• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.7
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• pp.113-121
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• 2005

In this paper, a constant output power supply system for ozonizer is proposed to remove the noise of ozonizer and control the output of ozonizer using feedback control. The proposed system is based on the rouble control loop such as the outer voltage control loop and inner current control loop. In the proposed system overshoots and oscillations due to the computation time-delay are compensated by explicit incorporation of the time-delay in the current control loop transfer function. The inner current control loop is adopted by an internal model controller. The internal model controller is designed to a second order deadbeat reference-to-output response which means that its response reaches the reference in two sampling time including computational time-delays. The outer voltage control loop employing P-Resonance controller is proposed. The resonance controller has an infinite gain at resonant frequency, and the resonant frequency is set to the fundamental frequency of the reference voltage in this paper. Thus the outer voltage control loop causes no steady state error as regard to both magnitude and phase. The effectiveness of the proposed control system has been verified by the experimental results.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.48.4-48.4
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• 2019

We studied the metal-distribution of isolated Milky-way mass galaxy using various hydrodynamic solvers and investigated the difference of the result between AMR and SPH codes. In particle-based codes, physical quantities like mass or metallicity defined in each particle are conserved unless being injected explicitly by the effect of the supernova, whereas in the Eulerian codes the diffusion is simply accomplished by hydro-equation. Therefore, without including explicit physics of diffusion on the SPH- codes, the metal mixing in the galaxy or CGM only can be accomplished by the direct motion of the particles, however, the standard-SPH codes depress the instability of the turbulent fluid mixing. In this work, we simulated under common initial conditions, common gas-physics like cooling-heating models, and star-formation feedback using ENZO(AMR) GIZMO and GADGET-2 codes. We additionally included a metal-diffusion algorithm on the SPH-codes, which follows the subgrid-turbulent mixing model investigated by Shen et al. (2010) and compared the effect of the metal-outflow on the halo region of the galaxy in different hydro-solvers. We also found that for the implementation of the diffusion scheme in the SPH-codes, the existence of a sufficient number of the gas-particles, which is the carrier of the metals, is necessary. So we tested a new initial condition for proper implementation of the diffusion scheme on the SPH simulations. By comparing the metal-contamination of the circumgalactic medium with different hydrodynamics models, we quantify the diffusion strength of AMR codes using diffusion parameterization of the SPH codes and also suggest the calibration solutions in the different behavior of codes in metal-outflow.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Journal of Elementary Mathematics Education in Korea
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• v.20 no.2
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• pp.353-370
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• 2016

This study examined elementary mathematics teacher's guidebook to determine the inclusion level of 11 critical features of evidence based instruction. And the inclusion level of the features in teacher's guidebook were interpreted as 'Low', 'Middle' and 'High'. The results are as followings. First, The overall inclusion level of the features in teacher's guidebook is 'Middle' The inclusion level of the features in teacher's guidebook for 1st, 2nd, 3rd and 4th were 'Middle' but for 5th and 6th were 'Low'. Second, the inclusion level of the features 'Clarity of Objective', 'Single Concepts and Skill Taught', 'Use of Manipulatives and Representation', 'Explicit Instruction', 'Provision of Examples for new concepts and skill', 'Adequate Independent Practice Opportunities' and 'Progress Monitoring' were 'Middle' The inclusion level of the features 'Review of Prerequisite Mathematical Skills', 'Error correction and Corrective Feedback' and 'Instruction of Strategies' were 'Low'. And discussed the results.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.451-462
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• 2009

Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information. However, despite its success and popularity, CF encounters a serious limitation with quality evaluation, called cold start problems. To alleviate this limitation, in this paper, we propose a unique method of building models derived from explicit ratings and applying the models to CF recommender systems. The proposed method is divided into two phases, an offline phase and an online phase. First, the offline phase is a building pre-computed model phase in which most of tasks can be conducted. Second, the online phase is either a prediction or recommendation phase in which the models are used. In a model building phase, we first determine a priori predicted rating and subsequently identify prediction errors for each user. From this error information, an error-reflected model is constructed. The error-reflected model, which is reflected average prior prediction errors of user neighbors and item neighbors, can make accurate predictions in the situation where users or items have few opinions; this is known as the cold start problems. In addition, in order to reduce the re-building tasks, the error-reflected model is designed such that the model is updated effectively and users'new opinions are reflected incrementally, even when users present a new rating feedback.

• 한국사회정책
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• v.23 no.1
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• pp.61-93
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• 2016

This research aims to analyze the role of social care services after the unification, assuming that the unification would occur in 2020 in a peaceful manner. While much has been discussed about the unification in recent years inside or outside academia, most of discussion tends to focus on political and economic dimensions. Also, social policy studies on North Korean defectors have increased, but few pay attention to social policy strategies after the possible unification. In this context, this study explores various explicit and implicit roles of social care services and possible strategies after the unification. As research methodology, it employs one of the scenario methods, 'TAIDA', for projecting and simulating uncertain future. In so doing, first, it reviews South and North Korean socio-economic experiences during last two decades as feedback and German unification experiences as feedforward. In addition, it utilizes a expert survey. Based on the reviews together with the survey result, it discusses various influences of social care services after the unification and draws policy implications. This research aruges that social care services could have profound effects on the stability of socio-economic conditions after the unification.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.