Search results for: Pattern Search

Authors: A. Mohajer, A. Noroozi, S. Norouzi

Abstract:

The bypass exhaust system of a 160 MW combined cycle has been modeled and analyzed using numerical simulation in 2D prospective. Analysis was carried out using the commercial numerical simulation software, FLUENT 6.2. All inputs were based on the technical data gathered from working conditions of a Siemens V94.2 gas turbine, installed in the Yazd power plant. This paper deals with reduction of pressure drop in bypass exhaust system using turning vanes mounted in diverter box in order to alleviate turbulent energy dissipation rate above diverter box. The geometry of such turning vanes has been optimized based on the flow pattern at diverter box inlet. The results show that the use of optimized turning vanes in diverter box can improve the flow pattern and eliminate vortices around sharp edges just before the silencer. Furthermore, this optimization could decrease the pressure drop in bypass exhaust system and leads to higher plant efficiency.

Keywords: Numerical simulation, Diverter box, Turning vanes, Exhaust system

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Authors: Kh. Ashrafi, Gh. A. Hoshyaripour

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Atmospheric stability plays the most important role in the transport and dispersion of air pollutants. Different methods are used for stability determination with varying degrees of complexity. Most of these methods are based on the relative magnitude of convective and mechanical turbulence in atmospheric motions. Richardson number, Monin-Obukhov length, Pasquill-Gifford stability classification and Pasquill–Turner stability classification, are the most common parameters and methods. The Pasquill–Turner Method (PTM), which is employed in this study, makes use of observations of wind speed, insolation and the time of day to classify atmospheric stability with distinguishable indices. In this study, a model is presented to determination of atmospheric stability conditions using PTM. As a case study, meteorological data of Mehrabad station in Tehran from 2000 to 2005 is applied to model. Here, three different categories are considered to deduce the pattern of stability conditions. First, the total pattern of stability classification is obtained and results show that atmosphere is 38.77%, 27.26%, 33.97%, at stable, neutral and unstable condition, respectively. It is also observed that days are mostly unstable (66.50%) while nights are mostly stable (72.55%). Second, monthly and seasonal patterns are derived and results indicate that relative frequency of stable conditions decrease during January to June and increase during June to December, while results for unstable conditions are exactly in opposite manner. Autumn is the most stable season with relative frequency of 50.69% for stable condition, whilst, it is 42.79%, 34.38% and 27.08% for winter, summer and spring, respectively. Hourly stability pattern is the third category that points out that unstable condition is dominant from approximately 03-15 GTM and 04-12 GTM for warm and cold seasons, respectively. Finally, correlation between atmospheric stability and CO concentration is achieved.

Keywords: Atmospheric stability, Pasquill-Turner classification, convective turbulence, mechanical turbulence, Tehran.

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

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One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: Rough Set Theory, Attribute Reduction, Fuzzy Logic, Memetic Algorithms, Record to Record Algorithm, Great Deluge Algorithm.

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Authors: S. Esakkirajan, T. Veerakumar, V. Senthil Murugan, R. Sudhakar

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This paper presents a new fingerprint coding technique based on contourlet transform and multistage vector quantization. Wavelets have shown their ability in representing natural images that contain smooth areas separated with edges. However, wavelets cannot efficiently take advantage of the fact that the edges usually found in fingerprints are smooth curves. This issue is addressed by directional transforms, known as contourlets, which have the property of preserving edges. The contourlet transform is a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks. The computation and storage requirements are the major difficulty in implementing a vector quantizer. In the full-search algorithm, the computation and storage complexity is an exponential function of the number of bits used in quantizing each frame of spectral information. The storage requirement in multistage vector quantization is less when compared to full search vector quantization. The coefficients of contourlet transform are quantized by multistage vector quantization. The quantized coefficients are encoded by Huffman coding. The results obtained are tabulated and compared with the existing wavelet based ones.

