Search results for: Network services.

Authors: J. Bouchard, B. Prosperi, G. Bavre, M. Daudé, E. Jeandupeux

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Young patients suffering from Cerebral Palsy are facing difficult choices concerning heavy surgeries. Diagnosis settled by surgeons can be complex and on the other hand decision for patient about getting or not such a surgery involves important reflection effort. Proposed software combining prediction for surgeries and post surgery kinematic values, and from 3D model representing the patient is an innovative tool helpful for both patients and medicine professionals. Beginning with analysis and classification of kinematics values from Data Base extracted from gait analysis in 3 separated clusters, it is possible to determine close similarity between patients. Prediction surgery best adapted to improve a patient gait is then determined by operating a suitable preconditioned neural network. Finally, patient 3D modeling based on kinematic values analysis, is animated thanks to post surgery kinematic vectors characterizing the closest patient selected from patients clustering.

Keywords: Cerebral Palsy, Clustering, Crouch Gait, 3-D Modeling.

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Authors: Ibrahim S AlNomay, Alaa Jaber, Ghada AlNasser

Abstract:

A learning management system (commonly abbreviated as LMS) is a software application for the administration, documentation, tracking, and reporting of training programs, classroom and online events, e-learning programs, and training content (Ellis 2009). (Hall 2003) defines an LMS as \"software that automates the administration of training events. All Learning Management Systems manage the log-in of registered users, manage course catalogs, record data from learners, and provide reports to management\". Evidence of the worldwide spread of e-learning in recent years is easy to obtain. In April 2003, no fewer than 66,000 fully online courses and 1,200 complete online programs were listed on the TeleCampus portal from TeleEducation (Paulsen 2003). In the report \" The US market in the Self-paced eLearning Products and Services:2010-2015 Forecast and Analysis\" The number of student taken classes exclusively online will be nearly equal (1% less) to the number taken classes exclusively in physical campuses. Number of student taken online course will increase from 1.37 million in 2010 to 3.86 million in 2015 in USA. In another report by The Sloan Consortium three-quarters of institutions report that the economic downturn has increased demand for online courses and programs.

Keywords: LMS, Interactive Materials, Exam Centers, Learning Outcomes

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Authors: Amir Lotfi, Seyed Hamid Hosseini

Abstract:

During the recent past, due to the increase of electrical energy demand and governmental resources constraints in creating additional capacity in the generation, transmission, and distribution, privatization, and restructuring in electrical industry have been considered. So, in most of the countries, different parts of electrical industry like generation, transmission, and distribution have been separated in order to create competition. Considering these changes, environmental issues, energy growth, investment of private equity in energy generation units and difficulties of transmission lines expansion, distributed generation (DG) units have been used in power systems. Moreover, reduction in the need for transmission and distribution, the increase of reliability, improvement of power quality, and reduction of power loss have caused DG to be placed in power systems. On the other hand, considering low liquidity need, private investors tend to spend their money for DGs. In this project, the main goal is to offer an algorithm for planning and placing DGs in order to reduce the need for transmission and distribution network.

Keywords: Planning, transmission, distributed generation, power security, power systems.

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Authors: B. Vahdani, R. Tavakkoli-Moghaddam, A. Baboli, S. M. Mousavi

Abstract:

Focusing on the environmental issues, including the reduction of scrap and consumer residuals, along with the benefiting from the economic value during the life cycle of goods/products leads the companies to have an important competitive approach. The aim of this paper is to present a new mixed nonlinear facility locationallocation model in recycling collection networks by considering multi-echelon, multi-suppliers, multi-collection centers and multifacilities in the recycling network. To make an appropriate decision in reality, demands, returns, capacities, costs and distances, are regarded uncertain in our model. For this purpose, a fuzzy mathematical programming-based possibilistic approach is introduced as a solution methodology from the recent literature to solve the proposed mixed-nonlinear programming model (MNLP). The computational experiments are provided to illustrate the applicability of the designed model in a supply chain environment and to help the decision makers to facilitate their analysis.

