• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.171-180
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• 2009

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2718-2720
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• 2002

The algorithm that can estimate frequency in real-time by using the resampling of a signal which was known frequency band like biological signals, was suggested in this study. A sinusoid signal is simulated as a practical measured signal. The sinusoid signal is sampled by using the impulse-train, and is subtracted the backward sample from forward by the sampled signals. The continuous sign, such as positive, negative or zero is counted from the calculation result of the subtraction, and those is stored. Therefore, the measured frequency is estimated by using the magnitude of continuous sign and the sampling period. The algorithm designed in this study is proven through the computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1044-1050
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• 2014

Robots used for pipe inspection have been studied for a long time and many mobile mechanisms have been proposed to achieve inspection tasks within pipelines. Localization is an important factor for an inpipe robot to perform successful autonomous operation. However, sensors such as GPS and beacons cannot be used because of the unique characteristics of inpipe conditions. In this paper, an inpipe localization algorithm based on elbow detection is presented. By processing the projected marker images of laser pointers and the attitude and heading data from an IMU, the odometer module of the robot determines whether the robot is within a straight pipe or an elbow and minimizes the integration error in the orientation. In addition, an off-line positioning algorithm has been performed with forward and backward estimation and Procrustes analysis. The experimental environment has consisted of several straight pipes and elbows, and a map of the pipeline has been constructed as the result.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.647-650
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• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.353-359
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• 1999

We present a matrix-based inference alorithm suitable for electronic commerce applications. For this purpose, an Extended AND-OR Graph (EAOG) was developed with the intention that fast inference process is enabled within the electronic commerce situations. The proposed EAOG inference mechanism has the following three characteristics. 1. Real-time inference: The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matric computation.2. Matrix operation: All the subjective knowledge is delineated in a matrix form, so that inference process can proceed based on the matrix operation which is computationally efficient.3. Bi-directional inference: Traditional inference method of expert systems is based on either forward chaining or backward chaining which is mutually exclusive in terms of logical process and exclusive in terms of logical process and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency. We have proved the validity of our approach with several propositions and an illustrative EC example.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.92-94
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• 1988

One of the recurring problems facing energy control center dispatchers each day is how to operate the system during the periods of high load pick up and fluctuation. This method reduces the problem to a forward - revised by backward - sequence of dispatch problem, with the generator Limits being carefully adjusted between each time interval in the total intervals to be optimized. It is applied to system of 31 units. It is observed that the method presented here achieves that generation fellows the load changes without any mismatch.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.507-509
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• 2000

This study developed the window based power flow analysis software for distribution system. This system was developed by using Visual C++ 6.0 in Microsoft Windows 98 environment; therefore, it utilized many advantages of window based programming for providing user-friendly interface. The backward/forward sweep algorithm is implemented in this software for power flow analysis. Especially, this software includes the newly developed classes of complex function, matrix function, and power flow calculation procedure, so it has high flexibility for enhancement and modifications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1109-1122
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• 1996

Generally, moving images contain the various components in motions, which reange from a static object and background to a fast moving object. To extract the accurate motion parameters, we must consider the various motions. That requires a wide search egion in motion estimation. The wide search, however, causes a high computational complexity. If we have a few knowledge about the motion direction and magnitude before motion estimation, we can determine the search location and search window size using the already-known information about the motion. In this paper, we present a local adaptive motion estimation approach that predicts a block motion based on spatio-temporal neighborhood blocks and adaptively defines the search location and search window size. This paper presents a technique for reducing computational complexity, while having high accuracy in motion estimation. The proposed algorithm is introduced the forward and backward projection techniques. The search windeo size for a block is adaptively determined by previous motion vectors and prediction errors. Simulations show significant improvements in the qualities of the motion compensated images and in the reduction of the computational complexity.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.1295-1298
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• 2005

기존 TCP Reno 의 혼잡 제어는 트래픽에 수동적으로 동작하는 방법으로서 혼잡이 이미 발생한 상태에서 동작하게 되므로 발생 시점의 라우터 버퍼는 이미 최대치에 도달해 있게 된다. 따라서 이후에 도착하는 모든 패킷은 폐기되므로 이 패킷들을 전송한 모든 송신원은 거의 동시에 윈도우 크기를 줄이는 Slow-start 단계에 들어가게 되어 일시적으로 링크 사용률이 떨어지는 전역 동기화(global synchronization)가 발생하게 된다. 이러한 문제를 해결하기 위해서는 네트워크의 혼잡이 발생하기 전에 능동적으로 대처하는 방안이 필요하다. 본 논문에서는 새로운 RTT 계산 알고리즘인 순방향/역방향 전송지연 알고리즘을 이용하여 네트워크의 혼잡을 미리 예측하고 네트워크 혼잡에 능동적으로 대처할 수 있는 새로운 알고리즘을 제안한다. 본 논문에서는 리눅스(Linux) 커널(Kernel)의 TCP Reno 의 흐름제어 및 혼잡제어를 수정하여 네트워크 혼잡에 능동적으로 대처 할 수 있는 새로운 TCP Reno 를 설계, 구현하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.109-115
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• 2008

Recently, autonomous parking problems have attracted a great deal of attention and have been examined in many papers in the literature. In this paper we design a fuzzy logic based parking system which is an important part for designing an autonomous parking system. We first analysis the existed papers and design a single-input fuzzy logic system for the parking algorithm and illustrate the effectiveness of the new method via the simulation results.