• 제목/요약/키워드: evolution task

검색결과 73건 처리시간 0.039초

A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics

  • Xi, Yaoyi;Li, Bicheng;Liu, Yang
    • Journal of Computing Science and Engineering
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    • 제9권2호
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    • pp.73-82
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    • 2015
  • Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

강화학습과 분산유전알고리즘을 이용한 자율이동로봇군의 행동학습 및 진화 (Behavior leaning and evolution of collective autonomous mobile robots using reinforcement learning and distributed genetic algorithms)

  • 이동욱;심귀보
    • 전자공학회논문지S
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    • 제34S권8호
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    • pp.56-64
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    • 1997
  • In distributed autonomous robotic systems, each robot must behaves by itself according to the its states and environements, and if necessary, must cooperates with other orbots in order to carray out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforement learning having delayed reward ability and distributed genectic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the perfodrmance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper, we verify the effectiveness of the proposed method by applying it to cooperative search problem.

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SVM을 이용한 군집로봇의 행동학습 및 진화 (Behavior Learning and Evolution of Swarm Robot System using Support Vector Machine)

  • 서상욱;양현창;심귀보
    • 한국지능시스템학회논문지
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    • 제18권5호
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    • pp.712-717
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    • 2008
  • 군집 로봇시스템에서 개개의 로봇은 스스로 주위의 환경과 자신의 상태를 스스로 판단하여 행동하고, 필요에 따라서는 다른 로봇과 협조를 통하여 어떤 주어진 일을 수행할 수 있어야 한다. 따라서 개개의 로봇은 동적으로 변화하는 환경에 잘 적응할 수 있는 학습과 진화능력을 갖는 것이 필수적이다. 본 논문에서는 구조적 위험 최소화를 기반으로 한 SVM을 이용 한 강화학습과 분산유전알고리즘을 이용한 새로운 자율이동로봇의 행동학습 및 진화방법을 제안한다. 또한 개개의 로봇이 통신을 통하여 염색체를 교환하는 분산유전알고리즘은 각기 다른 환경에서 학습한 우수한 염색체로부터 자신의 능력을 향상시킨다. 특히 본 논문에서는 진화의 성능을 향상시키기 위하여 SVM을 기반으로 한 강화학습의 특성을 이용한 선택 교배 방법을 채택하였다.

Q-learning과 Cascade SVM을 이용한 군집로봇의 행동학습 및 진화 (Behavior Learning and Evolution of Swarm Robot System using Q-learning and Cascade SVM)

  • 서상욱;양현창;심귀보
    • 한국지능시스템학회논문지
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    • 제19권2호
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    • pp.279-284
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    • 2009
  • 군집 로봇시스템에서 개개의 로봇은 스스로 주위의 환경과 자신의 상태를 스스로 판단하여 행동하고, 필요에 따라서는 다른 로봇과 협조를 통하여 어떤 주어진 일을 수행할 수 있어야 한다. 따라서 개개의 로봇은 동적으로 변화하는 환경에 잘 적응할 수 있는 학습과 진화능력을 갖는 것이 필수적이다. 본 논문에서는 SVM을 여러 개 이용한 강화학습과 분산유전알고리즘을 이용한 새로운 자율이동로봇의 행동학습 및 진화학습을 제안한다. 또한 개개의 로봇이 통신을 통하여 염색체를 교환하는 분산유전알고리즘은 각기 다른 환경에서 학습한 우수한 염색체로부터 자신의 능력을 향상시킨다. 특히 본 논문에서는 진화의 성능을 향상시키기 위하여 Cascade SVM을 기반으로 한 강화학습의 특성을 이용한 선택 교배방법을 채택하였다.

Propeller Perforator Flaps in Distal Lower Leg: Evolution and Clinical Applications

  • Georgescu, Alexandru V.
    • Archives of Plastic Surgery
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    • 제39권2호
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    • pp.94-105
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    • 2012
  • Simple or complex defects in the lower leg, and especially in its distal third, continue to be a challenging task for reconstructive surgeons. A variety of flaps were used in the attempt to achieve excellence in form and function. After a long evolution of the reconstructive methods, including random pattern flaps, axial pattern flaps, musculocutaneous flaps and fasciocutaneous flaps, the reappraisal of the works of Manchot and Salmon by Taylor and Palmer opened the era of perforator flaps. This era began in 1989, when Koshima and Soeda, and separately Kroll and Rosenfield described the first applications of such flaps. Perforator flaps, whether free or pedicled, gained a high popularity due to their main advantages: decreasing donor-site morbidity and improving aesthetic outcome. The use as local perforator flaps in lower leg was possible due to a better understanding of the cutaneous circulation, leg vascular anatomy, angiosome and perforasome concepts, as well as innovations in flaps design. This review will describe the evolution, anatomy, flap design, and technique of the main distally pedicled propeller perforator flaps used in the reconstruction of defects in the distal third of the lower leg and foot.

Optimal Trajectory Control for Robort Manipulators using Evolution Strategy and Fuzzy Logic

  • 박진현;김현식;최영규
    • 제어로봇시스템학회지
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    • 제1권1호
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    • pp.16-16
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    • 1995
  • Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

Optimal Trajectory Control for RobortManipulators using Evolution Strategy and Fuzzy Logic

  • Park, Jin-Hyun;Kim, Hyun-Sik;Park, Young-Kiu
    • Transactions on Control, Automation and Systems Engineering
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    • 제1권1호
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    • pp.16-20
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    • 1999
  • Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

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Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling

  • Tianhao Zhao;Linjie Wu;Di Wu;Jianwei Li;Zhihua Cui
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권4호
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    • pp.1100-1122
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    • 2023
  • Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems

  • Jin, Zilong;Zhang, Chengbo;Zhao, Guanzhe;Jin, Yuanfeng;Zhang, Lejun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제15권2호
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    • pp.383-403
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    • 2021
  • With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

소프트웨어 변경 이력의 최근 변경을 클래스 다이어그램으로 가시화하는 도구 (A Class Diagramming Tool for Visualizing the Latest Revision of Software Change History)

  • 심재경;조희태;박종열;이선아
    • 정보과학회 논문지
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    • 제45권2호
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    • pp.150-156
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    • 2018
  • 소프트웨어 가시화 연구는 개발자들이 소프트웨어 시스템을 이해하고 코드 변경을 수행할 때 도움을 줄 수 있다는 점에서 중요하다. 최근 제시된 상향식 소프트웨어 가시화 도구들은 개발자가 직접 작업하는 코드 정보만을 보여주는 이점으로 개발자들의 작업에 도움을 줄 수 있다는 효과를 입증하고 있다. 하지만 이러한 도구들은 개발자가 탐색한 코드만 한정되게 보여주는 약점이 있다. 본 논문은 상향식 가시화 도구에서 연관이 되는 코드 정보를 제공하여 코드 탐색을 돕기 위하여 소프트웨어 개정 이력을 클래스 다이어그램으로 보이는 도구를 제시한다. 제시 도구는 개발자들이 커밋한 코드 정보를 한 번의 클릭으로 클래스 다이어그램으로 보여줌으로써, 개발자들의 코드 변경에 대한 빠른 이해를 돕는다. 또한 본 논문은 사례 연구를 통하여 개발자들이 수일동안 지속적인 변경 작업을 수행할 때 제시 도구가 유용할 수 있음을 보인다.