• Proceedings of the KIEE Conference
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• 2007.04b
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• pp.75-78
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• 2007

To detect partial discharge of 154kV joint box, we have made experiment by using the HFCT sensor. Generally the signals which are detected in partial discharge test of underground power transmission cable are accompanied with both noises of high voltage and noises of surrounding power cable. The most noise in near to end part of joint box is corona, beside other noises flowed from surrounding area. Partial discharge test is difficulty due to these noises. First, we test reliability on both injection of calibration signal in NJB and removal of low frequency. After that, we had analyzed frequencies by measuring signals in IJB with 300[m] distance from NJB. Also we had measured S/N ratio by using the indirected injection method of calibration signal in IJB. In this experiment, two measurement methods were difference of detection acquisition, but these had the equal frequency properties.

• Smart Structures and Systems
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• v.24 no.3
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• pp.293-301
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• 2019

Bolted joints are commonly used in civil infrastructure and mechanical assembly structures. Monitoring and identifying the connection status of bolts is the frontier problem of structural research. The existing research is mainly on the looseness of a single bolt. This article presents a study of assessing the loosening/tightening health state and identifying the loose bolt by using ultrasonic guided wave in a bolt group joint. A bolt-tightening index was proposed for evaluating the looseness of a bolt connection based on correlation coefficient. The tightening/loosening state of the bolt was simulated by changing the bolt torque. More than 180 different measurement tests for total of six bolts were conducted. The results showed that with the bolt torque increases, value of the proposed bolt-tightening index increases. The proposed bolt-tightening index trend was very well reproduced by an analytical expression using a function of the torque applied with an overall percentage error lower than 5%. The developed damage index based on the proposed bolt-tightening index can also be applied to locate the loosest bolt in a bolt group joint. To verify the effectiveness of the proposed method, a bolt group joint experiment with different positions of bolt looseness was performed. Experimental results show that the proposed approach is effective to detect and locate bolt looseness and has a good prospect of finding applications in real-time structural monitoring.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.63-68
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• 2020

This paper proposes a face authentication system based on deep learning framework. The proposed system is consisted of face region detection and feature extraction using deep learning algorithm, and performed the face authentication using joint-bayesian matrix learning algorithm. The performance of proposed paper is evaluated by various face database , and the face image of one person consists of 2 images. The face authentication algorithm was performed by measuring similarity by applying 2048 dimension characteristic and combined Bayesian algorithm through Deep Neural network and calculating the same error rate that failed face certification. The result of proposed paper shows that the proposed system using deep learning and joint bayesian algorithms showed the equal error rate of 1.2%, and have a good performance compared to previous approach.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.449-452
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• 2000

본 논문은 다중의 사람을 동시에 검지 및 추적하기 위한 방법을 제안한다 여러 명의 검지된 사람들이 교차해서 움직이거나 폐색(occlusion) 되어 움직이는 경우 이를 검지하고 신뢰적으로 추적하기 위한 방법을 제시한다. 카메라의 시야 범위 안에 나타난 표적은 일정한 크기를 가지는 오브젝트이므로, 배경영상에서 전경 영상만을 분리하는 과정에서 오브젝트의 크기를 고려하여 표적을 검지 한다. 표적의 검지는 환경적인 요인에 의한 부가요소에 적응적으로 대치하기 위해 적응적인 영상처리기법을 사용한다. 최종적으로 검지 된 표적을 동시에 추적하기 위해 본 논문에서는 JPDA(Joint Probability Data Association) 필터를 이용하며 ,표적간의 폐색을 처리하기 위한 방법으로 전이모델을 첨가해서 사용한다. 다중 표적의 추적에 관한 실험의 유효성 및 강인함은 다양한 실제 영상의 실험을 통해 입증한다.

