• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1044-1050
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• 2021

After the occurrence of several passenger ship accidents in Korea, various systems are being developed for passenger ship safety management. A total of 162 passenger ships operate along the coast of Korea, of which 105 (65 %) are car-ferries with open vehicle decks. The car-ferry has a navigation pattern that passes through 2 to 4 islands. Safety inspections at the departure point(home port) are carried out by the crew, the operation supervisor of the operation management office, and the maritime safety supervisor. In some cases, self-inspections are carried out for safety inspections at layovers. As with any system, there are institutional and practical limitations. To this end, this study was conducted to suggest a method of detecting a vehicle using image processing and linking it to the calculations for ship stability. For vehicle detection, a method using a difference image and one using machine learning were used. However, a limitation was observed in these methods that the vehicle could not be identified due to strong background lighting from the pier and the ship in the cases where the camera was backlit such as during sunset or at night. It appears necessary to secure sufficient image data and upgrade the program for stable image processing.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.543-553
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• 2022

A collaborative robot(Cobot) is one of the production systems presented in the 4th industrial revolution and are systems that can maximize efficiency by combining the exquisite hand skills of workers and the ability of simple repetitive tasks of robots. Also, research on the development of an efficient interface method between the worker and the robot is continuously progressing along with the solution to the safety problem arising from the sharing of the workspace. In this study, a method for controlling the robot by recognizing the worker's hand signal was presented to enhance the convenience and concentration of the worker, and the safety of the worker was secured by introducing the concept of a safety zone. Various technologies such as robot control, PLC, image processing, machine learning, and ROS were used to implement this. In addition, the roles and interface methods of the proposed technologies were defined and presented for using educational media. Students can build and adjust the educational media system by linking the introduced various technologies. Therefore, there is an excellent advantage in recognizing the necessity of the technology required in the field and inducing in-depth learning about it. In addition, presenting a problem and then seeking a way to solve it on their own can lead to self-directed learning. Through this, students can learn key technologies of the 4th industrial revolution and improve their ability to solve various problems.

• Annual Conference of KIPS
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• 2024.10a
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• pp.393-396
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• 2024

본 논문에서는 자연·인적재해로 인한 건물, 도로 붕괴 시, 신속하고 안전한 이동을 위해 위성 이미지를 U-Net 딥러닝 학습모델과 A^* 알고리즘을 활용하여 위험지역을 우회한 경로 제안 시스템을 구현하였다. 이를 실제 재해 상황에 도입하면 안전이 확보된 최단 거리를 제공함에 따라 신속한 대피와 구호 등 재난 관리에 효율성을 제공하여 인명 및 물적 피해를 줄일 수 있을 것으로 예상한다.

• The Journal of Bigdata
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• v.5 no.2
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• pp.53-67
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• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.90-102
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• 2022

Currently, in our society with a substantial and increasing fraction of the elderly population, transport safety for elderly drivers is becoming the center of attention. However, deficient data on vehicle crashes in South Korea limits the growth of traffic accident research pertaining to the country. So, we complemented South Korean vehicle crash data by examining USA vehicle crash data, especially the data of Ohio State, and analyzing the influential factors of elderly driver-involved crashes of the State. Subsequently, we suggested a way of improving the South Korean dataset. Notably, our study showed that the influential factors were vehicle speed, posted speed, and following other vehicles too close and provided them in the South Korean dataset.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.1271-1276
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• 2017

산업이 고도화되고 기술발전이 가속화되고 있지만, 생산현장에서의 사고와 재해는 아직까지도 지속적으로 발생하고 있다. 이는 시스템의 대규모화, 복잡화, 다양화 등에 따라 나타나는 불안전한 상태(unsafe condition)와 근로자의 안전불감증, 낮은 학습효과, 안전문화 비활성화 등을 포함하는 불안전한 행동(unsafe behavior)에 기인한다. 최근 4차 산업혁명이 대두되면서, 인간과 기계 시스템 사이의 상호작용이 활발해지고, 데이터 가용성과 알고리즘 우수성이 확보되면서, 산업현장에서도 시스템과 공정안전을 위해 최신 기술을 활용하려는 시도가 시작되고 있다. 궁극적으로는, 품질 관리, 고장분석, 작업환경관리, 보건관리 등 생산관리의 다양한 범위에 새로운 산업안전혁신을 가져올 것으로 기대된다. 본 논문에서는 사물인터넷, 드론, 인공지능 등 4차 산업혁명 시대의 하드웨어와 소프트웨어의 결합의 사례를 통해 안전한 생산현장은 물론 신뢰성할 수 있는 공공 및 사회를 위한 지능형 시스템 구축의 필요성을 제시하고자 한다.

• Journal of the Korea Institute of Building Construction
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• v.19 no.4
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• pp.341-350
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• 2019

Construction industry is one of the most dangerous industry. As the construction accidents occur due to the repeated factors found in each accidents, there is a limitation in analyzing all types of occupational accidents by the existing descriptive analysis and statistical test. In this study, we classified safety accidents caused by falling objects among the accident types occurring at construction sites into fatal and nonfatal accidents and deduced the factors. In addition, we deduced the association rules among the safety accidents factors caused by falling objects through the association rule analysis method among the machine learning techniques. Therefore, considering the association rules for fatal and nonfatal accidents proposed in this study, it would be possible to prevent accidents by searching for countermeasures against safety accidents caused by falling objects.

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.205-205
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• 2018

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.127-136
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• 2024

Because the hazardous working environments and high labor intensity of the root industry can potentially impact the health of workers, current regulations have focused on measuring and controlling environmental factors, on a semi-annual basis. However, there is a lack of quantitative criteria addressing workers' health conditions other than the physical work environment. This gap makes it challenging to prevent occupational diseases resulting from continuous exposure to harmful substances below regulatory thresholds. Therefore, this paper proposes a machine learning-based method for determining the peak level of risk in root industry work environments and enables real-time safety assessment in workplaces utilizing this approach.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.603-606
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• 2023

인공지능 (AI) 서비스는 현대 사회에서 중요한 역할을 맡고 있다. 그러나 이러한 서비스는 보안과 관련된 문제들을 가지고 있다. 본 논문은 AI 서비스의 보안과 관련된 문제와 해결책을 조사하고자 한다. AI 서비스의 개요와 대표적인 상용 서비스를 간략히 소개 후, AI 서비스에서 발생할 수 있는 보안상의 문제와 Chat GPT를 중심으로 한 보안 문제에 대해 다루고자 한다. 또한, 향후 AI보안 서비스 연구 분야와 적재적 기계학습 연구에 대한 전망을 살펴볼 예정이다. 이를 통해 안전하고 신뢰성 있는 AI 서비스를 제공하는데 기여하고자 한다.