• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.39-55
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• 2011

Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.446-465
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• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.44-54
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• 2008

In this paper, an iterative multiple symbol differential detection for turbo coded differential unitary space-time modulation using a posteriori probability (APP) demodulator is investigated. Two approaches of different complexity based on linear prediction are presented to utilize the temporal correlation of fading for the APP demodulator. The first approach intends to take account of all possible previous symbols for linear prediction, thus requiring an increase of the number of trellis states of the APP demodulator. In contrast, the second approach applies Viterbi algorithm to assist the APP demodulator in estimating the previous symbols, hence allowing much reduced decoding complexity. These two approaches are found to provide a trade-off between performance and complexity. It is shown through simulation that both approaches can offer significant BER performance improvement over the conventional differential detection under both correlated slow and fast Rayleigh flat-fading channels. In addition, when comparing the first approach to a modified bit-interleaved turbo coded differential space-time modulation counterpart of comparable decoding complexity, the proposed decoding structure can offer performance gain over 3 dB at BER of $10^{-5}$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.1951-1972
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• 2018

Wi-Fi Access Point (AP) optimization approaches are used in indoor positioning systems for signal coverage enhancement, as well as positioning precision improvement. Although the huge power consumption of the AP optimization forms a serious problem due to the signal coverage requirement for large-scale indoor environment, the conventional approaches treat the problem of power consumption independent from the design of indoor positioning systems. This paper proposes a new Fast Water-filling algorithm Group Power Constraint (FWA-GPC) based Wi-Fi AP optimization approach for indoor positioning in which the power consumed by the AP optimization is significantly considered. This paper has three contributions. First, it is not restricted to conventional concept of one AP for one candidate AP location, but considered spare APs once the active APs break off. Second, it utilizes the concept of water-filling model from adaptive channel power allocation to calculate the number of APs for each candidate AP location by maximizing the location fingerprint discrimination. Third, it uses a fast version, namely Fast Water-filling algorithm, to search for the optimal solution efficiently. The experimental results conducted in two typical indoor Wi-Fi environments prove that the proposed FWA-GPC performs better than the conventional AP optimization approaches.

• Korean Medical Education Review
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• v.24 no.3
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• pp.180-192
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• 2022

Research suggests that medical students frequently experience mental health problems such as stress, burnout, and depression, which may, in turn, affect suicidal ideation and behaviors. Since mental health problems profoundly impact academic achievement and professionalism, it is vital to understand factors influencing students' mental health and identify strategies to provide the necessary support. Some relevant influencing factors range from the personal level, including gender, personality traits, perfectionism, and social support, to the environmental level, including the grading system, educational phases, exposure to patients' death, mistreatment, and culture of medicine. In this regard, a comprehensive mental health support system that encompasses environmental interventions, as well as personal-level support, is needed. Simultaneously, proactive approaches that address the improvement of self-care and alleviation of systemic burdens are essential, together with the predominant reactive approaches focusing on problems and deficits. Altogether, we proposed a framework for enhancing mental health constructed by four categories (personal-reactive, environmental-reactive, personal-proactive, environmental-proactive) based on the intervention level and goal of support. All four categories have important implications, and one cannot replace the other, but expanding environmental-proactive support will allow more students to learn how to pursue health independently. We expect that this comprehensive framework for enhancing mental health could expand support systems for medical students' personal and professional development.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.183-186
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• 2010

Advanced Railway countries are developing technologies of production and management for low-carbon and green growth of their railway industry to hold a dominant position under post-Tokyo protocol regime through integrated approach which uses environmental quantitative analysis of train life cycle by using LCA(Life Cycle Assessment). On the contrary, Korea railroad industry attempts to make an environmental improvement only for using regenerative energy and improvement in operating energy consumption through adapting reduction weight of material technology and etc. without systematic environmental analysis approaches such as comparing and analyzing energy consumption as well as GHG emission in each life cycle stages of train. Therefore, In this paper, low-carbon management and comprehensive environmental improvement for sustainable development of Korea railway industry through analyzing the result of life cycle analysis in abroad are suggested.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.9 no.4
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• pp.439-449
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• 1998

In this paper, error performance of DS-CDMA communication system has been analyzed in a radio channel which is characterized by multi-user interference (MUI). Also, performance improvement has been obtained by both numerical analysis and simulation techniques when 3-element array antenna and co-channel interference (CCI) canceller are employed as a means for overcoming an MUI effect. The MUI was modelled as a gaussian random process. In array antenna scheme we only considered a directivity of antennal. The results show that there is a substantial enhancement in performance by employing an array antenna or a CCI canceller. CCI canceller gives better improvement in performance than the array antenna with D=2.67. Additional improvement can be obtained when the array antenna and the CCI canceller are adopted in cascade form. In this case the error performance approaches that of AWGN environment.

• Journal of Computing Science and Engineering
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• v.4 no.1
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• pp.1-22
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• 2010

A field of spoken dialog systems is a rapidly growing research area because the performance improvement of speech technologies motivates the possibility of building systems that a human can easily operate in order to access useful information via spoken languages. Among the components in a spoken dialog system, the dialog management plays major roles such as discourse analysis, database access, error handling, and system action prediction. This survey covers design issues and recent approaches to the dialog management techniques for modeling the dialogs. We also explain the user simulation techniques for automatic evaluation of spoken dialog systems.

• ETRI Journal
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• v.29 no.3
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• pp.300-304
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• 2007

This paper proposes a new data-driven method for high-pass approaches, which suppresses slow-varying noise components. Conventional high-pass approaches are based on the idea of decorrelating the feature vector sequence, and are trying for adaptability to various conditions. The proposed method is based on temporal local decorrelation using the information-maximization theory for each utterance. This is performed on an utterance-by-utterance basis, which provides an adaptive channel normalization filter for each condition. The performance of the proposed method is evaluated by isolated-word recognition experiments with channel distortion. Experimental results show that the proposed method yields outstanding improvement for channel-distorted speech recognition.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.5
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• pp.467-473
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• 2013

Diverse approaches for reducing the total energy consumption of machine tools have been introduced and developed, to cope with rapid increase of total energy costs in world-wide manufacturing industries. To realize the improvement of the energy efficiency, systematic and integrated strategies must be considered, including energy-saving design, optimized control operation and concrete evaluation of the energy efficiency. This paper proposes key enabling technologies required to improve the energy efficiency of 5-axis multi-functional machining tools, considering both of system design and operation in the real production environments. Related standardized procedures of the energy efficiency evaluation is also represented.