Search results for: social media networks

Authors: M. Mahdavi, M. Ismail, K. Jumari, Z. M. Hanapi

Abstract:

For the sensor network to operate successfully, the active nodes should maintain both sensing coverage and network connectivity. Furthermore, scheduling sleep intervals plays critical role for energy efficiency of wireless sensor networks. Traditional methods for sensor scheduling use either sensing coverage or network connectivity, but rarely both. In this paper, we use random scheduling for sensing coverage and then turn on extra sensor nodes, if necessary, for network connectivity. Simulation results have demonstrated that the number of extra nodes that is on with upper bound of around 9%, is small compared to the total number of deployed sensor nodes. Thus energy consumption for switching on extra sensor node is small.

Keywords: Wireless sensor networks, energy efficient network, performance analysis, network coverage.

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feedforward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on selforganizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: Classification, SOFM, neural network, RGB images.

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Authors: Hichem Sassi, Tawfik Najeh, Noureddine Liouane

Abstract:

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

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

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Authors: Nada Azhar

Abstract:

The aim of the paper is to investigate the effect of corporate social responsibility (CSR) CSR on the National Commercial Bank (NCB) in Saudi Arabia. In order to achieve this, a case study was made of the CSR activities of this bank from the perspective of its branch managers. The NCB was chosen as it was one of the first Saudi banks to engage in CSR and currently has a wide range of CSR initiatives. A qualitative research method was used. Open-ended questionnaires were administered to eighty branch managers of the NCB, with fifty-five usable questionnaires returned and twenty managers were interviewed as part of the primary research. Data from both questionnaires and interviews were analysed using qualitative content analysis. Six themes emerged from the questionnaire findings were used to develop the interview questions. These themes are the following: Awareness of employees about CSR in the NCB; CSR activities as a type of investment; Government and media support; Increased employee loyalty in the NCB; Prestige and profit to the NCB; and View of CSR in Islam. This paper makes a theoretical contribution in that it investigates and increases understanding of the effect of CSR on the NCB in Saudi Arabia. In addition, it makes a practical contribution by making recommendations which can support the development of CSR in the NCB. A limitation of the paper is that it is a case study of only one bank. It is therefore recommended that future research could be conducted with other banks in Saudi Arabia, or indeed, with a range of other types of firm within the financial services area in Saudi Arabia. In this way, the same issues could be explored but with a greater potential generalisability of findings of CSR within the Saudi Arabian financial services industry. In addition, this paper takes a qualitative approach and it is suggested that future research be carried out using mixed methods, which could provide a greater depth of analysis.

Keywords: Branch managers, corporate social responsibility. national commercial bank, Saudi Arabia.

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Authors: Raj Kumar Samanta, Partha Bhattacharjee Gautam Sanyal

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In this paper, a mathematical model is proposed to estimate the dropping probabilities of cellular wireless networks by queuing handoff instead of reserving guard channels. Usually, prioritized handling of handoff calls is done with the help of guard channel reservation. To evaluate the proposed model, gamma inter-arrival and general service time distributions have been considered. Prevention of some of the attempted calls from reaching to the switching center due to electromagnetic propagation failure or whimsical user behaviour (missed call, prepaid balance etc.), make the inter-arrival time of the input traffic to follow gamma distribution. The performance is evaluated and compared with that of guard channel scheme.

Keywords: Cellular wireless networks, non-classical traffic, mathematicalmodel, guard channel, queuing, handoff.

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Authors: Werner Sandmann, Verena Wolf

Abstract:

Stochastic models of biological networks are well established in systems biology, where the computational treatment of such models is often focused on the solution of the so-called chemical master equation via stochastic simulation algorithms. In contrast to this, the development of storage-efficient model representations that are directly suitable for computer implementation has received significantly less attention. Instead, a model is usually described in terms of a stochastic process or a "higher-level paradigm" with graphical representation such as e.g. a stochastic Petri net. A serious problem then arises due to the exponential growth of the model-s state space which is in fact a main reason for the popularity of stochastic simulation since simulation suffers less from the state space explosion than non-simulative numerical solution techniques. In this paper we present transition class models for the representation of biological network models, a compact mathematical formalism that circumvents state space explosion. Transition class models can also serve as an interface between different higher level modeling paradigms, stochastic processes and the implementation coded in a programming language. Besides, the compact model representation provides the opportunity to apply non-simulative solution techniques thereby preserving the possible use of stochastic simulation. Illustrative examples of transition class representations are given for an enzyme-catalyzed substrate conversion and a part of the bacteriophage λ lysis/lysogeny pathway.

