Search results for: optimal one bit

Authors: R. Gomathi, D. Sharmila

Abstract:

The emergence of the Semantic Web technology increases day by day due to the rapid growth of multiple web pages. Many standard formats are available to store the semantic web data. The most popular format is the Resource Description Framework (RDF). Querying large RDF graphs becomes a tedious procedure with a vast increase in the amount of data. The problem of query optimization becomes an issue in querying large RDF graphs. Choosing the best query plan reduces the amount of query execution time. To address this problem, nature inspired algorithms can be used as an alternative to the traditional query optimization techniques. In this research, the optimal query plan is generated by the proposed SAPSO algorithm which is a hybrid of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms. The proposed SAPSO algorithm has the ability to find the local optimistic result and it avoids the problem of local minimum. Experiments were performed on different datasets by changing the number of predicates and the amount of data. The proposed algorithm gives improved results compared to existing algorithms in terms of query execution time.

Keywords: Semantic web, RDF, Query optimization, Nature inspired algorithms, PSO, SA.

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Authors: Abdulaziz Alsadhan, Naveed Khan

Abstract:

In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion detection system (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw dataset for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle component analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. This optimal feature subset is used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle component analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP).

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Authors: M. Hosseinzadeh, S. A. Zamani, A. Taheri

Abstract:

During the last few years, several sheet hydroforming processes have been introduced. Despite the advantages of these methods, they have some limitations. Of the processes, the two main ones are the standard hydroforming and hydromechanical deep drawing. A new sheet hydroforming die set was proposed that has the advantages of both processes and eliminates their limitations. In this method, a polyurethane plate was used as a part of the die-set to control the blank holder force. This paper outlines the Taguchi optimization methodology, which is applied to optimize the effective parameters in forming cylindrical cups by the new die set of sheet hydroforming process. The process parameters evaluated in this research are polyurethane hardness, polyurethane thickness, forming pressure path and polyurethane hole diameter. The design of experiments based upon L9 orthogonal arrays by Taguchi was used and analysis of variance (ANOVA) was employed to analyze the effect of these parameters on the forming pressure. The analysis of the results showed that the optimal combination for low forming pressure is harder polyurethane, bigger diameter of polyurethane hole and thinner polyurethane. Finally, the confirmation test was derived based on the optimal combination of parameters and it was shown that the Taguchi method is suitable to examine the optimization process.

Keywords: Sheet Hydroforming, Optimization, Taguchi Method

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Authors: Yingjie Zhang, Ming Li, Ying Zhang, Jing Zhang, Zuolei Hu

Abstract:

In the actual start-up process of DC motors, the DC drive system often faces a conflict between energy consumption and acceleration performance. To resolve the conflict, this paper proposes a comprehensive performance index that energy consumption index is added on the basis of classical control performance index in the DC motor starting process. Taking the comprehensive performance index as the cost function, particle swarm optimization algorithm is designed to optimize the comprehensive performance. Then it conducts simulations on the optimization of the comprehensive performance of the DC motor on condition that the weight coefficient of the energy consumption index should be properly designed. The simulation results show that as the weight of energy consumption increased, the energy efficiency was significantly improved at the expense of a slight sacrifice of fastness indicators with the comprehensive performance index method. The energy efficiency was increased from 63.18% to 68.48% and the response time reduced from 0.2875s to 0.1736s simultaneously compared with traditional proportion integrals differential controller in energy saving.

Keywords: Comprehensive performance index, energy consumption, acceleration performance, particle swarm optimal control.

