• 제목/요약/키워드: Real-time Optimization

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실수코딩 유전자알고리즘을 이용한 고정번지식 ATC 프레임의 토폴로지 최적화에 관한 연구 (A Study on the Topology Optimization of the fixed Address Type ATC frame Using a Real Number Coding Genetic Algorithm)

  • 허영진;임상헌;이춘만
    • 한국정밀공학회지
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    • 제21권9호
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    • pp.174-181
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    • 2004
  • Recently, many studies have been undergoing to reduce working time in field of machine tool. There are two ways of reducing working time to reduce actual working time by heighten spindle speed and to reduce stand-by time by shortening tool exchange time. Auto tool changer belongs to latter case. Fixed address type auto tool changer can store more number of tools in small space than magazine transfer Ope and can shorten tool exchange time. This study focuses on the topology optimization to reduce the weight of the fixed address type ATC. The optimization program using a real number coding genetic algorithm is developed and is applied to the 10-bar truss optimization problem to verify the developed program. And, it is shown that the developed program gives better results than other methods. Finally, The developed program applied to optimize the fixed address type ATC.

최적화 기반 인간 팔꿈치 관절각 실시간 추출 방법 (Optimization-based Real-time Human Elbow Joint Angle Extraction Method)

  • 최영진;유현재
    • 제어로봇시스템학회논문지
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    • 제14권12호
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    • pp.1278-1285
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    • 2008
  • An optimization-based real-time joint angle extraction method of human elbow is proposed by processing the biomedical signal of surface EMG (electromyogram) measured at the center point of biceps brachii. The EMG signal is known as non-stationary (time-varying) signal, but we assume that it is quasi-stationary because a physical or physiological system has limitations in the rate at which it can change its characteristics. Based on the assumption, a pre-processing method to obtain pre-angle values from raw EMG signal is firstly suggested, and then an optimization method to minimize the error between the pre-angle and real joint angle is proposed in this paper. Finally, we suggest the experimental results showing the effectiveness of the proposed algorithm.

Real-Time Optimization for Mobile Robot Based on Algorithmic Control

  • Kobayashi, Tomoaki;Maenishi, Junichi;Imae, Joe;Zhai, Guisheng
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2005년도 ICCAS
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    • pp.2102-2107
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    • 2005
  • In this paper, a real-time optimization method for nonlinear dynamical systems is proposed. The proposed method is based on the algorithms of numerical solutions for optimal control problems. We deal with a real-time collision-free motion control of a nonholonomic mobile robot, which has input restrictions of actuators. The effectiveness of the algorithmic method is demonstrated through numerical and experimental results. The mobile robot which we have developed is able to avoid moving obstacles skillfully. Therefore the proposed controller works well in real time.

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A Holistic Approach to Optimizing the Lifetime of IEEE 802.15.4/ZigBee Networks with a Deterministic Guarantee of Real-Time Flows

  • Kim, Kang-Wook;Park, Myung-Gon;Han, Junghee;Lee, Chang-Gun
    • Journal of Computing Science and Engineering
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    • 제9권2호
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    • pp.83-97
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    • 2015
  • IEEE 802.15.4 is a global standard designed for emerging applications in low-rate wireless personal area networks (LR-WPANs). The standard provides beneficial features, such as a beacon-enabled mode and guaranteed time slots for realtime data delivery. However, how to optimally operate those features is still an open issue. For the optimal operation of the features, this paper proposes a holistic optimization method that jointly optimizes three cross-related problems: cluster-tree construction, nodes' power configuration, and duty-cycle scheduling. Our holistic optimization method provides a solution for those problems so that all the real-time packets can be delivered within their deadlines in the most energy-efficient way. Our simulation study shows that compared to existing methods, our holistic optimization can guarantee the on-time delivery of all real-time packets while significantly saving energy, consequently, significantly increasing the lifetime of the network. Furthermore, we show that our holistic optimization can be extended to take advantage of the spatial reuse of a radio frequency resource among long distance nodes and, hence, significantly increase the entire network capacity.

실시간 차량 경로 계획 문제의 연구 동향 (A Survey on the Real Time Vehicle Routing Problems)

  • 양병학
    • 대한안전경영과학회지
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    • 제10권1호
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    • pp.155-166
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    • 2008
  • During last two decades the transportation system has developed into very intelligent system with GIS, GPS and ITS. The practical transportation management system provides real time response module to manage the customer's order. We have surveyed research papers on the real time vehicle routing problem in last two decades to figure out the dynamic vehicle routing problem. The papers are classified by basic routing algorithms and by managing the dynamic events which are the order management, the routing re-optimization, the routing post-optimization and the waiting strategy.

실시간 탄도 궤적 목표물 추적을 위한 GPU 기반 병렬적 입자군집최적화 기법 (Parallelized Particle Swarm Optimization with GPU for Real-Time Ballistic Target Tracking)

  • 한윤호;이헌철;권혁훈;최원석;정보라
    • 대한임베디드공학회논문지
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    • 제17권6호
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    • pp.355-365
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    • 2022
  • This paper addresses the problem of real-time tracking a high-speed ballistic target. Particle filters can be considered to overcome the nonlinearity in motion and measurement models in the ballistic target. However, it is difficult to apply particle filters to real-time systems because particle filters generally require much computation time. This paper proposes an accelerated particle filter using graphics processing unit (GPU) for real-time ballistic target tracking. The real-time performance of the proposed method was tested and analyzed on a widely-used embedded system. The comparison results with the conventional particle filter on CPU (central processing unit) showed that the proposed method improved the real-time performance by reducing computation time significantly.

