• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.1-15
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• 1999

This research involves the development and evaluation of a work flow control model for a type of flexible manufacturing system(FMS) called a flexible flow line(FFL). The control model can be considered as a kind of hybrid intelligent model in that it utilizes both computer simulation and neural network technique. Training data sets were obtained using computer simulation of typical FFL states. And these data sets were used to train the neural network model. The model can easily incorporate particular aspects of a specific FFL such as limited buffer capacity and dispatching rules used. It also dynamically adapts to system uncertainty caused by such factors as machine breakdowns. Performance of the control model is shown to be superior to the random releasing method and the Minimal Part Set(MPS) heuristic in terms of machine utilization and work-in-process inventory level.

• Journal of Environmental Science International
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• v.14 no.1
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• pp.1-8
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• 2005

CFD code are used for numerically testing a new concept of large space air control system. A workshop with air-conditioners products lines and air-conditioned by several floor type air-containers is tested. The whole room air distribution is controlled by boosters installed in a middle height horizontal plane. First, calculated results are compared with measured data to confirm the validity and applicability of the prediction method. Next, the method is applied to case studies heating seasons. Results under some operating conditions show effectiveness in avoid the temperature stratification in winter.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.579-583
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• 2001

In the area of data clustering in high dimensional space, one of the difficulties is the time-consuming process for computing vector similarities. It becomes worse in the case of the agglomerative algorithm with the group-average link and mean centroid method, because the cluster similarity must be recomputed whenever the cluster center moves after the merging step. As a solution of this problem, we present an incremental method of similarity computation, which substitutes the scalar calculation for the time-consuming calculation of vector similarity with several measures such as the squared distance, inner product, cosine, and minimum variance. Experimental results show that it makes clustering speed significantly fast for very high dimensional data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.4
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• pp.42-49
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• 1998

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surface from which the particles originated. The morphological identification of wear debris can therefore provide very early detection of a fault and can also often facilitate a diagnosis. The purpose of this study is to attempt the developement of intelligent system for moving condition diagnosis of the machine driving system. The four shape parameter(50% volumetric diameter, aspect, roundness and reflectivity) of war debris are used as inputs to the neural network and learned the moving condition of five values(material3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the moving condition and materials very well by neural network.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.59-62
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• 2001

Reliability engineering is regarded as the major and important roll for all industry. And advanced manufacturing systems with high speed and intelligent have been developing for the betterment of machining ability. In this study, we have systemized evaluation of reliability for machinery system. We proposed the reliability assessment and design review method using analyzing critical units of high speed and intelligent machine system. In addition, we have not only designed and developed test bed system for acquiring reliability data, but also apply QFD technique for satisfying quality function which is provided in design phase. From this study, we will expect to guide and introduce the reliability engineering in developing and processing phase of high quality product.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.3
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• pp.713-723
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• 1995

In intelligent machine tools, a computer based control system, which can adapt the machining parameters in an optimal fashion based on sensor measurements of the machining process, should be incorporated. In this paper, the technology for adaptively optimizing the cutting conditions to maximize the material removal rate in face milling operations is proposed using the exterior penalty function method combined with multilayered neural networks. Two neural networks are introduced ; one for estimating tool were length, the other for mapping input and output relations from experimental data. Then, the optimization of cutting conditions is adaptively implemented using tool were information and predicted process output. The results are demonstrated with respect to each level of machining such as rough, fine and finish cutting.

• Journal of Mechanical Science and Technology
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• v.16 no.3
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• pp.338-343
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• 2002

In the autonomous vehicle, the reference lane is continually detected by machine vision system. And then the vehicle is steered to follow the reference yaw rates which are generated by the deviations of lateral distance and the yaw angle between a vehicle and the reference lane. To cope with the steering delay and the side-slip of vehicle, PI controller is introduced by yaw rate feedback and tuned from the simulation where the vehicle is modeled as 2 DOF and 79 DOF and verified by the results of an actual vehicle test. The lateral control algorithm by yaw rate feedback has good performances of lane tracking and passenger comfort.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.157-162
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• 2016

In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.461-462
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• 2006

This paper reports on the development of a scanning probe microscopy(SPM) tip with caborn nanotubes. We used an electric field which causes dielectrophoresis(DEP), to align and deposit CNTs on a metal-coated SPM tip. Using the CNT attached SPM tip, we have obtained an enhanced resolution and wear property compared to that from the bare silicon tip through the scanning of the surface of the bio materials. The carbon nanotube tip align toward the source of the ion beam allowing their orientation to be changed at precise angles. By this technique, metal coated carbon nanotube tips that are several micrometer in length are prepared for scanning probe microscopy.