Keywords: Contourlet Transform, Directional Filter bank, Laplacian Pyramid, Multistage Vector Quantization

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Authors: R. Srinivasa Rao

Abstract:

Individually Network reconfiguration or Capacitor control perform well in minimizing power loss and improving voltage profile of the distribution system. But for heavy reactive power loads network reconfiguration and for heavy active power loads capacitor placement can not effectively reduce power loss and enhance voltage profiles in the system. In this paper, an hybrid approach that combine network reconfiguration and capacitor placement using Harmony Search Algorithm (HSA) is proposed to minimize power loss reduction and improve voltage profile. The proposed approach is tested on standard IEEE 33 and 16 bus systems. Computational results show that the proposed hybrid approach can minimize losses more efficiently than Network reconfiguration or Capacitor control. The results of proposed method are also compared with results obtained by Simulated Annealing (SA). The proposed method has outperformed in terms of the quality of solution compared to SA.

Keywords: Capacitor Control, Network Reconfiguration, HarmonySearch Algorithm, Loss Reduction, Voltage Profile.

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Authors: Ahmed Amin Mousa, M. Abd El-Salam

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This study discusses a simple solution for the problem of shortage in learning resources for kindergarten teachers. Occasionally, kindergarten teachers cannot access proper resources by usual search methods as libraries or search engines. Furthermore, these methods require a long time and efforts for preparing. The study is expected to facilitate accessing learning resources. Moreover, it suggests a potential direction for using QR code inside the classroom. The present work proposes that QR code can be used for digitizing kindergarten curriculums and accessing various learning resources. It investigates using QR code for saving information related to the concepts which kindergarten teachers use in the current educational situation. The researchers have established a guide for kindergarten teachers based on the Egyptian official curriculum. The guide provides different learning resources for each scientific and mathematical concept in the curriculum, and each learning resource is represented as a QR code image that contains its URL. Therefore, kindergarten teachers can use smartphone applications for reading QR codes and displaying the related learning resources for students immediately. The guide has been provided to a group of 108 teachers for using inside their classrooms. The results showed that the teachers approved the guide, and gave a good response.

Keywords: Kindergarten, child, learning resources, QR code, smart phone, mobile.

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Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas

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Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.

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Authors: M. Tahani, G. Karimi

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In this paper after reviewing some previous studies, in order to optimize the above knee prosthesis, beside the inertial properties a new controlling parameter is informed. This controlling parameter makes the prosthesis able to act as a multi behavior system when the amputee is opposing to different environments. This active prosthesis with the new controlling parameter can simplify the control of prosthesis and reduce the rate of energy consumption in comparison to recently presented similar prosthesis “Agonistantagonist active knee prosthesis". In this paper three models are generated, a passive, an active, and an optimized active prosthesis. Second order Taylor series is the numerical method in solution of the models equations and the optimization procedure is genetic algorithm. Modeling the prosthesis which comprises this new controlling parameter (SEP) during the swing phase represents acceptable results in comparison to natural behavior of shank. Reported results in this paper represent 3.3 degrees as the maximum deviation of models shank angle from the natural pattern. The natural gait pattern belongs to walking at the speed of 81 m/min.

Keywords: Above knee prosthesis, active controlling parameter, ballistic motion, swing phase.

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Authors: Tripatjot S. Panag, J. S. Dhillon

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The lifetime of a wireless sensor network can be effectively increased by using scheduling operations. Once the sensors are randomly deployed, the task at hand is to find the largest number of disjoint sets of sensors such that every sensor set provides complete coverage of the target area. At any instant, only one of these disjoint sets is switched on, while all other are switched off. This paper proposes a heuristic search method to find the maximum number of disjoint sets that completely cover the region. A population of randomly initialized members is made to explore the solution space. A set of heuristics has been applied to guide the members to a possible solution in their neighborhood. The heuristics escalate the convergence of the algorithm. The best solution explored by the population is recorded and is continuously updated. The proposed algorithm has been tested for applications which require sensing of multiple target points, referred to as point coverage applications. Results show that the proposed algorithm outclasses the existing algorithms. It always finds the optimum solution, and that too by making fewer number of fitness function evaluations than the existing approaches.

Keywords: Coverage, disjoint sets, heuristic, lifetime, scheduling, wireless sensor networks, WSN.

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Authors: C. N. Vanitha, M. Usha

Abstract:

In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: Neural networks, pattern learning, security, wireless sensor networks.

