• Computers and Concrete
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• v.25 no.1
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• pp.1-14
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• 2020

This study presents applications of the multivariate adaptive regression splines (MARS) method for predicting the ultimate loading carrying capacity (N_u) of rectangular concrete-filled steel tubular (CFST) columns subjected to eccentric loading. A database containing 141 experimental data was collected from available literature to develop the MARS model with a total of seven variables that covered various geometrical and material properties including the width of rectangular steel tube (B), the depth of rectangular steel tube (H), the wall thickness of steel tube (t), the length of column (L), cylinder compressive strength of concrete (f'_c), yield strength of steel (f_y), and the load eccentricity (e). The proposed model is a combination of the MARS algorithm and the grid search cross-validation technique (abbreviated here as GS-MARS) in order to determine MARS' parameters. A new explicit formulation was derived from MARS for the mentioned input variables. The GS-MARS estimation accuracy was compared with four available mathematical methods presented in the current design codes, including AISC, ACI-318, AS, and Eurocode 4. The results in terms of criteria indices indicated that the MARS model was much better than the available formulae.

• Proceedings of the KAIS Fall Conference
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• 2010.05b
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• pp.1197-1200
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• 2010

Post-genomic 시대에 접어들면서 단백질의 기능의 주석이 중요한 문제로 떠오르기 시작하였다. 이런 단백질 기능을 예측하기 위해 단백질 상호작용(Protein-Protein interaction) 데이터를 이용한 방법들이 지난 10여 년간 발표되어왔다. 단백질 상호작용(Protein-Protein interaction) 데이터는 단백질들 간의 서열 등의 특징을 이용해 상호간의 연결 관련성이 있는 단백질끼리의 관계를 네트워크로 나타낸 자료이다. 현재 이러한 단백질 상호작용(Protein-Protein interaction) 데이터들은 MIPS, DIP, BioGrid등 약 5~6군데에서 제공되고 있다. 각각의 데이터는 다른 형식을 가지고 있고, 중복되는 정보도 포함하고 있다. 여러 연구 방법에서 데이터를 사용할 때 한군데에서만 추출하기 보다는 여러 데이터에서 추출하는 경우가 많기 때문에 다른 형식의 데이터를 이용하는데 불필요한 수고가 들어가게 된다. 때문에 여러군데의 데이터를 한 가지 형식으로 맞추어 통합적으로 구축하여 연구 시 데이터 사용에 용이하도록 설계 하였다. 또한 발표된 단백질 기능 예측 방법에 대한 정리를 통해 앞으로의 연구를 하는데 있어서 필요한 자료를 얻고 열람할 수 있도록 설계하였다. 이를 통해 관련 연구를 하거나 관심이 있는 사람들의 데이터를 검색하는데 많은 도움이 될 것이다.

• Food Science of Animal Resources
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• v.37 no.1
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• pp.139-146
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• 2017

Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. In the analysis of response surface data, a second-order polynomial regression model is usually used. However, sometimes we encounter situations where the fit of the second-order model is poor. If the model fitted to the data has a poor fit including a lack of fit, the modeling and optimization results might not be accurate. In such a case, using a fullest balanced model, which has no lack of fit, can fix such problem, enhancing the accuracy of the response surface modeling and optimization. This article presents how to develop and use such a model for the better modeling and optimizing of the response through an illustrative re-analysis of a dataset in Park et al. (2014) published in the Korean Journal for Food Science of Animal Resources.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.79-85
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• 2018

This study focuses on autonomous exploration based on map expansion for an underwater robot equipped with acoustic sonars. Map expansion is applicable to large-area mapping, but it may affect localization accuracy. Thus, as the key contribution of this paper, we propose a method for underwater autonomous exploration wherein the robot determines the trade-off between map expansion ratio and position accuracy, selects which of the two has higher priority, and then moves to a mission step. An occupancy grid map is synthesized by utilizing the measurements of an acoustic range sonar that determines the probability of occupancy. This information is then used to determine a path to the frontier, which becomes the new search point. During area searching and map building, the robot revisits artificial landmarks to improve its position accuracy as based on imaging sonar-based recognition and EKF-SLAM if the position accuracy is above the predetermined threshold. Additionally, real-time experiments were conducted by using an underwater robot, yShark, to validate the proposed method, and the analysis of the results is discussed herein.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.515-522
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• 2015

