• 한국컴퓨터정보학회논문지
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• 제17권11호
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• pp.163-171
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• 2012

온라인게임에 관한 대다수의 연구들은 주로 심리학자들에 의한 인터넷 중독과 과학자들에 의한 관련기술의 개발에 관해 수행되어왔다. 온라인 비즈니스 모델의 관점에서 고객만족을 연구한 논문은 드문 편이다. 본 연구의 목적은 온라인게임 이용자들을 대상으로 고객만족과 주요 요인들 간의 인과관계를 조사하는 것이다. 이를 위해 연구모델은 고객만족에 영향을 미치는 주요 요인들을 규명하기 위해 상호작용, 즐거움, 도전, 보상, 디자인의 개념을 이용하였다. 연구결과 상호작용, 즐거움, 도전 요인이 온라인게임 이용자들의 고객만족에 유의한 영향을 미치는 요인으로 규명되었다. 온라인게임 이용자들의 만족도에 영향을 미치는 주요 요인에 관한 연구는 지속적으로 논의되고 있다.

• 한국콘텐츠학회논문지
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• 제13권2호
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• pp.488-497
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• 2013

본 연구는 지상, 수상, 항공 레저스포츠 중심의 관광활동 참가자들의 참여정도와 몰입의 관계를 규명하는데 그 목적이 있다. 지상(등산, 골프, 캠핑), 수상(래프팅, 스킨스쿠버, 낚시), 항공(패러글라이딩, 스카이다이빙) 레저스포츠 중심의 관광활동에 참가하는 총 437명의 설문지가 최종 분석에 사용되었다. 수집된 자료를 바탕으로 SPSSWIN 18.0을 이용하여 일원변량분석, 다중회귀분석을 실시하여 다음과 같은 결과를 도출하였다. 첫째, 레저스포츠 중심의 관광활동 유형에 따라 몰입은 명확한 목표, 시간감각 왜곡, 자의식 상실, 도전과 기술의 균형, 자기 목적적 경험, 즉각적인 피드백, 통제감에서 유의한 차이가 있었다. 둘째, 참가 기간과 빈도가 몰입에 정(+)적인 영향을 미치는 것으로 나타났으며, 구체적으로 참가 기간은 도전과 기술의 균형, 명확한 목표에, 참가 빈도는 도전과 기술의 균형, 시간감각 왜곡, 명확한 목표, 자기 목적적 경험, 즉각적인 피드백, 통제감에, 참가 강도는 시간감각의 왜곡, 자기 목적적 경험에 통계적으로 유의한 정의 영향을 미치는 것으로 나타났다.

• 한국동물위생학회지
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• 제44권4호
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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.

• 한국콘텐츠학회논문지
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• 제7권2호
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• pp.150-160
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• 2007

교실수업에서는 지속적인 면대면 피드백을 통해 학습동기를 부여하고 학습 성취 욕구를 충족시켜주어 몰입학습을 유도 할 수 있으나 이러닝에서는 교실수업에 준하는 상호작용수준에 미치지 못하고 있어 많은 학습자들이 중도탈락이나 포기를 하고 있는 실정이다. 이러한 이러닝 환경에서의 문제점을 개선하고자 본 연구에서는 교실수업에서 사용하는 대화 피드백을 시스템에서 제공해 주어 학습자의 몰입학습을 증진시키는 대화 기반 피드백 시스템을 개발하였다. 개발된 시스템을 학교 현장에 적용해 보았으며, 그 결과 실험집단이 통제집단에 비해 학습자 개인에게 명확한 학습 목표의식과 도전감, 구체적인 피드백이 이루어져 몰입학습을 증진시킨 것으로 나타났다. 이는 본 시스템에서 제공해 주는 대화 기반 피드백이 학습자에게 적극적인 학습참여 자세를 배양하였고 학습의 흥미와 관심을 유지시켜 몰입학습을 유도하였음을 시사하고 있다. 끝으로 본 연구가 가지는 의의로 이러닝 환경에서 새로운 학습 시스템의 개발 방향을 제시하였다고 결론지을 수 있다.

• 한국생산제조학회지
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• 제24권5호
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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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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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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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• 제17권2호
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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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• 제50권1호
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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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• 제46권1호
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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.

• 한국측량학회지
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• 제31권6_2호
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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.