Search results for: social networks

Authors: Peter Hardi, Bala Mulloth

Abstract:

To be considered a socially entrepreneurial organization today requires achieving what can be termed a “hybrid middle ground” equilibrium, comprising of economic as well as social sustainability. This middle ground requires some blend of both business and social commitments. In this paper, we use the case of Hungary's second ranked mobile operator, Telenor Hungary to illustrate an example of a company that is moving to the hybrid middle ground by transitioning from a for-profit company to a socially responsible business using the concept of strategic CSR. In this line of thinking, the organization explicitly supports programs and initiatives that have a direct link to the core business and bring operational and/or financial advantages for the company, while creating a positive social and/or environmental impact. The important lessons learned from the company transition are also discussed. 

Keywords: Hybrid middle ground, social entrepreneurship, strategic corporate social responsibility.

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Authors: Somayeh Komeylian

Abstract:

The antenna arrays for the entire 3D spherical coverage have been developed for their potential use in variety of applications such as radars and body-worn devices of the body area networks. In this study, we have rigorously revamped the hybrid antenna array using the optimum geometry of bowtie elements for achieving a significant improvement in the angular discrimination capability as well as in separating two adjacent targets. In this scenario, we have analogously investigated the effectiveness of increasing the virtual array length in fostering and enhancing the directivity and angular resolution in the 10 GHz frequency. The simulation results have extensively verified that the proposed antenna array represents a drastic enhancement in terms of size, directivity, side lobe level (SLL) and, especially resolution compared with the other available geometries. We have also verified that the maximum directivities of the proposed hybrid antenna array represent the robustness to the all  variations, which is accompanied by the uniform 3D scanning characteristic.

Keywords: Bowtie antenna, hybrid antenna array, array signal processing, body area networks.

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Authors: Mohammed Benbrahim, Adil Daoudi, Khalid Benjelloun, Aomar Ibenbrahim

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The automatic discrimination of seismic signals is an important practical goal for earth-science observatories due to the large amount of information that they receive continuously. An essential discrimination task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, two classes of seismic signals recorded routinely in geophysical laboratory of the National Center for Scientific and Technical Research in Morocco are considered. They correspond to signals associated to local earthquakes and chemical explosions. The approach adopted for the development of an automatic discrimination system is a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "modified Mexican hat wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.

Keywords: Seismic signals, Wavelets, Dimensionality reduction, Artificial neural networks, Classification.

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Authors: S.Baskar, P.S.Ramkumar, R.Kesavan

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This paper presents a new heuristic algorithm useful for long-term planning of survivable WDM networks. A multi-period model is formulated that combines network topology design and capacity expansion. The ability to determine network expansion schedules of this type becomes increasingly important to the telecommunications industry and to its customers. The solution technique consists of a Genetic Algorithm that allows generating several network alternatives for each time period simultaneously and shortest-path techniques to deduce from these alternatives a least-cost network expansion plan over all time periods. The multi-period planning approach is illustrated on a realistic network example. Extensive simulations on a wide range of problem instances are carried out to assess the cost savings that can be expected by choosing a multi-period planning approach instead of an iterative network expansion design method.

Keywords: Wavelength Division Multiplexing, Genetic Algorithm, Network topology, Multi-period reliable network planning

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Authors: Shilpi Sharma, J. S. Sodhi

Abstract:

The advent of social networking technologies has been met with mixed reactions in academic and corporate circles around the world. This study explored the influence of social network in current era, the relation being maintained between the Social networking site and its user by the extent of use, benefits and latest technologies. The study followed a descriptive research design wherein a questionnaire was used as the main research tool. The data collected was analyzed using SPSS 16. Data was gathered from 1205 users and analyzed in accordance with the objectives of the study. The analysis of the results seem to suggest that the majority of users were mainly using Facebook, despite of concerns raised about the disclosure of personal information on social network sites, users continue to disclose huge quantity of personal information, they find that reading privacy policy is time consuming and changes made can result into improper settings.

Keywords: Social Networking Sites, Privacy Policy, Disclosure of Personal Information.

