• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.882-885
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• 2003

In this paper, nonlinear echo cancellation using a correlation LMS (CLMS) algorithm is proposed to cancel the undesired nonlinear echo signals generated in the hybrid system of the telephone network. In the telephone network, the echo signals may result the degradation of the network performance. Furthermore, digital to analog converter (DAC) and analog to digital converter (ADC) may be the source of the nonlinear distortion in the hybrid system. The adaptive filtering technique based on the nonlinear Volterra filter has been the general technique to cancel such a nonlinear echo signals in the telephone network. But in the presence of the double-talk situation, the error signal for tap adaptations will be greatly larger, and the near-end signal can cause any fluctuation of tap coefficients, and they may diverge greatly. To solve a such problem, the correlation LMS (CLMS) algorithm can be applied as the nonlinear adaptive echo cancellation algorithm. The CLMS algorithm utilizes the fact that the far-end signal is not correlated with a near-end signal. Accordingly, the residual error for the tap adaptation is relatively small, when compared to that of the conventional normalized LMS algorithm. To demonstrate the performance of the proposed algorithm, the DAC of hybrid system of the telephone network is considered. The simulation results show that the proposed algorithm can cancel the nonlinear echo signals effectively and show robustness under the double-talk situations.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.193-201
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• 2023

In this paper, we consider the maximum scatter traveling salesman problem (MSTSP), a travelling salesman problem (TSP) variant. The problem aims to maximize the minimum length edge in a salesman's tour that travels each city only once in a network. It is a very complicated NP-hard problem, and hence, exact solutions can be found for small sized problems only. For large-sized problems, heuristic algorithms must be applied, and genetic algorithms (GAs) are found to be very successfully to deal with such problems. So, this paper develops a hybrid GA (HGA) for solving the problem. Our proposed HGA uses sequential sampling algorithm along with 2-opt search for initial population generation, sequential constructive crossover, adaptive mutation, randomly selected one of three local search approaches, and the partially mapped crossover along with swap mutation for perturbation procedure to find better quality solution to the MSTSP. Finally, the suggested HGA is compared with a state-of-art algorithm by solving some TSPLIB symmetric instances of many sizes. Our computational experience reveals that the suggested HGA is better. Further, we provide solutions to some asymmetric TSPLIB instances of many sizes.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.12-22
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• 2024

The travelling salesman problem is very famous and very difficult combinatorial optimization problem that has several applications in operations research, computer science and industrial engineering. As the problem is difficult, finding its optimal solution is computationally very difficult. Thus, several researchers have developed heuristic/metaheuristic algorithms for finding heuristic solutions to the problem instances. In this present study, a new hybrid genetic algorithm (HGA) is suggested to find heuristic solution to the problem. In our HGA we used comprehensive sequential constructive crossover, adaptive mutation, 2-opt search and a new local search algorithm along with a replacement method, then executed our HGA on some standard TSPLIB problem instances, and finally, we compared our HGA with simple genetic algorithm and an existing state-of-the-art method. The experimental studies show the effectiveness of our proposed HGA for the problem.

• 대한전자공학회논문지SD
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• 제48권8호
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• pp.25-32
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• 2011

네트워크-온-칩(Networks-on-Chip, NoC)은 고도로 복잡해지고 있는 다중 프로세서 시스템-온-칩(Multi-Processor System-on-chip, MPSoC)에서의 버스 트래픽 문제를 해결할 핵심기술이나 전통적인 전기적 상호 연결 구조로는 머지않아 대역폭 및 전력소비 등의 한계에 직면할 것으로 예상된다. 이러한 문제를 해결하기 위해 광학적 상호연결과 전기적 상호연결을 같이 사용하는 하이브리드 광학 NoC기술이 최근 활발히 연구되고 있다. 대부분의 하이브리드 광학 NoC에서 전기적인 연결은 웜홀 스위칭(Wormhole switching)과 deterministic 알고리즘인 X-Y 라우팅 알고리즘을 사용하며, 광학적 버스 기반 데이터 전송을 위한 경로 설정 및 광학 라우터 설정을 한다. 광학적 연결에서는 서킷 스위칭(Circuit switching) 방식을 사용하며, 미리 설정된 경로 및 라우터를 이용하여 payload 데이터만 전송을 하게 된다. 그러나 기존에 발표된 하이브리드 광학 NoC같은 경우에는 한 번에 하나의 경로에서만 데이터를 전송 할 수 있다는 단점을 가지고 있어 성능 향상에 한계가 있다. 본 논문에서는 하이브리드 광학 NoC에서 동시에 여러 경로를 이용하여 데이터를 전송하기 위해 전기적인 연결에서 서킷 스위칭 방식과 적응적(adaptive) 알고리즘을 이용하는 새로운 라우팅 알고리즘을 제안하며, 적응적 알고리즘의 문제점인 livelock을 제거할 수 있는 방법 또한 제안한다. 모의실험은 전기적인 NoC, 그리고 웜홀 스위칭 방식의 기존 하이브리드 광학 NoC와 비교 수행 하였다. 그 결과 제안된 방식은 기존 하이브리드 광학 NoC에 비해 60%의 throughput 증가, 그리고 전기적 NoC와 비교했을 때 65%의 전력 감소를 보였다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 방송공학회논문지
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• 제20권2호
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• pp.283-299
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• 2015

