• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.1
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• pp.109-118
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• 2016

Traffic congestions are rapidly increasing in urban areas. In order to reduce these problems, it needs real-time data and intelligent techniques to quickly identify traffic activities with useful information. This paper proposes a Fuzzy Domain Ontology(FDO)-based opinion mining system to monitor the transportation network in real-time as well to make a city polarity map for travelers. The proposed system retrieves tweets and reviews related to transportation activities and a city. The feature opinions are extracted from these tweets and reviews and then used FDO to identify transportation and city features polarity. This FDO and intelligent prototype are developed using $Prot{\acute{e}}g{\acute{e}}$ OWL (Web Ontology Language) and JAVA, respectively. The experimental result shows satisfactory improvement in tweets and review's analyzing and opinion mining.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.93-99
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• 2020

Text mining is one of the big data analysis methods that extracts meaningful information from atypical large-scale text data. In this study, text mining was used to monitor citizens' opinions on the policies and systems being implemented. We collected 5,108 newspaper articles and 748 online cafe posts related to 'Gyeonggi Lacal Currency' and performed frequency analysis, TF-IDF analysis, association analysis, and word tree visualization analysis. As a result, many articles related to the purpose of introducing local currency, the benefits provided, and the method of use. However, the contents related to the actual use of local currency were written in the online cafe posts. In order to revitalize local currency, the news was involved in the promotion of local currency as an informant. Online cafe posts consisted of the opinions of citizens who are local currency users. SNS and text mining are expected to effectively activate various policies as well as local currency.

• Safety and Health at Work
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• v.13 no.1
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• pp.40-50
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• 2022

Background: Underground mines have several hazards that could lead to serious consequences if they come into effect. Acquiring, evaluating, and using the real-time data from the atmospheric monitoring system and miner's positional information is crucial in deciding the best course of action. Methods: A graphical user interface-based software is developed that uses an AutoCAD-based mine map, real-time atmospheric monitoring system, and miners' positional information to guide on the shortest route to mine exit and other locations within the mine, including the refuge chamber. Several algorithms are implemented to enhance the visualization of the program and guide the miners through the shortest routes. The information relayed by the sensors and communicated by other personnel are collected, evaluated, and used by the program in proposing the best course of action. Results: The program was evaluated using two case studies involving rescue relating to elevated carbon monoxide levels and increased temperature simulating fire scenarios. The program proposed the shortest path from the miner's current location to the exit of the mine, nearest refuge chamber, and the phone location. The real-time sensor information relayed by all the sensors was collected in a comma-separated value file. Conclusion: This program presents an important tool that aggregates information relayed by sensors to propose the best rescue strategy. The visualization capability of the program allows the operator to observe all the information on a screen and monitor the rescue in real time. This program permits the incorporation of additional sensors and algorithms to further customize the tool.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.537-550
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• 2021

There are various technical problems need to be solved in the construction process of pre-setting an isolation wall into a double lane in the outburst prone mine. This study presents a methodology that pre-setting an isolation wall into a double lane without a coal pillar. This requires the excavation of two small section roadways to dig a wide section roadway, followed by construction of the separation wall. During this process the connecting lane is reserved. In order to ensure the stability of the separation wall, the required bearing capacity of the isolation wall is 4.66 MN/m and the deformation of the isolation wall is approximately 25 cm. To reduce the difficulty of implementing support the roadway is driven by 5 m/d. After the construction of the separation wall, the left side coal wall is brushed 1.5 m to make the width of the gas roadway reach 2.5 m and the roadway support utilizes anchor rod, ladder beam, anchor cable beam and net configuration. During construction, the concrete pump and removable self-propelled hydraulic wall mold are used to pump and pour the concrete of the isolation wall. In the process of mining, the stress distribution of coal body and isolation wall is detected and measured on site. The results demonstrate that the deformation of the surrounding rock of roadway and separation of roof in the roadway is small. The stress of the bolt and anchor cable is within equipment tolerance validating their selection. The roadway is well supported and the intended goal is achieved. The methodology can be used for reference for similar mine gas control.

• Journal of Ecology and Environment
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• v.41 no.5
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• pp.142-144
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• 2017

In recent decades, as it becomes increasingly important to monitor and research long-term ecological changes, worldwide attempts are being conducted to integrate and manage ecological data in a unified framework. Especially domestic ecological data in South Korea should be first standardized based on predefined common protocols for data integration, since they are often scattered over many different systems in various forms. Additionally, foreign ecological data should be converted into a proper unified format to be used along with domestic data for association studies. In this study, our interest is to integrate ECN data with Korean domestic ecological data under our unified framework. For this purpose, we employed our semi-automatic data conversion tool to standardize foreign data and utilized ground beetle (Carabidae) datasets collected from 12 different observatory sites of ECN. We believe that our attempt to convert domestic and foreign ecological data into a standardized format in a systematic way will be quite useful for data integration and association analysis in many ecological and environmental studies.