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Authors: Sajed Yaghoubnezhad, Mina Karimi, Seyede Marjan Modirkhazeni

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The goal of this research was to study the relationship between codependency, perceived social support and depression in mothers of children with intellectual disability (ID). The correlational method was used in this study. The research population is comprised of mothers of educable children with ID in the age range of 25 to 61 years. From among this, a sample of 251 individuals, in the multistage cluster sampling method, was selected from educational districts in Tehran, who responded to the Spann-Fischer Codependency Scale (SFCDS), the Social Support Questionnaire and the Beck Depression Inventory (BDI). The findings of this study indicate that among mothers of children with ID depression has a positive and significant correlation with codependency (P<0.01, r=0.4) and a negative and significant correlation with the total score of social support (P<0.01, r=-0.34). Moreover, the results of stepwise multiple regression analysis showed that codependency is allocated a higher variance than social support in explaining depression (R²=0.023).

Keywords: Codependency, social support, depression, mothers of children with ID.

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Authors: Shathya Duobiene, Gediminas Račiukaitis

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Wireless Sensor Networks (WSNs) are an emerging technology that opens up a new field of research. The significant advance in WSN leads to an increasing prevalence of various monitoring applications and real-time assistance in labs and factories. Selective surface activation induced by laser (SSAIL) is a promising technology that adapts to the WSN design freedom of shape, dimensions, and material. This article proposes and implements a WSN-based temperature and humidity monitoring system, and its deployed architectures made for the monitoring task are discussed. Experimental results of developed sensor nodes implemented in university campus laboratories are shown. Then, the simulation and the implementation results obtained through monitoring scenarios are displayed. At last, a convenient solution to keep the WSN alive and functional as long as possible is proposed. Unlike other existing models, on success, the node is self-powered and can utilize minimal power consumption for sensing and data transmission to the base station.

Keywords: Internet of Things, IoT, network formation, sensor nodes, SSAIL technology.

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Authors: Junghoon Lee, Gyung-Leen Park, Ho-Young Kwak, Cheol Min Kim

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This paper presents the design and implements the prototype of an intelligent data processing framework in ubiquitous sensor networks. Much focus is put on how to handle the sensor data stream as well as the interoperability between the low-level sensor data and application clients. Our framework first addresses systematic middleware which mitigates the interaction between the application layer and low-level sensors, for the sake of analyzing a great volume of sensor data by filtering and integrating to create value-added context information. Then, an agent-based architecture is proposed for real-time data distribution to efficiently forward a specific event to the appropriate application registered in the directory service via the open interface. The prototype implementation demonstrates that our framework can host a sophisticated application on the ubiquitous sensor network and it can autonomously evolve to new middleware, taking advantages of promising technologies such as software agents, XML, cloud computing, and the like.

Keywords: sensor network, intelligent farm, middleware, event detection

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Authors: Vishnu Pratap Singh Kirar

Abstract:

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

Keywords: Neural Network, Backpropagation, Local Minima, Fast Convergence Rate.

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Authors: S. Anna Durai, E. Anna Saro

Abstract:

In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Counter Propagation Neural Network, it takes longer time to converge. The reason for this is that the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbor with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative Distribution Function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used the Counter Propagation Neural Network yield high compression ratio as well as it converges quickly.

Keywords: Correlation, Counter Propagation Neural Networks, Cummulative Distribution Function, Image compression.

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Authors: Grigoris Giannarakis, Nikolaos Litinas, Ioannis Theotokas

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This study attempts to clarify major perspectives of Corporate Social Responsibility (CSR) in the Greek market related to companies that have sufficient CSR. An empirical analysis was undertaken, based on literature review and previous observations and surveys, in order to provide a general analysis of the CSR concept in Greece. The results of Accountability Rating institution were used in order to identify companies that adopt an integrated social responsibility approach. Companies that responded to the survey are both regional and international and belong to different industrial fields. Some of the main survey results reveal: multiple aspects for the CSR concept, weak consensus as regards the importance of stakeholders and benefits from the CSR implementation, the important role of CSR in the decision procedure and CSR practices concerning social issues that affect mostly company-s competitiveness. Sharing companies- experience could address common social issues through CSR best practices and develop new knowledge.

Keywords: Corporate Social Responsibility, Greece, Kendall's co-efficient of concordance.

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: Deep learning, long-short-term memory, energy, renewable energy load forecasting.

