• Animal cells and systems
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• 제16권2호
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• pp.121-126
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• 2012

In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. Experimental data deduced by Fl$\ddot{u}$ckiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.

• 전기화학회지
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• 제7권4호
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• pp.179-182
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• 2004

The aim of this study is to investigate the initiation and propagation of crevice corrosion for ferritic stainless steel in artificial crevice based on micro capillary tube method. The 430 stainless steel in artificial crevice is potentiostatically polarized in different sodium chloride solutions. Potentiodynamic and potentiostatic polarization data were measured in situ. The potentials in the crevice were measured by depth profile using the 0.04 mm diameter micro capillary tube inserted in the crevice. The potentials in the crevice ranged from -220 mV to -360 mV vs SCE from opening to bottom of crevice, which are lower than the external surface potential, -200 mV vs SCE. Such a potential drop induced the change of the metal surface state from passive to active. The surface of metal is located in passive state in -200 mV but the inner surface keeps active state below -220 mV, Thus these results show that the It drop mechanism in the crevice was more objective for evaluation and the method was easier to reproduce. Therefore the potential drop is one of the reasons for crevice corrosion by measuring the potentials in narrow crevice with a new micro measuring system.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1998년도 춘계학술대회 논문집
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• pp.293-296
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• 1998

Surface electrical conduction in insulator is most important factor to assess the insulation performances of outdoor insulating materials. In this paper, contamination performance of the widely used materials for outdoor insulator - porcelain, EPDM, Silicone rubber - were discussed by measuring properties of average leakage current and scintillation discharge pulses under artificial contamination conditions. The artificial contaminations used were deionized distilled water fog, 0.5wt% NaCl salt fog of light pollution and 2wt% NaCl salt fog of medium pollution. The average leakage current was appeared linearly with applied voltage at dry and clean surface condition. The magnitude of leakage current was almost same at different kinds of samples. In case of deionized distilled water fog, the characteristics of leakage current and applied voltage was most different to that in case of dry and clean condition. In case of salt fog pollution condition, The leakage current was increased above critical voltage. The scintillation discharges were also activated at the level. the leakage current and scintillation discharges were increased with increasing pollution degree. The resistance to pollution properties of silicone rubber appeared excellent among them.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권3호
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• pp.110-114
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.

• 한국자원식물학회지
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• 제28권6호
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• pp.710-717
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• 2015

Humankind has been searching for medicinal materials from various plant sources in an attempt to treat disease. Mistletoe is one indubitable plant source for these materials due to its effectiveness in treating various diseases, but it has almost disappeared from the mountainous areas of Korea due to excessive harvesting. In this study, in order to select host tree species for Korean mistletoe [Viscum album var. coloratum (Kom.) Ohwi] by seed inoculation and to clarify the effect of host specificity among various tree species were conducted for the purpose of gaining basic information for the artificial cultivation of Korean mistletoe. Almost all the seeds of Korean mistletoe germinated in vitro at the temperature of 15℃. Among host trees used in this study, Prunus mume showed the highest parasitic affinity with inoculated Korean mistletoe, compared with any other host plants. However, treatment of hormones could not increase the low survival rate of Korean mistletoe on the host trees.

• International Journal of Automotive Technology
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• 제6권2호
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• pp.95-105
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• 2005

Diesel engines have low specific fuel consumption, but high particulate emissions, mainly soot. Diesel soot is suspected to have significant effects on the health of living beings and might also affect global warming. Hence stringent measures have been put in place in a number of countries and will be even stronger in the near future. Diesel engines require either advanced integrated exhaust after treatment systems or modified engine models to meet the statutory norms. Experimental analysis to study the emission characteristics is a time consuming affair. In such situations, the real picture of engine control can be obtained by the modeling of trend prediction. In this article, an effort has been made to predict emissions smoke and NO$_{x}$ using cylinder combustion derived parameters and diesel particulate filter data, with artificial neural network techniques in MATLAB environment. The model is based on three layer neural network with a back propagation learning algorithm. The training and test data of emissions were collected from experimental set up in the laboratory for different loads. The network is trained to predict the values of emission with training values. Regression analysis between test and predicted value from neural network shows least error. This approach helps in the reduction of the experimentation required to determine the smoke and NO$_{x}$ for the catalyst coated filters.

• 유변학
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• 제10권2호
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• pp.74-81
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• 1998

사출성형품의 설계는 그 내부의 기계적 물성 변화보다는 전통적으로 용도에 부합하 는 형상을 위주로 하여 이루어져 왔기 때문에 설계조건의 개선을 통하여 성능이 우수한 제 품을 얻기까지 많은 시행착오가 요구되고 있다. 그런데 사출성형 실험이나 물성평가 시험을 하기 전에 성형품의 부위별 기계적 물성을 알수있다면 제품의 설계나 금형 설계에 많은 도 움이 될 수 있으므로 최근에 물성 예측을 위한 방법론들의 개발이 다양하게 시도되고 있다. 따라서 본 연구에서는 학습시스템, 사출성형 수치모사와 기계적 물성과의 상관관계를 밝히 는 방법을 만들어 사출물이 제작되기 전에 그들의 기계적 물성을 사출성형 수치모사에서 얻 어진 열적·기계적 이력으로부터 예측하고자 하였다. 이때 성형품의 기계적 물성과 열적· 기계적 이력 사이에는 매우 복잡하고 비선형적인 상관관계를 보이기 때문에 이들 사이를 비 매개변수적으로 연관짓기 위하여 역전파 인공신경망 알고리듬을 사용하였으며 열적·기계적 이력은 사출성형용 수치모사 소프트웨어를 이용하여 구하였다. 학습과정에서 전역최소값에 도달하지 못하는 인공신경망의 문제점을 해결하기 위하여 모멘텀변수와 잡음지수를 포함하 는 일련의 항을 첨가하여 연결가중치를 보정하였다. 그 결과 어떤 초기값에 의하여 학습이 되더라도 전역최소값에 도달하는 것을 확인하였으며 이를 이용하여 다른 사출조건에서 사출 물의 기계적 물성을 잘 예측할수 있었다.

• 한국정밀공학회지
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• 제20권3호
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• 대한원격탐사학회지
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• 제22권5호
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• pp.367-372
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• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

• 한국컴퓨터정보학회논문지
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• 제7권1호
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• pp.174-183
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• 2002

본 연구에서는 자동화된 데이터의 수집과 자동화된 제조환경하에서 수행될 수 있는 공정관리도의 패턴양상에 대하여 인공지능의 대표적인 기법인 인공신경망을 이용하여 각 패턴의 인식과 이의 검증, 그리고 이상패턴의 발생상황을 모니터링할 수 있는 지능형 공정관리 시스템을 개발하는데 중점을 두었다 개발된 패턴인식시스템을 이용하여 공정의 상태를 관리하는 작업자의 부담을 한층 덜어줄 수 있으며 작업자는 공정에 이상패턴이 발생하는 경우에 패턴인식시스템을 통하여 공정상태에 대한 정보를 전달받을 수 있어서 지속적인 품질개선활동을 수행할 수 있게 된다.