Search results for: Hybrid feature

Authors: Kayode A. Olaniyi, Adeola A. Ogunleye, Tola M. Osifeko

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Electric Vehicles (EV) appear to be gaining increasing patronage as a feasible alternative to Internal Combustion Engine Vehicles (ICEVs) for having low emission and high operation efficiency. The EV energy storage systems are required to handle high energy and power density capacity constrained by limited space, operating temperature, weight and cost. The choice of strategies for energy storage evaluation, monitoring and control remains a challenging task. This paper presents review of various energy storage technologies and recent researches in battery evaluation techniques used in EV applications. It also underscores strategies for the hybrid energy storage management and control schemes for the improvement of EV stability and reliability. The study reveals that despite the advances recorded in battery technologies there is still no cell which possess both the optimum power and energy densities among other requirements, for EV application. However combination of two or more energy storages as hybrid and allowing the advantageous attributes from each device to be utilized is a promising solution. The review also reveals that State-of-Charge (SoC) is the most crucial method for battery estimation. The conventional method of SoC measurement is however questioned in the literature and adaptive algorithms that include all model of disturbances are being proposed. The review further suggests that heuristic-based approach is commonly adopted in the development of strategies for hybrid energy storage system management. The alternative approach which is optimization-based is found to be more accurate but is memory and computational intensive and as such not recommended in most real-time applications.

Keywords: Hybrid electric vehicle, hybrid energy storage, battery state estimation, ate of charge, state of health.

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: Feature selection, multi-objective evolutionary computation, unsupervised classification, behavior assessment system for children.

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Authors: Hossein Nezamabadi-pour, Saeid Saryazdi

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In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.

Keywords: Object-based image retrieval, DCT domain, Image indexing, Image classification.

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Authors: Elif Derya UBEYLI, Inan GULER

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A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg- Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.

Keywords: Chaotic signal, Electroencephalogram (EEG) signals, Feature extraction/selection, Lyapunov exponents

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Authors: Alexandros Leontitsis, Archana P. Sangole

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This article presents a short discussion on optimum neighborhood size selection in a spherical selforganizing feature map (SOFM). A majority of the literature on the SOFMs have addressed the issue of selecting optimal learning parameters in the case of Cartesian topology SOFMs. However, the use of a Spherical SOFM suggested that the learning aspects of Cartesian topology SOFM are not directly translated. This article presents an approach on how to estimate the neighborhood size of a spherical SOFM based on the data. It adopts the L-curve criterion, previously suggested for choosing the regularization parameter on problems of linear equations where their right-hand-side is contaminated with noise. Simulation results are presented on two artificial 4D data sets of the coupled Hénon-Ikeda map.

Keywords: Parameter estimation, self-organizing feature maps, spherical topology.

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Authors: Hidehiko Okada, Jumpei Tokida

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Researchers have been applying tional intelligence (AI/CI) methods to computer games. In this research field, further researchesare required to compare AI/CI methods with respect to each game application. In th our experimental result on the comparison of three evolutionary algorithms – evolution strategy, genetic algorithm, and their hybrid applied to evolving controller agents for the CIG 2007 Simulated Car Racing competition. Our experimental result shows that, premature convergence of solutions was observed in the case of ES, and GA outperformed ES in the last half of generations. Besides, a hybrid which uses GA first and ES next evolved the best solution among the whole solutions being generated. This result shows the ability of GA in globally searching promising areas in the early stage and the ability of ES in locally searching the focused area (fine-tuning solutions).

Keywords: Evolutionary algorithm, autonomous agent, neuroevolutions, simulated car racing.

