• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.163-171
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• 2012

Many previous researches in the area of online games have been carried out about internet addiction by psychologist and about the development of related technologies by scientists. There are a few studies about the consumer satisfaction from the online business model. The purpose of this study is to examine the causal relationship between critical factors and consumer satisfaction of online game users. This study model used five themes(interactivity, enjoyment, challenge, feedback, design) in order to identify the effect of critical factors on consumer satisfaction. The result of this study identified that interactivity, enjoyment, challenge have a significant effect on consumer satisfaction of online game users. Future areas for studying on the Effect of Critical Factors on Consumer Satisfaction of Online Game Users are continuously discussed.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.488-497
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• 2013

The purpose of the study was to investigate the relationship between the degree of participation and flow for participants in ground, water, and air leisure sports-based tourism activities. A total of 437 participants' data was employed for the analyses (one-way ANOVA and multiple regression analysis) using SPSS Win 18.0 program. Main findings are as follows; First, there were differences in clear goals, transformation of time, loss of self-consciousness, challenge-skill balance, autotelic experience, unambiguous feedback, and sense of control out of flow dimensions based on types of leisure sports-based tourism activities. Second, participation period and frequence had positive effects on flow. Specifically, period had an influence on challenge-skill balance. clear goals: frequence had an effect on challenge-skill balance, transformation of time, clear goals, autotelic experience, unambiguous feedback, and sense of control: intensity had an influence on transformation of time and autotelic experience.

• Korean Journal of Veterinary Service
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• v.44 no.4
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• pp.291-297
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• 2021

Severe outbreaks of porcine epidemic diarrhoea virus (PEDV) have continued to re-emerge worldwide. Because of the high mortality rate of suckling piglets in PEDV outbreaks, the disease causes significant economic losses in the pig industry. The limited pre-existing immunity against this virus is thought to cause an explosive increase in infection in pig farms. This study aimed to evaluate the clinical symptoms of PEDV after intentional exposure (feedback). During the first few days of the outbreak in a breeding pig farm, 14 sows showed watery diarrhoea, and the disease subsequently spread rapidly throughout the barn. Pigs that were intentionally exposed to PEDV (n=251) showed watery diarrhoea (46.6%), reduced appetite (17.5%), and vomiting (6.0%). However, 75 exposed pigs (29.9%) showed no clinical signs of disease. Four weeks after the feedback challenge, 34 sows gave birth to litters of piglets, which survived with no diarrhoea. Five weeks after the start of the outbreak, PEDV was not detected in any of the examined samples, including environmental swabs. Thus, early diagnosis, prompt establishment of herd immunity, and strict application of biosecurity are good practices to reduce the mortality rates among new-born piglets and control economic losses in pig farms showing PEDV outbreaks.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.150-160
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• 2007

In the actual classroom the so-called flow learning is able to motivate the students through face-to-face feedback, and to meet their needs for educational achievement. By contrast, the so-called e-learning method falls short of the satisfactory level of real-life interaction, which makes many learners drop out or give up on their learning. In order to better the e-learning environment, this study presents a dialogue-based feedback system that improves the flow learning of the learners' in the classroom. This newly developed system was applied at the actual school. The result is that the experimented group improved its flow learning, compared with the controlled group. In the former group, each individual showed some consciousness of objective and challenge following the concrete feedback. That is to say, this system enhances the attitude of an active participation and induces the flow learning, thanks to the dialogue-based feedback and the sustained interest in learning. In conclusion, the significance of this study lies in suggesting the direction of a new learning method development in the e-learning environment.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.5
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• pp.548-552
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• 2015

In this paper, we describe the quadruped walking-control algorithm of the complete full-size humanoid DARPA Robotics Challenge-HUBO (DRC-HUBO) robot. Although DRC-HUBO is a biped robot, we require a quadruped walking function using two legs and two arms to overcome uneven terrains in the DRC. We design a wave-type quadruped walking pattern as a feedforward control using several walking parameters, and we design zero moment point (ZMP) controllers to maintain stable walking using an inverted pendulum model and an observed-state feedback control scheme. In particular, we propose a switching algorithm for ZMP controllers using supporting value and weighting factors in order to maintain the ZMP control performance during foot switching. Finally, we verify the proposed algorithm by performing quadruped walking experiments using DRC-HUBO.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.531-531
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• 2000