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Authors: Ebunoluwa G. Gabriel, Victoria A. Jideani, Daniel I. O. Ikhu-Omoregbe

Abstract:

The current desire in food and industrial emulsification is the use of natural emulsifiers. Bambara groundnut flour (BGNF) and its starch (BGNS) will serve both emulsifying and nutritional purposes if found suitable. This current study was aimed at investigating the emulsifying properties of BGNF/BGNS. BGNS was extracted from the BGNF. Emulsions were prepared using a wide range of flour-oil-water and starch-oil-water composition as generated through the application of Response Surface (D-optimal) design. Prepared emulsions were investigated for stability to creaming/sedimentation (using the kinetic information from turbiscan) and flocculation/coalescence (by monitoring the droplet diameter growth using optical microscope) over 5 days. The most stable emulsions (one BGNF-stabilized and the other BGNS-stabilized) were determined. The optimal emulsifier/oil composition was 9g/39g for BGNF and 5g/30g for BGNS. The two emulsions had only 30% and 50% growth in oil droplet diameter respectively by day 5, compared to over 3000% in the unstable ones. The BGNF-stabilized emulsions were more stable than the BGNS-stabilized ones. Emulsions were successfully stabilized with BGNF and BGNS.

Keywords: Bambara groundnut, coalescence, creaming, emulsification, emulsion, emulsion stability.

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Authors: Shen-Chun Wu, Chuo-Jeng Huang, Wun-Hong Yang, Jy-Cheng Chang, Chien-Chun Kung

Abstract:

This investigation examines the effect of the sintering temperature curve in manufactured nickel powder capillary structure (wick) for a loop heat pipe (LHP). The sintering temperature curve is composed of a region of increasing temperature; a region of constant temperature and a region of declining temperature. The most important region is that in which the temperature increases, as an index in the stage in which the temperature increases. The wick of nickel powder is manufactured in the stage of fixed sintering temperature and the time between the stage of constant temperature and the stage of falling temperature. When the slope of the curve in the region of increasing temperature is unity (equivalent to 10 °C/min), the structure of the wick is complete and the heat transfer performance is optimal. The result of experiment test demonstrates that the heat transfer performance is optimal at 320W; the minimal total thermal resistance is approximately 0.18°C/W, and the heat flux is 17W/cm2; the internal parameters of the wick are an effective pore radius of 3.1 μm, a permeability of 3.25×10-13m2 and a porosity of 71%.

Keywords: Loop heat pipe (LHP), capillary structure (wick), sintered temperature curve.

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Authors: Kyoungwoo Park, Ji-Won Han, Hyo-Jae Lim, Byeong-Sam Kim, Juhee Lee

Abstract:

Shape optimization of the airfoil with high aspect ratio of long endurance unmanned aerial vehicle (UAV) is performed by the multi-objective optimization technology coupled with computational fluid dynamics (CFD). For predicting the aerodynamic characteristics around the airfoil the high-fidelity Navier-Stokes solver is employed and SMOGA (Simple Multi-Objective Genetic Algorithm), which is developed by authors, is used for solving the multi-objective optimization problem. To obtain the optimal solutions of the design variable (i.e., sectional airfoil profile, wing taper ratio and sweep) for high performance of UAVs, both the lift and lift-to-drag ratio are maximized whereas the pitching moment should be minimized, simultaneously. It is found that the lift force and lift-to-drag ratio are linearly dependent and a unique and dominant solution are existed. However, a trade-off phenomenon is observed between the lift-to-drag ratio and pitching moment. As the result of optimization, sixty-five (65) non-dominated Pareto individuals at the cutting edge of design spaces that is decided by airfoil shapes can be obtained.

Keywords: Unmanned aerial vehicle (UAV), Airfoil, CFD, Shape optimization, Lift-to-drag ratio.

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Authors: Batin Latif Aylak, Bernd Noche, M. Baran Cantepe, Aydin Karakaya

Abstract:

Ultra-light overhead conveyor systems are rope-based conveying systems with individually driven vehicles. The vehicles can move automatically on the rope and this can be realized by energy and signals. The ultra-light overhead conveyor systems always must be integrated with a logistical process by finding a best way for a cheaper material flow in order to guarantee precise and fast workflows. This paper analyzes the process of an ultra-light overhead conveyor system using necessary assumptions. The analysis consists of three scenarios. These scenarios are based on raising the vehicle speeds with equal increments at each case. The correlation between the vehicle speed and system throughput is investigated. A discrete-event simulation model of an ultra-light overhead conveyor system is constructed using DOSIMIS-3 software to implement three scenarios. According to simulation results; the optimal scenario, hence the optimal vehicle speed, is found out among three scenarios. This simulation model demonstrates the effect of increased speed on the system throughput.

