• Journal of Mechanical Science and Technology
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• 제18권2호
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• pp.230-239
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• 2004

A compact model approach of a network of spring elements for elastic loading is presented for the thermal deformation analysis of BGA package assembly. High-sensitivity moire interferometry is applied to evaluate and calibrated the model quantitatively. Two ball grid array (BGA) package assemblies are employed for moire experiments. For a package assembly with a small global bending, the spring model can predict the boundary conditions of the critical solder ball excellently well. For a package assembly with a large global bending, however, the relative displacements determined by spring model agree well with that by experiment after accounting for the rigid-body rotation. The shear strain results of the FEM with the input from the calibrated compact spring model agree reasonably well with the experimental data. The results imply that the combined approach of the compact spring model and the local FE analysis is an effective way to predict strains and stresses and to determine solder damage of the critical solder ball.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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• pp.468-472
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• 2004

In this paper a VLSI design for the automatic magnetizing system has been presented. This is the design of a peripheral controller, which magnetizes CRTs and computers monitors and controls the automatic inspection system. We implemented a programmable peripheral interface(PPI) circuit of the control and protocol module for the magnetizer controller by using a O.8um CMOS SOG(Sea of Gate) technology of ETRI. Most of the PPI functions has been confirmed. In the conventional method, the propagation/ramp delay model was used to predict the delay of cells, but used to model on only a single cell. Later, a modified "apos;Linear delay predict model"apos; was suggested in the LODECAP(LOgic Design Capture) by adding some factors to the prior model. But this has not a full model on the delay chain. In this paper a new "apos;delay predict equationapos;" for the design of the timing control block in PPI system has been suggested. We have described the detail method on a design of delay chain block according to the extracted equation and applied this method to the timing control block design.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1282-1290
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• 2017

Since the mitigation of fear of crime significantly enhances the consumptions in a city, studies focusing on urban safety analysis have received much attention as means of revitalizing the local economy. In addition, with the development of computer vision and machine learning technologies, efficient and automated analysis methods have been developed. Previous studies have used global features to predict the safety of cities, yet this method has limited ability in accurately predicting abstract information such as safety assessments. Therefore we used a Convolutional Context Neural Network (CCNN) that considered "context" as a decision criterion to accurately predict safety of cities. CCNN model is constructed by combining a stacked auto encoder with a fully connected network to find the context and use it in the CNN model to predict the score. We analyzed the RMSE and correlation of SVR, Alexnet, and Sharing models to compare with the performance of CCNN model. Our results indicate that our model has much better RMSE and Pearson/Spearman correlation coefficient.

• Structural Engineering and Mechanics
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• 제45권1호
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• pp.53-67
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• 2013

This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively large-dimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the Levenberg-Marquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.

• 한국항공우주학회지
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• 제35권9호
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• pp.826-835
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• 2007

발사체 열환경 설계를 위해서 여러 종류의 태양열 모델을 비교 검토하였으며, 측정된 태양열과 잘 일치하는 태양열 모델을 개발하였다. 기존의 태양열 모델은 태양 직사광 예측은 정확하지만 산란광에 대해서는 오차가 포함되어 있었다. 이에 반하여 새롭게 개발된 산란광 모델은 등방성, 이방성 산란을 고려하였으며 기존의 어느 모델보다 관측값과 잘 일치하였다. 우주 센터의 태양광 측정 데이터가 매우 적기 때문에 본 모델은 발사체 열하중 설계에 필요한 설계 데이터를 제공할 수 있었다. 또한 본 모델은 위도, 경도, 날짜, 고도에 대한 제한이 없는 일반적인 모델이기 때문에 추후 태양열에 민감한 반응을 보이는 비행기구 등의 개발에 효과적인 열환경 예측 수단을 제공할 수 있다.