The challenge is to control unstable nonlinear dynamic systems using only sparse feedback from the environment concerning its performance. The design of such controllers can be achieved by evolving neural networks. An evolutionary approach to train neural networks in realtime is proposed. Evolutionary strategies adapt the weights of neural networks and the threshold values of neuron's synapses. The proposed method has been successfully implemented for pole balancing problem.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.2
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• pp.216-222
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• 2017

The most prominent challenge for MOSFET scaling is to reduce power consumption; however, the supply voltage ($V_{DD}$) cannot be scaled down because of the carrier injection mechanism. To overcome this limit, a new type of field-effect transistor using positive feedback as a carrier injection mechanism (FBFET) has been proposed. In this study we have investigated the electrical characteristics of a $Si_{1-x}Ge_x$ FBFET with one gate and one-sided $Si_3N_4$ spacer using TCAD simulations. To reduce the drain bias dependency, $Si_{1-x}Ge_x$ was introduced as a low-bandgap material, and the minimum subthreshold swing was obtained as 2.87 mV/dec. This result suggests that a $Si_{1-x}Ge_x$ FBFET is a promising candidate for future low-power devices.

• Nuclear Engineering and Technology
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• v.50 no.1
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• pp.97-106
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• 2018

Improved load-following capability is one of the most important technical tasks of a pressurized water reactor. Controlling the nuclear reactor core during load-following operation leads to some difficulties. These difficulties mainly arise from nuclear reactor core limitations in local power peaking: the core is subjected to sharp and large variation of local power density during transients. Axial offset (AO) is the parameter usually used to represent the core power peaking. One of the important local power peaking components in nuclear reactors is axial power peaking, which continuously changes. The main challenge of nuclear reactor control during load-following operation is to maintain the AO within acceptable limits, at a certain reference target value. This article proposes a new robust approach to AO control of pressurized water reactors during load-following operation. This method uses robust feedback-linearization control based on the multipoint kinetics reactor model (neutronic and thermal-hydraulic). In this model, the reactor core is divided into four nodes along the reactor axis. Simulation results show that this method improves the reactor load-following capability in the presence of parameter uncertainty and disturbances and can use optimum control rod groups to maneuver with variable overlapping.

• ETRI Journal
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• v.46 no.1
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• pp.82-95
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• 2024

As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi-trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT-Trans-CNN model, which combines transformer-based representations with a novel dual-scale bidirectional encoder representations from transformers (BERT) encoding approach at the document-level. By explicitly leveraging multi-trait representations in a multi-task learning (MTL) framework, our DualBERT-Trans-CNN emphasizes the interrelation between holistic and trait-based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single-task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi-trait performance on the ASAP++ dataset.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.585-592
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• 2013

CBIR is an effective tool to search and extract image contents in a large remote sensing image database queried by an operator or end user. However, as imaging principles are different by sensors, their visual representation thus varies among image modality type. Considering images of various modalities archived in the database, image modality difference has to be tackled for the successful CBIR implementation. However, this topic has been seldom dealt with and thus still poses a practical challenge. This study suggests a cross modality CBIR (termed as the CM-CBIR) method that transforms given query feature vector by a supervised procedure in order to link between modalities. This procedure leverages the skill of analyst in training steps after which the transformed query vector is created for the use of searching in target images with different modalities. Current initial results show the potential of the proposed CM-CBIR method by delivering the image content of interest from different modality images. Despite its retrieval capability is outperformed by that of same modality CBIR (abbreviated as the SM-CBIR), the lack of retrieval performance can be compensated by employing the user's relevancy feedback, a conventional technique for retrieval enhancement.