• International Journal of Advanced Culture Technology
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• v.8 no.1
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• pp.128-134
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• 2020

The purpose of this study is to investigate the role and function of tokens to form a healthy blockchain-based ecosystem. Tokens must be constructed in a way that enhances their desired behavior to grow into a healthy token economy. The actions required of ecosystem participants in designing tokens should enable each individual to receive appropriate incentives (rewards) and encourage voluntary participation in taking this action. Also, all ecosystem participants must design to make the token ecosystem self-sustainable by generating profits. For example, in Bitcoin's proof-of-work method, mining is designed as a desirable behavior. Token-based services should be designed to induce multiple engagements, to design penalties for undesirable behavior, and to take into account evolutionary development potentials. Besides, the economic value of the entire token ecosystem will increase if the value that is designed and designed to take into account the revolutionary Innovation Possibility is greater than the reward amount paid to tokens. This study will contribute to presenting relevant service model by presenting how to design tokens and criteria when establishing blockchain-based service model. Future research is needed to discover new facts through a detailed comparative analysis between Tokennomics models.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.123-128
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• 2004

A finite dam under $P_{\lambda,;,T}^M-policy$ is considered, where the input of water is formed by a Wiener process subject to random jumps arriving according to a Poisson process. Explicit expression is deduced for the stationary distribution of the level of water. And the long-run average cost per unit time is obtained after assigning costs to the changes of release rate, a reward to each unit of output, and a penalty which is a function of the level of water in the reservoir.

• The Journal of Korea Robotics Society
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• v.10 no.3
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• pp.133-138
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• 2015

This paper presents a modified task structure of coupled-constraints consensus based bundle algorithm especially to resolve the cooperative transportation problem. The cooperative transportation mission has various types of constraints. A modified framework to generate activities and subtasks to solve time and task constraints of the transportation mission by using coupled-constraints consensus based bundle algorithm is suggested. In this paper modifications on task structure, reward function and arrival time calculation are suggested to handle the constraints of cooperative transportation mission.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.462-465
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• 2004

수치적인 데이터를 분류하기 위한 대표적인 방법은 퍼지 규칙을 사용하는 것이다. 하지만, 이러한 방법은 퍼지 소속 함수를 어떻게 정의하느냐에 따라 퍼지 분류의 성능이 크게 영향을 받는다는 문제점과 퍼지 규칙을 쉽게 이해하기 위해 가능한 퍼지 규칙의 수를 적게 유지해야한다는 문제점이 있다. 본 논문에서는 효과적이며 이해하기 쉬운 퍼지 규칙을 생성하기 위해 기울기 강하법을 기반으로 하는 소속 함수 학습 방법을 제안한다. 에러율을 감소하기 위해 Penalty 연산과 Reward 연산을 통해 소속 함수가 반복적으로 조절된다. 새로운 소속 함수는 Coverage 연산에 의해 생성된다. 또한 이해하기 쉬운 퍼지 규칙을 최적화하기 위해 학습된 소속 함수를 퍼지 결정 트리에 적용한다. 본 논문에서 제안한 알고리즘의 타당성을 확인하기 위해 벤치 마크 데이터인 Iris, Wisconsin Breast Cancer, Pima. Bupa 데이터를 이용하여 실험 결과를 보인다. 실험 결과를 통해 제안한 알고리즘이 기존의 C4.5와 FID 3.1 알고리즘보다 더 효과적이거나 비슷한 성능을 보임을 알 수 있다.