Keywords: Computational Modeling, Biological Networks, Stochastic Models, Markov Chains, Transition Class Models.

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Authors: Marta Czechowska-Bieluga

Abstract:

Social work education in Poland focuses mostly on developing competencies that address the needs of individuals and families affected by a variety of life's problems. As a result of the ageing of the Polish population, much attention is equally devoted to adults, including the elderly. However, social work with children and young people is the area of education which should be given more consideration. Social work students are mostly trained to cater to the needs of families and the competencies aimed to respond to the needs of children and young people do not receive enough attention and are only offered as elective classes. This paper strives to review the social work programmes offered by the selected higher education institutions in Poland in terms of social work training aimed at helping children and young people to address their life problems. The analysis conducted in this study indicates that university education for social work focuses on training professionals who will provide assistance only to adults. Due to changes in the social and political situation, including, in particular, changes in social policy implemented for the needy, it is necessary to extend this area of education to include the specificity of the support for children and young people; especially, in the light of the appearance of new support professions within the area of social work. For example, family assistants, whose task is to support parents in performing their roles as guardians and educators, also assist children. Therefore, it becomes necessary to equip social work professionals with competencies which include issues related to the quality of life of underage people living in families. Social work curricula should be extended to include the issues of child and young person development and the patterns governing this phase of life.

Keywords: Social work education, social work programmes, social worker, university.

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Authors: A. P. Anvar, T. Dowling, T. Putland, A. M. Anvar, S.Grainger

Abstract:

This paper presents the application of Intelligent Techniques to the various duties of Intelligent Condition Monitoring Systems (ICMS) for Unmanned Aerial Vehicle (UAV) Robots. These Systems are intended to support these Intelligent Robots in the event of a Fault occurrence. Neural Networks are used for Diagnosis, whilst Fuzzy Logic is intended for Prognosis and Remedy. The ultimate goals of ICMS are to save large losses in financial cost, time and data.

Keywords: Intelligent Techniques, Condition Monitoring Systems, ICMS, Robots, Fault, Unmanned Aerial Vehicle, UAV, Neural Networks, Diagnosis, Fuzzy Logic, Prognosis, Remedy.

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Authors: Mohamed Othmani, Yassine Khlifi

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This paper presents a technique for compact three dimensional (3D) object model reconstruction using wavelet networks. It consists to transform an input surface vertices into signals,and uses wavelet network parameters for signal approximations. To prove this, we use a wavelet network architecture founded on several mother wavelet families. POLYnomials WindOwed with Gaussians (POLYWOG) wavelet families are used to maximize the probability to select the best wavelets which ensure the good generalization of the network. To achieve a better reconstruction, the network is trained several iterations to optimize the wavelet network parameters until the error criterion is small enough. Experimental results will shown that our proposed technique can effectively reconstruct an irregular 3D object models when using the optimized wavelet network parameters. We will prove that an accurateness reconstruction depends on the best choice of the mother wavelets.

Keywords: 3D object, optimization, parametrization, Polywog wavelets, reconstruction, wavelet networks.

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Authors: Maria E. Manioudaki, Panayiota Poirazi

Abstract:

Yeast cells live in a constantly changing environment that requires the continuous adaptation of their genomic program in order to sustain their homeostasis, survive and proliferate. Due to the advancement of high throughput technologies, there is currently a large amount of data such as gene expression, gene deletion and protein-protein interactions for S. Cerevisiae under various environmental conditions. Mining these datasets requires efficient computational methods capable of integrating different types of data, identifying inter-relations between different components and inferring functional groups or 'modules' that shape intracellular processes. This study uses computational methods to delineate some of the mechanisms used by yeast cells to respond to environmental changes. The GRAM algorithm is first used to integrate gene expression data and ChIP-chip data in order to find modules of coexpressed and co-regulated genes as well as the transcription factors (TFs) that regulate these modules. Since transcription factors are themselves transcriptionally regulated, a three-layer regulatory cascade consisting of the TF-regulators, the TFs and the regulated modules is subsequently considered. This three-layer cascade is then modeled quantitatively using artificial neural networks (ANNs) where the input layer corresponds to the expression of the up-stream transcription factors (TF-regulators) and the output layer corresponds to the expression of genes within each module. This work shows that (a) the expression of at least 33 genes over time and for different stress conditions is well predicted by the expression of the top layer transcription factors, including cases in which the effect of up-stream regulators is shifted in time and (b) identifies at least 6 novel regulatory interactions that were not previously associated with stress-induced changes in gene expression. These findings suggest that the combination of gene expression and protein-DNA interaction data with artificial neural networks can successfully model biological pathways and capture quantitative dependencies between distant regulators and downstream genes.

