Search results for: network protocol.

Authors: Won-Ho Kim, Chang-Ho Hyun, Moon-Jung Kim

Abstract:

In this paper, zigbee communication based wireless energy surveillance system is presented. The proposed system consists of multiple energy surveillance devices and an energy surveillance monitor. Each different standby power-off value of electric device is set automatically by using learning function of energy surveillance device. Thus adaptive standby power-off function provides user convenience and it maximizes the energy savings. Also, power consumption monitoring function is helpful to reduce inefficient energy consumption in home. The zigbee throughput simulator is designed to evaluate minimum transmission power and maximum allowable information quantity in the proposed system. The test result of prototype has been satisfied all the requirements. The proposed system has confirmed that can be used as an intelligent energy surveillance system for energy savings in home or office.

Keywords: Energy monitoring system, Energy surveillance system, Energy sensor network, Energy savings.

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Authors: Wang Ye, Wei Wei

Abstract:

Residential block construction of big cities in China began in the 1950s, and four models had far-reaching influence on modern residential block in its development process, including unit compound and residential district in 1950s to 1980s, and gated community and open community in 1990s to now. Based on analysis of the four models’ fabric, the article takes residential blocks in Hangzhou west area as an example and carries on the studies from urban structure level and block spacial level, mainly including urban road network, land use, community function, road organization, public space and building fabric. At last, the article puts forward “Semi-open Sub-community” strategy to improve the current fabric.

Keywords: Hangzhou West Area, residential block model, residential block fabric, “Semi-open Sub-community” strategy.

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Authors: Mogari I. Rapoo, Diteboho Xaba

Abstract:

This paper examines the forecasting performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) models with the published exchange rate obtained from South African Reserve Bank (SARB). ARIMA is one of the popular linear models in time series forecasting for the past decades. ARIMA and ANN models are often compared and literature revealed mixed results in terms of forecasting performance. The study used the MSE and MAE to measure the forecasting performance of the models. The empirical results obtained reveal the superiority of ARIMA model over ANN model. The findings further resolve and clarify the contradiction reported in literature over the superiority of ARIMA and ANN models.

Keywords: ARIMA, artificial neural networks models, error metrics, exchange rates.

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Authors: H. Zainuddin, F. Hanafi, M. H. Hairi, A. Aman, M.H.N. Talib

Abstract:

This paper investigates the effects of knowledge-based acceleration feedback control integrated with Automatic Generation Control (AGC) to enhance the quality of frequency control of governing system. The Intelligent Acceleration Feedback Controller (IAFC) is proposed to counter the over and under frequency occurrences due to major load change in power system network. Therefore, generator tripping and load shedding operations can be reduced. Meanwhile, the integration of IAFC with AGC, a well known Load-Frequency Control (LFC) is essential to ensure the system frequency is restored to the nominal value. Computer simulations of frequency response of governing system are used to optimize the parameters of IAFC. As a result, there is substantial improvement on the LFC of governing system that employing the proposed control strategy.

Keywords: Knowledge-based Supplementary Control, Acceleration Feedback, Load Frequency Control, Automatic Generation Control.

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Authors: Manal A. Mohsen, Ahmed Tawfik

Abstract:

Hydrogen production from cake wastewater by anaerobic dark fermentation via upflow anaerobic staged reactor (UASR) was investigated in this study. The reactor was continuously operated for four months at constant hydraulic retention time (HRT) of 21.57 hr, PH value of 6 ± 0.6, temperature of 21.1°C, and organic loading rate of 2.43 gCOD/l.d. The hydrogen production was 5.7 l H₂/d and the hydrogen yield was 134.8 ml H₂ /g COD_removed. The system showed an overall removal efficiency of TCOD, TBOD, TSS, TKN, and Carbohydrates of 40 ± 13%, 59 ± 18%, 84 ± 17%, 28 ± 27%, and 85 ± 15% respectively during the long term operation period. Based on the available results, the system is not sufficient for the effective treatment of cake wastewater, and the effluent quality of UASR is not complying for discharge into sewerage network, therefore a post treatment is needed (not covered in this study).