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Authors: Juliana Barcelos Cordeiro, Khashayar Mahani, Farbod Farzan, Mohsen A. Jafari

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Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.  

Keywords: Energy consumption forecasting, energy efficiency, load disaggregation, pattern recognition approach.

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Authors: K. Vijayalakshmi, S. Radhakrishnan

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In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate.

Keywords: Dynamic Group membership change, Hybrid Genetic Algorithm, Link / node failure, QoS Parameters.

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Authors: Abdullah Almusharraf, Andrew Whyte

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The sub-task pattern in terms of deviations and defects should be identified and understood in order to improve the quality of practices in construction projects. Therefore, sub-task susceptibility to exposure to deviations and defects has been evaluated and classified via six classifications proposed in this study. Thirty-four case studies of specific sub-tasks (from compression members in constructed concrete structures) were collected from seven construction projects in order to examine the study’s proposed classifications. The study revealed that the sub-task has a high sensitivity to deviation, where 91% of the cases were recorded as deviations; however, only 19% of cases were recorded as defects. Other findings were that the actual work during the execution process is a high source of deviation for this sub-task (74%), while only 26% of the source of deviation was due to both design documentation and the actual work. These findings significantly imply that the study’s proposed classifications could be used to determine the pattern of each sub-task and develop proactive actions to overcome issues of sub-task deviations and defects.

Keywords: Sub-tasks, deviations, defects, quality, construction projects.

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, capsule network, capacity optimization, character recognition, data augmentation; semantic segmentation.

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Authors: Chun-Yen Sung, Chung-Yao Yang, Tzu-Chun Liao, Wen-Shiang Chen, Chao-Min Cheng, J. Andrew Yeh

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The study describes chitosan membrane platform modified with nanostructure pattern which using nanotechnology to fabricate. The cell-substrate interaction between neuro-2a neuroblasts cell lines and chitosan membrane (flat, nanostructure and nanostructure pattern types) was investigated. The adhered morphology of neuro-2a cells depends on the topography of chitosan surface. We have found that neuro-2a showed different morphogenesis when cells adhered on flat and nanostructure chitosan membrane. The cell projected area of neuro-2a on flat chitosan membrane is larger than on nanostructure chitosan membrane. In addition, neuro-2a cells preferred to adhere on flat chitosan surface region than on nanostructure chitosan membrane to immobilize and differentiation. The experiment suggests surface topography can be used as a critical mechanism to isolate group of neuro-2a to a particular rectangle area on chitosan membrane. Our finding will provide a platform to take patch clamp to record electrophysiological behavior about neurons in vitro in the future.

Keywords: Chitosan membrane, neuro-2a, wet chemical etching, solvent casting.

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Authors: M. Palanivel, R. Uthayakumar

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In this paper, Economic Order Quantity (EOQ) based model for non-instantaneous Weibull distribution deteriorating items with power demand pattern is presented. In this model, the holding cost per unit of the item per unit time is assumed to be an increasing linear function of time spent in storage. Here the retailer is allowed a trade-credit offer by the supplier to buy more items. Also in this model, shortages are allowed and partially backlogged. The backlogging rate is dependent on the waiting time for the next replenishment. This model aids in minimizing the total inventory cost by finding the optimal time interval and finding the optimal order quantity. The optimal solution of the model is illustrated with the help of numerical examples. Finally sensitivity analysis and graphical representations are given to demonstrate the model.

Keywords: Power demand pattern, Partial backlogging, Time dependent holding cost, Trade credit, Weibull deterioration.

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Authors: Rekha P. Labade, Shankar B. Deosarkar, Narayan Pisharoty

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In this article design and optimization of square printed monopole antenna for wireless application is proposed. Theory of characteristics mode (TCM) is used for analysis of current modes on the antenna. TCM analysis shows that beveled ground plane improves the impedance bandwidth. The antenna operates over the frequency range from 1.860 GHz to 5 GHz for a VSWR ≤ 2, covering the GSM (1900-1990MHz), IMT-2000(1920-2170MHz), Bluetooth (2.400-2484 MHz) and lower band of ultrawideband (UWB). Stable radiation pattern shows minimal pulse distortion. The radiation pattern is omni-directional along the H-plane and figure of eight along the E-plane. Size of proposed antenna is 39 mm x 29 mm x 1.6mm. Antenna is simulated using CAD FEKO suite (6.2) using method of moment. A prototype antenna is fabricated using FR4 dielectric substrate with a dielectric constant of 4.4 and loss tangent of 0.02 to validate the simulated and measured results of the proposed antenna. Measured results are in good agreement with simulated results.