Through the correspondence works with IEC in the smart grid fields and power IT fields, we set up the interpretation work procedure and defined the work rule for correspondence by analyzing the work results. In addition, we suggest cases for discussion of terms and definitions in the IEC and analyze them and then propose a matrix classification system for standardization to solve the cases for discussion. The matrix classification system with 3-axes of classification has been applied to newly emerging terminologies followed by smart gird. We drew the usefulness in search of terms in application fields and showed the cases of applying the matrix classification. The IEC Electropedia classification standard is unclear and the classification is mixed with principle, application and product areas. We proposed a new working group in IEC TC1 for research on the matrix classification system and then TC 1 decided to organize a new WG titled in the "IEV structure and supporting tools".

• Korean Institute of Interior Design Journal
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• v.23 no.4
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• pp.42-51
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• 2014

The research aims to analyze the structural meaning of mobility design on 'Furnitecture' with the works of Andrea Zittel. The study consists of the literature review and the analysis of Zittel's works. The framework for the analysis is divided into four steps: identification of visual forms/structure/function, analysis of the principles of delivering the concept of mobility, interpretation & synthesis of the relational meanings derived from the concepts of mobility, and evaluation of Zittel's tendency toward design/social background/design history, etc. Total fifteen cases are selected from Zittel's works, and each case is analyzed following the above steps. The finding shows that Zittel likes to play with geometrical forms, grid & modular system to create a minimum space for living equipped with critical furnishing. Secondly, Zittel's works deliver the concept of mobility by applying movability, adaptation, combination and transformation. Thirdly, through the concepts of mobility, Zittel reflects the ideas of high efficiency and functionalism, harmony with natural environment, search for liberty, simple & humble life and success of the designers of American modernism. Finally, it is found that modernism from the era of Bauhaus, utopian values derived from constructionism, utilitarianism inspired by Shakers and homestead act & mobile home/capsule unit suggested since 19th century in America mainly affect Zittel's works to reflect the dynamic concepts of mobility through the design of furnitecture.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.236-236
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• 2017

본 연구에서는 강우의 시공간성을 파악할 수 있는 격자기반의 Average-point Tracking 프로그램을 이용하여 호우의 DAD(Depth-Area-Duration)를 분석하였다. IPCC 5차보고서에 따르면 1950년 이래로 다수의 극한 기상 및 기후 변화가 관측되었다. 그 중 일부는 인간의 활동과 관련된 것으로 많은 지역에서의 극한 호우 현상의 증가가 손꼽힌다. 이러한 극한 호우 현상 증가와 일부 저수지의 유출 증가 경향은 지역적 규모에서 홍수의 위험이 더 커졌음을 의미한다(Kim et al., 2016). 최근 이상기후 현상의 증가에 따른 강우양상의 변화로 게릴라성 집중 호우와 태풍의 빈도가 증가하고 있지만, 우리나라의 호우의 특성은 방위 및 진행방향에 따른 해석이 매우 복잡하여 강우를 정형화하기에 어려운 특징을 보인다. 또한 지속시간이 긴 호우의 경우에는 호우의 범위가 한반도 전체가 되는 특성 때문에 강우의 시 공간성과 관련된 관측 자료는 부족하며, 이러한 특성을 고려한 연구 또한 미진한 실정이다. 만약, 태풍과 같이 호우이동이 뚜렷한 경우, 기존의 적용되고 있는 유역중심의 DAD 분석 방법으로는 DAD 관계를 명확히 표현하기 어려우며 유역면적이 증가할수록 유역의 면적평균강우량의 오차도 증가하기 때문에 DAD 분석의 정확도는 낮아지게 된다. 따라서 본 연구에서는 호우의 형태와 이동을 고려하기 위해 시간에 따른 호우를 격자로 나누어 격자를 증가시키면서 면적평균최대강우량을 산정할 수 있는 Average-point Tracking 방법을 이용하여 DAD 분석을 실시하였다.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.694-706
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• 2019

In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.93-115
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• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.