• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.05a
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• pp.152-153
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• 2017

In the construction site, it is necessary to estimate the compressive strength of concrete in order to adjust the demolding time of the form, and establish and adjust the construction schedule. The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, six influential factors (Water, Cement, Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at three conferences in order to know the various correlations among data and the tendency of data. After using algorithm of various methods of machine learning techniques, we selected the most suitable regression analysis model for estimating the compressive strength.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.12
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• pp.34-41
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• 2003

This paper suggests a methodology fur improving the machining accuracy by compensating for the machining errors based on on-machine measurement process. Probing errors and machine tool errors included in the measurement data were calibrated or compensated to obtain the actual machining errors. Machine tool errors were modeled in forward and backward directions according to the axis movement direction to consider the effects of backlash errors on the measurement data, and model parameters were determined by measuring a cube array artifact. A rectangular workpiece was machined and then measured with a touch probe as a verification experiment. Machining experiments showed that the machining errors were reduced to within the designated tolerance after compensating for the actual machining errors by modifying the original footpath for the next-step machining.

• Journal of Biosystems Engineering
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• v.22 no.2
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• pp.217-226
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• 1997

This study was to develop a machine vision system to detect and to discriminate 5 kinds of apple surface defectbruise, decay. fleck, worm hole and scar. To detect the defects from an image of apple, thresholding technique was applied to images on various frames (R, G, B, H, S and I) of the color machine vision and an image of near infrared (NIR). To discriminate the detected region of defect, various features of the 5 kind defect regions were extracted from the 4 kinds of images selected above. The features were size of area, roundness, axes length ratio, mean and valiance of pixel values, standard deviation of real part of amplitude spectrum in frequency domain obtained by Fourier transform of pixel data and mean and standard deviation of power spectrum obtained by the same transform of pixel data. Routines to discriminate the defects from the features of image were developed and tested to prove their validity. The test resulted that I-frame and NIR images were the most desirable. Accuracies of the two images to discriminate the defects were noted as 76% and 77%, respectively.

• Korean Journal of Computational Design and Engineering
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• v.13 no.2
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• pp.153-161
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• 2008

This paper proposes an efficient algorithm for deriving inverse kinematics equation of 5-axis machine. Because the joint order and direction of 5-axis machine are different for each type of machine, each type of machine needs its own inverse kinematics equation for post-processing of NC data. Also derived inverse kinematics equation may cause problems of indeterminate and inconsistent solution. In order to resolve these problems, we have developed a generic method to derive direct kinematics equation by considering orthogonal joints of 5-axis machines. Using this method, we also have proposed a general algorithm for deriving inverse kinematics equation for various types of 5-axis machines.

• Journal of the Korea Safety Management & Science
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• v.7 no.1
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• pp.199-210
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• 2005

Customer segmentation prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. However, ANN approaches have suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the customer segmentation prediction problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with logistic regression analysis and ANN. The experiment results with real data of insurance company show that SVM superiors to them.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.

• ETRI Journal
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• v.42 no.2
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• pp.292-304
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• 2020

As side-channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side-channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning-based side-channel analysis. In particular, we outline how deep learning is applied to side-channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.498-503
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• 2002

This paper has studied thermal characteristics of machine tool to develope high speed spindle and optimum design considering the thermal deformation. Comparing the test data of temperature measurement and structural analysis data using FEM, we verified the test validity and predicted thermal deformation, influence of spindle generation of heat, and established cooling system to prevent the thermal deformation. 1) The temperature rise of spindle system depends on increasing number of rotation and shows sudden doubling increment of number of rotation over 7,000rpm. 2) Oil jacket cooling can be effective cooling method below 8,000rpm but, over 8,000rpm, it shows the decrement of cooling effect. 3) Comparing FEM analysis results and revolution test results, we can confirmn approximate temperature change consequently, it is possible to simulate temperature rise and thermal distribution on the inside of spindle system. 4) We can confirm that simulated approach by FEM analysis can be effective mettled in thermal-appropriate design.

• Journal of Korean Medical classics
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• v.35 no.1
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• pp.93-102
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• 2022

Objectives : We propose a method for constructing a thesaurus of Traditional East Asian Medicine terminology using machine learning. Methods : We presented a method of combining the 'Automatic Step' which uses machine learning and the 'Manual Step' which is the operator's review process. By applying this method to the sample data, we constructed a simple thesaurus and examined the results. Results : Out of the 17,874 sample data, a thesaurus was constructed targeting 749 terminologies. 200 candidate groups were derived in the automatic step, from which 79 synonym groups were derived in the manual step. Conclusions : The proposed method in this study will likely save resources required in constructing a thesaurus.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.399-404
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• 2020

There are many attempts to apply AI technology to diagnose facilities or improve the work efficiency of the power industry. The emergence of new machine learning technologies, such as deep learning, is accelerating the digital transformation of the power sector. The problem is that traditional power systems face security risks when adopting state-of-the-art AI systems. This adoption has convergence characteristics and reveals new cybersecurity threats and vulnerabilities to the power system. This paper deals with the security measures and implementations of the power system using machine learning. Through building a commercial facility operations forecasting system using machine learning technology utilizing power big data, this paper identifies and addresses security vulnerabilities that must compensated to protect customer information and power system safety. Furthermore, it provides security guidelines by generalizing security measures to be considered when applying AI.