• International Journal of Control, Automation, and Systems
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• 제4권3호
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• pp.382-393
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• 2006

Mechanical system dynamics plays an important role in the area of computational structural biology. Elastic network models (ENMs) for macromolecules (e.g., polymers, proteins, and nucleic acids such as DNA and RNA) have been developed to understand the relationship between their structure and biological function. For example. a protein, which is basically a folded polypeptide chain, can be simply modeled as a mass-spring system from the mechanical viewpoint. Since the conformational flexibility of a protein is dominantly subject to its chemical bond interactions (e.g., covalent bonds, salt bridges, and hydrogen bonds), these constraints can be modeled as linear spring connections between spatially proximal representatives in a variety of coarse-grained ENMs. Coarse-graining approaches enable one to simulate harmonic and anharmonic motions of large macromolecules in a PC, while all-atom based molecular dynamics (MD) simulation has been conventionally performed with an aid of supercomputer. A harmonic analysis of a macroscopic mechanical system, called normal mode analysis, has been adopted to analyze thermal fluctuations of a microscopic biological system around its equilibrium state. Furthermore, a structure-based system optimization, called elastic network interpolation, has been developed to predict nonlinear transition (or folding) pathways between two different functional states of a same macromolecule. The good agreement of simulation and experiment allows the employment of coarse-grained ENMs as a versatile tool for the study of macromolecular dynamics.

• 한국수자원학회논문집
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• 제53권6호
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• pp.395-408
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• 2020

본 연구논문은 북부아프리카의 알제리에 위치한 하천유역에서 다중선행일 유출량의 예측을 위하여 진화적 최적화기법과 k-fold 교차검증을 결합한 세 개의 서로 다른 기계학습 접근법 (인공신경망, 적응 뉴로퍼지 시스템, 그리고 웨이블릿 기반 신경망)을 개발하고 적용하는 것이다. 인공신경망과 적응 뉴로퍼지 시스템은 root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), 그리고 peak flow criteria (PFC) 의 네 개의 통계지표를 기반으로 하여 모형의 훈련 및 테스팅 결과 유사한 모형수행결과를 나타내었다. 웨이블릿 기반 신경망모형은 하루선행일 테스팅의 결과 RMSE = 8.590 ㎥/sec 과 PFC = 0.252로 분석되어서 인공신경망의 RMSE = 19.120 ㎥/sec, PFC = 0.446 과 적응 뉴로퍼지 시스템의 RMSE = 18.520 ㎥/sec, PFC = 0.444 보다 양호한 결과를 나타내었고, NSE와 R의 값도 웨이블릿 기반 신경망모형이 우수한 것으로 나타났다. 그러므로 웨이블릿 기반 신경망은 알제리 세이보스 하천에서 다중선행일의 예측을 위하여 효율적인 도구로 사용할 수 있다.

• 한국물환경학회지
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• 제36권6호
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• pp.611-620
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• 2020

Although process-based models have been a preferred approach for modeling freshwater aquatic systems over extended time intervals, the increasing utility of data-driven models in a big data environment has made the data-driven models increasingly popular in recent decades. In this study, international peer-reviewed journals for the relevant fields were searched in the Web of Science Core Collection, and an extensive literature review, which included total 2,984 articles published during the last two decades (2000-2020), was performed. The review results indicated that the rate of increase in the number of published studies using data-driven models exceeded those using process-based models since 2010. The increase in the use of data-driven models was partly attributable to the increasing availability of data from new data sources, e.g., remotely sensed hyperspectral or multispectral data. Consistently throughout the past two decades, South Korea has been one of the top ten countries in which the greatest number of studies using the data-driven models were published. Among the major data-driven approaches, i.e., artificial neural network, decision tree, and Bayesian model, were illustrated with case studies. Based on the review, this study aimed to inform the current state of knowledge regarding the biogeochemical water quality and ecological models using data-driven approaches, and provide the remaining challenges and future prospects.

• 한국시뮬레이션학회논문지
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• 제5권2호
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• pp.25-40
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• 1996

Modeling of systems for intensive knowledge-based processing requires a modeling methodology that makes efficient access to the information in huge data base models. The proposed level access mothodology is a modeling approach applicable to systems where data is stored in a hierarchical and modular modules of active memory cells(processor/memory pairs). It significantly reduces the effort required to create discrete event simulation models constructed in hierarchical, modular fashion for above application. Level access mothodology achieves parallel access to models within the modular, hierarchical modules(clusters) by broadcasting the desired operations(e.g. querying information, storing data and so on) to all the cells below a certain desired hierarchical level. Level access methodology exploits the capabilities of object-oriented programming to provide a flexible communication paradigm that combines port-to-port coupling with name-directed massaging. Several examples are given to illustrate the utility of the methodology.

• 상하수도학회지
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• 제10권2호
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• pp.88-103
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• 1996

The objective of this study is to develop stochastic and deterministic models which could be used to synthesize water application time series. Adaptive models using mulitivariate ARIMA(Transfer Function Model) are developed for daily urban water use forecasting. The model considers several variables on which water demands is dependent. The dynamic response of water demands to several factors(e.g. weekday, average temperature, minimum temperature, maximum temperature, humidity, cloudiness, rainfall) are characterized in the model by transfer functions. Daily water use data of Kumi city in 1992 are employed for model parameter estimation. Meteorological data of Seonsan station are utilized to input variables because Kumi has no records about the meteorological factor data.To determine the main factors influencing water use, autocorrelogram and cross correlogram analysis are performed. Through the identification, parameter estimation, and diagnostic checking of tentative model, final transfer function models by each month are established. The simulation output by transfer function models are compared to a historical data and shows the good agreement.

