• Title/Summary/Keyword: mining situation

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Analysis of Terrain Data Change using Digital Elevation Data (수치표고자료를 활용한 지형자료변화 분석)

  • 이형석;송승호;배상호
    • Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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    • 2004.11a
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    • pp.385-388
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    • 2004
  • Many environmental destruction factors are accompanied in the mining development work and the secondary environmental disaster and the induction factors are inhered. We aquired digital data using aerial photogrammetry to analyze the terrain current situation according to the development situation of the mining restoration plan. We made the object area to 3D model and conducted terrian change monitoring. Then, we presented the decision-making information to improve rational management according to the original state plan.

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The Current Situation of Mining and Smelting and the Mineral Policy of Japan

  • Shiga, Yoshihide
    • Proceedings of the KSEEG Conference
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    • 2003.04a
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    • pp.32-36
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    • 2003
  • The mining industry of Japan had rapidly grown after the World War II with the economic growth. There existed more than 350 mines all over Japan in the 1960’s. The mines however had been closed one after another under the influence of the world and domestic economic events such as the mining-related pollution in Japan after the middle of the 60’s, the Oil Crisises in 73 and 79 and the Strong Yen in 85 (Fig. 1). (omitted)

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Optimization of Robust Design Model using Data Mining (데이터 바이닝을 이용한 로버스트 설계 모형의 최적화)

  • Jung, Hey-Jin;Koo, Bon-Cheol
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.30 no.2
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    • pp.99-105
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    • 2007
  • According to the automated manufacturing processes followed by the development of computer manufacturing technologies, products or quality characteristics produced on the processes have measured and recorded automatically. Much amount of data daily produced on the processes may not be efficiently analyzed by current statistical methodologies (i.e., statistical quality control and statistical process control methodologies) because of the dimensionality associated with many input and response variables. Although a number of statistical methods to handle this situation, there is room for improvement. In order to overcome this limitation, we integrated data mining and robust design approach in this research. We find efficiently the significant input variables that connected with the interesting response variables by using the data mining technique. And we find the optimum operating condition of process by using RSM and robust design approach.

Applying Data Mining to ATCIS for Supporting Rapid Decision Making (ATCIS의 신속한 결심수립 지원을 위한 Data Mining 적용)

  • Lee, Hak-hun;Kim, Min-hwan
    • Journal of the Korea Institute of Military Science and Technology
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    • v.20 no.4
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    • pp.551-557
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    • 2017
  • Commanders want to receive quickly scientific and analytic results about the battlefield situation. Unfortunately, decision support system of Army Tactical Command Information System(ATCIS) is restricted to message procedures and searching function based on manual work. In this paper, we propose applying Data Mining to ATCIS for supporting rapid decision making based on the scientific and analytic method. The purpose of this proposal is to efficiently execute the tactical planning and employment of the subordinate units in order to achieve the mission.

The Evaluation of Personal Protective Equipment Usage Habit of Mining Employees Using Structural Equation Modeling

  • Kursunoglu, Nilufer;Onder, Seyhan;Onder, Mustafa
    • Safety and Health at Work
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    • v.13 no.2
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    • pp.180-186
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    • 2022
  • Background: In occupational studies, it is a known situation that technical and organizational attempts are used to prevent occupational accidents. Especially in the mining sector, if these attempts cannot prevent occupational accidents, personal protective equipment (PPE) becomes a necessity. Thus, in this study, the main objective is to examine the effects of the variables on the use of PPE and identify important factors. Methods: A questionnaire was implemented and structural equation modeling was conducted to ascertain the significant factors affecting the PPE use of mining employees. The model includes the factors that ergonomics, the efficiency of PPE and employee training, and PPE usage habit. Results: The results indicate that ergonomics and employee training have no significant effect (p > 0.05) on the use of PPE. The efficiency of PPE has a statistically meaningful effect (p < 0.05) on the use of PPE. Various variables have been evaluated in previous studies. However, none of them examined the variables simultaneously. Conclusion: The developed model in the study enables to better focus on ergonomics and employee training in the PPE usage. The effectiveness of a PPE makes its use unavoidable. Emphasizing PPE effectiveness in OHS training and even showing them in practice will increase employees' PPE usage. The fact that a PPE with high effectiveness is also ergonomic means that it will be used at high rates by the employee.

