• Journal of Korea Society of Industrial Information Systems
• /
• v.28 no.2
• /
• pp.31-45
• /
• 2023

This study deals with exploring qualitative steps and paths that appear as YouTube users' usage time increases quantitatively. For the study, I applied theories from psychology and neuroscience, subdivided the interval between the personalization algorithm of the recommendation system, and active use and analyzed the relationship between variables in this process. According to the theory behavioral model theory (FBM), variable reward, and dopamine addiction were applied. Personalization algorithms easy clicks as triggers according to associated content presentation functions in behavioral model theory (FBM). Variable rewards increase motivational effectiveness with unpredictability of the content you search, and dopamine nation is summarized as stimulating the dopaminergic nerve to continuously and actively consume content. This study is expected to make an academic and practical contribution in that it divides the purpose of use of content in the personalization algorithm and active use section into four stages from a psychological perspective: first use, reuse, continuous use, and active use, and analyzes the path.

• Journal of Korea Game Society
• /
• v.16 no.2
• /
• pp.87-96
• /
• 2016

The purpose of the study was the defining of gambling game using effect to adolescent's' morality. We conducted the survey on 1,331 students about adolescents' favorite game and made two groups, gambling and non-gambling game, and we checked the effects of game using time with personal psychological and social factors on the morality that was surveyed 6 months later. The study showed gambling game using time showed negative effect the morality but non-gambling game using time didn't show any relationship with morality. This means preferring gambling cause decrease of the morality, not game contents playing. And we proposed the follow-up study through the result.

• Journal of Communications and Networks
• /
• v.16 no.2
• /
• pp.140-145
• /
• 2014

In this paper, we propose an anti-interference cooperative spectrum sharing strategy for cognitive system, in which a secondary system can operate on the same spectrum of a primary system. Specifically, the primary system leases a fraction of its transmission time to the secondary system in exchange for cooperation to achieve the target rate. To gain access to the spectrum of the primary system, the secondary system needs to allocate a fraction of bandwidth to help forward the primary signal. As a reward, the secondary system can use the remaining bandwidth to transmit its own signal. The secondary system uses different bandwidth to transmit the primary and its own signal. Thus, there will be no interference felt at primary and secondary systems. We study the joint optimization of time and bandwidth allocation such that the transmission rate of the secondary system is maximized, while guaranteeing the primary system, as a higher priority, to achieve its target transmission rate. Numerical results show that the secondary system can gain significant improvement with the proposed strategy.

• Korean Management Science Review
• /
• v.28 no.2
• /
• pp.75-92
• /
• 2011

This study investigates how cognitive and affective conflict affect performance and social outcomes in the new product situation. We examine the corporate, project, and personal characteristics that determine the level of inter-functional conflict between marketing and R&D. From a survey of Korean high-tech companies, we found : (1) time pressure was positively related to cognitive and affective conflict, (2) joint reward systems, interdepartmental connectedness, and years of education were negatively associated to cognitive conflict, (3) while cognitive conflict was positively related to new product creativity and NPD performance, affective conflict was negatively associated to marketing-R&D integration and perceived effectiveness of the relationship, as well as new product creativity and NPD performance.

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

• Journal of the Korean Graphic Arts Communication Society
• /
• v.27 no.1
• /
• pp.89-106
• /
• 2009

The purpose of this study is to provide necessity of job stress management to chief executives officers at small and medium size printing companies. At the same time, this study offers information of successful job stress management. When managing job stress is successfully accomplished, it increases job involvement of workers and brings high productivity eventually. Independent variables are decision-making right at the job, insecurity of job, and unreasonable reward at the company, while job involvement is dependent variable at this study. After analyzing data gathered, decision-making right influences job attachment and pursuing job outcome. And insecurity of job, that is the difficult job transferring, is significant to job accomplishment.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.174-174
• /
• 2000

Q-learning, based on discrete state and action space, is a most widely used reinforcement Learning. However, this requires a lot of memory and much time for learning all actions of each state when it is applied to a real mobile robot navigation using continuous state and action space Region-based Q-learning is a reinforcement learning method that estimates action values of real state by using triangular-type action distribution model and relationship with its neighboring state which was defined and learned before. This paper proposes a new Region-based Q-learning which uses a reward assigned only when the agent reached the target, and get out of the Local optimal path with adjustment of random action rate. If this is applied to mobile robot navigation, less memory can be used and robot can move smoothly, and optimal solution can be learned fast. To show the validity of our method, computer simulations are illusrated.

• Proceedings of the Korea Contents Association Conference
• /
• 2016.05a
• /
• pp.99-100
• /
• 2016

본 연구는 경제적 가치가 있는 돈, 상품 등을 입자로 대체하여, 관람객의 행동에 대해 보상을 제공하는 "3D interactive reward system"을 만들었다. 관람객의 움직임과 속도에 따라 보상물의 종류, 생성량을 변화시켜 보상심리를 자극함으로써 보다 관람객의 관심과 적극적인 참여를 유도하고 동시에 자연스럽게 가상의 Fuzzy현상을 체험하고 소통할 수 있도록 시도하였다.

• Journal of Information Science Theory and Practice
• /
• v.4 no.2
• /
• pp.6-20
• /
• 2016

In the last few years, a new type of synchronous social networking services (SNSs) has emerged—social live streaming services (SLSSs). Studying SLSSs is a new and exciting research field in information science. What information behaviors do users of live streaming platforms exhibit? In our empirical study we analyzed information production behavior (i.e., broadcasting) as well as information reception behavior (watching streams and commenting on them). We conducted two quantitative investigations, namely an online survey with YouNow users (N = 123) and observations of live streams on YouNow (N = 434). YouNow is a service with video streams mostly made by adolescents for adolescents. YouNow users like to watch streams, to chat while watching, and to reward performers by using emoticons. While broadcasting, there is no anonymity (as in nearly all other WWW services). Synchronous SNSs remind us of the film The Truman Show, as anyone has the chance to consciously broadcast his or her own life real-time.

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