Keywords: Location-allocation model, recycling collection networks, fuzzy mathematical programming.

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Authors: Nieto B. W, Luna A. C, Ramos R. J.

Abstract:

This paper describes an experience of research, development and innovation applied in Industrial Naval at (Science and Technology Corporation for the Development of Shipbuilding Industry, Naval in Colombia (COTECMAR) particularly through processes of research, innovation and technological development, based on theoretical models related to organizational knowledge management, technology management and management of human talent and integration of technology platforms. It seeks ways to facilitate the initial establishment of environments rich in information, knowledge and content-supported collaborative strategies on dynamic processes missionary, seeking further development in the context of research, development and innovation of the Naval Engineering in Colombia, making it a distinct basis for the generation of knowledge assets from COTECMAR. The integration of information and communication technologies, supported on emerging technologies (mobile technologies, wireless, digital content via PDA, and content delivery services on the Web 2.0 and Web 3.0) as a view of the strategic thrusts in any organization facilitates the redefinition of processes for managing information and knowledge, enabling the redesign of workflows, the adaptation of new forms of organization - preferably in networking and support the creation of symbolic-inside-knowledge promotes the development of new skills, knowledge and attitudes of the knowledge worker

Keywords: Management Knowledge, Information andCommunication Technologies, Knowledge Worker.

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Authors: Reza Sigari Tabrizi, Yeap Peik Foong, Nazli Ebrahimi

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This paper is concerned with the establishment of relationships among knowledge management (KM) criteria that will ensure an essential foundation to evaluate KM outcomes. The major issue under investigation is to assess the popularity of criteria within organizations and to establish a structure of criteria for measuring KM results. An empirical survey was conducted among Malaysian organizations to investigate KM criteria for measuring success of KM initiatives. Therefore, knowledge workers as the respondents were targeted to establish a structure of criteria for evaluating KM outcomes. An established structure of criteria based on the Interpretive Structural Modeling (ISM) is used to map criteria relationships inside organizations. This structure is portrayed to identify that how these set of criteria are related. This network schema should be investigated and implemented to promote innovation and improve enterprise performance. To the researchers, this survey has significant insights into relationship between KM programs and business success.

Keywords: Knowledge Management, Knowledge ManagementOutcomes, KM Criteria, Innovation, Interpretive Structural Modeling

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Authors: Katleho Daniel Makatjane, Diteboho Lawrence Xaba, Ntebogang Dinah Moroke

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The current study investigates the behaviour of time-varying parameters that are based on the score function of the predictive model density at time t. The mechanism to update the parameters over time is the scaled score of the likelihood function. The results revealed that there is high persistence of time-varying, as the location parameter is higher and the skewness parameter implied the departure of scale parameter from the normality with the unconditional parameter as 1.5. The results also revealed that there is a perseverance of the leptokurtic behaviour in stock returns which implies the returns are heavily tailed. Prior to model estimation, the White Neural Network test exposed that the stock price can be modelled by a GAS model. Finally, we proposed further researches specifically to model the existence of time-varying parameters with a more detailed model that encounters the heavy tail distribution of the series and computes the risk measure associated with the returns.

Keywords: Generalized autoregressive score model, stock returns, time-varying.

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Authors: R. Byler

Abstract:

Innovation, particularly in technology development, is a crucial aspect of nanotechnology R&D and, although several approaches to effective innovation management exist, organizational structures that promote knowledge exchange have been found to be most effect in supporting new and emerging technologies. This paper discusses Integrated Knowledge Networks (IKNs) and evaluates its use within nanotechnology R&D to increase technology innovation. Specifically, this paper reviews the role of IKNs in bolstering national and international nanotechnology development and in enhancing nanotechnology innovation. Both physical and virtual IKNs, particularly IT-based network platforms for community-based innovation, offer strategies for enhanced technology innovation, interdisciplinary cooperation, and enterprise development. Effectively creating and managing technology R&D networks can facilitate successful knowledge exchange, enhanced innovation, commercialization, and technology transfer. As such, IKNs are crucial to technology development processes and, thus, in increasing the quality and access to new, innovative nanoscience and technologies worldwide.

