• Journal of the Korean earth science society
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• v.31 no.4
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• pp.392-402
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• 2010

The purpose of this study is to analyze and evaluate the self-directed learning contents of Earth science area in the basic course of the 7th grade. For this purpose, we applied the 'Cyber Home Study Content Quality Control Tool' presented in 'Elementary Secondary Education e-Learning Quality Management Guidelines (Ver.2.0)' of Korea Education & Research Information Service (2008). The results of contents analysis are as follow: First, it was presented that the study guide introduced the contents which should be studied for one class, properly. And it was not analyzed that the diagnosis assesment was not completed in the initiative study; Second, it was possible to study choosing the contents fitting the learner's level of learning in the main study, it was comprised of about 15 minutes. Third, it was performed without feedback for incorrect answers in the learning assessment, just the number of wrong questions. And the learning arrangement present the important contents learned in that class, summarizing and arranging again. The results of content evaluation are as follows: First, a big difference was not showed against the needs analysis, instructional design, interaction in each class. And the evaluation of the ethics was not included a word or sentence not suitable. The evaluation of copyright, it was analyzed that Work within the content display in compliance with international copyright Second, the evaluation of instructional design presented mainly the description of a simple picture based, the visible resources like flash card were poor. And in the evaluation of Supporting System, it was presented that the contents were installed so that it was freely available for learners. But it was analyzed that there was no memo-function learners were able to jot down something during the studying contents. And in the evaluation for evaluation, the clear valuation basis about the described content was not presented. So there were slightly differences for each class. Third, in the evaluation and analysis for learning content, it was presented that there were some big differences for each class because it was not composed of the latest information, not corrected and complementary.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.178-191
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• 2020

Purpose: This paper proposes a performance evaluation method for state-of-the-art facility inspection/diagnostic equipment through a trend survey of equipment and standardization systems of US, Japan, and Korea. This paper also suggests the priority of developing a performance evaluation method through expert interviews and surveys. Method: In this study, report for the last 5 years of FMS, state-of-the-art equipment of facility maintenance companies/safety diagnosis specialist agencies and papers/research reports/patents of NTIS were analyzed to identify recent trends of facility inspection/diagnostic equipment usages. standardization system of US, Japan, and Korea were analyzed to figure out a suitable form of a performance evaluation method for the domestic situation. And expert interview and survey were conducted to identify the priority of developing a performance evaluation method. Result: The performance evaluation method must be developed by the shape that only evaluates performance, regardless of types of equipment, on inspection item level for creative technology development. The priority of developing the performance evaluation method was identified as crack detection of concrete for durability evaluation and displacement/deformation/fatigue detection of concrete and steel for stability evaluation. Conclusion: The performance evaluation method will be developed firstly for the crack detection of concrete for durability evaluation and displacement/deformation/fatigue detection of concrete/steel for stability evaluation. In order to promote creative technology development, the performance evaluation method should be developed in a form that provides standardized specimens or testbeds and can be applied regardless of types of technologies.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.97-121
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• 2013

