• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.193-198
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• 2001

In the large scale B2B transaction like buying Express-Train or selling Daewoo Motor, a tremendous amount of variables and factors of chaos functionate in it directly or indirectly. To get effective information processing on the so called complex system like this, it should be possible to unite the global insight power of the human being and the local computing power of the machine. In this paper, we suggested a union model of human being and machine using Hugent concept. Hugent is defined as an agent model which allows us to chemically unite the human's component and the machine's component in terms of information processing. In this paper, we showed that some typical problems contained in the complex system can be treated more easily through the suggested Hugent concept.

• Journal of Mechanical Science and Technology
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• v.20 no.11
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• pp.1863-1872
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• 2006

In FMS (Flexible Manufacturing System) and CIM (Computer Integrated Manufacturing), machine-tools have been the target of integration in the last three decades. The conventional concept of integration is being changed into the autonomous manufacturing device based on the knowledge evolution by applying advanced information technology in which an open architecture controller, high-speed network and internet technology are included. In the advanced environment, the machine-tools is not the target of integration anymore, but has been the key subject of cooperation. In the near future, machine-tools will be more improved in the form of a knowledge-evolutionary intelligent device. The final goal of this study is to develop an intelligent machine having knowledge-evolution capability and a management system based on internet operability. The knowledge-evolutionary intelligent machine-tools is expected to gather knowledge autonomically, by producing knowledge, understanding knowledge, reasoning knowledge, making a new decision, dialoguing with other machines, etc. The concept of the knowledge-evolutionary intelligent machine is originated from the machine control being operated by human experts' sense, dialogue and decision. The structure of knowledge evolution in M2M (Machine to Machine) and the scheme for a dialogue agent among agent-based modules such as a sensory agent, a dialogue agent and an expert system (decision support agent) are presented in this paper, with intent to develop the knowledge-evolutionary machine-tools. The dialogue agent functions as an interface for inter-machine cooperation. To design the dialogue agent module in an M2M environment, FIPA (Foundation of Intelligent Physical Agent) standard platform and the ping agent based on FIPA are analyzed in this study. In addition, the dialogue agent is designed and applied to recommend cutting conditions and thermal error compensation in a tapping machine. The knowledge-evolutionary machine-tools are expected easily implemented on the basis of this study and shows a good assistance to sensory and decision support agents.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.9 no.13
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• pp.1-11
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• 1986

The mechanization of production facilities being made rapid progress, and its function being diversified and complicated for industrialized period, the relation between machine and Its operator brings about many problems which are concerned with accident. In these circumstance, the purpose of industrial safety can not be properly achieved as considered by only one side of machine or man. Therefore, it is necessary to study how to cope with the safety of man-machine system. It has to be considered in the above mentioned contents that safety management can not be attained through only technique of numerical control. The cause of accident being studied scientifically, the service of safety problems has to be systematized and operated in rational safety organization. The purpose of this thesis is to consider preventing and decreasing industrial accident from production system field by means of the improvement of worker's own safety consciousness and introducing the function of safety management to the duties of labour union.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.456-458
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• 2022

Due to the environmental regulations of the International Maritime Organization, shipyards are conducting various researches to improve the efficiency of ships, and efforts are being made to reduce the weight of ships. Recently, composite materials including CNT materials have the advantage of being able to reduce weight by 40% or more compared to general steel plate materials, and have the advantage of being able to be used as a substitute for ship clamps or door skins. Therefore, in this study, to predict the life of composite materials including CNT materials, the results were compared through the accelerated deterioration test method and the life prediction using machine learning techniques. The accelerated degradation test used the Arrhenius model equation, and the machine learning method predicted the life using a regression analysis algorithm.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.412-419
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• 2022

The amount of digital text data is growing exponentially, and many machine learning solutions are being used to monitor and manage this data. Artificial intelligence and machine learning are used in many areas of our daily lives, but the underlying processes and concepts are not easy for most people to understand. At a time when many experts are needed to run a machine learning solution, no-code machine learning tools are a good solution. No-code machine learning tools is a platform that enables machine learning functions to be performed without engineers or developers. The latest No-Code machine learning tools run in your browser, so you don't need to install any additional software, and the simple GUI interface makes them easy to use. Using these platforms can save you a lot of money and time because there is less skill and less code to write. No-Code machine learning tools make it easy to understand artificial intelligence and machine learning. In this paper, we examine No-Code machine learning tools and compare their features.

• Journal of Technology Innovation
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• v.13 no.2
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• pp.179-205
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• 2005

This study reveals that the user industry has a limited role in being a source of technological capability in the case of the machine tool industry in Korea where the user industry is relatively more advanced than other capital goods industries. This study examines the sources of technological capability in terms of migration of workforces and flow of product development knowledge. Although the capital goods sector is generally regarded as being the sector where user-producer interaction is important, the user industry is not the seed-bed of technological capability for machine development. Users and producers interact in terms of expressing 'needs', mainly in the form of specifications. As a result of receiving unique specifications from users, the producer learns to react by making specific customised special purpose machines. The user's specification could include information o the imported machine originally used. When confronted with technical problems in developing a new machine, the producer accesses foreign sources of knowledge. This study's finding reveals that users of special purpose machines have a significantly clearer role in providing specifications than do users of general purpose machine tools. Most intensive interactive learning between users and producers in the production process is found in special purpose machine tools. From the empirical findings, policy implications are discussed.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.1
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• pp.9-16
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• 2015

The machining-chatter stands for a sudden relative vibration appeared between a material and a tool while processing with a machine. This chatter is key factor that seriously affects the quality of processed materials as well as being a factor which causes serious damages to the tool and the machine. This study is related to the monitoring and smart control of chatter problem that can compensate machining-chatter faster and produce processed goods with more precision by autonomous compensation. The above-mentioned machining-chatter compensator includes the chatter vibration sensor and the chatter compensator that estimates the compensation value according to the sensor detecting the chatter vibration of machine-tool and the chatter vibration detected from the sensor while having a feature of being organized by interlocking with the machine-tool controller.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.47-55
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• 2023

The automotive industry is undergoing a paradigm shift due to the convergence of IT and rapid digital transformation. Various components, including embedded structures and systems with complex architectures that incorporate IC semiconductors, are being integrated and modularized. As a result, there has been a significant increase in vehicle defects, raising expectations for the quality of automotive parts. As more and more data is being accumulated, there is an active effort to go beyond traditional reliability analysis methods and apply machine learning models based on the accumulated big data. However, there are still not many cases where machine learning is used in product development to identify factors of defects in performance and durability of products and incorporate feedback into the design to improve product quality. In this paper, we applied a prediction algorithm to the defects of automotive door devices equipped with automatic responsive sensors, which are commonly installed in recent electric and hydrogen vehicles. To do so, we selected test items, built a measurement emulation system for data acquisition, and conducted comparative evaluations by applying different machine learning algorithms to the measured data. The results in terms of R² score were as follows: Ordinary multiple regression 0.96, Ridge regression 0.95, Lasso regression 0.89, Elastic regression 0.91.

• Journal of Welding and Joining
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• v.34 no.3
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• pp.1-5
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• 2016

At present, FSW(friction stir welding) process is being considered as an actual way for production of various industrial products. However FSW process involves high temperature and load on the tool during welding. These are make a difference between FSW machine and general machine tools. From this reason, development of FSW machine needs very careful consideration on stiffness of machine structure, spindle and moving axis including machine control system. In this study authors investigate on the trends of technology development of FSW machine in order to share the information for more extension of FSW technology with related researchers and engineers.