Keywords: Community-based innovation, integrated knowledge networks, nanotechnology, technology innovation.

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Authors: Bernardo Cedano, Ahmed H. Eltom, Bob Hay, Jim Glass, Raga Ahmed

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The deployment of smart grid devices such as smart meters and smart switches (SS) supported by a reliable and fast communications system makes automated distribution possible, and thus, provides great benefits to electric power consumers and providers alike. However, more research is needed before the full utility of smart switch data is realized. This paper presents new automated switching techniques using SS within the electric power grid. A concise background of the SS is provided, and operational examples are shown. Organization and presentation of data obtained from SS are shown in the context of the future goal of total automation of the distribution network. The description of application techniques, the examples of success with SS, and the vision outlined in this paper serve to motivate future research pertinent to disturbance analysis automation.

Keywords: Disturbance automation, electric power grid, smart grid, smart switch.

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Authors: N. M. Nawi, R. S. Ransing, M. R. Ransing

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The conjugate gradient optimization algorithm is combined with the modified back propagation algorithm to yield a computationally efficient algorithm for training multilayer perceptron (MLP) networks (CGFR/AG). The computational efficiency is enhanced by adaptively modifying initial search direction as described in the following steps: (1) Modification on standard back propagation algorithm by introducing a gain variation term in the activation function, (2) Calculation of the gradient descent of error with respect to the weights and gains values and (3) the determination of a new search direction by using information calculated in step (2). The performance of the proposed method is demonstrated by comparing accuracy and computation time with the conjugate gradient algorithm used in MATLAB neural network toolbox. The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm.

Keywords: Adaptive gain variation, back-propagation, activation function, conjugate gradient, search direction.

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Authors: Yousry El-Gamal, Khalid El-Gazzar, Magdy Saeb

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The development of distributed systems has been affected by the need to accommodate an increasing degree of flexibility, adaptability, and autonomy. The Mobile Agent technology is emerging as an alternative to build a smart generation of highly distributed systems. In this work, we investigate the performance aspect of agent-based technologies for information retrieval. We present a comparative performance evaluation model of Mobile Agents versus Remote Method Invocation by means of an analytical approach. We demonstrate the effectiveness of mobile agents for dynamic code deployment and remote data processing by reducing total latency and at the same time producing minimum network traffic. We argue that exploiting agent-based technologies significantly enhances the performance of distributed systems in the domain of information retrieval.

Keywords: Mobile Agent, performance evaluation, RMI, information retrieval, distributed systems, database.

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Authors: A. Melka, N. Slater, A. Mualem, Y. Louzoun

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Networks are often presented as containing a “core” and a “periphery.” The existence of a core suggests that some vertices are central and form the skeleton of the network, to which all other vertices are connected. An alternative view of graphs is through communities. Multiple measures have been proposed for dense communities in graphs, the most classical being k-cliques, k-cores, and k-plexes, all presenting groups of tightly connected vertices. We here show that the edge number thresholds for such communities to emerge and for their percolation into a single dense connectivity component are very close, in all networks studied. These percolating cliques produce a natural core and periphery structure. This result is generic and is tested in configuration models and in real-world networks. This is also true for k-cores and k-plexes. Thus, the emergence of this connectedness among communities leading to a core is not dependent on some specific mechanism but a direct result of the natural percolation of dense communities.

Keywords: Networks, cliques, percolation, core structure, phase transition.

