• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.1
• /
• pp.1-6
• /
• 2013

For predicting defect-prone module in software, SVM classifier showed good performance in a previous research. But there are disadvantages that SVM parameter should be chosen differently for every kernel, and algorithm should be performed iteratively for predict results of changed parameter. Therefore, we find these parameters using Genetic Algorithm and compare with result of classification by Backpropagation Algorithm. As a result, the performance of GA-SVM model is better.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1652-1653
• /
• 2007

가변제어구조로 동작하는 슬라이딩모드제어(SMC)는 플렌트의 파라미터 변동과 부하왜형에 관계없이 스위칭 제어 알고리즘에 의해 위상 평면에서 미리 예측된 궤적 또는 기준모델을 따라 구동응답을 주어진 슬라이딩 면을 따라 강제로 추종시키는 것이다. 여기서 슬라이딩 평면을 찾아내는 방법의 하나로 SVM(Suppot Vector Machine) 을 사용한다. 그런데 SVM을 사용하여 슬라이딩 평면을 찾아내는 과정에서, 초기치의 변동이 있을 경우, SVM 모델을 재구성해야 해야 한다. 이에 본 논문에서는 SVM 모델을 재구성할 필요 없이, 기존 초기치에 의한 SVM 모델에서, 원하는 초기치의 SVM모델로 변경할 수 있는 방법을 제안한다.

• Journal of the Korean Chemical Society
• /
• v.53 no.6
• /
• pp.653-662
• /
• 2009

Developing effective tools for predicting absorption, distribution, metabolism, excretion properties and toxicity (ADME/T) of new chemical entities in the early stage of drug design is one of the most important tasks in drug discovery and development today. As one of these attempts, support vector machines (SVM) has recently been exploited for the prediction of ADME/T related properties. However, two problems in SVM modeling, i.e. feature selection and parameters setting, are still far from solved. The two problems have been shown to be crucial to the efficiency and accuracy of SVM classification. In particular, the feature selection and optimal SVM parameters setting influence each other, which indicates that they should be dealt with simultaneously. In this account, we present an integrated practical solution, in which genetic-based algorithm (GA) is used for feature selection and grid search (GS) method for parameters optimization. hERG ion-channel inhibitor classification models of ADME/T related properties has been built for assessing and testing the proposed GA-GS-SVM. We generated 6 different models that are 3 different single models and 3 different ensemble models using training set - 1891 compounds and validated with external test set - 175 compounds. We compared single model with ensemble model to solve data imbalance problems. It was able to improve accuracy of prediction to use ensemble model.

• 한국HCI학회:학술대회논문집
• /
• 2006.02a
• /
• pp.1387-1392
• /
• 2006

본 논문에서는 SVM (Support Vector Machine)을 기반으로 하여 인체의 뇌 하부구조인 해마에 대한 지능적 형상분석 방법을 제공한다. 일반적으로 의료 영상으로부터 해마의 형상 분석을 하기 위해서는 충분한 임상 데이터를 필요로 한다. 하지만 현실적으로 많은 양의 표본들을 얻는 것이 쉽지 않기 때문에 전문가의 지식을 기반으로 한 작업이 수반되어야 한다. 결국 이러한 요소들이 분석 작업을 어렵게 한다. 의학 기술이 복잡해 지면서 최근의 형상 분석 연구는 점차 통계적 모델을 기반으로 진행되고 있다. 본 연구에서는 해마로부터 고해상도의 매개변수형 모델을 만들어 형상 표현으로 이용하고, 집단간 분류 작업에 SVM 알고리즘을 적용하는 지능적 분석 방법을 구현한다. 우선 메쉬 데이터로부터 물리변형모델 기반의 매개변수 모델을 구축하고, PDM (point distribution model) 방법을 적용하여 두 집단을 대표하는 평균 모델을 생성한다. 마지막으로 SVM 기반의 이진 분류기를 구축하여 집단간 분류 작업을 수행한다. 구현한 모델링 방법과 분류기의 성능을 평가하기 위하여 본 연구에서는 네 가지 커널 함수 (linear, radial basis function, polynomial, sigmoid)들을 적용한다. 본 논문에서 제시한 매개변수형 모델은 다양한 형태의 의료 데이터로부터 보편적인 3차원 모델을 생성하고, 또한 모델의 전역적, 국부적인 특징들을 복합적으로 표현할 수 있기 때문에 통계적 형상분석에 적합하다. 그리고 SVM 기반의 분류기는 적은 수의 학습 데이터로부터 정상인 해마 집단과 간질 환자 집단간의 정확한 분류를 가능하게 한다.

