• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.5
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• pp.1-15
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• 1993

In DQDB networks, the bandwidth can be increased considerably be using the EN(Erasure Node) algorithms and DR(Destination Release) algorithms. However, the important issue in implementing them is using method of extra capacity fairly. To improve it, this paper proposes AEN(Adaptive Erasure Node) algorithm which erasure function is activated by network traffic load. Its functional architecture consists of SESM, RCSM, LMSM in addition to the basic DQDB state machines (DQSM, RQM). The SESM and RCSM state machines are placed in front of the DQSM and RQM state machines in order for the node to take advantage of the newly cleared slots. This paper also presents some simulation results showing the effect of AEN algorithm on access delay, throughput and segment erasing ratio in the single and multiple priority networks. The results show that the AEN algorithm offer the better performance characteristics than existing algorithms under overload conditions.

• Asia-Pacific Journal of Business
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• v.2 no.2
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• pp.1-35
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• 2011

Facing information overload in today's complex environments, managers look to a concise set of marketing metrics to provide direction for marketing decision making. While there have been several papers dealing with the theoretical aspects of dashboard creation, no research creates and tests a dashboard using scientific techniques. This study develops and demonstrates an empirical approach to dashboard metric selection. In a fast moving consumer goods category, this research selects leading indicators for national-brand and store-brand sales and revenue premium performance from 99 brand-specific and relative-to-competition variables including price, brand equity, usage occasions, and multiple measures of awareness, trial/usage, purchase intent, and liking/satisfaction. Plotting impact size and wear-in time reveals that different kinds of variables predict sales at distinct lead times, which implies that managerial action may be taken to turn the metrics around before performance itself declines.

• Aerospace Engineering and Technology
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• v.1 no.2
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• pp.32-44
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• 2002

This paper contains the information that describes the test fixture design and technology for full-scale airframe static test. Obtained technologies consist of determination of design load for test fixture, design technique for loading system, counterbalance system, positioning system of test article, test equipment and overload protection method. Full-scale airframe static test of advanced jet trainer was implemented using test fixture which are applied these technique.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.597-604
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• 1986

This paper describes various mismatch phenomena in differential pulse code modulation (DPCM) coding, such as the mismatch effects of probability density functin(pdf), signal variance, and correlation. At a high transmission rate(i.e., above 32 kbits/s), the performance of DPCM can be improved by matching the pdf shape between the input signal and the quantizer. However, the same gain cannot be obtained at a lower transmission rate. Also, it is shown that the gamma quantizer is realtively robust to the variation of pdf shaper and signal variance. Moreover, as the transmission rate increases, the performance of DPCM for the input signal with large variance is worse than that of DPCM for the signal with small variance due to the increase of overload noise. According to our simuladiton results, the mismatch effects of pdf shape and variance appear to yield more degradatin than that of correlation in a DPCM system.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.31-42
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• 2024

Today, news avoidance has become an inevitable trend, particularly exacerbated since the outbreak of the COVID-19 pandemic in 2020. To delve deeper into the shifting tendencies of news consumers towards news avoidance and unveil the motivations behind this avoidance, this study recruited 500 Chinese news consumers aged between 20 and 60 years old, employing survey questionnaires as the research method. Through an indepth examination of their news consumption behavior at different stages of the COVID-19 pandemic, we discovered that individuals' risk perceptions and efficacy beliefs significantly influence their patterns of news consumption. Furthermore, we identified negative emotions, information overload, and media distrust as the primary reasons for news avoidance among Chinese news consumers during the COVID-19 crisis. These findings Not only provide crucial insights into understanding the dynamics of news consumption behavior but also offer valuable reference points for the news industry to better fulfill its role and value during crises in the future.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.5
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• pp.257-266
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• 2017

Two remarkable problems of recent online social network are information overflow and information overload. Since the mid-1990s, many researches to overcome these issues have been conducted with information recommender systems and context awareness based personalization techniques, the importance of trust or relationship between users to discover influential information has been increasing as recent online social networks become huge. But almost researches have not regarded trust or relationship in real world while reflecting them in online world. In this paper, we present a novel method how to discover influential and spreadable information that is highly personalized to a user. This valuable information is extracted from an information set that consists of lots of information user missed in the past, and we assumes important information is likely to exist in this set.

• Smart Media Journal
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• v.10 no.1
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• pp.70-78
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• 2021

The auditory information is used for urgent notification or warning in vehicle because it is not restricted by the direction compared to the visual. However, since the hearing impaired drivers cannot recognize sound signal, various methods of visualizing the auditory information have been attempted to replace it. When visualizing auditory information, only important information should be selected and provided to prevent cognitive overload concentrated on the vision. For this purpose, analysis of the type of auditory information in vehicle should be given in advance. In this study, the types of auditory information in vehicle were analyzed based on the user experience of hearing impaired drivers. Through the observation of the driving behavior of hearing impaired drivers, 33 auditory informations experienced in vehicle were collected. The collected auditory informations were classified into 12 groups through open card sorting by an expert group, and the types of auditory information in vehicle consisting of four levels were presented through a relative comparison of importance between groups. The presented type of auditory information in vehicle can be used as a guideline for selecting important information when the auditory information is converted into visual or tactile. This study is meaningful in that the user experience analysis was conducted by observing actual driving in daily life of hearing impaired drivers.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3559-3568
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• 1999

The information overload with many information resources is an inevitable problem in modern electronic life. It is more difficult to search some information with user's information needs from an uncontrolled flood of many digital information resources, such as the internet which has been rapidly increased. So, many information retrieval systems have been researched and appeared. In text retrieval systems, they have met with user's information needs. While, in image retrieval systems, they have not properly dealt with user's information needs. In this paper, for resolving this problem, we proposed the intelligent user interface for image retrieval. It is based on HCOS(Human-Computer Symmetry) model which is a layed interaction model between a human and computer. Its' methodology is employed to reduce user's information overhead and semantic gap between user and systems. It is implemented with machine learning algorithms, decision tree and backpropagation neural network, for user adaptation capabilities of intelligent image retrieval system(IIRS).

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.247-258
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• 2021

Recently, organizations are demanding strict information security behavior from their employees. Strict information security policies and techniques can cause information security related stress. The purpose of this study is to present the negative effects of information security related techno stress and role stress that reduce knowledge sharing behavior and person-organization fit. The survey was conducted to people working in organizations with information security policies and system, and the research hypothesis was verified by structural equation modeling using 309 samples. As a result of the study, person-organization fit had a positive effect on knowledge sharing behavior, but role stress had a negative effect. And, techno-stress negatively affected the person-organization fit. Additionally, role ambiguity had a moderating effect between person-organization fit and knowledge sharing behavior. The implications of the study were to confirm the negative effects of information security related techno stress and role stress, and to suggest directions for minimizing negative behavior of insiders.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.1-18
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• 2016

Collaborative Filtering (CF) predicts the focal user's preference for particular item based on user's preference rating data and recommends items for the similar users by using them. It is a popular technique for the personalization in e-commerce to reduce information overload. However, it has some limitations including sparsity and scalability problems. In this paper, we use a method to integrate social network information into collaborative filtering in order to mitigate the sparsity and scalability problems which are major limitations of typical collaborative filtering and reflect the user's qualitative and emotional information in recommendation process. In this paper, we use a novel recommendation algorithm which is integrated with collaborative filtering by using Social SVD++ algorithm which considers social network information in SVD++, an extension algorithm that can reflect implicit information in singular value decomposition (SVD). In particular, this study will evaluate the performance of the model by reflecting the real-world user's social network information in the recommendation process.