• Tunnel and Underground Space
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• v.30 no.6
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• pp.540-550
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• 2020

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.5
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• pp.1-6
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• 2011

Machine tool errors have to be characterized and predicted to improve machine tool accuracy. Therefore, it is very important to assess errors in machine tools. Volumetric error analysis has been developed by many researchers. This paper presents a useful technique for analyzing the volumetric errors in machine tools using the ball bar. The volumetric error model is proposed in specific vertical machining center and the program is developed for generating NC code, acquiring the ball bar data, and analyzing the volumetric errors. The developed system assesses the volumetric errors such as positional, straightness, squareness, and back lash. Also this system analyzes the dynamic performance such as servo gain mismatch. The radial data acquired by ball bar on 3D space is used for analyzing these errors. It is convenient to test the volumetric errors on 3D space because all errors are calculated at once. The developed system has been tested using an actual vertical machining center.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.3
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• pp.291-300
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• 2016

The emergence of smart devices and IoT leads to the appearance of O2O service to blur the difference between online and offline. As online services' merits were added to the offline market, it caused a change in the dynamics of the offline industry, which means the offline-space's digitization. Unlike these changing aspects of the offline market, exhibition industry grows steadily in the industry, however it is also possible to create a new value added by combining O2O service. We conducted a survey targeting 20 spectators in '2015 Seoul Design Festival' at COEX. The survey was used to analysis of the spatial structure and generate the dataset for machine learning. We identified problems with the analysis study of the existing spatial structure, and based on this investigation we propose a new method for analyzing a spatial structure. Also by processing a machine learning technique based on the generated dataset, we propose a novel evaluation model of exhibition-space cells for O2O service layout.

• The Bulletin of The Korean Astronomical Society
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• v.34 no.1
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• pp.82.1-82.1
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• 2009

• Korean Institute of Interior Design Journal
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• v.16 no.5
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• pp.81-88
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• 2007

Energy consumption has been increased world widely, and the energy retain is one of the most important economic alternatives. These tendencies expand the nuclear power plants not only quantitatively but also qualitatively. Despite of the increasing construction of nuclear power plants and related facilities, every system in main control room(MCR) has been designed and administered solely based on the safety-first principles because of the specificity of nuclear industry. However, recent main control rooms started with the concept that the operators' performance could be optimized though the organic interrelation between human, machine, and environments. Now, it has been recognised in the scope of Ergonomics and Space Design which acknowledge our living spaces as Man-Environment Interface and this change connotes the MCR spaces should be special spaces rather than ordinary spaces. This research investigated domestic and foreign nuclear power plants' MCRs to suggest basic alternatives which can be applied to future MCR. With the review of characteristics of MCR, an integration of interior design, lighting and Ergonomics was explored and classified as types. Futhermore, the classification of environmental characteristics within the relationships between human, machine, and environments was developed through the case analysis of nuclear power plants. The results of this study will provide a basis of space design for system environments that the high level of safety and function are extremely important.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.74.2-74.2
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• 2019

We present a possible bias in the estimation of velocity dispersions for galaxy groups due to the contribution of subgroups which are infalling into the groups. We execute a systematic search for flux-limited galaxy groups and subgroups based on the spectroscopic galaxies with r < 17.77 mag of SDSS data release 12, by using DBSCAN (Density-Based Spatial Clustering of Application with Noise) and Hierarchical Clustering Method which are well known unsupervised machine learning algorithm. A total of 2042 groups with at least 10 members are found and ~20% of groups have subgroups. We found that the estimation of velocity dispersions of groups using total galaxies including those in subgroups are underestimated by ~10% compared to the case of using only galaxies in main groups. This result suggests that the subgroups should be properly considered for mass measurement of galaxy groups based on the velocity dispersion.

• ETRI Journal
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• v.40 no.4
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• pp.499-510
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• 2018

A vector space based augmented structural kinematic (VSASK) feature descriptor is proposed for human activity recognition. An action descriptor is built by integrating the structural and kinematic properties of the actor using vector space based augmented matrix representation. Using the local or global information separately may not provide sufficient action characteristics. The proposed action descriptor combines both the local (pose) and global (position and velocity) features using augmented matrix schema and thereby increases the robustness of the descriptor. A multiclass support vector machine (SVM) is used to learn each action descriptor for the corresponding activity classification and understanding. The performance of the proposed descriptor is experimentally analyzed using the Weizmann and KTH datasets. The average recognition rate for the Weizmann and KTH datasets is 100% and 99.89%, respectively. The computational time for the proposed descriptor learning is 0.003 seconds, which is an improvement of approximately 1.4% over the existing methods.

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.28-33
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• 2012

The visual sensing of human hands plays an important part in many man-machine interaction/interface systems. Most existing visionbased hand detection techniques depend on the color cues of human skin. The RGB color image from a vision sensor is often transformed to another color space as a preprocessing of hand detection because the color space transformation is assumed to increase the detection accuracy. However, the actual effect of color space transformation has not been well investigated in literature. This paper discusses a comparative evaluation of the pixel classification performance of hand skin detection in four widely used color spaces; RGB, YIQ, HSV, and normalized rgb. The experimental results indicate that using the normalized red-green color values is the most reliable under different backgrounds, lighting conditions, individuals, and hand postures. The nonlinear classification of pixel colors by the use of a multilayer neural network is also proposed to improve the detection accuracy.

• ETRI Journal
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• v.10 no.3
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• pp.140-156
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• 1988

This paper presents methodological and theoretical principles for constructing a machine thanslation system between Korean and Japanese. We focus our discussion on the real time computing problem of the machine translation system. This problem is characterized in the time and space complexity during the machine translation. The NARA system has the real time computing algorithm which is based on a mathematical model integrating the linguistic competence and the linguistic performance of both languages, with consequence that the system NARA has also the functional characteristic : the two-way translation mechanism.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.394-397
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• 1987

This paper presents an analysis of the man-machine control system. A man-machine system depends on the performance of a human operator for proper operation. The analysis method is based upon the assumption that human operator will act in a near optimal controller. Optimal control theory and its associated state space representation is used as the basis for the analytic procedure. The computer simulation for a given plant shows that plant parameters have limited range by the human operator.