Task Assignment Strategies for a Complex Real-time Network System

  • Kim Hong-Ryeol;Oh Jae-Joon;Kim Dae-Won
    • International Journal of Control, Automation, and Systems
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    • 제4권5호
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    • pp.601-614
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    • 2006
  • In this paper, a study on task assignment strategies for a complex real-time network system is presented. Firstly, two task assignment strategies are proposed to improve previous strategies. The proposed strategies assign tasks with meeting end-to-end real-time constraints, and also with optimizing system utilization through period modulation of the tasks. Consequently, the strategies aim at the optimizationto optimize of system performance with while still meeting real-time constraints. The proposed task assignment strategies are devised using the genetic algorithmswith heuristic real-time constraints in the generation of new populations. The strategies are differentiated by the optimization method of the two objectives-meeting end-to-end real-time constraints and optimizing system utilization: the first one has sequential genetic algorithm routines for the objectives, and the second one has one multiple objective genetic algorithm routine to find a Pareto solution. Secondly, the performances of the proposed strategies and a well-known existing task assignment strategy using the BnB(Branch and Bound) optimization are compared with one other through some simulation tests. Through the comparison of the simulation results, the most adequate task assignment strategies are proposed for some as system requirements-: the optimization of system utilization, the maximization of running tasktasks, and the minimization of the number of network node nodesnumber for a network system.

Time-Predictable Java Dynamic Compilation on Multicore Processors

  • Sun, Yu;Zhang, Wei
    • Journal of Computing Science and Engineering
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    • 제6권1호
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    • pp.26-38
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    • 2012
  • Java has been increasingly used in programming for real-time systems. However, some of Java's features such as automatic memory management and dynamic compilation are harmful to time predictability. If these problems are not solved properly then it can fundamentally limit the usage of Java for real-time systems, especially for hard real-time systems that require very high time predictability. In this paper, we propose to exploit multicore computing in order to reduce the timing unpredictability that is caused by dynamic compilation and adaptive optimization. Our goal is to retain high performance comparable to that of traditional dynamic compilation, while at the same time, obtain better time predictability for Java virtual machine (JVM). We have studied pre-compilation techniques to utilize another core more efficiently, preoptimization on another core (PoAC) scheme to replace the adaptive optimization system (AOS) in Jikes JVM and the counter based optimization (CBO). Our evaluation reveals that the proposed approaches are able to attain high performance while greatly reducing the variation of the execution time for Java applications.

발전정지와 교환방법을 적용한 실시간급전문제 최적화 알고리즘 (Optimization Algorithm for Real-time Load Dispatch Problem Using Shut-off and Swap Method)

  • 이상운
    • 한국인터넷방송통신학회논문지
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    • 제17권4호
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    • pp.219-224
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    • 2017
  • 경제급전 최적화 문제를 해결하는 결정론적인 알고리즘에 존재하지 않아 지금까지는 비결정론적인 휴리스틱 알고리즘들이 제안되고 있다. 이와 더불어 실시간 급전문제에 대한 연구는 거의 없는 실정이다. 본 논문은 발전정지 개념을 도입하여 실시간 급전의 최적화 문제를 풀 수 있는 알고리즘을 제안하였다. 제안된 알고리즘은 단위 발전량당 최대 비용이 소요되는 발전기는 발전을 중지시키는 기준을 적용하였다. 본 논문에서 제안한 발전정지 기준은 발전비용함수에서 밸브효과에 따른 비선형 절대치 함수를 제외한 2차 함수만을 대상으로 하였다. 경제급전 문제의 시험사례로 빈번히 활용되고 있는 데이터에 대해 제안된 알고리즘을 적용한 결과 기존 알고리즘들의 해를 크게 감소시킬 수 있었다.

A Random Deflected Subgradient Algorithm for Energy-Efficient Real-time Multicast in Wireless Networks

  • Tan, Guoping;Liu, Jianjun;Li, Yueheng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권10호
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    • pp.4864-4882
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    • 2016
  • In this work, we consider the optimization problem of minimizing energy consumption for real-time multicast over wireless multi-hop networks. Previously, a distributed primal-dual subgradient algorithm was used for finding a solution to the optimization problem. However, the traditional subgradient algorithms have drawbacks in terms of i) sensitivity to iteration parameters; ii) need for saving previous iteration results for computing the optimization results at the current iteration. To overcome these drawbacks, using a joint network coding and scheduling optimization framework, we propose a novel distributed primal-dual Random Deflected Subgradient (RDS) algorithm for solving the optimization problem. Furthermore, we derive the corresponding recursive formulas for the proposed RDS algorithm, which are useful for practical applications. In comparison with the traditional subgradient algorithms, the illustrated performance results show that the proposed RDS algorithm can achieve an improved optimal solution. Moreover, the proposed algorithm is stable and robust against the choice of parameter values used in the algorithm.