Search results for: network pattern models

Authors: Zina Saheb, Ezz El-Masry, Jean-François Bousquet

Abstract:

This paper presents an enhanced efficiency simultaneous dual band energy harvesting system for wireless body area network. A bulk biasing is used to enhance the efficiency of the adapted rectifier design to reduce V_th of MOSFET. The presented circuit harvests the radio frequency (RF) energy from two frequency bands: 1 GHz and 2.4 GHz. It is designed with TSMC 65-nm CMOS technology and high quality factor dual matching network to boost the input voltage. Full circuit analysis and modeling is demonstrated. The simulation results demonstrate a harvester with an efficiency of 23% at 1 GHz and 46% at 2.4 GHz at an input power as low as -30 dBm.

Keywords: Energy harvester, simultaneous, dual band, CMOS, differential rectifier, voltage boosting, TSMC 65nm.

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Authors: L. Sellami, D. Idoughi, P. F. Tiako

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Cloud computing provides infrastructure to the enterprise through the Internet allowing access to cloud services at anytime and anywhere. This pervasive aspect of the services, the distributed nature of data and the wide use of information make cloud computing vulnerable to intrusions that violate the security of the cloud. This requires the use of security mechanisms to detect malicious behavior in network communications and hosts such as intrusion detection systems (IDS). In this article, we focus on the detection of intrusion into the cloud sing IDSs. We base ourselves on client authentication in the computing cloud. This technique allows to detect the abnormal use of ubiquitous service and prevents the intrusion of cloud computing. This is an approach based on client authentication data. Our IDS provides intrusion detection inside and outside cloud computing network. It is a double protection approach: The security user node and the global security cloud computing.

Keywords: Cloud computing, intrusion detection system, privacy, trust.

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Authors: Farooq Ahmad, Hejiao Huang, Xiaolong Wang

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Reachability graph (RG) generation suffers from the problem of exponential space and time complexity. To alleviate the more critical problem of time complexity, this paper presents the new approach for RG generation for the Petri net (PN) models of parallel processes. Independent RGs for each parallel process in the PN structure are generated in parallel and cross-product of these RGs turns into the exhaustive state space from which the RG of given parallel system is determined. The complexity analysis of the presented algorithm illuminates significant decrease in the time complexity cost of RG generation. The proposed technique is applicable to parallel programs having multiple threads with the synchronization problem.

Keywords: Parallel processes, Petri net, reachability graph, time complexity.

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Authors: Bharatendra Rai

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Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.

Keywords: Degradation signal, drill-bit breakage, random forest, multinomial logistic regression.

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Authors: A. Daftari, W. Kudla

Abstract:

Liquefaction is a phenomenon in which the strength  and stiffness of a soil is reduced by earthquake shaking or other rapid  cyclic loading. Liquefaction and related phenomena have been  responsible for huge amounts of damage in historical earthquakes  around the world.  Modeling of soil behavior is the main step in soil liquefaction  prediction process. Nowadays, several constitutive models for sand  have been presented. Nevertheless, only some of them can satisfy this  mechanism. One of the most useful models in this term is  UBCSAND model. In this research, the capability of this model is  considered by using PLAXIS software. The real data of superstition  hills earthquake 1987 in the Imperial Valley was used. The results of  the simulation have shown resembling trend of the UBC3D-PLM  model. 

Keywords: Liquefaction, Plaxis, Pore-Water pressure, UBC3D-PLM.

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Authors: Nasibeh Nasiri, Dawood Talebi Khanmiri

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Performance of any continuous speech recognition system is highly dependent on performance of the acoustic models. Generally, development of the robust spoken language technology relies on the availability of large amounts of data. Common way to cope with little data for training each state of Markov models is treebased state tying. This tying method applies contextual questions to tie states. Manual procedure for question generation suffers from human errors and is time consuming. Various automatically generated questions are used to construct decision tree. There are three approaches to generate questions to construct HMMs based on decision tree. One approach is based on misrecognized phonemes, another approach basically uses feature table and the other is based on state distributions corresponding to context-independent subword units. In this paper, all these methods of automatic question generation are applied to the decision tree on FARSDAT corpus in Persian language and their results are compared with those of manually generated questions. The results show that automatically generated questions yield much better results and can replace manually generated questions in Persian language.

