• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.3
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• pp.120-131
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• 2017

Recent studies have suggested that providing in-vehicle feedback on various driving behaviors promote eco-friendly driving behaviors. However, there was relatively little interest in cognitive overload that can be caused by the provision of information. Thus, the goal of this study was to investigate the relative effects of two types of feedback(visual feedback vs. visual-auditory feedback) to increase eco-driving performance while minimizing driving workload. Also, in this study, the complexity of the driving task was distinguished (secondary vs. tertiary task) in order to reflect the actual driving situation. The study adopted a counterbalancing design in which the two feedback types were delivered in a different order under the two different task conditions. Results showed that providing the visual-auditory feedback was more effective than the visual only feedback in both promoting eco-friendly driving behaviors and minimizing driving workload under both task conditions.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.1
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• pp.9-20
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• 2009

In a real-time system with both hard real-time periodic tasks and soft real-time aperiodic tasks, it is important to guarantee the deadlines of each periodic task as well as obtain fast response time for each aperiodic task. This paper proposes Enhanced Total Bandwidth Server (ETBS) with possibly shorter response time than Total Bandwidth Server (TBS), which is efficient and widely used for servicing aperiodic tasks. For uniprocessor system using Earliest Deadline First (EDF) scheduling algorithm, ETBS assigns an on-line deadline to each aperiodic task considering a surplus slack time which gained for every unit execution time of periodic job. The proposed method can fully utilize the processor while meeting all the deadlines of periodic tasks. We show that the proposed ETBS provides better response time of aperiodic tasks than TBS theoretically, but has the same computational complexity as TBS, O(1). Simulation results show that the response time of aperiodic tasks with ETBS are shorter than one with TBS.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.931-940
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• 2021

As information security(IS) is recognized as a critical success factor for organizational growth, organizations are increasing their investment in adopting and operating strict IS policies and technologies. However, when strict IS technology is adopted, IS-related techno-stress may occur in the employees who apply IS technology to their tasks. This study proposes the effect of IS-related techno-stress formed in individuals on IS policy resistance through IS strain and proves that task-technology fit mitigates the negative effect of techno-stress. Research models and hypotheses were presented through previous studies, and the secured samples were used, and structural equation modeling was applied to verify hypothesis. As a result of the study, it was confirmed that IS-related techno-stress (overload, complexity) affected IS policy resistance through IS strain (anxiety, fatigue), and that task-technology fit moderated the relationship between techno-stress and strain. This study suggests a strategic direction for improving the level of internal IS from the viewpoint of suggesting ways to mitigate the stress of employees that may occur when IS policies and technologies are adopted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.15-23
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• 2022

Among the learning methods for Continuous Learning environments, "Learning without Forgetting" has fixed regularization strengths, which can lead to poor performance in environments where various data are received. We suggest a way to set weights variable by identifying the features of the data we want to learn. We applied weights adaptively using correlation and complexity. Scenarios with various data are used for evaluation and experiments showed accuracy increases by up to 5% in the new task and up to 11% in the previous task. In addition, it was found that the adaptive weight value obtained by the algorithm proposed in this paper, approached the optimal weight value calculated manually by repeated experiments for each experimental scenario. The correlation coefficient value is 0.739, and overall average task accuracy increased. It can be seen that the method of this paper sets an appropriate lambda value every time a new task is learned, and derives the optimal result value in various scenarios.

• Knowledge Management Research
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• v.23 no.3
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• pp.213-229
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• 2022

This study aimed to reveal factors that contribute to the creation of collective intelligence (CI) and to provide a cornerstone for future studies on this subject. We hypothesized that effects of the complexity and meaningfulness of the task, diversity, openness to experience, independence, decentralization, and the use of information and communication technology (ICT) are preconditions for the creation of CI. To investigate these hypotheses, we surveyed 200 individuals in the research and development-based manufacturing industry and collected a total of 185 valid responses. The results of the analysis showed that the meaningfulness of the task, openness to experience, independence, decentralization, and the use of ICT had positive effects on CI. Both perceived dissimilarity and value diversity had negative effects on CI. When all variables were included, their significance for the creation of CI showed the following order: use of ICT, the meaningfulness of the task, openness to experience, perceived dissimilarity, and value difference. The theoretical and empirical implications of these results were discussed.