Keywords: Destructive Ground Surface (DGS), Method of moment, Theory of characteristics mode, UWB, VSWR.

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Authors: Hyun-Woo Cho

Abstract:

The fault detection and diagnosis of complicated production processes is one of essential tasks needed to run the process safely with good final product quality. Unexpected events occurred in the process may have a serious impact on the process. In this work, triangular representation of process measurement data obtained in an on-line basis is evaluated using simulation process. The effect of using linear and nonlinear reduced spaces is also tested. Their diagnosis performance was demonstrated using multivariate fault data. It has shown that the nonlinear technique based diagnosis method produced more reliable results and outperforms linear method. The use of appropriate reduced space yielded better diagnosis performance. The presented diagnosis framework is different from existing ones in that it attempts to extract the fault pattern in the reduced space, not in the original process variable space. The use of reduced model space helps to mitigate the sensitivity of the fault pattern to noise.

Keywords: Real-time Fault diagnosis, triangular representation of patterns in reduced spaces, Nonlinear kernel technique, multivariate statistical modeling.

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Authors: Amin Aghatabar Roodbary, Mohammad Hassan Bastani

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One of the defects of stepped frequency radar systems is their sensitivity to target motion. In such systems, target motion causes range cell shift, false peaks, Signal to Noise Ratio (SNR) reduction and range profile spreading because of power spectrum interference of each range cell in adjacent range cells which induces distortion in High Resolution Range Profile (HRRP) and disrupt target recognition process. Thus Target Motion Parameters (TMPs) effects compensation should be employed. In this paper, such a method for estimating TMPs (velocity and acceleration) and consequently eliminating or suppressing the unwanted effects on HRRP based on entropy minimization has been proposed. This method is carried out in two major steps: in the first step, a discrete search method has been utilized over the whole acceleration-velocity lattice network, in a specific interval seeking to find a less-accurate minimum point of the entropy function. Then in the second step, a 1-D search over velocity is done in locus of the minimum for several constant acceleration lines, in order to enhance the accuracy of the minimum point found in the first step. The provided simulation results demonstrate the effectiveness of the proposed method.

Keywords: ATR, HRRP, motion compensation, SFW, TMP.

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Authors: Shivakumar Krishnamurti, Ruchi Agarwal

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The study examines the influence of marital status on consumers of products and services using blogs as a source of information. A pre-designed questionnaire was used to collect the primary data from the respondents (experiences). Data were collected from one hundred and eighty seven respondents residing in and around the Emirates of Sharjah and Dubai of the United Arab Emirates. The collected data was analyzed with the help of statistical tools such as averages, percentages, factor analysis, Student’s t-test and Structural Equation Modelling Technique. Objectives of the study are to know the reasons how married and unmarried or single consumers of products and services are motivated to use blogs as a source of information, to know whether the consumers of products and services irrespective of their marital status share their views and experiences with other bloggers and to know the respondents’ future intentions towards blogging. The study revealed the following: Majority of the respondents have the motivation to blog because they are willing to receive comments on what they post about services, convenience of blogs to search for information about services and products, by blogging respondents share information on the symptoms of a disease/ disorder that may be experienced by someone, helps to share information about ready to cook mix products and are keen to spend more time blogging in the future.

Keywords: Blog, consumer, information, marital status.

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

Abstract:

The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: Automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection.

