• 대한기계학회논문집A
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• 제26권6호
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• pp.1092-1098
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• 2002

This paper discusses the composition of the theory of reinforcement teaming, which is applied in real-time teaming, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line teaming method. The individuals are reduced in order to team the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It is possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because of the teaming process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2000년도 춘계학술대회 논문집
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• pp.324-329
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• 2000

Object of a real-time system, that performs exact information based on the real-time constraint. is required for an improvement of high reliability. The fault-tolerant task scheduling strategy of multiprocessor as using a distributed memory based on a hardware redundancy can be improved into a high reliability of the real-time system. Therefore, this paper is shown to analyze the reliability of the system by using the transfer parameter and make the modeling in reference to a minimization of the fault-tolerant task scheduling strategy which uses a percentage of task missing and deadline parameter based on optimization task size.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.105-109
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• 2001

This paper discuss the real-time self-tuning learning control based on evolutionary computation, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• 대한기계학회논문집A
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• 제27권9호
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• pp.1463-1468
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• 2003

This paper presented the real-time self-tuning learning control based on evolutionary computation, which proves its superiority in finding of the optimal solution at the off-line learning method. The individuals of the populations are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations is proposed. It is possible to control the control object slightly varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied each sampling time because the learning process of an estimation, selection, mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• KIEE International Transactions on Power Engineering
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• 제5A권2호
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• pp.177-185
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• 2005

This paper proposes an expert system that can enhance the accuracy of real-time bus reconfiguration strategy by adopting the local minimum tree search method and that can minimize the spreading effect of the fault by considering the operating condition when a main transformer fault occurs in an automated substation. The local minimum tree search method is used to expand the best-first search method. This method has the advantage that it can improve the solution performance within the limits of the real-time condition. The inference strategy proposed expert system consists of two stages. The first stage determines the switching candidate set by searching possible switching candidates starting from the main transformer or busbar related to the event. The second stage determines the rational real-time bus reconfiguration strategy based on heuristic rules from the obtained switching candidate set. Also, this paper proposes generalized distribution substation modeling using graph theory, and a substation database based on the study results is designed.

• 한국자동차공학회논문집
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• 제24권2호
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• pp.127-136
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• 2016

The camera based object detection systems should satisfy the recognition performance as well as real-time constraints. Particularly, in safety-critical systems such as Autonomous Emergency Braking (AEB), the real-time constraints significantly affects the system performance. Recently, multi-core processors and system-on-chip technologies are widely used to accelerate the object detection algorithm by distributing computational loads. However, due to the advanced hardware, the complexity of system architecture is increased even though additional hardwares improve the real-time performance. The increased complexity also cause difficulty in migration of existing algorithms and development of new algorithms. In this paper, to improve real-time performance and design complexity, a task scheduling strategy is proposed for visual object tracking systems. The real-time performance of the vision algorithm is increased by applying pipelining to task scheduling in a multi-core processor. Finally, the proposed task scheduling algorithm is applied to crosswalk detection and tracking system to prove the effectiveness of the proposed strategy.

• 한국정보통신학회논문지
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• 제6권6호
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• pp.943-950
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• 2002

실시간 전략 게임은 인간 혹은 컴퓨터를 상대로 하는 게임 장르이다. 이것은 턴방식의 컴퓨터게임과는 게임 진행 방식이 상이하다. 체스와 같은 턴방식은 오직 한 명의 플레이어의 동작을 허용하지만 실시간 전략게임은 복수의 플레이어의 동시다발적인 동작을 허용한다. 따라서 실시간 전략게임에서는 게임 내 유닛들의 이동경로는 자원채취, 건물건설, 그리고 전투 프로세스들의 처리를 위한 충분한 시간을 확보하기 위해 신속히 계산되어야 한다. 경로계산에 필요한 메모리, 탐색공간, 유닛들의 반응속도를 향상시키려는 여러 접근방식들이 소개되고 있다. 현재의 경로계산 알고리즘들은 최상 경로계산에 치중한 나머지 실시간 전략게임에서의 계산 과부하 문제를 고려하고 있지 않다 .이런 점에서 본 논문은 탐색공간을 줄이고 유닛들의 반응속도를 높이는 DHA*(Dynamic Heuristic Af) 알고리듬을 제안하고, 기존 A* 알고리듬과 비교하여 본 제안 DHA* 알고리듬의 성능이 우수함을 입증한다.