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Authors: V. Sudha, D. Sriram Kumar

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In this paper, we present a low complexity hybrid scheme using conventional selective mapping (C-SLM) and clipping algorithms to reduce the high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signal. In the proposed scheme, the input data sequence (X) is divided into two sub-blocks, then clipping algorithm is applied to the first sub-block, whereas C-SLM algorithm is applied to the second sub-block in order to reduce both computational complexity and PAPR. The resultant time domain OFDM signal is obtained by combining the output of two sub-blocks. The simulation results show that the proposed hybrid scheme provides 0.45 dB PAPR reduction gain at CCDF value of 10-2 and 52% of computational complexity reduction when compared to C-SLM scheme at the expense of slight degradation in bit error rate (BER) performance.

Keywords: CCDF, Clipping, OFDM, PAPR, SLM.

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Authors: G. Renuka Devi

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This paper presents design and analysis of fault tolerate feature of n-phase induction motor drive. The n-phase induction motor (more than 3-phases) has a number of advantages over conventional 3-phase induction motor, it has low torque pulsation with increased torque density, more fault tolerant feature, low current ripple with increased efficiency. When increasing the number of phases, it has reduced current per phase without increasing per phase voltage, resulting in an increase in the total power rating of n-phase motors in the same volume machine. In this paper, the theory of operation of a multi-phase induction motor is discussed. The detailed study of d-q modeling of n-phase induction motors is elaborated. The d-q model of n-phase (5, 6, 7, 9 and 12) induction motors is developed in a MATLAB/Simulink environment. The steady state and dynamic performance of the multi-phase induction motor is studied under varying load conditions. Comparison of 5-phase induction is presented under normal and fault conditions.

Keywords: d-q model, dynamic Response, fault tolerant feature, matlab/simulink, multi-phase induction motor, transient response.

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Authors: K. Paradkar, S. N. Mudliar, A. Sharma, A. B. Pandit, R. A. Pandey

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The study explores the hybrid combination of Hydrodynamic Cavitation (HC) and Subcritical Wet Air Oxidation-based pretreatment of complex industrial effluent to enhance the biodegradability selectively (without major COD destruction) to facilitate subsequent enhanced downstream processing via anaerobic or aerobic biological treatment. Advanced oxidation based techniques can be less efficient as standalone options and a hybrid approach by combining Hydrodynamic Cavitation (HC), and Wet Air Oxidation (WAO) can lead to a synergistic effect since both the options are based on common free radical mechanism. The HC can be used for initial turbulence and generation of hotspots which can begin the free radical attack and this agitating mixture then can be subjected to less intense WAO since initial heat (to raise the activation energy) can be taken care by HC alone. Lab-scale venturi-based hydrodynamic cavitation and wet air oxidation reactor with biomethanated distillery wastewater (BMDWW) as a model effluent was examined for establishing the proof-of-concept. The results indicated that for a desirable biodegradability index (BOD: COD - BI) enhancement (up to 0.4), the Cavitation (standalone) pretreatment condition was: 5 bar and 88 min reaction time with a COD reduction of 36 % and BI enhancement of up to 0.27 (initial BI - 0.17). The optimum WAO condition (standalone) was: 150oC, 6 bar and 30 minutes with 31% COD reduction and 0.33 BI. The hybrid pretreatment (combined Cavitation + WAO) worked out to be 23.18 min HC (at 5 bar) followed by 30 min WAO at 150oC, 6 bar, at which around 50% COD was retained yielding a BI of 0.55. FTIR & NMR analysis of pretreated effluent indicated dissociation and/or reorientation of complex organic compounds in untreated effluent to simpler organic compounds post-pretreatment.

Keywords: BI, hybrid, hydrodynamic cavitation, wet air oxidation.

