• Journal of Electrical Engineering and Technology
• /
• 제10권3호
• /
• pp.877-887
• /
• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.124-127
• /
• 1997

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B lymphocyte(B cell), each environmental condition as an antigen, and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is simulated and suppressed by other robot using communication. Finally much simulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy.

• 한국지능시스템학회논문지
• /
• 제20권3호
• /
• pp.413-421
• /
• 2010

본 논문에서는 로봇 축구용 카메라를 사용하는 기존 경로계획의 제한적인 사항을 극복하기 위해서 컬러 인식법에 의한 경로계획방법을 제시한다. 제안된 연구에서는 움직이는 목표물이 스웜로봇과 멀리 있어도 로봇의 직선 시야를 기반으로 동료 로봇을 따라가며, 움직이는 목표물을 추적 할 수 있다. 제안된 포텐셜 필드는 동료 로봇과의 충돌과 장애물과의 충돌을 피하면서 스웜 로봇들이 움직이는 목표물을 향하여 이동하게 한다. 결국, 스웜 로봇들 사이의 시각적 도움에 의해 최종 목표물에 모든 스웜 로봇들이 도달하게 된다. 제안된 방법은 움직이는 파티클, 즉 점 로봇이 아닌 논홀로노믹 제한이 있는 유니 사이클 로봇들을 대상으로 자기 조직화 방법을 제시하기 때문에 실제 하드웨어 적용시 유용하다.

• Structural Engineering and Mechanics
• /
• 제47권4호
• /
• pp.513-530
• /
• 2013

In this paper, an improved version of particle swarm optimization based optimum design algorithm (IPSO) is presented for the steel grillage systems. The optimum design problem is formulated considering the provisions of American Institute of Steel Construction concerning Load and Resistance Factor Design. The optimum design algorithm selects the appropriate W-sections for the beams of the grillage system such that the design constraints are satisfied and the grillage weight is the minimum. When an improved version of the technique is extended to be implemented, the related results and convergence performance prove to be better than the simple particle swarm optimization algorithm and some other metaheuristic optimization techniques. The efficiency of different inertia weight parameters of the proposed algorithm is also numerically investigated considering a number of numerical grillage system examples.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권10호
• /
• pp.4987-5005
• /
• 2017

This paper introduces a new method to solve Similarity Recognition for Cloth Products, which is based on Fuzzy logic and Particle swarm optimization algorithm. For convenience, it is called the SRCPFP method hereafter. In this paper, the SRCPFP method combines Fuzzy Logic (FL) and Particle Swarm Optimization (PSO) algorithm to solve similarity recognition for cloth products. First, it establishes three features, length, thickness, and temperature resistance, respectively, for each cloth product. Subsequently, these three features are engaged to construct a Fuzzy Inference System (FIS) which can find out the similarity between a query cloth and each sampling cloth in the cloth database D. At the same time, the FIS integrated with the PSO algorithm can effectively search for near optimal parameters of membership functions in eight fuzzy rules of the FIS for the above similarities. Finally, experimental results represent that the SRCPFP method can realize a satisfying recognition performance and outperform other well-known methods for similarity recognition under considerations here.

• 제어로봇시스템학회논문지
• /
• 제16권1호
• /
• pp.61-68
• /
• 2010

This paper proposes networked swarm robotic systems with group based control scheme using spring damper impendence feature. The proposed algorithm is applied to keep system arrangement in unexpected situations based on the spring-damper impedance and fuzzy logic. Using the proposed scheme, each robot overcome collision problems efficiently. The structure of UBSR (UMPC Based Swarm Robot) system consists of user level, cognitive level, and executive level. This structure is designed to easily meet the different configuration requirements for other levels. Simulation results show an availability of the proposed method.

• 한국지능시스템학회논문지
• /
• 제16권1호
• /
• pp.72-78
• /
• 2006

군집 로봇시스템에서 협조 행동을 위기 위해서는 로봇의 센싱과 통신 기능은 필수적이다. 일반적으로 대역적 통신 시스템에서는 로봇의 대수가 증가하면 통신 자원의 제한과 정보의 범람이 발생한다. 따라서 이 경우 지역적 통신방법이 유리하다. 따라서 본 논문에서는 지역적 통신에 의한 정보의 전파 해석을 통하여 최적의 통신 반경을 결정하는 방법을 제안하고, 이를 이용하여 인공면역계에 기반한 군집 로봇시스템의 협조 전략과 군 행동 알고리즘을 제안한다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
• /
• pp.127-130
• /
• 1998

In this paper, we propose a method of cooperative control(T-cell modeling) and selection of group behavior strategy(B-cell modeling) based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-call respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based of clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• 전기학회논문지
• /
• 제57권12호
• /
• pp.2322-2329
• /
• 2008

In this study, we introduce the design methodology of an optimized fuzzy cascade controller with the aid of particle swarm optimization(PSO) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer(1st) controller and the inner(2nd) controller as two cascaded fuzzy controllers, and auto-tune the control parameters(scaling facrors) of each fuzzy controller using PSO. For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on PSO, is presented in comparison with the conventional PD cascade controller based on serial genetic alogritms.

• 산업경영시스템학회지
• /
• 제33권4호
• /
• pp.58-68
• /
• 2010

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.