• 소음진동
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• 제7권2호
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• pp.301-310
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• 1997

This paper presents active noise control method to form the near-field quiet zone for passengers in an automobile. The actuator model including interior acoustic plant, speaker and amplifier is experimentally identified in forms of auto-regressive and moving average by means of least mean square algorithm, The digital controller is composed of the regulator and Kalman filter to be designed based on LQG (linear quadratic gaussian). If the actuator model is prefiltered with digital filter to be properly designed for concentrating control performance index on the frequency band of primary noise source, LQG design approach can be effectively applied for the design of headset controller. Experimental results demonstrate that near-field quiet zone showing about 10dB noise reduction at microphone position can be formed using the headset located at passenger seat.

• Journal of Communications and Networks
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• 제7권3호
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• pp.385-393
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• 2005

In this paper, we propose a simple algorithm to adaptively adjust the value of dupthresh, the duplicate acknowledgement threshold that triggers the transmission control protocol (TCP) fast retransmission algorithm, to improve the TCP performance in a network environment with persistent packet reordering. Our algorithm uses an exponentially weighted moving average (EWMA) and the mean deviation of the lengths of the reordering events reported by a TCP receiver with the duplicate selective acknowledgement (DSACK) extension to estimate the value of dupthresh. We also apply an adaptive upper bound on dupthresh to avoid the retransmission timeout events. In addition, our algorithm includes a mechanism to exponentially reduce dupthresh when the retransmission timer expires. With these mechanisms, our algorithm is capable of converging to and staying at a near-optimal interval of dupthresh. The simulation results show that our algorithm improves the protocol performance significantly with minimal overheads, achieving a greater throughput and fewer false fast retransmissions.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.52.2-52
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• 2001

We propose a new optimization method to improve the productivity of dispenser machines in PCB assembly lines. The optimization problem for multi-nozzle dispensers is formulated as a variant TSP. A genetic algorithm is applied to the problem to get a near-optimal solution. Chromosome and some operator are newly defined to implement the genetic algorithm for multi-nozzle dispensers. Simulation results are then presented to verify the usefulness of the method.

• Nuclear Engineering and Technology
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• 제54권9호
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• pp.3283-3292
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• 2022

Based on the Deep Q-Network(DQN) algorithm of reinforcement learning, an active fault-tolerance method with incremental action is proposed for the control system with sensor faults of the once-through steam generator(OTSG). In this paper, we first establish the OTSG model as the interaction environment for the agent of reinforcement learning. The reinforcement learning agent chooses an action according to the system state obtained by the pressure sensor, the incremental action can gradually approach the optimal strategy for the current fault, and then the agent updates the network by different rewards obtained in the interaction process. In this way, we can transform the active fault tolerant control process of the OTSG to the reinforcement learning agent's decision-making process. The comparison experiments compared with the traditional reinforcement learning algorithm(RL) with fixed strategies show that the active fault-tolerant controller designed in this paper can accurately and rapidly control under sensor faults so that the pressure of the OTSG can be stabilized near the set-point value, and the OTSG can run normally and stably.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1993년도 제3회 정기총회 및 추계학술발표회
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• pp.16-16
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• 1993

The efficient use of capital intensive FMS requires determination of effective dispatching priority with which the parts of the selected part types are to be inputed into the system. This paper presents a simulation-optimization approach to find an appropriate dispatching priority. The study is based on a detailed simulator for a module-type commercial FMS, Specifically, after presenting the basic configuration and fundamental control logic of the system together with its main characteristics as a special type of a job shop, an algorithm is presented which combines simulated annealing and simulation to explore a dispatching priority of operations that minimizes the total tardiness, Computational performance of the algorithm shows that good solutions can be obtained within a reasonable amount of computations. The paper also compares the performance of the "optimal" or near optimal dispatching priority generated by the proposed algorithm with those generated by standard dispatching rules such as SPT, EDD and SLACK.

• 한국지능시스템학회논문지
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• 제23권2호
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• pp.166-171
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• 2013

The method proposed in this paper can improve the performance of the Boosting algorithm in machine learning. The proposed Boundary AdaBoost algorithm can make up for the weak points of Normal binary classifier using threshold boundary concepts. The new proposed boundary can be located near the threshold of the binary classifier. The proposed algorithm improves classification in areas where Normal binary classifier is weak. Thus, the optimal boundary final classifier can decrease error rates classified with more reasonable features. Finally, this paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Boundary AdaBoost in a simulation experiment of pedestrian detection using 10-fold cross validation.

• Structural Engineering and Mechanics
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• 제83권4호
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

• 전자공학회논문지C
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• 제36C권1호
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• pp.66-72
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• 1999

본 논문은 개체군 변환 알고리즘의 새로운 수명할당방식(allocation method)을 제안한다. 이 방법으로 개체군의 크기를 적합도(fitness)에 따라서 조절할 수 있다. 개체군(population)의 크기는 최적해(optimal value) 근처로 갈수록 안정된 상태로 가게 된다. 유전자 코딩에 있어서는 이배체(diploidy) 방식을 사용하였다. 시뮬레이션을 통하여 새로운 수명 할당 방식이 개체군의 크기를 조절할 수 있음을 확인한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.386-386
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• 2000

Cell mapping is a powerful computational technique for analyzing the global behavior of nonlinear dynamic systems. It simplifies the task of analyzing a continuous phase space by partitioning it into a finite number of disjoint cells and approximating system trajectories as cell transitions. A cell map for the system is then constructed based on the allowable control actions. Next search algorithms are employed to identify the optimal or near-optimal sequence(s) of control actions required to drive the system from each cell to the target cell by an "unravelling algorithm." Errors resulting from the cell center-point approximation could be reduced and eliminated by fuzzifying the bonders of cells. The dynamic system control method based on the cell mapping has been demonstrated for a motor control problem.l problem.

• 한국지능시스템학회논문지
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• 제12권3호
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.