• International journal of advanced smart convergence
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• v.11 no.4
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.131-132
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• 2011

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1671-1686
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• 2022

Face manipulation tools represented by Deepfake have threatened the security of people's biological identity information. Particularly, manipulation tools with deep learning technology have brought great challenges to Deepfake detection. There are many solutions for Deepfake detection based on traditional machine learning and advanced deep learning. However, those solutions of detectors almost have problems of poor performance when evaluated on different quality datasets. In this paper, for the sake of making high-quality Deepfake datasets, we provide a preprocessing method based on the image pixel matrix feature to eliminate similar images and the residual channel attention network (RCAN) to resize the scale of images. Significantly, we also describe a Deepfake detector named Cascaded-Hop which is based on the PixelHop++ system and the successive subspace learning (SSL) model. By feeding the preprocessed datasets, Cascaded-Hop achieves a good classification result on different manipulation types and multiple quality datasets. According to the experiment on FaceForensics++ and Celeb-DF, the AUC (area under curve) results of our proposed methods are comparable to the state-of-the-art models.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.406-411
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• 2013

Big data issue has been considered in diverse fields. Also, big data learning has been required in all areas such as engineering and social science. Statistics and machine learning algorithms are representative tools for big data learning. In this paper, we study learning tools for big data and propose an efficient methodology for big data learning via legacy data to practical application. We apply our big data learning to patent analysis, because patent is one of big data. Also, we use patent analysis result for technology forecasting. To illustrate how the proposed methodology could be applied in real domain, we will retrieve patents related to big data from patent databases in the world. Using searched patent data, we perform a case study by text mining preprocessing and multiple linear regression of statistics.

• International Journal of Contents
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• v.13 no.1
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• pp.38-44
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• 2017

Increased use of software and complexity of software functions, as well as shortened software quality evaluation periods, have increased the importance and necessity for automation of software testing. Automating software testing by using machine learning not only minimizes errors in manual testing, but also allows a speedier evaluation. Research on machine learning in automated software testing has so far focused on solving special problems with algorithms, leading to difficulties for the software developers and testers, in applying machine learning to software testing automation. This paper, proposes a new machine learning framework for software testing automation through related studies. To maximize the performance of software testing, we analyzed and categorized the machine learning algorithms applicable to each software test phase, including the diverse data that can be used in the algorithms. We believe that our framework allows software developers or testers to choose a machine learning algorithm suitable for their purpose.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.58-63
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• 2007

A compact gun drill machine was developed to improve productivity and economical efficiency for small and medium enterprise tool makers. Gun drilling works are mainly using at molding, automobile, aircraft industry and special tool makers to make deep holes. As gun drill machines are very expensive and big burden for small tool makers, so that works used to execute through outside orders but it was required lot of cost too. Most of gun drill machines are providing for high volume and large capacity enterprises. In order to use for small and medium enterprises that compact gun drill machine was designed and developed. It could be improved product quality, productivity and manufacturing cost for small and medium enterprises by using this machine.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.2
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• pp.129-138
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• 2012

In this study, three types of large scale multi-tasking machine tools together with core technologies involved have been developed and introduced; a multi-tasking machine tool for large scale marine engine crankshafts, a multi-tasking vertical lathe for windmill parts, and a large scale 5-axis machine tool of gantry type. Several special purpose devices has been necessarily developed for the purpose of handling and machining big and heavy workpieces accurately, such as PTD (Pin Turning Device) with revolving ring spindle for machining eccentric crankshaft pins, hydrostatic rotary table and steady rest for supporting and resting heavy workpieces, and 2-axis automatic swiveling head for high-quality free surface machining. Core technologies have been also developed and adopted on their detail design stage; 1) structural design optimization with FEM structural analysis, 2) theoretical hydrostatic analysis for the PTD and rotary table bearings, 3) box-in-box type cross-rail and octagonal ram design to secure machine rigidity and accuracy, 4) constant spindle rpm control against gravitational torque due to unbalanced workpiece.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.3
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• pp.27-32
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• 2010

The OMM(On-Machine-Measurement) system has many advantages compare to conventional measurement in the way the time and cost. But, the sensor suitable to OMM system is restrictive use. Touch trigger probe sensor has long time for measurement and non-contact sensor has directional demerit. Because the long mechanical parts such as gear and lead screw for pump, injector and machine tools has big and heavy, unclamp and transferring for measurement in machining process is very difficult. This paper presents a development of ultra miniature eddy current displacement sensor with 3 axes for On-Machine-Measurement system. The accuracy of the sensor is experimentally proved in the grinding machine. In experimental results, the accuracy has under ${\pm}5\;{\mu}m$.

• Language and Information
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• v.2 no.2
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• pp.185-220
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• 1998

The purpose of this research is to propose a set of basic guidelines for the construction of English test suites, a set of basic guidelines for the construction of Korean test suites to objectively evaluate the performance of machine translation systems. For this end, we constructed 650 English test sentences, 650 Korean test sentences, and developed the statistical methods and tools for the comparative evaluation of the English-Korean machine translation systems. It also evaluates the existing commercial English-Korean machine translation systems. The importance of this research lies in that it will promote an awareness of the importance and need of testing machine translation systems within the Natural Language Community. This research will also make a big contribution to the development of evaluation methods and techniques for appropriate test suites for Korean information processing systems. The results of this research can be used by the natural language community to test the performance and development of their information processing systems or machine translation systems.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.3
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• pp.189-197
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• 2014

Diffractive optical elements (DOEs) - in general a complex pattern of micro- and nano-scale structures - can modulate and transform light in a predetermined way. Their importance is being increased nowadays because they can be designed to handle a number of simultaneous tasks. In view point of machining DOEs, it is a big challenge to fabricate micro- and nano-scale structures on a free-form surfaces. In this paper, the state-of-the-art of the ultra-precision machine tools is reviewed. Also some technical issues which determine the machine tool accuracy are discussed.