• 대한인간공학회지
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• 제19권2호
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• pp.87-99
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• 2000

This study aims to examine a fuzzy logic-based human expert EMG prediction model (FLHEPM) for predicting electromyographic responses of trunk muscles due to manual lifting based on two task (control) variables. The FLHEPM utilizes two variables as inputs and ten muscle activities as outputs. As the results, the lifting task variables could be represented with the fuzzy membership functions. This provides flexibility to combine different scales of model variables in order to design the EMG prediction system. In model development, it was possible to generate the initial fuzzy rules using the neural network, but not all the rules were appropriate (87% correct ratio). With regard to the model precision, the EMG signals could be predicted with reasonable accuracy that the model shows mean absolute error of 8.43% ranging from 4.97% to 13.16% and mean absolute difference of 6.4% ranging from 2.88% to 11.59%. However, the model prediction accuracy is limited by use of only two task variables which were available for this study (out of five proposed task variables). Ultimately, the neuro-fuzzy approach utilizing all five variables to predict either the EMG activities or the spinal loading due to dynamic lifting tasks should be developed.

• 한국콘텐츠학회논문지
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• 제21권2호
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• pp.594-603
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• 2021

본 연구는 예비교사들의 원격 PBL 수업에서 학습자의 몰입에 대한 학습동기와 흥미의 영향을 인식된 과제 난이도가 조절효과를 가지는지 탐색하고자 수행되었다. 이를 위해서 선행연구 탐색을 통해서 연구모형을 구성하였다. 연구모형의 검증을 위해 교육과정 수업을 수강하는 사범학부 학생 105명을 대상으로 원격PBL을 운영하였다. 원격PBL은 실시간 화상 회의 시스템을 이용하여, 실시간으로 협업 활동을 수행하였다. 원격PBL 활동이 종료된 이후에, 학습동기, 흥미, 몰입, 과제난이도 인식 수준을 측정하였다. 수집된 자료는 구조방정식 모형을 이용한 집단 간 비교(test of the structural model invariance across the groups) 분석을 수행하여, 측정모형 간의 경로계수의 차이를 검증하여 과제난이도에 따른 조절효과를 검증하였다. 그 결과 몰입에 대한 학습동기의 영향을 흥미가 매개하는 것으로 나타났으며, 인식된 과제난이도 수준에 의해서 학습동기에서 흥미로 향하는 경로가 조절되는 것으로 나타났다.

• 간호행정학회지
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• 제13권2호
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• pp.156-166
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• 2007

Purpose: The purpose of the present study was to examine the causal relationships among hospital nursing organizational characteristics (organizational climate, workload), individual characteristics (experience, education) and outcome variables (job satisfaction, job stress, task performance) by constructing and testing a conceptual framework. Method: Five large general hospitals located in Seoul were selected to participated. The total sample of 245 registered nurses represents a response rate of 94 percent. Data for this study was collected from January to February in 2006 by questionnaire. Path analyses with LISREL program were used to test the fit of the proposed model to the data and to examine the causal relationships among variables. Result: Both the proposed model and the modified model fit the data excellently. The model revealed relatively high explanatory power of work stress (40%), job satisfaction (46%) and task performance (27%) by predicted variables. In predicting work stress, job satisfaction and task performance, the finding of this study clearly demonstrate organizational climate might be the most important variable. Conclusion: Based on the findings of the study, it was suggested that desirable organizational climate was needed to increase the nurses' mental and physical health as well as qualified task performance.

• 응용통계연구
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• 제21권6호
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• pp.997-1006
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• 2008

This paper proposes a new model by combining an infinite-server queueing model for multi-task processing software system with a perfect debugging model based on Markov process with two types of faults suggested by Lee et al. (2001). We apply this model for module and integration testing in the testing process. Also, we compute several measure, such as the expected number of tasks whose processes can be completed and the task completion probability are investigated under the proposed model.

