• Architectural research
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• 제17권1호
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• pp.31-40
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• 2015

A simple force-finding process based on an optimization technique is proposed for tensegrity structures. For this purpose, the inverse problem of form-finding process is formulated. Therefore, the position vector of nodes and element connectivity information are provided as priori. Several benchmark tests are carried out to demonstrate the performance of the present force-finding process. In particular, the force density distributions of simplex tensegrity are thoroughly investigated with the important parameters such as the radius, height and twisting angle of simplex tensegrity. Finally, the force density distribution of arch tensegrity is produced by using the present force-finding process for a future reference solution.

• 제어로봇시스템학회논문지
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• 제19권6호
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• pp.527-533
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• 2013

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

• Structural Engineering and Mechanics
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• 제62권6호
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• pp.709-717
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• 2017

This paper develops a two-stage method for structural damage identification by using modal data. First, the Residual Force Vector (RFV) is introduced to detect any potentially damaged elements of structures. Second, data of the frequency domain are used to build up the objective function, and then the Imperialist Competitive Algorithm (ICA) is utilized to estimate damaged extents. ICA is a heuristic algorithm with simple structure, which is easy to be implemented and it is effective to deal with high-dimension nonlinear optimization problem. The advantages of this present method are: (1) Calculation complexity can be decreased greatly after eliminating many intact elements in the first step. (2) Robustness, ICA ensures the robustness of the proposed method. Various damaged cases and different structures are investigated in numerical simulations. From these results, anyone can point out that the present algorithm is effective and robust for structural damage identification and is also better than many other heuristic algorithms.

• Steel and Composite Structures
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• 제25권4호
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• pp.485-496
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• 2017

In this paper, damage detection has been introduced as an optimization problem and a two-step method has been proposed that can detect the location and severity of damage in truss structures precisely and reduce the volume of computations considerably. In the first step, using the residual force vector concept, the suspected damaged members are detected which will result in a reduction in the number of variables and hence a decrease in the search space dimensions. In the second step, the precise location and severity of damage in the members are identified using the genetic algorithm and the results of the first step. Considering the reduced search space, the algorithm can find the optimal points (i.e. the solution for the damage detection problem) with less computation cost. In this step, the Efficient Correlation Based Index (ECBI), that considers the structure's first few frequencies in both damaged and healthy states, is used as the objective function and some examples have been provided to check the efficiency of the proposed method; results have shown that the method is innovatively capable of detecting damage in truss structures.

• ETRI Journal
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• 제31권2호
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• pp.121-128
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• 2009

In this paper, we describe a fixed-threshold sequential minimal optimization (FSMO) for structured SVM problems. FSMO is conceptually simple, easy to implement, and faster than the standard support vector machine (SVM) training algorithms for structured SVM problems. Because FSMO uses the fact that the formulation of structured SVM has no bias (that is, the threshold b is fixed at zero), FSMO breaks down the quadratic programming (QP) problems of structured SVM into a series of smallest QP problems, each involving only one variable. By involving only one variable, FSMO is advantageous in that each QP sub-problem does not need subset selection. For the various test sets, FSMO is as accurate as an existing structured SVM implementation (SVM-Struct) but is much faster on large data sets. The training time of FSMO empirically scales between O(n) and O($n^{1.2}$), while SVM-Struct scales between O($n^{1.5}$) and O($n^{1.8}$).

• 대한기계학회논문집A
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• 제26권6호
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• pp.987-992
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• 2002

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. In this paper, pareto-based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. This algorithm is based on Continuous Evolutionary Algorithms to solve single objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche-formation method fur fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto-optimal tradeoff surface. Finally, the validity of this method has been demonstrated through a numerical example.

• 지능정보연구
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• 제3권2호
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• pp.1-9
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• 1997

본 논푼에서는 경수로 원자력 발전소의 사용 후 핵연료를 중수로의 핵연료로 재사용하기 위해 사용 후 경수로 핵연료의 최적 조합을 찾는데 진화 알고리즘(Evolutionary Algorithm)을 이용하여 해결해 보고자 한다. 진화 알고리즘은 대규모 문제 공간에서 최적화 문제를 해결하는데 적합한 알고리즘이다. 사용 후 경수로 핵연료에는 중수로에서 사용할 수 있는 유용한 원자들을 많이 포함하고 있지만 핵연료 봉마다 그 함량이 다양하고, 중수로 연료가 되기 위한 제약 조건 때문에 최적 조합 전략이 펼요하다. 사용후 핵연료의 조합 문제는 알고리즘 분야에서 대표적인 조합 최적화 문제인 0/1 Knapsack문제와 같이 Non-Polynomial (NP) Complete문제에 해당한다. 이러한 문제를 해결하기 위해셔는 고전적언 전화 알고리즘의 전략에 기반하여 랜덤 연산자를 이용하되 평가 함수 값이 좋은 방향으로만 탐색을 수행하는 방법이 있으나 이것은 탐색의 효율면에셔 좋지 않다. 따라서 본 연구에서는 벡터 연산자를 이용하여 최적의 해를 보다 빨리 얻을 수 있는 휴리스틱을 사용하는 방법을 제안한다. 본 논문에서는 경수로 핵연료 조합 문제 영역의 모든 지식을 벡터화하여 벡터의 연산만으로 가능성 검사, 해를 평가 하는 방법을 소개한다. 또한 벡터 휴리스틱이 고전적인 진화 알고리즘에 비해 어느 정도의 성능을 보이는지 비교한다.

• Management Science and Financial Engineering
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• 제19권1호
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• pp.25-28
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• 2013

Consider the inverse bin-packing number problem. Given a set of items and a prescribed number K of bins, the inverse bin-packing number problem, IBPN for short, is concerned with determining the minimum perturbation to the item-size vector so that all the items can be packed into K bins or less. It is known that this problem is NP-hard (Chung, 2012). In this paper, we investigate some special cases of IBPN that can be solved in polynomial time. We propose an optimal algorithm for solving the IBPN instances with two distinct item sizes and the instances with large items.

• Journal of Power Electronics
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• 제17권5호
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• 대한산업공학회지
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• 제22권4호
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• pp.533-549
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• 1996

This paper is concerned with a sequencing problem in mixed model assembly lines, which is important to efficient utilization of the lines. In the problem, we deal with the two objectives of minimizing the risk of stoppage and leveling part usage, and consider sequence-dependent setup time. In this paper, we present a genetic algorithm(GA) suitable for the multi-objective optimization problem. The aim of multi-objective optimization problems is to find all possible non-dominated solutions. The proposed algorithm is compared with existing multi-objective GAs such as vector evaluated GA, Pareto GA, and niched Pareto GA. The results show that our algorithm outperforms the compared algorithms in finding good solutions and diverse non-dominated solutions.