• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.901-911
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• 2005

With the development of e-commerce and the proliferation of easily accessible information, information filtering has become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative filtering systems have succeeded in capturing the similarities among users or items based on ratings to provide good recommendations, there are still some challenges for them to be more efficient, especially the user bias problem, non-transitive association problem and cold start problem. Those three problems impede us to capture more accurate similarities among users or items. In this paper, we provide probabilistic model approaches for UCHM and ICHM which are suggested to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, objects (users or items) are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.167-181
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• 2024

Utilization of a driving simulator in the development of autonomous driving technology allows us to perform various tests effectively in criticial environments, thereby reducing the development cost and efforts. However, there exists a serious drawback that the driving simulator has a big difference from the real environment, so a problem occurs when the autonomous driving algorithm developed using the driving simulator is applied directly to the real vehicle system. This is defined as so-called Sim2Real problem and can be classified into scenarios, sensor modeling, and vehicle dynamics. This Paper presensts on a method to solve the Sim2Real problem in autonomous driving simulator focusing on IMU sensor. In order to reduce the difference between emulated virtual IMU sensor real IMU sensor, IMU sensor emulation techniques through precision error modeling of IMU sensor are introduced. The error model of IMU sensors takes into account bias, scale factor, misalignmnet, and random walk by IMU sensor grades.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.47-53
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• 2019

The idea of ensemble learning is to train multiple models, each with the objective to predict or classify a set of results. Most of the errors from a model's learning are from three main factors: variance, noise, and bias. By using ensemble methods, we're able to increase the stability of the final model and reduce the errors mentioned previously. By combining many models, we're able to reduce the variance, even when they are individually not great. In this paper we propose an ensemble model and applied it to classification problem. In iris, Pima indian diabeit and semiconductor fault detection problem, proposed model classifies well compared to traditional single classifier that is logistic regression, SVM and random forest.

• ETRI Journal
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• v.44 no.2
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• pp.244-259
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• 2022

Bicycle- or bike-sharing systems (BSSs) have received increasing attention as a secondary transportation mode due to their advantages, for example, accessibility, prevention of air pollution, and health promotion. However, in BSSs, due to bias in bike demands, the bike rebalancing problem should be solved. Various methods have been proposed to solve this problem; however, it is difficult to apply such methods to small cities because bike demand is sparse, and there are many practical issues to solve. Thus, we propose a demand prediction model using multiple classifiers, time grouping, categorization, weather analysis, and station correlation information. In addition, we analyze real-world relocation data by relocation managers and propose a relocation algorithm based on the analytical results to solve the bike rebalancing problem. The proposed system is compared experimentally with the results obtained by the real relocation managers.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.1-18
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• 2021

Idol music, which began to gain huge popularity in the domestic music market in the mid-1990s, has become one of the major global contents thanks to the global popularity of K-POP. As a result, the Korean music market continues to grow, forming the world's sixth-largest music market, and domestic music agencies are focusing more on fostering idol groups and producing idol music. The global success of idol music is surprising, but this has resulted in the domestic music market being biased toward idol music. As a result of the study, it was confirmed that there are several problems with the phenomenon of being biased toward similar types of musicians and music content. First, just as trend-oriented cultural contents face the problem of life expectancy all the time, the "Korean Wave" is also forced to think in terms of identity and sustainability. Second, it was observed that only consumers of a certain age may cause cultural alienation of other age groups, and thirdly, various problems such as shrinking creative paths due to the size of the cost required for idol group production and the lifespan of idol musicians' art activities. This paper derives the reality of the domestic idol bias phenomenon through comparative analysis of the English-American music market and the domestic music market, which have had a profound influence on the global music market in popular music history, and verified the theory and results through an expert survey using the Likert scale. In addition, the problems caused by the idol bias phenomenon were considered based on the theory of cultural diversity, and improvement directions were also suggested to solve this problem.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.190-199
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• 2019

This quantitative study analyzed the influence of college students' preferred type of occupation on a confirmation bias and job anxiety during the process of making a career decision. The questionnaires were distributed to university students in Seoul and the metropolitan area for 500 weeks from July 10 2017 to August 8, 2017. Among them, 482 valid samples of data were analyzed by data coding and data cleaning usin SPSS 18.0 statistics and the AMOS 18.0 program. The main results of this study are that the type of business preference for an affirmative bias has a positive (+) direct influence (${\beta}=.374$) and the type of freedom has a positive direct influence (${\beta}=.326$) and a negative direct influence (${\beta}=-.274$). In the case of job anxiety, the influence of job type is more increased. The confirmation bias shows that the business type and freestyle type find cause in effort or achievement motive, while rect type is recognized as social environment and structural problem. In conclusion, there is a difference in the degree of confirmation bias and job insecurity. This study shows that college students' preferred occupation types can help them to understand the bias and anxiety that they have in preparing for the job and help to reduce job anxiety, and these findings are expected to be useful for career guidance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2806-2811
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• 2013

New technologies such as multi-antenna and small cell were proposed as key technology for the next generation mobile system to cope with the explosively increasing mobile data traffic. In particular, heterogeneous mobile communication network which can improve spatial reuse factor by exploiting macro and small cells simultaneously is attracting attention. However, the heterogeneous network has a problem that the utilization of small cells becomes low because the transmit power of macro base stations is much higher than that of small base stations and then the probability that mobile stations are attached to the macro base stations becomes high. This problem is dominant in uplink. The concept of cell range expansion bias to mitigate the problem was proposed by 3GPP and the corresponding standardization is in progress. In this paper, we analyze the downlink performance of the heterogeneous mobile communication network based on a system level simulator with the cell range expansion bias in terms of average cell spectral efficiency.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1419-1422
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• 1997

This paper deals with the Appliation of robust fault detection problem in uncertain linear systems, having both model mismatch and noise. A robust fault detection method presented by Kwon et al.(1994) for SISO uncertain systems. Here we experimented this method to the diesel engine systems described by difference ARMA models. The model mismatch includes here linearization error as well as undermodeling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.169-171
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• 2007

We need two operation modes to obtain the complex hologram without bias and the conjugate image in the modified triangular interferometer(MTI). To solve the problem, we proposed the optimized MTI with one wave plate, which can obtain cosine and sine functions by the combination of one wave plate and one linear polarizer. In the extraction of phase term using the combination of polarization components, the phase error occurs, and we simulated such potential phase errors in the optimized MTI.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.27-32
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• 2000

To overcome boundary problems with wavelet regression, we propose a simple method that reduces bias at the boundaries. It is based on a combination of wavelet functions and low-order polynomials. The utility of the method is illustrated with simulation studies and a real example. Asymptotic results show that the estimators are competitive with other nonparametric procedures.