• 한국윤활학회:학술대회논문집
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• 한국윤활학회 1998년도 제27회 춘계학술대회
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• pp.83-90
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• 1998

The morphologies of the wear debris are directly indicative of wear processes occuring in machinery and their severity. The neural network was applied to identify friction condition from the lubricated moving system. The four parameter(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction coefficient. 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 characteristic and recognized the friction condition and materials very well by neural network. We dicuss between the characteristic of wear debris and the friction coefficient and how the network determines difference in wear debris feature.

• 한국생산제조학회지
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• 제7권5호
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• pp.92-97
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• 1998

The morpholgies of the wear particles are directly indicative of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the machine driving system. The four parameters(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different patter characteristic and recognized the friction condition and materials very well by artificial neural network. We discussed how the network determines differencee in wear debris feature, and this approach can be applied to foreseeability and decisio for moving condition of the Machine driving system.

• 한국산학기술학회논문지
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• 제15권5호
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• pp.2610-2616
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• 2014

이젝터는 펌프의 일종으로서 고압의 유체가 지닌 압력에너지를 이용하여 흡입 유체를 빨아들여 이송하는 기계장치이다. 본 논문은 유한체적법 기반의 CFD 분석을 이용하여 이젝터의 성능에 영향을 미치는 혼합실 형상에 따른 영향을 조사하였다. 혼합실 내부의 노즐 직경과 노즐목 길이, 그리고 노즐 끝단과 유체가 외부로 빠져나가는 디퓨저 입구까지의 거리를 변화시키면서 성능을 좌우하는 흡입유체가 가장 잘 흡입되는 최적의 조건을 조사하였다. 연구 결과 이젝터의 성능은 노즐의 직경이 가장 큰 영향을 나타내는 것을 확인하였다. 혼합실 내부 노즐의 직경이 감소함에 따라 혼입율이 증가하는 것을 확인하였고 노즐 직경이 증가할수록 혼입율이 감소되는 것을 확인하였다. 반면 노즐목 길이, 노즐끝단과 디퓨저 입구까지의 거리에 대한 영향은 미비한 것으로 확인되었다. 마지막으로 CFD분석 자료를 토대로 인공신경망을 이용하여 더욱 구체적인 이젝터 혼합실 형상, 노즐 직경 23.8mm를 제시하였다.

• 상하수도학회지
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• 제27권4호
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• pp.413-428
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• 2013

In this study, Ant Colony Algorithm(ACO) was used for optimal model. ACO which are metaheuristic algorithm for combinatorial optimization problem are inspired by the fact that ants are able to find the shortest route between their nest and food source. For applying the model to water distribution systems, pipes, tanks(reservoirs), pump construction and pump operation cost were considered as object function and pressure at each node and reservoir level were considered as constraints. Modified model from Ostfeld and Tubaltzev(2008) was verified by applying 2-Looped, Hanoi and Ostfeld's networks. And sensitivity analysis about ant number, number of ants in a best group and pheromone decrease rate was accomplished. After the verification, it was applied to real water network from S water treatment plant. As a result of the analysis, in the Two-looped network, the best design cost was found to $419,000 and in the Hanoi network, the best design cost was calculated to $6,164,384, and in the Ostfeld's network, the best design cost was found to $3,525,096. These are almost equal or better result compared with previous researches. Last, the cost of optimal design for real network, was found for 66 billion dollar that is 8.8 % lower than before. In addition, optimal diameter for aged pipes was found in this study and the 5 of 8 aged pipes were changed the diameter. Through this result, pipe construction cost reduction was found to 11 percent lower than before. And to conclusion, The least cost design model on water distribution system was developed and verified successfully in this study and it will be very useful not only optimal pipe change plan but optimization plan for whole water distribution system.

• 전자공학회논문지CI
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• 제37권6호
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• pp.1-9
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• 2000

본 논문에서는 클러스터 지향 다단계 상호 연결 망(Cluster Oriented Multistage Interconnection Network)인 COMR을 소개한다. COMR은 통신이 빈번하게 발생되는 프로세서-메모리 클러스터에 보다 짧은 경로를 제공하여 지역화 된 통신 형태를 갖는 병렬 응용 분야에 적합하도록 구성할 수 있다. COMR에 대한 성능 분석은 네트워크에서의 경로 설정 성공 확률(probability of acceptance), 대역폭(bandwidth), 지역 참조성의 변화에 따른 평균 거리(weighted average distance) 및 비용-효율성(cost-effectiveness)에 대해 평가하였다. 성능 평가에 대한 분석 결과에 따르면, COMR은 지역화의 정도가 높은 통신 형태에서 동일한 네트워크 크기를 갖는 MIN보다 높은 성능을 나타내었다. 최악의 경우(worst case)에서의 N×N COMR의 직경(diameter)은 n+1로서 이것은 동일한 네트워크 크기의 MIN과 비교했을 때 단지 1개의 스테이지만을 더 가지고 있는 것이다. 따라서 COMR은 공유 메모리 다중 프로세서 시스템(shared memory multiprocessor system)에서 지역화 된 통신 분포뿐만 아니라 균등 분포 통신를 갖는 병렬 응용 분야에 적합한 MIN으로 활용될 수 있을 것이다.

• Design & Manufacturing
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• 제17권4호
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• 한국멀티미디어학회논문지
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• 제17권6호
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• pp.716-724
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• 2014

The pancake graph is node symmetric and is utilized on the data sorting algorithm. We propose a new half pancake graph that improve pancake graph's network cost. The half pancake degree is approximately half of pancakes degree and diameter is 3n+4. The pancake graph's network cost is $O(1.64n^2)$ and half pancake's is $O(1.5n^2)$. Additionally half pancake graph is sub graph of pancake graph. As this result, The several algorithms developed in pancake graph has the advantage of leverage on the pancake by adding constant cost.

• Tribology and Lubricants
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• 제11권3호
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• pp.24-30
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• 1995

This paper was undertaken to recognize the pattern of the wear debris by neural network as a link for the development of diagnosis system for movable condition of the lubricated machine surface. The wear test was carried out under different experimental conditions using the wear test device was made in laboratory and wear testing specimen of the pin-on-disk type were rubbed in paraffine series base oil, by varying applied load, sliding distance and mating material. The neural network has been used to pattern recognition of four parameter (diameter, elongation, complex and contrast) of the wear debris and learned the friction condition of five values (material 3, applied load 1, sliding distance 1). The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by the neural network. The characteristic parameter of the large wear debris over a few micron size enlarged recognition ability.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집B
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• pp.365-370
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• 2001

In order to minimize unstable flow occurred at a multiblade fan/scroll system, optimal angle and shape of cut-off was determined by using two-dimensional turbulent fluid field analyses and neural network. The results of CFD analyses were used for learning as data of input and output of neural network. After learning neural network optimization process was accomplished for design variables, the angle and the shape of cut-off, in the design domain. As a result of optimization, the optimal angle and shape were obtained as 71 and 0.092 times the outer diameter of impeller, respectively, which are very similar values to previous studies. Finally, it was verified that the fluid field is very stable for optimal angle and shape of cut-off by two-dimensional CFD analysis.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 1997년도 제26회 추계학술대회
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• pp.135-144
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• 1997

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surfaces from which the particles originated. The morphologies of the wear particles are therefore directly indica- rive of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the lubricated moving system. The four parameter(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, 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 characteristic and recognized the friction condition and materials very well by neural network. We dicuss how the network determines difference in wear debris feature, and this approach can be applied to condition diagnosis of the lubricated moving system.