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Authors: M. Invigorito, G. Elia, M. Panelli

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Hybrid propulsion combines beneficial properties of both solid and liquid rockets, such as multiple restarts, throttability as well as simplicity and reduced costs. A nitrous oxide (N₂O)/paraffin-based hybrid rocket engine demonstrator is currently under development at the Italian Aerospace Research Center (CIRA) within the national research program HYPROB, funded by the Italian Ministry of Research. Nitrous oxide belongs to the class of self-pressurizing propellants that exhibit a high vapor pressure at standard ambient temperature. This peculiar feature makes those fluids very attractive for space rocket applications because it avoids the use of complex pressurization systems, leading to great benefits in terms of weight savings and reliability. To avoid feed-system-coupled instabilities, the phase change is required to occur through the injectors. In this regard, the oxidizer is stored in liquid condition while target chamber pressures are designed to lie below vapor pressure. The consequent cavitation and flash vaporization constitute a remarkably complex phenomenology that arises great modelling challenges. Thus, it is clear that the design of the injection system is fundamental for the full exploitation of hybrid rocket engine throttability. The Analytical Hierarchy Process has been used to select the injection architecture as best compromise among different design criteria such as functionality, technology innovation and cost. The impossibility to use engineering simplified relations for the dimensioning of the injectors led to the needs of applying a numerical approach based on OpenFOAM^®. The numerical tool has been validated with selected experimental data from literature. Quantitative, as well as qualitative comparisons are performed in terms of mass flow rate and pressure drop across the injector for several operating conditions. The results show satisfactory agreement with the experimental data. Modeling assumptions, together with their impact on numerical predictions are discussed in the paper. Once assessed the reliability of the numerical tool, the injection plate has been designed and sized to guarantee the required amount of oxidizer in the combustion chamber and therefore to assure high combustion efficiency. To this purpose, the plate has been designed with multiple injectors whose number and diameter have been selected in order to reach the requested mass flow rate for the two operating conditions of maximum and minimum thrust. The overall design has been finally verified through three-dimensional computations in cavitating non-reacting conditions and it has been verified that the proposed design solution is able to guarantee the requested values of mass flow rates.

Keywords: Hybrid rocket, injection system design, OpenFOAM®, cavitation

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Authors: M. Z Md Zain, M. O. Tokhi, M. S. Alam

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This paper describes a practical approach to design and develop a hybrid learning with acceleration feedback control (HLC) scheme for input tracking and end-point vibration suppression of flexible manipulator systems. Initially, a collocated proportionalderivative (PD) control scheme using hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate a further hybrid control scheme of the collocated PD control and iterative learning control with acceleration feedback using genetic algorithms (GAs) to optimize the learning parameters. Experimental results of the response of the manipulator with the control schemes are presented in the time and frequency domains. The performance of the HLC is assessed in terms of input tracking, level of vibration reduction at resonance modes and robustness with various payloads.

Keywords: Flexible manipulator, iterative learning control, vibration suppression.

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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: Christian Castro Samaniego, Daniel Icaza Alvarez, Juan Portoviejo Brito

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For this research work, hybrid wind-photovoltaic (SHEF) systems were considered as renewable energy sources that take advantage of wind energy and solar radiation to transform into electrical energy. In the present research work, the feasibility of a wind-photovoltaic hybrid generation system was analyzed for the La Tranca tourist viewpoint of the Chordeleg canton in Ecuador. The research process consisted of the collection of data on solar radiation, temperature, wind speed among others by means of a meteorological station. Simulations were carried out in MATLAB/Simulink based on a mathematical model. In the end, we compared the theoretical radiation-power curves and the measurements made at the site.

Keywords: Hybrid system, wind turbine, modeling, simulation, validation, experimental data, panel, Ecuador.

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Authors: Hossein Sahoolizadeh, Mahdi Rahimi, Hamid Dehghani

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We introduce an algorithm based on the morphological shared-weight neural network. Being nonlinear and translation-invariant, the MSNN can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The output is then learned by interacting with the classification process. The feature extraction and classification networks are trained together, allowing the MSNN to simultaneously learn feature extraction and classification for a face. For evaluation, we test for robustness under variations in gray levels and noise while varying the network-s configuration to optimize recognition efficiency and processing time. Results show that the MSNN performs better for grayscale image pattern classification than ordinary neural networks.

