• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2004.09a
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• pp.201-209
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• 2004

As the European Community's Directive on the Restriction of Hazardous Substances in Electrical and Electronic Equipment banning lead (Pb) in electronics products will take effect on July 1, 2006, most electronics manufacturers will be commencing with volume production of Pb-free components by the middle of 2004. Electrodeposited pure tin finishes on electronic components are a leading contender to replace the industry standard tin-lead. Commensurate with this shift will be a somewhat steep learning curve as manufacturers adapt a variety of equipment and processes to contend with the issues surrounding this critical, industry-wide material conversion. Since the electrodeposited finish directly influences the critical reliability characteristics of the component itself, the nature of the Pb-free component finish must be well characterized and understood. Only through a thorough examination of the attributes of the electroplated tin deposit can critical decisions be made regarding component finish reliability. This paper investigates the properties of electrodeposited tin that may have an effect on component reliability, namely, grain structure (size and shape), oxide formation, tin whisker formation, and solderability. Data will be presented from laboratory and production settings, with the objective being to enable manufacturers to draw their own conclusions regarding previously established perceptions and misconceptions about electrodeposited tin properties.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.21-28
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• 2019

Side effects of drugs mean harmful and unintended effects resulting from drugs used to prevent, diagnose, or treat diseases. These side effects can lead to patients' death and are the main causes of drug developmental failures. Thus, various methods have been tried to identify side effects. These can be divided into biological and systems biology approaches. In this study, we use systems biology approach and focus on using various phenotypic information in addition to the chemical structure and target proteins. First, we collect datasets that are used in this study, and calculate similarities individually. Second, we generate a set of features using the similarities for each drug-side effect pair. Finally, we confirm the results by AUC(Area Under the ROC Curve), and showed the significance of this study through a comparison experiment.

• Korean Journal of Child Studies
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• v.36 no.6
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• pp.165-188
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• 2015

The purpose of this study was to examine the relationships among Korean early adolescents' perceived negative parenting practices, trajectories of mobile phone dependency(MPD), and self-regulated learning(SRL), while taking into account gender differences. Early adolescents are required to acquire self-regulation in Korean cultural contexts of a strong emphasis on academic achievement and recent technological advancements. The study made use of data from the Korean Children and Youth Panel Study(KCYPS), and three waves of data collected from 1,953 adolescents in $7^{th}$, $8^{th}$ and $9^{th}$ grade were analyzed. The results can be summarized as follows. Growth-curve longitudinal analysis indicates that their initial value of MPD through $7^{th}$ to $9^{th}$ grade had increased, but the initial value and rate of change were significantly different according to gender. Furthermore, the results of multiple group analysis revealed that some path weights appeared different according to gender. For male students, the rate of change in MPD did not have a significant effect on either SRL in $7^{th}$ or $9^{th}$ grade, whereas for female students, it predicted the existence of significant relationships with them. The implications of these findings were also discussed.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.370-375
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• 2012

국가개발연구원(KDI)에서는 새만금에 40MW 용량의 국산 풍력발전단지 조성을 위한 예비타당성 조사를 실시하였다. 본 사업에 대한 KDI의 비용편익 분석 결과 순 현재가치가 0보다 작고, B/C 비율은 0.73으로 경제적 타당성이 나타나지 않았다. 하지만 모든 비용과 편익을 화폐가치로 환산해 내는 비용편익분석은 여러 가지 외부효과로 인해 사업의 타당성 연구자들에 의해 주관적인 결과물이 나오기도 한다. 이에 따라 본 논문에서는 새만금 풍력발전 시범단지의 경제성 평가 재검토 연구를 실시하였다. 본 논문에서는 기존 경제성 평가 항목에 대하여 여러 가지 쟁점을 제시하고, 그 중에서 대기오염물질 저감 비용과 학습효과(learning effect)로 인한 비용 절감 편익을 추가적으로 산정하였다. 여기서 학습효과의 학습속도(learning rate) 를 세 가지 시나리오로 나누어 분석하였다. 두 가지의 추가 편익을 KDI의 기존 예비타당성 조사 분석에 추가한 순 현재가치는 상당한 양의 값이 나왔고, B/C 비율은 8.8 로 편익이 비용에 비해 크게 증가했다. 이러한 항목들의 포함 여부에 따라 타당성 결과가 현저하게 달라짐을 알 수 있다. 향 후 비용편익 분석이 정책 결정에 적절히 반영되기 위해서는 외부효과를 고려한 환경 비용, 그리고 학습효과와 같은 추가적인 사항들이 면밀히 검토되어야 한다. 시장에 기반하지 않은 이러한 외부효과로 인한 항목들은 대상과 시기에 따라 매우 다른 결과를 보여주기에 이에 대한 세부적인 연구가 필요한 시점이다.

