• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.873-887
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• 2013

The axis of rotation in biomechanics is a major tool to investigate joint function; therefore, many methods to estimate the axis of rotation have been developed. However, there exist several problems to describe, estimate, and test the axis statistically. The axis is directional data(axial data) and it should not be analyzed with traditional statistics. A proper comparative method should be considered to compare axis estimating methods for the same given data ANOVA (analysis of variance) is a frequently used statistical method to compare treatment means in experimental designs. In case of the axial data response assumed to come from Watson distribution, there are a few ANOVA method options. This study constructed ANOVA models for within-subjects designs of axial data. Two models (one within-subjects factor and two within-subjects factors crossed design) were considered. The empirical data used in this study were instantaneous axes of rotation of flexion/extension at the knee joint and the flexion/extension and pronation/supination at the elbow joint. The results of this study can be further applied to the various analysis of experimental designs.

• Journal of Korean Society of Forest Science
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• v.19 no.1
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• pp.49-54
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• 1973

Surveys of stock volume on steep and vast expanse of mountains, involves various difficulties. And it is extremely uneconomical in forest management point of view, to spend lots of time and man power for surveying such tree volume as the value is much cheaper in comparison with volume and weight. Therefore, a stand volume table estimate easely stock volume per hectare basis from aerial photographs was prepared and correlations to stand volume among factors affecting tree volumation, were studied. Data were 114 places selected from planted Korean white pine, Pinus koreiensis Sieb. et Zucc. stands in Kwangnung Experiment Forest and were computed and analysed by the means of the quantification in the multi-variate statistical analysis. Electronic Data Processing System was applied for data processing at Korean Instiute of Science and Technology. Coefficients of multiple correlations of stand volume table was ranged 0.85~0.88.

• Journal of the Korea Concrete Institute
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• v.17 no.4 s.88
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• pp.559-567
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• 2005

The objective of this study is to optimize the use of mineral admixtures (silica fume, fly ash, and blast furnace slag) in high-performance concrete for bridge deck overlay. For this purpose, high-performance concrete, incorporating mineral admixtures, was tested for compressive strength and permeability. The Box Behnken design was used to determine the optimum mix proportions of the mineral admixtures. The optimized mix compositions were then technically evaluated. Test results are compare with the performance specification for high performance concrete overlay on bridge deck. The optimum mix proportions were shown to possess acceptable properties. Also, it is possible to save the construction and materials costs result from a reduction In actual material cost and from the use of widely avaliable truck mixers instead of mobile mixers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.95-108
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• 2019

Quality management has become an pervasive philosophy in most sectors of business. Specific movements such as statistical quality control, quality circle, total quality management, and quality management system have become embedded in business organizations. Only the companies with competitive edge can survive in the competition in global market. KSA(Korean Standards Association) established in 1962 has launched all kinds of quality education, quality standard certification service for business, and KNQA(Korean National Quality Award) system. This article considers quality competitiveness excellent company award among KNQA. We performed a statistical analysis of audit data for quality competitiveness excellent company for three years, from 2015 to 2017. By using ANOVA and two sample t-tests, the average scores of 13 evaluation fields were significantly different depending on company size and type. We proposed ways to improve the current hall of fame system. We discovered that the average scores of 13 evaluation fields in the audit data according to years and hall of fame status were not significantly different. We also showed linear relationships among 13 evaluation fields by correlation analysis and obtained an estimated linear regression equation : Business Performance, which is a comprehensive index, as a dependent variable was significantly related to Customer Focus and Product Liability as regressor variables among 13 evaluation fields by regression analysis.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.151-154
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• 2021

Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study was analyzed for 21 papers in the March 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 305 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.326-327
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• 2021

Newly, big data utilization has been widely interested in a wide variety of industrial fields. Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study were analyzed for 29 papers in a specific journal. In the final analysis results, the most frequently mentioned keyword was "Research", which ranked first 743 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.182-196
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• 2024

This study introduces driving pattern analysis and change detection methods using smartphone sensors, based on contrastive learning. These methods characterize driving patterns without labeled data, allowing accurate classification with minimal labeling. In addition, they are robust to domain changes, such as different vehicle types. The study also examined the applicability of these methods to smartphones by comparing them with six lightweight deep-learning models. This comparison supported the development of smartphone-based driving pattern analysis and assistance systems, utilizing smartphone sensors and contrastive learning to enhance driving safety and efficiency while reducing the need for extensive labeled data. This research offers a promising avenue for addressing contemporary transportation challenges and advancing intelligent transportation systems.

• Spatial Information Research
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• v.1 no.1
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• pp.73-87
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• 1993

This paper reviews the topic of GIS, the academic setting of GIS, GIS model curricula and the possibility GIS education in Korea. The topics which might be included in a science of geographic information consist of data collection and measurement, data capture, spatial statistics, data modeling and theories of spatial data, data structures, algorithms and processes, display, analytical tools, institutional, managerial and ethical issues. The problems in teaching a course on GIS in higher education are reviewed. Because of their technological, integrative, and rapidly changing nature, GIS pose major challenges to their education system which it is ill equipped to meet. In higher education a number of initiatives have been taken to provide education about and training with, GIS. The possible GIS curricula are suggested. These curricula are divided into 3 major sections, relating GIS context, technical issues and application issues. The prospects of GIS appears lo depend largely upon the future cooperation of academia, government, and industry

• Tunnel and Underground Space
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• v.22 no.6
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• pp.412-428
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• 2012

To achieve mine development, a large amount of data concerned with the geological structure and the ore body had to be investigated and collected through geological survey, drilling and geophysical explorations. In most previous cases, however, the data were usually analyzed two dimensionally and those results showed some limits because of their 2D presentation. Those 2D maps such as geological plane sections or longitudinal sections cause lots of difficulties in understanding the complex geological structure or the feature of ore body in a spatial way. In this study, research area was set on the abandoned Yangyang iron mine in Korea and the Sugaeng ore body within the mine was selected as the research target to design a mine haulage system for reopening. A 3D mine model of this area was tried to be constructed using a 3D modelling software, GEMS. An accurate 3D model including the ore body, the geological structure, the old underground mine drifts and the new mine drifts was constructed under the purpose of reopening of the abandoned iron mine. Especially, mine design for trackless haulage system was conducted. New inclines and drifts were planned and modelled 3 dimensionally considering the utilization of old drifts and shaft. In addition to the 3D modelling, geostatistical technique was adopted to generate a spatial distribution of the ore grade and the rock physical properties. 3D model would be able to contribute in solving problems such as evaluating ore reserves, planning the mine development and additional explorations and changing the development plans, etc.