Keywords: Decision Tree, Markov Models, Speech Recognition, State Tying.

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Authors: H. Rafiei, M. Rabbani

Abstract:

A novel concept to balance and tradeoff between make-to-stock and make-to-order has been hybrid MTS/MTO production context. One of the most important decisions involved in the hybrid MTS/MTO environment is determining whether a product is manufactured to stock, to order, or hybrid MTS/MTO strategy. In this paper, a model based on analytic network process is developed to tackle the addressed decision. Since the regarded decision deals with the uncertainty and ambiguity of data as well as experts- and managers- linguistic judgments, the proposed model is equipped with fuzzy sets theory. An important attribute of the model is its generality due to diverse decision factors which are elicited from the literature and developed by the authors. Finally, the model is validated by applying to a real case study to reveal how the proposed model can actually be implemented.

Keywords: Fuzzy analytic network process, Hybrid make-tostock/ make-to-order, Order partitioning, Production planning.

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Authors: Seyed Sadegh Naseralavi, Najmeh Bemani

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In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio.

Keywords: Concrete design code, anticipate method, artificial neural network, multi-variable regression, adaptive neuro fuzzy inference system.

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Authors: Zhi-Jun Lu, Qi Lu, Meng Wu, Qian Xiang, Jun Gu

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The analysis of perforated steel members is a 3D problem in nature, therefore the traditional analytical expressions for the ultimate load of thin-walled steel sections cannot be used for the perforated steel member design. In this study, finite element method (FEM) and artificial neural network (ANN) were used to simulate the process of stub column tests based on specific codes. Results show that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments. The ANN model for the solving the hard problem of complex steel perforated sections is very promising.

Keywords: Artificial neural network, finite element method, perforated sections, thin-walled steel, ultimate load.

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Authors: Alireza Dehghani Pilehvarani, Mojtaba Hakimzadeh, Mohammad Jafari Far, Reza Sedaghati

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Distributed Generation (DG) can help in reducing the cost of electricity to the costumer, relieve network congestion and provide environmentally friendly energy close to load centers. Its capacity is also scalable and it provides voltage support at distribution level. Hence, DG placement and penetration level is an important problem for both the utility and DG owner. DG allocation and capacity determination is a nonlinear optimization problem. The objective function of this problem is the minimization of the total loss of the distribution system. Also high levels of penetration of DG are a new challenge for traditional electric power systems. This paper presents a new methodology for the optimal placement of DG and penetration level of DG in distribution system based on General Algebraic Modeling System (GAMS) and Genetic Algorithm (GA).

Keywords: Distributed Generation, Location, Loss Reduction, Distribution Network, GA, GAMS.

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Authors: Kesari Verma, Aniruddha S. Thoke, Pritam Singh

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Automatic face detection is a complex problem in image processing. Many methods exist to solve this problem such as template matching, Fisher Linear Discriminate, Neural Networks, SVM, and MRC. Success has been achieved with each method to varying degrees and complexities. In proposed algorithm we used upright, frontal faces for single gray scale images with decent resolution and under good lighting condition. In the field of face recognition technique the single face is matched with single face from the training dataset. The author proposed a neural network based face detection algorithm from the photographs as well as if any test data appears it check from the online scanned training dataset. Experimental result shows that the algorithm detected up to 95% accuracy for any image.

Keywords: Face Detection, Face Recognition, NN Approach, PCA Algorithm.