• Smart Structures and Systems
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• v.30 no.6
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1329-1338
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• 1999

In this paper, an efficient signal interference control technique to improve the convergence speed of LMS algorithm is introduced. The convergence characteristics of the proposed algorithm, whose coefficients are multiply adapted in a symbol time period by recycling the received data, are analyzed to prove theoretically the improvement of convergence speed. According as the step-size parameter $\mu$ is increased, the rate of convergence of the algorithm is controlled. Also, a increase in the step-size parameter $\mu$ has the effect of reducing the variation in the experimentally computed learning curve. Increasing the eigenvalue spread has the effect of controlling down the rate of convergence of the adaptive equalizer and also increasing the steady-state value of the mean squared error and also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS technique.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.494-501
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• 1999

In this research, ANN modeling method of SMES unit is developed for stability analysis, and the optimal site is selected to maximize stabilization effect of SMES unit. The ANN is trained by learning data which is obtained through the application of complex test function into the traditional mathematical mode. In order to verify the validity of proposed modeling method, fault data of sample power system is applied to both the traditional and the ANN models. When the response of traditional and proposed models are compared, the average error for the active and reactive power are 2.51[%], and 0.24[%], respectively. From the comparison, the relevance of proposed method is validated. For the transient stability analysis, an application method of the proposed model is presented, and the transient stability performance index, which describes system stabilization effect of SMES at disturbance, is also suggested, and optimal site selection method of SMES is presented. In the viewpoint of the voltage stability, system stabilization criterion of local bus is presented from P­V curve, and then optimal site which can maximize the voltage stabilization of the whole power system, is decided from the proposed voltage stability performance index.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.1
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• pp.5-19
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• 2015

Literatures on open innovation have two major limitations. First, either on a firm level or on an industry level did they analyze the open innovation issues. The results of a firm's innovation can be diffused through the whole network and the firm can learn back from the network knowledge. Prior literatures did not consider the feedback loop among firms and network in which the firms are involved. Second, most open innovation research had a static perspective on firm's innovation performance. Since the diffusion, spill-over and learning among network members are involved over time, the open innovation is intrinsically dynamic. From the dynamic perspective, we can appreciate the fundamental attributes of the open innovation network which involves diverse firms, research institutes, and universities. In order to overcome the limitations, we suggest a dynamic model for open innovation network. We build an agent-based model which consists of heterogeneous firms. The firms are connected through a scale-free network which is formed by preferential attachment. Through the diverse scenario of simulation, we collect massive data on the firm level and analyze them both on firm and industry level. From the analysis, we found that, on industry level, the overall performance of open innovation increases as the internal research capability, absorptive capacity, and learning curve coefficient increase. Noticeably, as the deprecation rate of knowledge increases, the variability of knowledge increases. From the firm level analysis, we found that the industry-level variables had a significant effect on the firm's innovation performance lasting through all the time, whereas the firm-level variables had only on the early phase of innovation.

• The Journal of the Acoustical Society of Korea
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• v.37 no.2
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• pp.118-128
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• 2018

In this paper, we have studied how to search an underwater object by learning the image generated by the side scan sonar in the convolution neural network. In the method of human side analysis of the side scan image or the image, the convolution neural network algorithm can enhance the efficiency of the analysis. The image data of the side scan sonar used in the experiment is the public data of NSWC (Naval Surface Warfare Center) and consists of four kinds of synthetic underwater objects. The convolutional neural network algorithm is based on Faster R-CNN (Region based Convolutional Neural Networks) learning based on region of interest and the details of the neural network are self-organized to fit the data we have. The results of the study were compared with a precision-recall curve, and we investigated the applicability of underwater object detection in convolution neural networks by examining the effect of change of region of interest assigned to sonar image data on detection performance.

• Structural Engineering and Mechanics
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• v.85 no.3
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• pp.315-335
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• 2023

Limited studies are available on the mathematical estimates of the compressive strength (CS) of glass fiber-embedded polymer (glass-FRP) compressive elements. The present study has endeavored to estimate the CS of glass-FRP normal strength concrete (NSTC) compression elements (glass-FRP-NSTC) employing two various methodologies; mathematical modeling and artificial neural networks (ANNs). The dataset of 288 glass-FRP-NSTC compression elements was constructed from the various testing investigations available in the literature. Diverse equations for CS of glass-FRP-NSTC compression elements suggested in the previous research studies were evaluated employing the constructed dataset to examine their correctness. A new mathematical equation for the CS of glass-FRP-NSTC compression elements was put forwarded employing the procedures of curve-fitting and general regression in MATLAB. The newly suggested ANN equation was calibrated for various hidden layers and neurons to secure the optimized estimates. The suggested equations reported a good correlation among themselves and presented precise estimates compared with the estimates of the equations available in the literature with R²= 0.769, and R² =0.9702 for the mathematical and ANN equations, respectively. The statistical comparison of diverse factors for the estimates of the projected equations also authenticated their high correctness for apprehending the CS of glass-FRP-NSTC compression elements. A broad parametric examination employing the projected ANN equation was also performed to examine the effect of diverse factors of the glass-FRP-NSTC compression elements.