• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term power load prediction method using temporal pattern mining from AMR (Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• 한국해양공학회지
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• 제27권2호
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• pp.80-86
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• 2013

KIOST developed a deep-sea mining robot called "MineRo" to collect manganese nodules in 2007. MineRo operates on flat ground. SMS (seafloor massive sulfide) deposits are shaped like undulating mountains. This paper deals with a numerical analysis model of a mining robot for SMS deposits. The mining robot consists of a tracked vehicle, chassis structure with a turntable, boom arm with 2 articulations, excavation tool, discharging unit, hydro-electric system, and sensing-and-monitoring system. In order to compare and analyze the dynamic responses of the driving mechanism, various tracked vehicles are modeled using commercial software. Straight driving simulations are conducted under undulating ground conditions. A conceptual design of a mining robot with four track systems for SMS deposits is modeled on the basis of these results.

• 산업경영시스템학회지
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• 제42권4호
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• pp.126-134
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• 2019

Nowadays, since there are so many big data available everywhere, those big data can be used to find useful information to improve design and operation by using various analysis methods such as data mining. Especially if we have event log data that has execution history data of an organization such as case_id, event_time, event (activity), performer, etc., then we can apply process mining to discover the main process model in the organization. Once we can find the main process from process mining, we can utilize it to improve current working environment. In this paper we developed a new method to find a final diagnosis of a patient, who needs several procedures (medical test and examination) to diagnose disease of the patient by using process mining approach. Some patients can be diagnosed by only one procedure, but there are certainly some patients who are very difficult to diagnose and need to take several procedures to find exact disease name. We used 2 million procedure log data and there are 397 thousands patients who took 2 and more procedures to find a final disease. These multi-procedure patients are not frequent case, but it is very critical to prevent wrong diagnosis. From those multi-procedure taken patients, 4 procedures were discovered to be a main process model in the hospital. Using this main process model, we can understand the sequence of procedures in the hospital and furthermore the relationship between diagnosis and corresponding procedures.

• Geomechanics and Engineering
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• 제23권6호
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• pp.503-512
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• 2020

Mining activities focus on the production of mineral resources for energy generation and raw material requirements worldwide and it is a known fact that shallow reserves become scarce. For this reason, exploration of new resources proceeds consistently to meet the increasing energy and raw material demand of industrial activities. Rock mechanics has a vital role in underground mining and surface mining. Devices and instruments used in laboratory testing to determine rock mechanics related parameters might have limited sensing capability of the failure behavior. However, methodologies such as, thermal cameras, digital speckle correlation method and acoustic emission might enable to investigate the initial crack formation in detail. Regarding this, in this study, thermographic analysis was performed to analyze the failure behaviors of different types of rock specimens during uniaxial compressive strength experiments. The energy dissipation profiles of different types of rocks were characterized by the temperature difference recorded with an infrared thermal camera during experiments. The temperature increase at the failure moment was detected as 4.45℃ and 9.58℃ for andesite and gneiss-schist specimens, respectively. Higher temperature increase was observed with respect to higher UCS value. Besides, a temperature decreases of about 0.5-0.6℃ was recorded during the experiments of the marble specimens. The temperature change on the specimen is related to release of radiation energy. As a result of the porosity tests, it was observed that increase in the porosity rate from 5.65% to 20.97% can be associated to higher radiation energy released, from 12.68 kJ to 297.18 kJ.

• 산업경영시스템학회지
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• 제44권2호
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• pp.66-77
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• 2021

Companies widely use survey to identify customer requirements, but the survey has some problems. First of all, the response is passive due to pre-designed questionnaire by companies which are the surveyor. Second, the surveyor needs to have good preliminary knowledge to improve the quality of the survey. On the other hand, text mining is an excellent way to compensate for the limitations of surveys. Recently, the importance of online review is steadily grown, and the enormous amount of text data has increased as Internet usage higher. Also, a technique to extract high-quality information from text data called Text Mining is improving. However, previous studies tend to focus on improving the accuracy of individual analytics techniques. This study proposes the methodology by combining several text mining techniques and has mainly three contributions. Firstly, able to extract information from text data without a preliminary design of the surveyor. Secondly, no need for prior knowledge to extract information. Lastly, this method provides quantitative sentiment score that can be used in decision-making.