Keywords: Cake wastewater industry, chemical oxygen demand (COD), hydrogen production (HP), up-flow anaerobic staged reactor (UASR).

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Authors: Yuan-Jye Tseng, Jia-Shu Li

Abstract:

To design a product with the given product requirement and design objective, there can be alternative ways to propose the detailed design specifications of the product. In the design modeling stage, alternative design cases with detailed specifications can be modeled to fulfill the product requirement and design objective. Therefore, in the design evaluation stage, it is required to perform an evaluation of the alternative design cases for deciding the final design. The purpose of this research is to develop a product evaluation model for evaluating the alternative design cases by integrated evaluating the criteria of functional design, Kansei design, and design for supply chain. The criteria in the functional design group include primary function, expansion function, improved function, and new function. The criteria in the Kansei group include geometric shape, dimension, surface finish, and layout. The criteria in the design for supply chain group include material, manufacturing process, assembly, and supply chain operation. From the point of view of value and cost, the criteria in the functional design group and Kansei design group represent the design value of the product. The criteria in the design for supply chain group represent the supply chain and manufacturing cost of the product. It is required to evaluate the design value and the supply chain cost to determine the final design. For the purpose of evaluating the criteria in the three criteria groups, a fuzzy analytic network process (FANP) method is presented to evaluate a weighted index by calculating the total relational values among the three groups. A method using the technique for order preference by similarity to ideal solution (TOPSIS) is used to compare and rank the design alternative cases according to the weighted index using the total relational values of the criteria. The final decision of a design case can be determined by using the ordered ranking. For example, the design case with the top ranking can be selected as the final design case. Based on the criteria in the evaluation, the design objective can be achieved with a combined and weighted effect of the design value and manufacturing cost. An example product is demonstrated and illustrated in the presentation. It shows that the design evaluation model is useful for integrated evaluation of functional design, Kansei design, and design for supply chain to determine the best design case and achieve the design objective.

Keywords: Design evaluation, functional design, Kansei design, supply chain, design value, manufacturing cost, fuzzy analytic network process, technique for order preference by similarity to ideal solution.

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Authors: H. AbdelRahman, S. Rostom, Y. Lotfy, S. Salah Eldeen, R. Yassein, N. Awny

Abstract:

People are growing more concerned with their appearance in today's society. But they are terrified of what they will look like after a plastic surgery. People's mental health suffers when they have accidents, burns, or genetic issues that cause them to cleave certain body parts, which makes them feel uncomfortable and unappreciated. The method provides an innovative deep learning-based technique for image inpainting that analyzes different picture structures and fixes damaged images. This study proposes a model based on the Stable Diffusion Inpainting method for in-painting medical images. One significant advancement made possible by deep neural networks is image inpainting, which is the process of reconstructing damaged and missing portions of an image. The patient can see the outcome more easily since the system uses the user's input of an image to identify a problem. It then modifies the image and outputs a fixed image.

Keywords: Generative Adversarial Network, GAN, Large Mask Inpainting, LAMA, Stable Diffusion Inpainting.

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Authors: Igor Astrov, Mikhail Pikkov

Abstract:

This paper focuses on a critical component of the situational awareness (SA), the neural control of depth flight of an autonomous underwater vehicle (AUV). Constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. With the SA strategy, we proposed a multirate neural control of an AUV trajectory using neural network model reference controller for a nontrivial mid-small size AUV "r2D4" stochastic model. This control system has been demonstrated and evaluated by simulation of diving maneuvers using software package Simulink. From the simulation results it can be seen that the chosen AUV model is stable in the presence of high noise, and also can be concluded that the fast SA of similar AUV systems with economy in energy of batteries can be asserted during the underwater missions in search-and-rescue operations.

Keywords: Autonomous underwater vehicles, multirate systems, neurocontrollers, situational awareness.

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

Abstract:

This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: Image forensics, computer graphics, classification, deep learning, convolutional neural networks.