• The Transactions of the Korea Information Processing Society
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• v.1 no.2
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• pp.237-243
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• 1994

Fast curve generation is one of important techniques which facilitate fast process of computer graphics applications. It is possible to resolve the task through hardware or software. But rapid development of computer graphics area need methods for more fast generation of curves. This paper propose a algorithm (called CDAUD), which computes the coordinates of points on curve only with add operations using for ward differences and draw the curve fast. It also contains the method for generation of smooth and fully connected curves. Time complexity of CDAUD shows that it is superior than the existing method for constrained case, and it's superiority was validated through experimental implementation.

• Journal of IKEEE
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• v.23 no.4
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• pp.1134-1139
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• 2019

One of the most important applications of computer vision algorithms is the detection of threat objects in x-ray security images. However, in the practical setting, this task is complicated by two properties inherent to the dataset, namely, the problem of class imbalance and visual complexity. In our previous work, we resolved the class imbalance problem by using a GAN-based anomaly detection to balance out the bias induced by training a classification model on a non-practical dataset. In this paper, we propose a new method to alleviate the visual complexity problem by using a KNN-based automatic cropping algorithm to remove distracting and irrelevant information from the x-ray images. We use the cropped images as inputs to our current model. Empirical results show substantial improvement to our model, e.g. about 3% in the practical dataset, thus further outperforming previous approaches, which is very critical for security-based applications.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2050-2059
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• 1999

Multiprocessor scheduling problem has been an important research area for the past decades. The problem is defined as finding an optimal schedule which minimizes the parallel execution time of an application on a target multiprocessor system. Duplication Based Scheduling (DBS) is a relatively new approach for solving multiprocessor scheduling problems. This paper classifies DBS algorithms into two categories according to the task duplication method used. The paper then presents a new DBS algorithm that extracts the strong features of the two categories of DBS algorithms. The simulation study shows that the proposed algorithm achieves considerable performance improvement over existing DBS algorithms with similar time complexity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.463-468
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• 2016

The overall performance of multiuser systems significantly depends on how effectively and fairly manage resources shared by them. The efficient resource management strategies are even more important for multimedia users since multimedia data is delay-sensitive and massive. In this paper, we focus on resource allocation based on a game-theoretic approach, referred to as Nash bargaining solution (NBS), to provide a quality of service (QoS) guarantee for each user. While the NBS has been known as a fair and optimal resource management strategy, it is challenging to find the NBS efficiently due to the computationally-intensive task. In order to reduce the computation requirements for NBS, we propose an approach that requires significantly low complexity even when networks consist of a large number of users and a large amount of resources. The proposed approach linearizes utility functions of each user and formulates the problem of finding NBS as a convex optimization, leading to nearly-optimal solution with significantly reduced computation complexity. Simulation results confirm the effectiveness of the proposed approach.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.249-254
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• 2009

The today's decision making tasks in globalized business and manufacturing become more complex, and ill-defined, and typically multiaspect or multi-discipline due to many influencing factors. The requirement of obtaining fast and reliable decision solutions further complicates the task. Intelligent decision support system (DSS) currently exhibit wide spread applications in business and manufacturing because of its ability to treat ill-structuredness and vagueness associated with complex decision making problems. For multi-dimensional decision problems, generally an optimum single DSS can be developed. However, with an increasing number of influencing dimensions, increasing number of their factors and relationships, complexity of such a system exponentially grows. As a result, software development and maintenance of an optimum DSS becomes cumbersome and is often practically unfeasible for real situations. This paper presents a technically feasible approximation of an optimum DSS through decreasing its complexity by a modular structure. It consists of multiple DSSs, each of which contains the homogenous knowledge's, decision making tools and possibly expertise's pertaining to a certain decision making dimension. Simple, efficient and practical integration mechanism is introduced for integrating the individual DSSs within the proposed overall DSS architecture.