• Journal of Welding and Joining
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• v.13 no.4
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• pp.65-74
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• 1995

The weld seam tracking sensor is indispensable to improve the flexibility of automatic arc welding systems. Among the position sensing methods available, a laser displacement sensor is one of the most prevailing methods. In this study, a laser displacement sensor was examined on detecting the weld seam of lap joints in sheet metal arc welding. The output signal of the laser displacement sensor may ontain severe fluctuation from the effect of arc light, spatters, fume, etc. So a variety of signal processing methods was applied to smooth the output signal of the sensor. And then the weld joint was determined by using the central difference method. It was revealed that the quadratic mean method plays an important role in detecting the weld seam during welding especially.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.729-733
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• 1997

Due to the variety of disturbance, it is not ease to accomplish the in-process detection of weld line with non-contact sensor. To get around this difficulties problem develop an automatic seam tracking weld system, the reliable signal processing algorithm has been recommanded. In this research, laser displacement sensor is applied as a seam finder in the automatic tracking system. The sensor is controlled by a dc servo motor which is mounted at X-Y moving table. X-Y moving table manipulated by an ac servo motor controls the position and velocity of the welding torch. First, X-Y table moves to Y-axis to search the welding joint feature before starting the welding, and welding joint is from the scanning data and weighting factor for each other. Second, weld line is determined using proposed signal processing algorithm during welding process. Form the experimental results, we could see the possibility that laser displacement sensor with procesed algorithm can be used as a seam finder in welding process under the severe noise (spatter,arc light etc.) condition

• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.167-172
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• 2015

In this paper, we propose a new approach to design and control a smart gripper of robot system. A control method for flexible grasping a object in partially unknown environment was proposed, where a proximate sensor detecting the distance between the fingertip and object was used. Based on the proximate sensor signal the finger motion controller could plan the grasping process divided in three phases. The first step is scanning process which two first joints were moved to mid-position of the detected range by a state-variable feedback position controller, after the scanning was finished. The contact force of fingertip was then controlled using the detection sensor of the servo controller for finger joint control. The proposed grasping planning was tested on rectangular bar.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.616-617
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• 2013

This paper introduces a new method that controls a humanoid robot detecting a human motion using a Kinect sensor. Processing the output of a depth seneor of the Kinect sensor, we build a human stick model which represents each joint of human body. We detect a specific motion by calculating the distance and angle between joints. We send the control message to the robot using Bluetooth wireless communication.

• Journal of muscle and joint health
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• v.28 no.2
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• pp.91-101
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• 2021

Purpose: This study was conducted to identify factors related to osteoporosis prevalence in postmenopausal women. Methods: This study was a secondary analysis research using data from the Eighth Korea National Health and Nutrition Examination Survey (KNHANES VIII-1), 2019, which were downloaded from the KNHANES website. The subjects of this study were 1,791 postmenopausal women who participated in the KNHANES VIII-1, 2019. Data analysis was performed using the IBM SPSS 21.0 program and complex sample design analysis was performed considering factors such as weight, cluster, and strata. Results: Osteoporosis prevalence of in postmenopausal women was 17.5%. Factors related to osteoporosis prevalence were age (65~74 years old, ≥75 years old), house income (low), household type (one-person household), postmenopausal period (10~19 years), drinking (non-drinking). Conclusion: Interventions for osteoporosis prevention and management in postmenopausal women need to focus on women less than 10 years after menopause and one-person household women. Furthermore, it is necessary to expand bone density testing for the early detection of osteoporosis in postmenopausal women.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.124-131
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• 2023

Object detection techniques are increasingly used to obtain information on physical characteristics or situations of a specific area from remote images. The accuracy of object detection is decreased in remote sensing images with low resolution because the low resolution reduces the amount of detail that can be captured in an image. A single neural network is proposed to joint the super-resolution method and object detection method. The proposed method constructs a deep residual-based network to restore object features in low-resolution images. Moreover, the proposed method is used to improve the performance of object detection by jointing a single network with YOLOv5. The proposed method is experimentally tested using VEDAI data for low-resolution images. The results show that vehicle detection performance improved by 81.38% on mAP@0.5 for VISIBLE data.