• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.9-14
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• 2002

In this paper we propose a method for performance enhancement of speech recognizer under noisy conditions. The parallel combination model which is presented at the PMC method using multiple Gaussian-distributed mixtures have been adapted to the variation of each mixture. The CDHMM(continuous observation density HMM) which has multiple Gaussian distributed mixtures are combined by the proposed PMC method. Also, the EM(expectation maximization) algorithm is used for adapting the model mean parameter in order to reduce the variation of the mixture density. The result of simulation, the proposed PMC adaptation method show better performance than the conventional PMC method.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1000-1002
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• 1998

We present an approach to parallelizing optimal power flow (OPF) that is suitable for distributed implementation and is applicable to very large interconnected power systems. The objective of this paper is to find a set of control parameters with which the Auxiliary Problem Principle (Algorithm - APP) can be best implemented in solving optimal power flow (OPF) Problems. We employed several IEEE Reliability Test Systems to demonstrate the alternative parameter sets.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.465-470
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• 2012

This paper proposes a distributed autonomous control method of swarm robot behavior strategy based on artificial immune system and an optimization strategy for artificial immune system. The behavior strategies of swarm robot in the system are depend on the task distribution in environment and we have to consider the dynamics of the system environment. In this paper, the behavior strategies divided into dispersion and aggregation. For applying to artificial immune system, an individual of swarm is regarded as a B-cell, each task distribution in environment as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows: When the environmental condition changes, the agent selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other agent using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. In order to decide more accurately select the behavior strategy, the optimized parameter learning procedure that is represented by stimulus function of antigen to antibody in artificial immune system is required. In this paper, particle swarm optimization algorithm is applied to this learning procedure. The proposed method shows more adaptive and robustness results than the existing system at the viewpoint that the swarm robots learning and adaptation degree associated with the changing of tasks.

• Journal of IKEEE
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• v.23 no.2
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• pp.710-715
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• 2019

We consider parameter estimation in distributed systems in which measurements at local nodes are quantized in a non-regular manner, where multiple codewords are mapped into a single local measurement. For the system with non-regular quantization, to ensure a perfect independent encoding at local nodes, a local measurement can be encoded into a set of a great number of codewords which are transmitted to a fusion node where estimation is conducted with enormous computational cost due to the large cardinality of the sets. In this paper, we propose an efficient estimation technique that can handle the non-regular quantized data by efficiently finding the feasible combination of codewords without searching all of the possible combinations. We conduct experiments to show that the proposed estimation performs well with respect to previous novel techniques with a reasonable complexity.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.408-411
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• 1995

In this paper, we discuss the force control of flexible manipulators. Since the force control of flexible manipulators with planar one or two links using the distributed-parameter modeling has been the subject of a considerable number of publications until now, real time computations of the force control schemes are possible. But, application of those control schemes to multi-link spatial manipulators is fairly complicated. In this paper, we apply a concise hybrid position/force control scheme for a flexible manipulators. We use a lumped-parameter modeling for the flexible manipulators. The Hamilton's principle is applied to derive the equations of motion for the system and then, state-space model is obtained by the Lagrange's method. Finally, comparison of simulation results with experimental results is given to show the performance of our method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.749-756
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• 2005

This paper presents vibration analyses of hard disk drive (HDD) spindle systems based on the finite element method. The systems under investigation have a cantilevered shaft rotating on hydrodynamic bearings. In particular, the influence of stator's flexibility on major modes has been taken into account in dual ways lumped and distributed-parameter model approfches. Even the latter employs relatively macroscopic elements instead of extremely fine ones Popular in commercial codes. In order to prove the effectiveness of such formulated models, two types of HDD prototypes featuring different hub and stator structures are selected as examples. Compared to the first, the second type has a reinforced stator that would raise the natural frequency of the hub's translational (or sideway) mode. Both free and forced vibration characteristics are computed, and subsequently compared with the experimental data. It is our conclusion that Particularly the Proposed distributed model method is an efficient design tool for state-of-the-art HDD spindle systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.993-996
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• 1993

This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.

• Journal of Power Electronics
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• v.9 no.4
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• pp.643-653
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• 2009

Voltage-controlled voltage source inverter (VCVSI) based distributed generation systems (DGS) using renewable energy sources (RES) is becoming increasingly popular as grid support systems in both remote isolated grids as well as end of rural distribution lines. In VCVSI based DGS for load voltage stabilization, the power angle between the VCVSI output voltage and the grid is an important design parameter because it affects not only the power flow and the power factor of the grid but also the capacity of the grid, the sizing of the decoupling inductor and the VCVSI. In this paper, the steady state modeling and analysis in terms of power flow and power demand of the each component in the system at the different values of maximum power angle is presented. System design considerations are examined for various load and grid conditions. Experimental results conducted on a I KVA VCVSI based DGS prove the analysis and simulation results.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.627-633
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• 1999

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a ？3-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robot using communication (immune network). Finally much stimulated strateby is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of optimal swarm strategy. Adaptation ability of robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• Bulletin of the Korean Mathematical Society
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• v.25 no.2
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• pp.185-194
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• 1988

In [2], we have shown that the optimal boundary controls for hyperbolic systems in L$^{2}$-spaces can be attained in a feedback form via Riccati operators. A number of authors [1, 5, 7 and 10] have investigated approximations of Riccati operators arising in distributed parameter systems. They assumed bounded controls for parabolic systems. However, we in this paper study Galerkin approximations of Riccati operators and feedback controls for hyperbolic systems with unbounded control actions. Let us briefly introduce some results of [2]. Let .ohm. be an open bounded region in R$^{n}$ with smooth boundary .GAMMA. where n is a fixed positive integer. We consider a strictly hyperbolic differential operator H(x) of order 1 on .ohm. with noncharacteristic boundary on .GAMMA.