• The Journal of the Korea Contents Association
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• v.14 no.3
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• pp.393-400
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• 2014

The purpose of this study is to investigate the effects of extrinsic and intrinsic compensation on organizational commitment. It is also analyzed whether the compensation selection may play a moderating role between the types of compensation and organizational commitment. A total of 295 questionnaires from 12 Chinese companies are used for data analysis. The empirical results show that the types of compensation have a positive effect on organizational commitment. It is found a moderating effect of compensation selection only between extrinsic compensation and organizational commitment. The results reveal that more focus on external compensation and adopting a flexible benefit plan are necessary to improve organizational commitment.

• Korean Journal of Culture and Social Issue
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• v.27 no.2
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• pp.191-216
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• 2021

How an individual construes one's future influences everyday decisions. For example, savings and impulsive purchasing, which is highly familiar with our life, are related to future time perception. Drawing on the idea of future self-continuity, the perceived connectedness between the current and future self, this study demonstrated whether media images with various age ranges influence a sense of connectedness with one's future self as well as impatience. Furthermore, the study measured whether these relationships were moderated by the positivity of older adults and an individual's dispositional optimism in general. Results showed that watching various images of people with a wide range of age (from the 20s to 90s) in social media increased young adults' (the 20s) self-continuity and decreased their intention of impatient consumption. This effect was also moderated by the degree to which the participants perceive aging positively.

• Journal of Chest Surgery
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• v.35 no.10
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• pp.724-729
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• 2002

Although variable surgical methods of sympathetic nerve for palmar hyperhidrosis are curative and safe therapeutic options, they have some limitations such as compensatory sweating and anhidrosis of hand in long term satisfaction rate. Material and Method: Therefore, we tried to decrease severity of compensatory sweating and prevent excessive dryness of hand through selective division of rami communicantes of thoracic sympathetic ganglia distributed to the hands(ramicotomy). Result: In postoperative results, about half of the patients maintained humidity of hands and most of them showed no more than mild degree of compensatory sweating. Conclusion: Therefore, ramicotomy of thoracic sympathetic ganglia can be recommended as selective and physiologic surgical method for palmar hyperhidrosis.

• Journal of Broadcast Engineering
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• v.7 no.4
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• pp.317-326
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• 2002

The adaptive coding schemes in H.264 standardization provide a significant ceding efficiency and some additional features like error resilience and network friendliness. The variable block size motion compensation using multiple reference frames is one of the key H.264 coding elements to provide main performance gain, but also the main culprit that increases the overall computational complexity. For this reason, this paper proposes a selective motion estimation algorithm based on variable block size for fast motion estimation in H.264. After we find the SAD(Sum of Absolute Difference) at initial points using diamond search, we decide whether to perform additional motion search in each block. Simulation results show that the proposed method is five times faster than the conventional full search in case of search range $\pm$32.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.559-562
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• 2020

시각-언어 이동 문제는 시각 이해와 언어 이해 능력을 함께 요구하는 복합 지능 문제이다. 본 논문에서는 시각-언어 이동 에이전트를 위한 새로운 학습 모델을 제안한다. 이 모델은 데모 데이터에 기초한 모방 학습과 행동 보상에 기초한 강화 학습을 함께 결합한 복합 학습을 채택하고 있다. 따라서 이 모델은 데모 데이타에 편향될 수 있는 모방 학습의 문제와 상대적으로 낮은 데이터 효율성을 갖는 강화 학습의 문제를 상호 보완적으로 해소할 수 있다. 또한, 제안 모델은 서로 다른 두 학습 간에 발생 가능한 학습 불균형도 고려하여 손실 정규화를 포함하고 있다. 또, 제안 모델에서는 기존 연구들에서 사용되어온 목적지 기반 보상 함수의 문제점을 발견하고, 이를 해결하기 위해 설계된 새로은 최적 경로 기반 보상 함수를 이용한다. 본 논문에서는 Matterport3D 시뮬레이션 환경과 R2R 벤치마크 데이터 집합을 이용한 다양한 실들을 통해, 제안 모델의 높은 성능을 입증하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.50-55
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• 2021

Multi-path system is a system in which utilizes various networks simultaneously. It is expected that multi-path system can enhance communication speed, reliability, security of network. In this paper, we focus on path selection in multi-path system. To select optimal path, we propose deep reinforcement learning algorithm which is rewarded by the round-trip-time (RTT) of each networks. Unlike multi-armed bandit model, deep Q learning is applied to consider rapidly changing situations. Due to the delay of RTT data, we also suggest compensation algorithm of the delayed reward. Moreover, we implement testbed learning server to evaluate the performance of proposed algorithm. The learning server contains distributed database and tensorflow module to efficiently operate deep learning algorithm. By means of simulation, we showed that the proposed algorithm has better performance than lowest RTT about 20%.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.51-58
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• 2009

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocup Keepaway which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.9
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• pp.281-290
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• 2020

The Vision-and-Language Navigation(VLN) task is a complex intelligence problem that requires both visual and language comprehension skills. In this paper, we propose a new learning model for visual-language navigation agents. The model adopts a hybrid learning that combines imitation learning based on demo data and reinforcement learning based on action reward. Therefore, this model can meet both problems of imitation learning that can be biased to the demo data and reinforcement learning with relatively low data efficiency. In addition, the proposed model uses a novel path-based reward function designed to solve the problem of existing goal-based reward functions. In this paper, we demonstrate the high performance of the proposed model through various experiments using both Matterport3D simulation environment and R2R benchmark dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.408-418
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• 2004

In this paper, we propose a novel scalable video coding algorithm based on adaptively weighted multiple reference frame method. To improve the coding efficiency in the enhancement layer, the enhancement frame is predicted by the sum of adaptively weighted double motion compensated frames in the enhancement layer and the current frame in the base layer, according to the input video characteristics. By employing adaptive reference selection scheme at the decoder, the proposed method reduce the drift problem significantly. From the experimental results, the proposed algorithm shows more than 1.0 ㏈ PSNR improvement, compared with the conventional scalable H.263+ for various packet loss rate channel conditions.

• Environmental and Resource Economics Review
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• v.30 no.3
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• pp.365-393
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• 2021

Using the Choice Experiment Method, this paper identified whether subsidy to the household around landfil in Seoul metropolitan area is being provided corresponding to the scale of the environmental damage. Since 2001, the subsidy program has been operating for nearly 20 years to compensate for various environmental damage (foul odor, noise, air pollution, water pollution, etc.) from landfill site in the metropolitan area, but it is not clear on what ground the subsidy is allocated. This paper estimated the marginal WTP by attribute (odor, noise, air pollution, and water pollution) based on mixed logit model and compared them with current subsidy level per household in each town. As a result of the comparison, it was found that the subsidy for each town was not allocated in proportion to the amount of the marginal WTP for each household in the corresponding town. In addition, this paper constructs a level-by-level scenario for environmental improvement attributes and compares economic benefits and current subsidy levels. As a result, the current subsidy level is insufficient compared to the level at which environmental damage is completely eliminated, but excessive subsidy is allocated compared to partial improvement levels.