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Automatic target-recognition technique using a neural network (신경회로망을 이용한 표적의 자동인식 기법)

  • Tahk, Min-Je;Rew, hyuk;Yoo, Inn-Eark;Lee, Won-Sang
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10a
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    • pp.430-435
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    • 1992
  • This paper presents a real-time algorithm for an infrared seeker to find the real target automatically against various background noises without changing the reticle configuration. The modeling technique of infrared sources and analysis results of the various source types based on the FFT algorithm are included. Futhermore, a neural network is used to recognize the source type using the results of FFT analysis. The evaluation of target recognition for cases which can happen in real situation is also treated.

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Split Model Speech Analysis Techniques for Speech Signal Enhancement

  • Park, Young-Ho;You, Kwang-Bock;Bae, Myung-Jin
    • Proceedings of the IEEK Conference
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    • 1999.11a
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    • pp.1135-1138
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    • 1999
  • In this paper, The Split Model Analysis Algorithm, which can generate the wideband speech signal from the spectral information of narrowband signal, is developed. The Split Model Analysis Algorithm deals with the separation of the 10$\^$th/ order LPC model into five cascade-connected 2$\^$nd/ order model. The use of the less complex 2$\^$nd/ order models allows for the exclusion of the complicated nonlinear relationships between model parameters and all the poles of the LPC model. The relationships between the model parameters and its corresponding analog poles is proved and applied to each 2$\^$nd/ order model. The wideband speech signal is obtained by changing only the sampling rate.

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Competitive Learning Neural Network with Dynamic Output Neuron Generation (동적으로 출력 뉴런을 생성하는 경쟁 학습 신경회로망)

  • 김종완;안제성;김종상;이흥호;조성원
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.9
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    • pp.133-141
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    • 1994
  • Conventional competitive learning algorithms compute the Euclidien distance to determine the winner neuron out of all predetermined output neurons. In such cases, there is a drawback that the performence of the learning algorithm depends on the initial reference(=weight) vectors. In this paper, we propose a new competitive learning algorithm that dynamically generates output neurons. The proposed method generates output neurons by dynamically changing the class thresholds for all output neurons. We compute the similarity between the input vector and the reference vector of each output neuron generated. If the two are similar, the reference vector is adjusted to make it still more like the input vector. Otherwise, the input vector is designated as the reference vector of a new outputneuron. Since the reference vectors of output neurons are dynamically assigned according to input pattern distribution, the proposed method gets around the phenomenon that learning is early determined due to redundant output neurons. Experiments using speech data have shown the proposed method to be superior to existint methods.

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ACCURATE AND EFFICIENT COMPUTATIONS FOR THE GREEKS OF EUROPEAN MULTI-ASSET OPTIONS

  • Lee, Seunggyu;Li, Yibao;Choi, Yongho;Hwang, Hyoungseok;Kim, Junseok
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.18 no.1
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    • pp.61-74
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    • 2014
  • This paper presents accurate and efficient numerical methods for calculating the sensitivities of two-asset European options, the Greeks. The Greeks are important financial instruments in management of economic value at risk due to changing market conditions. The option pricing model is based on the Black-Scholes partial differential equation. The model is discretized by using a finite difference method and resulting discrete equations are solved by means of an operator splitting method. For Delta, Gamma, and Theta, we investigate the effect of high-order discretizations. For Rho and Vega, we develop an accurate and robust automatic algorithm for finding an optimal value. A cash-or-nothing option is taken to demonstrate the performance of the proposed algorithm for calculating the Greeks. The results show that the new treatment gives automatic and robust calculations for the Greeks.

Performance Improvement of Attitude Estimation Using Modified Euler Angle Based Kalman Filter (변형된 오일러각 기반의 칼만필터를 이용한 자세 추정 성능 향상)

  • Kang, Chul-Woo;Yoo, Young-Min;Park, Chan-Gook
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.9
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    • pp.881-885
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    • 2008
  • To calculate the attitude in ARS(Attitude Reference System) using 3 gyros and 3 accelerometers, gyro drift must be compensated with accelerometer to avoid divergence of attitude error. Kalman filter is most popular method to integrate those two sensor outputs. In this paper, new Kalman filtering method is proposed for roll and pitch attitude estimation. New states are defined to make linear equation and algorithm for changing Kalman filter parameters is proposed to ignore disturbances of acceleration. This algorithm can be easily applied to low cost ARS.

