• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.74-90
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• 2014

Routing in mobile wireless sensor networks (MWSNs) is an extremely challenging issue due to the features of MWSNs. In this paper, we present a novel bio-inspired trusted routing protocol (B-iTRP) based on artificial immune system (AIS), ant colony optimization (ACO) and Physarum optimization (PO). For trust mechanism, B-iTRP monitors neighbors' behavior in real time and then assesses neighbors' trusts based on AIS. For routing strategy, each node proactively finds routes to the Sink based on ACO. When a backward ant is on the way to return source, it senses the energy residual and trust value of each node on the discovered route, and calculates the link trust and link energy of the route. Moreover, B-iTRP also assesses the availability of route based on PO to maintain the route table. Simulation results show how B-iTRP can achieve the effective performance compared to existing state-of-the-art algorithms.

• Agricultural and Biosystems Engineering
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• v.1 no.1
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• pp.22-29
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• 2000

The development of bio-response feedback control system known as the speaking plant approach has been a challenging task for plant production engineers and scientists. In order to achieve the aim of developing such a bio-response feedback control system, the primary concern should be to develop a practical non-invasive technique for monitoring plant growth. Those who are skilled in raising plants can sense whether their plants are under adequate water conditions or not, for example, by merely observing minor color and tone changes before the plants wilt. Consequently, using machine vision, it may be possible to recognize changes in indices that describe plant conditions based on the appearance of growing plants. The interpretation of image information of plants may be based on image features extracted from the original pictorial image. In this study, the performance of a finite element retina was optimized by a genetic algorithm. The optimized finite element retina was evaluated based on the performance of neural plant growth monitor that requires input data given by the finite element retina.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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• pp.161-167
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• 2015

The performance of high power LEDs highly depends on the junction temperature. Operating at high junction temperature causes elevation of the overall thermal resistance which causes degradation of light intensity and lifetime. Thus, appropriate thermal management is critical for LED packaging. The main goal of this research is to improve thermal resistance by optimizing and comparing nano-pore silicon-based thermal substrate to insulated metal substrate and direct bonded copper thermal substrate. The thermal resistance of the packages are evaluated using computation fluid dynamic approach for 1 W single chip LED module.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.780-784
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• 2017

Data discovery and distribution application that is utilized by climate, high energy physics, and other scientific communities are experiencing performance and large scale data managing problems, that are rooted from the shortcomings of IP architecture. To solve this problem, newly developed data managing applications based on NDN architecture were introduced. In this letter, we present the federated NDN testbed with an NDN-based climate science application and the set of experiments that reflect the performance of NDN based climate application in general with determined and applied optimization.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.1
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• pp.47-51
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• 2018

This paper is concerned with an optimization design of Incremental Granular Model(IGM) based Genetic Algorithm (GA) as an evolutionary approach. The performance of IGM has been successfully demonstrated to various examples. However, the problem of IGM is that the same number of cluster in each context is determined. Also, fuzzification factor is set as typical value. In order to solve these problems, we develop a design method for optimizing the IGM to optimize the number of cluster centers in each context and the fuzzification factor. We perform energy analysis using 12 different building shapes simulated in Ecotect. The experimental results on energy efficiency data set of building revealed that the proposed GA-based IGM showed good performance in comparison with LR and IGM.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.1
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• pp.135-142
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• 2012

In this paper, optimal selection of the unified power flow controller (UPFC) damping controller parameters in order to improve the power system dynamic response and its stability based on two modified intelligent algorithms have been proposed. These algorithms are based on a modified intelligent particle swarm optimization (PSO) and continuous genetic algorithm (GA). After extraction of UPFC dynamic model, intelligent PSO and genetic algorithms are used to select the effective feedback signal of the damping controller; then, to compare the performance of the proposed UPFC controller in damping the critical modes of a single-machine infinite-bus (SMIB) power system, the simulation results are presented. The comparison shows the good performance of both presented PSO and genetic algorithms in an optimal selection of UPFC damping controller parameters and damping oscillations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.3
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• pp.8-15
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• 2011

The computer-based simulation tools are currently used overwhelmingly to simulate the performance of automotive designs. Then, the search for an optimal solution that satisfies a number of performance requirements usually involves numerous iterations among several simulation tools. Therefore, meta-modeling techniques are becoming widely used to build approximations of computationally expensive computer analysis tools. The set-based approach proposed in the first report of a four-part paper has been a test bed for the innovation of vehicle structure design process in the Structural Design and Fabrication Committee of JSAE(Society of Automotive Engineers of Japan). In the second report, the proposed design approach is illustrated with a side-door impact beam design example using meta-modeling techniques.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• ETRI Journal
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• v.46 no.2
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• pp.175-183
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• 2024

Recently, polar code has attracted the attention of many scholars and has been developed as a code technology in coded-cooperative communication. We propose a polar code scheme based on Plotkin structure and quasi-uniform punching (PC-QUP). Then we apply the PC-QUP to coded-cooperative scenario and built to a new coded-cooperative scheme, which is called PCC-QUP scheme. The coded-cooperative scheme based on polar code is studied on the aspects of codeword construction and performance optimization. Further, we apply the proposed schemes to space-time block coding (STBC) to explore the performance of the scheme. Monte Carlo simulation results show that the proposed cooperative PCC-QUP-STBC scheme can obtain a lower bit error ratio (BER) than its corresponding noncooperative scheme.

• Nuclear Engineering and Technology
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• v.19 no.4
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• pp.266-278
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• 1987

This paper addresses the issues in setting performance criteria for safety regulation of nuclear power plants. Since setting criteria at the low level is a much more difficult task than it is at the top level, the low-level performance criteria should be derived consistently from the more easily determinable top-level performance criteria. The paper also proposes several approaches to characterizing uncertainties in performance criteria, by extending the reliability allocation methodology that is based on the mean-to-mean mapping to a stochastic multi-objective optimization problem where the state variables are uncertain.