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Authors: M. Ali, K. Masood

Abstract:

In this paper methodology to exploit creeping wave for body area network BAN communication reliability are described. Creeping wave propagation effects are visualized & analyzed. During this work Dipole, IA antennas various antennas were redesigned using existing designs and their propagation characteristics were verified for optimum performance when used on BANs. These antennas were then applied on body shapes-including rectangular, spherical and cylindrical so that all the effects of actual human body can be taken nearly into account. Parametric simulation scheme was devised so that on Body channel characterization can be visualized at front, curved and back region. In the next phase multiple inputs multiple output MIMO scheme was introduced where virtual antennas were used in order to diminish the effects of antennas on the propagation of waves. Results were, extracted and analyzed at different heights. Finally based on comparative measurement and analysis it was concluded that on body propagation can be exploited to gain spatial diversity.

Keywords: BAN, Creeping Wave, MIMO, WIAs.

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

Abstract:

Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: Customer relationship management, churn prediction, telecom industry, deep learning, Artificial Neural Networks, ANN.

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Authors: Wagdi S. Soliman, Shu-ichi Sugiyama

Abstract:

The C3 plants are frequently suffering from exposure to high temperature stress which limits the growth and yield of these plants. This study seeks to clarify the physiological mechanisms of heat tolerance in relation to oxidative stress in C3 species. Fifteen C3 species were exposed to prolonged moderately high temperature stress 36/30°C for 40 days in a growth chamber. Chlorophyll fluorescence (Fv/Fm) showed great difference among species at 40 days of the stress. The species showed decreases in Fv/Fm and increases in malondialdehyde (MDA) content under stress condition as well as negative correlation between Fv/Fm and MDA (r = -0.61*) at 40 days of the stress. Hydrogen peroxide (H2O2) content before and after stress in addition to its response under stress showed great differences among species. The results suggest that the difference in heat tolerance among C3 species is closely associated with the ability to suppress oxidative damage but not with the content of reactive oxygen species (ROS) which is regulated by complex network.

Keywords: C3 species, Fv/Fm, heat stress, oxidative stress.

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Authors: Florin Leon, Silvia Curteanu

Abstract:

The Bacterial Foraging Optimization (BFO) algorithm is inspired by the behavior of bacteria such as Escherichia coli or Myxococcus xanthus when searching for food, more precisely the chemotaxis behavior. Bacteria perceive chemical gradients in the environment, such as nutrients, and also other individual bacteria, and move toward or in the opposite direction to those signals. The application example considered as a case study consists in establishing the dependency between the reaction yield of hydrogels based on polyacrylamide and the working conditions such as time, temperature, monomer, initiator, crosslinking agent and inclusion polymer concentrations, as well as type of the polymer added. This process is modeled with a neural network which is included in an optimization procedure based on BFO. An experimental study of BFO parameters is performed. The results show that the algorithm is quite robust and can obtain good results for diverse combinations of parameter values.

Keywords: Bacterial foraging optimization, hydrogels, neural networks, modeling.

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Authors: San Nah Sze, Wei King Tiong

Abstract:

The multiple traveling salesman problem (mTSP) can be used to model many practical problems. The mTSP is more complicated than the traveling salesman problem (TSP) because it requires determining which cities to assign to each salesman, as well as the optimal ordering of the cities within each salesman's tour. Previous studies proposed that Genetic Algorithm (GA), Integer Programming (IP) and several neural network (NN) approaches could be used to solve mTSP. This paper compared the results for mTSP, solved with Genetic Algorithm (GA) and Nearest Neighbor Algorithm (NNA). The number of cities is clustered into a few groups using k-means clustering technique. The number of groups depends on the number of salesman. Then, each group is solved with NNA and GA as an independent TSP. It is found that k-means clustering and NNA are superior to GA in terms of performance (evaluated by fitness function) and computing time.

Keywords: Multiple Traveling Salesman Problem, GeneticAlgorithm, Nearest Neighbor Algorithm, k-Means Clustering.