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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: Abdel Ilah Alshabtat, Liang Dong

Abstract:

In this paper, we proposed a new routing protocol for Unmanned Aerial Vehicles (UAVs) that equipped with directional antenna. We named this protocol Directional Optimized Link State Routing Protocol (DOLSR). This protocol is based on the well known protocol that is called Optimized Link State Routing Protocol (OLSR). We focused in our protocol on the multipoint relay (MPR) concept which is the most important feature of this protocol. We developed a heuristic that allows DOLSR protocol to minimize the number of the multipoint relays. With this new protocol the number of overhead packets will be reduced and the End-to-End delay of the network will also be minimized. We showed through simulation that our protocol outperformed Optimized Link State Routing Protocol, Dynamic Source Routing (DSR) protocol and Ad- Hoc On demand Distance Vector (AODV) routing protocol in reducing the End-to-End delay and enhancing the overall throughput. Our evaluation of the previous protocols was based on the OPNET network simulation tool.

Keywords: Mobile Ad-Hoc Networks, Ad-Hoc RoutingProtocols, Optimized link State Routing Protocol, Unmanned AerialVehicles, Directional Antenna.

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Authors: Syed Muhammad Aqil Burney, Tahseen Ahmed Jilani, C. Ardil

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Using neural network we try to model the unknown function f for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain quasi-fuzzy weight sets (QFWS) using repeated simulation of the crisp neural network. Such type of fuzzy weight functions may be applied where we have multivariate crisp input that needs to be adjusted after iterative learning, like claim amount distribution analysis. As real data is subjected to noise and uncertainty, therefore, QFWS may be helpful in the simplification of such complex problems. Secondly, these QFWS provide good initial solution for training of fuzzy neural networks with reduced computational complexity.

Keywords: Crisp neural networks, fuzzy systems, extraction of logical rules, quasi-fuzzy numbers.

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Authors: R. Ramli, S. Sameon

Abstract:

There has been a strong link between computermediated education and constructivism learning and teaching theory.. Acknowledging how well the constructivism doctrine would work online, it has been established that constructivist views of learning would agreeably correlate with the philosophy of open and distance learning. Asynchronous and synchronous communications have placed online learning on the right track of a constructive learning path. This paper is written based on the social constructivist framework, where knowledge is constructed from social communication and interaction. The study explores the possibility of practicing this theory through incorporating online discussion in the syllabus and the ways it can be implemented to contribute to young people-s personality and social development by addressing some aspects that may contribute to the social problem such as prejudice, ignorance and intolerance.

Keywords: Educational Technology, Internet, Personal Development, Student Exchange

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Authors: Chia-Feng Lu, Yuh-Jen Wang, Yu-Te Wu, Sui-Hing Yan

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This study aimed at investigating whether the functional brain networks constructed using the initial EEG (obtained when patients first visited hospital) can be correlated with the progression of cognitive decline calculated as the changes of mini-mental state examination (MMSE) scores between the latest and initial examinations. We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions, and the network analysis based on graph theory to investigate the organization of functional networks in aMCI. Our finding suggested that higher integrated functional network with sufficient connection strengths, dense connection between local regions, and high network efficiency in processing information at the initial stage may result in a better prognosis of the subsequent cognitive functions for aMCI. In conclusion, the functional connectivity can be a useful biomarker to assist in prediction of cognitive declines in aMCI.

Keywords: Cognitive decline, functional connectivity, MCI, MMSE.

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Authors: P. Harazin, Gy. Horváth

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There are four challenges of sustainable development and in corporate level sustainability management-s role is to answer for ecological sustainability challenge, social sustainability challenge, economic sustainability challenges to environment and social management and integration challenge of corporate sustainable challenges by the help of different concepts, methods, instruments, which are in the toolbox of sustainability management. These instruments, concepts have different relevance in these challenges, and according to different literatures environmental management is outside of social and integration challenge. Main aim of this paper is to represent the answer for the question that: is it true that social and integration point of view is outside of the concept environmental accounting? Using literature review and primer research at the end of the paper the answer will be confirmed.

