• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
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• pp.737-747
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• 2011

5678SMRT has installed various sensor for operating conditions(field of electric, facilities, signal, communication equipment and track) and environment of Every Function Room for remotely detecting and monitoring. Installed sound sensor for analyzed after remotely heard the noise of every equipment at Every Function Room and temperature sensor for check the temperature condition of Every Function Room. Additional installed voltage sensor in signal equipment room for monitoring RF track-circuit's voltage condition. Installed displacement sensor at The Chungdam bridge's railway for measuring and monitoring track displacement caused by temperature change and Pan/Tilt camera at sub-station and drainage for remotely field monitoring. Installed sensor for each equipment's operating condition and failure at Every Function Room then periodic check of workforce turned to around-the-clock surveillance by sensor therefore improvement of operating equipment. SMRT is lots of prevent a failure by Immediately detect of precondition of equipment failure by analyzed the sensor data. If the occurrence of an failure, become detected Immediately so possibility correct diagnosis and order by remotely field check by installed camera and sound sensor at field.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술대회 논문집 전문대학교육위원
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• pp.132-134
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• 2003

Insulator that is used in electric power equipment superannuation and being damaged disorder can. Such as defected insulator has repair or change must because defective influence in electrical equipment. There are many method developed for detecting the defected insulators, analyzed various test of devide voltage, electric field, ultrasonic and discharge pulse. But methods of detecting ultrasonic of discharge, electric field or leakage current could not application in the field, In this paper, we compare the advantage and the limitation of the method to detect defected insulator, and wish proposed using corona camera.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2002년도 춘계학술대회논문집
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• pp.193-199
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• 2002

According to the NOx level requirement of annex Vl to IMO(International Maritime Organization) MARPOL 73/78, this regulation shall apply to each diesel engine with a power output of more than 130 ㎾ which is installed on a ship constructed and undergoes a major conversion on or after 1 January 2000. It is inevitable to adopt IMO standard for marine engines. Therefore, most of diesel engines which are being currently built should be tested and surveyed in accordance with the NOx technical code. In this study, various technics of NOx reduction methods were investigated for the diesel engines and the methods of NOx measuring were introduced by the new and simplified field detecting equipment. These results can be utilized for the basic design and developement of diesel engine for NOx reduction.

• 한국철도학회논문집
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• 제9권2호
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• pp.230-236
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• 2006

For a short-term measurement to reduce an accident on a railroad crossing, it is necessary to find out the weakness and problems of existing equipment in the railroad transportation system. The analysis of railroad crossing accidents shows that main causes of accidents (24.9%) are the careless driving including an engine problem, damage on the safety equipment, slide on the snow, and an engine stop on the railroad crossing. Therefore, it is required to install a sensor, or to make a crossing gate at the exist for detecting obstacles on a railroad to prevent collisions among trains. This paper suggests 'a panel sensor using the strain gauge' to make up the weaknesses of the sensing Dead Zone, which are caused by using a laser method, and to detect any small objects passing railroads in order to reduce the errors, which are occurred in the earth magnetic field for controlling Exit Slide Crossing Arm Detector. The device, suggested in this paper, is verified with detecting any small objects passing the railroad, with judging entry decision clearly, and with controlling passing gate and obstruction warnings/cautions obstacles at the same time, through an experimental test.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.736-743
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• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• 인터넷정보학회논문지
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• 제21권2호
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• pp.9-18
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• 2020

영상 처리 기법을 이용한 영상 인식 분야는 버스 승차 및 하차 시에 승객을 움직이는 객체로 검출하고 개수하는 방법이 연구되고 있다. 이러한 기술 중에는 인공지능 기법의 하나인 딥러닝 기법이 사용되고 있다. 또 다른 방법으로 스테레오 비전 카메라를 이용하여 객체를 검출하는 방법도 사용되고 있다. 그러나 이러한 방법들은 객체를 검출할 때 사용되는 장비의 연산량이 많이 들어 고가의 하드웨어 장비가 필요하다. 그러나 대중교통 중 하나인 버스 승객을 검출하기 위해 상대적으로 연산량이 적은 기법을 이용하여 다양한 장비에 맞는 영상 처리 기술이 필요하다. 이에 본 논문에서는 다양한 장비에 맞는 이동 객체 검출 기법 중 배경 제거를 통한 객체의 윤곽선을 검출하여 대중교통 중의 하나인 버스에 탑승객의 수를 효율적으로 획득 할 수 있는 기법을 제안한다. 실험 결과 스테레오 비전을 장착한 장비보다 더 저사양의 장비에서 약 70%의 정확도로 승객을 개수하였다.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.302-309
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• 2022

Occupational safety accidents are caused by various factors, and it is difficult to predict when and why they occur, and it is directly related to the lives of workers, so the interest in safety accidents is increasing every year. Therefore, in order to reduce safety accidents at industrial fields, workers are required to wear personal protective equipment. In this paper, we proposes a method to automatically check whether workers are wearing safety helmets among the protective equipment in the industrial field. It detects whether or not the helmet is worn using YOLOv5, a computer vision-based deep learning object detection algorithm. We transfer learning the s model among Yolov5 models with different learning rates and epochs, evaluate the performance, and select the optimal model. The selected model showed a performance of 0.959 mAP.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 하계학술대회 논문집 Vol.3 No.2
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• pp.900-903
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• 2002

Partial discharges(PD) in air, corona discharges, deteriorate the insulation strength. So, it is necessary to detect PD at an early stage before the system failure and extensive damage to the equipment. PD emit signals which can be detected by applying an capacitive electric field diagnosis. In this paper, We measured signals by capacitive electric field sensor and analyzed FFT from PD, and then compared conventional electric PD detecting method to capacitive electric field diagnosis by $\Psi-q-n$, $\Psi-v-n$, $\Psi-n$, $\Psi-q$ distributions.

• 상하수도학회지
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• 제30권3호
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• pp.253-261
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• 2016

A variety of methods for detecting the location of an underground water pipeline are being used across the world; the current main methods used in South Korea, however, have the problems of low precision and efficiency and the limitations in actual application. On this, this study developed locator capable of detecting the location of a water pipe by the use of an IMU sensor, and technology for using the extended karman filter to correct error in location detection and to plot the location on the coordinate system. This study carried out a tract test and a road test as basic experiments to measure the performance of the developed technology and equipment. As a result of the straight line, circular and ellipse track tests, the 1750 IMU sensor showed the average error of 0.08-0.11%; and thus it was found that the developed locator can detect a location precisely. As a result of the 859.6-m road test, it was found that the error was 0.31 m in case the moving rate of the sensor was 0.3-0.6 m/s; and thus it was judged that the equipment developed by this study can be applied to long-distance water pipes of over 1 km sufficiently. It is planned to evaluate its field applicability in the future through an actual pipe network pilot test, and it is expected that locator capable of detecting the location of a water pipe more precisely will be developed through research for the enhancement of accuracy in the algorithm of location detection.