• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.507-513
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• 2004

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• 로봇학회논문지
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• 제15권2호
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• pp.169-176
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• 2020

This paper studied the collision detection of robot manipulators for safe collaboration in human-robot interaction. Based on sensor-based collision detection, external torque is detached from subtracting robot dynamics. To detect collision using joint torque sensor data, a comparative study was conducted using data-based machine learning algorithm. Data was collected from the actual 3 degree-of-freedom (DOF) robot manipulator, and the data was labeled by threshold and handwork. Using support vector machine (SVM), decision tree and k-nearest neighbors KNN method, we derive the optimal parameters of each algorithm and compare the collision classification performance. The simulation results are analyzed for each method, and we confirmed that by an optimal collision status detection model with high prediction accuracy.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.233-238
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• 2000

CNC machine tool is assembled by central processor, PLC(Programmable Logic Controller), and actuator. The sequential control of machine generally controlled by a PLC. The main fault occured at PLC in 3 control parts. In LC faults, operational fault is charged over 70%. This paper describes diagnosis model and data processing for remote monitoring and diagnosis system in machine tools with open architecture controller. Two diagnostic models based on the ladder diagram. Logical Diagnosis Model(LDM), Sequential Diagnosis Model(SDM), are proposed. Data processing structure is proposed ST(Structured Text) based on IEC1131-3. The faults from CNC are received message form open architecture controller and faults from PLC are gathered by sequential data.. To do this, CNC and PLC's logical and sequential data is constructed database.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제10권1호
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• pp.1-9
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• 2006

Photomask 고해상도 검사기를 개발함에 있어서 기계 설계, 제어 설계, 소프트웨어 설계 등 3개의 카테고리가 필요하다. 그 중에서 소프트웨어 설계는 CAD 데이터와 Photomask로부터 카메라로 읽어 들인 실제 영상과의 비교를 통하여 불량을 검출한다. 픽셀 단위로 비교하기 때문에 전체 영역이 커서 블록으로 나누어서 비교하는 블록 매칭 알고리즘을 사용한다. 그리고 기계 조립 정도에서 오는 오차를 보정하기 위하여 Calibration 알고리즘을 쓰고, Photomask의 곡면을 보정하기 위하여 카메라 사전 초점 알고리즘을 제시한다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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• pp.61-68
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• 2000

Now that the complete genomes of numerous organisms have been ascertained, key problems in molecular biology include determining the functions of the genes in each organism, the relationships that exist among these genes, and the regulatory mechanisms that control their operation. These problems can be partially addressed by using machine learning methods to induce predictive models from available data. My group is applying and developing machine learning methods for several tasks that involve characterizing gene regulation. In one project, for example, we are using machine learning methods to identify transcriptional control elements such as promoters, terminators and operons. In another project, we are using learning methods to identify and characterize sets of genes that are affected by tumor promoters in mammals. Our approach to these tasks involves learning multiple models for inter-related tasks, and applying learning algorithms to rich and diverse data sources including sequence data, microarray data, and text from the scientific literature.

• Journal of the Korean Data and Information Science Society
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• 제23권4호
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• pp.825-832
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• 2012

We propose a novel way of forecasting the market shares of several brands simultaneously in a multiplicative competitive interaction model, which uses kernel regression technique incorporated with kernel machine technique applied in support vector machines and other machine learning techniques. Traditionally, the estimations of the market share attraction model are performed via a maximum likelihood estimation procedure under the assumption that the data are drawn from a normal distribution. The proposed method is shown to be a good candidate for forecasting method of the market share attraction model when normal distribution is not assumed. We apply the proposed method to forecast the market shares of 4 Korean car brands simultaneously and represent better performances than maximum likelihood estimation procedure.

• Journal of information and communication convergence engineering
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• 제17권2호
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.131-136
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• 2018

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.

• 한국안전학회지
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• 제33권6호
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• pp.42-49
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• 2018

In this study, we have developed a prediction model for expansion and contraction behaviors of expansion joint in Gwangan Bridge using machine learning techniques and bridge monitoring data. In the development of the prediction model, two famous machine learning techniques, multiple regression analysis (MRA) and artificial neural network (ANN), were employed. Structural monitoring data obtained from bridge monitoring system of Gwangan Bridge were used to train and validate the developed models. From the results, it was found that the expansion and contraction behaviors predicted by the developed models are matched well with actual expansion and contraction behaviors of Gwangan Bridge. Therefore, it can be concluded that both MRA and ANN models can be used to predict the expansion and contraction behaviors of Gwangan Bridge without actual measurements of those behaviors.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.169-180
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• 2021

Since machine learning was invented, there have been many different machine learning-based algorithms, from shallow learning to deep learning models, that provide solutions to the classification tasks. But then it poses a problem in choosing a suitable classification algorithm that can improve the classification/detection efficiency for a certain network context. With that comes whether an algorithm provides good performance, why it works in some problems and not in others. In this paper, we present a data-centric analysis to provide a way for selecting a suitable classification algorithm. This data-centric approach is a new viewpoint in exploring relationships between classification performance and facts and figures of data sets.