• Journal of Information Technology Applications and Management
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• v.25 no.1
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• pp.67-85
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• 2018

We propose a noise reduced risk aversion index for measuring risk aversion through a laboratory experiment to overcome disadvantages of the multiple pricing list format developed by Holt and Laury (2002). We use randomized multiple list choices with coarser classification and reward weighting, supplement the rank of risk aversion with extra individual characteristics of risk attitude, and construct an index of risk aversion by standardizing the risk aversion ranking with quantile normalization. Our method reduces multiple switching problems that noisy decision makers mistakenly commit in experimental approaches, so that it is free of the framing effect which severely occurred in the HL. Furthermore, the index doesn't utilize any specific utility function or probability weighting, which allows researcher to hold the independence axiom. Since our noise reduced index of risk aversion has many good traits, it is widely used and applied to reveal fundamental characteristics of risk-related behaviors in economics and finance regardless of experimental environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5654-5673
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• 2016

Participatory sensing applications rely on recruiting appropriate participants to share their surrounding conditions with others, and have been widely used in many areas like environmental monitoring, health care, and traffic congestion monitoring, etc. In such applications, how to ensure the privacy of a participant is important, since incentive mechanisms are used to maintain their enthusiasm for sustainable participation by offering certain amount of reward. In this paper, we propose a pseudonym-based privacy protection scheme, that takes both privacy protection and user incentives into consideration. The proposed scheme uses the pseudonym mechanism and one-way hash function to achieve user incentives, while protecting their identity. We also show extensive analysis of the proposed scheme to demonstrate that it can meet the security and performance the requirement of a participatory sensing application.

• ETRI Journal
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• v.27 no.6
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• pp.725-732
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• 2005

The reconfiguration management scheme changes a logical topology in response to changing traffic patterns in the higher layer of a network or the congestion level on the logical topology. In this paper, we formulate a reconfiguration scheme with a shared buffer-constrained cost model based on required quality-of-service (QoS) constraints, reconfiguration penalty cost, and buffer gain cost through traffic aggregation. The proposed scheme maximizes the derived expected reward-cost function as well as guarantees the required flow's QoS. Simulation results show that our reconfiguration scheme significantly outperforms the conventional one, while the required physical resources are limited.

• Knowledge Management Research
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• v.6 no.2
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• pp.149-168
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• 2005

Owing to the limits of IT System-driven knowledge management(KM) for innovation processes, alternative KM methods has been suggested such as: (1) the knowledge network of experts or (2) communities-of-practice. This study analyzes two cases in terms of on-line expert knowledge networks for problem-solving, with the dimensions of analysis based on a theoretical framework. By analyzing the cases of S company's expert network and Naver's Ji-sik-iN, we found that system quality(e.g., ease of use, accessibility, and searching function), information/knowledge quality(e.g., usefulness, accuracy, and timeliness), knowledge-sharing culture, social capital and relevant reward systems are important for stimulating a Q&A-based problem-solving knowledge network. Implications of the findings and future research directions are discussed.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.613-615
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• 2004

수치적인 데이터를 분류하기 위한 대표적인 방법은 퍼지 규칙을 사용하는 것이다. 하지만 퍼지 규칙을 이용하는 방법은 퍼지 소속 함수를 어떻게 정의하느냐에 따라 퍼지 분류의 성능이 크게 영향을 받는다는 문제점이 있다. 따라서 퍼지 규칙을 쉽게 이해하기 위해서는 가능한 퍼지 규칙의 수를 적게 유지하는 것이 필요하다. 본 논문에서는 효과적이며 이해하기 쉬운 퍼지 규칙을 생성하기 위해 기울기 강하법을 기반으로 하는 소속 함수 학습 방법을 제안한다 에러율을 감소하기 위해 Penalty 연산과 Reward 연산을 통해 소속 함수가 반복적으로 조절된다 새로운 소속 함수는 Coverage 연산에 의해 생성된다. 또한 이해하기 쉬운 퍼지 규칙을 최적화하기 위해 학습된 소속 함수골 퍼지 결정 트리에 적용한다. 본 논문에서 제안한 알고리즘의 타당성을 확인하기 위해 벤치 마크 데이터인 Iris, Wisconsin Breast Cancer, Plma, Bupa 데이터를 이용하여 실험 결과를 보인다. 실험 결과를 통해 제안한 알고리즘이 기존의 C4.5와 FID 3.1 알고리즘보다 더 효과적이거나 비슷한 성능을 보임을 알 수 있다.

• ETRI Journal
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• v.43 no.5
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.