• Journal of the Korea Safety Management & Science
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• v.8 no.1
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• pp.17-26
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• 2006

Basis frame-work's base in a semiconductor industry have gas, chemical, electricity and various facilities in bring to it. That it is a foundation by fire, power failure, blast, spill of toxicant huge by large size accident human and physical loss and damage because it can bring this efficient, connect with each kind mechanical, physical thing to prevent usefully need that control finding achievement factor of human factor of human action. Large size accident in a semiconductor industry to machine and human and it is involved that present, in system by safety interlock defect of machine is conclusion for error of behaviour. What is not construing in this study, do safety in a semiconductor industry to do improvement. Control human error analyzes in human control with and considers mechanical element and several elements. Also, apply achievement factor using O'conner Model by control method of human error. In analyze by failure mode effect using actuality example.

• Journal of The Korean Society For Urban Railway
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• v.6 no.4
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• pp.287-298
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• 2018

This research is intended to understand and make practical suggestions on various aspects of the railroad traffic controllers focusing on railroad control of the railroad safety management system's technical standards. The method of analysis is using the causal loop diagram, which is the soft method of System Dynamics. Content of analysis is about the system. 11.7 of the work of the railroad control system. In addition, we compared and analyzed railroad controllers by four railroad operators, suggesting the importance of railroad control and future practical improvement plans.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.196-212
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• 2014

Facial expression recognition is an important part in emotional interaction between human and machine. This paper proposes a facial expression recognition approach based on multi-classifier fusion with stacking algorithm. The kappa-error diagram is employed in base-level classifiers selection, which gains insights about which individual classifier has the better recognition performance and how diverse among them to help improve the recognition accuracy rate by fusing the complementary functions. In order to avoid the influence of the chance factor caused by guessing in algorithm evaluation and get more reliable awareness of algorithm performance, kappa and informedness besides accuracy are utilized as measure criteria in the comparison experiments. To verify the effectiveness of our approach, two public databases are used in the experiments. The experiment results show that compared with individual classifier and two other typical ensemble methods, our proposed stacked ensemble system does recognize facial expression more accurately with less standard deviation. It overcomes the individual classifier's bias and achieves more reliable recognition results.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1897-1902
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• 2008

The computer system is used in many application domains and any system error in these domains may either cause critical loss or threaten environment or human life. Though examples of these domains can be found in many areas, the system, which is used in domains for carrying passengers including rolling stocks in particular, is expected to show satisfactory operation all the time. The relay control logic, which is used in rolling stocks, is complex in hardware and occupies considerably large volume. Nevertheless, it has been used for a long time, to let the system safely operate even in the occurrence of an error in the computer system. However, the relay control logic circuit is so complex that the analysis of proper circuit operation and interlocking tends to be dependent only on the designer's experiences instead of being systematically performed. Especially, the analysis following a change, addition and deletion of a previous circuit according to the requirements from a source of demand is significantly limited. In this paper, the accuracy of relay control logic is verified by the use of properties of Petri Net model. In addition, how main circuit breaker (MCB) control circuit is modeled and analyzed by the design methodology is shown.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.776-783
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• 2022

Since Mechanical, Electrical, and Plumbing(MEP) routing is a repetitive and experience-centered process that requires considerable time and human resources, if automated, design errors can be prevented and the previously required time and human resources can be reduced. Although research on automatic routing has been conducted in many industries, the MEP routing in AEC projects has yet to be identified due to the complexity of system configuration, distributed expertise, and various constraints. Therefore, the purpose of this study is to identify the target subjects for MEP routing automation and the constraints of each subject. The MEP design checklist provided by a CM company and existing literature review were conducted, and target subjects and constraints were identified through process observation and in-depth expert interviews for five days by visiting a MEP design company. The target subjects were largely divided into six categories: air conditioning plumbing, air conditioning duct, restroom sanitary plumbing, heating plumbing, and diagram. The findings from interviews show that work reduction and error reduction has the greatest effect on air conditioning plumbing while the level of difficulty is the highest in air conditioning duct and restroom sanitary plumbing. Major constraints for each subject include preventing cold drafts on air conditioning pipes, deviation in ventilation volume in air conditioning ducts, routing order on restroom sanitary plumbing, and separation distance from the wall on heating plumbing. In this way, subjects and constraints identified in this study can be used for MEP automatic routing.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.107-114
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• 2005

Edge enhanced image is needed for processing images for special purpose such as a circuit diagram or a design composed of lines. Error diffusion halftoning, among digital halftoning methods to represent a continuous grayscale image for the binary output device such as printers, facsimiles, LCD televisions and etc. also makes edges of objects blurred. This paper proposes the method to enhance the edge of a binary image for the binary output device as well as a circuit diagram or a design. Based on that the human eyes perceive the local average luminance rather than the pixel's luminance itself, the proposed system uses a local activitymeasure (LAM), which is the difference between a pixel luminance and the average of its $3{\times}3$ neighborhood pixels' luminances weighted according to the spatial positioning. The system also usesinformation of edge enhancement(IEE), which is computed from the LAM multiplied by the average luminance. The IEE is added to the quantizer's input pixel and feeds into the halftoning quantizer. The quantizer produces the halftone image having the enhanced edge. The simulation results show that the proposed method produces more fine halftoning images than conventional methods due to the enhanced edges. Also the performance of the proposed method is compared with that of the conventional method by measuring the edge correlation and the local average accordance over a range of viewing distances.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.68-77
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• 1990

In this paper, a neural patten recognition method for the automatic circuit diagram reading system is proposed. The proposed procedure to recognize a deformed logic symbols is composed of three stages: feature detection, log mapping, and pattern classification. In the feature detection stage, a modified competitive learning algorithm where each pattern has the inhibition weight as well as the activation weight is developed. The global information of hand-written logic symbols is obtained by the feature detection neural network having both the inhibition and activation weights. The obtained global data is then transformed into a log space by the conformal mapping where according to the Schwartz's theory about the human visual signal process-ing, the degree of rotation and the scale change are mapped into the translation change. Logic symbols are finally classified by a three layer perceptron trained by the error back propagation algorithm. The computer simulation demonstrates that the proposed multistage neural network system can recognize well the deformed patterns of hand-written logic circuit diagrams.