Search results for: passive method

Authors: Kittiwann Nimkerdphol, Masahiro Nakagawa

Abstract:

Biological reactions of individuals of a testing animal to toxic substance are unique and can be used as an indication of the existing of toxic substance. However, to distinguish such phenomenon need a very complicate system and even more complicate to analyze data in 3 dimensional. In this paper, a system to evaluate in vitro biological activities to acute toxicity of stochastic self-affine non-stationary signal of 3D goldfish swimming by using fractal analysis is introduced. Regular digital camcorders are utilized by proposed algorithm 3DCCPC to effectively capture and construct 3D movements of the fish. A Critical Exponent Method (CEM) has been adopted as a fractal estimator. The hypothesis was that the swimming of goldfish to acute toxic would show the fractal property which related to the toxic concentration. The experimental results supported the hypothesis by showing that the swimming of goldfish under the different toxic concentration has fractal properties. It also shows that the fractal dimension of the swimming related to the pH value of FD Ôëê 0.26pH + 0.05. With the proposed system, the fish is allowed to swim freely in all direction to react to the toxic. In addition, the trajectories are precisely evaluated by fractal analysis with critical exponent method and hence the results exhibit with much higher degree of confidence.

Keywords: 3D locomotion, bioassay, critical exponent method, CEM, fractal analysis, goldfish.

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Authors: Dariush Semnani, Mohammad Sheikhzadeh

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Circular knitting machine makes the fabric with more than two knitting tools. Variation of yarn tension between different knitting tools causes different loop length of stitches duration knitting process. In this research, a new intelligent method is applied to control loop length of stitches in various tools based on ideal shape of stitches and real angle of stitches direction while different loop length of stitches causes stitches deformation and deviation those of angle. To measure deviation of stitch direction against variation of tensions, image processing technique was applied to pictures of different fabrics with constant front light. After that, the rate of deformation is translated to needed compensation of loop length cam degree to cure stitches deformation. A fuzzy control algorithm was applied to loop length modification in knitting tools. The presented method was experienced for different knitted fabrics of various structures and yarns. The results show that presented method is useable for control of loop length variation between different knitting tools based on stitch deformation for various knitted fabrics with different fabric structures, densities and yarn types.

Keywords: Circular knitting, Radon transformation, Knittedfabric, Regularity, Fuzzy control

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

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

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

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Authors: Maysam Saidi, Hassan Basirat Tabrizi, Reza Maddahian

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In this paper, numerical simulations are performed to investigate the effect of disturbance block on flow field of the classical square lid-driven cavity. Attentions are focused on vortex formation and studying the effect of block position on its structure. Corner vortices are different upon block position and new vortices are produced because of the block. Finite volume method is used to solve Navier-Stokes equations and PISO algorithm is employed for the linkage of velocity and pressure. Verification and grid independency of results are reported. Stream lines are sketched to visualize vortex structure in different block positions.

Keywords: Disturbance Block, Finite Volume Method, Lid-Driven Cavity

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Authors: C. Ardil

Abstract:

Fighter aircraft selection is one of the most critical strategies for defense multiple criteria decision-making analysis to increase the decisive power of air defense and its superior power in the defense strategy. Vague set theory is an adequate approach for modeling vagueness, uncertainty, and imprecision in decision-making problems. This study integrates vague set theory and the technique for order of preference by similarity to ideal solution (TOPSIS) to support fighter aircraft selection. The proposed method is applied in the selection of fighter aircraft for the Air Force. In the proposed approach, the ratings of alternatives and the importance weights of criteria for fighter aircraft selection are represented by the vague set theory. Finally, an illustrative example for fighter aircraft selection is given to demonstrate the applicability and effectiveness of the proposed approach. The fighter aircraft candidates were selected under six criteria including costability, payloadability, maneuverability, speedability, stealthility, and survivability. Analysis results show that the best fighter aircraft is selected with the highest closeness coefficient value. The proposed method can also be applied to solve other multiple criteria decision analysis problems. 