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Authors: Armando Cartenì, Ilaria Henke

Abstract:

Recently, the Italian government has approved a new law for public contracts and has been laying the groundwork for restarting a planning phase. The government has adopted the indications given by the European Commission regarding the estimation of the external costs within the Cost-Benefit Analysis, and has been approved the ‘Guidelines for assessment of Investment Projects’. In compliance with the new Italian law, the aim of this research was to perform a feasibility study applying quantitative methods regarding the revamping of an Italian tourist railway line. A Cost-Benefit Analysis was performed starting from the quantification of the passengers’ demand potentially interested in using the revamped rail services. The benefits due to the external costs reduction were also estimated (quantified) in terms of variations (with respect to the not project scenario): climate change, air pollution, noises, congestion, and accidents. Estimations results have been proposed in terms of the Measure of Effectiveness underlying a positive Net Present Value equal to about 27 million of Euros, an Internal Rate of Return much greater the discount rate, a benefit/cost ratio equal to 2 and a PayBack Period of 15 years.

Keywords: Cost-benefit analysis, evaluation analysis, demand management, external cost, transport planning, quality.

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Authors: Brett E. Shelton, Joel Duffin, Yuxuan Wang, Justin Ball

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With a growing number of digital libraries and other open education repositories being made available throughout the world, effective search and retrieval tools are necessary to access the desired materials that surpass the effectiveness of traditional, allinclusive search engines. This paper discusses the design and use of Folksemantic, a platform that integrates OpenCourseWare search, Open Educational Resource recommendations, and social network functionality into a single open source project. The paper describes how the system was originally envisioned, its goals for users, and data that provides insight into how it is actually being used. Data sources include website click-through data, query logs, web server log files and user account data. Based on a descriptive analysis of its current use, modifications to the platform's design are recommended to better address goals of the system, along with recommendations for additional phases of research.

Keywords: Digital libraries, open education, recommendation system, social networks

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Authors: Choon Ling Sia, Yani Shi, Jiaqi Yan, Huaping Chen

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With the development of the Internet, E-commerce is growing at an exponential rate, and lots of online stores are built up to sell their goods online. A major factor influencing the successful adoption of E-commerce is consumer-s trust. For new or unknown Internet business, consumers- lack of trust has been cited as a major barrier to its proliferation. As web sites provide key interface for consumer use of E-Commerce, we investigate the design of web site to build trust in E-Commerce from a design science approach. A conceptual model is proposed in this paper to describe the ontology of online transaction and human-computer interaction. Based on this conceptual model, we provide a personalized webpage design approach using Bayesian networks learning method. Experimental evaluation are designed to show the effectiveness of web personalization in improving consumer-s trust in new or unknown online store.

Keywords: Trust, Web site design, Human-ComputerInteraction, E-Commerce, Design science, Bayesian network.

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Authors: Byung-Wan Jo, Kwang-Won Yoon, Ji-Sun Choi

Abstract:

Load managing method on road became necessary since overloaded vehicles occur damage on road facilities and existing systems for preventing this damage still show many problems.Accordingly, efficient managing system for preventing overloaded vehicles could be organized by using the road itself as a scale by applying genetic algorithm to analyze the load and the drive information of vehicles.Therefore, this paper organized Ubiquitous sensor network system for development of intelligent overload vehicle regulation system, also in this study, to use the behavior of road, the transformation was measured by installing underground box type indoor model and indoor experiment was held using genetic algorithm. And we examined wireless possibility of overloaded vehicle regulation system through experiment of the transmission and reception distance.If this system will apply to road and bridge, might be effective for economy and convenience through establishment of U-IT system..