Keywords: gene modules, artificial neural networks, yeast, stress

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Authors: K. L. Verma

Abstract:

Analysis for the propagation of elastic waves in arbitrary anisotropic plates is investigated, commencing with a formal analysis of waves in a layered plate of an arbitrary anisotropic media, the dispersion relations of elastic waves are obtained by invoking continuity at the interface and boundary of conditions on the surfaces of layered plate. The obtained solutions can be used for material systems of higher symmetry such as monoclinic, orthotropic, transversely isotropic, cubic, and isotropic as it is contained implicitly in the analysis. The cases of free layered plate and layered half space are considered separately. Some special cases have also been deduced and discussed. Finally numerical solution of the frequency equations for an aluminum epoxy is carried out, and the dispersion curves for the few lower modes are presented. The results obtained theoretically have been verified numerically and illustrated graphically.

Keywords: Anisotropic, layered, dispersion, elastic waves, frequency equations.

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Authors: Sepehr M.H.Jamarani, M.H.Moradi, H.Behnam, G.A.Rezai Rad

Abstract:

This paper presents an approach for early breast cancer diagnostic by employing combination of artificial neural networks (ANN) and multiwaveletpacket based subband image decomposition. The microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands,, reconstructing the mammograms from the subbands containing only high frequencies. For this approach we employed different types of multiwaveletpacket. We used the result as an input of neural network for classification. The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases and images collected from local hospitals. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve.

Keywords: Breast cancer, neural networks, diagnosis, multiwavelet packet, microcalcification.

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Authors: S.L. Loh, S. Salleh, N.H. Sarmin

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A spanning tree of a connected graph is a tree which consists the set of vertices and some or perhaps all of the edges from the connected graph. In this paper, a model for spanning tree transformation of connected graphs into single-row networks, namely Spanning Tree of Connected Graph Modeling (STCGM) will be introduced. Path-Growing Tree-Forming algorithm applied with Vertex-Prioritized is contained in the model to produce the spanning tree from the connected graph. Paths are produced by Path-Growing and they are combined into a spanning tree by Tree-Forming. The spanning tree that is produced from the connected graph is then transformed into single-row network using Tree Sequence Modeling (TSM). Finally, the single-row routing problem is solved using a method called Enhanced Simulated Annealing for Single-Row Routing (ESSR).

Keywords: Graph theory, simulated annealing, single-rowrouting and spanning tree.

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Authors: Ball J., Capanni N., Watt S.

Abstract:

This paper argues that fostering mutual understanding in landscape planning is as much about the planners educating stakeholder groups as the stakeholders educating the planners. In other words it is an epistemological agreement as to the meaning and nature of place, especially where an effort is made to go beyond the quantitative aspects, which can be achieved by the phenomenological experience of the Virtual Reality (VR) environment. This education needs to be a bi-directional process in which distance can be both temporal as well as spatial separation of participants, that there needs to be a common framework of understanding in which neither 'side' is disadvantaged during the process of information exchange and it follows that a medium such as VR offers an effective way of overcoming some of the shortcomings of traditional media by taking advantage of continuing technological advances in Information, Technology and Communications (ITC). In this paper we make particular reference to this as an extension to Geographical Information Systems (GIS). VR as a two-way communication tool offers considerable potential particularly in the area of Public Participation GIS (PPGIS). Information rich virtual environments that can operate over broadband networks are now possible and thus allow for the representation of large amounts of qualitative and quantitative information 'side-by-side'. Therefore, with broadband access becoming standard for households and enterprises alike, distributed virtual reality environments have great potential to contribute to enabling stakeholder participation and mutual learning within the planning context.