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Authors: Gintarė Sauliutė, Gintaras Svecevičius

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Landfill leachates contain a number of persistent pollutants, including heavy metals. They have the ability to spread in ecosystems and accumulate in fish which most of them are classified as top-consumers of trophic chains. Fish are freely swimming organisms; but perhaps, due to their species-specific ecological and behavioral properties, they often prefer the most suitable biotopes and therefore, did not avoid harmful substances or environments. That is why it is necessary to evaluate the persistent pollutant dispersion in hydroecosystem using fish tissue metal concentration. In hydroecosystems of hybrid type (e.g. river-pond-river) the distance from the pollution source could be a perfect indicator of such a kind of metal distribution. The studies were carried out in the Kairiai landfill neighboring hybrid-type ecosystem which is located 5 km east of the Šiauliai City. Fish tissue (gills, liver, and muscle) metal concentration measurements were performed on two types of ecologically-different fishes according to their feeding characteristics: benthophagous (Gibel carp, roach) and predatory (Northern pike, perch). A number of mathematical models (linear, non-linear, using log and other transformations) have been applied in order to identify the most satisfactorily description of the interdependence between fish tissue metal concentration and the distance from the pollution source. However, the only one log-multiple regression model revealed the pattern that the distance from the pollution source is closely and positively correlated with metal concentration in all predatory fish tissues studied (gills, liver, and muscle).

Keywords: Bioaccumulation in fish, heavy metals, hydroecosystem, landfill leachate, mathematical model.

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Authors: Eric Jacob Bacon, Chaoyang Jin, Dianning He, Shuaishuai Hu, Lanbo Wang, Han Li, Shouliang Qi

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Refractory epilepsy is a complicated type of epilepsy that can be difficult to diagnose. Recent technological advancements have made resting-state functional magnetic resonance (rsfMRI) a vital technique for studying brain activity. However, there is still much to learn about rsfMRI. Investigating rsfMRI connectivity may aid in the detection of abnormal activities. In this paper, we propose studying the functional connectivity of rsfMRI candidates to diagnose epilepsy. 45 rsfMRI candidates, comprising 26 with refractory epilepsy and 19 healthy controls, were enrolled in this study. A data-driven approach known as Independent Component Analysis (ICA) was used to achieve our goal. First, rsfMRI data from both patients and healthy controls were analyzed using group ICA. The components that were obtained were then spatially sorted to find and select meaningful ones. A two-sample t-test was also used to identify abnormal networks in patients and healthy controls. Finally, based on the fractional amplitude of low-frequency fluctuations (fALFF), a chi-square statistic test was used to distinguish the network properties of the patient and healthy control groups. The two-sample t-test analysis yielded abnormal in the default mode network, including the left superior temporal lobe and the left supramarginal. The right precuneus was found to be abnormal in the dorsal attention network. In addition, the frontal cortex showed an abnormal cluster in the medial temporal gyrus. In contrast, the temporal cortex showed an abnormal cluster in the right middle temporal gyrus and the right fronto-operculum gyrus. Finally, the chi-square statistic test was significant, producing a p-value of 0.001 for the analysis. This study offers evidence that investigating rsfMRI connectivity provides an excellent diagnosis option for refractory epilepsy.

Keywords: Independent Component Analysis, Resting State Network, refractory epilepsy, rsfMRI.

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Authors: In Lee, Kyung-Kyu Kang, Jaewoon Lee, Dongho Kim

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Previous the 3D model texture generation from multi-view images and mapping algorithms has issues in the texture chart generation which are the self-intersection and the concentration of the texture in texture space. Also we may suffer from some problems due to the occluded areas, such as inside parts of thighs. In this paper we propose a texture mapping technique for 3D models using multi-view images on the GPU. We do texture mapping directly on the GPU fragment shader per pixel without generation of the texture map. And we solve for the occluded area using the 3D model depth information. Our method needs more calculation on the GPU than previous works, but it has shown real-time performance and previously mentioned problems do not occur.

Keywords: Texture Mapping, Multi-view Images, Camera Calibration, GPU Shader.

