• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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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.

• Journal of KIBIM
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• v.11 no.2
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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.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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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.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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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.

• Proceedings of the KIEE Conference
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• 2005.10b
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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.

• Journal of the Korean Society of Safety
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• v.10 no.4
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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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• v.16 no.5
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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%.

• Journal of the Korean Society of Safety
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• v.37 no.2
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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.

• Fire Science and Engineering
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• v.32 no.3
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• pp.116-122
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• 2018

In Chemical, Biological, Radiological and Nuclear (CBRN) disaster, integrated and optimized equipment package including stretcher, isolation unit, patient monitoring and treatment equipment is essential to achieve proper treatment and prevent secondary contamination. The purpose of this study was to evaluate the efficiency and ease of use of integrated CBRN disaster equipment package for disaster medical response. This study was a randomized crossover study using a manikin simulation for emergency medical technitian (EMT). All participants used the existing devices and prototype of integrated CBRN disaster equipment package alternately. Efficiency was measured by time from vital sign change to detection or treatment application. Ease was use was measured by questionnaires for each patient monitor, stretcher care and isolation unit. 12 EMTs were enrolled. hypoxia-detection time of integrated equipment group was significantly shorter than existing equipment group (4.9 s (3.8-3.9) vs 3.5 s (2.5-3.9), p < 0.05). There was decreasing tendency of ECG change detection and facial mask oxygen supply but no statistical significance was observed. Overall satisfaction of patient monitoring device in integrated equipment group was significantly higher than existing devices (4(3.5-5) vs 3(3-3), p < 0.05). The use of integrated CBRN disaster equipment package shortened the hypoxia detection time and improved usability of vital sign monitor compared to existing devices.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.