• 국제초고층학회논문집
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• 제3권2호
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• pp.155-165
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• 2014

This study measured and compared the variation of ventilation rate and fan energy consumption according to various control strategies after installing wireless sensor-based pilot ventilation system in order to verify the applicability of demand-controlled ventilation (DCV) strategy that was efficient ventilation control strategy for underground parking lot. The underground parking lot pilot ventilation system controlled the ventilation rate by directly or indirectly tracking the traffic load in real-time after sensing data, using vehicle detection sensors and carbon monoxide (CO) and carbon dioxide ($CO_2$) sensor. The ventilation system has operated for 9 hours per a day. It responded real-time data every 10 minutes, providing ventilation rate in conformance with the input traffic load or contaminant level at that time. A ventilation rate of pilot ventilation system can be controlled at 8 levels. The reason is that a ventilation unit consists of 8 high-speed nozzle jet fans. This study proposed vehicle detection sensor based demand-controlled ventilation (VDS-DCV) strategy that would accurately trace direct traffic load and CO sensor based demand-controlled ventilation (CO-DCV) strategy that would indirectly estimate traffic load through the concentration of contaminants. In order to apply DCV strategy based on real-time traffic load, the minimum required ventilation rate per a single vehicle was applied. It was derived through the design ventilation rate and total parking capacity in the underground parking lot. This is because current ventilation standard established per unit floor area or unit volume of the space made it difficult to apply DCV strategy according to the real-time variation of traffic load. According to the results in this study, two DCV strategies in the underground parking lot are considered to be a good alternative approach that satisfies both energy saving and healthy indoor environment in comparison with the conventional control strategies.

• 전기학회논문지
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• 제63권5호
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• pp.644-651
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• 2014

The renewable energy system and the real-time pricing can provide the significant economic advantage for end-user of residential house. However, according to recent studies, high initial cost of renewable energy system such as photovoltaic (PV) system and lack of suitable load control methods adjusting electric power consumption in response to time-varying price are regarded as the major obstruction for introduction of renewable energy system and real-time pricing in residental household. In this paper, we propose automated optimal load control strategy which aim to achieve not only minimizing the electricity cost but also the increase in the utilization rates of PV generation power of residential PV house in real-time pricing environment. Simulation results show that our proposed optimal load control strategy leads to significant reduction in the electricity costs and increase in the utilization rates of power generated by PV system in comparison with the conventional PV house. Therefore, the proposed optimal load control strategy can provide more economic benefit to end-user.

• Smart Structures and Systems
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• 제31권5호
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• pp.437-454
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• 2023

Real-time hybrid simulation (RTHS), which has the advantages of a substructure pseudo-dynamic test, is widely used to investigate the rate-dependent mechanical response of structures under earthquake excitation. However, time delay in RTHS can cause inaccurate results and experimental instabilities. Thus, this study proposes a model-based adaptive control strategy using a Kalman filter (KF) to minimize the time delay and improve RTHS stability and accuracy. In this method, the adaptive control strategy consists of three parts-a feedforward controller based on the discrete inverse model of a servohydraulic actuator and physical specimen, a parameter estimator using the KF, and a feedback controller. The KF with the feedforward controller can significantly reduce the variable time delay due to its fast convergence and high sensitivity to the error between the desired displacement and the measured one. The feedback control can remedy the residual time delay and minimize the method's dependence on the inverse model, thereby improving the robustness of the proposed control method. The tracking performance and parametric studies are conducted using the benchmark problem in RTHS. The results reveal that better tracking performance can be obtained, and the KF's initial settings have limited influence on the proposed strategy. Virtual RTHSs are conducted with linear and nonlinear physical substructures, respectively, and the results indicate brilliant tracking performance and superb robustness of the proposed method.