• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.385-391
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• 2007

In this paper, we propose and observe the Adaptive Modulation system with optimal Turbo Coded V-BLAST (Vertical-Bell-lab Layered Space-Time) technique that is applied the extrinsic information from MAP (Maximum A Posteriori) Decoder with Iterative Decoding to use as a priori probability in two decoding procedures of V-BLAST: ordering and slicing. Also, comparing with the Adaptive Modulation system using conventional Turbo Coded V-BLAST technique that is simply combined V-BLAST with Turbo Coding scheme, we observe how much throughput performance has been improved. As a result of simulation, in the Adaptive Modulation systems with several Turbo Coded V-BLAST techniques, the optimal Turbo Coded V-BLAST technique has higher throughput gain than the conventional Turbo Coded V-BLAST technique. Especially, the results show that the proposed scheme achieves the gain of 1.5 dB SNR compared to the conventional system at 2.5 Mbps throughput.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.114-122
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• 2010

The Probabilistic Traveling Salesman Problem (PTSP) is an important topic in the study of traveling salesman problem and stochastic routing problem. The goal of PTSP is to find a priori tour visiting all customers with a minimum expected length, which simply skips customers not requiring a visit in the tour. There are many existing researches for the homogeneous version of the problem, where all customers have an identical visiting probability. Otherwise, the researches for the heterogeneous version of the problem are insufficient and most of them have focused on search base algorithms. In this paper, we propose a simple construction algorithm to solve the heterogeneous PTSP. The Minimum Expected Length Insertion (MELI) algorithm is a construction algorithm and consists of processes to decide a sequence of visiting customers by inserting the one, with the minimum expected length between two customers already in the sequence. Compared with optimal solutions, the MELI algorithm generates better solutions when the average probability is low and the customers have different visiting probabilities. We also suggest a local search method which improves the initial solution generated by the MELI algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.134-139
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• 1994

In this paper the comparison between the neural networks and traditional approaches as system identification method are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. The examples considered do not represent any physical system, no a priori knowledge concerning their structure has been used in the identification process. Testing inputs for comparison are the sinusoidal, ramp and the noise ramp.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2441-2443
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• 2003

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed loop system is guaranteed. The computer simulation results for an inverted pendulum system show the performance of the proposed robust adaptive fuzzy controller.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.235-249
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• 2004

This paper proposes a method for estimating isotropic out-of-focus blur and a fully digital auto-focusing based on a priori estimate set of PSFs. The proposed algorithm for identifying the isotropic PSF is performed by approximating an isotropic blur to a novel discrete PSF model and estimating the PSF model coefficients from degraded edges. After acquiring the set of PSFs by proposed PSF estimation algorithm the proposed fully digital auto-focusing system can restore out-of-focused images by two steps: i) selecting an optimal PSF and ii) restoring the out-of-focused image by digital image restoration.

• Ocean and Polar Research
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• v.36 no.4
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• pp.367-371
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• 2014

In early 1990s, the Korean government has launched a deep-sea research program to secure the stable long-term supply of strategic metallic minerals including Cr, Cu and Ni. Through the pioneering surveys, Korea registered $150,000km^2$ of Mn-nodule field in the Clarion-Clipperton area, the NE equatorial Pacific to the international sea-bed authority (ISA) in 1994. Following the ISA exploration code, the final exclusive exploration area of $75,000km^2$ was assigned in 2002, based on results of eight-year researches of chemico-physical properties of nodules, bottom profiles and sediment properties. Since that time, environmental studies, mining technical developments including robot miner and lifting system and establishment of smelting systems were accompanied with the detailed geophysical studies to decipher the priori mining area until 2009. Major points of the recent Korea Mn-nodule program are deployed on a commercial scale until 2015. In order to meet the goals, we developed a 1/5 scaled robot miner compared to commercial one in 2012 and performed a mining test at the water depth of 1,370 m in 2013. In addition, detailed 25,000 scaled mining maps in the priori area, which can provide operation roots of the miner, will be prepared and an environmental-friendly mining strategy will be pursued based on the environmental impact test and environmental monitoring.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.24-26
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• 2005

At present, KS-1000 which is one of a commercial measurement instrument for motor fault diagnosis has been used in industrial field. The measurement system of KS-1000 is composed of three part : harmonic acquisition, signal processing by KS-1000 algorithm, diagnosis for motor fault. First of all, voltage signal taken from harmonic sensor is analysed for frequency by KS-1000 algorithm. Then, based on the result values of analysis skilled expert makes a judgment about whether motor system is the abnormality or degradation state. But the expert system such a motor fault diagnosis is very difficult to bring the expectable results by mathematical modeling due to the complexity of judgment process. In this reason, we propose an automation system using fuzzy model based on genetic algorithm(GA) that builded a qualitative model of a system without priori knowledge about a system provided numerical input output data.

• International Journal of Contents
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• v.2 no.1
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• pp.5-8
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• 2006

This paper describes a new mechanism of detecting object of interest from a noisy image, without using any a-priori knowledge about the target. It employs a parallel set of filters inspired upon biological findings of mammalian vision. In our proposed system, several basic features are extracted directly from original input visual stimuli, and these features are integrated based on their local competitive relations and statistical information. Through integration process, unnecessary features for detecting the target are spontaneously decreased, while useful features are enhanced. Experiments have been performed on a set of computer generated and real images corrupted with noise.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.207-211
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• 2004

We consider a class of uncertain nonlinear systems containing the uncertainties without a priori information except that they are bounded. For such systems, we assume that the upper bound of the uncertainties is represented as a Fredholm integral equation of the first kind and we propose an adaptation law that is capable of estimating the upper bound. Using this adaptive upper bound, a continuous robust control which renders uncertain nonlinear systems uniformly ultimately bounded is designed.