• 한국초등과학교육학회지:초등과학교육
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• 제39권3호
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• pp.433-448
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• 2020

In this study, the tasks of thermal equilibrium and heat insulation concept were divided into scientific and everyday contexts to analyzed the level of familiarity, feeling of knowing, state curiosity, and state anxiety that students feel in task contexts and their relationship. The subjects of this study were One hundred nine students in sixth grade of elementary schools located in metropolitan cities. The results of this study were as follows. First, there was no difference in the level of feeling of knowing, state curiosity, and state anxiety in the task of scientific and everyday contexts. In the case of familiarity, there was no consistent tendency in the concept of thermal equilibrium and heat insulation. And the group who recognized the task context familiarly had higher feeling of knowing and lower state anxiety than the group who recognized the task context unfamiliarly. Second, familiarity and feeling of knowing showed high positive correlation, state anxiety and familiarity showed negative correlation, and state anxiety and feeling of knowing had also negative correlation. In addition, familiarity had a negative effect on state anxiety, and FOK had a positive effect on state curiosity and a negative effect on state anxiety. There was no significant moderating effect of the task context. Third, in case of state curiosity, the group perceived the knowledge gap was very small had the highest state curiosity, and the group perceived the knowledge gap was very large had the lowest state curiosity. In case of state anxiety, the less the knowledge gap was perceived, the lower the anxiety was triggered. This study broadens our understanding of the learning process and provides implications for effective instruction strategies for students' cognitive and emotional states.

• 로봇학회논문지
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• 제19권2호
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• pp.196-202
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• 2024

The ability to understand given environments and plan a sequence of actions leading to goal state is crucial for personal service robots. With recent advancements in deep learning, numerous studies have proposed methods for state representation in planning. However, previous works lack explicit information about relationships between objects when the state observation is converted to a single visual embedding containing all state information. In this paper, we introduce graph-based state representation that incorporates both object and relationship features. To leverage these advantages in addressing the task planning problem, we propose a Graph Neural Network (GNN)-based subgoal prediction model. This model can extract rich information about object and their interconnected relationships from given state graph. Moreover, a search-based algorithm is integrated with pre-trained subgoal prediction model and state transition module to explore diverse states and find proper sequence of subgoals. The proposed method is trained with synthetic task dataset collected in simulation environment, demonstrating a higher success rate with fewer additional searches compared to baseline methods.

• 한국초등과학교육학회지:초등과학교육
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• 제42권4호
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• pp.513-522
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• 2023

과학 문제를 대면했을 때 학생들이 인지하는 과제난이도에 따른 학생이 느끼는 상태불안 수준에 대한 실증적인 연구는 부족하다. 본 연구의 목적은 학생들이 과학 문제를 대면했을 때 학생이 인지한 과제 난이도가 과학불안과 상태불안의 관계를 조절하는지 검증하는 것이다. 이를 위하여 초등학생 5~6학년 410명(5학년 186명; 여학생 194명)을 대상으로 2개의 과학 문제를 제시하였다. 그리고 PROCESS macro 1번 모델을 활용하여 과학 문제 대면 상황에서 과학불안과 상태불안 유발 사이의 관계에서 학생이 인지한 과제난이도의 조절 효과를 검증하였다. 분석 결과, 학생의 과학불안 수준과 학생이 인지한 과제난이도는 상태불안을 정적으로 유의하게 예측하였다. 그리고 학생이 인지한 과제난이도는 학생이 문제를 대면했을 때 유발되는 상태불안 수준을 부적으로 유의하게 조절하였다. 즉, 학생이 문제를 어렵다고 생각할수록 상태불안 유발에 대한 과학불안의 영향력이 낮아졌으며, 학생이 문제를 쉽다고 생각하면 상태불안 유발에 대한 과학불안의 영향력이 높아졌다. 이러한 연구 결과를 바탕으로 과학교육 및 향후 연구 방향에 대한 시사점을 논의하였다.