Keywords: fighter aircraft selection, vague set theory, fuzzy set theory, neutrosophic set theory, multiple criteria decision making analysis, MCDMA, TOPSIS

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Authors: Marina Velikova, Hennie Daniels, Ad Feelders

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In many data mining applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. In this paper we consider partially monotone prediction problems, where the target variable depends monotonically on some of the input variables but not on all. We propose a novel method to construct prediction models, where monotone dependences with respect to some of the input variables are preserved by virtue of construction. Our method belongs to the class of mixture models. The basic idea is to convolute monotone neural networks with weight (kernel) functions to make predictions. By using simulation and real case studies, we demonstrate the application of our method. To obtain sound assessment for the performance of our approach, we use standard neural networks with weight decay and partially monotone linear models as benchmark methods for comparison. The results show that our approach outperforms partially monotone linear models in terms of accuracy. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.

Keywords: mixture models, monotone neural networks, partially monotone models, partially monotone problems.

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Authors: Bayar Shahab

Abstract:

The fast development of technology that has advanced neuroscience and human interaction with computers has enabled solutions to various problems and issues of this new era. The Brain-Computer Interface (BCI) has opened the door to several new research areas and have been able to provide solutions to critical and vital issues such as supporting a paralyzed patient to interact with the outside world, controlling a robot arm, playing games in VR with the brain, driving a wheelchair. This review presents the state-of-the-art methods and improvements of canonical correlation analyses (CCA), an SSVEP-based BCI method. These are the methods used to extract EEG signal features or, to be said differently, the features of interest that we are looking for in the EEG analyses. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers understand the most state-of-the-art methods available in this field, their pros and cons, and their mathematical representations and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) stating most of the main methods used in this field in a hierarchical way, (2) explaining the pros and cons of each method and their performance, (3) presenting the gaps that exist at the end of each method that can improve the understanding and open doors to new researches or improvements. 

Keywords: BCI, CCA, SSVEP, EEG

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Authors: M. J. Kao, S.F. Chang, C.C. Chen, C.G. Kuo

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This paper aims to fabricated high quality anodic aluminum oxide (AAO) film by anodization method. AAO pore size, pore density, and film thickness can be controlled in 10~500 nm, 108~1011 pore．cm-2, and 1~100 μm. AAO volume and surface area can be computed based on structural parameters such as thickness, pore size, pore density, and sample size. Base on the thetorical calculation, AAO has 100 μm thickness with 15 nm, 60 nm, and 500 nm pore diameters AAO surface areas are 1225.2 cm2, 3204.4 cm2, and 549.7 cm2, respectively. The large unit surface area which is useful for adsorption application. When AAO adsorbed pH indictor of bromphenol blue presented a sensitive pH detection of solution change. This testing method can further be used for the precise measurement of biotechnology, convenience measurement of industrial engineering.

Keywords: AAO, Pore, Surface area, Adsorption, Indicator

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Authors: Wookyong Kwon, Sangmoon Lee

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In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method.

Keywords: Model predictive control, sampled-data control, linear parameter varying systems, LPV.

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Authors: M. Jalali Azizpour, S.Norouzi H. Mohammadi Majd

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In this paper, the babbitting of a bearing in boiler feed pump of an electromotor has been studied. These bearings have an important role in reducing the shut down times in the pumps, compressors and turbines. The most conventional method in babbitting is casting as a melting method. The comparison between thermal spray and casting methods in babbitting shows that the thermal spraying babbitt layer has better performance and tribological behavior. The metallurgical and tribological analysis such as SEM, EDS and wet chemical analysis has been made in the Babbitt alloys and worn surfaces. Two type of babbitt materials: tinbase and lead-base babbitt was used. The benefits of thermally sprayed babbitt layers are completely clear especially in large bearings.