Keywords: Face recognition, Neural Networks, Multi-layer Perceptron, masking.

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Authors: V. Kabeer, N.K.Narayanan

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This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.

Keywords: Face recognition, Image analysis, Wavelet feature extraction, Pattern recognition, Classifier algorithms

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original dataset. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 dataset is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: Distributed intrusion detection system, mobile agent, feature selection, Bees Algorithm, decision tree.

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

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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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Authors: Sandipan Chakroborty, Goutam Saha

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A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for speech related applications. On a recent contribution by authors, it has been shown that the Inverted Mel- Frequency Cepstral Coefficients (IMFCC) is useful feature set for SI, which contains complementary information present in high frequency region. This paper introduces the Gaussian shaped filter (GF) while calculating MFCC and IMFCC in place of typical triangular shaped bins. The objective is to introduce a higher amount of correlation between subband outputs. The performances of both MFCC & IMFCC improve with GF over conventional triangular filter (TF) based implementation, individually as well as in combination. With GMM as speaker modeling paradigm, the performances of proposed GF based MFCC and IMFCC in individual and fused mode have been verified in two standard databases YOHO, (Microphone Speech) and POLYCOST (Telephone Speech) each of which has more than 130 speakers.

Keywords: Gaussian Filter, Triangular Filter, Subbands, Correlation, MFCC, IMFCC, GMM.

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Authors: Sandipan Chakroborty, Anindya Roy, Goutam Saha

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A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for SI applications. However, due to the structure of its filter bank, it captures vocal tract characteristics more effectively in the lower frequency regions. This paper proposes a new set of features using a complementary filter bank structure which improves distinguishability of speaker specific cues present in the higher frequency zone. Unlike high level features that are difficult to extract, the proposed feature set involves little computational burden during the extraction process. When combined with MFCC via a parallel implementation of speaker models, the proposed feature set outperforms baseline MFCC significantly. This proposition is validated by experiments conducted on two different kinds of public databases namely YOHO (microphone speech) and POLYCOST (telephone speech) with Gaussian Mixture Models (GMM) as a Classifier for various model orders.

Keywords: Complementary Information, Filter Bank, GMM, IMFCC, MFCC, Speaker Identification, Speaker Recognition.

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Authors: Gorazd Karer, Igor Skrjanc, Borut Zupancic

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The complex hybrid and nonlinear nature of many processes that are met in practice causes problems with both structure modelling and parameter identification; therefore, obtaining a model that is suitable for MPC is often a difficult task. The basic idea of this paper is to present an identification method for a piecewise affine (PWA) model based on a fuzzy clustering algorithm. First we introduce the PWA model. Next, we tackle the identification method. We treat the fuzzy clustering algorithm, deal with the projections of the fuzzy clusters into the input space of the PWA model and explain the estimation of the parameters of the PWA model by means of a modified least-squares method. Furthermore, we verify the usability of the proposed identification approach on a hybrid nonlinear batch reactor example. The result suggest that the batch reactor can be efficiently identified and thus formulated as a PWA model, which can eventually be used for model predictive control purposes.

Keywords: Batch reactor, fuzzy clustering, hybrid systems, identification, nonlinear systems, PWA systems.

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Authors: M. Kulesza, G. Szwoch, A. Czyżewski

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A novel approach to speech coding using the hybrid architecture is presented. Advantages of parametric and perceptual coding methods are utilized together in order to create a speech coding algorithm assuring better signal quality than in traditional CELP parametric codec. Two approaches are discussed. One is based on selection of voiced signal components that are encoded using parametric algorithm, unvoiced components that are encoded perceptually and transients that remain unencoded. The second approach uses perceptual encoding of the residual signal in CELP codec. The algorithm applied for precise transient selection is described. Signal quality achieved using the proposed hybrid codec is compared to quality of some standard speech codecs.

Keywords: CELP residual coding, hybrid codec architecture, perceptual speech coding, speech codecs comparison.

