• 패션비즈니스
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• 제26권3호
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• pp.155-167
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• 2022

Hanbok, a traditional Korean clothing, became a hip culture for young people in the late 2010s. As hanbok brands for young people appeared and distribution channels changed for them, hanbok appeared on the crowdfunding platform. This study summarized characteristics of hanbok provided as rewards by funding projects in Wadiz, the largest crowdfunding platform in Korea. Results of this study are as follows. First, since the first successful crowdfunding in 2015, it has shown rapidly growth. Second, as a result of examining the name of reward hanbok, 167 reward hanbok appeared. They could be divided into three periods: women's hanbok, unisex hanbok and trendy hanbok period. Third, looking at characteristics of reward hanbok from each period, feminine Chollic onepiece during the women's Hanbok period adopted the feminine interpretation of the original men's Hanbok. Characteristics of reward hanbok during the Unisex Hanbok period are in the direction of the closure. By adopting the direction of the closure of hanbok that both men and women can use, unisex hanbok is appeared. Finally, reward hanbok during the trendy hanbok period reflected trends such as genderless and hip. Hanbok reflected various trends from home culture to COVID-19 that occurred around the world at that time.

• 경영과학
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• 제27권3호
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• pp.149-160
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• 2010

This paper focuses on frequent flyer programs (FFPs), which have long been used by most airlines as a powerful marketing tool. Since the preference for FFPs reward services and the customer perceived values of mileage points differ among FFPs members, airlines should design a customer-oriented reward service based on customer preference to motivate the use of mileage points. The intention for using mileage points is affected by various kinds of attributes such as reword items, consuming mileage points for rewards and time of usage. In this paper, we focus on evaluating customer perceived values of attributes of FFPs reward services. A conjoint analysis model is applied to get the preference value of each attribute. Some empirical experiments are conducted in relation to Korean customers. From the empirical survey, the preference values of attributes are evaluated for different scenarios with respect to the number of mileage points. With the preference values of attributes, we can find several implications for airlines regarding the development of various FFPs strategies.

• 로봇학회논문지
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• 제16권2호
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• pp.137-146
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• 2021

When controlling manipulator, degree of freedom is lost in singularity so specific joint velocity does not propagate to the end effector. In addition, control problem occurs because jacobian inverse matrix can not be calculated. To avoid singularity, we apply Deep Deterministic Policy Gradient(DDPG), algorithm of reinforcement learning that rewards behavior according to actions then determines high-reward actions in simulation. DDPG uses off-policy that uses 𝝐-greedy policy for selecting action of current time step and greed policy for the next step. In the simulation, learning is given by negative reward when moving near singulairty, and positive reward when moving away from the singularity and moving to target point. The reward equation consists of distance to target point and singularity, manipulability, and arrival flag. Dual arm manipulators hold long rod at the same time and conduct experiments to avoid singularity by simulated path. In the learning process, if object to be avoided is set as a space rather than point, it is expected that avoidance of obstacles will be possible in future research.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.166-169
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• 2003

Reinforcement learning if a kind of machine learning. It aims to adapt an agent to a given environment with a clue to a reward and a penalty. Q-learning [8] that is a representative reinforcement learning system treats a reward and a penalty at the same time. There is a problem how to decide an appropriate reward and penalty values. We know the Penalty Avoiding Rational Policy Making algorithm (PARP) [4] and the Penalty Avoiding Profit Sharing (PAPS) [2] as reinforcement learning systems to treat a reward and a penalty independently. though PAPS is a descendant algorithm of PARP, both PARP and PAPS tend to learn a local optimal policy. To overcome it, ion this paper, we propose the Multi Best method (MB) that is PAPS with the multi-start method[5]. MB selects the best policy in several policies that are learned by PAPS agents. By applying PS, PAPS and MB to a soccer game environment based on the SoccerBots[9], we show that MB is the best solution for the soccer game environment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권3호
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• pp.219-223
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• 2009

Recently, Zhao et al. [Fuzzy Optimization and Decision Making (2007) 6, 279-295] characterized the interarrival times as fuzzy random variables and presented a fuzzy random elementary renewal theorem on the limit value of the expected renewal rate of the process in the fuzzy random renewal process. They also depicted both the interarrival times and rewards are depicted as fuzzy random variables and provided fuzzy random renewal reward theorem on the limit value of the long run expected reward per unit time in the fuzzy random renewal reward process. In this note, we simplify the proofs of two main results of the paper.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.9-11
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• 2022