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Authors: Tarek M. Alguhane, Ayman H. Khalil, M. N. Fayed, Ayman M. Ismail

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This paper deals with different modeling aspects of masonry infill: no infill model, Layered shell infill model, and strut infill model. These models consider the complicated behavior of the in-filled plane frames under lateral load similar to an earthquake load. Three strut infill models are used: NBCC (2005) strut infill model, ASCE/SEI 41-06 strut infill model and proposed strut infill model based on modification to Canadian, NBCC (2005) strut infill model. Pushover and modal analyses of a masonry infill concrete frame with a single storey and an existing 5-storey RC building have been carried out by using different models for masonry infill. The corresponding hinge status, the value of base shear at target displacement as well as their dynamic characteristics have been determined and compared. A validation of the structural numerical models for the existing 5-storey RC building has been achieved by comparing the experimentally measured and the analytically estimated natural frequencies and their mode shapes. This study shows that ASCE/SEI 41-06 equation underestimates the values for the equivalent properties of the diagonal strut while Canadian, NBCC (2005) equation gives realistic values for the equivalent properties. The results indicate that both ASCE/SEI 41-06 and Canadian, NBCC (2005) equations for strut infill model give over estimated values for dynamic characteristic of the building. Proposed modification to Canadian, NBCC (2005) equation shows that the fundamental dynamic characteristic values of the building are nearly similar to the corresponding values using layered shell elements as well as measured field results.

Keywords: Masonry infill, framed structures, RC buildings, non-structural elements.

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Authors: S. Pazi, C. Chatwin

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ICT services are a key element of communications and important for socio-economic development. In recognition of the importance of this, the Tanzanian Government started to implement a National ICT Broadband Infrastructure Fibre Optic Backbone (NICTBB) in 2009; this development was planned to be implemented in four phases using an optical dense wavelength division multiplexing (DWDM) network technology in collaboration with the Chinese Government through the Chinese International Telecommunications Construction Corporation (CITCC) under a bilateral agreement. This paper briefly explores the NICTBB network technologies implementation, operations and Internet bandwidth costs. It also provides an in depth assessment of the delivery of ICT services such as e-services and m-services in both urban and rural areas following commissioning of the NICTBB system. Following quantitative and qualitative approaches, the study shows that there have been significant improvements in utilization efficiency, effectiveness and the reliability of the ICT service such as e-services and m-services the NICTCBB was commissioned.

Keywords: NICTBB, DWDM, Optic Fibre, Internet, ICT services, e-services, m-services.

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Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm

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In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. Several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature, and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Keywords: Robust fault detection, cascade control, independent RBF model, RBF neural networks, Chylla-Haase reactor, FDI under closed-loop control.

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Authors: Chee Peng Lim, Wei Yee Goh

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In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.

Keywords: AdaBoost, Elman network, neural network ensemble, time series regression.

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Authors: Sh. Jamali, A. Eftekhari

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Transmission control protocol (TCP) Vegas detects network congestion in the early stage and successfully prevents periodic packet loss that usually occurs in TCP Reno. It has been demonstrated that TCP Vegas outperforms TCP Reno in many aspects. However, TCP Vegas suffers several problems that affect its congestion avoidance mechanism. One of the most important weaknesses in TCP Vegas is that alpha and beta depend on a good expected throughput estimate, which as we have seen, depends on a good minimum RTT estimate. In order to make the system more robust alpha and beta must be made responsive to network conditions (they are currently chosen statically). This paper proposes a modified Vegas algorithm, which can be adjusted to present good performance compared to other transmission control protocols (TCPs). In order to do this, we use PSO algorithm to tune alpha and beta. The simulation results validate the advantages of the proposed algorithm in term of performance.

Keywords: Self-managing, Congestion control, TCP.