Keywords: Logistics, material flow, simulation, ultra-light overhead conveyor.

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Authors: C. Chitra, P. Subbaraj

Abstract:

The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.

Keywords: Multiobjective optimization, Non-dominated SortingGenetic Algorithm, Routing, Weighted sum.

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Authors: D. Dib, W. Guebabi, M. B. Guesmi

Abstract:

The objective of this paper is a contribution to a study of power supply by solar energy system called a common Ferkène north of Algerian desert in the semi-arid area. The optimal exploitation of the system, goes through stages of study and essential design, the choice of the model of the photovoltaic panel, the study of behavior with all the parameters involved in simulation before fixing the trajectory tracking the maximum point the power to extract (MPPT), form the essential platform to shape the design of the solar system set up to supply the town Ferkène without considering the grid. The identification of the common Ferkène by the collection of geographical, meteorological, demographic and electrical provides a basis uniform and important data. The results reflect a valid fictive model for any attempt to study and design a solar system to supply an arid or semi-arid zone by electrical energy from photovoltaic panels.

Keywords: Solar power, photovoltaic panel, Boost converter, supply, design, electric power, Ferkène, Algeria.

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Authors: C. B. Masera, M. Imprialou, L. Budd, C. Morton

Abstract:

Even though signalised intersections are necessary for urban road traffic management, they can act as bottlenecks and disrupt traffic operations. Interrupted traffic flow causes congestion, delays, stop-and-go conditions (i.e. excessive acceleration/deceleration) and longer journey times. Vehicle and infrastructure connectivity offers the potential to provide improved new services with additional functions of assisting drivers. This paper focuses on one of the applications of vehicle-to-infrastructure communication namely Green Light Optimal Speed Advisory (GLOSA). To assess the effectiveness of GLOSA in the urban road network, an integrated microscopic traffic simulation framework is built into VISSIM software. Vehicle movements and vehicle-infrastructure communications are simulated through the interface of External Driver Model. A control algorithm is developed for recommending an optimal speed that is continuously updated in every time step for all vehicles approaching a signal-controlled point. This algorithm allows vehicles to pass a traffic signal without stopping or to minimise stopping times at a red phase. This study is performed with all connected vehicles at 100% penetration rate. Conventional vehicles are also simulated in the same network as a reference. A straight road segment composed of two opposite directions with two traffic lights per lane is studied. The simulation is implemented under 150 vehicles per hour and 200 per hour traffic volume conditions to identify how different traffic densities influence the benefits of GLOSA. The results indicate that traffic flow is improved by the application of GLOSA. According to this study, vehicles passed through the traffic lights more smoothly, and waiting times were reduced by up to 28 seconds. Average delays decreased for the entire network by 86.46% and 83.84% under traffic densities of 150 vehicles per hour per lane and 200 vehicles per hour per lane, respectively.

Keywords: Connected vehicles, GLOSA, intelligent transportation systems, infrastructure-to-vehicle communication.

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Authors: K. Krishna Veni, S. Raja Balachandar

Abstract:

This paper presents a heuristic to solve large size 0-1 Multi constrained Knapsack problem (01MKP) which is NP-hard. Many researchers are used heuristic operator to identify the redundant constraints of Linear Programming Problem before applying the regular procedure to solve it. We use the intercept matrix to identify the zero valued variables of 01MKP which is known as redundant variables. In this heuristic, first the dominance property of the intercept matrix of constraints is exploited to reduce the search space to find the optimal or near optimal solutions of 01MKP, second, we improve the solution by using the pseudo-utility ratio based on surrogate constraint of 01MKP. This heuristic is tested for benchmark problems of sizes upto 2500, taken from literature and the results are compared with optimum solutions. Space and computational complexity of solving 01MKP using this approach are also presented. The encouraging results especially for relatively large size test problems indicate that this heuristic can successfully be used for finding good solutions for highly constrained NP-hard problems.