Applying Reinforcement Learning in everyday applications and varied environments has proved the potential of the of the field and revealed pitfalls along the way. In robotics, a learning agent takes over gradually the control of a robot by abstracting the navigation model of the robot with its inputs and outputs, thus reducing the human intervention. The challenge for the agent is how to implement a feedback function that facilitates the learning process of an MDP problem in an environment while reducing the time of convergence for the method. In this paper we will implement a reward shaping system avoiding sparse rewards which gives fewer data for the learning agent in a ROS environment. Reward shaping prioritizes behaviours that brings the robot closer to the goal by giving intermediate rewards and helps the algorithm converge quickly. We will use a pseudocode implementation as an illustration of the method.

• Asia Marketing Journal
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• 제26권1호
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• pp.1-10
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• 2024

This study explores incomplete decision-making dynamics on reward-based crowdfunding platforms, focusing on temporal and social factors influencing backers' decisions. Utilizing the temporal aspect (i.e., pledging campaign phase) and social aspect (i.e., current pledged amount ratio) as stimuli within the stimulus-organism-response framework, our findings reveal that nearly 50.9% of respondents change their initial decisions, highlighting widespread incomplete information processing. Backers are more prone to altering decisions under heightened time pressure and display herding behaviors. Furthermore, backers exhibit an increased likelihood of changing decisions under heightened time pressure, coupled with a greater chance that the pledged goal amount will not be achieved. The study discusses theoretical and practical implications.

• 대한지역사회영양학회지
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• 제22권1호
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• pp.13-21
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• 2017

Objectives: This study was conducted to evaluate the nutrition quotient (NQ) by mother's parenting style which may influence the NQ in preschool children. Methods: Subjects were 310 mothers and their 4-6 year old children. The questionnaire composed of demographic characteristics, mother's parenting style at meal time and eating behavior as measured by NQ questions. The NQ questions consisted of 19 food behavior checklist items and all items were grouped into 5 factors: balance, diversity, moderation, regularity, and practice. Mother's parenting style was classified by using words for nutrition education at meal time. All data were statistically analyzed by SPSS program (Ver. 23) and the statistical differences in variables were evaluated by Student's t-test, ${\chi}^2$-test, One-way ANOVA. Results: We observed that in children whose mothers use the parenting style at meal time of 'explanation' and 'compliment & cheer up' had high dietary regularity, diversity, practice. The children of mothers who use the parenting style at meal time of 'persuasion' and 'reward' were found to have a lower degree of balance, diversity, and practice. Especially, children of 'reward' style mothers had lower moderation of dietary life. On the other hand, among the parenting style at meal time of 'comparison & demand', 'treating' and 'faire', there was no significant difference in the NQ factor by each group. NQ grade was higher among those who used more explanation (p < 0.001) and persuasion (p < 0.01) and with use of less persuasion (p < 0.01) and reward (p < 0.01). The positive association observed between the frequency of dietary education of mothers and higher NQ grade indicated the degree of dietary practices of those children. On the other hand, the children of mothers who rarely practice the dietary education at home had lower NQ grade (p < 0.001). Conclusions: In order to promote children's proper dietary behaviors, it is important to provide nutrition education to children as well as provide guidance on parenting style at meal time.

• 한국경영과학회지
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• 제31권3호
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• pp.27-40
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• 2006

Today's customer demands in supply chains tend to change quickly, variously even in a short time Interval. The uncertainties of customer demands make it difficult for supply chains to achieve efficient inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. Un this paper, we propose an adaptive vendor managed inventory (VMI) model for a two-echelon supply chain with non-stationary customer demands using the action-reward learning method. The Purpose of this model is to decrease the inventory cost adaptively. The control Parameter, a compensation factor, is designed to adaptively change as customer demand pattern changes. A simulation-based experiment was performed to compare the performance of the adaptive VMI model.

• International Journal of Contents
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• 제6권4호
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• pp.30-34
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• 2010

Effective job scheduling scheme is a crucial part of complex heterogeneous distributed systems. Gang scheduling is a scheduling algorithm for grid systems that schedules related grid jobs to run simultaneously on servers in different local sites. In this paper, we address grid jobs (gangs) schedule modeling using Stochastic reward nets (SRNs), which is concerned for static and dynamic scheduling policies. SRN is an extension of Stochastic Petri Net (SPN) and provides compact modeling facilities for system analysis. Threshold queue is adopted to smooth the variations of performance measures. System throughput and response time are compared and analyzed by giving reward measures in SRNs.