• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.117-124
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• 2014

This paper studies on introduction of online education to provide opportunities for spreading university-level program by analyzing perception of high school students and teachers. The university-level program can be defined as the fulfillment of learning needs and the value offer of excellence education for outstanding high school students who want to improve their potential capabilities. For the study, a survey was conducted at high school students and teachers. As the result of the survey for high school students, the efficiency of education was the most important factor for the university-level program. The order of next important factors was the aid to entering university, the method of education, the satisfaction, and the recommendation of others. The result of high school teacher indicated that the efficiency of education was the most important factor as the high school students. The order of next important factors by high school teachers was the satisfaction, the aid to entering university, and the method of education. An activation of the university-level programs can be spread by analyzing the results of the survey. With the introduction of online education for the university-level program can conclude the guarantee of the right of studying and the reduction of education gap. This paper proposed an online education for the university-level program to guarantee the right of studying and to reduce the education gap.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.309-314
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• 2017

In general, there are many difficulties in collecting, storing, and analyzing SNS (social network service) data, since those data have big data characteristics, which occurs very fast with the mixture form of structured and unstructured data. In this paper, we propose a new data visualization framework that works on Apache Storm, and it can be useful for real-time and dynamic analysis of SNS data. Apache Storm is a representative big data software platform that processes and analyzes real-time streaming data in the distributed environment. Using Storm, in this paper we collect and aggregate the real-time Twitter data and dynamically visualize the aggregated results through the tag cloud. In addition to Storm-based collection and aggregation functionalities, we also design and implement a Web interface that a user gives his/her interesting keywords and confirms the visualization result of tag cloud related to the given keywords. We finally empirically show that this study makes users be able to intuitively figure out the change of the interested subject on SNS data and the visualized results be applied to many other services such as thematic trend analysis, product recommendation, and customer needs identification.

• Education of Primary School Mathematics
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• v.4 no.2
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• pp.111-125
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• 2000

This paper presents cross-national perspectives on challenges in implementing current mathematics education reform ideals. This paper includes detailed qualitative descriptions of mathematics instruction from unevenly successful second-grade classrooms both in Koran and in the U. S with regared to reform recommendations. Despits dramatic differences in mathematics achivement between Korean and the U.S student. problems in both countries with regard to mathematics education are perceived to be very similar. The shared problems have a common origin in teacher-centered instruction. Educational leaders in both countries have persistently attempted to change the teacher-centered pedagogy to a student-centered approach. Many teachers report familiarity with and adherence to reform ideas, but their actual classroom teaching practices do not reflect the full implications of the reform ideals. Given the challenges in implementing reform, this study explored the breakdown that may occur between teachers adoption of reform objectives and their successful incorporation of reform ideals by comparing and contrasting two reform-oriented classrooms in both countries. This comparison and contrast provided a unique opportunity to reflect on possible subtle but crucial issues with regard to reform implementation. Thus, this study departed from past international comparisons in which the common objective has been to compare general social norma of typical mathematics classes across countries. This study was and exploratory, qualitative, comparative case study using grounded theory methodology based on constant comparative analysis for which the primary data sources were classroom video recordings and transcripts. The Korean portion of this study was conducted by the team of four researchers, including the author. The U.S portion of this study and a brief joint analysis were conducted by the author. This study compared and contrasted the classroom general social norms and sociomathematical norms of two Korean and two U.S second-grade teachers who aspired to implement reform. The two classrooms in each country were chosen because of their unequal success in activating the reform recommendation. Four mathematics lessons were videotaped from Korean classes, whereas fourteen lessons were videotaped from the U.S. classes. Intensive interviews were conducted with each teacher. The two classes within each country established similar participation patterns but very different sociomathematical norms. In both classes open-ended questioning, collaborative group work, and students own problem solving constituted the primary modes of classroom participation. However in one class mathematical significance was constituted as using standard algorithm with accuracy, whereas the other established a focus on providing reasonable and convincing arguments. Given these different mathematical foci, the students in the latter class had more opportunities to develop conceptual understanding than their counterparts. The similarities and differences to between the two teaching practices within each country clearly show that students learning opportunities do not arise social norms of a classroom community. Instead, they are closely related to its sociomathematical norms. Thus this study suggests that reform efforts highlight the importance of sociomathematical norms that established in the classroom microculture. This study also provides a more caution for the Korean reform movement than for its U.S. counterpart.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.141-148
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• 2014

As the quality of the medical treatment service provided by large hospitals grow, the number of patients utilizing the facilities is increasing dramatically. Various studies such as order communication system and treatment guidance system are under their process in order to shorten the waiting time for patients. However, the existing methods assign the treatments in successive order without recognizing the situation of each treatment, therefore increasing a patient's standby time at a hospital. This paper proposes a context-aware treatment guidance system, which recognizes the previously undermined estimated waiting time of each treatment for a patient and recommends a treatment with shorter estimated sojourn time first. This context-aware treatment guidance system provides detailed information of treatment services based on the recommended order of treatments to a patient's smartphone. By utilizing the context-aware treatment guidance system introduced in this paper, patients can reduce their standby time at hospitals to the minimum while hospitals can efficiently service more patients at the same amount of time. The proposed context-aware treatment guidance system proves to be outstanding in treatment order recommendation through comparisons to previously used methods.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.3 s.118
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• pp.334-340
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• 2007

