• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.301-308
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• 2009

In the modern life, machines are used for various areas in industries as the advance of science and industrial development has proceeded. In many machines, the rotating machines play an important role in many processes. Therefore, the development of fault diagnosis and monitoring system for rotating machines is required. An ubiquitous sensor network (USN) is a combination of the key computer science and engineering area technology including the wireless network, embedded system hardware and software, communication, real-time system, etc. It collects environmental information to realize a variety of functions. In this work, a data reduction algorithm for USN based remote fault diagnostic system which can be easily applied to previously built factories is proposed. To verify the feasibility of the proposed scheme, some simulations and experiments are executed.

• Journal of Platform Technology
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• v.6 no.4
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• pp.18-25
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• 2018

This paper describes a real-time human and robot tracking method in Intelligent Space with multi-camera networks. The proposed method detects candidates for humans and robots by using the histogram of oriented gradients (HOG) feature in an image. To classify humans and robots from the candidates in real time, we apply cascaded structure to constructing a strong classifier which consists of many weak classifiers as follows: a linear support vector machine (SVM) and a radial-basis function (RBF) SVM. By using the multiple view geometry, the method estimates the 3D position of humans and robots from their 2D coordinates on image coordinate system, and tracks their positions by using stochastic approach. To test the performance of the method, humans and robots are asked to move according to given rectangular and circular paths. Experimental results show that the proposed method is able to reduce the localization error and be good for a practical application of human-centered services in the Intelligent Space.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• Informatization Policy
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• v.23 no.2
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• pp.3-18
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• 2016

Radical paradigm shift is being expected due to the coming of the so-called intelligent society. In the intelligent society, things or machines are able to use intelligence for the first time in history and this will bring about fundamental changes at every corner of human society. This article analyzes views of the world in order to figure out basic directions of this paradigm shift. A dualistic view of world mediated by technology is suggested for a new world view of the intelligent society, based on comparison of a traditional dualistic view of world between human-nature and a tripartite view of world between human-machine-nature. This article summarizes paradigms of the intelligent society into four ; externalization of intelligence, productivity explosion, platform society, and self-organizing society. These new paradigms will provide lots of benefits such as intelligence augmentation, production capacity increase, and self-organizing effect. But at the same time, it will increase risks of system failure because of loss of human control on technologies. In conclusion, it is argued that human choices and efforts will decide the future of the intelligent society becuase the paradigm shift is value neutral in essence.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.37-44
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• 2005

The creation of automotive systems of active safety with intelligent functions needs the use of new control principles for the wheel and automobile. One of such directions is the pre-extreme control strategy. Its aim is the ensuring of wheel's work in pre-extreme, stable area of tire grip wheel slip dependence. The simplest realization of pre-extreme control in automotive anti-lock brake systems consists in the threshold and gradient algorithms. A comparative analysis of these algorithms, which has been made on 'hardware in-the-loop' simulation results of the braking for bus with various anti-lock brake systems (ABS), indicated their high efficiency.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.1
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• pp.15-20
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• 2014

The manufacturing equipment on most shop floors consists of numerical control machines, and the condition of each piece of equipment is monitored and controlled by an internal sensor or programmable logic controller (PLC). To control and monitor production lines that consist of an equipment or production module, a separate control and monitoring system such as a manufacturing execution system should be introduced. However, there is no standardized system, and it is costly and difficult to build a system for small or medium-sized plants. In this paper, a PLC-based supervisory system for operation control of a group of production machines is proposed, and the developed PLC-based system is evaluated by applying it to a computer numerical control machine.

• Journal of the Korea Safety Management & Science
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• v.9 no.3
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• pp.49-54
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• 2007

신뢰성이란 단기간에 측정되는 성능과는 다른 지표로서 흔히 장기간에 걸쳐 평가되는 품질의 척도이다. Oil Cooler는 공작기계(machine tools)의 주축 및 구동부 등에서 발생하는 열 변형을 제어하는 장치로서 공작기계의 신뢰성 향상을 위해서는 oil cooler의 신뢰성 개선이 이루어져야 한다. 본 연구에서는 oil cooler의 신뢰성 개선을 위해 고장률 데이터베이스를 이용한 신뢰성 예측과 이를 통한 취약부품 분석을 실시하고 신뢰성 시험기를 통한 oil cooler의 신뢰성을 평가하였다. 이를 통해 oil cooler의 정량적 신뢰도를 계산하였으며 신뢰성호 향상을 위한 공정기법을 개발하여 적용하였다. Oil cooler의 신뢰성 개선을 통해 공작기계 및 반도체 제조 장비 등과 같은 제조 시스템의 신뢰성 향상을 기대할 수 있으며, 제안된 기법을 이용하여 다른 기계류 부품의 신뢰성 평가 및 개선에 적용할 수 있다.

• International Journal of Contents
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• v.12 no.3
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• pp.12-16
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• 2016

Brain computer interfaces (BCI) usually have focused on classifying the explicitly-expressed intentions of humans. In contrast, implicit intentions should be considered to develop more intelligent systems. However, classifying implicit intention is more difficult than explicit intentions, and the difficulty severely increases for subject independent classification. In this paper, we address the subject independent classification of implicit intention based on electroencephalography (EEG) signals. Among many machine learning models, we use the support vector machine (SVM) with radial basis kernel functions to classify the EEG signals. The Fisher scores are evaluated after extracting the gamma, beta, alpha and theta band powers of the EEG signals from thirty electrodes. Since a more discriminant feature has a larger Fisher score value, the band powers of the EEG signals are presented to SVM based on the Fisher score. By training the SVM with 1-out of-9 validation, the best classification accuracy is approximately 65% with gamma and theta components.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.809-816
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• 2008

With increasing demands for reducing cost of maintenance which can detect machine fault automatically; low cost and intelligent functionality sensors are required. Rapid developments, in semiconductor, computing, and communication have led to a new generation of sensor called "smart" sensors with functionality and intelligence. The purpose of this research is comparison of machine fault classification between general analyzer signals and smart sensor signals. Three types of sensors are used in induction motors faults diagnosis, which are vibration, current and flux. Classification results are satisfied.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.37-41
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• 2002

This study has been focused on the development of an open architecture CNC system and integration of core application technology for machine tool such as a remote monitoring/diagnosis system, NURBS interpolation, and cutting process simulation. To do this, we have developed a comprehensive CNC software including the basic HMI, screen editor, ASF, and visual builder. As a control hardware system for machine tool, the universal I/O module and CNC main unit have been developed. Then the remote monitoring/diagnosis system and NURBS interpolation have been implemented in the CNC software. The cutting simulation software will be used for enhancing the productivity of machine tools. Through a simulator and test bed, the whole technology has been verified.