Keywords: 0-1 Multi constrained Knapsack problem, heuristic, computational complexity, NP-Hard problems.

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Authors: Ifeyinwa E. Achumba, Djamel Azzi, Rinat Khusainov

Abstract:

There are three approaches to complete Bayesian Network (BN) model construction: total expert-centred, total datacentred, and semi data-centred. These three approaches constitute the basis of the empirical investigation undertaken and reported in this paper. The objective is to determine, amongst these three approaches, which is the optimal approach for the construction of a BN-based model for the performance assessment of students- laboratory work in a virtual electronic laboratory environment. BN models were constructed using all three approaches, with respect to the focus domain, and compared using a set of optimality criteria. In addition, the impact of the size and source of the training, on the performance of total data-centred and semi data-centred models was investigated. The results of the investigation provide additional insight for BN model constructors and contribute to literature providing supportive evidence for the conceptual feasibility and efficiency of structure and parameter learning from data. In addition, the results highlight other interesting themes.

Keywords: Bayesian networks, model construction, parameterlearning, structure learning, performance index, model comparison.

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Authors: Sangeeta Mondal, Vasundhara, Rajib Kar, Durbadal Mandal, S. P. Ghoshal

Abstract:

This paper presents an optimal design of linear phase digital high pass finite impulse response (FIR) filter using Improved Particle Swarm Optimization (IPSO). In the design process, the filter length, pass band and stop band frequencies, feasible pass band and stop band ripple sizes are specified. FIR filter design is a multi-modal optimization problem. An iterative method is introduced to find the optimal solution of FIR filter design problem. Evolutionary algorithms like real code genetic algorithm (RGA), particle swarm optimization (PSO), improved particle swarm optimization (IPSO) have been used in this work for the design of linear phase high pass FIR filter. IPSO is an improved PSO that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. A comparison of simulation results reveals the optimization efficacy of the algorithm over the prevailing optimization techniques for the solution of the multimodal, nondifferentiable, highly non-linear, and constrained FIR filter design problems.

Keywords: FIR Filter, IPSO, GA, PSO, Parks and McClellan Algorithm, Evolutionary Optimization, High Pass Filter

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Authors: Amir Bak Khoshnevis, Mahdieh Khodadadi, Aghil Lotfi

Abstract:

For a bluff body, roughness elements in simulating a turbulent boundary layer, leading to delayed flow separation, a smaller wake, and lower form drag. In the present work, flow past a circular cylinder with using tripping wires is studied experimentally. The wind tunnel used for modeling free stream is open blow circuit (maximum speed = 30m/s and maximum turbulence of free stream = 0.1%). The selected Reynolds number for all tests was constant (Re = 25000). The circular cylinder selected for this experiment is 20 and 400mm in diameter and length, respectively. The aim of this research is to find the optimal operation mode. In this study installed some tripping wires 1mm in diameter, with a different number of wires on the circular cylinder and the wake characteristics of the circular cylinder is studied. Results showed that by increasing number of tripping wires attached to the circular cylinder (6, 8, and 10, respectively), The optimal angle for the tripping wires with 1mm in diameter to be installed on the cylinder is 60̊ (or 6 wires required at angle difference of 60̊). Strouhal number for the cylinder with tripping wires 1mm in diameter at angular position 60̊ showed the maximum value.

Keywords: Wake of a circular cylinder, trip wire, velocity defect, Strouhal number.

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Authors: C. Chitra, P. Subbaraj

Abstract:

The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.

Keywords: Multiobjective optimization, Non-dominated Sorting Genetic Algorithm, Routing, Weighted sum.