Keywords: Thermal spray, Babbitting, Bonding, Bearing, BFP, CWP

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Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: Spatial Information Network, Traffic prediction, Wavelet decomposition, Time series model.

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Authors: R. Guliyev

Abstract:

The enhanced need for food items is receiving more importance due to a gradual increase in the world population and, in this scenario, fertilizers play a very important role in agriculture. In this study, the production of the normal superphosphate was investigated with a continuous chamber method by adding potassium chloride to a mixture of Hibin apatite and Kingisepp phosphorite. In the experiments, the following parameters were selected: The concentration of sulfuric acid (54–66% (w/w)), the stoichiometric norm of sulfuric acid (100, 107, 110, 114% (w/w)), the ratio of apatite/phosphorite in the mixture of phosphate (95/5, 90/10, 85/15, 80/20, 75/25, 70/30, 65/35,60/40, 55/45, 50/50 (w/w)), potassium chloride/the mixture of phosphate (1/50, 2/50, 3/50,4/50, 5/50 (w/w)), and the reaction time (2–8 min). It was observed that by adding potassium chloride to a low-grade phosphorite and using it to substitute a fraction of high-grade apatite in the normal superphosphate production not only resulted in a high-quality product but also eliminated the waiting period for the maturation of superphosphate in the storage. The objective of this study was to produce a normal superphosphate fertilizer by using a continuous chamber method in order to accelerate the production process and to reduce the environmental pollution caused by fluoride gases by eliminating the maturation time in the storage.

Keywords: Continuous chamber method, environmental pollution, fluoride gases.

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Authors: M. N. Zelenin, V. S. Mikhailov, R. P. Zhivotovsky

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It is known that residual welding deformations give negative effect to processability and operational quality of welded structures, complicating their assembly and reducing strength. Therefore, selection of optimal technology, ensuring minimum welding deformations, is one of the main goals in developing a technology for manufacturing of welded structures. Through years, JSC SSTC has been developing a theory for estimation of welding deformations and practical activities for reducing and compensating such deformations during welding process. During long time a methodology was used, based on analytic dependence. This methodology allowed defining volumetric changes of metal due to welding heating and subsequent cooling. However, dependences for definition of structures deformations, arising as a result of volumetric changes of metal in the weld area, allowed performing calculations only for simple structures, such as units, flat sections and sections with small curvature. In case of complex 3D structures, estimations on the base of analytic dependences gave significant errors. To eliminate this shortage, it was suggested to use finite elements method for resolving of deformation problem. Here, one shall first calculate volumes of longitudinal and transversal shortenings of welding joints using method of analytic dependences and further, with obtained shortenings, calculate forces, which action is equivalent to the action of active welding stresses. Further, a finiteelements model of the structure is developed and equivalent forces are added to this model. Having results of calculations, an optimal sequence of assembly and welding is selected and special measures to reduce and compensate welding deformations are developed and taken.

Keywords: Finite elements method, modeling, expected welding deformations, welding, assembling.

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Authors: A. A. Kudysheva, A. N. Kudyshev

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In present article the model of Blended Learning, its advantage at foreign language teaching, and also some problems that can arise during its use are considered. The Blended Learning is a special organization of learning, which allows to combine classroom work and modern technologies in electronic distance teaching environment. Nowadays a lot of European educational institutions and companies use such technology. Through this method: student gets the opportunity to learn in a group (classroom) with a teacher and additionally at home at a convenient time; student himself sets the optimal speed and intensity of the learning process; this method helps student to discipline himself and learn to work independently.

