• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1115-1124
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• 2022

The impact of climate change on water resources was evaluated for Chungju Dam and Soyang-gang Dam basins by constructing an integrated modeling framework consisting of a dam inflow prediction model based on the Variable Infiltration Capacity (VIC) model, a distributed hydrologic model, and an LSTM based dam outflow prediction model. Considering the uncertainty of future climate data, four models of CMIP6 GCM were used as input data of VIC model for future period (2021-2100). As a result of applying future climate data, the average inflow for period increased as the future progressed, and the inflow in the far future (2070-2100) increased by up to 22% compared to that of the observation period (1986-2020). The minimum value of dam discharge lasting 4~50 days was significantly lower than the observed value. This indicates that droughts may occur over a longer period than observed in the past, meaning that citizens of Seoul metropolitan areas may experience severe water shortages due to future droughts. In addition, compared to the near and middle futures, the change in water storage has occurred rapidly in the far future, suggesting that the difficulties of water resource management may increase.

• Journal of Music and Human Behavior
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• v.15 no.1
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• pp.1-23
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• 2018

Recently, there has been a growing focus on the concerns pre-service teachers have as they move through different stages of their teacher preparation programs. Accordingly, the purpose of this study was to investigate pre-service secondary music teachers' concerns about music teaching and learning. The specific research questions were as follows: (a) What concerns are most common among participants? (b) What are differences in the concerns of pre-service secondary music teachers based on their stage of teacher preparation? (c) What are differences in the concerns of pre-service secondary music teachers based on their previous teaching experiences? A survey was distributed to pre-service secondary music teachers in a university in Seoul, and responses from a total of 93 participants were included in the final analysis. Data analysis indicated that pre-service music teachers experienced more concerns related to teaching tasks and student impact than themselves. Also, as participants progressed through their teacher preparation programs, self concerns decreased while task concerns and student impact concerns increased. Pre-service secondary music teachers who had previously taught in a school had fewer concerns than participants with no teaching experience. Suggestions for how teacher preparation programs can address the concerns of pre-service secondary music teachers are offered.

• Journal of Korea Multimedia Society
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• v.2 no.3
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• pp.277-288
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• 1999

A distributed multimedia school system is developed for the multimedia classroom at high school and university. The system is designed and implemented for students to improve the learning efficiency through the personalized multimedia contents and pace of learning. The previously developed multimedia information retrieval systems have some limitations on being applied to the multimedia classroom: expensive cost per stream or poor retrieval quality inappropriate for education, unscalability of system and service, unfamiliar proprietary client environment, and difficulty for teachers to use the authoring tools and manage the authored teaching materials. The system we developed overcomes the above problems. It is so scalable as to be applicable not only to a segmented classroom but also to the world wide Internet. The stream server is one of the components of the system: stream servers clients, a service gateway system, and a authoring management system. This paper describes the design and implementation of the stream server. A single stream server can simultaneously playback the multimedia streams as many as clients at one classroom. This is achieved only by the software engine without any changes of the hardware architecture. The systematic coupling with other components gives the scalability of the system and the flexibility of services.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.427-432
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• 2012

The article herein suggested a compound repository and a descriptive method to develop a compound knowledge process. A data target saved in a compound knowledge repository suggested in this article includes all compound knowledge meta data and digital resources, which can be divided into the three following factors according to the purpose: user roles, functional elements, and service ranges. The three factors are basic components to describe abstract models of repository. In this article, meta data of compound knowledge are defined by being classified into the two factors. A component stands for the property about a main agent, activity unit or resource that use and create knowledge, and a context presents the context in which knowledge object are included. An agent of the compound knowledge process performs classification, registration, and pattern information management of composite knowledge, and serves as data flow and processing between compound knowledge repository and user. The agent of the compound knowledge process consists of the following functions: warning to inform data search and extraction, data collection and output for data exchange in an distributed environment, storage and registration for data, request and transmission to call for physical material wanted after search of meta data. In this article, the construction of a compound knowledge repository for recommendation system to be developed can serve a role to enhance learning productivity through real-time visualization of timely knowledge by presenting well-put various contents to users in the field of industry to occur work and learning at the same time.

• Korea Journal of Hospital Management
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• v.21 no.4
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• pp.1-12
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• 2016

The purpose of this study are suggest to performance measurement model of Electronic Medical Record(EMR) and Key Performance Index(KPI). For data collection, 665 questionnaires were distributed to medical record administrators and insurance reviewers at 31 hospitals, and 580 questionnaires were collected(collection rate: 87.2%). Regarding methodology, Critical Success Factor(CSF) and index of the information system were derived based on previous studies, and these were set as performance measurement factors of EMR system. The performance measurement factors were constructed by perspective using BSC, and analysis on causal relationship between factors was conducted. A model of causal relationship was established, and performance measurement model of EMR system was proposed through model validation. Analysis on causal relationship between performance management factors revealed that utility cognition of the learning & growth perspective factor had causal relationship with job efficiency(${\beta}=0.20$) and decision support(${\beta}=0.66$) of the internal process perspective factors, and security had causal relationship with system satisfaction(${\beta}=0.31$) of the customer perspective factor. System quality had causal relationship with job efficiency(${\beta}=0.66$) and decision support(${\beta}=0.76$) of the internal process perspective factors, all of which were statistically significant(P<0.01). Job efficiency of the internal process perspective had causal relationship with system satisfaction(${\beta}=0.43$), and decision support had causal relationship with decision support satisfaction(${\beta}=0.91$) and job satisfaction (${\beta}=0.74$), all of which were statistically significant(P<0.01). System satisfaction of the customer perspective had causal relationship with job satisfaction(${\beta}=0.12$), job satisfaction had causal relationship with cost reduction(${\beta}=0.53$) of the financial perspective, and decision support satisfaction had causal relationship with productivity improvement(${\beta}=0.40$)of the financial perspective(P<0.01). Also, cost reduction of the financial perspective had causal relationship with productivity improvement(${\beta}=0.37$), all which were statistically significant(P<0.05). Suitability index verification of the performance measurement model whose causal relationship was found to be statistically significant revealed that $X^2/df=2.875$, RMR=0.036, GFI=0.831, AGFI=0.810, CFI=0.887, NFI=0.838, IFI=0.888, RMSEA=0.057, PNFI=0.781, and PCFI=0.827, all of which were in suitable levels. In conclusion, the performance measurement indices of EMR system include utility cognition, security, and system quality of the learning & growth perspective, decision support and job efficiency of the internal process perspective, system satisfaction, decision support satisfaction, and job satisfaction of the customer perspective, and productivity improvement and cost reduction of the financial perspective. In this study, it is expected that the performance measurement indices and model of EMR system which are suggested by the author, will be a measurement tool available for system performance measurement of EMR system in medical institutions.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.599-604
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• 2005

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature in put using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.525-531
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• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.287-297
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• 2012

In order to prepare for the introduction of the E-Discovery system from the United States and to cope with some causable changes of legal systems, we propose a general E-Discovery process and essential tasks of the each phase. The proposed process model is designed by the analysis of well-known projects such as EDRM, The Sedona Conference, which are advanced research for the standardization of E-Discovery task procedures and for the supply of guidelines to hands-on workers. In addition, Machine Learning Algorithms, Open-source libraries for the Information Retrieval and Distributed Processing technologies based on the Hadoop for big data are introduced and its application methods on the E-Discovery work scenario are proposed. All this information will be useful to vendors or people willing to develop the E-Discovery service solution. Also, it is very helpful to company owners willing to rebuild their business process and it enables people who are about to face a major lawsuit to handle a situation effectively.