• Journal of Distribution Research
• /
• v.10 no.3
• /
• pp.1-14
• /
• 2005

It is important to control performance and a Sustainable Collaboration (SC) for the successful Supply Chain Management (SCM). This research developed a control model which analyzed SCM performances based on a Balanced Scorecard (ESC) and an SC using Support Vector Machine (SVM). 108 specialists of an SCM completed the questionnaires. We analyzed experimental data set using SVM. This research compared the forecasting accuracy of an SCMSC through four types of SVM kernels: (1) linear, (2) polynomial (3) Radial Basis Function (REF), and (4) sigmoid kernel (linear > RBF > Sigmoid > Polynomial). Then, this study compares the prediction performance of SVM linear kernel with Artificial Neural Network. (ANN). The research findings show that using SVM linear kernel to forecast an SCMSC is the most outstanding. Thus SVM linear kernel provides a promising alternative to an SC control level. A company which pursues an SCM can use the information of an SC in the SVM model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.10a
• /
• pp.626-628
• /
• 2018

In this paper, we compare the performances of SVM (Support Vector Machine) and ANN (Artificial Neural Network) machine learning models for predicting solar energy by using meteorological data. Two machine learning models were built by using fifteen kinds of weather data such as long and short wave radiation average, precipitation and temperature. Then the RBF (Radial Basis Function) parameters in the SVM model and the number of hidden layers/nodes and the regularization parameter in the ANN model were found by experimental studies. MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) were considered as metrics for evaluating the performances of the SVM and ANN models. Sjoem Simulation results showed that the SVM model achieved the performances of MAPE=21.11 and MAE=2281417.65, and the ANN model did the performances of MAPE=19.54 and MAE=2155345.10776.

• KIPS Transactions on Software and Data Engineering
• /
• v.4 no.6
• /
• pp.261-268
• /
• 2015

In spoken chatting systems, users'spoken queries are converted to text queries using automatic speech recognition (ASR) engines. If the top-1 results of the ASR engines are incorrect, these errors are propagated to the spoken chatting systems. To improve the top-1 accuracies of ASR engines, we propose a post-processing model to rearrange the top-n outputs of ASR engines using a ranking support vector machine (RankSVM). On the other hand, a number of chatting sentences are needed to train chatting systems. If new chatting sentences are not frequently added to training data, responses of the chatting systems will be old-fashioned soon. To resolve this problem, we propose a data collection model to automatically select chatting sentences from TV and movie scenarios using a support vector machine (SVM). In the experiments, the post-processing model showed a higher precision of 4.4% and a higher recall rate of 6.4% compared to the baseline model (without post-processing). Then, the data collection model showed the high precision of 98.95% and the recall rate of 57.14%.

• Korean Journal of Construction Engineering and Management
• /
• v.7 no.2 s.30
• /
• pp.118-126
• /
• 2006

There is a greater importance for underground work designed and built in the urban areas when it comes to considering the cost-effectiveness and the period of construction commensurate with an increasing trend of skyscrapers. At this stage of underground work, it's extremely necessary to choose a proper earth retaining method. Therefore, the study has suggested the rational retaining wall method by developing the support vector machine(SVM) model as a tool to choose a proper retaining wall method applied at the stage of selecting the earth retaining method. In order to develop the SVM model, the binary SVM classifier is expanded into a multi-class classifier. and to present the feasibility of our SVM model, we considered 129 projects. Applying the 'SVM Model' developed in the study to the designing and developing stages of the earth retaining work will contribute to the successful outcomes by decreasing any changes of design from implementing the earth retaining.

• Journal of Digital Contents Society
• /
• v.19 no.9
• /
• pp.1819-1824
• /
• 2018

This paper proposes a control method of ASMR(autonomous sensory meridian response) contents to relieve user's stress and improve his attention. The proposed method measures EEG(electroencephalography), attention, meditation, and eyeblink data from an EEG device and sends them to an oneM2M-compliant IoT(internet of things) server platform through an Android IoT Application. Then a SVM(support vector machine) model is built to classify user's mental health status by using EEG, attention and meditation data collected in the server platform. The ASMR contents are controlled by the mental health status classified by a SVM model and the eyeblink data. When comparing the SVM models according to types of data used, the SVM model with attention and meditation data showed accuracy of 85.7%. It was verified that the proposed control algorithm of ASMR contents properly worked as the mental health status from the SVM model and the eyeblink data changed.

• Membrane Journal
• /
• v.33 no.1
• /
• pp.34-45
• /
• 2023

Two computational intelligence techniques namely artificial neural networks (ANN) and support vector machine (SVM) are employed to model the permeate flux based on seven input variables including time, transmembrane pressure, rotating velocity, the pore diameter of the membrane, dynamic viscosity, concentration and density of the feed fluid. The best-fit model was selected through the trial-error method and the two statistical parameters including the coefficient of determination (R²) and the average absolute relative deviation (AARD) between the experimental and predicted data. The obtained results reveal that the optimized ANN model can predict the permeate flux with R² = 0.999 and AARD% = 2.245 versus the SVM model with R² = 0.996 and AARD% = 4.09. Thus, the ANN model is found to predict the permeate flux with high accuracy in comparison to the SVM approach.