• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.722-728
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• 2012

Urban sewer systems has a limitation of capacity of rainwater storage and problem of occurrence of untreated sewage, so adopting a storage facility for sewer flooding prevention and urban non-point pollution reduction has a big attention. The Korea Ministry of Environment has recently introduced a new concept of "multi-functional storage facility", which is crucial not only in preventive stormwater management but also in dealing with combined sewer overflow and sanitary sewer discharge, and also has been promoting its adoption. However, reserving a space for a single large-scale storage facility might be difficult especially in urban areas. Thus, decentralized construction of small- and midium-sized storage facilities and its operation have been introduced as an alternative way. In this paper, we propose a model predictive control scheme for an optimized operation of distributed storage facilities and sewer networks. To this aim, we first describe the mathematical model of each component of networks system which enables us to analyze its detailed dynamic behavior. Second, overflow locations and volumes will be predicted based on the developed network model with data on the external inflow occurred at specific locations of the network. MPC scheme based on the introduced particle swarm optimization technique then produces the optimized the gate setting for sewer network flow control, which minimizes sewer flooding and maximizes the potential storage capacity. Finally, the operational efficacy of the proposed control scheme is demonstrated by simulation study with virtual rainstorm event.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2739-2746
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• 2011

This paper proposes the method for more effectively calculating X/R which is the ratio of equivalent reactance(X) and resistance(R) of the bulk power system and analyses the characteristic of X/R values by applying the proposed method to the real bulk power systems. X/R is used to determine the rating of the relay in the bulk power systems and its value has been accepted to be big enough to ignore the equivalent resistance of the bulk power systems. However, X/R is calculated as a big number when only the upper transformer and transmission line are considered. The correct approach to calculating X/R needs to consider all the parameters including generators, transformers, lines and loads. This paper calculates X/R of the bulk power systems using dynamic models which have been used to analyse the power system stability. The effectiveness of the proposed method is verified by applying it to the test system and X/R values of the real bulk power systems are analyzed. In addition, the dependence of X/R on the closeness of its calculating locations to the generator is verified by using the marginal loss factor which has been used in the electricity market.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.73-85
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• 2014

As the importance of big data analysis increases to improve the competitiveness of a corporate, a unified big data project development methodology is required in order to study the problem of a corporate in a systematic way and evaluate the problem w.r.t. a business value after solving the problem. This paper propose Scientific Data Anslysis and Development methodology(SDAD) which are integrated methodology of software development and project management for easier application into a field project. SDAD consisits of 6 stages(problem definition stage, data preparation stage, model design stage, model development stage, result extraction stage, service development state), each stages has detailed processes(47) and productions(93). SDAD, furthermore, unified previous ISP, DW, SW development methodologies in terms of the data analysis and can easily interchange the productions with them. This paper, lastly, introduces a way to assign responsible persons for each process and provide communication procedures in RACI chart to improves the efficiency of the interaction among professionals from different subjects. SDAD is applied to a Bigdata project in Korea Employment Information Services institution and the result turned out to be acceptable when evaluated by the supervision.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.10
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• pp.421-428
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• 2017

The need for various approaches to non-face-to-face identification technology for registering and authenticating users online is being required because of the growth of online financial services and the rapid development of financial technology. In general, non-face-to-face approaches can be exposed to a greater number of threats than face-to-face approaches. Therefore, identification policies and technologies to verify users by using various factors and channels are being studied in order to complement the risks and to be more reliable non-face-to-face identification methods. One of these new approaches is to collect and verify a large number of personal information of user. Therefore, we propose a big-data based non-face-to-face Identity Proofing method that verifies identity on online based on various and large amount of information of user. The proposed method also provides an identification information management scheme that collects and verifies only the user information required for the identity verification level required by the service. In addition, we propose an identity information sharing model that can provide the information to other service providers so that user can reuse verified identity information. Finally, we prove by implementing a system that verifies and manages only the identity assurance level required by the service through the enhanced user verification in the non-face-to-face identity proofing process.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.27-35
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• 2022

The purpose of this study is to analyze the metaverse-related issues in the news big data from an educational perspective, explore their characteristics, and provide implications for the educational applicability of the metaverse and future education. To this end, 41,366 cases of metaverse-related data searched on portal sites were collected, and weight values of all extracted keywords were calculated and ranked using TF-IDF, a representative term weight model, and then word cloud visualization analysis was performed. In addition, major topics were analyzed using topic modeling(LDA), a sophisticated probability-based text mining technique. As a result of the study, topics such as platform industry, future talent, and extension in technology were derived as core issues of the metaverse from an educational perspective. In addition, as a result of performing secondary data analysis under three key themes of technology, job, and education, it was found that metaverse has issues related to education platform innovation, future job innovation, and future competency innovation in future education. This study is meaningful in that it analyzes a vast amount of news big data in stages to draw issues from an education perspective and provide implications for future education.

• Journal of the Korean Society of Safety
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• v.31 no.6
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• pp.113-120
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• 2016

People use social media platforms such as Twitter to leave traces of their personal thoughts and opinions. In other words, social media platforms retain the emotions of the people as it is, and accurately understanding the emotions of the people through social media will be used as a significant index for disaster management. In this research, emotion type modeling method and emotional quotient quantification method will be proposed to understand the emotions present in social media platforms. Emotion types are primarily analyzed based on 3 major emotions of affirmation, caution, and observation. Then, in order to understand the public's emotional progress according to the progress of disaster or accident and government response in detail, negative emotions are broken down into anxiety, seriousness, sadness, and complaint to enhance the analysis. Ultimately, positive emotions are further broken down into 3 more emotions, and Russell emotion model was used as a reference to develop a model of 8 primary emotions in order to acquire an overall understanding of the public's emotions. Then, the emotional quotient of each emotion was quantified. Based on the results, overall emotional status of the public is monitored, and in the event of a disaster, the public's emotional fluctuation rate could be quantitatively observed.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.561-566
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• 2005

This paper presents a visual tracking algorithm for the golf swing motion analysis by using the information of the pixels of video frames and movement of the golf club to solve the problem fixed center point in model based tracking method. The model based tracking method use the polynomial function for trajectory displaying of upswing and downswing. Therefore it is under the hypothesis of the no movement of the center of gravity so this method is not for the amateurs. we proposed method using the information of pixel and movement, we first detected the motion by using the information of pixel in the frames in golf swing motion. Then we extracted the club head and hand by a properties of club shaft that consist of the parallel line and the moved location of club in up-swing and down-swing. In addition, we can extract the center point of user by tracking center point of the line between center of head and both foots. And we made an experiment with data that movement of center point is big. Finally, we can track the real trajectory of club head, hand and center point by using proposed tracking algorithm.

• Journal of Information Management
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• v.41 no.4
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• pp.93-118
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• 2010

This study was aimed to establish a workable strategies to operate KESLI consortium reliably. To encourage the research purposes, it was mainly investigated international literature on current challenges for the e-journals consortium in comparison with the domestic implications. Major issues and challenges are divided to six items by scholarly communication changes and e-journals percentage increase, a big deal for a contract -based consortium pricing model in vogue, a consortium of the price hikes and making budget difficulties, use the standard statistics by the results of the use, archiving and archive security uncertainty, and contracts public availability of such external conditions. As a result, the international challenges of E-journals consortium was similar to KESLI, but the depth of information and research on domestic was weaker than the international research. To see more research and a rational perception based on scientific evidence and alternatives that enable KESLI working in the field was needed to be.