Keywords: 3D, communication, geographical information systems, planning, public participation, virtual reality, visualisation.

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Authors: Pimploi Tirastittam, Suppara Charoenpoom

Abstract:

Nowadays the asynchronous learning has granted the permission to the anywhere and anything learning via the technology and E-media which give the learner more convenient. This research is about the design of the blended and online learning for the asynchronous learning of the process management subject in order to create the prototype of this subject asynchronous learning which will create the easiness and increase capability in the learning. The pattern of learning is the integration between the in-class learning and online learning via the internet. This research is mainly focused on the online learning and the online learning can be divided into 5 parts which are virtual classroom, online content, collaboration, assessment and reference material. After the system design was finished, it was evaluated and tested by 5 experts in blended learning design and 10 students which the user’s satisfaction level is good. The result is as good as the assumption so the system can be used in the process management subject for a real usage.

Keywords: Blended Learning, Asynchronous Learning, Design, Process Management.

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Authors: Praveen Boinee, Alessandro De Angelis, Gian Luca Foresti

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Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques with classifiers such as random forests, neural networks and support vector machines. The data sets are from MAGIC, a Cherenkov telescope experiment. The task is to classify gamma signals from overwhelmingly hadron and muon signals representing a rare class classification problem. We compare the individual classifiers with their ensemble counterparts and discuss the results. WEKA a wonderful tool for machine learning has been used for making the experiments.

Keywords: Ensembles, WEKA, Neural networks [NN], SupportVector Machines [SVM], Random Forests [RF].

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Authors: J. D. Mallapur, Supriya H., Santosh B. K., Tej H.

Abstract:

The cellular network is one of the emerging areas of communication, in which the mobile nodes act as member for one base station. The cluster based communication is now an emerging area of wireless cellular multimedia networks. The cluster renders fast communication and also a convenient way to work with connectivity. In our scheme we have proposed an optimization technique for the fuzzy cluster nodes, by categorizing the group members into three categories like long refreshable member, medium refreshable member and short refreshable member. By considering long refreshable nodes as static nodes, we compute the new membership values for the other nodes in the cluster. We compare their previous and present membership value with the threshold value to categorize them into three different members. By which, we optimize the nodes in the fuzzy clusters. The simulation results show that there is reduction in the cluster computational time and iterational time after optimization.

Keywords: Clusters, fuzzy and optimization.

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Authors: Magdalena Musiał-Karg

Abstract:

The expansion of telecommunication and progress of electronic media constitute important elements of our times. The recent worldwide convergence of information and communication technologies (ICT) and dynamic development of the mass media is leading to noticeable changes in the functioning of contemporary states and societies. Currently, modern technologies play more and more important roles and filter down to almost every field of contemporary human life. It results in the growth of online interactions that can be observed by the inconceivable increase in the number of people with home PCs and Internet access. The proof of it is undoubtedly the emergence and use of concepts such as e-society, e-banking, e-services, e-government, e-government, e-participation and e-democracy. The newly coined word e-democracy evidences that modern technologies have also been widely used in politics. Without any doubt in most countries all actors of political market (politicians, political parties, servants in political/public sector, media) use modern forms of communication with the society. Most of these modern technologies progress the processes of getting and sending information to the citizens, communication with the electorate, and also – which seems to be the biggest advantage – electoral procedures. Thanks to implementation of ICT the interaction between politicians and electorate are improved. The main goal of this text is to analyze electronic voting (e-voting) as one of the important forms of electronic democracy in terms of security aspects. The author of this paper aimed at answering the questions of security of electronic voting as an additional form of participation in elections and referenda.

Keywords: Electronic democracy, electronic participation, electronic voting, security of e-voting, ICT.

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Authors: D. Seenivasan, K. Shanthi

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Botnets are one of the most serious and widespread cyber threats. Today botnets have been facilitating many cybercrimes, especially financial, top secret thefts. Botnets can be available for lease in the market and are utilized by the cybercriminals to launch massive attacks like DDoS, click fraud, phishing attacks etc., Several large institutions, hospitals, banks, government organizations and many social networks such as twitter, facebook etc., became the target of the botmasters. Recently, noteworthy researches have been carried out to detect bot, C&C channels, botnet and botmasters. Using many sophisticated technologies, botmasters made botnet a titan of the cyber world. Innumerable challenges have been put forth by the botmasters to the researchers in the detection of botnet. In this paper we present a survey of different types of botnet C&C channels and also provide a comparison of various botnet categories. Finally we hope that our survey will create awareness for forthcoming botnet research endeavors.

