• IE interfaces
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• v.11 no.1
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• pp.199-205
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• 1998

This paper describes the development of on-the-machine measuring(OMM) system which can directly measure the two and three dimensional machined accuracy using a scanning probe in milling machine. Two algorithms, NC program based continuous path(CP) measurement and CAD data assisted point to point(PTP) measurement, are developed for three dimensional measurements, with consideration of the characteristics of the scanning probe. The algorithms are used to develop an auto measuring system. The delveloped system is compared with the CMM (Coordinate Measuring Machine) in terms of accuracy and repeatability. The OMM system is expected to realize measurement time reduction and hence result in high productivity.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.45-51
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• 2020

Recently, many games provide data related to the users' game play, and there have been a few studies that predict opponent move by combining machine learning methods. This study predicts opponent move using match data of a real-time strategy game named ClashRoyale and a multi-label classification based on machine learning. In the initial experiment, binary card properties, binary card coordinates, and normalized time information are input, and card type and card coordinates are predicted using random forest and multi-layer perceptron. Subsequently, experiments were conducted sequentially using the next three data preprocessing methods. First, some property information of the input data were transformed. Next, input data were converted to nested form considering the consecutive card input system. Finally, input data were predicted by dividing into the early and the latter according to the normalized time information. As a result, the best preprocessing step was shown about 2.6% improvement in card type and about 1.8% improvement in card coordinates when nested data divided into the early.

• The Korean Journal of Archival Studies
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• no.67
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• pp.99-138
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• 2021

In the era of big data, analyzing not only structured data but also unstructured data is emerging as an important task. Official documents produced by government agencies are also subject to big data analysis as large text-based unstructured data. From the perspective of internal work efficiency, knowledge management, records management, etc, it is necessary to analyze big data of public documents to derive useful implications. However, since many of the public documents currently held by public institutions are not in open format, a pre-processing process of extracting text from a bitstream is required for big data analysis. In addition, since contextual metadata is not sufficiently stored in the document file, separate efforts to secure metadata are required for high-quality analysis. In conclusion, the current official documents have a low level of machine readability, so big data analysis becomes expensive.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.817-828
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• 2021

Purpose: In this paper, we propose a common dataset structure which includes the incidents investigation information and features data for machine learning. Most of the data is from the incidents reports of the governmental part and restricts on the social disaster and safety areas. Method: Firstly, we extract basic incidents data from the several incident investigation reports. The data includes the cause, damage, date, classification of the incidents and additionally considers the feature data for the machine learning. All data is represented by XML standard notation. Result: We defined the standard XML schema and the example for the incidents investigation information. Conclusion: We defined the common incidents dataset structure for the machine learning. It may play roles of the common infrastructure for the disaster and safety applications areas

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.55-58
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• 2024

In this paper, we propose a design and implementation of big data-based fine dust anomaly detection machine learning system. The proposed is system that classifies the fine dust air quality index through meteorological information composed of fine dust and big data. This system classifies fine dust through the design of an anomaly detection algorithm according to the outliers for each air quality index classification categories based on machine learning. Depth data of the image collected from the camera collects images according to the level of fine dust, and then creates a fine dust visibility mask. And, with a learning-based fingerprinting technique through a mono depth estimation algorithm, the fine dust level is derived by inferring the visibility distance of fine dust collected from the monoscope camera. For experimentation and analysis of this method, after creating learning data by matching the fine dust level data and CCTV image data by region and time, a model is created and tested in a real environment.

• Journal of Smart Tourism
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• v.4 no.2
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• pp.5-14
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• 2024

This study explores the transformative potential of machine learning (ML) and ML-driven data analytics in the hospitality industry. It provides a comprehensive overview of this emerging method, from explaining ML's origins to introducing the evolution of ML-driven data analytics in the hospitality industry. The present study emphasizes the shift embodied in ML, moving from explicit programming towards a self-learning, adaptive approach refined over time through big data. Meanwhile, social media analytics has progressed from simplistic metrics deriving nuanced qualitative insights into consumer behavior as an industry-specific example. Additionally, this study explores innovative applications of these innovative technologies in the hospitality sector, whether in demand forecasting, personalized marketing, predictive maintenance, etc. The study also emphasizes the integration of ML and social media analytics, discussing the implications like enhanced customer personalization, real-time decision-making capabilities, optimized marketing campaigns, and improved fraud detection. In conclusion, ML-driven hospitality data analytics have become indispensable in the strategic and operation machinery of contemporary hospitality businesses. It projects these technologies' continued significance in propelling data-centric advancements across the industry.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.1-12
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• 2020

As one of the essential resources in the agricultural process, soil moisture has been carefully managed by predicting future changes and deficits. In recent years, statistics and machine learning based approach to predict soil moisture has been preferred in academia for its generalizability and ease of use in the field. However, little is known that machine learning based soil moisture prediction is applicable in the situation of South Korea. In this sense, this paper aims to examine 1) whether publicly available weather data generated in South Korea has sufficient quality to predict soil moisture, 2) which machine learning algorithm would perform best in the situation of South Korea, and 3) whether a single machine learning model could be generally applicable in various regions. We used various machine learning methods such as Support Vector Machines (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting Machines (GBM), and Deep Feedforward Network (DFN) to predict future soil moisture in Andong, Boseong, Cheolwon, Suncheon region with open source weather data. As a result, GBM model showed the lowest prediction error in every data set we used (R squared: 0.96, RMSE: 1.8). Furthermore, GBM showed the lowest variance of prediction error between regions which indicates it has the highest generalizability.

• Korean Journal of Computational Design and Engineering
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• v.12 no.1
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• pp.1-7
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• 2007

In this paper, we present a machining time estimation algorithm for 5-axis high-speed machining. Estimation of machining time plays an important role in process planning and production scheduling of a shop. In contrast to the rapid evolution of machine tools and controllers, machining time calculation is still based on simple algorithms of tool path length divided by input feedrates of NC data, with some additional factors from experience. We propose an algorithm based on 5-axis machine behavior in order to predict machining time more exactly. For this purpose, we first investigated the operational characteristics of 5-axis machines. Then, we defined some dominant factors, including feed angle that is an independent variable for machining speed. With these factors, we have developed a machining time calculation algorithm that has a good accuracy not only in 3-axis machining, but also in 5-axis high-speed machining.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.4 s.175
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• pp.937-945
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• 2000

The assessment of machined surface is difficult because the freeform surface must be evaluated by surface fairness as well as dimensional accuracy. In this study, the machined freeform surface is modeled by interpolating the data measured on the machine tool into the mathematical continuous surface, and then the surface model is improved with the parameterization to minimize surface fairness. The accuracy reliability of the measured data is confirmed through compensation of volumetric errors of the machine tool and of probing errors. Non-uniform B-spline surface interpolation method is adopted to guarantee the continuity of surface model. Surface fairness is evaluated with the consideration of normal curvature on the interpolated surface. The validity and usefulness of the proposed method is examined through computer simulation and experiment on the machine tool.

• Tribology and Lubricants
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• v.19 no.6
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• pp.335-341
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• 2003

Recently, the automotive industry is being developed rapidly. On this, a demand of high quality performance­test­machine is increased too. But it is progressive technology that must be combined hydraulic, mechanic and electronic technologies. To construct this system, the design of oil hydraulic circuit, interface skill between sensor and personal computer, data acquisition & display system and integrated control are very important skill. Moreover, reliable data is obtained with vacuum system and complex heat exchange system. Therefore, in this study, we designed a performance­test­machine by using above key technologies and we also made a integrated PC control system using personal computer which is more progressive and flexible method than PLC control.