With the growing importance of knowledge for sustainable competitive advantages and innovation in a volatile environment, many researches on knowledge sharing have been conducted. However, previous researches have mostly relied on the questionnaire survey which has inherent perceptive errors of respondents. The current research has drawn the relationship among primary participant behaviors towards the participation level in knowledge sharing, basically from online user behaviors on Wikipedia, a representative community for online knowledge collaboration. Without users' participation in knowledge sharing, knowledge collaboration for creating knowledge cannot be successful. By the way, the editing patterns of Wikipedia users are diverse, resulting in different revisiting periods for the same number of edits, and thus varying results of shared knowledge. Therefore, we illuminated the participation level of knowledge sharing from two different angles of number of edits and revisiting period. The behavioral dimensions affecting the level of participation in knowledge sharing includes the article talk for public discussion and user talk for private messaging, and community registration, which are observable on Wiki platform. Public discussion is being progressed on article talk pages arranged for exchanging ideas about each article topic. An article talk page is often divided into several sections which mainly address specific type of issues raised during the article development procedure. From the diverse opinions about the relatively trivial things such as what text, link, or images should be added or removed and how they should be restructured to the profound professional insights are shared, negotiated, and improved over the course of discussion. Wikipedia also provides personal user talk pages as a private messaging tool. On these pages, diverse personal messages such as casual greetings, stories about activities on Wikipedia, and ordinary affairs of life are exchanged. If anyone wants to communicate with another person, he or she visits the person's user talk page and leaves a message. Wikipedia articles are assessed according to seven quality grades, of which the featured article level is the highest. The dataset includes participants' behavioral data related with 2,978 articles, which have reached the featured article level, with editing histories of articles, their article talk histories, and user talk histories extracted from user talk pages for each article. The time period for analysis is from the initiation of articles until their promotion to the featured article level. The number of edits represents the total number of participation in the editing of an article, and the revisiting period is the time difference between the first and last edits. At first, the participation levels of each user category classified according to behavioral dimensions have been analyzed and compared. And then, robust regressions have been conducted on the relationships among independent variables reflecting the degree of behavioral characteristics and the dependent variable representing the participation level. Especially, through adopting a motivational theory adequate for online environment in setting up research hypotheses, this work suggests a theoretical framework for the participation level of online knowledge sharing. Consequently, this work reached the following practical behavioral results besides some theoretical implications. First, both public discussion and private messaging positively affect the participation level in knowledge sharing. Second, public discussion exerts greater influence than private messaging on the participation level. Third, a synergy effect of public discussion and private messaging on the number of edits was found, whereas a pretty weak negative interaction effect of them on the revisiting period was observed. Fourth, community registration has a significant impact on the revisiting period, whereas being insignificant on the number of edits. Fifth, when it comes to the relation generated from private messaging, the frequency or depth of relation is shown to be more critical than the scope of relation for the participation level.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• Korean Journal of Community Nutrition
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• v.24 no.3
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• pp.232-244
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• 2019

Objectives: This study evaluated the nutritional status and prevalence of metabolic syndrome of the people who participated in the KNHANES according to the number of household members. They were assessed by using information from the 2013~2014 Korea National Health and Nutrition Examination Survey (KNHANES). Methods: A total of 6,088 persons aged 19 years and over participated in 2013~2014 KNHANES, and they were classified into three groups according to the number of household members (single-person, two-person, three-person & over). The dietary behavior, nutritional status, health-related factors and the prevalence of metabolic syndrome of the subjects were investigated with using information from the survey questionnaires of KNHANES. The nutrient intake data of the subjects were obtained by the 24-hour recall method and this was analyzed for evaluating the nutrition adequacy ratio and the index of nutritional quality. The prevalence of metabolic syndrome among the subjects, and according to the study groups, was estimated using the blood and physical measurement data of the subjects. Results: As for EQ-5D index available for all the health states generated by the EQ-5D descriptive system, the single-person household member was the lowest among all the household types. The index of nutrition quality for protein, crude fiber, calcium, phosphorus, potassium, riboflavin and vitamin C in the single-person household was lower than that of the two-person or the three-person and over households (p<0.001). The mean adequacy ratio of single-person households was significantly decreased compared with that of the other types of households (p<0.001). The prevalence of metabolic syndrome was higher in the single-person households than that in the multiple-person households (p<0.001). Conclusions: These results showed that dietary behaviors, nutrition status and health status might be influenced by the number of household members. The results from this study would be useful for improving Korean people's dietary life and health status by implementing evidence-based, specialized intervention for the members of diverse types of households.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.63-72
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• 2019

As a sub-concept of corporate or organization governance, business governance and IT governance have become major research topics in academia. However, despite the importance of process as a construct for mediating the domain between business and information technology, research on process governance is relatively inadequate. Process Governance focuses on activities that link business strategy with IT system implementation and explains the creation of corporate core values. The researcher studied the basic conceptual governance models of political science, sociology, public administration, and classified governance styles into six categories. The researcher focused on the series of metamodels. For examples, the traditional Strategy Alignment Model(SAM) by Henderson and Venkatraman which is replaced by the neo-SAM model, organizational governance network model, sequential organization governance model, organization governance meta model, process governance CUBE model, COSO and process governance CUBE comparison model, and finally Process Governance Framework and etc. The Major difference between SAM and neo-SAM model is Process Governance domain inserted between Business Governance and IT Governance. Among several metamodels, Process Governance framework, the core conceptual model consists of four activity dimensions: strategic aligning, human empowering, competency enhancing, and autonomous organizing. The researcher designed five variables for each activity dimensions, totally twenty variables. Besides four activity dimensions, there are six driving forces for Process Governance cycle: De-normalizing power, micro-power, vitalizing power, self-organizing power, normalizing power and sense-making. With four activity dimensions and six driving powers, an organization can maintain the flexibility of process governance cycle to cope with internal and external environmental changes. This study aims to propose the Process Governance competency model and Process Governance variables. The situation of the industry is changing from the function-oriented organization management to the process-oriented perspective. Process Governance framework proposed by the researcher will be the contextual reference models for the further diffusion of the research on Process Governance domain and the operational definition for the development of Process Governance measurement tools in detail.

• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.701-711
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• 2020

Purpose: In this study, the risk of flammability of a liquid mixture was experimentally confirmed because the purpose of this study was to confirm the increase or decrease of the flammability risk in a mixture of two substances (combustible+combustible) and to present the risk of the mixture. Method: Flash point test method and result processing were tested based on KS M 2010-2008, a tag sealing test method used as a flash point test method for crude oil and petroleum products. The manufacturer of the equipment used in this experiment was Japan's TANAKA. The flash point was measured with a test equipment that satisfies the test standards of KS M 2010 with equipment produced by the company, and LP gas was used as the ignition source and water as the cooling water. In addition, when measuring the flash point, the temperature of the cooling water was tested using cooling water of about 2℃. Results: First of all, in the case of flammable + combustible mixtures, there was little change in flash point if the flash point difference between the two substances was not large, and if the flash point difference between the two substances was low, the flash point tended to increase as the number of substances with high flash point increased. However, in the case of toluene and methanol, the flash point of the mixture was lower than that of the material with a lower flash point. Also, in the case of a paint thinner, it was not easy to predict the flash point of the material because it was composed of a mixture, but as a result of experimental measurement, it was measured between -24℃ and 7℃. Conclusion: The results of this study are to determine the risk of mixtures through experimental studies on flammable mixtures for the purpose of securing the effectiveness of the details of the criteria for determining dangerous goods in the existing dangerous goods safety management method and securing the reliability and reproducibility of the determination of dangerous goods Criteria have been presented, and reference data on experimental criteria for flammable liquids that are regulated in firefighting sites can be provided. In addition, if this study accumulates know-how on differences in test methods, it is expected that it can be used as a basis for research on risk assessment of dangerous goods and as a basis for research on dangerous goods determination.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.722-732
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• 2021

Purpose: Due to the coronavirus infection-19 (COVID) pendemics, all educational institutions were required to provide full non-face-to-face real-time education, and fire officials were required to provide fire-fighting education by applying non-face-to-face education. In this difficult situation, the National Fire Service Academy tries to find the direction of the non-face-to-face real-time education and suggest ways to improve it through a survey of the status of non-face-to-face real-time education conducted by the NFSA to fire officials. Method: A survey was conducted on fire officials under the theme of "Consciousness Survey for Improving the Quality and Specialization of Non-face-to-face Real-Time Remote Education" and an in-depth analysis was conducted based on the results. Result & Conclusion: First, professors or educational operators shall actively utilize remote education programs suitable for educational characteristics by utilizing various programs. Second, a dedicated notebook for non-face-to-face training should be provided to provide an educational environment where all learners can participate in the training without difficulty. Third, in the case of education and training that requires the use of equipment due to the nature of fire officials' education and training, it is necessary to consider it as a non-face-to-face training place by arranging educational equipment at each fire station. Fourth, it is hard to expect a satisfactory educational effect to cope with practical education with theoretical education. Therefore, facilities and programs that enable non-face-to-face real-time hands-on training should be developed. It is worth considering the proper combination of face-to-face education while maintaining the social distance as much as possible until such non-face-to-face training is possible. Fifth, non-face-to-face education is considered to have high eye fatigue due to the light and electromagnetic waves of the computer screen, and as time goes by, the concentration level decreases. Therefore, it is necessary to form an education time to reduce the eye fatigue of learners and increase concentration through proper class and rest time. Finally, professors should operate a learner participation-oriented education that allows professors and learners to interact rather than one-sided knowledge transfer education. In addition, technical problems of non-face-to-face remote education should be thoroughly prepared through preliminary system checks to ensure that education is not disrupted.