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Authors: Mohd Azrul Hisham Mohd Adib, Nur Hazreen Mohd Hasni

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A three-dimensional and pulsatile blood flow in the left ventricle of heart model has been studied numerically. The geometry was derived from a simple approximation of the left ventricle model and the numerical simulations were obtained using a formulation of the Navier-Stokes equations. In this study, simulation was used to investigate the pattern of flow velocity in 3D model of heart with consider the left ventricle based on critical parameter of blood under steady condition. Our results demonstrate that flow velocity focused from mitral valve channel and continuous linearly to left ventricle wall but this skewness progresses into outside wall in atrium through aortic valve with random distribution that is irregular due to force subtract from ventricle wall during cardiac cycle. The findings are the prediction of the behavior of the blood flow velocity pattern in steady flow condition which can assist the medical practitioners in their decision on the patients- treatments.

Keywords: Mitral Valve, Aortic Valve, Cardiac Cycle, Leaflet, Biomechanics, Left Ventricle

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Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed

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Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).

Keywords: Classification, Bayesian network; structure learning, K2 algorithm, expert knowledge, surface water analysis.

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Authors: C. Adam, G. Henri, T. Levent, J-B Mauro, A-L Mayet

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Energy Efficiency Management is the heart of a worldwide problem. The capability of a multi-agent system as a technology to manage the micro-grid operation has already been proved. This paper deals with the implementation of a decisional pattern applied to a multi-agent system which provides intelligence to a distributed local energy network considered at local consumer level. Development of multi-agent application involves agent specifications, analysis, design, and realization. Furthermore, it can be implemented by following several decisional patterns. The purpose of present article is to suggest a new approach for a decisional pattern involving a multi-agent system to control a distributed local energy network in a decentralized competitive system. The proposed solution is the result of a dichotomous approach based on environment observation. It uses an iterative process to solve automatic learning problems and converges monotonically very fast to system attracting operation point.

Keywords: Energy Efficiency Management, Distributed Smart- Grid, Multi-Agent System, Decisional Decentralized Competitive System.

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Authors: M. Pandi, K. Premalatha

Abstract:

The DNA microarray technology concurrently monitors the expression levels of thousands of genes during significant biological processes and across the related samples. The better understanding of functional genomics is obtained by extracting the patterns hidden in gene expression data. It is handled by clustering which reveals natural structures and identify interesting patterns in the underlying data. In the proposed work clustering gene expression data is done through an Advanced Nelder Mead (ANM) algorithm. Nelder Mead (NM) method is a method designed for optimization process. In Nelder Mead method, the vertices of a triangle are considered as the solutions. Many operations are performed on this triangle to obtain a better result. In the proposed work, the operations like reflection and expansion is eliminated and a new operation called spread-out is introduced. The spread-out operation will increase the global search area and thus provides a better result on optimization. The spread-out operation will give three points and the best among these three points will be used to replace the worst point. The experiment results are analyzed with optimization benchmark test functions and gene expression benchmark datasets. The results show that ANM outperforms NM in both benchmarks.

Keywords: Spread out, simplex, multi-minima, fitness function, optimization, search area, monocyte, solution, genomes.

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Authors: Vaibhav Kant Singh, Vijay Shah, Yogendra Kumar Jain, Anupam Shukla, A.S. Thoke, Vinay KumarSingh, Chhaya Dule, Vivek Parganiha

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Data mining, which is the exploration of knowledge from the large set of data, generated as a result of the various data processing activities. Frequent Pattern Mining is a very important task in data mining. The previous approaches applied to generate frequent set generally adopt candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how the different approaches achieve the objective of frequent mining along with the complexities required to perform the job. This paper will also look for hardware approach of cache coherence to improve efficiency of the above process. The process of data mining is helpful in generation of support systems that can help in Management, Bioinformatics, Biotechnology, Medical Science, Statistics, Mathematics, Banking, Networking and other Computer related applications. This paper proposes the use of both upward and downward closure property for the extraction of frequent item sets which reduces the total number of scans required for the generation of Candidate Sets.

Keywords: Data Mining, Candidate Sets, Frequent Item set, Pruning.

