• Tunnel and Underground Space
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• v.32 no.1
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• pp.78-92
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• 2022

In this study, we proposed a diagnostics and assessment model for mining sites that can evaluate the smart mining technology level in a systematic and structured way. For this, the maturity of the smart mining was defined, and detailed assessment items of the diagnostics and assessment model for smart mining were derived by considering the smart factory diagnostics and assessment model (KS X 9001-3) used in the manufacturing industry. While maintaining the existing system, the existing 46 detailed assessment items were modified to be suitable for mining. As a result, a total of 29 detailed assessment items were derived in the areas of promotion strategy, process, information system and automation, and performance. Based on this, a questionnaire was designed to diagnose the level of smart mining technology, and assessment was performed by applying it to domestic iron mines. The level of smart mining technology in the study area was found to be level 2, and it could be inferred that it was about 40% lower than the average smart level of the general manufacturing industry. In addition, by using the developed model, it was possible to recognize the weak points of the mine at each stage of the introduction, operation, and advancement of smart mining, and to suggest investment and improvement directions.

• Korean Journal of Ecology and Environment
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• v.38 no.3 s.113
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• pp.361-371
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• 2005

The objective of study was to diagnose integrative ecological health of Daejeon Stream, one of the tributaries of Guem River, during May 2004 ${\sim}$ April 2005. The research approach was primarily based on a Qualitative Habitat Evaluation Index (QHEI) and the Index of Biological Integrity (IBI) using fish assemblage. These outcomes were compared with conventional chemical dataset. For the experiment, four sampling sites were chosen from Daejeon Stream and long-term water quality data during 1995 ${\sim}$ 2004 (obtained from the Ministry of Environment) were analyzed in the spatial and temporal aspects. For the biological health assessment, we developed a stream health assessment model (SHA model) far regional applications. We found that current water quality conditions, based on the COD, BOD, TN and TP, were enhanced by 1.6 ${\sim}$ 5.3 fold over the period of 1995 ${\sim}$ 2004 and that the parameters showed a typical longitudinal decline from the upstream to downstream reach. The differences of water quality between the two reaches were more than 4.4 times, indicating a large spatial variations within the stream. The health conditions, based on the SHA model, averaged 23 and varied from 20 to 26 depending on the sampling stations. Values of the QHEI varied from 39 (Poor condition) to 124 (Cood condition)and values of QHEI in the reach of S2 ${\sim}$ S4 had significantly lower than in the headwater site (S1). Also, biological stream health, based on the criteria of US EPA (1993), was judged as 'Poor condition', in the S4 where TN, TP, BOD and COD were highest. In the meantime, maximum value of SHA (26) was found in the upstream reach (S1) where the water quality and QHEI were best. We also found that compositions of sensitive species showed a linear function with water quality conditions and this pattern was evident in the tolerant species. Thus, the biological stream health, based on the SHA model, matched well water chemistry. Overall outcomes suggest that the biological health impact was a function of chemical degradation and physical habitat quality in the stream.

• Journal of Christian Education in Korea
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• v.61
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• pp.203-232
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• 2020

The purpose of this study was to analyze various factors that influence the academic life of students through a theoretical review. As a result of the analysis, sub-factors were derived to diagnose the factors. From the study's findings, principles and models for developing learning status diagnosis tools were designed. The study first, based upon the competencies of university students, university life, students' low academic achievement, and academic probation studies, confirmed what factors affect student learning and from them derived a set of sub-factors. The setting dimension was divided into psychology, learning, and career factors, while also including a factor of faith for Christian university students. Next, in the draft model, sub-factors were constructed for each factor: faith maturity and faith training in the faith factor, positive thinking, emotion regulation, and self-esteem in the psychology factor, self-directed learning ability, learning motivation, and learning strategies in the learning factor, and career reflection, career exploration, career management, and career barriers in the career factor. By using the Delphi method, the final model of learning status diagnosis was confirmed. As a result, we completed the model comprised of nine sub-factors in four parts. A follow-up study should be conducted that examines learning status diagnosis tools development research.

• Knowledge Management Research
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• v.25 no.2
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• pp.95-114
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• 2024

Because it is difficult to respond to a constantly changing environment with individual ability and creativity alone, many organizations are forming teams and seeking ways to make the teams more active. Team learning behavior allows team members to and create better performance based on such accumulated knowledge and experience within a team. In particular, the process of team learning not only explicit and formalized knowledge but also implicit and informal experiences is important from the perspective of knowledge management. However, there were limitations in utilizing research results on team learning behavior because the concepts were fragmented and the measurements were different for each researcher. In this study, an integrated model was presented by examining concepts related to team learning behaviors. Moreover, the measurement model of team learning behaviors was validated for the Korean context. The measurement model consisted of five factors: sharing and elaboration, constructive conflict, team reflection, team activity, and storage and utilization. This tool was confirmed through exploratory factor analysis and confirmatory factor analysis. The results of this study are expected to have implications for team researchers and practitioners who diagnose and improve the level of team learning behavior within an organization.