Keywords: Overload Vehicle. Genetic Algorithm, EmbeddedSystem, Wim Sensor, overload vehicle regulation

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Authors: Labar Hocine, Dgeghader Yacine, Kelaiaia Mounia Samira, Bounaya Kamel

Abstract:

To maximise furnace production it-s necessary to optimise furnace control, with the objectives of achieving maximum power input into the melting process, minimum network distortion and power-off time, without compromise on quality and safety. This can be achieved with on the one hand by an appropriate electrode control and on the other hand by a minimum of AC transformer switching. Electrical arc is a stochastic process; witch is the principal cause of power quality problems, including voltages dips, harmonic distortion, unbalance loads and flicker. So it is difficult to make an appropriate model for an Electrical Arc Furnace (EAF). The factors that effect EAF operation are the melting or refining materials, melting stage, electrode position (arc length), electrode arm control and short circuit power of the feeder. So arc voltages, current and power are defined as a nonlinear function of the arc length. In this article we propose our own empirical function of the EAF and model, for the mean stages of the melting process, thanks to the measurements in the steel factory.

Keywords: Modelling, electrical arc, melting, power, EAF, steel.

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Authors: Samee Ullah Khan, Ishfaq Ahmad

Abstract:

This paper proposes a novel game theoretical technique to address the problem of data object replication in largescale distributed computing systems. The proposed technique draws inspiration from computational economic theory and employs the extended Vickrey auction. Specifically, players in a non-cooperative environment compete for server-side scarce memory space to replicate data objects so as to minimize the total network object transfer cost, while maintaining object concurrency. Optimization of such a cost in turn leads to load balancing, fault-tolerance and reduced user access time. The method is experimentally evaluated against four well-known techniques from the literature: branch and bound, greedy, bin-packing and genetic algorithms. The experimental results reveal that the proposed approach outperforms the four techniques in both the execution time and solution quality.

Keywords: Auctions, data replication, pricing, static allocation.

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Authors: Jinghui Peng, Shanyu Tang, Jia Li

Abstract:

Steganalysis seeks to detect the presence of secret data embedded in cover objects, and there is an imminent demand to detect hidden messages in streaming media. This paper shows how a steganalysis algorithm based on Fast Fourier Transform (FFT) can be used to detect the existence of secret data embedded in streaming media. The proposed algorithm uses machine parameter characteristics and a network sniffer to determine whether the Internet traffic contains streaming channels. The detected streaming data is then transferred from the time domain to the frequency domain through FFT. The distributions of power spectra in the frequency domain between original VoIP streams and stego VoIP streams are compared in turn using t-test, achieving the p-value of 7.5686E-176 which is below the threshold. The results indicate that the proposed FFT-based steganalysis algorithm is effective in detecting the secret data embedded in VoIP streaming media.

Keywords: Steganalysis, security, fast Fourier transform, streaming media.

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Authors: N. Ploysook, R. Rugsaj, C. Suvanjumrat

Abstract:

The characteristic requirement for producing rectangular shape bottles was a uniform thickness of the plastic bottle wall. Die shaping was a good technique which controlled the wall thickness of bottles. An advance technology which was the finite element method (FEM) for blowing parison to be a rectangular shape bottle was conducted to reduce waste plastic from a trial and error method of a die shaping and parison control method. The artificial intelligent (AI) comprised of artificial neural network and genetic algorithm was selected to optimize the die gap shape from the FEM results. The application of AI technique could optimize the suitable die gap shape for the parison blow molding which did not depend on the parison control method to produce rectangular bottles with the uniform wall. Particularly, this application can be used with cheap blow molding machines without a parison controller therefore it will reduce cost of production in the bottle blow molding process.

Keywords: AI, bottle, die shaping, FEM.

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Authors: Byung-Kon Kim

Abstract:

According as the Architecture, Engineering and Construction (AEC) Industry projects have grown more complex and larger, the number of utilization of BIM for 3D design and simulation is increasing significantly. Therefore, typical applications of BIM such as clash detection and alternative measures based on 3-dimenstional planning are expanded to process management, cost and quantity management, structural analysis, check for regulation, and various domains for virtual design and construction. Presently, commercial BIM software is operated on single-user environment, so initial cost is so high and the investment may be wasted frequently. Cloud computing that is a next-generation internet technology enables simple internet devices (such as PC, Tablet, Smart phone etc) to use services and resources of BIM software. In this paper, we suggested developing method of the BIM software based on cloud computing environment in order to expand utilization of BIM and reduce cost of BIM software. First, for the benchmarking, we surveyed successful case of BIM and cloud computing. And we analyzed needs and opportunities of BIM and cloud computing in AEC Industry. Finally, we suggested main functions of BIM software based on cloud computing environment and developed a simple prototype of cloud computing BIM software for basic BIM model viewing.

