• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.636-640
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• 2002

In contrast to conventional CNC, PC-NC opens a new era for machine tools to be more intelligent. For instance, machine tool with PC-NC can be a machine agent system with capability of reacting autonomously to changing operating conditions. This paper introduces a concept of intelligent machine agent system, composed of machine agent and cell manager. Machine agent performs the functions such as process monitoring, diagnosis, maintenance management, condition assessment and schedule negotiation, while cell manager coordinates the negotiation process among multiple machine agents.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1244-1255
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• 2015

This paper presents a new multi-machine robust control based on an electric differential system for electric vehicle (EV) applications which is composed of four in-wheel permanent magnet synchronous motors. It is based on a new master-slave direct torque control (DTC) algorithm, which is used for the control of bi-machine traction systems based on a speed model reference adaptive system observer. The use of an electric differential in the design of a new EV constitutes a technological breakthrough. A classical system with a multi-inverter and a multi-machine comprises a three-phase inverter for each machine to be controlled. Another approach consists of only one three-phase inverter for several permanent magnet synchronous machines. The control of multi-machine single-inverter systems is the subject of this study. Several methods have been proposed for the control of multi-machine single-inverter systems. In this study, a new master-slave based DTC strategy is developed to generate an electric differential system. The entire system is simulated by Matlab/Simulink. The simulation results show the effectiveness of the new multi-machine robust control based on an electric differential system for use in EV applications.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.1
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• pp.19-25
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• 1998

Remote fault diagnosis and maintenance system using general telecommunication network is necessary for an effective fault diagnosis and higher productivity of NC machine tools. In order to monitor machine tool condition and diagnose alarm states due to electrical and mechanical faults, a remote data communication system for monitoring of NC machine fault diagnosis and status is developed. The developed system consists of (1) remote communication module among NC's and host PC using PSTN. (2) 8 channels analog data sensing module, (3) digital I/O module for control or NC machine, (4) communication module between NC machine and remote data communication system via RS-232C, and (5) software man-machine interface. Diagnostic monitoring results generated through a successive type inference engine are displayed in user-friendly graphics. The validity and reliability of the developed system is verified to be a powerful commercial version on a vertical machining center through a series of experiments.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.9
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• pp.75-81
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• 1999

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 consist of 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 tools diagnosis system by combining the world wide web technology and backward reasoning expert system.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.7-13
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• 2021

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.11-19
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• 2022

In this paper, we propose a fault tolerant embedded system using memory redundancy on the full-virtualization based virtual machine monitor. The proposed virtual machine monitor first virtualizes main memory of embedded system utilizing efficient shadow page table scheme so that the embedded system runs as a virtual machine on the virtual machine monitor. The virtual machine monitor makes the backup of the embedded system run as another virtual machine by copying memory contents of the embedded system into memory space of backup system according to predefined schedules. When an error occurs in the target virtual machine, the corresponding standby virtual machine takes the role of target virtual machine and continues its operation. Performance evaluation studies show that such backups and switches of virtual machines are performed with minor performance degradation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.305-325
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• 2014

In cloud computing infrastructure, current virtual machine trust measurement methods have many shortcomings in dynamism, security and concurrency. In this paper, we present a new method to measure the trust of virtual machine. Firstly, we propose "behavior trace" to describe the state of virtual machine. Behavior trace is a sequence of behaviors. The measurement of behavior trace is conducted on the basis of anticipated trusted behavior, which not only ensures security of the virtual machine during runtime stage but also reduces complexity of the trust measurement. Based on the behavior trace, we present a Dynamic Tree Style Trust Measurement Model (DTSTM). In this model, the measurement of system domain and user domain is separated, which enhances the extensibility, security and concurrency of the measurement. Finally, based on System Call Interceptor (SCI) and Virtual Machine Introspection (VMI) technology, we implement a DTSTM prototype system for virtual machine trust measurement. Experimental results demonstrate that the system can effectively verify the trust of virtual machine and requires a relatively low performance overhead.

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.1073-1074
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• 2005

• Proceedings of the Korean Nuclear Society Conference
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• 2005.05a
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• pp.1293-1294
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• 2005

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.11
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• pp.30-35
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• 2021

In this study, a machine simulation system was built using the actual scale of an AC-type 5-axis machine tool for mold surface machining that can be used in applications, such as, modeling and machine building, stroke, and collision detection. The validity of the 5-axis machine simulation system was verified by performing tool path generation, post-processing, machine simulation, prototype motion simulation, and an actual cutting experiment. This entire process was intended to activate the 5-axis machining in mold surface machining.