• Information Systems Review
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• v.20 no.1
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• pp.61-80
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• 2018

Individuals often receive smishing campaigns (mobile phishing messages), which they treat as spam. Thus, firms should understand how their customers distinguish their promotion messages from smishing. However, only a few studies examined this important issue. The present study employs the elaboration likelihood model to develop research hypotheses on the relationship between message cue and message credibility. The message cue in this study is classified as content cue, which is found in the content of promotion messages, and as leakage cue, which is found in peripheral information in the message. Leakage cue includes orthography (inclusion of special characters)and an abbreviated link sent by a faithless sender. We also propose that contextualization has a moderating effect on the relationship between content cue and credibility. We conducted a survey experiment to examine the effect of message cues on message credibility in the context of respondents receiving discount coupons through mobile messages. The result of data analysis based on 166 responses suggests that leakage cue had a negative effect on message credibility. A message with defective content cue has a marginally negative effect on message credibility. In particular, defective content cue in a high-contextual message has a strong negative impact on message credibility. This effect was not observed in low-contextual messages. Moreover, message credibility is significantly low regardless of the degree of contextualization if there is a leakage cue in the message. Our findings suggest that mobile promotion messages should be customized for message receivers and should have no leakage cues.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.4
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• pp.86-97
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• 2006

For the design of DC traction power supply system at new Light Rail Transit(LRT) construction, it is very important to determine system configuration, location and power capacity of substation. However, a LRT system consists of a number of subsystems such as train movement, power supply and traction drives, which inevitably contains many complexities and diversities. The objective of this paper is to clarify and systematize the design procedure and its assessment for the electrification system of a LRT line. This paper discusses in detail our approach to system design and its assessment. The whole DC-feeding network configuration, characteristics of a train, and design method of substation arrangements is thoroughly investigated for the design. As a result of the investigations, the design procedure is clarified and systematized and a computer program for the design and evaluation of the system is developed using the most suitable iterative method with nodal equation. To verify the proposed design and its assessment procedure, case studies for the DC traction power supply system of a planed Korean LRT line are performed.

• Journal of KIISE:Software and Applications
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• v.36 no.6
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• pp.443-450
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• 2009

In an earlier development phase, the simulation technique is one of the key analysis methods for checking the correctness of system's functional requirements. In general, simulation is manually or randomly performed by executing state machine diagrams according to the requirement scenarios. Therefore, simulation is one of the most effort-consuming tasks. In this paper, an automatic simulation technique of state machine diagrams is provided according to the scenarios of the sequence diagrams. It is not easy to generate detailed simulation traces from sequence diagrams due to different abstraction levels between sequence diagrams and state machine diagrams. In order to adjust for different abstraction levels, state machine diagrams and sequence diagrams are transformed into LTS models and compositional analysis and transition reduction are performed. After checking behavior conformance between them, detailed simulation traces for the state machine diagrams are generated. These simulation traces are used not only for performing automatic simulation but also for assisting analyzers to reach a specific system state in order to guide further efficient simulation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.508-514
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• 2018

An electroencephalogram (EEG) evaluates the electrical activity generated by brain cell interactions that occur during brain activity, and an EEG can evaluate the brain activity caused by hand movement. In this study, a 16-channel EEG was used to measure the EEG generated before and after hand movement. The measured data can be classified as a supervised learning model, a support vector machine (SVM). To shorten the learning time of the SVM, a feature extraction and vector dimension reduction by filtering is proposed that minimizes motion-related information loss and compresses EEG information. The classification results showed an average of 72.7% accuracy between the sitting position and the hand movement at the electrodes of the frontal lobe.

• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.248-257
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• 2002

Clustering is a method for grouping objects with similar properties into a same cluster. SVD(Singular Value Decomposition) is known as an efficient preprocessing method for clustering because of dimension reduction and noise elimination for a high dimensional and sparse data set like E-Commerce data set. However, it is hard to evaluate the worth of original attributes because of information loss of a converted data set by SVD. This research proposes a cluster feature selection method, called ENTROPY-SVD, to find important attributes for each cluster based on entropy weighting and SVD. Using SVD, one can take advantage of the latent structures in the association of attributes with similar objects and, using entropy weighting one can find highly dense attributes for each cluster. This paper also proposes a model-based collaborative filtering recommendation system with ENTROPY-SVD, called CFS-CF and evaluates its efficiency and utilization.

• Journal of IKEEE
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• v.20 no.3
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• pp.260-264
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• 2016

The MAX77846, which is compatible with MAX77826, is a sub-power management IC (PMIC) for the latest Wearable Watch and 3G/4G smart phones. The MAX77846 contains N-MOSFET (N channel Metal-Oxide Semiconductor Field-Effect Transistor), a high-efficiency regulator, and comparator, etc to power up peripherals. The MAX77846 also provides power on/off control logic for complete flexibility and an $I^2C$ (Inter Integrated Circuit) serial interface to program individual regulator output voltages. In this paper, the simplified power macro-model based on MAX77846 is designed to verify the performance of the battery voltage in terms of current and time, and simulated by using of the LTspice. In addition, it is verified how much time can the charged battery capacity for Samsung Galaxy Gear 2 be used to operate a specified function after measuring the currents flowing to carry out the main functions in real time, which will be applicable to design parameters for the advanced power module

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2406-2419
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• 2010