• Journal of The Korean Association of Information Education
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• v.10 no.2
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• pp.241-248
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• 2006

Recently, as the importance of the information communication ethics education has increased, the research about new instructional method or contents have been progressed actively. On the other hand, due to the advance of e-learning technology, instead of teacher-centered instruction, the development of learning contents and learning method for satisfying students' requirements is proceeded actively. In this paper, in order to increase the learning effect for information communication ethics education for primary school students, we propose a new way to design an instruction which considers the characteristics and the requirements of students. We decompose instructional design features into 4 components such as goal, model, contents, and media, and then we pose questionnaire to the 5th grade students of a primary school. After that, we analyze data by using the conjoint analysis. Based on the result of the conjoint analysis, we give instructions to two classes in order to compare the learning achievement of the two classes. Finally, by evaluating the students and analyzing their satisfaction levels, we diagnose the effectiveness of the proposed method.

• Fire Science and Engineering
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• v.18 no.4
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• pp.42-51
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• 2004

This paper presents a study on the analysis of fire detection system using fuzzy logic with input variables of temperature and smoke density. The input variables for the fuzzy logic algorithm are measured by fire experiment of small scale with temperature detector and smoke detector. The antecedent part of fuzzy rules consists of temperature and smoke density, and the consequent part consists of fire possibility. Also the triangular fuzzy membership function is chosen for input variables and fuzzy rules to simplify computation. In order to calculate fuzzy values of such fuzzy system, a computer program is developed with Matlab based on graphics user interface. The experiment was conducted with paper and ethanol to simulate flaming fire and with plastic and sawdust to model smoldering fire. The results showed that the fire detection system presented here was able to diagnose fire very precisely. With the help of algorithms using fuzzy logic we could distinguish whether fire or not.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.408-415
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• 2020

In this study, the AE-SOM method, which combines auto-encoder and self-organizing map, is used to detect and diagnose faults in EVA production process. Then, the fault propagation pathways are identified using Granger causality test. One year and seven months of operation data were obtained to detect faults of the process, and the process variables of the autoclave reactor are mainly analyzed. In the data pretreatment process, the data are standardized and 200 samples of each grade are randomly chosen to obtain a fault detection model. After that, the best matching unit (BMU) of each grade is confirmed by applying AE-SOM. The faults are determined based on each BMU. When a fault is found, the most causative variable of the fault is identified by using a contribution plot, and the fault propagation pathway is identified by Granger causality test. The prognostic of the two shutdowns is detected, and the fault propagation pathway caused by the faulty variable was analyzed.

• Journal of the Korean Institute of Gas
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• v.4 no.4 s.12
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• pp.42-49
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• 2000

Due to their size and complexity, it is very difficult to make diagnostic system for the whole chemical processes. Therefore, a systematic approach is required to decompose larpge-scale process into sub-processes and then diagnose them. This paper suggests a method for the minimization of knowledge base and flexible diagnosis to be used in qualitative fault diagnosis based on Fault-Effect Tree model. The system can be decomposed for flexible diagnosis, size reduction of knowledge base, and consistent construction of complex knowledge base. The new node, gate-variable, is introduced to connect the cause-effect relationships of each sub-process. For on-line diagnosis, off-line analysis is performed to construct Fault-Effect Trees of gate-variables as well as activation conditions of gate-variables. On-line diagnosis strategy is modified to get the same diagnosis result without system decomposition. The proposed method is illustrated with a fault diagnosis system for a large-scale boiler plant.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.1
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• pp.59-74
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• 2020

The web space where web applications run is the cyber information warfare of attackers and defenders due to the open HTML. In the cyber attack space, about 84% of worldwide attacks exploit vulnerabilities in web applications and software. It is very difficult to detect web vulnerability attacks with security products such as web firewalls, and high labor costs are required for security verification and assurance of web applications. Therefore, rapid vulnerability detection and response in web space by automated software is a key and effective cyber attack defense strategy. In this paper, we establish a security risk management model by intensively analyzing security threats against web applications and software, and propose a method to effectively diagnose web and application vulnerabilities. The testing results on the commercial service are analyzed to prove that our approach is more effective than the other existing methods.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.55-65
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• 2019

With the rapid development of the national industry, the importance of electrical safety was recognized because of a lot of new electrical equipment are installing and the electrical accidents have been occurring annually. Today, the electrical equipments is inspect by using the portable Infrared thermal imaging camera. but the most negative element of using the camera is inspected for only state of heating, the reliable diagnosis is depended with inspector's knowledge, and real-time monitoring is impossible. This paper present the infrared thermal imaging safety diagnosis system. This system is able to monitor in real time, predict the state of fault, and diagnose the state with analysis of thermal and power data. The system consists of a main processor, an infrared camera module, the power data acquisition board, and a server. The diagnostic algorithm is based on a mathematical model designed by analyzing the Pearson's Correlation Coefficient between temperature and power data. To test the prediction algorithm, the simulations were performed by damaging the terminals or cables on the switchboard to generate a large amount of heat. Utilizing these simulations, the developed prediction algorithm was verified.