Keywords: Corporate social responsibility, Environmentalaccounting, Integration, Sustainability management

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Authors: Nashrawan N. Taha, Andrew M. Cox

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In a multi-cultural learning context, where ties are weak and dynamic, combining qualitative with quantitative research methods may be more effective. Such a combination may also allow us to answer different types of question, such as about people’s perception of the network. In this study the use of observation, interviews and photos were explored as ways of enhancing data from social network questionnaires. Integrating all of these methods was found to enhance the quality of data collected and its accuracy, also providing a richer story of the network dynamics and the factors that shaped these changes over time.

Keywords: Mixed Methods, Social Network Analysis, multi-cultural learning, Social Network Dynamics.

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Authors: V. Nikkhah Rashidabad, M. Manteghian, M. Masoumi, S. Mousavian, D. Ashouri

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In this research, the capability of neural networks in  modeling and learning complicated and nonlinear relations has been  used to develop a model for the prediction of changes in the diameter  of bubbles in pool boiling distilled water. The input parameters used  in the development of this network include element temperature, heat  flux, and retention time of bubbles. The test data obtained from the  experiment of the pool boiling of distilled water, and the  measurement of the bubbles form on the cylindrical element. The  model was developed based on training algorithm, which is  typologically of back-propagation type. Considering the correlation  coefficient obtained from this model is 0.9633. This shows that this  model can be trusted for the simulation and modeling of the size of  bubble and thermal transfer of boiling.

Keywords: Bubble Diameter, Heat Flux, Neural Network, Training Algorithm.

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Authors: Mahmood Reza Shakarami, Reza Sedaghati

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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: L. Feleagă, M. Dumitrașcu, N. Feleagă

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This article is a research on corporate governance. The aim of the study is to focus a special attention on the importance of corporate social responsibility and corporate governance, which are relevant, indeed necessary, for organizations. In this regard, we analyzed the corporate social responsibility in the context of corporate governance for companies listed on Bucharest and Bombay Stock Exchange. Therefore, we bring into the spotlight some differences between India and Romania linked with the importance ascribed to corporate social responsibility of a company. We presented the results of the demarche and we concluded suggestions regarding further research in this area. The study increases the awareness, identifies and articulates desirable behaviors, which are not intended to be exhaustive.

Keywords: Corporate governance, corporate social responsibility, disclosure, listed companies.

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Authors: Aline F. Marcon, Eduardo F. da Silva, Marina Bouzon

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The impact of lean management on environmental sustainability is the research line that receives the most attention from academicians. Therefore, the social dimension of sustainable development has so far received less attention. This paper aims to evaluate the impact of intra-plant lean manufacturing practices on social sustainability indicators extracted from the Global Reporting Initiative (GRI) parameters. The method is two-phased, including MCDM approach to uncover the most relevant practices regarding social performance and Interpretive Structural Modeling (ISM) method to reveal the structural relationship among lean practices. Professionals from the academic and industrial fields answered the questionnaires. From the results of this paper, it is possible to verify that practices such as “Safety Improvement Programs”, “Total Quality Management” and “Cross-functional Workforce” are the ones which have the most positive influence on the set of GRI social indicators.

Keywords: Indicators, ISM, lean, social, sustainability.

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Authors: Alaa Tag Eldin Mohamed

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The environmental, cultural, social, and technological changes have led higher education institutes to question their traditional roles. Many declarations and frameworks highlight the importance of fulfilling social responsibility of higher education institutes. The study aims at developing a framework of university social responsibility and sustainability (USR&S) with focus on South Valley University (SVU) as a case study of Egyptian Universities. The study used meetings with 12 vice deans of community services and environmental affairs on social responsibility and environmental issues. The proposed framework integrates social responsibility with strategic management through the establishment and maintenance of the vision, mission, values, goals and management systems; elaboration of policies; provision of actions; evaluation of services and development of social collaboration with stakeholders to meet current and future needs of the community and environment. The framework links between different stakeholders internally and externally using communication and reporting tools. The results show that SVU integrates social responsibility and sustainability in its strategic plans. It has policies and actions however fragmented and lack of appropriate structure and budgeting. The proposed framework could be valuable for researchers and decision makers of the Egyptian Universities. The study proposed recommendations and highlighted building on the results and conducting future research.