Keywords: Construction IT, BIM(Building Information Modeling), Cloud Computing, BIM Service Based Cloud Computing, Viewer Based BIM Server, 3D Design.

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Authors: Anusua Ghosh, Andrew Nafalski, Jeffery Tweedale, Maureen Dollard

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This paper presents anapproach of hybridizing two or more artificial intelligence (AI) techniques which arebeing used to fuzzify the workstress level ranking and categorize the rating accordingly. The use of two or more techniques (hybrid approach) has been considered in this case, as combining different techniques may lead to neutralizing each other-s weaknesses generating a superior hybrid solution. Recent researches have shown that there is a need for a more valid and reliable tools, for assessing work stress. Thus artificial intelligence techniques have been applied in this instance to provide a solution to a psychological application. An overview about the novel and autonomous interactive model for analysing work-stress that has been developedusing multi-agent systems is also presented in this paper. The establishment of the intelligent multi-agent decision analyser (IMADA) using hybridized technique of neural networks and fuzzy logic within the multi-agent based framework is also described.

Keywords: Fuzzy logic, intelligent agent, multi-agent systems, neural network, workplace stress.

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Authors: Mahmood Reza Shakarami, Reza Sedaghati

Abstract:

Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering wind turbines. The main objective of the DFR is to minimize the deviation of the bus voltage. Since the DFR is a nonlinear optimization problem, we apply the Adaptive Modified Firefly Optimization (AMFO) approach to solve it. As a result of the conflicting behavior of the single- objective function, a fuzzy based clustering technique is employed to reach the set of optimal solutions called Pareto solutions. The approach is tested on the IEEE 32-bus standard test system.

Keywords: Adaptive Modified Firefly Optimization (AMFO), Pareto solutions, feeder reconfiguration, wind turbines, bus voltage.

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Authors: Y. Parvizi, M. Gorji, M.H. Mahdian, M. Omid

Abstract:

Soil organic carbon (SOC) plays a key role in soil fertility, hydrology, contaminants control and acts as a sink or source of terrestrial carbon content that can affect the concentration of atmospheric CO2. SOC supports the sustainability and quality of ecosystems, especially in semi-arid region. This study was conducted to determine relative importance of 13 different exploratory climatic, soil and geometric factors on the SOC contents in one of the semiarid watershed zones in Iran. Two methods canonical discriminate analysis (CDA) and feed-forward back propagation neural networks were used to predict SOC. Stepwise regression and sensitivity analysis were performed to identify relative importance of exploratory variables. Results from sensitivity analysis showed that 7-2-1 neural networks and 5 inputs in CDA models output have highest predictive ability that explains %70 and %65 of SOC variability. Since neural network models outperformed CDA model, it should be preferred for estimating SOC.

Keywords: Soil organic carbon, modeling, neural networks, CDA.

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Authors: R. Khadim, M. Erritali, A. Maaden

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Localization of nodes is one of the key issues of Wireless Sensor Network (WSN) that gained a wide attention in recent years. The existing localization techniques can be generally categorized into two types: range-based and range-free. Compared with rang-based schemes, the range-free schemes are more costeffective, because no additional ranging devices are needed. As a result, we focus our research on the range-free schemes. In this paper we study three types of range-free location algorithms to compare the localization error and energy consumption of each one. Centroid algorithm requires a normal node has at least three neighbor anchors, while DV-hop algorithm doesn’t have this requirement. The third studied algorithm is the amorphous algorithm similar to DV-Hop algorithm, and the idea is to calculate the hop distance between two nodes instead of the linear distance between them. The simulation results show that the localization accuracy of the amorphous algorithm is higher than that of other algorithms and the energy consumption does not increase too much.