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Authors: C. Bunsanit

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This paper presents the refinement method for two beams formation of wideband smart antenna. The refinement method for weighting coefficients is based on Fully Spatial Signal Processing by taking Inverse Discrete Fourier Transform (IDFT), and its simulation results are presented using MATLAB. The radiation pattern is created by multiplying the incoming signal with real weights and then summing them together. These real weighting coefficients are computed by IDFT method; however, the range of weight values is relatively wide. Therefore, for reducing this range, the refinement method is used. The radiation pattern concerns with five input parameters to control. These parameters are maximum weighting coefficient, wideband signal, direction of mainbeam, beamwidth, and maximum of minor lobe level. Comparison of the obtained simulation results between using refinement method and taking only IDFT shows that the refinement method works well for wideband two beams formation.

Keywords: Fully spatial signal processing, beam forming, refinement method, smart antenna, weighting coefficient, wideband.

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Authors: Razib M. Othman, Safaai Deris, Rosli M. Illias, Hany T. Alashwal, Rohayanti Hassan, FarhanMohamed

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The most important property of the Gene Ontology is the terms. These control vocabularies are defined to provide consistent descriptions of gene products that are shareable and computationally accessible by humans, software agent, or other machine-readable meta-data. Each term is associated with information such as definition, synonyms, database references, amino acid sequences, and relationships to other terms. This information has made the Gene Ontology broadly applied in microarray and proteomic analysis. However, the process of searching the terms is still carried out using traditional approach which is based on keyword matching. The weaknesses of this approach are: ignoring semantic relationships between terms, and highly depending on a specialist to find similar terms. Therefore, this study combines semantic similarity measure and genetic algorithm to perform a better retrieval process for searching semantically similar terms. The semantic similarity measure is used to compute similitude strength between two terms. Then, the genetic algorithm is employed to perform batch retrievals and to handle the situation of the large search space of the Gene Ontology graph. The computational results are presented to show the effectiveness of the proposed algorithm.

Keywords: Gene Ontology, Semantic similarity measure, Genetic algorithm, Ontology search

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Authors: Rami N. Khushaba, Ahmed Al-Ani, Adel Al-Jumaily

Abstract:

One of the approaches enabling people with amputated limbs to establish some sort of interface with the real world includes the utilization of the myoelectric signal (MES) from the remaining muscles of those limbs. The MES can be used as a control input to a multifunction prosthetic device. In this control scheme, known as the myoelectric control, a pattern recognition approach is usually utilized to discriminate between the MES signals that belong to different classes of the forearm movements. Since the MES is recorded using multiple channels, the feature vector size can become very large. In order to reduce the computational cost and enhance the generalization capability of the classifier, a dimensionality reduction method is needed to identify an informative yet moderate size feature set. This paper proposes a new fuzzy version of the well known Fisher-s Linear Discriminant Analysis (LDA) feature projection technique. Furthermore, based on the fact that certain muscles might contribute more to the discrimination process, a novel feature weighting scheme is also presented by employing Particle Swarm Optimization (PSO) for estimating the weight of each feature. The new method, called PSOFLDA, is tested on real MES datasets and compared with other techniques to prove its superiority.

Keywords: Discriminant Analysis, Pattern Recognition, SignalProcessing.

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Authors: Sheng-Lan Zeng, Ting-Ting Zhang, Yu Gao, Zu-Tao Ouyang, Jia-Kuan Chen, Bo Li, Bin Zhao

Abstract:

Urbanization and related anthropogenic modifications cause extent of habitat fragmentation and directly lead to decline of local biodiversity. Conservation biologists advocate corridor creation as one approach to rescue biodiversity. Here we examine the utility of roads as corridors in preserving plant diversity by investigating roadside vegetation in Yellow River Delta (YRD), China. We examined the spatio-temporal distribution pattern of plant species richness, diversity and composition along roadside. The results suggest that roads, as dispersal conduits, increase occurrence probability of new settlers to a new area, meanwhile, roads accumulate the greater propagule pressure and favourable survival condition during operation phase. As a result, more species, including native and alien plants, non- halophyte and halophyte species, threatened and cosmopolitic species, were found prosperous at roadside. Roadside may be a refuge for more species, and the pattern of vegetation distribution is affected by road age and the distance from road verge.

Keywords: Native and alien species, Plant diversity conservation, Road construction, Road disturbance

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