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Authors: Jihye Jeon

Abstract:

This paper analyzes the political and economic issues that people with disabilities face related to globalization; how people with disabilities have been adapting globalization and surviving under worldwide competition system. It explains that economic globalization exacerbates inequality and deprivation of people with disabilities. The rising tide of neo-liberal welfare policies emphasized efficiency, downsized social expenditure for people with disabilities, excluded people with disabilities against labor market, and shifted them from welfare system to nothing. However, there have been people with disabilities' political responses to globalization, which are characterized by a global network of people with disabilities as well as participation to global governance. Their resistance can be seen as an attempt to tackle the problems that economic globalization has produced. It is necessary paradigm shift of disability policy from dependency represented by disability benefits to independency represented by labor market policies for people with disabilities.

Keywords: Economic Globalization, People with Disability, Deprivation, Welfare Cut, Disability Right Movement, Resistance.

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Authors: Zhichao Zhao, Yi Liu, Shunping Xiao

Abstract:

A new data fusion method called joint probability density matrix (JPDM) is proposed, which can associate and fuse measurements from spatially distributed heterogeneous sensors to identify the real target in a surveillance region. Using the probabilistic grids representation, we numerically combine the uncertainty regions of all the measurements in a general framework. The NP-hard multisensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion method, the JPDM method dose not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRLB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.

Keywords: Cramer-Rao lower bound (CRLB), data fusion, probabilistic grids, joint probability density matrix, localization, sensor network.

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Authors: Shahin Zanbaghi, Saeed Samet

Abstract:

The internet has transformed the way we shop. Previously, most of our purchases came in the form of shopping trips to a nearby store. Now, it is as easy as clicking a mouse. We have to be constantly vigilant about our personal information. In this work, our proposed approach is to encrypt the information printed on the physical packages, which include personal information in plain text using a symmetric encryption algorithm; then, we store that encrypted information into a Blockchain network rather than storing them in companies or corporations centralized databases. We present, implement and assess a blockchain-based system using Ethereum smart contracts. We present detailed algorithms that explain the details of our smart contract. We present the security, cost and performance analysis of the proposed method. Our work indicates that the proposed solution is economically attainable and provides data integrity, security, transparency and data traceability.

Keywords: Blockchain, Ethereum, smart contract, commit-reveal scheme.

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Authors: Lien-Fa Lin, Yung-Ming Li, Fu-Shun Hsieh

Abstract:

With the advancement of information technology and development of group commerce, people have obviously changed in their lifestyle. However, group commerce faces some challenging problems. The products or services provided by vendors do not satisfactorily reflect customers’ opinions, so that the sale and revenue of group commerce gradually become lower. On the other hand, the process for a formed customer group to reach group-purchasing consensus is time-consuming and the final decision is not the best choice for each group members. In this paper, we design a social decision support mechanism, by using group discussion message to recommend suitable options for group members and we consider social influence and personal preference to generate option ranking list. The proposed mechanism can enhance the group purchasing decision making efficiently and effectively and venders can provide group products or services according to the group option ranking list.

Keywords: Social network, group decision, text mining, group commerce.

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Authors: Seunghee Park, Junkyeong Kim, Eun-Seok Shin, Sang-Hun Han

Abstract:

In this study, two kinds of nondestructive evaluation  (NDE) techniques (rebound hardness and ultrasonic pulse velocity  methods) are investigated for the effective maintenance of underwater  concrete structures. A new methodology to estimate the underwater  concrete strengths more effectively, named “artificial neural network  (ANN) – based concrete strength estimation with the combination of  rebound hardness and ultrasonic pulse velocity methods” is proposed  and verified throughout a series of experimental works.

 

Keywords: Underwater Concrete, Rebound Hardness, Schmidt hammer, Ultrasonic Pulse Velocity, Ultrasonic Sensor, Artificial Neural Networks, ANN.

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Authors: D. Triantakonstantis, D. Stathakis

Abstract:

Urban areas have been expanded throughout the globe. Monitoring and modelling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modelling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: Artificial Neural Networks, CORINE, Urban Atlas, Urban Growth Prediction.