Keywords: Corporate social responsibility (CSR), South Valley University, Sustainable University, university social responsibility and sustainability (USR&S).

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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: Zhao Linfei, Gu Qingliang

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China apparel industry, which is deeply embedded in the global production network (GPN), faces the dual pressures of social upgrading and economic upgrading. Based on the survey in Ningbo apparel cluster, the paper shows the state of corporate social responsibility (CSR) in China apparel industry is better than before. And the investigation indicates that the firms who practice CSR actively perform better both socially and economically than those who inactively. The research demonstrates that CSR can be an initial capital rather than cost, and “doing well by doing good" is also existed in labor intensive industry.

Keywords: Global production network, corporate social responsibility, China apparel industry.

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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: Mohamed Adnan Landolsi, Ali F. Almutairi

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The paper addresses the problem of line-of-sight (LOS) vs. non-line-of-sight (NLOS) propagation link identification in ultra-wideband (UWB) wireless networks, which is necessary for improving the accuracy of radiolocation and positioning applications. A LOS/NLOS likelihood hypothesis testing approach is applied based on exploiting distinctive statistical features of the channel impulse response (CIR) using parameters related to the “skewness” of the CIR and its root mean square (RMS) delay spread. A log-normal fit is presented for the probability densities of the CIR parameters. Simulation results show that different environments (residential, office, outdoor, etc.) have measurable differences in their CIR parameters’ statistics, which is then exploited in determining the nature of the propagation channels. Correct LOS/NLOS channel identification rates exceeding 90% are shown to be achievable for most types of environments. Additional improvement is also obtained by combining both CIR skewness and RMS delay statistics.

Keywords: Ultra-wideband, propagation, line-of-sight, non-line-of-sight, identification.

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Authors: Wenji Zhu, Yigang He

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This paper presents a new method of analog fault diagnosis based on back-propagation neural networks (BPNNs) using wavelet decomposition and fractal dimension as preprocessors. The proposed method has the capability to detect and identify faulty components in an analog electronic circuit with tolerance by analyzing its impulse response. Using wavelet decomposition to preprocess the impulse response drastically de-noises the inputs to the neural network. The second preprocessing by fractal dimension can extract unique features, which are the fed to a neural network as inputs for further classification. A comparison of our work with [1] and [6], which also employs back-propagation (BP) neural networks, reveals that our system requires a much smaller network and performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques.

Keywords: Analog circuits, fault diagnosis, tolerance, wavelettransform, fractal dimension, box dimension.

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Authors: B. Ghanbarian-Alavijeh, A.M. Liaghat, S. Sohrabi

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Saturated hydraulic conductivity is one of the soil hydraulic properties which is widely used in environmental studies especially subsurface ground water. Since, its direct measurement is time consuming and therefore costly, indirect methods such as pedotransfer functions have been developed based on multiple linear regression equations and neural networks model in order to estimate saturated hydraulic conductivity from readily available soil properties e.g. sand, silt, and clay contents, bulk density, and organic matter. The objective of this study was to develop neural networks (NNs) model to estimate saturated hydraulic conductivity from available parameters such as sand and clay contents, bulk density, van Genuchten retention model parameters (i.e. r θ , α , and n) as well as effective porosity. We used two methods to calculate effective porosity: : (1) eff s FC φ =θ -θ , and (2) inf φ =θ -θ eff s , in which s θ is saturated water content, FC θ is water content retained at -33 kPa matric potential, and inf θ is water content at the inflection point. Total of 311 soil samples from the UNSODA database was divided into three groups as 187 for the training, 62 for the validation (to avoid over training), and 62 for the test of NNs model. A commercial neural network toolbox of MATLAB software with a multi-layer perceptron model and back propagation algorithm were used for the training procedure. The statistical parameters such as correlation coefficient (R2), and mean square error (MSE) were also used to evaluate the developed NNs model. The best number of neurons in the middle layer of NNs model for methods (1) and (2) were calculated 44 and 6, respectively. The R2 and MSE values of the test phase were determined for method (1), 0.94 and 0.0016, and for method (2), 0.98 and 0.00065, respectively, which shows that method (2) estimates saturated hydraulic conductivity better than method (1).

Keywords: Neural network, Saturated hydraulic conductivity, Soil physical properties.

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