• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.377-382
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• 2005

This paper describes the design of micro machine tools system for mechanical machining of micro/meso scale mechanical parts. The micro machining systems such as $\mu-Late$, $\mu-milling/drilling$ machine and $\mu-grinding$ machine are the basic elements constructing $\mu-factory$ which gains more attention recently because of increasing needs of mico and nano-parts in various industrial and medical area. A miniaturized 3-axis milling machine with VCM stage and air spindle and palm-top size micro-late are designed, and air bearing stage and stepwise linear motion system with PZT are studied for motion system. The micro cutting characteristics are investigated experimentally, and reconfigurable machine structures are also considered.

• Nuclear Engineering and Technology
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• v.37 no.2
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• pp.151-158
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• 2005

Advanced nuclear power plants are generally large complex systems automated by computers. Whenever a rare plant emergency occurs the plant operators must cope with the emergency under severe mental stress without committing any fatal errors. Furthermore, The operators must train to improve and maintain their ability to cope with every conceivable situation, though it is almost impossible to be fully prepared for an infinite variety of situations. In view of the limited capability of operators in emergency situations, there has been a new approach to preventing the human error caused by improper human-machine interaction. The new approach has been triggered by the introduction of advanced information systems that help operators recognize and counteract plant emergencies. In this paper, the adverse effect of automation in human-machine systems is explained. The discussion then focuses on how to configure a joint human-machine system for ideal human-machine interaction. Finally, there is a new proposal on how to organize technologies that recognize the different states of such a joint human-machine system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.361-367
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• 1998

The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.600-607
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• 2006

For the effective operation of manufacturing system, FMS(Flexible Manufacturing System) and CIM(Computer Integrated Manufacturing) system are developed. In these systems, a machine tool is the target of integration in last 3 decades. In nowadays, the conventional concept of machine tools is changing to the autonomous manufacturing device based on knowledge-evolution through applying advanced information technology in which open architecture controller, high speed network and internet technology are contained. In this environment, a machine tool is not the target of integration but the subject of cooperation. In the future, a machine tool will be more improved in the form of a knowledge-evolution based device. In order to develop the knowledge-evolution based machine tools, this paper proposes the structure of knowledge evolution in M2M(Machine To Machine) and the scheme of a dialogue agent among agent-based modules such as a sensory module, a dialogue module, and an expert system. The dialogue agent has a role of interfacing with another machine for cooperation. To design the dialogue agent module in M2M environment, FIPA-OS and ping agent based on FIPA-OS are analyzed in this study. Through this, it is expected that the dialogue agent module can be more efficiently designed and the knowledge-evolution based machine tools can be hereafter more easily implemented.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.1-8
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• 2004

In this paper, we implement the Digital Signal Processing System based on Human Machine Interface technology for the telematics with embedded noise-robust speech recognition engine and develop the communication system which can be applied to the automobile information center through the human-machine interface technology. Through the embedded speech recognition engine, we can develop the total DSP system based on Human Machine Interface for the telematics in order to test the total system and also the total telematics services.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.788-791
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• 2004

This paper presents a general description of single and dual contact tooling systems, finite element analysis, and discussions on the application of the system to the 5-head router machine which is in particular for aerospace components. This study has been performed as part of the development of the new generation 5-head router machine which is designed for high productivity. Such high productivity in essence requires high speed rotation and multiple spindles in one machine. The high speed rotation may exceed a range in a conventional single contact tooling system. The conventional tooling system is reevaluated in comparison with the dual-contact system. Finite element analysis using simplified spindle models compares major differences in the two systems. Some problems in the application to the 5-head router machine are discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.868-871
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• 1995

In order to remain competitive, a manufacturing company needs to maintain the optimal condition of its manufacturing system. Machine tools as an important element of a manufacturing system comprises complex mechanical as well as electronic components. Therefore, diagnosing the troubles of machine tools is a tricky process which requires a lot of experience and knowledge. Since providing machine tool users with necessary services at the right time is very difficult and expensive, a remote diagnosis system is to be regarded as a good alternative,with which users can diagnose and fix the machine troubles. This paper presents a framework for a remote machine tool diagnosis system using the world wide web technology and backward reasoning expert system.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.5
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• pp.177-185
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• 2001

This paper describes a machine-vision-based prototype system for automatically feeding engine-mount parts to a swaging machine which assembles engine mounts. The system developed consists of a robot, a feeding device with two cylinders and two photo sensors, and a machine vision system. The machine vision system recognizes the type of different parts being fed from the feeding device and estimates the angular difference between the inner-hole center of the part and the point predetermined for assembling. The robot then picks up each part and rotated it through the estimated angle such that the parts are well assembled together as specified. An algorithm has been developed to recognize different part types and estimate the angular difference. The test results obtained for a set of real specimens indicate that the algorithm performs well enough to be applied to prototype system.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.42-49
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• 2009

This study proposes a web-based remote monitoring system for evaluating degradation of machine tools using ART2(Adaptive Resonance Theory 2) neural network. A number of studies on the monitoring of machine tools using neural networks have been reported. However, when normal condition is changed due to factors such as maintenance, tool change etc., or a new failure signal is generated, such algorithms need to be entirely retrained in order to accommodate the new signals. To cope with such problems, this study develops a remote monitoring system using ART2 in which new signals when required are simply added to the classes previously trained. This system can monitor degradation as well as failure of machine tools. To show the effectiveness of the proposed approach, the system is experimentally applied to monitoring a simulator similar to the main spindle of a machine tool, and the results show that the proposed system can be extended to monitoring of real industrial machine tools and equipment.