• International Journal of Control, Automation, and Systems
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• 제3권4호
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• pp.533-541
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• 2005

In this paper, a wind turbine with power splitting transmission, which is realized through a novel three-shaft planetary, is presented. The input shaft of the transmission is driven by the rotor of the wind turbine, the output shaft is connected to the grid via the main generator (asynchronous generator), and the third shaft is driven by a control motor with variable speed. The dynamic models of the sub systems of this wind turbine, e.g. the rotor aerodynamics, the drive train dynamics and the power generation unit dynamics, were given and linearized at an operating point. These sub models were integrated in a multidisciplinary dynamic model, which is suitable for control syntheses to optimize the utilization of wind energy and to reduce the excessive dynamic loads. The important dynamic behaviours were investigated and a wind turbine with a soft main shaft was recommend.

• Steel and Composite Structures
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• 제29권3호
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• pp.309-318
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• 2018

Steel girders are the structural members often used for passing long spans. Mostly being subjected to patch loading, or concentrated loading, steel girders are likely to face sudden deformation or damage e.g., web breathing. Horizontal or vertical stiffeners are employed to overcome this phenomenon. This study aims at assessing the feasibility of a machine learning method, namely the support vector machines (SVM) in predicting the patch loading resistance of longitudinally stiffened webs. A database consisting of 162 test data is utilized to develop SVM models and the model with best performance is selected for further inspection. Existing formulations proposed by other researchers are also investigated for comparison. BS5400 and other existing models (model I, model II and model III) appear to yield underestimated predictions with a large scatter; i.e., mean experimental-to-predicted ratios of 1.517, 1.092, 1.155 and 1.256, respectively; whereas the selected SVM model has high prediction accuracy with significantly less scatter. Robust nature and accurate predictions of SVM confirms its feasibility of potential use in solving complex engineering problems.

• ETRI Journal
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• 제46권3호
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• pp.526-537
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• 2024

Monitoring fetal growth in utero is crucial to anomaly diagnosis. However, current computer-vision models struggle to accurately assess the key metrics (i.e., head circumference and occipitofrontal and biparietal diameters) from ultrasound images, largely owing to a lack of training data. Mitigation usually entails image augmentation (e.g., flipping, rotating, scaling, and translating). Nevertheless, the accuracy of our task remains insufficient. Hence, we offer a U-Net fetal head measurement tool that leverages a hybrid Dice and binary cross-entropy loss to compute the similarity between actual and predicted segmented regions. Ellipse-fitted two-dimensional ultrasound images acquired from the HC18 dataset are input, and their lower feature layers are reused for efficiency. During regression, a novel region of interest pooling layer extracts elliptical feature maps, and during segmentation, feature pyramids fuse field-layer data with a new scale attention method to reduce noise. Performance is measured by Dice similarity, mean pixel accuracy, and mean intersection-over-union, giving 97.90%, 99.18%, and 97.81% scores, respectively, which match or outperform the best U-Net models.

• 한국식품위생안전성학회지
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• 제28권2호
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• pp.168-173
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• 2013

본 연구는 신선편이 식품에서 오염 가능성이 있는 병원성 식중독 균 E. coli O157:H7에 대해 파프리카에서 성장 예측 모델을 적용하고, 본 연구에서 개발된 성장 예측 모델을 내부 검증하였다. 이를 비교하여 신선편이 식품을 안전하게 관리하기 위한 적절한 모델을 제시하고자 하였다. 파프리카에 E. coli O157:H7접종하여 온도에 따라 12, 24, 30, $36^{\circ}C$에 보관하여 성장을 측정하였다. Gompertz 식을 이용하여 온도에 따른 성장곡선을 그리고 LT와 SGR을 산출하였다. 산출된 LT와 SGR은 각각 Davey model와 squareroot model를 이용하여 2차 모델을 개발하였다. 개발된 2차 모델에 대하여 LT와 SGR model의 $R^2$값은 각각 0.999, 0.994로 1에 근접하는 높은 적합성을 보였다. 또한 내부 검정 결과 LT와 SGR model의 Bf 값은 각각 1.01, 0.89, LT model은 안전하게 SGR model 위험하게 예측되었다. 파프리카의 LT와 SGR의 상대적인 오차 값은 모두 허용 가능한 오차 범위에 포함 되었다. 따라서 개발된 모델을 이용하여 온도에 따른 E. coli O157:H7성장을 추정할 수 있으며, 이를 위해평가 자료로 활용할 수 있을 것으로 보인다.

• ETRI Journal
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• 제37권5호
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• pp.929-939
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• 2015

Ultra-high-frequency radio-frequency identification (UHF RFID) is widely applied in different industries. The Frame Slotted ALOHA in EPC C1G2 suffers severe collisions that limit the efficiency of tag recognition. An efficient full-duplex anti-collision scheme is proposed to reduce the rate of collision by coordinating the transmitting process of CDMA UWB uplink and UHF downlink. The relevant mathematical models are built to analyze the performance of the proposed scheme. Through simulation, some important findings are gained. The maximum number of identified tags in one slot is g/e (g is the number of PN codes and e is Euler's constant) when the number of tags is equal to mg (m is the number of slots). Unlike the Frame Slotted ALOHA, even if the frame size is small and the number of tags is large, there aren't too many collisions if the number of PN codes is large enough. Our approach with 7-bit Gold codes, 15-bit Gold codes, or 31-bit Gold codes operates 1.4 times, 1.7 times, or 3 times faster than the CDMA Slotted ALOHA, respectively, and 14.5 times, 16.2 times, or 18.5 times faster than the EPC C1 G2 system, respectively. More than 2,000 tags can be processed within 300 ms in our approach.