• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_1
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• pp.977-983
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• 2023

The tank uses a twin turbo diesel engine equipped with two turbocharger systems for high output. The main component of the turbocharger system is the turbine housing through which the exhaust flows. Turbine housing is manufactured through a sand casting process, taking into account the shape and material characteristics according to the environmental conditions in which it is used. Currently, turbine housing is imported, and local production is necessary. In this study, basic research was conducted to localize the turbine housing of a tank diesel turbo engine. Reverse engineering and finite element analysis of the imported turbine housing were performed. The prototype of the turbine housing was printed using FDM and PBF 3D printers. The prototype of the turbine housing printed with an FDM 3D printer has an overall appearance similar to 3D modeling, but the printed surface of the whorl part is rough. The prototype printed with the PBF 3D printer is completely identical to the 3D modeling, including the whorl part.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.180-186
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• 2024

A common language for modelling software requirements and design in recent years is Unified Modeling Language (UML). Essential principles and rules are provided by UML to help visualize and comprehend complex software systems. It has therefore been incorporated into the curriculum for software engineering courses at several institutions all around the world. However, it is commonly recognized that UML is challenging for beginners to understand, mostly owing to its complexity and ill-defined nature. It is unavoidable that we need to comprehend their preferences and issues considerably better than we do presently in order to approach the problem of teaching UML to beginner students in an acceptable manner. This paper offers a hint based approach that can be implemented along with an ordinary lab task. Some keywords are heighted to indicate class diagram component and make students to understand the textual descriptions. The experimental results indicate significant improvement in students learning skills. Furthermore, majority of students also positively responded to the survey conducted in the end experimental study.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.

• The KSFM Journal of Fluid Machinery
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• v.19 no.6
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• pp.43-49
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• 2016

A performance analysis of a gasoline engine with a 2-stage turbocharger system for unmanned aerial vehicle(UAV) was conducted. One dimensional system analysis was conducted for the requirements of turbochargers and adequate turbochargers were selected from commercially available models for automobiles. Modeling and simulation were performed by Ricardo WAVE. Gasoline engine modeling was based on a 2.4 L 4-cylinder engine specification. The selected turbochargers and intercoolers were added to the engine model and simulated at 40,000 ft altitude condition. The results of the engine model and 2-stage turbocharger system model simulation showed break power 93 kW which is appropriate power required for the engine operation at the ambient conditions of 40,000 ft altitude.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.278-282
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• 2001

The discovery of tacit knowledge from domain experts is one of the most exciting challenges in today\`s knowledge management. The nature of decision knowledge in determining the quality a firm\`s short-term liquidity is full of abstraction, ambiguity, and incompleteness, and presents a typical tacit knowledge extraction problem. In dealing with knowledge discovery of this nature, we propose a scheme that integrates both knowledge elicitation and knowledge discovery in the knowledge engineering processes. The knowledge elicitation component applies the Verbal Protocol Analysis to establish industrial cases as the basic knowledge data set. The knowledge discovery component then applies fuzzy clustering to the data set to build a fuzzy knowledge based system, which consists of a set of fuzzy rules representing the decision knowledge, and membership functions of each decision factor for verifying linguistic expression in the rules. The experimental results confirm that the proposed scheme can effectively discover the expert\`s tacit knowledge, and works as a feedback mechanism for human experts to fine-tune the conversion processes of converting tacit knowledge into implicit knowledge.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1139-1158
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• 2015

This paper proposes an efficient system identification method for modeling nonlinear behavior of civil structures. This method is developed by integrating three different methodologies: principal component analysis (PCA), artificial neural networks, and fuzzy logic theory, hence named PANFIS (PCA-based adaptive neuro-fuzzy inference system). To evaluate this model, a 3-story building equipped with a magnetorheological (MR) damper subjected to a variety of earthquakes is investigated. To train the input-output function of the PANFIS model, an artificial earthquake is generated that contains a variety of characteristics of recorded earthquakes. The trained model is also validated using the1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes. The adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. It is demonstrated from the training and validation processes that the proposed PANFIS model is effective in modeling complex behavior of the smart building. It is also shown that the proposed PANFIS produces similar performance with the benchmark ANFIS model with significant reduction of computational loads.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.201-208
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• 2005

To guarantee the quality of service for user request, we should consider various kinds of things. The important thing of QoS is that response time of service is transparently suggested 'to network users. We can know the response time of service using the information of network latency, system latency, and software component latency, In this paper, we carried out the modeling of network latency and analyzed the effects of packets loss to the network latency, Also, we showed the effectiveness of modeling using the NS-2. This research can help to provide the effective methods in case of SLA(Service Level Agreement) agreement between service provider and user.

• Journal of the Korean Society of Systems Engineering
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• v.9 no.1
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• pp.65-84
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• 2013

This paper introduces a systems engineering approach to reliability centered maintenance to address some of the weaknesses. Reliability centered maintenance is a systematic, disciplined process that produces an efficient equipment management strategy to reduce the probability of failure [1]. The study identifies the need for RCM, requirements analysis, design for RCM implementation. Value modeling is used to evaluate the value measures of RCM. The system boundary for the study has been selected as containment spray pump and its motor drive. Failure Mode and Criticality Effects analysis is applied to evaluate the failure modes while the logic tree diagram used to determine the optimum maintenance strategy. It is concluded that condition based maintenance tasks should be enhanced to reduce component degradation and thus improve reliability and availability of the component. It is recommended to apply time directed tasks to age related failures and failure finding tasks to hidden failures.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.231-236
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• 2000

In order to predict depth of the pile forward modeling and inversion of magnetic logging data was conducted by using a finite line of dipoles model. The horizontal component as well as the vertical component of magnetic fields can be measured in the borehole, and the magnetic anomalies can be obtained by subtracting the Earth's magnetic field from the measurement. The magnetic anomalies of the pile are considered as vector sum of induced magnetization due to the Earth's magnetic field and remnant magnetization possessed by steel strings in the pile. The magnetic anomalies are used as input data for inversion from which the length, the magnetic moment per unit length, and the dip angle of the pile can be obtained. From the inversion of synthetic noisy data, and the data obtained from the field model test it is found that the driving depth of the pile can be determined as close to the order of measuring interval (5∼10㎝). It is also found that the resultant magnetic anomalies due to an individual steel string in the pile are almost same as those due to a group of steel strings located at the center of the pile. The magnetic logging method also can be used for locating reinforced bars, pipes, and steel casings.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.173-180
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• 2011

This paper shows an approach for real-time object segmentation on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). Recently, many applications that is monitoring system, motion analysis, object tracking or etc require real-time processing. It is not suitable for object segmentation to procedure real-time in CPU. NVIDIA provide CUDA platform for Parallel Processing for General Computation to upgrade limit of Hardware Graphic. In this paper, we use adaptive Gaussian Mixture Background Modeling in the step of object extraction and CCL(Connected Component Labeling) for classification. The speed of GPU and CPU is compared and evaluated with implementation in Core2 Quad processor with 2.4GHz.The GPU version achieved a speedup of 3x-4x over the CPU version.