This study aims to elevate the usefulness of the current annual Accreditation of Hospitals. To achieve this purpose, A modeling of an efficiency analysis based on DEA and AHP to the Accreditation of Hospitals Data from 2004 to 2008. By applying to AHP and DEA_AR to the scores derived from the various domains in data, An adequate prediction model about conversion factor in fee contract is made. By summarizing information derived from DEA, factor analysis and Generalized Linear Model, The linear functions combining conversion factor and efficiency index is successfully established. The factor analysis with AHP was used to merge diverse scores from the domains of evaluation. Not only the input and output initially introduced, AHP scores, dummy variables of hospital classification, geographical location are effective variables to forecast a conversion factor. If a predicted conversion factors from efficiency is used, It will be a great contributions to the annul doctor's fee contract.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.882-885
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• 2014

이미지 혹은 영상에 대한 자동 태깅은 해당 콘텐츠에 대한 추가적인 정보를 자동으로 시스템에 제공하는 기술로써 영상 인식, 콘텐츠 매시업, 정보 검색 등 다양한 기술/서비스 분야에서 여러 목적으로 활용되고 있다. 특히, 방송 콘텐츠는 많은 양의 정보를 제한된 영역 및 시간에 축약하여 담고 있기 때문에 영상 처리 기술을 통한 객체 인식이나, 콘텐츠 매시업, 추천 서비스 등의 성능 향상을 위해 자동 혹은 수동 태깅을 통한 정보 제공이 요구된다. 본 논문에서는 블로그를 이용한 프레임 단위의 방송 콘텐츠 태깅 기술을 제안한다. 제안하는 기술은 기존의 콘텐츠 단위의 정보 제공이나, 수동 태깅 된 정보를 제공하는 기술들과 달리, 영상의 각 프레임에 대한 자동 태깅을 목표로 한다. 제안하는 방법은 거리 학습을 통해 영상의 각 프레임이 가지는 특성을 고려한 모델을 구축한 후, 이를 토대로 영상의 프레임들과 블로그의 이미지를 매칭한다. 매칭된 결과를 기반으로 특정 블로그는 영상 내 특정 프레임 구간에 태깅 된다. 제안한 방법은 이미지 매칭 성능을 측정하여 평가하였다. 블로그 이미지에 대해 Top 1 매칭 프레임을 살펴본 결과, 70%의 정확률을 보였다. 소프트 매칭(Top n)의 경우, 최대 90%의 성능을 얻을 수 있음을 실험을 통해 알 수 있었다.

• Investigative Magnetic Resonance Imaging
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• v.7 no.1
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• pp.39-46
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• 2003

Purpose : To develop a theoretical model for magnetic relaxation behavior of the superparamagnetic nano-particle agent, which demonstrates multi-functionality such as liver- and lymp node-specificity. Based on the developed model, the computer simulation was performed to clarify the relationship between relaxation time and the applied magnetic field strength. Materials and Methods : The ultrasmall superparamagnetic iron oxide (USPIO) was encapsulated with biocompatiable polymer, to develop a relaxation model based on outsphere mechanism, which was resulting from diffusion and/or electron spin fluctuation. In addition, Brillouin function was introduced to describe the full magnetization by considering the fact that the low-field approximation, which was adapted in paramagnetic case, is no longer valid. The developed model describes therefore the T1 and T2 relaxation behavior of superparamagnetic iron oxide both in low-field and in high-field. Based on our model, the computer simulation was performed to test the relaxation behavior of superparamagnetic contrast agent over various magnetic fields using MathCad (MathCad, U.S.A.), a symbolic computation software. Results : For T1 and T2 magnetic relaxation characteristics of ultrasmall superparamagnetic iron oxide, the theoretical model showed that at low field (<1.0 Mhz), $\tau_{S1}(\tau_{S2}$, in case of T2), which is a correlation time in spectral density function, plays a major role. This suggests that realignment of nano-magnetic particles is most important at low magnetic field. On the other hand, at high field, $\tau$, which is another correlation time in spectral density function, plays a major role. Since $\tau$ is closely related to particle size, this suggests that the difference in R1 and R2 over particle sizes, at high field, is resulting not from the realignment of particles but from the particle size itself. Within normal body temperature region, the temperature dependence of T1 and T2 relaxation time showed that there is no change in T1 and T2 relaxation times at high field. Especially, T1 showed less temperature dependence compared to T2. Conclusion : We developed a theoretical model of r magnetic relaxation behavior of ultrasmall superparamagnetic iron oxide (USPIO), which was reported to show clinical multi-functionality by utilizing physical properties of nano-magnetic particle. In addition, based on the developed model, the computer simulation was performed to investigate the relationship between relaxation time of USPIO and the applied magnetic field strength.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.199-232
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• 2018

There are increasing needs for understanding and fathoming of business management environment through big data analysis at industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm level analyses using publicly available company disclousre data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels. Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries' topic trend, software and hardware industries are compared in recent 20 years. Also, the changes of management subject at firm level are observed with comparison of two companies in software industry. The changes of topic trends provides lens for identifying decreasing and growing management subjects at industrial and firm level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at firm level in software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades. For suggesting methodology to develop analysis model based on public management data at industrial and corporate level, there may be contributions in terms of making ground of practical methodology to identifying changes of managements subjects. However, there are required further researches to provide microscopic analytical model with regard to relation of technology management strategy between management performance in case of related to various pattern of management topics as of frequent changes of management subject or their momentum. Also more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.