• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.635-638
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• 2001

본 논문에서는 수중 환경에서 분산 소나 시스템의 최적 정보 융합에 관한 알고리즘을 제시하였다. 기존의 방법은 Bayesian 법칙을 이용하여 local 소나와 퓨전 센터의 문턱치를 적절히 조절하여 분산 소나 시스템을 최적화했다. 그러나, 이러한 최적화 과정에서 소나의 개수를 늘려감에 따라 P/sub F/(false alarm probability)가 단조 증가하는 현상이 발생하였고 이러한 단점을 보완하기 위해 P/sub F/를 작은 간에 제한시키고 Bayesian 법칙과 Neyman-Pearson 법칙을 함께 적용하여 분산 소나 시스템을 최적화시킨다. 그러나, 이러한 조건 하에 시스템을 최적화시키는 것은 N-P hard 문제에 의해 계산 부하가 매우 크므로 unate 함수와 SQP(Sequential Quadratic Programming)을 이용하여 계산 부하를 감소시켰다.

• 한국IT서비스학회:학술대회논문집
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• 2008.11a
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• pp.445-448
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• 2008

최근의 비디오 영상을 사용한 상황 판단 기법들은 사용자의 인식과 판단에 의존하고 있을 뿐만아니라 실시간 대응이 어렵다는 단점이 있었다. 따라서 본 논문에서는 순차패턴을 이용하여 실시간으로 영상에 나타나는 객체들의 비정상 행위를 탐지하는 자동화 방법을 제안한다.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.209-209
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• 2004

• Journal of Korea Society of Industrial Information Systems
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• v.3 no.1
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• pp.258-268
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• 1998

This paper presents two sequential advanced split and merge algorithms and a parallel image segmentation algorithm based on them. First, the two advanced methods are obtained from the combination of edge detection and classic split and merge to solve the inherent problems of the classical method. Besides, the parallel image segmentation algorithm on mesh-connected MIMD system considers three types in the merge stage to reduce the communication overhead between processors, such as intraprocessor merge, interprocessor with boundary merge, and interprocessor without boundary merge. Finally , we prove that the proposed algorithms achieve the improved performance by implementing them.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• ETRI Journal
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• v.27 no.5
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• pp.639-642
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• 2005

This paper proposes two kinds of complexity-reduced algorithms for a low density parity check (LDPC) decoder. First, sequential decoding using a partial group is proposed. It has the same hardware complexity and requires a fewer number of iterations with little performance loss. The amount of performance loss can be determined by the designer, based on a tradeoff with the desired reduction in complexity. Second, an early detection method for reducing the computational complexity is proposed. Using a confidence criterion, some bit nodes and check node edges are detected early on during decoding. Once the edges are detected, no further iteration is required; thus early detection reduces the computational complexity.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.74-81
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• 2008

This paper describes a low-cost omnidirectional camera system designed for the intruder detection capability of a security robot. Moving targets on sequential images are detected first by an adaptive background subtraction technique, and the targets are identified as intruders if they fail to enter a password within a preset time. A warning message is then sent to the owner's mobile phone. The owner can check scene pictures posted by the system on the web. The system developed worked well in experiments including a situation when the indoor lighting was suddenly changed.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.601-604
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• 1994

This paper describes an effective method to estimate a position of an automous vehicle equipped with a single CCD-camera along indoor passageways. Using the sequential image data from the self-movement of the vehicle, the position is estimated by integrating the approximated motion parameters. The detection of the yaw angle that is one of the motion parameter is difficult in general, e.g. slip or error for noise, therefore the different detection is presented, which is, without shaft encoders, based on a projection function for 2D-image data and a cross-correlation function so as to be robust for noise. The approximated geometric function to estimate the position is used to reduce the computational effort. To verify the effectiveness of the method, the analysis and the computational results are shown through the simulations. Furthermore, the experimental results by using the test vehicle for the real indoor passageway are shown.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.839-845
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• 2003

This paper introduces multichannel blind source separation (BSS) and multichannel blind deconvolution (MBD) based on higher order statistics of signals from convolutive mixtures. In particular, we are concerned with the case that the number of inputs is the same as the number of outputs. Simulations for two input two output cases are carried out and their performances are assessed. One of the major applications of those sequential algorithms (BSS and MBD) is demonstrated through the fault signal detection from only a single measurement of rotating machine, which offers a certain degree of practicability in the engineering field such as machine health monitoring or condition monitoring.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3764-3771
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• 2021

Based on the convolutional neural network (CNN), a novel technique is investigated for lost gamma source detection in a room. The CNN is trained with the result of a GEANT4 simulation containing a gamma source inside a meshed room. The dataset for the training process is the deposited energy in the meshes of different n-step paths. The neural network is optimized with parameters such as the number of input data and path length. Based on the proposed method, the place of the gamma source can be recognized with reasonable accuracy without human intervention. The results show that only by 5 measurements of the energy deposited in a 5-step path, (5 sequential points 50 cm apart within 1600 meshes), the gamma source location can be estimated with 94% accuracy. Also, the method is tested for the room geometry containing the interior walls. The results show 90% accuracy with the energy deposition measurement in the meshes of a 5-step path.