• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.346-349
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• 2003

For a traveling crane, various control methods such as neural network predictive control and TDOFPID(Two Degree of Freedom Proportional Integral Derivative) are studied. So in this paper, we proposed improved navigation method to reduce transfer time and sway with anti-collision path for avoiding collision in its movement to the finial coordinate. And we constructed the NNPPID(Neural Network Predictive PID) controller to control the precise move and speedy navigation. The proposed predictive control system is composed of the neural network predictor, TDOFPID controller, and neural network self-tuner. We analyzed ASC(Automated Stacking Crane) system and showed some computer simulations to prove excellence of the proposed controller than other conventional controllers.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.41-47
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• 2012

Data mining and knowledge discovery techniques have shown to be effective in finding hidden underlying rules inside large database in an automated fashion. On the other hand, analyzing, assessing, and applying students' survey data are very important in science and engineering education because of various reasons such as quality improvement, engineering design process, innovative education, etc. Among those surveys, analyzing the students' views on science-technology-society can be helpful to engineering education. Because, although most researches on the philosophy of science have shown that science is one of the most difficult concepts to define precisely, it is still important to have an eye on science, pseudo-science, and scientific misconducts. In this paper, we report the experimental results of applying decision tree induction algorithms for analyzing the questionnaire results of high school students' views on science-technology-society (HS-VOSTS). Empirical results on various settings of decision tree induction on HS-VOSTS results from one South Korean university students indicate that decision tree induction algorithms can be successfully and effectively applied to automated knowledge discovery from students' survey data.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.951-958
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• 2007

Radio Frequency Identification (RFID) is a technology for automated identification of objects and people. RFID may be viewed as a means of explicitly labeling objects to facilitate their "perception" by computing devices. RFlD systems have been gaining more popularity in areas especially in supply chain management and automated identification systems. However, there are many existing and potential problems in the RFlD systems which could threat the technology s future. To successfully adopt RFID technology in various applications. we need to develop the solutions to protect the RFID system s data information. This study investigates important issues related to privacy and security of RF1D based on the recent literature and suggests solutions to cope with the problem.

• Annual Conference of KIPS
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• 2003.05b
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• pp.791-794
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• 2003

자동화 컨테이너터미널은 ATC(Automated Transfer Crane)와 AGV(Automated Guided Vehicle)와 같은 자동화 운송 장비를 사용하는 컨테이너터미널이다. 이러한 자동화 컨테이너터미널의 핵심은 생산성을 향상시키기 위한 효율적인 장비 운영이 결정적인 역할을 한다. 본 논문에서는 AGV의 이동 위치 결정을 위한 RTK-GPS(Real Time Kinematic-Global Positioning System) 위치 정보 전송 및 운용 방법에 대해서 연구하였다. 이를 위해서 기존의 무선모뎀을 이용한 RTK-GPS 전송 방법을 무선 LAN(Local Area Network) 환경에서 RTK-GPS 위치 정보 전송 방법에 대해서 제안하고, AGV 이동 위치 결정 및 계산을 실험하였다.

• Mycobiology
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• v.30 no.4
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• pp.228-232
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• 2002

This study focused on the production of high quality compost for the growth of aero-thermophilic fungi, which has a promoting effect on the growth rate and production of oyster mushrooms. The automated solid-state bioreactor system was designed on the basis of a Three-Phase-One system, which controls the serial steps of prewetting, pasteurization and fermentation processes. High numbers of thermophilic fungi and bacteria were recovered from the mushroom composts prepared by this solid-state bioreactor. The rates of composting process were depended on physical as well as chemical factors. Among these factors, the parameters of moisture content and temperature were found to be particularly important. In our automated system, constant levels of moisture content, temperature and ventilation via mixing were provided by a centralized control apparatus including PLC, water tank and water jacket systems. These features induced higher microbiological activity of aero-thermophiles.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.1
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• pp.43-48
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• 2002

This paper describes an automated computer-aided engineering (CAE) system for three-dimensional structures. An automatic finite element mesh-generation technique, which is based on fuzzy knowledge processing and computational geometry techniques, is incorporated into the system, together with a commercial FE analysis code, and a commercial solid modeler. The system allows a geometry model of interest to be automatically converted to different FE models, depending on the physical phenomena of the structures to be analyzed, i.e., electrostatic analysis, stress analysis, modal analysis, and so on. Also, with the aid of multilayer neural networks, the present system allows us to obtain automatically a design window in which a number of satisfactory design solutions exist in a multi-dimensional design parameter space. The developed CAE system is successfully applied to evaluate an electrostatic micromachines.

• IE interfaces
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• v.19 no.3
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• pp.214-224
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• 2006

The container handling times of automated transfer cranes(ATCs) significantly affect the productivity of container terminals. In this paper, evaluation models for the container handling times of ATCs are suggested for import container blocks with different transfer point configurations. Firstly, evaluation models for various motion times of stacking and retrieving operations of ATC are suggested for two basic alternatives of import container blocks. In addition, in considering the space allocation, evaluation methods for the container handling times of ATC are suggested. Finally, the container handling times for each case are compared with each other in order to analyze how the block shape and the transfer point locations affect the container handling times of ATC.

• Structural Engineering and Mechanics
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• v.69 no.1
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• pp.105-120
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• 2019

In this paper, a new efficient method for structural modal reanalysis is proposed, which can handle large finite element (FE) models requiring frequent design modifications. The global FE model is divided into a residual part not to be modified and a target part to be modified. Then, an automated matrix permutation and substructuring algorithm is applied to these parts independently. The reduced model for the residual part is calculated and saved in the initial analysis, and the target part is reduced repeatedly, whenever design modifications occur. Then, the reduced model for the target part is assembled with that of the residual part already saved; thus, the final reduced model corresponding to the new design is obtained easily and rapidly. Here, the formulation of the proposed method is derived in detail, and its computational efficiency and reanalysis ability are demonstrated through several engineering problems, including a topological modification.

• Annual Conference of KIPS
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• 2009.11a
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• pp.811-812
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• 2009

프로젝트 개발에서 소프트웨어의 품질을 높이기 위한 방법 중 하나는 소스코드에 대한 잠재적인 결함을 초기에 발견하는 것이다. 이를 실현하기 위해 정형화된 기법으로 코드 인스펙션을 자동화하였으며, 개발자들이 ACI 규칙을 수립하였다. 논문에서는 실제 진행 중인 프로젝트를 기반으로 하여 결함 점검 수행에 따른 결함 발견 건수와 결함밀도가 감소되는 증명을 다룬다.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.660-669
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• 2020

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.