• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1651-1654
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• 2003

The process equipment for handling high level radioactive materials, such as spent nuclear fuel, is operated within a sealed facility, called a hot cell, due to high radioactivity. Thus, this equipment should be maintained and repaired by remotely operated manipulator. In this study, to carry out the sale and effective maintenance of the process equipment installed in the hot cell by a servo type manipulator, a collision free motion planning method of the manipulator using virtual prototyping technology is suggested. To do this, the parts are modelled in 3-D graphics, assembled, and kinematics are assigned and the virtual workcell is implemented in the graphical environment which is the same as the real environment. The method proposed in this paper is to find the optimal path of the manipulator using the function of the collision detection in the graphic simulator. The proposed path planning method and this graphic simulator of manipulator can be effectively used in designing of the maintenance processes for the hot cell equipment and enhancing the reliability of the spent fuel management.

• 한국BIM학회 논문집
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• 제11권2호
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• 한국군사과학기술학회지
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• 제27권2호
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• pp.213-221
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• 2024

Aircraft's operational effectiveness is reduced due to threats from enemy anti-aircraft weapons, which is a weak point. In particular, guided missiles, which pose a threat to aircraft, are rapidly developing due to technological advancements in seekers, and are classified as one of the important technologies in weapon systems. Missile approach warning equipment installed to ensure aircraft survivability detects guided missiles and provides relevant information to respond. Tests were conducted domestically to verify the detection level of missile approach warning equipment, and test results were presented under various test conditions.

• 반도체디스플레이기술학회지
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• 제18권4호
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• pp.143-148
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• 2019

Numerous pipe lines can be found on various plant-based industrial sites. These pipelines should be periodically checked for defects. Most of these pipelines are internally inaccessible and difficult to visually inspect. Therefore, the inspection is being carried out with the help of non-contact inspection equipment such as ultrasonic flaw detection equipment. The use of ultrasonic flaw detection equipment can raise time and efficiency issues. In order to solve this problem, we will study the basic technology necessary for the development of automated inspection system equipped with ultrasonic measuring equipment and verify the validity through the fabrication of the demonstration device.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.240-242
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• 2005

It is well known that the PD (Partial Discharge) signals are generated if insulators have some defects such as voids in electrical facility and various PD detection methods are developed for preventing electrical troubles. So, an interest for the PD signals is higher and higher according to the high concern for the defects detection method of the aging electrical facility. When the equipment to detect PD signals installed at site and it works, a lot of noises flow in the equipment from surrounding situation and it will be mixed with original PD waveform. So we can not get the desired PD waveform. Therefore, there are many trial to reject or suppress the noise from the PD signals from long times ago. The greater of them used the hardware such as bridge circuits and frequency filters to suppress the noise. This paper proposed a novel noise rejection method in acquired data from PD detection equipment. The noise has the irregular phase and higher signal level than real PD, and noise decision is performed after inspection of pulse distribution in ${\Phi}$-q-n graph of acquired data from PD detection equipments. By experimental results on high voltage electric equipments, it is shown that proposed method has good performance. It is expected that this noise rejection technology is useful in numeric calculation and trend management of PD level.

• 한국안전학회지
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• 제10권4호
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• pp.9-12
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• 1995

Detection of small defects by medical ultrasonic testing when the thermal sprayed coating by $MoSi_2$ on the metal is done. The defects may occur at the bonded surface. So, the detecting method of the defects by non-destructive in spection is desired. Here, in order to examine the possibility of the detection of the small defects by the ultrasonic. The electronic scanning ultrasonic equipment using an array probe developed as the medical ultrasonic diagnostic equipment is applied for the detection of the metal material defects. It's validity is investigated.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1008-1014
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• 2020

Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on the optimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on the maximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter banks with 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the one-dimensional image entropy algorithm and the two-dimensional image entropy algorithm respectively. Thirdly, these two optimized Gabor filters are compared to realize the common defect detection of denim fabric, such as normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has better detection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.

