• Journal of the Ergonomics Society of Korea
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• v.24 no.2
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• pp.25-33
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• 2005

In order to minimize a biased view of evaluators in usability test, a multiple evaluation method so called Multi-Centered Usability Evaluation Method(MCUEM) was suggested and structured in this study. To use MCUEM, the existing evaluation methods were re-categorized into 'developer and evaluator-centered', 'evaluator-centered', 'user-centered', 'evaluator and user-centered', and the 'developer, evaluator, and user-centered methods. Furthermore, in order to verify the relevance of MCUEM, the usability of mobile phone was tested in this study. In results, it was found that different evaluation methods generated greatly different results, that showed the risk of using a particular usability method due to different perspectives of evaluators. At the same time, it was found that MCUEM could greatly minimize such biases. Therefore, it was concluded that using MCUEM could help the usability engineer design a test protocol with a minimal risk of bias due to different perspective of evaluators.

• The Journal of Information Systems
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• v.24 no.2
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• pp.189-208
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• 2015

Purpose While individuals have unique abilities for planned behavior, they also often act irrationally. In this study, we draw on myopic loss aversion perspective as a meta-theoretical lens to explain why mobile applications users have inertia from updating their applications, ultimately leading them to use current version of applications. Design/methodology/approach Based on a survey of 219 users, this study conducts its research model using partial least square analysis and also demonstrates that both subconscious triggers (habit and anxiety) of system 1 thinking and conscious triggers (sunk cost and transition cost) of system 2 thinking promotes user's inertia, thus leading to the willness to continue use current versions. Findings By grounding the research model in the combination of both status quo bias and dual information processing theory from the behavioral economics, this study provide an alternative theoretical lens to describe why mobile users hesitate to update their applications. The results of this research show that all triggers have significant impacts on inertia. This study also found that the relationship between inertia and willingness to continue to use current version was positively significant.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.68.2-68.2
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• 2012

한국우주전파관측망(KVN)을 이루는 각 망원경에 22GHz, 43GHz, 86GHz, 129GHz의 4개의 주파수 대역을 동시에 관측할 수 있는 전파 수신시스템을 구축하고 있다. 현재, 22GHz와 43GHz의 수신기가 설치되어 있고, 86GHz와 129GHz 수신기가 2012년 상반기에 설치 완료할 예정이다. 이들 중, 129GHz 수신기의 핵심인 초전도 전파수신소자에 공급하는 바이어스 전원의 공급 시스템을 2년에 걸쳐 설계 및 제작 완료하였다. 개발된 Bias Supply System은 주 보드, User Interface, 전용 정전압 모듈, Protection 모듈 등이 한 Sub-Rack에 결합되어 있다. 먼저, Bias Supply System 시제품 1set를 우선 제작하여 연세전파천문대의 129GHz수신기 초전도 전파수신소자 제어에 설제 적용하였다. 이를 바탕으로, 수정과 보완 및 Remote M&C 기능을 추가하여 새로 설계하고 제작한 최종 Bias Supply System 3set를 개발 완료하였고, 129GHz 수신기 3기의 초전도 전파수신소자 구동에 운용하고 있다. 본 발표에서는 그 개발 내용을 소개하고자 한다.

• The Journal of Information Systems
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• v.24 no.3
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• pp.75-94
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• 2015

Purpose While new versions of mobile applications could offer users better computing environment, users are not always comfortable with them for various reasons. Considering making update decisions is important task for users, it is crucial for us to understand users' behavior and attitude on app updates. The purpose of this study is to explain why mobile users succumb to both reactance toward the update and satisfaction to the current version, ultimately leading them to feel the regret by employing three theoretical perspectives including regret theory, status quo bias theory and the dual model. Design/methodology/approach Survey data collected from 204 mobile users was used to test the research model using partial least squares analysis. The results have shown that both reactance toward the update and satisfaction to the current version have negative impacts on individuals' decisions to update, which leading to their regret after updating the applications Findings By integrating both status quo bias and regret theory in the model, this study tried to explain why mobile users feel regret in application update settings. More specifically, this study has proposed a novel framework that introduces an individual's update decision on mobile applications.

