• Journal of Wind Energy
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• v.4 no.1
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• pp.20-29
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• 2013

Load measurement, which is performed based on IEC 61400-13, consists of three stages: the stage of collecting huge amounts of load measurement data through a measurement campaign lasting for several months; the stage of processing the measured data, including data validation and classification; and the stage of analyzing the processed data through time series analysis, load statistics analysis, frequency analysis, load spectrum analysis, and equivalent load analysis. In this research, we pursued the development of an analysis software in MATLAB to save labor and to secure exact and consistent performance evaluation data in processing and analyzing load measurement data. The completed analysis software also includes the functions of processing and analyzing power performance measurement data in accordance with IEC 61400-12. The analysis software was effectively applied to process and analyse the load measurement data from a demonstration research for a 750 kW direct-drive wind turbine generator system (KBP-750D), performed at the Daegwanryeong Wind Turbine Demonstration Complex. This paper describes the details of the analysis software and its processing and analysis stages for load measurement data and presents the analysis results.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.443-452
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• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.125-132
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• 2020

Due to the rapidly increasing number of cars and power generation, environmental pollution caused by fine dust is becoming a serious social problem. Especially fine dust becomes an important issue nowadays. More than 50 countries are suffering from fine dust above the recommended level, and each affected country is studying the measures to reduce fine dust and minimize its occurrence. However, at present, it is difficult to collect fine dust data from the various points with fixed fine dust acquisition drones, and also to collect accurate data due to the influence of rotating blades even in the existing drone method. In this paper, we propose a method for collecting fine dust using drones and a sensing parts architecture and show its effectiveness.

• Hwankyungkyoyuk
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• v.20 no.4
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• pp.12-26
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• 2007

The purpose of this research is to develop a water environmental education (EE) program using streams, based on the core ideas of ENVISION and materializing elements that were extracted in this research. This research realized the elements and presented a model of the water EE program using a local stream. First, this research developed a basic model of a water EE program using streams by extracting 10 materializing elements and realizing the elements in 4 stage-procedural model. The 10 materializing elements were 1. experiencing the process of inquiry, 2. inquiring local environments, 3. self-directing learning and mutual interaction with colleagues, 4. collecting real data and interpreting, 5. utilizing the ICT(information and communication technology), 6. inquiring with the view point of the 'Environmental Studies for EE', 7. inquiring with the watershed concept, 8. inquiring with the integrating and the holistic view point, 9. pursuing the macroscopic understanding about environment, and 10. connecting the real world phenomena with the environmental concepts and theories. This research materialized these 10 elements in 4 stage model, following the previous ENVISION research, which are 1. preparing stage and visual assessment, 2. writing the report of the inquiry plan, 3. collecting the real data in the environment and performing the investigation, and 4. presenting the inquiry results. Second, with using this basic model, this research developed and presented a model of the specific water EE program using a case stream called 'Baig Cheon' stream, which is a local stream. This research is considered to have a considerable meaning in developing a EE program with ENVISION ideas for the watershed concept and inquiry with environmental science using local streams. The developed model can help the professional development of teachers and teacher education of water EE.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.9-16
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• 2023

Recently, there is an increasing demand to quickly identify changes in the field by applying Digital-twin for on-site management control and analyzing changes in real-time data transmitted. When technologies such as Digital-twin are applied, early problem response and quick response to situations are possible. However, in order to maximize the advantages of digital twin technology, a method of collecting and managing sensor data that collects field information at an appropriate period is required. need. In addition, it is necessary to consider how sensor information is transmitted to each management domain and how to identify and respond to abnormal situations so that it can be visualized for each management purpose. In this paper, we propose a method for collecting heterogeneous sensor information and related system configuration for on-site inspection management based on digital twin.