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Authors: A. Ould-Abbas, M. Bouchaour, , M. Madani, D. Trari, O. Zeggai, M. Boukais, N.-E.Chabane-Sari

Abstract:

The porous silicon (PS), formed from the anodization of a p+ type substrate silicon, consists of a network organized in a pseudo-column as structure of multiple side ramifications. Structural micro-topology can be interpreted as the fraction of the interconnected solid phase contributing to thermal transport. The reduction of dimensions of silicon of each nanocristallite during the oxidation induced a reduction in thermal conductivity. Integration of thermal sensors in the Microsystems silicon requires an effective insulation of the sensor element. Indeed, the low thermal conductivity of PS consists in a very promising way in the fabrication of integrated thermal Microsystems.In this work we are interesting in the measurements of thermal conductivity (on the surface and in depth) of PS by the micro-Raman spectroscopy. The thermal conductivity is studied according to the parameters of anodization (initial doping and current density. We also, determine porosity of samples by spectroellipsometry.

Keywords: micro-Raman spectroscopy, mono-crysatl silicon, porous silicon, thermal conductivity

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Authors: Siavash Asadi Ghajarloo

Abstract:

Nowadays predicting political risk level of country has become a critical issue for investors who intend to achieve accurate information concerning stability of the business environments. Since, most of the times investors are layman and nonprofessional IT personnel; this paper aims to propose a framework named GECR in order to help nonexpert persons to discover political risk stability across time based on the political news and events. To achieve this goal, the Bayesian Networks approach was utilized for 186 political news of Pakistan as sample dataset. Bayesian Networks as an artificial intelligence approach has been employed in presented framework, since this is a powerful technique that can be applied to model uncertain domains. The results showed that our framework along with Bayesian Networks as decision support tool, predicted the political risk level with a high degree of accuracy.

Keywords: Bayesian Networks, Data mining, GECRframework, Predicting political risk.

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Authors: Vitalice K. Oduol, C. Ardil

Abstract:

The paper presents frame and burst acquisition in a satellite communication network based on time division multiple access (TDMA) in which the transmissions may be carried on different transponders. A unique word pattern is used for the acquisition process. The search for the frame is aided by soft-decision of QPSK modulated signals in an additive white Gaussian channel. Results show that when the false alarm rate is low the probability of detection is also low, and the acquisition time is long. Conversely when the false alarm rate is high, the probability of detection is also high and the acquisition time is short. Thus the system operators can trade high false alarm rates for high detection probabilities and shorter acquisition times.

Keywords: burst acquisition, burst time plan, frame acquisition, satellite access, satellite TDMA, unique word detection

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Authors: Rosemarie Velik, Dietmar Bruckner

Abstract:

Today, building automation is advancing from simple monitoring and control tasks of lightning and heating towards more and more complex applications that require a dynamic perception and interpretation of different scenes occurring in a building. Current approaches cannot handle these newly upcoming demands. In this article, a bionically inspired approach for multimodal, dynamic scene perception and interpretation is presented, which is based on neuroscientific and neuro-psychological research findings about the perceptual system of the human brain. This approach bases on data from diverse sensory modalities being processed in a so-called neuro-symbolic network. With its parallel structure and with its basic elements being information processing and storing units at the same time, a very efficient method for scene perception is provided overcoming the problems and bottlenecks of classical dynamic scene interpretation systems.

Keywords: building automation, biomimetrics, dynamic scene interpretation, human-like perception, neuro-symbolic networks.

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Authors: S. Safi, M. Frikel, M. M'Saad, A. Zeroual

Abstract:

This paper describes a blind algorithm for estimating a time varying and frequency selective fading channel. In order to identify blindly the impulse response of these channels, we have used Higher Order Statistics (HOS) to build our algorithm. In this paper, we have selected two theoretical frequency selective channels as the Proakis-s 'B' channel and the Macchi-s channel, and one practical frequency selective fading channel called Broadband Radio Access Network (BRAN A). The simulation results in noisy environment and for different data input channel, demonstrate that the proposed method could estimate the phase and magnitude of these channels blindly and without any information about the input, except that the input excitation is i.i.d (Identically and Independent Distributed) and non-Gaussian.

Keywords: Frequency response, system identification, higher order statistics, communication channels, phase estimation.

