• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1278-1282
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• 2003

A new interacting multiple model (IMM) method using intelligent input estimation (IIE) is proposed to track a maneuvering target. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of acceleration input. Then, multiple models are composed of these fuzzy systems, which are optimized for different ranges of acceleration input. In computer simulation for an incoming ballistic missile, the tracking performance of the proposed method is compared with those of the input estimation (IE) technique and the adaptive interacting multiple model (AIMM) method.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.915-920
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• 2016

A power system designer may have difficulties in choosing a suitable multiple-input converter topology for a specific target application because each multiple-input converter topology presented in the literature has its own advantages and disadvantages. In this perspective, this paper presents a systematic topology selection method for multiple-input converters with three comparison criteria including cost-saving effect, modularity potential and flexibility. Based on these criteria, this paper proposes a strategic flow chart example for choosing a proper multiple-input converter topology. This flow chart will provide a powerful selection tool to a power system designer when he or she chooses a specific multiple-input converter for a given application.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.99-102
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• 2003

A new interacting multiple model (IMM) method using genetic algorithm (GA)-based intelligent input estimation(IIE) is proposed to track a maneuvering target. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one. The GA is utilized to optimize a fuzzy system fur a sub-model within a fixed range of acceleration input. Then, multiple models are composed of these fuzzy systems, which are optimized for different ranges of acceleration input. In computer simulation for an incoming ballistic missile, the tracking performance of the proposed method is compared with those of the input estimation(IE) technique and the adaptive interacting multiple model (AIMM) method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1291-1298
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• 2017

In this paper, we propose a decoding method that improves the transmission rate and reliability by integrating MIMO(Multiple Input Multiple Output) communication scheme using turbo code and FTN(Faster Than Nyquist) scheme among high rate and high reliability wireless communication schemes in wireless communication. The existing MIMO-FTN(Multiple Input Multiple Output-Faster Than Nyquist) scheme based on hierarchical space-time coded method induced data rate loss due to the application of the space-time trellis coding scheme to remove adjacent symbol interference due to FTN scheme. To solve these problems, this paper proposes a method using W-ZF(Weighted-Zero Forcing) which overcomes the disadvantages of ZF(Zero Forcing) scheme in MIMO-FTN scheme using ZF scheme. In this paper, we compared the performance and the transmission rate of the MIMO-FTN scheme based on the hierarchical space-time coding, the MIMO-FTN scheme using W-ZF and the SISO-FTN scheme. As a result, the MIMO-FTN scheme using the W-ZF scheme is two times better than the other two schemes.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.1997-2004
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• 1994

A model analysis method has been developed in the paper. The method estimates the modal parameters of multiple input-output systems, assesses their quality, and seperates structural modes form computation ones. The modal parameter extraction algorithm is the least squares method with a finite difference model relating input and output time data. The quality of the estimated system model can be assessed in narrow frequency bands by comparing the measured and model predicted responses in time domain with the aid of digital filters. Structural modes can be effectively separated from computational ones using the convergence factor which represents the pole convergence rate. The modal analysis method has been applied to simulated and experimental vibration data to evaluate its utility and limitations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6A
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• pp.494-499
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• 2009

In this paper, we provide an efficient method of user selection for achieving the maximum system throughput in downlink multiuser Multiple-Input Multiple-Output (MIMO) systems. A proposed method is for selecting a fine user set only with powers of each user channel and angles between them. This algorithm is simpler than SUS because there is no considering about the optimal value of correlation. The proposed method finds the user set toward maximizing system throughput, so it has high performance.

• Journal of Power Electronics
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• v.12 no.6
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• pp.912-924
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• 2012

In hybrid renewable power systems, the use of a multiple-input dc/dc converter (MIC) leads to simpler circuit and lower cost, when compared to the conventional use of several single-input converters. This paper proposed a novel three-input buck/boost/buck-boost converter, which can be used in applications with various values of input voltage. The energy sources in this converter can deliver power to the load either simultaneously or individually in one switching period. The steady relationship, the power management strategy and the small-signal circuit model of this converter have been derived. With decoupling technology, modeling and regulator design can be obtained under multi-loop control modes. Finally, three generating methods of a multiple-input buck/boost/buck-boost converter is given, and this method can be extended to the other multiple-input dc/dc converters.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.234-239
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• 2006

In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking errors for maneuvering targets. In the proposed filter, the unknown acceleration input for each sub-model is determined by mismatches between the modelled target dynamics and the actual target dynamics. After a acceleration input is detected, the state estimates for each sub-filter are modified. To modify the accurate estimation, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). The tracking performance of the proposed method is compared with those of the adaptive interacting multiple model(AIMM) method and input estimation (IE) method through computer simulations.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.41-45
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• 2017

This paper presents a method of constructing the efficiency multiple output switching function of the combinational logic systems. The proposed method reduce the optimized input variable pair and output variable pair after we obtained the final multiple output switching function which was time based multiplexing and obtained the common multiple end node extension logic decision diagram. Also the proposed method have an advantage of the cost, input-output node number, circuit simplification, increment of the arithmetic speed, and more regularity and extensibility compare with previous method.

• ETRI Journal
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• v.41 no.2
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• pp.145-159
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• 2019

To reduce the number of radio frequency (RF) chains in multiple input multiple output (MIMO) systems, generalized spatial modulation (GSM) techniques have been proposed in the literature. In this paper, we propose a zero-forcing (ZF)-based detector, which performs an initial pruning of the search tree that will be considered as the initial condition in a sphere decoding (SD) algorithm. The proposed method significantly reduces the computational complexity of GSM systems while achieving a near maximum likelihood (ML) performance. We analyze the performance of the proposed method and provide an analytic performance difference between the proposed method and the ML detector. Simulation results show that the performance of the proposed method is very close to that of the ML detector, while achieving a significant computational complexity reduction in comparison with the conventional SD method, in terms of the number of visited nodes. We also present some simulations to assess the accuracy of our theoretical results.