch normalization, spatial dropout, and various convergence methodologies on the resulting detection accuracy are discussed. For comparative Return-on-Investment study (per industry request), end-2-end performance is benchmarked on both Nvidia Titan GPUs and Intel’s Xeon CPUs. A new real-estate dataset from San Francisco Bay Area is used for this work. Sufficient duplicate detection accuracy is achieved to supplement other database-grounded methods of duplicate removal. The implemented method is used in a Proof-of-Concept project in the real-estate industry.

Keywords: visual recognition, convolutional neural networks, triplet loss, spatial batch normalization with dropout, duplicate removal, advertisement technologies, performance benchmarking

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Authors: Danladi Ali, Onah Festus Iloabuchi

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In this paper, GSM signal strength was measured in order to detect the type of the signal fading phenomenon using one-dimensional multilevel wavelet residual method and neural network clustering to determine the average GSM signal strength received in the study area. The wavelet residual method predicted that the GSM signal experienced slow fading and attenuated with MSE of 3.875dB. The neural network clustering revealed that mostly -75dB, -85dB and -95dB were received. This means that the signal strength received in the study is a weak signal.

Keywords: one-dimensional multilevel wavelets, path loss, GSM signal strength, propagation, urban environment

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Authors: M. Nekri, A. Khelladi

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The world wide web network is a network with a complex topology, the main properties of which are the distribution of degrees in power law, A low clustering coefficient and a weak average distance. Modeling the web as a graph allows locating the information in little time and consequently offering a help in the construction of the research engine. Here, we present a model based on the already existing probabilistic graphs with all the aforesaid characteristics. This work will consist in studying the web in order to know its structuring thus it will enable us to modelize it more easily and propose a possible algorithm for its exploration.

Keywords: clustering coefficient, preferential attachment, small world, web community

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Authors: I. Gahlouz, A. Chellil

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The paper concerns the estimation of system components faults by using an unknown inputs observer. To reach this goal, we used the Bond Graph approach to physical modelling. We showed that this graphical tool is allowing the representation of system components faults as unknown inputs within the state representation of the considered physical system. The study of the causal and structural features of the system (controllability, observability, finite structure, and infinite structure) based on the Bond Graph approach was hence fulfilled in order to design an unknown inputs observer which is used for the system component fault estimation.

Keywords: estimation, bond graph, controllability, observability

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Authors: Shin-Shin Kao, Hsiu-Chunj Pan

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Given a graph G. A cycle of G is a sequence of vertices of G such that the first and the last vertices are the same. A hamiltonian cycle of G is a cycle containing all vertices of G. The graph G is k-ordered (resp. k-ordered hamiltonian) if for any sequence of k distinct vertices of G, there exists a cycle (resp. hamiltonian cycle) in G containing these k vertices in the specified order. Obviously, any cycle in a graph is 1-ordered, 2-ordered and 3-ordered. Thus the study of any graph being k-ordered (resp. k-ordered hamiltonian) always starts with k = 4. Most studies about this topic work on graphs with no real applications. To our knowledge, the chordal ring families were the first one utilized as the underlying topology in interconnection networks and shown to be 4-ordered [1]. Furthermore, based on computer experimental results in [1], it was conjectured that some of them are 4-ordered hamiltonian. In this paper, we intend to give some possible directions in proving the conjecture.

Keywords: Hamiltonian cycle, 4-ordered, Chordal rings, 3-regular

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Authors: Bingquan Shen

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Convolutional Neural Networks (CNN) have demonstrated their effectiveness in synthesizing 3D views of object instances at various viewpoints. Given the problem where one have limited viewpoints of a particular object for classification, we present a pose normalization architecture to transform the object to existing viewpoints in the training dataset before classification to yield better classification performance. We have demonstrated that this Pose Normalization Network (PNN) can capture the style of the target object and is able to re-render it to a desired viewpoint. Moreover, we have shown that the PNN improves the classification result for the 3D chairs dataset and ShapeNet airplanes dataset when given only images at limited viewpoint, as compared to a CNN baseline.

Keywords: convolutional neural networks, object classification, pose normalization, viewpoint invariant

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Authors: Yasser F. Hassan

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The reduction or removal of noise in a color image is an essential part of image processing, whether the final information is used for human perception or for an automatic inspection and analysis. This paper describes the modeling system based on the rough neural network model to adaptive cellular automata for various image processing tasks and noise remover. In this paper, we consider the problem of object processing in colored image using rough neural networks to help deriving the rules which will be used in cellular automata for noise image. The proposed method is compared with some classical and recent methods. The results demonstrate that the new model is capable of being trained to perform many different tasks, and that the quality of these results is comparable or better than established specialized algorithms.

Keywords: rough sets, rough neural networks, cellular automata, image processing

Procedia PDF Downloads 417