• 지역사회간호학회지
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• 제16권2호
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• pp.177-185
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• 2005

Purpose: This study was to examine the effects of an exercise program on physical fitness, cardiopulmonary function and life satisfaction for adult women. Method: The exercise program combined dance and resistance training. The subject group consisted of 114 women aged between 33 and 60. Three 8-week sessions consisted of 55-80% HR max for 60-90 minutes a day and 3 times a week from March to November, 2004. Data were collected through pre- and post-exercise tests before and after each session. Data were collected with dynamometer, sphygmomamometer, spirometer and structured questionnaires. Data were analyzed employing descriptive statistics and paired t-test with SPSS/PC(10.0version) program. Results: There were significantly positive changes in muscle strength, flexibility, balance quality, forced vital capacity and life satisfaction, but no significantly positive changes in agility and blood pressure. Conclusion: This study showed that an exercise program has partially positive effects for adult women. The results of this study suggest that there should be programs of continuous exercise at community health centers for adult women's health.

• 생명과학회지
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• 제12권5호
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• pp.622-631
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• 2002

중년여성을 대상으로 유산소성 운동으로서 댄스스포츠 동작을 이용한 운동프로그램을 12주간 실시하여 실험군과 통제군의 면역기능과 체력 및 체지방률의 변화를 살펴보았다. 면역기능은 실험군에서 임파구, 호산구, T3, T4의 경우 사후에 유의하게 증가하는 것으로 나타났으며, 호중구와 B4는 유의하게 감소하는 것으로 나타났다. 통제군에서는 74가 유의하게 증가하고,78이 유의하게 감소한 외에는 거의 변화가 나타나지 않았다. 그리고 실험군과 통제군 간에서는 통계적으로 유의한 변화가 나타나지 않았다. 이러한 증감은 면역기능 지표의 정상범위내에서 나타난 현상이다. 그리고 체력은 실험군에서 근력과 근지구력에서 유의하게 증가하였으며, 배근력의 제외한 모든 항목에서 실험군이 통제군에 비해 유의하게 증가한 것으로 나타났다. 즉, 지속적인 유산소성 운동이 중년 여성의 전신의 근육을 적절히 단련시킴으로써 근골격계의 기능감소를 지연, 개선시킬 것으로 사료된다 또한 체중과 체지방률의 변화는 모두 실험군에서 유의하게 감소하는 경향을 보였으며, 집단간에서도 실험군이 통제군보다 유의하게 감소하는 것으로 나타났다. 이상과 같은 결과들을 요약하자면, 중년여성을 대상으로 댄스스포츠 동작을 이용한 유산소성 운동이 면역기능과 근력, 근지구력 등의 체력 및 비만 인자에 긍정적인 효과를 나타냄으로서 성인병의 예방뿐만 아니라 건강 증진으로 향후 활기찬 생활에 기여하여 삶의 질을 향상시킬 것으로 사료된다.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• 한국전자파학회논문지
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• 제30권4호
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• pp.261-269
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• 2019

본 논문은 레이돔과 같은 다층구조의 주파수 선택적 표면(frequency selective surfaces: FSS)을 설계하는데, 편파나 입사각 등 다양한 고려사항에 대한 유연성을 갖는 픽셀 형태의 주파수 선택적 표면을 설계하는 것에 관한 것이다. 픽셀 형태의 FSS를 설계할 때 이산 공간 문제를 해결할 수 있는 다양한 방법 중 이진 입자 군집 최적화(binary particle swarm optimization: BPSO) 알고리즘은 FSS의 주기구조 패턴을 결정하는데 쉽게 적용 가능한 기술 중 하나이며, 따라서 향상된 BPSO 알고리즘을 통해 롤 오프 전파 투과특성을 갖는 FSS를 효율적으로 설계하는 기법을 제안하였다. 원하는 솔루션에 입자를 유도하기 위한 적합성 함수 설계에 대하여 수렴속도 문제를 해결하기 위해, '기울기'를 입력 변수로 한 적합성 함수를 적용할 경우 쉽게 원하는 전파특성을 갖는 FSS를 얻을 수 있었다.

