• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.363-366
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• 2001

The present process of grading leather quality by the rare eyes is not reliable. Because inconsistency of grading due to eyes strain for long time can cause incorrect result of grading. Therefore it is necessary to automate the process of grading quality of leather based on objective standard for it. In this paper, leather automatic classification system consists of the process obtaining the information of leather and the process grading the quality of leather from the information. Leather is graded by its information such as texture density, types and distribution of defects. This paper proposes the algorithm which sorts out leather information like texture density and defects from the gray-level images obtained by digital camera. The density information is sorted out by the distribution value of Fourier spectrum which comes out after original image is converted to the image in frequency domain. And the defect information is obtained by the statistics of pixels which is relevant to Window using searching Window after sort out boundary lines from preprocessed images. The information for entire leather is used as standard of grading leather quality, and the proposed algorithm is practically applied to machine vision system.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.450-454
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• 2007

In line with the construction projects, which have been increasingly getting larger and complex, safety accident has been on the rise, particularly those in association with the construction equipment. In a bid to prevent the safety accident, it's needed to analyze the cause of such accidents. The thesis was intended to identify the cause of safety accident by reviewing the cases of construction disaster complied by Korea Occupational Safety and Health Agency. The cases subject to study were limited to the accident by excavator. Summarizing the study is as follows. 1) Among the cause of accidents caused by excavator were, in order of high frequency, being caught in equipment or machine, falling, being crashed or bumped. 2) Among the causes of accident were, in order of high frequency, worker's unauthorized presence within the range of equipment operation. inappropriate use, failure of equipment inspection prior to starting work and inappropriate work method. The study is highly expected to pave the foundation for further study as well as to make commitment to mitigating the safety accident.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.499-505
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• 2018

Shipping and logistics safety, security system is strengthening worldwide, the development of shipping and logistics safety security core technology for national security logistics system construction has been carried out. In addition, it is necessary to localize the Array Detection System, which is a core component of the container search machine, to cope with the 100% pre-inspection of the container scheduled for 2018 in the United States. In this research, we propose a test software program developed by using TI-RTOS (Texas Instruments - Real Time Operating System) with a test digital signal processing board which is developed self development. We have developed a program that can test GPIO, SRAM, TCP/IP, and SDcard using M4 MCU. Also we propose a study on a self-developed Digital Signal Processing Board among the array detection systems that replace foreign products. We have developed a test board that can test M4 MCU and developed an X-Ray Detector Digital Signal Processing Board that combines MCU and FPGA.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.481-486
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• 2019

Smart Factory requires real-time monitoring and analysis of all process processes for optimal production environment. Monitoring system for data collection from various sensors is necessary to make all production processes automatic. By storing and analyzing the collected data, we can check whether there are any signs of abnormalities in any machine or equipment. Thus, in this paper, an integrated monitoring system for smart factory incorporating a working environment monitoring system and an automatic storage system of measurement values was implemented. By using the automatic storage system of measurement values, it is possible to carry out reliable inspection in any place without misentry. Also, through working environment monitoring system using LoRa, production environments such as temperature, humidity and atmospheric pressure can be monitored in real time.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.327-340
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• 2021

Purpose: The purpose of this study is to propose a system predicting whether an electricity distribution system is abnormal by analyzing the temperature of the deteriorated system. Traditional electricity distribution system abnormality diagnosis was mainly limited to post-inspection. This research presents a remote monitoring system for detecting thermal images of the deteriorated electricity distribution system efficiently hereby providing safe and efficient abnormal diagnosis to electricians. Methods: In this study, an object detection algorithm (YOLOv5) is performed using 16,866 thermal images of electricity distribution systems provided by KEPCO(Korea Electric Power Corporation). Abnormality/Normality of the extracted system images from the algorithm are classified via the limit temperature. Each classification model, Random Forest, Support Vector Machine, XGBOOST is performed to explore 463,053 temperature datasets. The process capability index is employed to indicate the quality of the electricity distribution system. Results: This research performs case study with transformers representing the electricity distribution systems. The case study shows the following states: accuracy 100%, precision 100%, recall 100%, F1-score 100%. Also the case study shows the process capability index of the transformers with the following states: steady state 99.47%, caution state 0.16%, and risk state 0.37%. Conclusion: The sum of caution and risk state is 0.53%, which is higher than the actual failure rate. Also most transformer abnormalities can be detected through this monitoring system.

