• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2968-2976
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• 2021

This paper introduces a method to reconstruct the three-dimensional (3D) microstructure of two-phase materials, e.g., porous materials such as highly irradiated nuclear fuel, from two-dimensional (2D) sections via a multi-objective optimization genetic algorithm. The optimization is based on the comparison between the reference and reconstructed 2D sections on specific target properties, i.e., 2D pore number, and mean value and standard deviation of the pore-size distribution. This represents a multi-objective fitness function subject to weaker hypotheses compared to state-of-the-art methods based on n-points correlations, allowing for a broader range of application. The effectiveness of the proposed method is demonstrated on synthetic data and compared with state-of-the-art methods adopting a fitness based on 2D correlations. The method here developed can be used as a cost-effective tool to reconstruct the pore structure in highly irradiated materials using 2D experimental data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5487-5495
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• 2012

The important issue that applies security key are secure rekeying, processing time and cost reduction. Because of sensor node's limited energy, energy consumption for rekeying affects lifetime of network. Thus it is necessary a secure and efficient security key management method. In this paper, I propose an energy efficient group-based cluster key management (EEGCK) in the large scale sensor networks. EEGCK uses five security key for efficient key management and different polynomial degree using security fitness function of sector, cluster and group is applied for rekeying and security processing. Through both analysis and simulation, I also show that proposed EEGCK is better than previous security management method at point of network energy efficiency.

• Journal of Animal Reproduction and Biotechnology
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• v.34 no.3
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• pp.166-172
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• 2019

Heart rate is a relatively simple and non-invasive method that is used as an important physiological indicator in many studies and has a close relationship with heart structure and function, cardiovascular disease and sudden cardiac death. In general, appropriately low heart rate during resting means effective heart function and cardiovascular fitness; heart rate at recovery is an important indicator of health and disease condition. We found a beagle dog (Dog_1) with a high heart rate in the previous preliminary experiment. Therefore, purpose of this study was to compare the heart rate response of the Dog_1 with the control group during 12 weeks of interval exercise, to evaluate the structural and functional abnormalities of the heart and to verify the applicability of exercise program. Heart rate was checked during 12 weeks of interval exercise, and after the exercise was over, imaging examination and hematological and serum biochemistry were performed. As a result, Dog_1 (165.6 ± 1.5) showed significantly higher heart rate in low intensity session of interval exercise than control group (133.3 ± 0.5, p < 0.01). In addition, Dog_1 (181.2 ± 1.4) showed significantly higher heart rate than control group (155.1 ± 0.9) in high intensity session (p < 0.01). The heart rate (30 sec, 60 sec) during recovery state was higher in Dog_1 (30 sec: 156.8 ± 4.0, 60 sec: 166.8 ± 5.8) than in the control group (30 sec: 111.2 ± 2.5, 60 sec: 104.0 ± 5.1, p < 0.01). The results of the imaging examination of Dog_1 with high heart rate confirmed that the heart had no functional and structural abnormalities. All beagles with the interval exercise program did not show maladjustment, and in the hematological and serum biochemistry results, all the parameter were within the reference range. If the interval exercise program of this study is used in the future, it is expected to be used as an important basic data to achieve the purpose of health, welfare, and physical fitness improvement of dogs.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.6
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• pp.13-23
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• 2008

In this study, a finite element (FE) model updating method based on the hybrid genetic algorithm (HGA) is proposed to improve the grillage FE model for plate girder bridges. HGA consists of a genetic algorithm (GA) and direct search method (DS) based on a modification of Nelder & Mead's simplex optimization method (NMS). Fitness functions based on natural frequencies, mode shapes, and static deflections making use of the measurements and analytical results are also presented to apply in the proposed method. In addition, a multi-objective function has been formulated as a linear combination of fitness functions in order to simultaneously improve both stiffness and mass. The applicability of the proposed method to girder bridge structures has been verified through a numerical example on a two-span continuous grillage FE model, as well as through an experimental test on a simply supported plate girder skew bridge. In addition, the effect of measuring error is considered as random noise, and its effect is investigated by numerical simulation. Through numerical and experimental verification, it has been proven that the proposed method is feasible and effective for FE model updating on plate girder bridges.