Keywords: Foreign language, information and communication technology (ICT), model of Blended Learning, virtual cool room, technophobia

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Authors: Qasim Idrees, Gao Liangtian, Liu Bo, Miao Yiran

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In order to improve the efficiency and accuracy of the pressure hull structure, optimization of underwater vehicle based on response surface methodology, a method for optimizing the design of pressure hull structure was studied. To determine the pressure shell of five dimensions as a design variable, the application of thin shell theory and the Chinese Classification Society (CCS) specification was carried on the preliminary design. In order to optimize variables of the feasible region, different methods were studied and implemented such as Opt LHD method (to determine the design test sample points in the feasible domain space), parametric ABAQUS solution for each sample point response, and the two-order polynomial response for the surface model of the limit load of structures. Based on the ultimate load of the structure and the quality of the shell, the two-generation genetic algorithm was used to solve the response surface, and the Pareto optimal solution set was obtained. The final optimization result was 41.68% higher than that of the initial design, and the shell quality was reduced by about 27.26%. The parametric method can ensure the accuracy of the test and improve the efficiency of optimization.

Keywords: Parameterization, response surface, structure optimization, pressure hull.

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Authors: Anthony Overmars, Sitalakshmi Venkatraman

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This paper presents a method for determining all of the co-prime right angle triangles in the Euclidean field by looking at the intersection of the Pythagorean and Platonic right angle triangles and the corresponding lattice that this produces. The co-prime properties of each lattice point representing a unique right angle triangle are then considered. This paper proposes a conjunction between these two ancient disparaging theorists. This work has wide applications in information security where cryptography involves improved ways of finding tuples of prime numbers for secure communication systems. In particular, this paper has direct impact in enhancing the encryption and decryption algorithms in cryptography.

Keywords: Pythagorean triples, platonic triples, right angle triangles, co-prime numbers, cryptography.

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Authors: Rampal Singh, S. Balasundaram

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In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layered feedforward neural networks to non-linear chaotic time series problems. In this algorithm the input weights and the hidden layer bias are randomly chosen. The ELM formulation leads to solving a system of linear equations in terms of the unknown weights connecting the hidden layer to the output layer. The solution of this general system of linear equations will be obtained using Moore-Penrose generalized pseudo inverse. For the study of the application of the method we consider the time series generated by the Mackey Glass delay differential equation with different time delays, Santa Fe A and UCR heart beat rate ECG time series. For the choice of sigmoid, sin and hardlim activation functions the optimal values for the memory order and the number of hidden neurons which give the best prediction performance in terms of root mean square error are determined. It is observed that the results obtained are in close agreement with the exact solution of the problems considered which clearly shows that ELM is a very promising alternative method for time series prediction.

Keywords: Chaotic time series, Extreme learning machine, Generalization performance.

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Authors: Pathinathan Theresanathan, Ajay Minj

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Ignatian Discernment Process (IDP) is an intense decision-making tool to decide on life-issues. Decisions are influenced by various factors outside of the decision maker and inclination within. This paper develops IDP in the context of Fuzzy Multi-criteria Decision Making (FMCDM) process. Extended VIKOR method is a decision-making method which encompasses even conflict situations and accommodates weightage to various issues. Various aspects of IDP, namely three ways of decision making and tactics of inner desires, are observed, analyzed and articulated within the frame work of fuzzy rules. The decision-making situations are broadly categorized into two types. The issues outside of the decision maker influence the person. The inner feeling also plays vital role in coming to a conclusion. IDP integrates both the categories using Extended VIKOR method. Case studies are carried out and analyzed with FMCDM process. Finally, IDP is verified with an illustrative case study and results are interpreted. A confused person who could not come to a conclusion is able to take decision on a concrete way of life through IDP. The proposed IDP model recommends an integrated and committed approach to value-based decision making.

Keywords: Analytical hierarchy process, fuzzy multi-criteria decision making, Ignatian discernment process, Ignatian discernment, multi-criteria decision making, VIKOR.

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Authors: Behrouz Fathi-Vajargah, Sara Ghasemalipour

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This paper studies a random fuzzy queueing system that the interarrival times of customers arriving at the server and the service times are independent and identically distributed random fuzzy variables. We match the random fuzzy queueing system with the random fuzzy alternating renewal process and we do not use from α-pessimistic and α-optimistic values to estimate the average chance of the event ”random fuzzy queueing system is busy at time t”, we employ the fuzzy simulation method in practical applications. Some theorem is proved and finally we solve a numerical example with fuzzy simulation method.