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Authors: Ghada Elshafei, Abdelazim Negm

Abstract:

Recently, the green architecture becomes a significant way to a sustainable future. Green building designs involve finding the balance between comfortable homebuilding and sustainable environment. Moreover, the utilization of the new technologies such as artificial intelligence techniques are used to complement current practices in creating greener structures to keep the built environment more sustainable. The most common objectives in green buildings should be designed to minimize the overall impact of the built environment that effect on ecosystems in general and in particularly human health and natural environment. This will lead to protecting occupant health, improving employee productivity, reducing pollution and sustaining the environmental. In green building design, multiple parameters which may be interrelated, contradicting, vague and of qualitative/quantitative nature are broaden to use. This paper presents a comprehensive critical state- ofart- review of current practices based on fuzzy and its combination techniques. Also, presented how green architecture/building can be improved using the technologies that been used for analysis to seek optimal green solutions strategies and models to assist in making the best possible decision out of different alternatives.

Keywords: Green architecture/building, technologies, optimization, strategies, fuzzy techniques and models.

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Authors: Dimitriya A. Mihaylova, Zlatka V. Valkova-Jarvis, Georgi L. Iliev

Abstract:

One possible approach for maintaining the security of communication systems relies on Physical Layer Security mechanisms. However, in wireless time division duplex systems, where uplink and downlink channels are reciprocal, the channel estimate procedure is exposed to attacks known as pilot contamination, with the aim of having an enhanced data signal sent to the malicious user. The Shifted 2-N-PSK method involves two random legitimate pilots in the training phase, each of which belongs to a constellation, shifted from the original N-PSK symbols by certain degrees. In this paper, legitimate pilots’ offset values and their influence on the detection capabilities of the Shifted 2-N-PSK method are investigated. As the implementation of the technique depends on the relation between the shift angles rather than their specific values, the optimal interconnection between the two legitimate constellations is investigated. The results show that no regularity exists in the relation between the pilot contamination attacks (PCA) detection probability and the choice of offset values. Therefore, an adversary who aims to obtain the exact offset values can only employ a brute-force attack but the large number of possible combinations for the shifted constellations makes such a type of attack difficult to successfully mount. For this reason, the number of optimal shift value pairs is also studied for both 100% and 98% probabilities of detecting pilot contamination attacks. Although the Shifted 2-N-PSK method has been broadly studied in different signal-to-noise ratio scenarios, in multi-cell systems the interference from the signals in other cells should be also taken into account. Therefore, the inter-cell interference impact on the performance of the method is investigated by means of a large number of simulations. The results show that the detection probability of the Shifted 2-N-PSK decreases inversely to the signal-to-interference-plus-noise ratio.

Keywords: Channel estimation, inter-cell interference, pilot contamination attacks, wireless communications.

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Authors: Vijay Kumar Nath, Deepika Hazarika, Anil Mahanta,

Abstract:

Discrete Cosine Transform (DCT) based transform coding is very popular in image, video and speech compression due to its good energy compaction and decorrelating properties. However, at low bit rates, the reconstructed images generally suffer from visually annoying blocking artifacts as a result of coarse quantization. Lapped transform was proposed as an alternative to the DCT with reduced blocking artifacts and increased coding gain. Lapped transforms are popular for their good performance, robustness against oversmoothing and availability of fast implementation algorithms. However, there is no proper study reported in the literature regarding the statistical distributions of block Lapped Orthogonal Transform (LOT) and Lapped Biorthogonal Transform (LBT) coefficients. This study performs two goodness-of-fit tests, the Kolmogorov-Smirnov (KS) test and the 2- test, to determine the distribution that best fits the LOT and LBT coefficients. The experimental results show that the distribution of a majority of the significant AC coefficients can be modeled by the Generalized Gaussian distribution. The knowledge of the statistical distribution of transform coefficients greatly helps in the design of optimal quantizers that may lead to minimum distortion and hence achieve optimal coding efficiency.