• The Journal of Asian Finance, Economics and Business
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• 제5권4호
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• pp.95-100
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• 2018

The study investigates the factors that affect Kyrgyzstan's bilateral trade flows with its main trading partners and attempts to predict trade potential for Kyrgyzstan. Using panel data, the gravity model is applied to estimate Kyrgyzstan's trade from 2000 to 2016 for its 35 main trading partners. The coefficients derived from the gravity-model estimation are then used to predict trade potential for Kyrgyzstan. Results proved to be successful and explained 63% of the fluctuations in Kyrgyzstan's trade. According to the results, Kyrgyzstan's and its partners' GDP have a positive effect on trade, while distance and partners' population prove to have a negative effect. Predicted trade potential reveals that neighboring countries (China, Kazakhstan, Uzbekistan, and Tajikistan) and Russia still have a significant trade potential. Kyrgyzstan, being a less developed economy, even by Central Asia standards, can only achieve its goals of reducing poverty and becoming more developed by increasing its overall trade with the rest of the world. Therefore, it is essential to study the main determinants of Kyrgyzstan's bilateral trade. In this way, we can help policy makers formulate policies to expand Kyrgyzstan's trade. This study is the first attempt to apply to the gravity model to Kyrgyzstan in an attempt to predict trade potential.

• Industrial Engineering and Management Systems
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• 제15권4호
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• pp.324-334
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• 2016

Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

• 전자공학회논문지C
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• 제34C권6호
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• pp.11-21
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• 1997

The delay models for CMOS invertr presented so far predicted the delay time quite accurately whens input transition-time is very small. But the problem that the accuracy is inclined to decrease becomes apparent as input transition tiem increases. In this paper, a delay model for CMOS inverter is presented, which accuractely predicts the delay time even though input transition-time increases. To inverter must be included in modeling process because the main reason of inaccuracy as input transition tiem is the leakage current through the complementary MOS. For efficient modeling, this paper first models the MOSes with simple I-V charcteristic, with which both the pMOS and the nMOS are considered easily in calculating the inverter delay times. This resulting model needs few parameters and re-models each MOS effectively and simply evaluates output voltage to predict delay time, delay values obtained from this effectively and simply evaluates output voltage to predict delay time, delay values obtained from this model have been found to be within about 5% error rate of the SPICE results. The calculation time to predict the delay time with the model from this paper has the speed of more than 70times as fast as to the SPICE.

• Advances in Computational Design
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• 제4권4호
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• pp.367-379
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• 2019

Multiple Inputs and Multiple Outputs (MIMO) Fuzzy logic model is developed to predict the engine performance and emission characteristics of pongamia pinnata biodiesel with hydrogen injection. Engine performance and emission characteristics such as brake thermal efficiency (BTE), brake specific energy consumption (BSEC), hydrocarbon (HC), carbon monoxide (CO), carbon dioxide ($CO_2$) and nitrous oxides ($NO_X$) were considered. Experimental investigations were carried out by using four stroke single cylinder constant speed compression ignition engine with the rated power of 5.2 kW at variable load conditions. The performance and emission characteristics are measured using an Exhaust gas analyzer, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends (Diesel, B10, B20 and B30) and engine load conditions. Fuzzy logic model uses triangular and trapezoidal membership function because of its higher predictive accuracy to predict the engine performance and emission characteristics. Computational results clearly demonstrate that, the proposed fuzzy model has produced fewer deviations and has exhibited higher predictive accuracy with acceptable determination correlation coefficients of 0.99136 to 1 with experimental values. The developed fuzzy logic model has produced good correlation between the fuzzy predicted and experimental values. So it is found to be useful for predicting the engine performance and emission characteristics with limited number of available data.

• Structural Engineering and Mechanics
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• 제60권6호
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• pp.1039-1061
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• 2016

Based on Vlasov's torsional theory of open thin-walled members and the nonlinear constitutive relations of materials, a nonlinear analysis model to predict response of open thin-walled RC members subjected to pure torsion is proposed in the current study. The variation of the circulatory torsional stiffness and warping torsional stiffness over the entire loading process and the impact of warping shear deformation on the torsion-induced rotation of the member are considered in the formulation. The torque equilibrium differential equation is then solved by Runge-Kutta method. The proposed nonlinear model is then applied to predict the behavior of five U-shaped thin-walled RC members under pure torsion. Four of them were tested in an earlier experimental study by the authors and the testing data of the fifth one were reported in an existing literature. Results show that the analytical predictions based on the proposed model agree well with the experimental data of all five specimens. This clearly shows the validity of the proposed nonlinear model analyzing behavior of U-shaped thin-walled RC members under pure torsion.