Keywords: Random fuzzy variables, Fuzzy simulation, Queueing system, Interarrival times.

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Authors: Yong-JuJeon, Doo-chul Choi, Jong Pil Yun, Changhyun Park, Homoon Bae, Sang Woo Kim

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This paper presents an automated inspection algorithm for a thick plate. Thick plates typically have various types of surface defects, such as scabs, scratches, and roller marks. These defects have individual characteristics including brightness and shape. Therefore, it is not simple to detect all the defects. In order to solve these problems and to detect defects more effectively, we propose a dual light switching lighting method and a defect detection algorithm based on Gabor filters.

Keywords: Thick plate, Defect, Inspection, Gabor filter, Dual Light Switching.

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Authors: Wichuda Kunnu, Aungkana Sukwises

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Audio-lingual Method (ALM) is a teaching approach that is claimed that ineffective for teaching second/foreign languages. Because some linguists and second/foreign language teachers believe that ALM is a rote learning style. However, this study is done on a belief that ALM will be able to solve Thais’ English speaking problem. This paper aims to report the findings on teaching English speaking to adult learners with an “adapted ALM”, one distinction of which is to use Thai as the medium language of instruction. The participants are consisted of 9 adult learners. They were allowed to speak English more freely using both the materials presented in the class and their background knowledge of English. At the end of the course, they spoke English more fluently, more confidently, to the extent that they applied what they learnt both in and outside the class.

Keywords: Teaching English, Audio Lingual Method.

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Authors: H. Shayeghi, H. A. Shayanfar, G. Azimi

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The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.

Keywords: Large Neural Network, Short-Term Load Forecasting, Particle Swarm Optimization.

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Authors: J. Prakash, K. Rajesh

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In this paper we propose a novel method for human face segmentation using the elliptical structure of the human head. It makes use of the information present in the edge map of the image. In this approach we use the fact that the eigenvalues of covariance matrix represent the elliptical structure. The large and small eigenvalues of covariance matrix are associated with major and minor axial lengths of an ellipse. The other elliptical parameters are used to identify the centre and orientation of the face. Since an Elliptical Hough Transform requires 5D Hough Space, the Circular Hough Transform (CHT) is used to evaluate the elliptical parameters. Sparse matrix technique is used to perform CHT, as it squeeze zero elements, and have only a small number of non-zero elements, thereby having an advantage of less storage space and computational time. Neighborhood suppression scheme is used to identify the valid Hough peaks. The accurate position of the circumference pixels for occluded and distorted ellipses is identified using Bresenham-s Raster Scan Algorithm which uses the geometrical symmetry properties. This method does not require the evaluation of tangents for curvature contours, which are very sensitive to noise. The method has been evaluated on several images with different face orientations.

Keywords: Circular Hough Transform, Covariance matrix, Eigenvalues, Elliptical Hough Transform, Face segmentation, Raster Scan Algorithm.

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Authors: M. Winkens, P. Nyhuis

Abstract:

Components with sensory properties such as gentelligent components developed at the Collaborative Research Centre 653 offer a new angle in terms of the full utilization of the remaining service life as well as preventive maintenance. The developed methodology of component status driven maintenance analyzes the stress data obtained during the component's useful life and on the basis of this knowledge assesses the type of maintenance required in this case. The procedure is derived from the case-based reasoning method and will be explained in detail. The method's functionality is demonstrated with real-life data obtained during test runs of a racing car prototype.

Keywords: Gentelligent Components, Preventive Maintenance, Case based Reasoning.