• Safety and Health at Work
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• v.9 no.1
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• pp.30-41
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• 2018

Background: The purpose of the work is to develop a system that allows processing of information for analysis and industrial risk management, to monitor the level of industrial safety and to perform necessary measures aimed at the prevention of accidents, casualties, and development of professional diseases for effective management of industrial safety at hazardous industrial sites. Methods: Risk assessment of accidents and incidents is based on expert evaluations. Based on the lists of criteria parameters and their possible values, provided by the experts, a unified information and analytical database is compiled, which is included in the final interrogation questionnaires. Risk assessment of industrial injuries and occupational diseases is based on statistical methods. Results: The result of the research is the creation of Guidelines for risk management on hazardous industrial sites of the Republic of Kazakhstan. The Guidelines determine the directions and methods of complex assessment of the state of industrial safety and labor protection and they could be applied as methodological basis at the development of preventive measures for emergencies, casualties, and incidents at hazardous industrial sites. Conclusion: Implementation of the information-analytical system of risk level assessment allows to analyze the state of risk of a possible accident at industrial sites, make valid management decisions aimed at the prevention of emergencies, and monitor the effectiveness of accident prevention measures.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.363-370
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• 2018

Ransomware detection has become a hot topic in computer security for protecting digital contents. Unfortunately, current signature-based and static detection models are often easily evadable by compress, and encryption. For overcoming the lack of these detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as RF, SVM, SL and NB algorithms. We monitor the actual behaviors of software to generate API calls flow graphs. Thereafter, data normalization and feature selection were applied to select informative features. We improved this analysis process. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. We conduct our experiment using more suitable real ransomware samples. and it's results show that our proposed system can be more effective to improve the performance for ransomware detection.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.441-441
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• 2002

In order to ensure a balance between economic development and a healthy Mekong Basin environment supporting natural resources diversity and productivity critical to the livelihood of its 65 million inhabitants, the Mekong River Commission (MRC) has been investigating the use of radar to remotely characterize and monitor the diversity, complexity, size and connectivity of the Basin's aquatic habitats. The PACRIM AIRSAR Mission provided an opportunity to evaluate the usefulness of radar technology to derive information for assessing, forecasting and mitigating possible cumulative and long-term impacts of development on the natural environment and the people's livelihood. This paper presents the results of mapping wetland cover types using multi-polarimetric radar for an area of the north-western corner of the Tonle Sap basin with data acquired from the AIRSAR Mission in September 2000. The implementation of a newly developed segmentation classification routine used to derive the image classification is described and the results of a fieldwork campaign to check the classification is presented.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.410-413
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• 2008

Existing Outlier detections have been widely studied in geosensor networks. Recently, machine learning and data mining have been applied the outlier detection method to build a model that distinguishes outliers based on anchored criterion. However, it is difficult for the existing methods to detect outliers against incoming time-variant data, because outlier detection needs to monitor incoming data and classify irregular attacks. Therefore, in order to solve the problem, we propose a time-variant outlier detection using 2-dimensional grid method based on unanchored criterion. In the paper, outliers using geosensor data was performed to classify efficiently. The proposed method can be utilized applications such as network intrusion detection, stock market analysis, and error data detection in bank account.

• Animal Bioscience
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• v.36 no.2_spc
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• pp.374-384
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• 2023

Skeletal muscle metabolism regulates homeostatic balance in animals. The metabolic impact persists even after farm animal skeletal muscle is converted to edible meat through postmortem rigor mortis and aging. Muscle metabolites resulting from animal growth and postmortem storage have a significant impact on meat quality, including flavor and color. Metabolomics studies of postmortem muscle aging have identified metabolisms that contain signatures inherent to muscle properties and the altered metabolites by physiological adaptation, with glycolysis as the pivotal metabolism in postmortem aging. Metabolomics has also played a role in mining relevant postmortem metabolisms and pathways, such as the citrate cycle and mitochondrial metabolism. This leads to a deeper understanding of the mechanisms underlying the generation of key compounds that are associated with meat quality. Genetic background, feeding strategy, and muscle type primarily determine skeletal muscle properties in live animals and affect post-mortem muscle metabolism. With comprehensive metabolite detection, metabolomics is also beneficial for exploring biomarker candidates that could be useful to monitor meat production and predict the quality traits. The present review focuses on advances in farm animal muscle metabolomics, especially postmortem muscle metabolism associated with genetic factors and muscle type.