• Journal of Society of Preventive Korean Medicine
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• v.21 no.2
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• pp.105-116
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• 2017

Objectives : The purpose of this study was to investigate the effects of Korean medicine based arthritis management program for community dwelling elderly women with osteoarthritis. Methods : A Nonequivalent control group pretest-posttest design was used for this study. Elderly people who agreed to participated in the study were assigned to a experimental group(n = 29) or a control group (n = 30). The Korean medicine based arthritis management program conducted for 12 weeks, 2 times a week. Study outcomes were measured by structured questionnaires from April, 2016 to July. For data analysis, independent t-test, Mann-Whitney U test and ANCOVA were performed using SPSS version 21.0. Results : Pain, stiffness, balance, strength of lower extremity, and self care activity were significant difference between the two groups in pretest and posttest(F = 8.23, p = 006), (F = 9.13, p = .004), (F = 5.74, p = .020), (F = 4.98, p = .030), (F = 8.47, p = .005). Conclusions : The Korean medicine based arthritis management program was effective on decreasing joint pain, stiffness, and increasing balance, strength of lower extremity and self care activity of community dwelling elderly women with osteoarthritis. This program was found to be useful in alleviating and managing joint symptoms of elderly women with osteoarthritis.

• Toxicological Research
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• v.8 no.2
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• pp.265-272
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• 1992

Effects of repeated physical exercise on the carbon tetrachloride ($CCl_4$) hepatotoxicity were examined in adult female rats. Rats were introduced into a cylindrical rotating cage and forced to exercise for 1 hr each day, 6days/week, for 5 consecutive weeks at a speed starting from 10m/min, increased by 1m/min per day until the speed reached 27m/min. Significantly less body weight gain was observed in the exercise group suggesting that physical fitness had been induced in these animals. Eighteen hours following termination of the last exercise bout rats were treated with $CCl_4$(2 mmol/kg.ip). The $CCl_4$-induced heptotoxicity was significantly potentiated in the repeated exercise group compared to the resting sedentary animals as determined by changes in serum sorbitol dehydrogenase (SDH), glutamic oxaloacetic transaminase(GOT), glutamic pyruvic transaminase (GPT), and glucose-6-phosphatase(G-6-Pase) activities when measured 24hrs following the $CCl_4$ treatment. Hepatic drug metabolizing activity was determined in order to elucidate the underlying mechanism of potentiating action of the $CCl_4$ hepatotoxicity induced by repeated physical exercise. Repeated exercise increased the hepatic microsomal cytochrome P-450 contents and aminopyrine N-demethylase activity. The results suggest that the potentiation of $CCl_4$ hepatotoxicity by repeated exercise is associated with induction of the mixed function oxidase (MFO) enzyme system mediating the metabolism of $CCl_4$ to its active metabolite(s).

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.1
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• pp.1-11
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• 2013

The purpose of this study was to investigate the analysis on exercise effect of basic physical fitness, flexibility and muscle function using electric horseback riding machine. Subjects were ten males and ten females who were 20th aged young adults. They exercised every 45-min a day. Horse riding exercise has progressed 3-days a week till 4-weeks. We measured contents of basic physical fitness, flexibility and joint torque in lumbar. The results showed that contents of human body effect were higher than before exercise. We thought that stimulated by 3-D movement in horseback riding exercise instrument could be activated joints and muscles with the unused generally This continuous movement could reduce tonus of muscle and relax stiff muscles. horseback riding have significant effect with whole body exercise, also it could provide efficient exercise for the elder and disabled person.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.943-948
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• 2018

In this paper, we propose a hybrid approach of combining $A^*$ and Genetic algorithm in the path search problem. In $A^*$, the cost from a start node to the intermediate node is optimized in principle but the path from that intermediate node to the goal node is generated and tested based on the cumulated cost and the next node in a priority queue is chosen to be tested. In that process, we adopt the genetic algorithm principle in that the group of nodes to generate the next node from an intermediate node is tested by its fitness function. Top two nodes are selected to use crossover or mutation operation to generate the next generation. If generated nodes are qualified, those nodes are inserted to the priority queue. The proposed method is compared with the original sequential selection and the random selection of the next searching path in $A^*$ algorithm and the result verifies the superiority of the proposed method.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.1-10
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• 2019

Since big-data text mining extracts many features and data, clustering and classification can result in high computational complexity and low reliability of the analysis results. In particular, a term document matrix obtained through text mining represents term-document features, but produces a sparse matrix. We designed an advanced genetic algorithm (GA) to extract features in text mining for detection model. Term frequency inverse document frequency (TF-IDF) is used to reflect the document-term relationships in feature extraction. Through a repetitive process, a predetermined number of features are selected. And, we used the sparsity score to improve the performance of detection model. If a spam mail data set has the high sparsity, detection model have low performance and is difficult to search the optimization detection model. In addition, we find a low sparsity model that have also high TF-IDF score by using s(F) where the numerator in fitness function. We also verified its performance by applying the proposed algorithm to text classification. As a result, we have found that our algorithm shows higher performance (speed and accuracy) in attack mail classification.

• Advances in concrete construction
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• v.14 no.3
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• pp.185-194
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• 2022

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R² metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.