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Authors: Jincan Li, Mingyu Gao, Zhiwei He, Yuxiang Yang, Zhongfei Yu, Yuanyuan Liu

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Building an appropriate motion model is crucial for trajectory planning of robots and determines the operational quality directly. An adaptive acceleration and deceleration motion planning based on trigonometric functions for the end-effector of 6-DOF robots in Cartesian coordinate system is proposed in this paper. This method not only achieves the smooth translation motion and rotation motion by constructing a continuous jerk model, but also automatically adjusts the parameters of trigonometric functions according to the variable inputs and the kinematic constraints. The results of computer simulation show that this method is correct and effective to achieve the adaptive motion planning for linear trajectories.

Keywords: 6-DOF robots, motion planning, trigonometric function, kinematic constraints

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Authors: M. Lakshmi, J. Senthil Kumar

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Digital cameras to reduce cost, use an image sensor to capture color images. Color Filter Array (CFA) in digital cameras permits only one of the three primary (red-green-blue) colors to be sensed in a pixel and interpolates the two missing components through a method named demosaicking. Captured data is interpolated into a full color image and compressed in applications. Color interpolation before compression leads to data redundancy. This paper proposes a new Vector Quantization (VQ) technique to construct a VQ codebook with Differential Evolution (DE) Algorithm. The new technique is compared to conventional Linde- Buzo-Gray (LBG) method.

Keywords: Color Filter Array (CFA), Biorthogonal Wavelet, Vector Quantization (VQ), Differential Evolution (DE).

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Authors: Ali Mchirgui, Nejib Hidouri, Mourad Magherbi, Ammar Ben Brahim

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The paper provides a numerical investigation of the entropy generation analysis due to natural convection in an inclined square porous cavity. The coupled equations of mass, momentum, energy and species conservation are solved using the Control Volume Finite-Element Method. Effect of medium permeability and inclination angle on entropy generation is analysed. It was found that according to the Darcy number and the porous thermal Raleigh number values, the entropy generation could be mainly due to heat transfer or to fluid friction irreversibility and that entropy generation reaches extremum values for specific inclination angles.

Keywords: Porous media, entropy generation, convection, numerical method.

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Authors: Hyunsang Kim, Younghun Kim, Younghee Kim, Sangku Lee

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We investigated ecotoxicity and performed experiment for removing ZnO nanoparticles in water. Short term exposure of hatching test using fertilized eggs (O. latipes) showed deformity in 5ppm of ZnO nanoparticles solution. And in 10ppm ZnO nanoparticles solution delayed hatching was observed. Hereine, chemical precipitation method was suggested for removing ZnO nanoparticles in water. The precipitated ZnO nanoparticles showed the form of ZnS after addition of Na2S, and the form of Zn3(PO4)2 for Na2HPO4. The removal efficiency of ZnO nanoparticles in water was closed to 100% for two cases. In ecotoxicity evaluation of as-precipitated ZnS and Zn3(PO4)2, they did not cause any acute toxicity for D. magna. It is noted that this precipitation treatment of ZnO is effective to reduce the potential cytotoxicity.

Keywords: ZnO nanoparticles, ZnS, Zn3(PO4)2, ecotoxicity evaluation, chemical precipitation.

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Authors: Muhammad Tariq, Jafar Khan Kasi, Samiullah, Ajab Khan Kasi

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Biosensors are playing vital role in industrial, clinical, and chemical analysis applications. Among other techniques, ZnO based biosensor is an easy approach due to its exceptional chemical and electrical properties. ZnO nanorods have positively charged isoelectric point which helps immobilize the negative charge glucose oxides (GOx). Here, we report ZnO nanorods based biosensors for the immobilization of GOx. The ZnO nanorods were grown by hydrothermal method on indium tin oxide substrate (ITO). The fabrication of biosensors was carried through batch processing using conventional photolithography. The buffer solutions of GOx were prepared in phosphate with a pH value of around 7.3. The biosensors effectively immobilized the GOx and result was analyzed by calculation of voltage and current on nanostructures.

Keywords: Hydrothermal growth, zinc dioxide, biosensors.

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

Abstract:

Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: Cancer classification, feature selection, deep learning, genetic algorithm.

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