Keywords: Bot, Botmaster, Botnet, Botnet cloud, Mobile Botnet.

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Authors: J. Tospornsampan, I. Kita, M. Ishii, Y. Kitamura

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In this paper a combination approach of two heuristic-based algorithms: genetic algorithm and tabu search is proposed. It has been developed to obtain the least cost based on the split-pipe design of looped water distribution network. The proposed combination algorithm has been applied to solve the three well-known water distribution networks taken from the literature. The development of the combination of these two heuristic-based algorithms for optimization is aimed at enhancing their strengths and compensating their weaknesses. Tabu search is rather systematic and deterministic that uses adaptive memory in search process, while genetic algorithm is probabilistic and stochastic optimization technique in which the solution space is explored by generating candidate solutions. Split-pipe design may not be realistic in practice but in optimization purpose, optimal solutions are always achieved with split-pipe design. The solutions obtained in this study have proved that the least cost solutions obtained from the split-pipe design are always better than those obtained from the single pipe design. The results obtained from the combination approach show its ability and effectiveness to solve combinatorial optimization problems. The solutions obtained are very satisfactory and high quality in which the solutions of two networks are found to be the lowest-cost solutions yet presented in the literature. The concept of combination approach proposed in this study is expected to contribute some useful benefits in diverse problems.

Keywords: GAs, Heuristics, Looped network, Least-cost design, Pipe network, Optimization, TS

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Authors: Svitov David, Alyamkin Sergey

Abstract:

The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network.

Keywords: ArcFace, distillation, face recognition, margin-based softmax.

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Authors: Wenqin Wang, Shouming Zhong

Abstract:

This paper presents the robust stability criteria for uncertain genetic regulatory networks with time-varying delays. One key point of the criterion is that the decomposition of the matrix ˜D into ˜D = ˜D1 + ˜D2. This decomposition corresponds to a decomposition of the delayed terms into two groups: the stabilizing ones and the destabilizing ones. This technique enables one to take the stabilizing effect of part of the delayed terms into account. Meanwhile, by choosing an appropriate new Lyapunov functional, a new delay-dependent stability criteria is obtained and formulated in terms of linear matrix inequalities (LMIs). Finally, numerical examples are presented to illustrate the effectiveness of the theoretical results.

Keywords: Genetic regulatory network, Time-varying delay, Uncertain system, Lyapunov-Krasovskii functional

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Authors: W. Fethallah, L. Chraibi, N. Sefiani

Abstract:

The present paper is concerned with a statistical approach involving latent and manifest variables applied in order to assess the organization's social responsibility performance. The main idea is to develop an assessment tool and a measurement of the Social Responsibility Performance, enabling the company to characterize her performance regarding to the ISO 26000 standard's seven core subjects. For this, we conceptualize a structural equation modeling (SEM) which describes various causal connections between the Social Responsibility’s components. The SEM’s resolution is based on the Partial Least squares (PLS) method and the implementation is running in the XLSTAT software.

Keywords: Corporate social responsibility, latent and manifest variable, partial least squares, structural equation model.

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Authors: Qitao Xie, Qingquan Zhang, Xiaofei Zhang, Di Tian, Ruixuan Wen, Ting Zhu, Ping Yi, Xin Li

Abstract:

Inspired by the recent movement of digital currency, we are building a question answering system concerning the subject of cryptocurrency using Bidirectional Encoder Representations from Transformers (BERT). The motivation behind this work is to properly assist digital currency investors by directing them to the corresponding knowledge bases that can offer them help and increase the querying speed. BERT, one of newest language models in natural language processing, was investigated to improve the quality of generated responses. We studied different combinations of hyperparameters of the BERT model to obtain the best fit responses. Further, we created an intelligent chatbot for cryptocurrency using BERT. A chatbot using BERT shows great potential for the further advancement of a cryptocurrency market tool. We show that the BERT neural networks generalize well to other tasks by applying it successfully to cryptocurrency.