• 한국안전학회지
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• 제37권2호
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• pp.69-75
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• 2022

Fire-alarm systems are safety equipment that facilitate rapid evacuation and early suppression in case of fire. It is highly desirable that fire-alarm systems have low false-alarm rates and are thus reliable. Until now, researchers have attempted to improve detector performance by applying new technologies such as IoT. To this end, IoT-based fire-detection systems have been developed. However, due to scarcity of large-scale operational data, researchers have barely studied malfunctioning in fire-alarm systems or attempted to reduce false-alarm rates in these systems. In this study, we analyzed false-alarm rates of smoke/temperature detectors and unwanted fire-alarm signal patterns at K institution, where Korea's largest IoT-based fire-detection system operates. After analyzing the fire alarm occurrences at the institution for five years, we inferred that the IoT-based fire-detection system showed lower false-alarm rates compared to the automatic fire-detection equipment. We analyzed the detection pattern by dividing it into two parts: normal operation and unwanted fire alarms. When a specific signal pattern was filtered out, the false-alarm rate was reduced to 66.9% in the smoke detector and to 46.9% in the temperature detector.

• 한국화재소방학회논문지
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• 제32권3호
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• pp.116-122
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• 2018

본 연구의 목적은 개발 중인 화학, 생물학, 방사능 및 원자력 특수재난 대응 격리 이송 장비의 효율성과 사용 용이성을 평가하는 것이다. 상기 개발 장비는 자체 개발한 격리, 이송, 환자 감시 모듈을 통합하여 제작하였다. 응급구조사를 대상으로 한 마네킹을 이용한 무작위 교차 실험(또는 시뮬레이션)연구이며, 모든 연구대상자는 기존 장비와 특수재난 대응 격리 이송 장비의 시제품을 교대로 사용하였다. 생체신호 변화 검출 소요 시간과 치료 적용 소요 시간으로 효율성을 평가하였고 각 감시장치, 이송카트, 격리 장치 편의성에 대한 설문조사를 통해 사용 편의성으로 평가하였다. 총 12명의 응급구조사가 연구에 참여하였고 특수재난 대응 격리 이송 장비 군의 저산소증 검출 시간이 3.5초(2.5-3.9)로 기존 장비군의 4.9초(3.8-3.9)보다 유의하게 짧았다(p < 0.05). 심전도 변화 감지 소요 시간 및 안면 마스크 산소 공급 소요 시간의 감소 경향은 있었으나 통계적 유의성은 관찰되지 않았다. 특수재난 대응 격리 이송 장비 군의 환자 감시 장치의 전반적 만족도도 특수재난 대응 격리 이송 장비 군이 4점(3.5-5)으로 기존 장비군의 3점(3-3)에 비해 높았다(p < 0.05). 특수재난 대응 격리 이송 장비 사용군이 저산소증 검출 시간이 짧았으며 기존장비에 비해 환자 감시 장치의 전반적 만족도가 높은 것을 확인하였다.

• 한국재난정보학회 논문집
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• 제18권2호
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• pp.269-279
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• 2022

연구목적: 본 연구의 목적은 건설현장의 재해 예방을 위해 딥러닝기반의 개인보호구 검출 모델을 개발하고, 실제 건설현장에 적용하여 분석하는 것이다. 연구방법: 본 연구의 수행 방법은 실제 환경의 데이터를 구축하고, 개발된 개인보호구 검출 모델을 적용하였다. 개인보호구 검출 모델은 크게 근로자 검출 및 개인보호구 착용 분류 모델로 구성되어 있다. 근로자 검출 모델은 딥러닝 기반의 알고리즘을 실제 현장에서 획득한 데이터셋을 구축하여 학습 및 근로자를 검출하였고, 개인보호구 착용 분류 모델은 앞단에서 추출된 근로자 검출영역에서 학습된 개인보호구 검출 알고리즘을 적용하였다. 구축된 모델의 검증을 위해 건설현장 3곳에서 획득된 데이터를 통해 실험결과를 도출하였다. 연구결과: 데이터베이스 12,000장을 구축하여 정상검출 9,460장(78.8%), 오검출 1,468(12.2%), 미검출 1,072장(8.9%)으로 나타났으며 주요 원인은 영상에서의 객체 크기, 객체간 중첩(Occulusion), 객체 잘림, 그림자에 의한 오검출로 분류되었다. 결론: 개인보호구 검출모델은 현장 상황마다 다른 검출률을 확인할 수 있었고, 본 연구의 결과가 차후 현장적용을 위한 연구에 활용될 수 있을 것으로 여겨진다.