• Culinary science and hospitality research
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• v.24 no.3
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• pp.167-176
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• 2018

In this study, Big Data was used to collect the information related to 'Cooking Academy' keywords. After collecting all the data, we calculated the frequency through the text mining and selected the main words for future data analysis. Data collection was conducted from Google Web and News during the period from January 1, 2013 to December 31, 2017. The selected 64 words were analyzed by using UCINET 6.0 program, and the analysis results were visualized with NetDraw in order to present the relationship of main words. As a result, it was found that the most important goal for the students from cooking school is to work as a cook, likewise to have practical classes. In addition, we obtained the result that SNS marketing system that the social sites, such as Facebook, Twitter, and Instagram are actively utilized as a marketing strategy of the institute. Therefore, the results can be helpful in searching for the method of utilizing big data and can bring brand-new ideas for the follow-up studies. In practical terms, it will be remarkable material about the future marketing directions and various programs that are improved by the detailed curriculums through semantic network of cooking school by using big data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2080-2085
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• 2008

KEPCO's AMR (Automatic Meter Reading) is a system that performs the real-time inspection and management of the 15-minute load profile of electric power consumption through a wired and/or wireless network such as CDMA. It has been utilized widely for real-time collection and data analysis. So far, KEPCO has focused on establishing wireless networks using CDMA and collecting data in real time but failed to consider sufficiently performances that can improve the quality of the original data required in terms of data utilization as well as establish the summary information. In this paper, we are going to show the functions that improve data quality by recording the final renewal time of any erroneous data and maintaining such data lists to use them in the rebuilding of summary information. The goals are to reduce any load applied mainly on the DBMS (Database Management System) of AMR, to enable the real-time performance of establishment in the summary information, and to obtain high-quality inspection data. The performance evaluation result has revealed a 10-fold improvement compared to the traditional disk-based DBMS system when the summary information is established.

• Journal of Multimedia Information System
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• v.3 no.3
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• pp.63-68
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• 2016

Environmental downtime produces a significant cost to organizations and makes them unable to do business because what happens in the data center affects everyone. In addition, the amount of electrical energy consumed by data centers increases with the amount of computing power installed. Installation of physical Information Technology and facilities related to environmental concerns, such as monitoring temperature, humidity, power, flood, smoke, air flow, and room entry, is the most proactive way to reduce the unnecessary costs of expensive hardware replacement or unplanned downtime and decrease energy consumed by servers. In this paper, we present remote system for monitoring datacenter implementing using open-source hardware platforms; Arduino, Raspberry Pi, and the Gobetwino. The sensed data displayed through Arduino are transferred using Gobetwino to the nearest host server such as temperature, humidity and distance every time an object hitting another object or a person coming in entrance. The raspberry Pi records the sensed data at the remote location. The objective of collecting temperature and humidity data allows monitoring of the server's health and getting alerts if things start to go wrong. When the temperature hits $50^{\circ}C$, the supervisor at remote headquarters would get a SMS, and then they would take appropriate actions to reduce electrical costs and preserve functionality of servers in data centers.

• Culinary science and hospitality research
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• v.23 no.3
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• pp.257-270
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• 2017

The purpose of this study was to visualize the semantic network with big data related to food exhibition at convention center. For this, this study collected data containing 'coex food exhibition/bexco food exhibition' keywords from web pages and news on Google during one year from January 1 to December 31, 2016. Data were collected by using TEXTOM, a data collecting and processing program. From those data, degree centrality, closeness centrality, betweenness centrality and eigenvector centrality were analyzed by utilizing packaged NetDraw along with UCINET 6. The result showed that the web visibility of hospitality and destinations was high. In addition, the web visibility was also high for convention center programs, such as festival, exhibition, k-pop and event; hospitality related words, such as tourists, service, hotel, cruise, cuisine, travel. Convergence of iterated correlations showed 4 clustered named "Coex", "Bexco", "Nations" and "Hospitality". It is expected that this diagnosis on food exhibition at convention center according to changes in domestic environment by using these web information will be a foundation of baseline data useful for establishing convention marketing strategies.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.22-32
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• 2017

The purpose of this study was to explore awareness of food tourism using big data analysis. For this, this study collected data containing 'food tourism' keywords from google web search, google news, and google scholar during one year from January 1 to December 31, 2016. Data were collected by using SCTM (Smart Crawling & Text Mining), a data collecting and processing program. From those data, degree centrality and eigenvector centrality were analyzed by utilizing packaged NetDraw along with UCINET 6. The result showed that the web visibility of 'core service' and 'social marketing' was high. In addition, the web visibility was also high for destination, such as rural, place, ireland and heritage; 'socioeconomic circumstance' related words, such as economy, region, public, policy, and industry. Convergence of iterated correlations showed 4 clustered named 'core service', 'social marketing', 'destinations' and 'social environment'. It is expected that this diagnosis on food tourism according to changes in international business environment by using these web information will be a foundation of baseline data useful for establishing food tourism marketing strategies.