• Korean Journal of Artificial Intelligence
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• v.12 no.2
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• pp.9-16
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• 2024

In this study, we delve into the effects of personalization algorithms on the creation of "filter bubbles," which can isolate individuals intellectually by reinforcing their pre-existing biases, particularly through personalized Google searches. By setting up accounts with distinct ideological learnings-progressive and conservative-and employing deep neural networks to simulate user interactions, we quantitatively confirmed the existence of filter bubbles. Our investigation extends to the deployment of an LSTM model designed to assess political orientation in text, enabling us to bias accounts deliberately and monitor their increasing ideological inclinations. We observed politically biased search results appearing over time in searches through biased accounts. Additionally, the political bias of the accounts continued to increase. These results provide numerical evidence for the existence of filter bubbles and demonstrate that these bubbles exert a greater influence on search results over time. Moreover, we explored potential solutions to mitigate the influence of filter bubbles, proposing methods to promote a more diverse and inclusive information ecosystem. Our findings underscore the significance of filter bubbles in shaping users' access to information and highlight the urgency of addressing this issue to prevent further political polarization and media habit entrenchment. Through this research, we contribute to a broader understanding of the challenges posed by personalized digital environments and offer insights into strategies that can help alleviate the risks of intellectual isolation caused by filter bubbles.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2627-2647
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• 2016

With the rapid growth of microblog, expert identification on microblog has been playing a crucial role in many applications. While most previous expert identification studies only assess global authoritativeness of a user, there is no way to differentiate the authoritativeness in a particular aspect of topics. In this paper, we propose a novel model, which jointly models text and following relationship in the same generative process. Furthermore, we integrate a similarity-based weight scheme into the model to address the popular bias problem, and use followee topic distribution as prior information to make user's topic distribution more precisely. Our empirical study on two large real-world datasets shows that our proposed model produces significantly higher quality results than the prior arts.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.9
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• pp.146-155
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• 1994

In this paper, we desribe a new system, called BEST, that is used to develop a macromodel or behavioral model easily. It automatically calculates the component values of macromodel represented by equations to satisfy the given specification. Also, it gives the way to analyze both the behavioral model and transistor level circuit, and then compare the analysis results of them to check the correspondence under specific temperature and bias condition, and BEST optimizes the component values of macromodel. Other feature is to characterize MOSFET as switch model which consists of PWL-RC network. Finally, it is possible to generage multi-level netlist which consists of macro/switch/transistor level circuits, and user can determine the trade-off between simulation speed and accuracy. With the graphic user interface form of macromodel development system described above. BEST enable designers to make macromodel by themselves and to uas it. We applied BEST to develop the macromodel for the test circuit and got the 18.6 times simulation speed up with preserving the accuracy within 10% compared to the conventional transistor level circuit simulation. Also, applicability of optimization capability was verified.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.970-973
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• 1996

In this paper, Modified GPS Kalman filter algorithms which allow user to estimate its position when the number of visible GPS satellites becomes less than four are presented. They are derived using the previous estimation of altitude and clock bias. Thus, it is possible to estimate 3-dimensional user position even when only two GPS satellites are visible. The algorithms are ideally suited to car navigation in urban areas where lack of GPS visibility is the major problem because of the frequent blockage of the GPS signals by tall buildings and other structures. Simulation results in this paper show that modified GPS Kalman filter provide better performances than a general GPS Kalman filter or any other instantaneous GPS solution algorithm, especially in the case which the number of visible GPS satellites becomes less than four.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.159-168
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• 2021

Precise Point Positioning-Real Time Kinematic (PPP-RTK) is an improved PPP method that provides the user receiver with satellite code and phase bias correction information in addition to the satellite orbit and clock, thus enabling single-receiver ambiguity resolution. Single station PPP-RTK concept is special case of PPP-RTK in that corrections are computed, instead of a network, by only one single GNSS receiver. This study is performed to experimentally verify the positioning accuracy performance of single baseline RTK level by a user who utilizes correction for a single station PPP-RTK using dual frequencies. As an experimental result, the horizontal and vertical 95% accuracy was 2.2 cm, 4.4 cm, respectively, which verify the same performance as the single baseline RTK.