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Authors: Elham Jelodari Mamaghani, Christian Prins, Haoxun Chen

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In this paper, there is concentration on collaborative transportation planning (CTP) among multiple carriers with pickup and delivery requests and time windows. This problem is a vehicle routing problem with constraints from standard vehicle routing problems and new constraints from a real-world application. In the problem, each carrier has a finite number of vehicles, and each request is a pickup and delivery request with time window. Moreover, each carrier has reserved requests, which must be served by itself, whereas its exchangeable requests can be outsourced to and served by other carriers. This collaboration among carriers can help them to reduce total transportation costs. A mixed integer programming model is proposed to the problem. To solve the model, a hybrid algorithm that combines Genetic Algorithm and Simulated Annealing (GASA) is proposed. This algorithm takes advantages of GASA at the same time. After tuning the parameters of the algorithm with the Taguchi method, the experiments are conducted and experimental results are provided for the hybrid algorithm. The results are compared with those obtained by a commercial solver. The comparison indicates that the GASA significantly outperforms the commercial solver.

Keywords: Centralized collaborative transportation, collaborative transportation with pickup and delivery, collaborative transportation with time windows, hybrid algorithm of GA and SA.

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Authors: Napat Watjanatepin, Wikorn Wong-SatieanNapat Watjanatepin, Wikorn Wong-Satiean

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The objective of this research is to design and construct the PV-Wind hybrid autonomous system for the greenhouse plantation, and analyze the technical performance of the PV-Wind energy system. This design depends on the water consumption in the greenhouse by using 24 of the fogging mist each with the capability of 24 liter/min. The operating time is 4 times per day, each round for 15 min. The fogging system is being driven by water pump with AC motor rating 0.5 hp. The load energy consumed is around 1.125 kWh/d. The designing results of the PV-Wind hybrid energy system is that sufficient energy could be generated by this system. The results of this study can be applied as a technical data reference for other areas in the central part of Thailand.

Keywords: Central part of Thailand, fogging system, greenhouse plantation, PV-Wind hybrid autonomous system.

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Authors: N. A. Zambri, A. Mohamed, H. Shareef, M. Z. C. Wanik

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This paper presents the modeling and simulation of a hybrid proton exchange membrane fuel cell (PEMFC) with an energy storage system for use in a stand-alone distributed generation (DG) system. The simulation model consists of fuel cell DG, lead-acid battery, maximum power point tracking and power conditioning unit which is modeled in the MATLAB/Simulink platform. Poor loadfollowing characteristics and slow response to rapid load changes are some of the weaknesses of PEMFC because of the gas processing reaction and the fuel cell dynamics. To address the load-tracking issues in PEMFC, a hybrid PEMFC and battery storage system is considered and modelled. The model utilizes PEMFC as the main energy source whereas the battery functions as energy storage to compensate for the limitations of PEMFC.Simulation results are given to show the overall system performance under light and heavyloading conditions.

Keywords: Hybrid, Lead–Acid Battery, Maximum Power Point Tracking, Proton Exchange Membrane Fuel Cell.

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Authors: Tomohiro Hachino, Kenji Shimoda, Hitoshi Takata

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This paper presents a method of model selection and identification of Hammerstein systems by hybridization of the genetic algorithm (GA) and particle swarm optimization (PSO). An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The weighting parameters of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. On the other hand, the adjusting parameters of the ACF model structure are properly selected by the hybrid algorithm of the GA and PSO, where the Akaike information criterion is utilized as the evaluation value function. Simulation results are shown to demonstrate the effectiveness of the proposed hybrid algorithm.

Keywords: Hammerstein system, identification, automatic choosing function model, genetic algorithm, particle swarm optimization.

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Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: Fake news detection, feature selection, support vector machine, K-means clustering, machine learning, social media.