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Authors: G.V.K Murthy, S. Sivanagaraju, S. Satyanarayana, B. Hanumantha Rao

Abstract:

Distributed Generation (DG) systems are considered an integral part in future distribution system planning. Appropriate size and location of distributed generation plays a significant role in minimizing power losses in distribution systems. Among the benefits of distributed generation is the reduction in active power losses, which can improve the system performance, reliability and power quality. In this paper, Artificial Bee Colony (ABC) algorithm is proposed to determine the optimal DG-unit size and location by loss sensitivity index in order to minimize the real power loss, total harmonic distortion (THD) and voltage sag index improvement. Simulation study is conducted on 69-bus radial test system to verify the efficacy of the proposed method.

Keywords: Distributed generation, artificial bee colony method, loss reduction, radial distribution network.

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Authors: Xiaoli Shen, Yuehui Chen

Abstract:

Interactions among proteins are the basis of various life events. So, it is important to recognize and research protein interaction sites. A control set that contains 149 protein molecules were used here. Then 10 features were extracted and 4 sample sets that contained 9 sliding windows were made according to features. These 4 sample sets were calculated by Radial Basis Functional neutral networks which were optimized by Particle Swarm Optimization respectively. Then 4 groups of results were obtained. Finally, these 4 groups of results were integrated by decision fusion (DF) and Genetic Algorithm based Selected Ensemble (GASEN). A better accuracy was got by DF and GASEN. So, the integrated methods were proved to be effective.

Keywords: protein interaction sites, features, sliding windows, radial basis functional neutral networks, genetic algorithm basedselected ensemble.

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Authors: Düzgün Akmaz, Hüseyin Erişti

Abstract:

In the last few years, harmonics have been occurred with the increasing use of nonlinear loads, and these harmonics have been an ever increasing problem for the line systems. This situation importantly affects the quality of power and gives large losses to the network. An efficient way to solve these problems is providing harmonic compensation through parallel active power filters. Many methods can be used in the control systems of the parallel active power filters which provide the compensation. These methods efficiently affect the performance of the active power filters. For this reason, the chosen control method is significant. In this study, Fourier analysis (FA) control method and synchronous reference frame (SRF) control method are discussed. These control methods are designed for both eliminate harmonics and perform reactive power compensation in MATLAB/Simulink pack program and are tested. The results have been compared for each two methods.

Keywords: Harmonics, Harmonic compensation, Parallel active power filters, Power quality.

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Authors: Jeffrey A. Dunne, Kevin Ligozio

Abstract:

In many applications there is a broad variety of information relevant to a focal “object" of interest, and the fusion of such heterogeneous data types is desirable for classification and categorization. While these various data types can sometimes be treated as orthogonal (such as the hull number, superstructure color, and speed of an oil tanker), there are instances where the inference and the correlation between quantities can provide improved fusion capabilities (such as the height, weight, and gender of a person). A service-oriented architecture has been designed and prototyped to support the fusion of information for such “object-centric" situations. It is modular, scalable, and flexible, and designed to support new data sources, fusion algorithms, and computational resources without affecting existing services. The architecture is designed to simplify the incorporation of legacy systems, support exact and probabilistic entity disambiguation, recognize and utilize multiple types of uncertainties, and minimize network bandwidth requirements.

Keywords: Data fusion, distributed computing, service-oriented architecture, SOA

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Authors: Hamdi. A. Awad, Mohamed A. Al-Zorkany

Abstract:

Developing techniques for mobile robot navigation constitutes one of the major trends in the current research on mobile robotics. This paper develops a local model network (LMN) for mobile robot navigation. The LMN represents the mobile robot by a set of locally valid submodels that are Multi-Layer Perceptrons (MLPs). Training these submodels employs Back Propagation (BP) algorithm. The paper proposes the fuzzy C-means (FCM) in this scheme to divide the input space to sub regions, and then a submodel (MLP) is identified to represent a particular region. The submodels then are combined in a unified structure. In run time phase, Radial Basis Functions (RBFs) are employed as windows for the activated submodels. This proposed structure overcomes the problem of changing operating regions of mobile robots. Read data are used in all experiments. Results for mobile robot navigation using the proposed LMN reflect the soundness of the proposed scheme.

Keywords: Mobile Robot Navigation, Neural Networks, Local Model Networks

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