• Proceedings of the KSR Conference
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• 2011.10a
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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.

• Proceedings of the KIEE Conference
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• 2003.07e
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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.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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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.

• Journal of the Korean Society for Railway
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• v.9 no.2 s.33
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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.

• Journal of KIBIM
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• v.13 no.3
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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.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.9-18
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• 2020

In the field of image processing, a method of detecting and counting passengers as moving objects when getting on and off the bus has been studied. Among these technologies, one of the artificial intelligence techniques, the deep learning technique is used. As another method, a method of detecting an object using a stereo vision camera is also used. However, these techniques require expensive hardware equipment because of the computational complexity of used to detect objects. However, most video equipments have a significant decrease in computational processing power, and thus, in order to detect passengers on the bus, there is a need for an image processing technology suitable for various equipment using a relatively low computational technique. Therefore, in this paper, we propose a technique that can efficiently obtain the number of passengers on the bus by detecting the contour of the object through the background subtraction suitable for low-cost equipment. Experiments have shown that passengers were counted with approximately 70% accuracy on lower-end machines than those equipped with stereo vision camera.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.07b
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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.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.3
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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.