Keywords: Lapped orthogonal transform, Lapped biorthogonal transform, Image compression, KS test,

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Authors: Amadi Ugwulo Chinyere, Lewis D. Gbarayorks, Emem N. H. Inamete

Abstract:

In this paper, the mandatory contribution, additional voluntary contribution (AVC) and administrative charges are merged together to determine the optimal investment strategy (OIS) for a pension plan member (PPM) in a defined contribution (DC) pension scheme under the modified constant elasticity of variance (M-CEV) model. We assume that the voluntary contribution is a stochastic process and a portfolio consisting of one risk free asset and one risky asset modeled by the M-CEV model is considered. Also, a stochastic differential equation consisting of PPM’s monthly contributions, voluntary contributions and administrative charges is obtained. More so, an optimization problem in the form of Hamilton Jacobi Bellman equation which is a nonlinear partial differential equation is obtained. Using power transformation and change of variables method, an explicit solution of the OIS and the value function are obtained under constant absolute risk averse (CARA). Furthermore, numerical simulations on the impact of some sensitive parameters on OIS were discussed extensively. Finally, our result generalizes some existing result in the literature.

Keywords: DC pension fund, modified constant elasticity of variance, optimal investment strategies, voluntary contribution, administrative charges.

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Authors: Amr Abdel Azim Ali, G. A. Elsheikh, Moutaz Hegazy

Abstract:

This paper presents verification of a modeling and simulation for a Spacecraft (SC) attitude and orbit control system. Detailed formulation of coupled SC orbital and attitude equations of motion is performed in order to achieve accepted accuracy to meet the requirements of multitargets tracking and orbit correction complex modes. Correction of the target parameter based on the estimated state vector during shooting time to enhance pointing accuracy is considered. Time-optimal nonlinear feedback control technique was used in order to take full advantage of the maximum torques that the controller can deliver. This simulation provides options for visualizing SC trajectory and attitude in a 3D environment by including an interface with V-Realm Builder and VR Sink in Simulink/MATLAB. Verification data confirms the simulation results, ensuring that the model and the proposed control law can be used successfully for large and fast tracking and is robust enough to keep the pointing accuracy within the desired limits with considerable uncertainty in inertia and control torque.

Keywords: Attitude and orbit control, time-optimal nonlinear feedback control, modeling and simulation, pointing accuracy, maximum torques.

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Authors: Ivana T. Karabegović, Saša S. Stojičević, Dragan T. Veličković, Nada Č. Nikolić, Miodrag L. Lazić

Abstract:

Optimization of a microwave-assisted extraction of cherry laurel (Prunus laurocerasus) fruit using methanol was studied. The influence of process parameters (microwave power, plant material-to-solvent ratio and the extraction time) on the extraction efficiency were optimized by using response surface methodology. The predicted maximum yield of extractive substances (41.85 g/100 g fresh plant material) was obtained at microwave power of 600 W and plant material to solvent ratio of 0.2 g/cm3 after 26 minutes of extraction, while a mean value of 40.80±0.41 g/100 g fresh plant material was obtained from laboratory experiments. This proves applicability of the model in predicting optimal extraction conditions with minimal laborious and time consuming. The results indicated that all process parameters were effective on the extraction efficiency, while the most important factor was extraction time. In order to rationalize production the optimal economical condition which gave a large total extract yield with minimal energy and solvent consumption was found.

Keywords: Cherry laurel, Extraction, Multiple regression modeling, Microwave.

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Authors: Wang Yichen, Haruka Yamashita

Abstract:

In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: Recurrent Neural Network, players lineup, basketball data, decision making model.