The prediction for vegetation attenuation using the RET model recommended in the ITU-R requires six RET input parameters. Among these, 4 parameters are related to the scattering characteristics of vegetation. To extract these parameters, two methods can be used. One is to extract the parameters by curve fitting of the measured vegetation-attenuation curve with the RET prediction model, and the other is to use the additional phase function measurement data. In the former method, fitting is quite complex and it does not result in the unique results in some cases. In addition, the extracted parameters lack the physical meaning as well. Thus, in this paper, the measurement method of phase function, and the method of extracting the RET model parameters which lead to more accurate and physically more meaningful results are presented. The extracted RET model parameters are also presented. The RET modeling method, measurement data, and the extracted RET model parameters presented in this paper were submitted to the ITU-R meeting in 2006, and adapted for ITU-R report and recommendation P.833.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.633-642
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• 2006

In the Internet environment many interactions between many users and unknown users take place and it is usually rare to have the trust information about others. Due to the lack of trust information, entities have to take some risks in transactions with others. In this perspective, it is crucial for the entities to be equipped with functionality to accumulate and manage the trust information on other entities in order to reduce risks and uncertainty in their transactions. This paper is concerned with a quantitative computational trust model which takes into account multiple evaluation criteria and uses the recommendation from others in order to get the trust for an entity. In the proposed trust model, the trust for an entity is defined as the expectation for the entity to yield satisfactory outcomes in the given situation. Once an interaction has been made with an entity, it is assumed that outcomes are observed with respect to evaluation criteria. When the trust information is needed, the satisfaction degree, which is the probability to generate satisfactory outcomes for each evaluation criterion, is computed based on the empirical outcome outcome distributions and the entity's preference degrees on the outcomes. Then, the satisfaction degrees for evaluation criteria are aggregated into a trust value. At that time, the reputation information is also incorporated into the trust value. This paper also shows that the model could help the entities effectively choose other entities for transactions with some experiments in e-commerce.

• Journal of Environmental Policy
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• v.9 no.1
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• pp.57-81
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• 2010

Since the Republic of Korea is highly dependent on fossil fuels despite high oil prices, it urgently needs to renew its economic and social system to cut carbon emissions and achieve green growth. Therefore, reforming or eliminating subsidies related to the use of fossil fuels is a timely and oppropriate policy recommendation for Korea. It would be a win-win deal for Korean society as it would not only reduce the use of environmentally harmful fossil fuels but also enhance economic efficiency. In particular, cross-subsidies for industrial, agricultural and night thermal-storage power services make up more than 80 percent of all subsidies provided to the entire electric power industry sector of Korea. Of these cross-subsidies, this paper analyzes the electricity subsidy for industries, which takes up the largest share (about KRW 1.6583 trillion yearly), among the environmentally harmful subsidies in the electric power sector. Thus, the paper focuses on the analysis of ripple effect anticipated when this is reformed. To examine the effects of this subsidy reform, price elasticities were estimated using the ARDL (autoregressive distributed lag) model and quarterly data from 1990 to 2007. The main results of this study show that 1) annual energy demand for electric power in the industrial sector would drop by 12,475,930MWh and 2) $CO_2$ emissions would plummet by 2,644,897 tons per year if the subsidy were reformed. We can deduct from this that the abolition of environmentally harmful subsidies in the electric power sector in the Republic of Korea would considerably contribute to $CO_2$ emissions abatement in the country.

• Journal of IKEEE
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• v.23 no.2
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• pp.739-742
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• 2019

In this paper, we propose enhanced DAP(Dose Area Product). The development of enhanced DAP proposed in this paper has optimized the area dose meter that was developed previously. The development of enhanced DAP performed Optimized design of charge integrator and ADC circuit, optimization of line transceiver for RS-485 communication, optimization of display circuit, and optimization of PC-based control program for interlocking and aging. As a result of evaluating the performance of the proposed system in an accredited testing laboratory, Radiation dose dependence and Radiation quality dependence were measured to be 4.2%, which is below ${\pm}15%$ of international standard. Energy range/Tube voltage was confirmed in the range of 30~150kV. The sensitivity difference between sensor field and sensor field area dose sensitivity was measured to be 4.3%, and it was confirmed that it operates normally under ${\pm}15%$ of international standard. In order to measure the reproducibility of the area dosimeter, it was confirmed that it was 0% and it was operated normally at less than 2% of IEC60580 recommendation. Digital resolution was confirmed to be a minimum unit of $0.01{\mu}Gy{\cdot}m^2$ within the error range for the reference dose per hour.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.167-183
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• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.244-251
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• 2007

Intention reading technique is essential to provide personalized services toward more convenient and human-friendly services in complex ubiquitous environment such as a smart home. If a system has knowledge about an user's intention of his/her behavioral pattern, the system can provide mote qualified and satisfactory services automatically in advance to the user's explicit command. In this sense, learning capability is considered as a key function for the intention reading technique in view of knowledge discovery. In this paper, ore introduce a personalized media control method for a possible application iii a smart home. Note that data pattern such as human behavior contains lots of inconsistent data due to limitation of feature extraction and insufficiently available features, where separable data groups are intermingled with inseparable data groups. To deal with such a data pattern, we introduce an effective engineering approach with the combination of fuzzy logic and probabilistic reasoning. The proposed learning system, which is based on IFCS (Iterative Fuzzy Clustering with Supervision) algorithm, extract probabilistic fuzzy rules effectively from the given numerical training data pattern. Furthermore, an extended architectural design methodology of the learning system incorporating with the IFCS algorithm are introduced. Finally, experimental results of the media contents recommendation system are given to show the effectiveness of the proposed system.