• Food Science of Animal Resources
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• v.41 no.4
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• pp.563-588
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• 2021

Increasing meat demand in terms of both quality and quantity in conjunction with feeding a growing population has resulted in regulatory agencies imposing stringent guidelines on meat quality and safety. Objective and accurate rapid non-destructive detection methods and evaluation techniques based on artificial intelligence have become the research hotspot in recent years and have been widely applied in the meat industry. Therefore, this review surveyed the key technologies of non-destructive detection for meat quality, mainly including ultrasonic technology, machine (computer) vision technology, near-infrared spectroscopy technology, hyperspectral technology, Raman spectra technology, and electronic nose/tongue. The technical characteristics and evaluation methods were compared and analyzed; the practical applications of non-destructive detection technologies in meat quality assessment were explored; and the current challenges and future research directions were discussed. The literature presented in this review clearly demonstrate that previous research on non-destructive technologies are of great significance to ensure consumers' urgent demand for high-quality meat by promoting automatic, real-time inspection and quality control in meat production. In the near future, with ever-growing application requirements and research developments, it is a trend to integrate such systems to provide effective solutions for various grain quality evaluation applications.

• Journal of Veterinary Clinics
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• v.39 no.3
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• pp.114-120
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• 2022

The purpose of this study is to examine the status of quality control using multipurpose phantom of ultrasound equipment used in hospital of veterinary college in South Korea by using ATS-539 multipurpose phantom so as to examine quantitative and objective new image evaluation method. Specialists discussed and analyzed multipurpose phantom images acquired by using convex transducer of 10 ultrasound imaging devices, currently used in 9 veterinary colleges, at 4.0-6.0 MHz. Total 8 items that can be measured with ATS-539 multipurpose phantom including dead zone, vertical and horizontal measurement, axial/lateral resolution, sensitivity, focal zone, functional resolution and gray scale/dynamic range were evaluated. For qualitative evaluation, valid decisions were made based on dead zone, axial/lateral resolution, and gray scale/dynamic range which are resolution index, and coefficient of variation (COV) and blind referenceless image spatial quality evaluator (BRISQUE) were found to increase objectivity. As a result of experiment, all the targeted ultrasonic devices were found appropriate from qualitative evaluation items of dead zone, axial/lateral resolution, and gray scale/dynamic range. In other evaluation items, they were found to be appropriate from focal zone and vertical measurement of quantitative evaluation while inappropriate from horizontal measurement, sensitivity, and functional resolution. COV value was 0.12 ± 0.04, and BRISQUE value was 47.77 ± 2.77, both analysis results show that the noise level of all ultrasonic devices was located within tolerance range. Upon image examination using ATS-539 multipurpose phantom, they were 100% appropriate with inspection standards of dead zone, axial/lateral resolution, and gray scale/dynamic range, and besides, focal zone and functional resolution can be used as evaluation items. In the field of veterinary medicine, 8 standard items using ATS-539 multipurpose phantom and image evaluation items using COV and BRISQUE can be used as standards for quality control of ultrasonography machine.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.4
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• pp.593-599
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• 2022

In this study, a crack monitoring system capable of detecting cracks based on image processing techniques was developed to effectively check cracks, which are the main damage of concrete structures, and a program capable of imaging and analyzing cracks was developed using machine vision. This system provides objective and quantitative data by replacing the appearance inspection that checks cracks with the naked eye. The verification of the development system was applied to the construction site of a self-healing concrete water tank to monitor the crack and the amount of change in the crack width according to age. In the case of crack width detected by image analysis, the difference from the measured value using a digital microscope was up to 0.036 mm, and the crack healing effect of self-healing concrete could be confirmed through the reduction of crack width.

• Convergence Security Journal
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• v.23 no.5
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• pp.117-123
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• 2023

Recently, as the demand for Generative Artificial Intelligence (AI) and artificial intelligence has increased, the seriousness of misuse and abuse has emerged. However, intelligent CCTV, which maximizes detection of abnormal behavior, is of great help to prevent crime in the military and police. AI performs learning as taught by humans and then proceeds with self-learning. Since AI makes judgments according to the learned results, it is necessary to clearly understand the characteristics of learning. However, it is often difficult to visually judge strange and abnormal behaviors that are ambiguous even for humans to judge. It is very difficult to learn this with the eyes of artificial intelligence, and the result of learning is very many False Positive, False Negative, and True Negative. In response, this paper presented standards and methods for clarifying the learning of AI's strange and abnormal behaviors, and presented learning measures to maximize the judgment ability of intelligent CCTV's False Positive, False Negative, and True Negative. Through this paper, it is expected that the artificial intelligence engine performance of intelligent CCTV currently in use can be maximized, and the ratio of False Positive and False Negative can be minimized..