Keywords: BERT, chatbot, cryptocurrency, deep learning.

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Authors: M. R. Ghasemi, A. Ehsani

Abstract:

In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Algorithm (GA) as an optimization technique with a direct use of real variables was employed. Yet, since the optimization via GAs is a long process, and the major time is consumed through the analysis, Radial Basis Function Neural Networks (RBFNN) was employed in predicting the output from the analysis. Thus, the process of optimization will be carried out through a hybrid neuro-GA environment, and the procedure will be carried out until a predicted optimum solution is achieved.

Keywords: Composite Laminates, GA, Multi-objectiveOptimisation, Neural Networks, RBFNN.

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

Abstract:

The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: Convolutional neural networks, deep learning, foot recognition, knee rehabilitation.

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Authors: Hazem M. El-Bakry

Abstract:

In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR signal. To calculate these parameters efficiently, a new model called modified Hopfield neural network is designed. The main achievement of this paper over the work in literature [30] is that the speed of the modified Hopfield neural network is accelerated. This is done by applying cross correlation in the frequency domain between the input values and the input weights. The modified Hopfield neural network can accomplish complex dignals perfectly with out any additinal computation steps. This is a valuable advantage as NMR signals are complex-valued. In addition, a technique called “modified sequential extension of section (MSES)" that takes into account the damping rate of the NMR signal is developed to be faster than that presented in [30]. Simulation results show that the calculation precision of the spectrum improves when MSES is used along with the neural network. Furthermore, MSES is found to reduce the local minimum problem in Hopfield neural networks. Moreover, the performance of the proposed method is evaluated and there is no effect on the performance of calculations when using the modified Hopfield neural networks.

Keywords: Hopfield Neural Networks, Cross Correlation, Nuclear Magnetic Resonance, Magnetic Resonance Spectroscopy, Fast Fourier Transform.

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Authors: K. Kalaiselvan, A. Elango, N. M. Nagarajan

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Today automobile and aerospace industries realise Laser Beam Welding for a clean and non contact source of heating and fusion for joining of sheets. The welding performance is mainly based on by the laser welding parameters. Some concepts related to Artificial Neural Networks and how can be applied to model weld bead geometry and mechanical properties in terms of equipment parameters are reported in order to evaluate the accuracy and compare it with traditional modeling schemes. This review reveals the output features of Titanium and Aluminium weld bead geometry and mechanical properties such as ultimate tensile strength, yield strength, elongation and reduction of the area of the weld using Artificial Neural Network.

Keywords: Laser Beam Welding (LBW), Artificial Neural Networks (ANN), Optimization, Titanium and Aluminium sheets.

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Authors: Mohd Herwan Sulaiman, Omar Aliman, Siti Rafidah Abdul Rahim

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This paper proposes a new methodology for the optimal allocation and sizing of Embedded Generation (EG) employing Real Coded Genetic Algorithm (RCGA) to minimize the total power losses and to improve voltage profiles in the radial distribution networks. RCGA is a method that uses continuous floating numbers as representation which is different from conventional binary numbers. The RCGA is used as solution tool, which can determine the optimal location and size of EG in radial system simultaneously. This method is developed in MATLAB. The effect of EG units- installation and their sizing to the distribution networks are demonstrated using 24 bus system.

Keywords: Embedded generation (EG), load flow study, optimal allocation, real coded genetic algorithm (RCGA).

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Authors: Salvatore L., Pires B., Campos M. C. M., De Souza Jr M. B.

Abstract:

It is estimated that the total cost of abnormal conditions to US process industries is around $20 billion dollars in annual losses. The hydrotreatment (HDT) of diesel fuel in petroleum refineries is a conversion process that leads to high profitable economical returns. However, this is a difficult process to control because it is operated continuously, with high hydrogen pressures and it is also subject to disturbances in feed properties and catalyst performance. So, the automatic detection of fault and diagnosis plays an important role in this context. In this work, a hybrid approach based on neural networks together with a pos-processing classification algorithm is used to detect faults in a simulated HDT unit. Nine classes (8 faults and the normal operation) were correctly classified using the proposed approach in a maximum time of 5 minutes, based on on-line data process measurements.

Keywords: Fault detection, hydrotreatment, hybrid systems, neural networks.

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