• Journal of electromagnetic engineering and science
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• v.17 no.4
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• pp.208-220
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• 2017

This paper proposes an experimental optimization method for a wireless power transfer (WPT) system. The power transfer characteristics of a WPT system with arbitrary loads and various types of coupling and compensation networks can be extracted by frequency domain measurements. The various performance parameters of the WPT system, such as input real/imaginary/apparent power, power factor, efficiency, output power and voltage gain, can be accurately extracted in a frequency domain by a single passive measurement. Subsequently, the design parameters can be efficiently tuned by separating the overall design steps into two parts. The extracted performance parameters of the WPT system were validated with time-domain experiments.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.64-68
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• 2012

In this paper, a design method for minimizing the cogging torque of an Interior Permanent Magnet Motor (IPM) is proposed based on a hybrid algorithm. The suggested optimization algorithm is based on a combination of the Response Surface Method (RSM) and Simplex Method. The results show that the proposed method provides improved characteristics compared to the conventional methods, such as a shorter calculation time and the acquisition of a more correct solution.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.131-138
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• 2022

Soft sensors are used to anticipate complicated model parameters using data from classifiers that are comparatively easy to gather. The goal of this study is to use artificial intelligence techniques to design and build soft sensors. The combination of a Long Short-Term Memory (LSTM) network and Grey Wolf Optimization (GWO) is used to create a unique soft sensor. LSTM is developed to tackle linear model with strong nonlinearity and unpredictability of manufacturing applications in the learning approach. GWO is used to accomplish input optimization technique for LSTM in order to reduce the model's inappropriate complication. The newly designed soft sensor originally brought LSTM's superior dynamic modeling with GWO's exact variable selection. The performance of our proposal is demonstrated using simulations on real-world datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1075-1089
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• 2024

In this paper, the optimizations of multi-hop cooperative molecular communication (CMC) system in cylindrical anomalous-diffusive channel in three-dimensional enviroment are investigated. First, we derive the performance of bit error probability (BEP) of CMC system under decode-and-forward relay strategy. Then for achieving minimum average BEP, the optimization variables are detection thresholds at cooperative nodes and destination node, and the corresponding optimization problem is formulated. Furthermore, we use conjugate gradient (CG) algorithm to solve this optimization problem to search optimal detection thresholds. The numerical results show the optimal detection thresholds can be obtained by CG algorithm, which has good convergence behaviors with fewer iterations to achieve minimized average BEP compared with gradient decent algorithm and Bisection method which are used in molecular communication.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.17-22
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• 2012

Lagrangian relaxation is the most widely adopted method for solving unit commitment (UC) problems. It consists of two steps: dual optimization and primal feasible solution construction. The dual optimization step is crucial in determining the overall performance of the solution. This paper intends to evaluate two dual optimization methods - one based on subgradient (SG) and the other based on the cutting plane. Large-scale UC problems with hundreds of thousands of variables and constraints have been generated for evaluation purposes. It is found that the evaluated SG method yields very promising results.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.83-97
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• 2015

IEEE 802.15.4 is a global standard designed for emerging applications in low-rate wireless personal area networks (LR-WPANs). The standard provides beneficial features, such as a beacon-enabled mode and guaranteed time slots for realtime data delivery. However, how to optimally operate those features is still an open issue. For the optimal operation of the features, this paper proposes a holistic optimization method that jointly optimizes three cross-related problems: cluster-tree construction, nodes' power configuration, and duty-cycle scheduling. Our holistic optimization method provides a solution for those problems so that all the real-time packets can be delivered within their deadlines in the most energy-efficient way. Our simulation study shows that compared to existing methods, our holistic optimization can guarantee the on-time delivery of all real-time packets while significantly saving energy, consequently, significantly increasing the lifetime of the network. Furthermore, we show that our holistic optimization can be extended to take advantage of the spatial reuse of a radio frequency resource among long distance nodes and, hence, significantly increase the entire network capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3321-3341
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• 2014

This paper investigates the outage performance and optimization for the four-phase two-way transmission network with an energy harvesting (EH) relay. To enable the simultaneous information processing and energy harvesting at the relay, we firstly propose a power splitting-based two-way relaying protocol (PSTWR). Then, we discuss its outage performance theoretically and derive an explicit expression for the system outage probability. In order to find the optimal system configuration parameters such as the optimal power splitting ratio and the optimal transmit power redistribution factor, we formulate an outage-minimized optimization problem. As the problem is difficult to solve, we design a genetic algorithm (GA) based algorithm for it. Besides, we also investigate the effects of the power splitting ratio, the power redistribution factor at the relay, and the source to relay distance on the system outage performance. Finally, extensive simulation results are provided to demonstrate the accuracy of the analytical results and the effectiveness of the GA-based algorithm. Moreover, it is also shown that, the relay position greatly affects the system performance, where relatively worse outage performance is achieved when the EH relay is placed in the middle of the two sources.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.177-183
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• 2012

Cell-phones functions have advanced so rapidly and they are now called 'smart-phones.' Typical approach to optimization the performance of a smartphone is the increasing the speed of device and acquiring more free memory. In this paper, we propose relatively simple techniques that average users can apply to their devices to optimize the performance. For performance upgrade, we proposed an over-clocking technique usually used by computer manufacturers. For memory optimization, we proposed deleting unnecessary apps and replacing with better-functioning apps. We also performed experimentation by applying these techniques to a popular Android phone model and presented the results.