• 한국응용과학기술학회지
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• 제37권1호
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• pp.76-82
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• 2020

Big data analysis in healthcare research seems to be a necessary strategy for the convergence of sports science and technology in the era of the Fourth Industrial Revolution. The purpose of this study is to provide the basic review to secure the diversity of big data and healthcare convergence by discussing the concept, analysis method, and application examples of big data and by exploring the application. Text mining, data mining, opinion mining, process mining, cluster analysis, and social network analysis is currently used. Identifying high-risk factor for a certain condition, determining specific health determinants for diseases, monitoring bio signals, predicting diseases, providing training and treatments, and analyzing healthcare measurements would be possible via big data analysis. As a further work, the big data characteristics provide very appropriate basis to use promising software platforms for development of applications that can handle big data in healthcare and even more in sports science.

• 인터넷정보학회논문지
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• 제19권1호
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• pp.155-166
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• 2018

These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers' opinions and competitive advantage in the competing market. Analyzing consumers' opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers' opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제32권4호
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• pp.555-573
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• 2018

본 연구는 텍스트 마이닝 기법을 활용하여 수학수업에서 나타나는 교사의 감성적 언어를 확인하고자 하였다. 이를 위해 우수 수업 동영상을 활용하여 수업에서 발생하는 교사의 수업 언어 데이터를 수집하였다. 추출한 비정형 데이터에 대한 분석 과정은 데이터 수집, 데이터 전처리, 텍스트 마이닝 분석의 세 가지 단계로 진행하였다. 분석 결과 수학 수업에서 오고가는 담화 중에서 교사의 감성적 반응을 나타내는 언어는 거의 나타나지 않았으며, 이를 통해 수업의 정의적 영역 측면에서의 시사점을 도출하였다.

• 한국정보시스템학회지:정보시스템연구
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• 제29권4호
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• pp.171-191
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• 2020

Purpose The study aims to compare the online review writing behavior of users in China and the United States through text mining on online reviews' text content. In particular, existing studies have verified that there are differences in online reviews between different cultures. Therefore, the purpose of this study is to compare the differences between reviews written by Chinese and American tourists by analyzing text contents of online reviews based on cultural theory. Design/methodology/approach This study collected and analyzed online review data for hotels, targeting Chinese and US tourists who visited Korea. Then, we analyzed review data through text mining like sentiment analysis and topic modeling analysis method based on previous research analysis. Findings The results showed that Chinese tourists gave higher ratings and relatively less negative ratings than American tourists. And American tourists have more negative sentiments and emotions in writing online reviews than Chinese tourists. Also, through the analysis results using topic modeling, it was confirmed that Chinese tourists mentioned more topics about the hotel location, room, and price, while American tourists mentioned more topics about hotel service. American tourists also mention more topics about hotels than Chinese tourists, indicating that American tourists tend to provide more information through online reviews.

• 화약ㆍ발파
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• 제30권2호
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• pp.9-20
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• 2012

최근 광업계는 지하개발이 주로 이루어지며, 대형 굴착장비의 보급으로 인해 갱도 및 채굴공동이 지하심 부화되고 있다. 채광설계 시 채굴공동의 붕락방지와 채광작업의 안전성 확보를 위해 채굴공동 및 광주의 설계가 매우 중요하게 대두되고 있다. 이에 본 연구는 지하채굴공동의 발파진동계측을 통한 발파진동 예측식 도출을 수행하였고, 채굴공동의 발파시 발생되는 비산석의 파쇄입도 분석과 비산거리 측정을 통하여 광주에 가해지는 이론적인 충격진동을 산정하였다. 지하채굴공동의 발파에 따른 광주의 영향을 검토하기 위하여 유한요소해석을 수행하여 발파진동 예측식과 비교하였으며, 채굴공동의 발파로 인해 발생되는 비산석이 광주에 충격이 가해졌을 때의 충격진동과 이론적인 충격진동을 비교, 분석하였다.