본 논문에서는 MMT(MPEG Media Transport) 표준과 MPEG-DASH(Dynamic Adaptive Streaming over HTTP)를 결합한 새로운 방송 서비스들을 제안한다. MMT 표준은 IP 친화적이고 방송 물리 채널과 인터넷을 동시에 사용하는 하이브리드 방송에 적합한 기능들을 제공하는 차세대 멀티미디어 전송 표준이다. MPEG-DASH는 유무선 인터넷에서 망 상태 및 단말 환경에 동적, 적응적으로 미디어 스트리밍이 가능한 기능을 제공한다. 본 논문에서 제안하는 방송 서비스들의 시나리오를 설명하고, MMT와 MPEG-DASH의 결합을 통해서 매우 다양한 하이브리드 서비스가 쉽게 실현 가능하다는 것을 보였다. MMT 표준의 내용을 바탕으로 PC 상에서 시험 콘텐츠를 제작하고 수신기 백엔드 소프트웨어를 구현하여 실험을 시행함으로써 이러한 시나리오들이 실현 가능함을 검증하였다.

• 대한전자공학회논문지TC
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• 제42권2호
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• pp.49-59
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• 2005

본 논문에서는 이동 수신 및 다중경로 환경에서의 ATSC(advanced television system committee) DTV(digital television) 수신기의 성능개선을 위한 적응 하이브리드 빔형성기를 제안한다. ATSC DTV 수신기는 이동 수신 환경에서 동적 다중 경로와 도플러 천이로 인하여 수신 성능이 심각하게 열화 된다. 본 논문에서는 입사각 추정 기반의 Capon 빔형성 기법과 훈련열기반의 LMS(lease mean square) 빔형성 알고리듬을 혼합한 CLMS (Capon and LMS) 빔형성 기법을 제안한다. 제안된 CLMS 빔형성 기법은 동적 다중경로 신호를 효율적으로 제거하고, 수신기의 이동 수신시 도플러 천이에 의해서 발생되는 위상 왜곡에 대한 보상이 가능하다. CLMS 빔형성기와 직렬 연결된 등화기는 어레이 출력의 잔존하는 다중경로 간섭신호들을 효율적으로 제거해줌으로써 좀더 향상된 수신 성능을 보장할 수 있다. 모의 실험을 통하여 CLMS 빔형성 기법과 기존의 빔형성 기법 및 등화기 결합 시스템의 성능을 분석한다.

• Smart Structures and Systems
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• 제25권5호
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• pp.569-580
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• 2020

The identification of delays and delay compensation are critical problems in real-time hybrid simulations (RTHS). Conventional delay compensation methods are mostly based on the assumption of a constant delay. However, the system delay may vary during tests owing to the nonlinearity of the loading system and/or the behavioral variations of the specimen. To address this issue, this study presents an adaptive delay compensation method based on a discrete model of the loading system. In particular, the parameters of this discrete model are identified and updated online with the least-squares method to represent a servo hydraulic loading system. Furthermore, based on this model, the system delays are compensated for by generating system commands using the desired displacements, achieved displacements, and previous displacement commands. This method is more general than the existing compensation methods because it can predict commands based on multiple displacement categories. Moreover, this method is straightforward and suitable for implementation on digital signal processing boards because it relies solely on the displacements rather than on velocity and/or acceleration data. The virtual and real RTHS results show that the studied method exhibits satisfactory estimation smoothness and compensation accuracy. Furthermore, considering the measurement noise, the low-order parameter models of this method are more favorable than that the high-order parameter models.

• Smart Structures and Systems
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• 제31권3호
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• pp.259-273
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• 2023

Real-time hybrid simulation (RTHS) is an effective experimental technique for structural dynamic assessment. However, time delay causes displacement de-synchronization at the interface between the numerical and physical substructures, negatively affecting the accuracy and stability of RTHS. To this end, the authors have proposed a model-based adaptive control strategy with a Kalman filter (MAC-KF). In the proposed method, the time delay is mainly mitigated by a parameterized feedforward controller, which is designed using the discrete inverse model of the control plant and adjusted using the KF based on the displacement command and measurement. A feedback controller is employed to improve the robustness of the controller. The objective of this study is to further validate the power of dealing with a nonlinear control plant and to investigate the potential challenges of the proposed method through actual experiments. In particular, the effect of the order of the feedforward controller on tracking performance was numerically investigated using a nonlinear control plant; a series of actual RTHS of a frame structure equipped with a magnetorheological damper was performed using the proposed method. The findings reveal significant improvement in tracking accuracy, demonstrating that the proposed method effectively suppresses the time delay in RTHS. In addition, the parameters of the control plant are timely updated, indicating that it is feasible to estimate the control plant parameter by KF. The order of the feedforward controller has a limited effect on the control performance of the MAC-KF method, and the feedback controller is beneficial to promote the accuracy of RTHS.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.570-572
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• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.