• 한국응용과학기술학회지
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• 제39권4호
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• pp.527-534
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• 2022

본 연구에서는 초등학교 남자 축구선수들을 대상으로 12주간 근파워 및 민첩성 트레이닝이 체력요인과 등속성근기능에 미치는 효과를 규명하고자 하였다. 이를 위해 초등학교 남자 축구선수 12명을 대상으로 근파워와 민첩성 트레이닝 프로그램 6개를 구성하여 12주간 주 3회 실시하였다. 근파워, 민첩성 트레이닝 전과 후 운동에 대한 체력요인들을 측정하여 분석한 결과는 다음과 같다. 첫째, 근파워의 제자리멀리뛰기에서는 유의한 차이가 나타났다(p<.001). 둘째, 근파워의 제자리높이뛰기에서 유의한 차이가 나타났다(p<.05). 셋째, 민첩성의 사이드스텝에서 유의한 차이가 나타났다(p<.01). 이상의 결과로 12주간의 근파워 민첩성 트레이닝은 초등학교 남자 축구선수들의 순발력과 민첩성 향상에 긍정적 효과를 기대할 수 있으며, 상해 예방과 경기력 향상을 위한 트레이닝 기초 자료를 제공할 수 있을 것으로 판단된다.

• 대한간호학회지
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• 제39권4호
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• pp.584-593
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• 2009

Purpose: The purpose of this study was to investigate the effect of stretching, muscle strengthening, and walking exercise on the cardiopulmonary function and health-related quality of life in hemodialysis patients. Methods: Twenty-one patients in the intervention and the control group participated in the exercise respectively on maintenance hemodialysis at four university hospitals. The exercise was composed of 20 to 60 min per session, 3 sessions a week for 12 weeks. The effect of exercise was assessed by cardiopulmonary function (peak oxygen uptake, peak ventilation, peak respiration rate, maximal heart rate, and exercise duration) using a cycle ergometer. Grip strength was measured by dynamometer, and flexibility was measured by sit and reach measuring instrument. Health-related quality of life was measured using Medical Outcomes Study Short Form-36. Results: Peak oxygen uptake, peak ventilation, peak respiration rate, exercise duration, grip strength, flexibility, and physical component scale were significantly improved in the intervention group after 12 week's exercise compared to the control group. Conclusion: These findings indicate the exercise can improve cardiopulmonary function, grip strength, flexibility, and physical component scale of health-related quality of life in hemodialysis patients.

• 한국인공지능학회지
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• 제5권1호
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• pp.18-28
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• International Journal of Advanced Culture Technology
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• 제5권2호
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• pp.74-81
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA(Dropout Genetic Algorithm) which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• Structural Engineering and Mechanics
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• 제55권1호
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• pp.229-244
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• 2015

H-infinity norm relates to the maximum in the frequency response function and H-infinity control method focuses on the case that the vibration is excited at the fundamental frequency, while 2-norm relates to the output energy of systems with the input of pulses or white noises and 2-norm control method weighs the overall vibration performance of systems. The trade-off between the performance in frequency-domain and that in time-domain may be achieved by integrating two indices in the mixed vibration control method. Based on the linear fractional state space representation in the modal space for a piezoelectric flexible structure with uncertain modal parameters and un-modeled residual high-frequency modes, a mixed dynamic output feedback control design method is proposed to suppress the structural vibration. Using the linear matrix inequality (LMI) technique, the initial populations are generated by the designing of robust control laws with different H-infinity performance indices before the robust 2-norm performance index of the closed-loop system is included in the fitness function of optimization. A flexible beam structure with a piezoelectric sensor and a piezoelectric actuator are used as the subject for numerical studies. Compared with the velocity feedback control method, the numerical simulation results show the effectiveness of the proposed method.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.17-32
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• 2023

Centralized hierarchical routing protocols are often used to solve the problems of uneven energy consumption and short network life in wireless sensor networks (WSNs). Clustering and cluster head election have become the focuses of WSNs. In this paper, an energy balanced clustering routing algorithm optimized by sine cosine algorithm (SCA) is proposed. Firstly, optimal cluster head number per round is determined according to surviving node, and the candidate cluster head set is formed by selecting high-energy node. Secondly, a random population with a certain scale is constructed to represent a group of cluster head selection scheme, and fitness function is designed according to inter-cluster distance. Thirdly, the SCA algorithm is improved by using monotone decreasing convex function, and then a certain number of iterations are carried out to select a group of individuals with the minimum fitness function value. From simulation experiments, the process from the first death node to 80% only needs about 30 rounds. This improved algorithm balances the energy consumption among nodes and avoids premature death of some nodes. And it greatly improves the energy utilization and extends the effective life of the whole network.