Keywords: Wireless Sensor Networks, Node Localization, Centroid Algorithm, DV–Hop Algorithm, Amorphous Algorithm.

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence/pattern recognition/classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: Hybrid systems, Hidden Markov Models, Recurrent neural networks, Deterministic finite state automata.

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Authors: Shahzad Hassan, Kamran Saeed

Abstract:

People are always in search of Wi-Fi hotspots because Internet is a major demand nowadays. But like all other technologies, there is still room for improvement in the Wi-Fi technology with regards to the speed and quality of connectivity. In order to address these aspects, Harald Haas, a professor at the University of Edinburgh, proposed what we know as the Li-Fi (Light Fidelity). Li-Fi is a new technology in the field of wireless communication to provide connectivity within a network environment. It is a two-way mode of wireless communication using light. Basically, the data is transmitted through Light Emitting Diodes which can vary the intensity of light very fast, even faster than the blink of an eye. From the research and experiments conducted so far, it can be said that Li-Fi can increase the speed and reliability of the transfer of data. This paper pays particular attention on the assessment of the performance of this technology. In other words, it is a 5G technology which uses LED as the medium of data transfer. For coverage within the buildings, Wi-Fi is good but Li-Fi can be considered favorable in situations where large amounts of data are to be transferred in areas with electromagnetic interferences. It brings a lot of data related qualities such as efficiency, security as well as large throughputs to the table of wireless communication. All in all, it can be said that Li-Fi is going to be a future phenomenon where the presence of light will mean access to the Internet as well as speedy data transfer.

Keywords: Communication, LED, Li-Fi, Wi-Fi.

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Authors: Eleni Aggelogiannaki, Haralambos Sarimveis

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In this work, a radial basis function (RBF) neural network is developed for the identification of hyperbolic distributed parameter systems (DPSs). This empirical model is based only on process input-output data and used for the estimation of the controlled variables at specific locations, without the need of online solution of partial differential equations (PDEs). The nonlinear model that is obtained is suitably transformed to a nonlinear state space formulation that also takes into account the model mismatch. A stable robust control law is implemented for the attenuation of external disturbances. The proposed identification and control methodology is applied on a long duct, a common component of thermal systems, for a flow based control of temperature distribution. The closed loop performance is significantly improved in comparison to existing control methodologies.

Keywords: Hyperbolic Distributed Parameter Systems, Radial Basis Function Neural Networks, H∞ control, Thermal systems.

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Authors: Dai Miyamoto

Abstract:

This paper examines the factors, which determine R&D outsourcing behaviour at Japanese firms, from the viewpoints of transaction cost and strategic management, since the latter half of the 1990s. This study uses empirical analysis, which involves the application of large-sample data. The principal findings of this paper are listed below. Firms that belong to a wider corporate group are more active in executing R&D outsourcing activities. Diversification strategies such as the expansion of product and sales markets have a positive effect on the R&D outsourcing behaviour of firms. Moreover, while quantitative R&D resources have positive influences on R&D outsourcing, qualitative indices have no effect. These facts suggest that R&D outsourcing behaviour of Japanese firms are consistent with the two perspectives of transaction cost and strategic management. Specifically, a conventional corporate group network plays an important role in R&D outsourcing behaviour. Firms that execute R&D outsourcing leverage 'old' networks to construct 'new' networks and use both networks properly.

Keywords: Corporate Group Networks, R&D Outsourcing, Strategic Management Perspective, Transaction Cost Perspective.

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on hyperspectral image (HSI) dataset on Indian Pines. The results confirm the capability of the proposed method.

Keywords: Continual learning, data reconstruction, remote sensing, hyperspectral image segmentation.

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