• Journal of Power Electronics
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• 제14권1호
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• pp.194-201
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• 2014

To increase the capacity of secondary cells, an appropriate serial composition of the battery modules is essential. The unbalance that may occur due to the series connection in such a serial composition is the main cause for declines in the efficiency and performance of batteries. Various studies have been conducted on the use of a passive or active topology to eliminate the unbalance from the series circuit of battery modules. Most topologies consist of a complex structure in which the Battery Management System (BMS) detects the voltage of each module and establishes the voltage balancing in the independent electrical power converters installed on each module by comparing the module voltage. This study proposes a new magnetic flux sharing type DC/DC converter topology in order to remove voltage unbalances from batteries. The proposed topology is characterized by a design in which all of the DC/DC convertor outputs connected to the modules converge into a single transformer. In this structure, by taking a form in which all of the battery balancing type converters share magnetic flux through a single harmonic wave transformer, all of the converter voltages automatically converge to the same voltage. This paper attempts to analyze the dynamic properties of the proposed circuit by using a Programmable Synthesizer Interface Module (PSIM), which is useful for power electronics analysis, while also attempting to demonstrate the validity of the proposed circuit through experimental results.

• 로봇학회논문지
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• 제10권1호
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• pp.9-15
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• 2015

We present a robust power transmission lines detection method based on vanishing point estimation. Vanishing point estimation can be helpful to detect power transmission lines because parallel lines converge on the vanishing point in a projected 2D image. However, it is not easy to estimate the vanishing point correctly in an image with complex background. Thus, we first propose a vanishing point estimation method on power transmission lines by using a probabilistic voting procedure based on intersection points of line segments. In images obtained by our system, power transmission lines are located in a fan-shaped area centered on this estimated vanishing point, and therefore we select the line segments that converge to the estimated vanishing point as candidate line segments for power transmission lines only in this fan-shaped area. Finally, we detect the power transmission lines from these candidate line segments. Experimental results show that the proposed method is robust to noise and efficient to detect power transmission lines.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4220-4241
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• 2017

For the downlink energy-harvesting small cell network, this paper proposes an interference management algorithm based on distributed coalitional game. The cooperative interference management problem of the energy-harvesting small cells is modeled as a coalitional game with transfer utility. Based on the energy harvesting strategy of the small cells, the time sharing mode of the small cells in the same coalition is determined, and an optimization model is constructed to maximize the total system rate of the energy-harvesting small cells. Using the distributed algorithm for coalition formation proposed in this paper, the stable coalition structure, optimal time sharing strategy and optimal power distribution are found to maximize the total utility of the small cell system. The performance of the proposed algorithm is discussed and analyzed finally, and it is proved that this algorithm can converge to a stable coalition structure with reasonable complexity. The simulations show that the total system rate of the proposed algorithm is superior to that of the non-cooperative algorithm in the case of dense deployment of small cells, and the proposed algorithm can converge quickly.

• 한국자동차공학회논문집
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• 제6권4호
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• pp.76-85
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• 1998

Recently, Genetic Algorithm(GA) is widely adopted into a search procedure for structural optimization, which is a stochastic direct search strategy that mimics the process of genetic evolution. This methods consist of three genetics operations maned selection, crossover and mutation. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA, being zero-order method, is very simple. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher probability of converge to global optimum compared to traditional techniques which take one-point search method. In this study, a method of finding the optimum values of suspension parameters is proposed by using the GA. And vehicle is modelled as planar vehicle having 5 degree-of-freedom. The generalized coordinates are vertical motion of passenger seat, sprung mass and front and rear unsprung mass and rotate(pitch) motion of sprung mass. For rapid converge and precluding local optimum, share function which distribute chromosomes over design bound is introduced. Elitist survival model, remainder stochastic sampling without replacement method, multi-point crossover method are adopted. In the sight of the improvement of ride comfort, good result can be obtained in 5-D.O.F. vehicle model by using GA.