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Authors: Chapman Eze Nnadozie

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Cloud computing, a technology that is made possible through virtualization within networks represents a shift from the traditional ownership of infrastructure and other resources by distinct organization to a more scalable pattern in which computer resources are rented online to organizations on either as a pay-as-you-use basis or by subscription. In other words, cloud computing entails the renting of computing resources (such as storage space, memory, servers, applications, networks, etc.) by a third party to its clients on a pay-as-go basis. It is a new innovative technology that is globally embraced because of its renowned benefits, profound of which is its cost effectiveness on the part of organizations engaged with its services. In Nigeria, the services are provided either directly to companies mostly by the key IT players such as Microsoft, IBM, and Google; or in partnership with some other players such as Infoware, Descasio, and Sunnet. This action enables organizations to rent IT resources on a pay-as-you-go basis thereby salvaging them from wastages accruable on acquisition and maintenance of IT resources such as ownership of a separate data centre. This paper intends to appraise the challenges of cloud computing adoption in Nigeria, bearing in mind the country’s peculiarities’ in terms of infrastructural development. The methodologies used in this paper include the use of research questionnaires, formulated hypothesis, and the testing of the formulated hypothesis. The major findings of this paper include the fact that there are some addressable challenges to the adoption of cloud computing in Nigeria. Furthermore, the country will gain significantly if the challenges especially in the area of infrastructural development are well addressed. This is because the research established the fact that there are significant gains derivable by the adoption of cloud computing by organizations in Nigeria. However, these challenges can be overturned by concerted efforts in the part of government and other stakeholders.

Keywords: Cloud computing, data centre, infrastructure, IT resources, network, servers, virtualization.

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Authors: Muhammad Marsudi, Dzuraidah Abdul Wahab, Che Hassan Che Haron

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Modeling of a manufacturing system enables one to identify the effects of key design parameters on the system performance and as a result to make correct decision. This paper proposes a manufacturing system modeling approach using a spreadsheet model based on queuing network theory, in which a static capacity planning model and stochastic queuing model are integrated. The model was used to improve the existing system utilization in relation to product design. The model incorporates few parameters such as utilization, cycle time, throughput, and batch size. The study also showed that the validity of developed model is good enough to apply and the maximum value of relative error is 10%, far below the limit value 32%. Therefore, the model developed in this study is a valuable alternative model in evaluating a manufacturing system

Keywords: Manufacturing system, product design, spreadsheet model, utilization.

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Authors: Sumitra Nuanmeesri

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The research focus on study, analyze and design the model of the infrastructure and computer networks for distance education, online learning via new media, e-learning and blended learning. The collected information from study and analyze process that information was evaluated by the index of item objective congruence (IOC) by 9 specialists to design model. The results of evaluate the model with the mean and standard deviation by the sample of 9 specialists value is 3.85. The results showed that the infrastructure and computer networks are designed to be appropriate to a great extent appropriate to a great extent.

Keywords: Blended Learning, New Media, Infrastructure and Computer Network, Tele-Education, Online Learning.

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Authors: Siamak Rasulzadeh

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Graph transformation has recently become more and more popular as a general visual modeling language to formally state the dynamic semantics of the designed models. Especially, it is a very natural formalism for languages which basically are graph (e.g. UML). Using this technique, we present a highly understandable yet precise approach to formally model and analyze the behavioral semantics of UML 2.0 Activity diagrams. In our proposal, AGG is used to design Activities, then using our previous approach to model checking graph transformation systems, designers can verify and analyze designed Activity diagrams by checking the interesting properties as combination of graph rules and LTL (Linear Temporal Logic) formulas on the Activities.

Keywords: UML 2.0 Activity, Verification, Model Checking, Graph Transformation, Dynamic Semantics.

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Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

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In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: Brain-computer interface, speech recognition, electroencephalography EEG, Wernicke area, artificial neural network.

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: Information Gain (IG), Intrusion Detection System (IDS), K-means Clustering, Weka.

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Authors: Zulkeflee Kusin, Mohamad Shanudin Zakaria

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Hierarchical Mobile IPv6 (HMIPv6) was designed to support IP micro-mobility management in the Next Generation Networks (NGN) framework. The main design behind this protocol is the usage of Mobility Anchor Point (MAP) located at any level router of network to support hierarchical mobility management. However, the distance MAP selection in HMIPv6 causes MAP overloaded and increase frequent binding update as the network grows. Therefore, to address the issue in designing MAP selection scheme, we propose a dynamic load control mechanism integrates with a speed detection mechanism (DMS-DLC). From the experimental results we obtain that the proposed scheme gives better distribution in MAP load and increase handover speed.