• 한국도서관정보학회지
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• 제49권3호
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• pp.407-428
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• 2018

메타데이터 표준은 디지털 정보자원의 기록생애주기를 기술하는 데 있어 매우 중요한 요소이다. 메타데이터 표준은 정보자원에 행해지는 업무의 목적과 내용에 영향을 받고 있으나, 일반적으로 정보자원의 관점을 중심으로 정의 및 설계되었기 때문에 기록생애주기 전체를 보는 업무의 관점은 반영되어 있지 않다. 그래서 정보자원의 기록생애주기 전체를 커버하기 위해서는 정보자원의 업무 관점에서 메타데이터 속성을 적절하게 선택하고 조합하는 것이 필요하다. 이에 본 연구에서는 기록생애주기를 커버하고 정보자원에서 실행되는 업무와 요구조건에 따라 적절한 메타데이터 속성을 선택하여 조합할 수 있는 업무중심의 어플리케이션 프로파일을 작성하여 제안하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3523-3543
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• 2022

Autonomous vehicles are gradually being regarded as the mainstream trend of future development of the automobile industry. Autonomous driving networks generate many intensive and delay-sensitive computing tasks. The storage space, computing power, and battery capacity of autonomous vehicle terminals cannot meet the resource requirements of the tasks. In this paper, we focus on the task scheduling problem of autonomous driving in software-defined edge networks. By analyzing the intensive and delay-sensitive computing tasks of autonomous vehicles, we propose an emotion model that is related to task urgency and changes with execution time and propose an optimal base station (BS) task scheduling (OBSTS) algorithm. Task sentiment is an important factor that changes with the length of time that computing tasks with different urgency levels remain in the queue. The algorithm uses task sentiment as a performance indicator to measure task scheduling. Experimental results show that the OBSTS algorithm can more effectively meet the intensive and delay-sensitive requirements of vehicle terminals for network resources and improve user service experience.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• 컴퓨터교육학회논문지
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• 제9권2호
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• pp.89-99
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• 2006

본 논문은 인지신경과학의 연구 방법으로 주로 사용되는 어휘판단과제LDT:ILexical decision task) 시 보이는 언어현상을 모사할 수 있는 계산주의 모델(computational model)을 제안한다. 제안하는 모델은 LDT 시 언어와 독립적으로 관찰되는 언어현상인 빈도효과, 어휘성효과, 단어유사성효과, 시각적쇠퇴효과, 의미점화효과, 그리고 반복점화효과 등을 모사할 수 있도록 설계되었다. 실험결과, 제안한 모델은 통계적으로 유의미하게 빈도효과, 어휘성 효과, 단어유사성 효과, 시각적 쇠퇴효과 그리고 의미점화 효과를 모사할 수 있었으며, LDT 시 인간 피험자와 유사한 양상의 수행 양식을 보였다.

• 한국정보시스템학회지:정보시스템연구
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• 제31권2호
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• pp.197-216
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• 2022

Purpose In this study, in order to understand the effects of NTIS users' behavioral scientific behaviors and determinants, an integrated model of the UTAUT(Unified Theory of Acceptance and Use of Technology) model and TTF(Task-Technology Fit) model, which are frequently used MIS field, was presented and empirical analysis was conducted. Design/methodology/approach In this study, an online survey was conducted on researchers from organizations carrying out national R&D projects, institutions, universities, and dedicated management institutions and the collected data verified hypotheses established using the SPSS 25.0 statistical package and structural equation model using AMOS. Findings The results showed that NTIS users' business activities had a positive(+) effect on task-technology fit and task-technology fit had a positive(+) effect on performance expectation, effort expectation, and continuous intention-to-use intention. In addition, the performance expectation, effort expectation, and promotion conditions presented in research model had a positive(+) effect on the continuous intention-to-use. The research results derived through this study are expected to contribute substantially to subsequent research in the field related to information sharing platforms.

• Educational Technology International
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• 제10권2호
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• pp.145-166
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• 2009

The purpose of this study was to investigate why advance organizers (AO) are effective in promoting comprehension and mental model formation in terms of cognitive load. Two experimental groups: a concept-map AO group and a key-word AO group and one control group were used. This study considered cognitive load in view of Baddeley's working memory model: central executive (CE), phonological loop (PL), and visuo-spatial sketch pad (VSSP). The present experiment directly examined cognitive load using dual task methodology. The results were as follows: central executive (CE) suppression task achievement for the concept map AO group was higher than the key word AO group and control group. Comprehension and mental model construction for the concept map AO group were higher than the other groups. These results indicated that the superiority of concept map AO owing to CE load decrement occurred with comprehension and mental model construction in learning. Thus, the available resources produced by CE load reduction may have been invested for comprehension and mental model construction of learning contents.