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Authors: Thella Babu Rao, A. Gopala Krishna

Abstract:

This investigation presents the formulation of kerf (width of slit) and optimal control parameter settings of wire electrochemical discharge machining which results minimum possible kerf while machining Al7075/SiCp MMCs. WEDM is proved its efficiency and effectiveness to cut the hard ceramic reinforced MMCs within the permissible budget. Among the distinct performance measures of WEDM process, kerf is an important performance characteristic which determines the dimensional accuracy of the machined component while producing high precision components. The lack of available of the machinability information such advanced MMCs result the more experimentation in the manufacturing industries. Therefore, extensive experimental investigations are essential to provide the database of effect of various control parameters on the kerf while machining such advanced MMCs in WEDM. Literature reviled the significance some of the electrical parameters which are prominent on kerf for machining distinct conventional materials. However, the significance of reinforced particulate size and volume fraction on kerf is highlighted in this work while machining MMCs along with the machining parameters of pulse-on time, pulse-off time and wire tension. Usually, the dimensional tolerances of machined components are decided at the design stage and a machinist pay attention to produce the required dimensional tolerances by setting appropriate machining control variables. However, it is highly difficult to determine the optimal machining settings for such advanced materials on the shop floor. Therefore, in the view of precision of cut, kerf (cutting width) is considered as the measure of performance for the model. It was found from the literature that, the machining conditions of higher fractions of large size SiCp resulting less kerf where as high values of pulse-on time result in a high kerf. A response surface model is used to predict the relative significance of various control variables on kerf. Consequently, a powerful artificial intelligence called genetic algorithms (GA) is used to determine the best combination of the control variable settings. In the next step the conformation test was conducted for the optimal parameter settings and found good agreement between the GA kerf and measured kerf. Hence, it is clearly reveal that the effectiveness and accuracy of the developed model and program to analyze the kerf and to determine its optimal process parameters. The results obtained in this work states that, the resulted optimized parameters are capable of machining the Al7075/SiCp MMCs more efficiently and with better dimensional accuracy.

Keywords: Al7075SiCP MMC, kerf, WEDM, optimization.

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Authors: H. Abdizadeh, Y. Vahidshad

Abstract:

The present paper discusses the selection of process parameters for obtaining optimal nanocrystallites size in the CuOZrO2 catalyst. There are some parameters changing the inorganic structure which have an influence on the role of hydrolysis and condensation reaction. A statistical design test method is implemented in order to optimize the experimental conditions of CuO-ZrO2 nanoparticles preparation. This method is applied for the experiments and L16 orthogonal array standard. The crystallites size is considered as an index. This index will be used for the analysis in the condition where the parameters vary. The effect of pH, H2O/ precursor molar ratio (R), time and temperature of calcination, chelating agent and alcohol volume are particularity investigated among all other parameters. In accordance with the results of Taguchi, it is found that temperature has the greatest impact on the particle size. The pH and H2O/ precursor molar ratio have low influences as compared with temperature. The alcohol volume as well as the time has almost no effect as compared with all other parameters. Temperature also has an influence on the morphology and amorphous structure of zirconia. The optimal conditions are determined by using Taguchi method. The nanocatalyst is studied by DTA-TG, XRD, EDS, SEM and TEM. The results of this research indicate that it is possible to vary the structure, morphology and properties of the sol-gel by controlling the above-mentioned parameters.

Keywords: CuO-ZrO2 Nanoparticles, Sol-gel, Taguchi method.

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Authors: Diako Azizi, Ahmad Gholami, Vahid Abbasi

Abstract:

Optimal selection of electrical insulations in electrical machinery insures reliability during operation. From the insulation studies of view for electrical machines, stator is the most important part. This fact reveals the requirement for inspection of the electrical machine insulation along with the electro-thermal stresses. In the first step of the study, a part of the whole structure of machine in which covers the general characteristics of the machine is chosen, then based on the electromagnetic analysis (finite element method), the machine operation is simulated. In the simulation results, the temperature distribution of the total structure is presented simultaneously by using electro-thermal analysis. The results of electro-thermal analysis can be used for designing an optimal cooling system. In order to design, review and comparing the cooling systems, four wiring structures in the slots of Stator are presented. The structures are compared to each other in terms of electrical, thermal distribution and remaining life of insulation by using Finite Element analysis. According to the steps of the study, an optimization algorithm has been presented for selection of appropriate structure.

