• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1373-1379
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• 2018

Wireless sensors in Internet of Things are getting closer to our daily lives. Since wireless sensors have limited battery power, energy efficient schemes should be employed. In this paper, we analyzed a system by using stochastic model and then solved an optimization problem, given that the gathered sensor data are aggregated before being transmitted to the sensor servers from a wireless sensor device. Using the developed model, we also proposed a optimal solution to determine the energy efficient sensor data transmitting interval. We also conducted performance evaluations of our proposals using numerical examples.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.32-39
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• 2004

Biological neurons generally have chaotic characteristics for permanent or transient period. The effects of chaotic response of biological neuron have not yet been verified by using analytical methods. Even though the transient chaos of neuron could be beneficial to overcoming the local minimum problem, the permanent chaotic response gives adverse effect on optimization problems in general. To solve optimization problems, which are needed in almost all neural network applications such as pattern recognition, identification or prediction, and control, the neuron should have one stable fixed point. In this paper, the dynamic characteristics of the chaotic dynamic neuron and the condition that produces the chaotic response are analyzed, and the convergence conditions are presented.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.372-381
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• 2006

The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.67-71
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• 2015

In this Paper, we proposed optimization of system performance, power splitting protocols applied to relay in the cooperative communication undergo co-channel interference. When relay adjust power distribution factors undergo co-channel interference, it is possible to optimize and maximize the channel capacity of the receiver. Because of energy haversting, interfence transfer to new power source. If finding the optimal power levels, to solve inability in system, and to increase the efficiency of the network. Finally, performance of the proposed protocol is analyzed in terms of outage probability, capacity of system.

• Smart Structures and Systems
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• v.25 no.6
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• pp.765-778
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• 2020

The present study intends to find a proper solution for the estimation of the physical behaviors of enlarged piles through a combination of small-scale laboratory tests and a hybrid computational predictive intelligence process. In the first step, experimental program is completed considering various critical influential factors. The results of the best multilayer perceptron (MLP)-based predictive network was implemented through three mathematical-based solutions of dragonfly algorithm (DA), whale optimization algorithm (WOA), and ant lion optimization (ALO). Three proposed models, after convergence analysis, suggested excellent performance. These analyses varied based on neurons number (e.g., in the basis MLP hidden layer) and of course, the level of its complexity. The training R² results of the best hybrid structure of DA-MLP, WOA-MLP, and ALO-MLP were 0.996, 0.996, and 0.998 where the testing R² was 0.995, 0.985, and 0.998, respectively. Similarly, the training RMSE of 0.046, 0.051, and 0.034 were obtained for the training and testing datasets of DA-MLP, WOA-MLP, and ALO-MLP techniques, while the testing RMSE of 0.088, 0.053, and 0.053, respectively. This obtained result demonstrates the excellent prediction from the optimized structure of the proposed models if only population sensitivity analysis performs. Indeed, the ALO-MLP was slightly better than WOA-MLP and DA-MLP methods.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.57-63
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• 2018

The operating system for an isolated microgrid, which is completely disconnected from the central power system, aims at preventing blackouts and minimizing power generation costs of diesel generators through efficient operation of the energy storage system (ESS) that stores energy produced by renewable energy generators and diesel generators. In this paper, we predict the amount of renewable energy generation using the weather forecast and build an optimal diesel power generation plan using a genetic algorithm. In order to avoid inefficiency due to inaccurate prediction of renewable energy generation, our search algorithm imposes penalty on candidate diesel power generation plans that fail to maintain the SOC (state of charge) of ESS at an appropriate level. Simulation experiments show that our optimization method for maintaining an appropriate SOC balance can prevent the blackout better when compared with the previous method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.1
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• pp.18-26
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• 2014

A microgrid can play a significant role for enlargement of renewable energy sources and emission reduction because it is a network of small, distributed electrical power generators operated as a collective unit. In this paper, an application of optimization method to economical operation of a microgrid is studied. The microgrid to be studied here is composed of distributed generation system(DGS), battery systems and loads. The distributed generation systems include combined heat and power(CHP) and small generators such as diesel generators and the renewable energy generators such as photovoltaic(PV) systems, wind power systems. Both of thermal loads and electrical loads are included here as loads. Also the emissions trading scheme to be applied in near future, the cost of unit start-up and the operational characteristics of battery systems are considered as well as the probabilistic characteristics of the renewable energy generation and load. A mathematical equation for optimal operation of this system is modeled based on the mixed integer programming. It is shown that this optimization methodology can be effectively used for economical operation of a microgrid by the case studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.107-110
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• 2017

According to researches, while the speed of processor and network in embedded devices is fast enough to meet user requirement, the IO speed is recognized as the main performance bottleneck. Meanwhile it is known that more than 70 percent of IOs are issued from SQLite database. Many researches related SQLite performance optimization is based on WAL mode because WAL mode optimized for write IO performance. In this paper, I propose to optimize SQLite with Atomic Write in the Rollback Journal Mode, which is mainly used in Android and Tizen. I have observed that Atomic Write have a significant write performance improvement(300%) by reducing write, file sync operation and memory usage improvement(80%). Additionally it can block JOJ(Journaling of Journal) and extend the life of the flash memory.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.439-448
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• 1999

In this paper, two optimization algorithms based on artificial neural networks and genetic algorithms are proposed for cellular radio channel assignment problems. The channel assignment process is characterized as minimization of the energy function which represents constraints of the channel assignment problems. All three constraints such as the co-channel constraint, the adjacent channel constraint and the co-site channel constraint are considered. In the neural networks approach, certain techniques such as the forced assignment and the changing cell order are developed, and in the genetic algorithms approach, data structure and proper genetic operators are developed to find optimal solutions, As simulation results, the convergence rates of the two approaches are presented and compared.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07a
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• pp.33-36
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• 2004

The process modeling for the growth rate in pulsed laser deposition(PLD)-grown ZnO thin films is investigated using neural networks(NNets) and the process recipes is optimized via genetic algorithms(GAs). D-optimal design is carried out and the growth rate is characterized by NNets based on the back-propagation(BP) algorithm. GAs is then used to search the desired recipes for the desired growth rate. The statistical analysis is used to verify the fitness of the nonlinear process model. This process modeling and optimization algorithms can explain the characteristics of the desired responses varying with process conditions.