• 한국문헌정보학회지
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• 제44권3호
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• pp.25-59
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• 2010

이 연구의 목적은 성격유형별로 독서성향이나 행태에 차이가 나는지를 조사해 봄으로써 성격유형에 기반한 독서치료가 가능할지를 모색하는 것이다. 이 목적을 달성하기 위하여 책이 읽고 싶어지는 상황과 독서를 통하여 추구하고자 하는 발달과업이 감정형과 사고형, 본능형에 따라 다르게 나타나는지 조사하였다. 연구에 필요한 데이터는 수도권에 소재한 K대학의 재학생을 대상으로 수집하였으며, 통계에 이용된 설문서는 815부였다. 연구결과 감정형, 본능형, 사고형에 속하는 학생들의 독서성향이 다른 것으로 나타났다. 이 결과를 바탕으로 성격유형별 독서치료안을 개발할 수 있을 것이라고 본다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.361-367
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• 1998

The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.

• Journal of Electrical Engineering and Technology
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• 제3권2호
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• pp.177-183
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• 2008

The particle swarm optimization(PSO) has been shown to converge rapidly during the initial stages of a global search, but around global optimum, the search process becomes very slow. On the other hand, the genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient and can find the optimal solution more accurately and with less computational time. Optimal location of SVC using this hybrid PSO-GA algorithm is found. We have also found the optimal place of SVC using GA and PSO separately and have compared the results. It has been shown that the new algorithm is more effective and efficient. An IEEE 68 bus test system is used for simulation.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.204-209
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• 1999

일반적으로 신경회로망은 다중분광 영상의 카테고리 분류를 위해 많이 사용되나 다중분광 영상의 경우 카테고리간 명암도차가 얼마나지 않아 오차 수렴시간이 많이 걸리고 분류성능이 떨어진다. 이와 같은 문제점을 해결하기 위해 본 논문에서는 평활화 과정, 주된 골을 찾는 과정, 그리고 향상 과정으로 구성되는 새로운 영상 향상법을 제안하고, 제안한 방법으로 향상된 다중분광 영상을 신경회로망의 입력으로 하여 카테고리 분류하였다. 제안한 방법을 LANDSAT TM 영상에 적용한 결과 신경회로망의 오차 수렴속도가 빨라졌고, 분류 성능이 향상되었음을 확인할 수 있었다.

• Current Research on Agriculture and Life Sciences
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• 제2권
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• pp.98-108
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• 1984

Generally, in case of constructing the embankments on the soft clay layers, one-dimensional consolidation settlement under the assumption of a middle position stress in a single layer makes a great difference with the integral value, ie. the final settlement. Consequently, to find how many equal segments of the soft clay layer are needed to converge into the integral value and which position should be taken as a position of mean stress, authors compared the theoretical value of the settlement due to one-dimensional consolidation with the practical value of the settlement due to two dimensional consolidation. The obtained results are as follows. 1) The practical value of the two-dimensional consolidation settlement can be estimated by the 74-83% theoretical value of the one-dimensional consolidation settlement. 2) When the soft clay layer was cut into 8-16 equal segments according to the depth, one-dimensional consolidation settlement converge into the integral value. 3) Assuming a total soft clay layer as a single one, the depth of a mean stress position is 0.29-0.37 of the thickness of the total soft clay layer. 4) The Hyperbola Method which presumes the long-term settlement from the short-term practical value of settlement is credible, because all practical value of the settlement are in safe side of the standard error of estimation and the correlation coefficient is up to 0.95.

• 공업화학
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• 제29권6호
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• pp.789-798
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• 2018

고분자전해질 연료전지(PEMFC)의 성능을 예측할 수 있는 empirical equation의 역할이 중요하게 대두되고 있다. 본 연구에서는 polarization curve에서 activation loss, ohmic loss, mass transfer loss 영역을 분리하였고, 현재까지 개발된 model 중 Kim의 model과 Hao의 model을 선정하여 각 영역의 fitting을 시행하였다. 온도, 압력, 산소 농도 및 막 두께를 운전변수로 설정하여 조건 변화에 대한 각 loss의 변화를 비교하였다. 기존 model은 전반적으로 좋은 fitting 정확도를 보였지만, 분리된 loss 영역에서는 부정확한 fitting 결과를 보이기도 하였다. 연료전지 성능 예측의 정확도를 개선하기 위하여 converge coefficient를 도입한 새로운 model을 제안하였다. 본 연구에서 제안한 model을 연료전지 성능 예측에 적용한 경우에 신뢰도 평가에서 개선된 결과를 얻을 수 있었다.