Keywords: Electrical field, field distribution, insulation, winding, finite element method, electro thermal

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Authors: G. Kloudova, M. Stehlik

Abstract:

Optimal human performance is a key goal in the professional setting of military pilots, which is a highly challenging atmosphere. The aviation environment requires substantial cognitive effort and is rich in potential stressors. Therefore, it is important to analyze variables such as mental workload to ensure safe conditions. Pilot mental workload could be measured using several tools, but most of them are very subjective. This paper details research conducted with military pilots using psychophysiological methods such as electroencephalography (EEG) and heart rate (HR) monitoring. The data were measured in a simulator as well as under real flight conditions. All of the pilots were exposed to highly demanding flight tasks and showed big individual response differences. On that basis, the individual pattern for each pilot was created counting different EEG features and heart rate variations. Later on, it was possible to distinguish the most difficult flight tasks for each pilot that should be more extensively trained. For training purposes, an application was developed for the instructors to decide which of the specific tasks to focus on during follow-up training. This complex system can help instructors detect the mentally demanding parts of the flight and enhance the training of military pilots to achieve optimal performance.

Keywords: Cognitive effort, human performance, military pilots, psychophysiological methods.

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Authors: Raksha Sharma, Vishnu Kant Soni, Manoj Kumar Mishra, Prachet Bhuyan, Utpal Chandra Dey

Abstract:

Grid computing is a group of clusters connected over high-speed networks that involves coordinating and sharing computational power, data storage and network resources operating across dynamic and geographically dispersed locations. Resource management and job scheduling are critical tasks in grid computing. Resource selection becomes challenging due to heterogeneity and dynamic availability of resources. Job scheduling is a NP-complete problem and different heuristics may be used to reach an optimal or near optimal solution. This paper proposes a model for resource and job scheduling in dynamic grid environment. The main focus is to maximize the resource utilization and minimize processing time of jobs. Grid resource selection strategy is based on Max Heap Tree (MHT) that best suits for large scale application and root node of MHT is selected for job submission. Job grouping concept is used to maximize resource utilization for scheduling of jobs in grid computing. Proposed resource selection model and job grouping concept are used to enhance scalability, robustness, efficiency and load balancing ability of the grid.

Keywords: Agent, Grid Computing, Job Grouping, Max Heap Tree (MHT), Resource Scheduling.

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Authors: V.Sekar, T.G.Palanivelu, B.Revathi

Abstract:

Electromagnetic interference (EMI) is one of the serious problems in most electrical and electronic appliances including fluorescent lamps. The electronic ballast used to regulate the power flow through the lamp is the major cause for EMI. The interference is because of the high frequency switching operation of the ballast. Formerly, some EMI mitigation techniques were in practice, but they were not satisfactory because of the hardware complexity in the circuit design, increased parasitic components and power consumption and so on. The majority of the researchers have their spotlight only on EMI mitigation without considering the other constraints such as cost, effective operation of the equipment etc. In this paper, we propose a technique for EMI mitigation in fluorescent lamps by integrating Frequency Modulation and Evolutionary Programming. By the Frequency Modulation technique, the switching at a single central frequency is extended to a range of frequencies, and so, the power is distributed throughout the range of frequencies leading to EMI mitigation. But in order to meet the operating frequency of the ballast and the operating power of the fluorescent lamps, an optimal modulation index is necessary for Frequency Modulation. The optimal modulation index is determined using Evolutionary Programming. Thereby, the proposed technique mitigates the EMI to a satisfactory level without disturbing the operation of the fluorescent lamp.

Keywords: Ballast, Electromagnetic interference (EMI), EMImitigation, Evolutionary programming (EP), Fluorescent lamp, Frequency Modulation (FM), Modulation index.

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Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

Abstract:

High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: Artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch.

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