• 한국시뮬레이션학회논문지
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• 제7권1호
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• pp.15-26
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• 1998

In building a shell-based FMS, which is known as one of the top-down approaches in the field of factory automation, we may take a hybrid simulation into consideration. The modeling of a hybrid simulation consists of real physical entities, virtual simulation, and central clock algorithm, etc. to carry out the whole system operation. In this paper, we sow a way to construct a hybrid simulation software system in manufacturing environments. We bring in the object-oriented methodology in system design and it can contribute in dealing with a wide variety of production types and configurations. Some classes such as project, product, process, order, schedule, stage are defined. These are used and tested by implementing a specific LSI circuit assembly line process.

• 한국잠사학회:학술대회논문집
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• 한국잠사학회 1997년도 Progress and Future Development of Sericultural Science and Technology 40th Anniversary Commemoration Symposium
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• pp.103-117
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• 1997

Decrease in the cocoon production of Japan is drastic because of low price of cocoon, scarcity of successors and so on. To tide over the difficulty, the automation system in the sericulture was discussed and some trials have been conducted by the Ministry of Agriculture, Forestry and Fisheries of Japan. The attempts are based on a low cost artificial diet which does not rely on mulberry leaves. Automatic machines developed until now are a rearing machine constituted with repeated belt conveyor, an reformation type of former rearing machine "Bombyx" and a mounting machine. Running parallel with them, utilization of 20-hydroxyecdysone extracted from a plant to the mounting was also studied to use their machines efficiently in the fields. In conclusion, 10 tons of law cocoon will come to be produced by two persons labor. At present, an automatic rearing system on low cost artificial diet has been developing for the future sericulture.

• 한국기계가공학회지
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• 제19권10호
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• pp.74-80
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• 2020

Crawler-type hydraulic devices facilitate forward and backward driving of construction equipment by converting power into mechanical energy. The existing hydraulic device performance test process is time- and labor-intensive. This study aims to improve efficiency and productivity by automating the hydraulic device production performance test processes, which have been separately conducted so far. We also used SolidWorksⓇ, a 3D modeling program, and ANSYSⓇ, a structural analysis tool, for structural analysis and to verify the suitability of fixing pins required for connecting a hydraulic device to performance test equipment. Our results that employing an automated hydraulic device performance test process improves efficiency.

• Journal of Multimedia Information System
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• 제8권1호
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• pp.57-60
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• 2021

Industry 4.0 is continuous automation by applying the latest smart technologies to traditional manufacturing industries. It means that large-scale M2M (Machine-to-Machine) communication and IoT (Internet of Things) technologies are well integrated to build efficient production systems by analyzing and diagnosing various issues without human intervention. Edge computing is widely used for M2M services that handle real-time interactions between devices at industrial machinery tool sites. Here, secure data transmission is required while interacting. Thus, this paper focused on a method of creating and maintaining secret key and security tag used for message authentication between end-devices and edge-device.

• 한국기계가공학회지
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• 제21권6호
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• pp.22-29
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• 2022

In this study, an automatic marking system for fabric inspection machines was developed. The main objectives of the study were to promote intelligence and automation for the inspection process, as well as to increase textile industrial productivity. Generally, when a worker manually inspects and marks a fabric, human error and reduced efficiency are unavoidable. To overcome these problems, we developed an automatic marking system that uses robots. This system incorporates a vision camera to automatically recognize defects, and an optical fiber sensor to detect the side of the fabric. To verify the performance, the control system sends a command directly to the robot to mark the fabric. Finally, the actual production confirmed that the proposed system could perform the desired motion.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.420-426
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• 2022

Developing a machine learning model and putting it into production goes through a number of steps. Automated Machine Learning(AutoML) appeared to increase productivity and efficiency by automating inefficient tasks that occur while repeating this process whenever machine learning is applied. The high degree of automation of AutoML models allows non-experts to use machine learning models and techniques without the need to become machine learning experts. Automating the process of applying machine learning end-to-end with AutoML models has the added benefit of creating simpler solutions, generating these solutions faster, and often generating models that outperform hand-designed models. In this paper, the AutoML data is collected and AutoML's Color Petri net model is created and analyzed based on it.

• 한국멀티미디어학회논문지
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• 제25권7호
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• pp.903-911
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• 2022

A shrimp quality detection model using YOLOv4 deep learning algorithm is designed, which is superior in terms of network architecture, data processing and feature extraction. The shrimp images were taken and data expanded on their own, the LableImage platform was used for data annotation, and the network model was trained under the Darknet framework. Through comparison, the final performance of the model was all higher than other common target detection models, and its detection accuracy reached 93.7% with an average detection time of 47 ms, indicating that the method can effectively detect the quality of shrimp in the production process.

• Design & Manufacturing
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• 제16권4호
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• pp.60-66
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• 2022

Recently, with the development of industry and the improvement of living standards, various wastes are generated along with the production of various products. Most of these wastes are used as containers for products, and plastic or aluminum is used. Various attempts are being made to automate the classification of these wastes due to the high labor cost, but most of them are solved by manpower due to the geometrical shape change due to the nature of the waste. In this study, in order to automate the waste sorting task, Deep Learning technology is applied to a robot system for waste sorting and a vision system for waste sorting to effectively perform sorting tasks according to the shape of waste. As a result of the experiment, a Deep Learning parameter suitable for waste sorting was selected. In addition, through various experiments, it was confirmed that 99% of wastes could be selected in individual & group image learning. It is expected that this will enable automation of the waste sorting operation.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.41-49
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• 2020

Concurrent construction offers considerable improvement for shorten the project duration of its production process. Therefore, standardized concurrent construction is widely applied in building construction projects. However, resources planning for standardized concurrent construction project is manually developed by construction manager. This practice is not effective since it is time-consuming and error-prone for managers to identify all project-specific information, distinguish different activity-resource types, interpret these types and analyze how they affect resource allocated on an ad hoc basis. Therefore, this research investigates the opportunity for leveraging activity modeling to enable automated resource planning for standardized concurrent construction during project development, with identifying the characteristics of construction activities under standardized concurrent planning and determining the activity-resources types that affects resource planning. Both will function as a basis for modeling these construction activities in a computer-interpretable manner and for automation in resource planning.

• 대한임베디드공학회논문지
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• 제18권5호
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• pp.241-248
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• 2023

GAN (Generative Adversarial Networks) creates highly sophisticated counterfeit products by learning real images or text and inferring commonalities. Therefore, it can be useful in fields that require the creation of large-scale images or graphics. In this paper, we implement GAN-based game character creation AI that can dramatically reduce illustration design work costs by providing expansion and automation of game character image creation. This is very efficient in game development as it allows mass production of various character images at low cost.