Development of Active Tracking System for Efficiency Improvement of PV Generation (태양광 발전의 효율 개선을 위한 능동형 추적시스템 개발)

  • Ko, Jae-Sub;Choi, Jung-Sik;Jung, Chul-Ho;Kim, Do-Yeon;Jung, Byung-Jin;Park, Ki-Tae;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2008.07a
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    • pp.1122-1123
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    • 2008
  • This paper proposes a the high efficiency tracking system regarding power loss when operating a tracking system for environment variable such as a rapidly changing insolation and shadow effect to improve the power of PV tracking system. To reduce the power loss, this paper proposes a novel control algorithm of the tracking system. And paper suggests a method controlling an altitude for length which is received the shadow influence of PV array. The paper is analyzed efficiency about conventional PV tracking method, comparing proposed algorithm with high performance method.

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Inertia and Coefficient of Friction Estimation of Electric Motor using Recursive Least-Mean-Square Method (순환 최소자승법을 이용한 전동기 관성과 마찰계수 추정)

  • Kim, Ji-Hye;Choi, Jong-Woo
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.56 no.2
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    • pp.311-316
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    • 2007
  • This paper proposes the algorithm which estimates moment of the inertia and friction coefficient of friction for high performance speed control of electric motor. The proposed algorithm finds the moment of inertia and friction coefficient of friction by observing the speed error signal generated by the speed observer and using Recursive Least-Mean-Square method(RLS). By feedbacking the estimated inertia and estimated coefficient of friction to speed controller and full order speed observer, then the errors of the inertia and coefficient of friction and speed due to the inaccurate initial value are decreased. Inertia and coefficient of friction converge to the actual value within several times of speed changing. Simulation and actual experiment results are given to demonstrate the effectiveness of the proposed parameter estimator.

Comparative Study on Performance of Metaheuristics for Weapon-Target Assignment Problem (무기-표적 할당 문제에 대한 메타휴리스틱의 성능 비교)

  • Choi, Yong Ho;Lee, Young Hoon;Kim, Ji Eun
    • Journal of the Korea Institute of Military Science and Technology
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    • v.20 no.3
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    • pp.441-453
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    • 2017
  • In this paper, a new type of weapon-target assignment(WTA) problem has been suggested that reflects realistic constraints for sharing target with other weapons and shooting double rapid fire. To utilize in rapidly changing actual battle field, the computation time is of great importance. Several metaheuristic methods such as Simulated Annealing, Tabu Search, Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization have been applied to the real-time WTA in order to find a near optimal solution. A case study with a large number of targets in consideration of the practical cases has been analyzed by the objective value of each algorithm.

A Maximum Power Point Tracking Control for Photovoltaic Array without Voltage Sensor

  • Senjyu, Tomonobu;Shirasawa, Tomiyuki;Uezato, Katsumi
    • Journal of Power Electronics
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    • v.2 no.3
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    • pp.155-161
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    • 2002
  • This paper presents a maximum power point tracking algorithm for Photovoltaic array using only instantaneous output current information. The conventional Hill climbing method of peak power tracking has a disadvantage of oscillations about the maximum power point. To overcome this problem, we have developed an algorithm that will estimate the duty ratio corresponding to maximum power operation of solar cell. The estimation of the optimal duty ratio involves, finding the duty ratio at which integral value of output current is maximum. For the estimation, we have used the well know Lagrange's interpolation method. This method can track maximum power point quickly even for changing solar isolation and avoids oscillations after reaching the maximum power point.

Analog Control Algorithm for Maximum Power Trackers Employed in Photovoltaic Applications

  • Ji, Sang-Keun;Jang, Du-Hee;Hong, Sung-Soo
    • Journal of Power Electronics
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    • v.12 no.3
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    • pp.503-508
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    • 2012
  • Tracking the Maximum Power Point (MPP) of a photovoltaic (PV) array is usually an essential part of a PV system. The problem addressed by Maximum Power Point Tracking (MPPT) techniques is to find the voltage $V_{MPP}$ or current $I_{MPP}$ at which a PV array should operate to generate the maximum power output $P_{MPP}$ under a given temperature and irradiance. MPPT control methods such as the perturb and observe method and the incremental conductance method require a microprocessor or DSP to determine if the duty cycle should be increased or not. This paper proposes a simple and fast analog MPPT method. The proposed control scheme tracks the MPP very quickly and its hardware implementation is simple when compared with the conventional techniques. The new algorithm can successfully track the MPP even in the case of rapidly changing atmospheric conditions. In addition, it has higher efficiency than ordinary algorithms.