Keywords: Dynamic load control, HMIPv6, Mobility AnchorPoint, MAP selection scheme

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Authors: Mohammad Asif

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An accurate prediction of the minimum fluidization velocity is a crucial hydrodynamic aspect of the design of fluidized bed reactors. Common approaches for the prediction of the minimum fluidization velocities of binary-solid fluidized beds are first discussed here. The data of our own careful experimental investigation involving a binary-solid pair fluidized with water is presented. The effect of the relative composition of the two solid species comprising the fluidized bed on the bed void fraction at the incipient fluidization condition is reported and its influence on the minimum fluidization velocity is discussed. In this connection, the capability of packing models to predict the bed void fraction is also examined.

Keywords: Bed void fraction, Binary solid mixture, Minimumfluidization velocity, Packing models

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Authors: Ning Gong, Michael Korostelev, Qiangguo Ren, Li Bai, Saroj Biswas, Frank Ferrese

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This paper investigates the joint effect of the interconnected (n,k)-star network topology and Multi-Agent automated control on restoration and reconfiguration of power systems. With the increasing trend in development in Multi-Agent control technologies applied to power system reconfiguration in presence of faulty components or nodes. Fault tolerance is becoming an important challenge in the design processes of the distributed power system topology. Since the reconfiguration of a power system is performed by agent communication, the (n,k)-star interconnected network topology is studied and modeled in this paper to optimize the process of power reconfiguration. In this paper, we discuss the recently proposed (n,k)-star topology and examine its properties and advantages as compared to the traditional multi-bus power topologies. We design and simulate the topology model for distributed power system test cases. A related lemma based on the fault tolerance and conditional diagnosability properties is presented and proved both theoretically and practically. The conclusion is reached that (n,k)-star topology model has measurable advantages compared to standard bus power systems while exhibiting fault tolerance properties in power restoration, as well as showing efficiency when applied to power system route discovery.

Keywords: (n, k)-star Topology, Fault Tolerance, Conditional Diagnosability, Multi-Agent System, Automated Power System.

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Authors: Hichem Sassi, Tawfik Najeh, Noureddine Liouane

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Information of nodes’ locations is an important criterion for lots of applications in Wireless Sensor Networks. In the hop-based range-free localization methods, anchors transmit the localization messages counting a hop count value to the whole network. Each node receives this message and calculates its own distance with anchor in hops and then approximates its own position. However the estimative distances can provoke large error, and affect the localization precision. To solve the problem, this paper proposes an algorithm, which makes the unknown nodes fix the nearest anchor as a reference and select two other anchors which are the most accurate to achieve the estimated location. Compared to the DV-Hop algorithm, experiment results illustrate that proposed algorithm has less average localization error and is more effective.

Keywords: Wireless Sensors Networks, Localization problem, localization average error, DV–Hop Algorithm, MATLAB.

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Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta

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Cosmic showers, during the transit through space, produce sub - products as a result of interactions with the intergalactic or interstellar medium which after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of High Energy Particle Showers involve a plethora of theoretical and experimental works with a host of constraints resulting in inaccuracies in measurements. Therefore, there exist a necessity to develop a readily available system based on soft-computational approaches which can be used for EAS analysis. This is due to the fact that soft computational tools such as Artificial Neural Network (ANN)s can be trained as classifiers to adapt and learn the surrounding variations. But single classifiers fail to reach optimality of decision making in many situations for which Multiple Classifier System (MCS) are preferred to enhance the ability of the system to make decisions adjusting to finer variations. This work describes the formation of an MCS using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN) with data inputs from correlation mapping Self Organizing Map (SOM) blocks and the output optimized by another SOM. The results show that the setup can be adopted for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

Keywords: EAS, Shower, Core, ANN, Location.

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