• Journal of the Microelectronics and Packaging Society
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• v.20 no.3
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• pp.59-62
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• 2013

Experimental analyses on the solder joint reliability of plastic ball grid array under harsh random vibration were presented. The chips were assembled on the daisy chained circuit boards for the test samples preparation, half of which were processed for underfill to investigate the underfill effects on the solder failures. Acceptance and qualification levels were applied for the solder failure tests, and the overall controlled RMS of the power spectrum densities of the steps were 22.7 Grms and 32.1 Grms, respectively. It was found that the samples survived without any solder failure during the tests, demonstrating the robustness of the packaging structure for potential avionics and space applications.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.7
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• pp.619-625
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• 2012

Since the fuel is always exposed to turbulent flow, the grid strap shows flow induced vibration characteristics that impact on the nuclear fuel soundness. The dynamic behavior of grids in nuclear fuels is quite complex, since two pairs of spring and dimple support are contacted with rods by friction in the limited space. This paper focuses on investigation of the grid strap(test fuel strap, TFS) vibration in one cell. TFS consists of a single spring and double dimples. To identify the grid strap vibration, modal analysis of the strap is performed using finite element method(FEM). Modal testing on a $5{\times}5$ grid structure without rods is performed. The modal testing results are compared to analytic results. In addition, random test considering rod effect is performed about a $5{\times}5$ grid with rods under real contact condition in the air. Finally, the strap vibration of a $5{\times}5$ fuel bundle in investigation of flow induced vibration(INFINIT) facility is measured in real fluid velocity condition without heating. It is shown that modal frequencies from the test are almost equal to those peak frequencies in the INFINIT test.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1493-1510
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• 2000

Theoretical models that can be used to predict the range of main lobe widths and the probability distribution of the peak sidelobe levels of two-dimensionally sparse arrays are presented here. The arrays are considered to comprise microphones that are randomly positioned on a segmented grid of a given size. First, approximate expressions for the expected squared magnitude of the aperture smoothing function and the variance of the squared magnitude of the aperture smoothing function about this mean are formulated for the random arrays considered in the present study. By using the variance function, the mean value and the lower end of the range i.e., the first I percent of the mainlobe distribution can be predicted with reasonable accuracy. To predict the probability distribution of the peak sidelobe levels, distributions of levels are modeled by a Weibull distribution at each peak in the sidelobe region of the expected squared magnitude of the aperture smoothing function. The two parameters of the Weibull distribution are estimated from the means and variances of the levels at the corresponding locations. Next, the probability distribution of the peak sidelobe levels are assumed to be determined by a procedure in which the peak sidelobe level is determined as the maximum among a finite number of independent random sidelobe levels. It is found that the model obtained from the above approach predicts the probability density function of the peak sidelobe level distribution reasonably well for the various combinations of two different numbers of microphones and grid sizes tested in the present study. The application of these models to the design of random, sparse arrays having specified performance levels is also discussed.

• Fisheries and Aquatic Sciences
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• v.21 no.8
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• pp.24.1-24.9
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• 2018

The Korean bottom trawl survey has been deployed on a regular basis for about the last decade as part of groundfish stock assessments. The regularity indicates that they sample groundfish once per grid cell whose sides are half of one latitude and that of one longitude, respectively, and whose inside is furthermore divided into nine nested grids. Unless they have a special reason (e.g., running into a rocky bottom), their sample location is at the center grid of the nine nested grids. Given data collected by the survey, we intended to show how to appropriately estimate not only the survey index of a fish stock but also its uncertainty. For the regularity reason, we applied the systematic sampling theory for the above purposes and compared its results with a reference, which was based on the simple random sampling. When using the survey data about 11 fish stocks, collected by the spring and fall surveys in 2014, the survey indices of those stocks estimated under the systematic sampling were overall more precise than those under the simple random sampling. In estimates of the survey indices in number, the standard errors of those estimates under the systematic sampling were reduced from those under the simple random sampling by 0.23~27.44%, while in estimates of the survey indices in weight, they decreased by 0.04~31.97%. In bias of the estimates, the systematic sampling was the same as the simple random sampling. Our paper is first in formally showing how to apply the systematic sampling theory to the actual data collected by the Korean bottom trawl surveys.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.97-115
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• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.630-633
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• 2004

Generally, component and FR-4 board are connected by solder joint. Because material properties of components and FR-4 board are different, component and FR-4 board show different coefficients of thermal expansion (CTE) and thus strains in component and board are different when they are heated. That is, the differences in CTE of component and FR-4 board cause the dissimilarity in shear strain and BGA solder joint s failure. The first order Taylor series expansion of the limit state function incorporating with thermal fatigue models is used in order to estimate the failure probability of solder joints under heated condition. A model based on plastic-strain rate such as the Coffin-Manson Fatigue Model is utilized in this study. The effects of random variables such as frequency, maximum temperature, and temperature variations on the failure probability of the BGA solder joint are systematically investigated by using a failure probability model with the first order reliability method(FORM).

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.834-836
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• 2009

The authors have been discussed the optimal voltage regulation method and on-line real time method using artificial neural networks in the distribution system interconnected with Distributed Generation and Storage(DSG) systems. However, these methods have difficulty in dealing with the random load variations and operation characteristics of a number of DSG systems. To overcome these problems, this paper shows the basic concepts of smart grid system which is considered as one of the power delivery system in the near future and presents an evaluation method on the impacts of customer voltages by the operation of smart grid system. The smart grid system can change the system configuration in a flexible manner by using the static switches and offer the different power qualities in power services through the power quality control centers.

• Journal of Korea Water Resources Association
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• v.48 no.1
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• pp.37-44
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• 2015

A Bayesian nonstationary probability rainfall estimation model using the Grid method is developed. A hierarchical Bayesian framework is consisted with prior and hyper-prior distributions associated with parameters of the Gumbel distribution which is selected for rainfall extreme data. In this study, the Grid method is adopted instead of the Matropolis Hastings algorithm for random number generation since it has advantage that it can provide a thorough sampling of parameter space. This method is good for situations where the best-fit parameter values are not easily inferred a priori, and where there is a high probability of false minima. The developed model was applied to estimated target year probability rainfall using hourly rainfall data of Seoul station from 1973 to 2012. Results demonstrated that the target year estimate using nonstationary assumption is about 5~8% larger than the estimate using stationary assumption.