An Enhanced Text Mining Approach using Ensemble Algorithm for Detecting Cyber Bullying

  • Z.Sunitha Bai;Sreelatha Malempati
    • International Journal of Computer Science & Network Security
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    • v.23 no.5
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    • pp.1-6
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    • 2023
  • Text mining (TM) is most widely used to process the various unstructured text documents and process the data present in the various domains. The other name for text mining is text classification. This domain is most popular in many domains such as movie reviews, product reviews on various E-commerce websites, sentiment analysis, topic modeling and cyber bullying on social media messages. Cyber-bullying is the type of abusing someone with the insulting language. Personal abusing, sexual harassment, other types of abusing come under cyber-bullying. Several existing systems are developed to detect the bullying words based on their situation in the social networking sites (SNS). SNS becomes platform for bully someone. In this paper, An Enhanced text mining approach is developed by using Ensemble Algorithm (ETMA) to solve several problems in traditional algorithms and improve the accuracy, processing time and quality of the result. ETMA is the algorithm used to analyze the bullying text within the social networking sites (SNS) such as facebook, twitter etc. The ETMA is applied on synthetic dataset collected from various data a source which consists of 5k messages belongs to bullying and non-bullying. The performance is analyzed by showing Precision, Recall, F1-Score and Accuracy.

Efficient Dynamic Weighted Frequent Pattern Mining by using a Prefix-Tree (Prefix-트리를 이용한 동적 가중치 빈발 패턴 탐색 기법)

  • Jeong, Byeong-Soo;Farhan, Ahmed
    • The KIPS Transactions:PartD
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    • v.17D no.4
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    • pp.253-258
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    • 2010
  • Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

Recent Technique Analysis, Infant Commodity Pattern Analysis Scenario and Performance Analysis of Incremental Weighted Maximal Representative Pattern Mining (점진적 가중화 맥시멀 대표 패턴 마이닝의 최신 기법 분석, 유아들의 물품 패턴 분석 시나리오 및 성능 분석)

  • Yun, Unil;Yun, Eunmi
    • Journal of Internet Computing and Services
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    • v.21 no.2
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    • pp.39-48
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    • 2020
  • Data mining techniques have been suggested to find efficiently meaningful and useful information. Especially, in the big data environments, as data becomes accumulated in several applications, related pattern mining methods have been proposed. Recently, instead of analyzing not only static data stored already in files or databases, mining dynamic data incrementally generated in a real time is considered as more interesting research areas because these dynamic data can be only one time read. With this reason, researches of how these dynamic data are mined efficiently have been studied. Moreover, approaches of mining representative patterns such as maximal pattern mining have been proposed since a huge number of result patterns as mining results are generated. As another issue, to discover more meaningful patterns in real world, weights of items in weighted pattern mining have been used, In real situation, profits, costs, and so on of items can be utilized as weights. In this paper, we analyzed weighted maximal pattern mining approaches for data generated incrementally. Maximal representative pattern mining techniques, and incremental pattern mining methods. And then, the application scenarios for analyzing the required commodity patterns in infants are presented by applying weighting representative pattern mining. Furthermore, the performance of state-of-the-art algorithms have been evaluated. As a result, we show that incremental weighted maximal pattern mining technique has better performance than incremental weighted pattern mining and weighted maximal pattern mining.

Association Rule Mining Algorithm and Analysis of Missing Values

  • Lee, Jae-Wan;Bobby D. Gerardo;Kim, Gui-Tae;Jeong, Jin-Seob
    • Journal of information and communication convergence engineering
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    • v.1 no.3
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    • pp.150-156
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    • 2003
  • This paper explored the use of an algorithm for the data mining and method in handling missing data which had generated enhanced association patterns observed using the data illustrated here. The evaluations showed that more association patterns are generated in the second analysis which suggests more meaningful rules than in the first situation. It showed that the model offer more precise and important association rules that is more valuable when applied for business decision making. With the discovery of accurate association rules or business patterns, strategies could be efficiently planned out and implemented to improve marketing schemes. This investigation gives rise to a number of interesting issues that could be explored further like the effect of outliers and missing data for detecting fraud and devious database entries.