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Authors: S. S. Ghodsi, M. H. Mehrabi, Zainah Ibrahim, Meldi Suhatril

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This research examines the performance of a hybrid passive control device for enhancing the seismic response of steel frame structures. The device design comprises a damper which employs a viscoelastic material to control both shear and axial strain. In the design, energy is dissipated through the shear strain of a two-layer system of viscoelastic pads which are located between steel plates. In addition, viscoelastic blocks have been included on either side of the main shear damper which obtains compressive strains in the viscoelastic blocks. These dampers not only dissipate energy but also increase the stiffness of the steel frame structure, and the degree to which they increase the stiffness may be controlled by the size and shape. In this research, the cyclical behavior of the damper was examined both experimentally and numerically with finite element modeling. Cyclic loading results of the finite element modeling reveal fundamental characteristics of this hybrid viscoelastic damper. The results indicate that incorporating a damper of the design can significantly improve the seismic performance of steel frame structures.

Keywords: Cyclic loading, energy dissipation, hybrid damper, passive control system, viscoelastic damper.

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Authors: Ali Vaseghi Ardekani, Hamid Reza Forutan, Amir Habibi, Ali Reza Rajabi, Hasan Adloo

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Distributed generation units (DGs) are grid-connected or stand-alone electric generation units located within the electric distribution system at or near the end user. It is generally accepted that centralized electric power plants will remain the major source of the electric power supply for the near future. DGs, however, can complement central power by providing incremental capacity to the utility grid or to an end user. This paper presents an efficient power dispatching model for hybrid wind-Solar power generation system, to satisfy some essential requirements, such as dispersed electric power demand, electric power quality and reducing generation cost and so on. In this paper, presented some elements of the main parts in the hybrid system; and then made fundamental dispatching strategies according to different situations; then pointed out four improving measures to improve genetic algorithm, such as: modify the producing way of selection probability, improve the way of crossover, protect excellent chromosomes, and change mutation range and so on. Finally, propose a technique for solving the unit's commitment for dispatching problem based on an improved genetic algorithm.

Keywords: Power Quality, Wind-Solar System, Genetic Algorithm, Hybrid System.

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Authors: Rosshairy Abd Rahman, Chooi-Leng Ang, Razamin Ramli

Abstract:

Feed is one of the factors which play an important role in determining a successful development of an aquaculture industry. It is always critical to produce the best aquaculture diet at a minimum cost in order to trim down the operational cost and gain more profit. However, the feed mix problem becomes increasingly difficult since many issues need to be considered simultaneously. Thus, the purpose of this paper is to review the current techniques used by nutritionist and researchers to tackle the issues. Additionally, this paper introduce an enhance algorithm which is deemed suitable to deal with all the issues arise. The proposed technique refers to Hybrid Genetic Algorithm which is expected to obtain the minimum cost diet for farmed animal, while satisfying nutritional requirements. Hybrid GA technique with artificial bee algorithm is expected to reduce the penalty function and provide a better solution for the feed mix problem.

Keywords: Artificial bee algorithm, feed mix problem, hybrid genetic algorithm.

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Authors: Awais Ahmad Khan, Emad Abouel Nasr, H. M. A. Hussein, Abdulrahman Al-Ahmari

Abstract:

Sheet-metal parts have been widely applied in electronics, communication and mechanical industries in recent decades; but the advancement in sheet-metal part design and manufacturing is still behind in comparison with the increasing importance of sheet-metal parts in modern industry. This paper presents a methodology for automatic extraction of some common 2D internal sheet metal features. The features used in this study are taken from Unipunch ™ catalogue. The extraction process starts with the data extraction from STEP file using an object oriented approach and with the application of suitable algorithms and rules, all features contained in the catalogue are automatically extracted. Since the extracted features include geometry and engineering information, they will be effective for downstream application such as feature rebuilding and process planning.

Keywords: Feature Extraction, Internal Features, Punch Shapes, Sheet metal, STEP.

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