• 한국마이크로전자및패키징학회:학술대회논문집
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• 한국마이크로전자및패키징학회 2004년도 ISMP Pb-free solders and the PCB technologies related to Pb-free solders
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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.

• 한국정보기술학회논문지
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• 제17권12호
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• pp.21-28
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• 2019

약물 부작용이란 질병의 예방, 진단 또는 치료에 사용된 약물로부터 발생한 유해하고 의도하지 않은 현상이다. 이러한 부작용은 환자를 죽음에 이르게 할 수 있으며, 약물 개발 실패의 주요 원인 중 하나이다, 따라서, 다양한 방법들이 부작용을 알아내기 위하여 시도되었다. 본 연구에서는 시스템스 바이올로지 접근법을 기반으로 기존 연구에서 주로 사용되었던 화학적 구조, 생물학적 정보 이외에도 다양한 표현형 정보를 사용하는 것에 주목하였다. 먼저, 5가지 적응증 데이터베이스, 화학적 구조, 타겟 유전자 정보를 수집하고 개별로 유사도를 계산하였다. 테이블은 하나의 약물-부작용에 대하여 앞서 생성된 유사도를 이용하여 생성되었고 다양한 기계학습 기법이 적용되었다. 결과는 AUC(Area Under the ROC Curve)값을 통해 확인하였다. 본 연구의 유의성은 비교 실험을 통하여 확인하였다.

• 아동학회지
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• 제36권6호
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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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• 한국태양에너지학회 2012년도 춘계학술발표대회 논문집
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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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• 제30권6호
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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.

• 한국통신학회논문지
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• 제24권9A호
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• pp.1329-1338
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• 1999

본 논문에서, LMS 알고리즘의 수렴 속도를 향상시키기 위한 효율적인 신호간섭 제어기법을 제안한다. 수신 데이터를 재활용하여 심볼 시간 주기에 계수들을 곱함으로써 적응되는 제안된 알고리즘의 수렴특성이 수렴 속도의 향상을 이론적으로 증명하기 위해 분석한다. 스텝-크기 매개변수 $\mu$가 증가됨에 따라 알고리즘의 수렴 속도가 제어된다. 또한, 스텝-크기 매개변수 $\mu$의 증가는 실험적으로 계산된 학습 곡선에서 분산을 감소시키는 효과를 갖는다. 고유치 확산을 증가시킴에 따라 적응 등화기의 수렴속도를 천천히 제어하고 평균 자승 에러의 안정-상태 값을 증가시키는 효과를 나타내며 데이터-재사용 LMS 기술이 수렴속도를 (B+1)배만큼 증가시켜 필터 알고리즘에서 신호간섭제어의 우수성을 입증한다.

• 대한전기학회논문지:전력기술부문A
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• 제48권5호
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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.

• 한국경영과학회지
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• 제40권1호
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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.

• 한국음향학회지
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• 제37권2호
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• pp.118-128
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• 2018

본 논문은 사이드 스캔 소나 영상을 컨볼루션 신경망으로 학습하여 수중물체를 탐색하는 방법을 다루었다. 사이드 스캔 소나 영상을 사람이 직접 분석하던 방법에서 컨볼루션 신경망 알고리즘이 보강되면 분석의 효율성을 높일 수 있다. 연구에 사용한 사이드 스캔 소나의 영상 데이터는 미 해군 수상전센터에서 공개한 자료이고 4종류의 합성수중물체로 구성되었다. 컨볼루션 신경망 알고리즘은 관심영역 기반으로 학습하는 Faster R-CNN(Region based Convolutional Neural Networks)을 기본으로 하며 신경망의 세부사항을 보유한 데이터에 적합하도록 구성하였다. 연구의 결과를 정밀도-재현율 곡선으로 비교하였고 소나 영상 데이터에 지정한 관심영역의 변경이 탐지성능에 미치는 영향을 검토함으로써 컨볼루션 신경망의 수중물체 탐지 적용성에 대해 살펴보았다.

• Structural Engineering and Mechanics
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• 제85권3호
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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.