• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.8
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• pp.405-411
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• 2000

When faults occur in transmission lines, the classification of faults is very important. If the fault is HIF(High Impedance Fault), it cannot be detected or removed by conventional overcurrent relays (OCRs), and results in fire hazards and causes damages in electrical equipment or personal threat. The fast discrimination of fault needs to effective protection and treatment and is important problem for power system protection. This paper propolsed the fault detection and discrimination algorithm for LIFs(Low Impedance Faults) and HIFs(High Impedance Faults). This algorithm uses artificial neural networks and variation of 3-phase maximum currents per period while faults. A double lines-to-ground and line-to-line faults can be detected using Neural Network. Also, the other faults can be detected using the value of variation of maximum current. Test results show that the proposed algorithms discriminate LIFs and HIFs accurately within a half cycle.

• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1712-1714
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• 2001

This paper deals with the development and application of an inner corrosion detector for the ACSR(Aluminum Conductor Steel Reinforced) power lines. The detector runs on an ACSR power line and inspects the inner corrosion of the conductor using the technology of the nondestructive eddy current test. It is consists of an ECT sensor, signal processing units, a RF transmitter/receiver, and etc.. The experimental result through field tests shows the detector can efficiently find both the zinc and aluminum loss of ACSR power lines.

• Proceedings of the IEEK Conference
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• 2000.11b
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• pp.59-62
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• 2000

As the density of memories increases, unwanted interference between cells and coupling noise between bit-lines are increased and testing high density memories for a high degree of fault coverage can require either a relatively large number of test vectors or a significant amount of additional test circuitry. From now on, conventional test algorithms have focused on faults between neighborhood cells, not neighborhood bit-lines. In this paper, a new algorithm for NPSFs, and neighborhood bit-line sensitive faults (NBLSFs) based on the NPSFs are proposed. Instead of the conventional five-cell and nine-cell physical neighborhood layouts to test memory cells, a three-cell layout which is minimum size for NBLSFs detection is used. To consider faults by maximum coupling noise by neighborhood bit-lines, we added refresh operation after write operation in the test procedure(i.e., write \longrightarrow refresh \longrightarrow read). Also, we present properties of the algorithm, such as its capability to detect stuck-at faults, transition faults, conventional pattern sensitive faults, and neighborhood bit-line sensitive faults.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1963-1965
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• 2000

This paper deals with a problem of detecting the inner corrosion of the ACSR(Aluminum Conductor Steel Reinforced) power lines using a detector which consists of an ECT(Eddy Current Test) sensor, a constant current service, a signal processing unit, and a RF transmitter/receiver unit. The detector runs on the ACSR transmission line and inspects the corrosion of the conductor using the technic of the nondestructive eddy current test. The experimental result shows this detector can efficiently find the zinc loss in ACSR power lines.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.19-26
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• 2020

Steel production requires high-quality surfaces with minimal defects. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. To meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. In this paper, we proposed a ShuffleDefectNet defect detection system based on deep learning. The proposed defect detection system exceeds state-of-the-art performance for defect detection on the Northeastern University (NEU) dataset obtaining a mean average accuracy of 99.75%. We train the best performing detection with different amounts of training data and observe the performance of detection. We notice that accuracy and speed improve significantly when use the overall architecture of ShuffleDefectNet.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1216-1224
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• 2015

In this paper, a detection algorithm for the removal of needle in oriental clinic is presented. First, in the proposed method, the candidate areas of each needle penetrated are selected by using the RGB color information of needle head, and the false candidates are removed by considering their area size. Next, two main edges of the needle are extracted through using the edges of selected candidate areas and their radon transformation. The final verification of penetrated needle is accomplished by using the morphological analysis of these two edge lines. In the experiments, the detection rate of proposed method reaches to 99% for the 36 images containing 294 needles.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.34-36
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• 2009

This paper proposes an effective lane detection algorithm using hugh transform and lane model. The proposed lane detection algorithm includes two major components, i.e., lane marks segmentation and an exact lane extraction using a novel postprocessing technique. The first step is to segment lane marks from background images using HSV color model. Then, a novel postprocessing is used to detect an exact lane using Hugh transform and lane models(linear and curved lane models). The postprocessing consists of three parts, i.e, thinning process, Hugh Transform and filtering process. We divide input image into three regions of interests(ROIs). Based on lane curve function(LCF), we can detect an exact lane from various extracted lane lines. The lane models(linear and curved lane mode]) are used in order to judge whether each lane segment is fit or not in each ROIs. Experimental results show that the proposed scheme is very effective in lane detection.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.1
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• pp.27-32
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• 2017

This paper presents a Daubechies wavelet-based fault detection method for fault identification in transmission lines. After the Daubechies wavelet coefficients are calculated, the proposed algorithm has been implemented difference equation using C language. We have modeled a 154kV transmission line using the ATPDraw software and have acquired test data. In order to evaluate effects of DC offset, simulations carried out while varying an inception angle of the voltage $0^{\circ}$, $45^{\circ}$, $90^{\circ}$. For performance evaluation, fault distance was varied. As we can see from the off-line simulation, the proposed algorithm shows rapid and accurate fault detection. Also we can see the proposed algorithm is not affected by the fault inception angle change.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.465-472
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• 2004

Analog CPPN-based optimal road boundary detection algorithm for autonomous vehicle is proposed. The CPPN is a massively connected analog parallel array processor. In the paper, the dynamic programming which is an efficient algorithm to find the optimal path is implemented with the CPPN algorithm. If the image of road-boundary information is utilized as an inter-cell distance, and goals and start lines are positioned at the top and the bottom of the image, respectively, the optimal path finding algorithm can be exploited for optimal road boundary detection. By virtue of the parallel and analog processing of the CPPN and the optimal solution of the dynamic programming, the proposed road boundary detection algorithm is expected to have very high speed and robust processing if it is implemented into circuits. The proposed road boundary algorithm is described and simulation results are reported.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.537-542
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• 2008

The protection against transient instability and consequent out-of-step condition is a major concern for the utility industry. Unstable system may cause serious damage to system elements such as generators and transmission lines. Therefore, out-of-step detection is essential to operate a system safely. Also, a multi-agent system is one that consists of a number of agents, which interact with one another. Multi-agent systems(MAS) can offer the flexibility and the adaptability to the previous algorithm. In this paper, the detection algorithm of out-of-step is designed by multi-agent system and implemented by EMTP-MODELS. To verify performance of the proposed algorithm based on multi-agent system, simulations by EMTP have been carried out.