• 로봇학회논문지
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• 제1권2호
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• pp.107-116
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• 2006

We propose a planning algorithm to automatically generate a robust behavior plan (RBP) with which mobile robots can achieve their task goal from any initial states under dynamically changing environments. For this, task description space (TDS) is formulated, where a redundant task configuration space and simulation model of physical space are employed. Successful task episodes are collected, where $A^*$ algorithm is employed. Interesting TDS state vectors are extracted, where occurrence frequency is used. Clusters of TDS state vectors are found by using state transition tuples and features of state transition tuples. From these operations, characteristics of successfully performed tasks by a simulator are abstracted and generalized. Then, a robust behavior plan is constructed as an ordered tree structure, where nodes of the tree are represented by attentive TDS state vector of each cluster. The validity of our method is tested by real robot's experimentation for a box-pushing-into-a-goal task.

• International Journal of Advanced Culture Technology
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• 제12권3호
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• pp.250-256
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• 2024

This study aims to examine the impact of perceived control on individuals' state boredom. We hypothesized that perceived control is negatively correlated with state boredom and is anticipated to be a significant predictor of state boredom after controlling for pre-task boredom. An experiment was conducted with 98 university students. All participants engaged in an identical task designed to induce boredom, with only participants in the experimental group given a choice during the task to enhance their perceived control. Correlational analysis revealed a significant negative relationship between perceived control and state boredom. Hierarchical regression analysis demonstrated that perceived control remained a significant predictor of post-task boredom even after controlling for pre-task boredom. The study concludes with a discussion of the limitations, the implications of the findings, and suggestions for future research.

• 로봇학회논문지
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• 제3권1호
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• pp.23-32
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• 2008

This paper presents a task planning system for a steward robot, which has been developed as an interactive intermediate agent between an end-user and a complex smart home environment called the ISH (Intelligent Sweet Home) at KAIST (Korea Advanced Institute of Science and Technology). The ISH is a large-scale robotic environment with various assistive robots and home appliances for independent living of the elderly and the people with disabilities. In particular, as an approach for achieving human-friendly human-robot interaction, we aim at 'simplification of task commands' by the user. In this sense, a task planning system has been proposed to generate a sequence of actions effectively for coordinating subtasks of the target subsystems from the given high-level task command. Basically, the task planning is performed under the framework of STRIPS (Stanford Research Institute Problem Solver) representation and the split planning method. In addition, we applied a state-partitioning technique to the backward split planning method to reduce computational time. By analyzing the obtained graph, the planning system decomposes an original planning problem into several independent sub-problems, and then, the planning system generates a proper sequence of actions. To show the effectiveness of the proposed system, we deal with a scenario of a planning problem in the ISH.

• Smart Structures and Systems
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• 제31권4호
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.308-310
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• 2022

With the rise of the Al wave, task-oriented dialogue systems have become one of the popular research directions in academia and industry. Currently, task-oriented dialogue systems mainly adopt pipelined form, which mainly includes natural language understanding, dialogue state decision making, dialogue state tracking and natural language generation. However, pipelining is prone to error propagation, so many task-oriented dialogue systems in the market are only for single-round dialogues. Usually single- domain dialogues have relatively accurate semantic understanding, while they tend to perform poorly on multi-domain, multi-round dialogue datasets. To solve these issues, we developed a paddle-based multi-domain task-oriented Chinese dialogue system. It is based on NEZHA-base pre-training model and CrossWOZ dataset, and uses intention recognition module, dichotomous slot recognition module and NER recognition module to do DST and generate replies based on rules. Experiments show that the dialogue system not only makes good use of the context, but also effectively addresses long-term dependencies. In our approach, the DST of dialogue tracking state is improved, and our DST can identify multiple slotted key-value pairs involved in the discourse, which eliminates the need for manual tagging and thus greatly saves manpower.

• 한국초등과학교육학회지:초등과학교육
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• 제40권2호
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• pp.175-190
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• 2021

본 연구의 목적은 열 개념에 대한 흥미와 학생이 인식하는 과제난이도 수준이 과학상태호기심 유발에 어떠한 영향을 미치는지를 알아보는 것이다. 이를 위해 초등학교 5~6학년 410명을 대상으로 열 개념 과제의 내용에 대한 흥미와 과제를 해결하면서 학생이 인식하는 과제난이도 및 과학상태호기심을 측정하였다. 과학상태호기심 유발에 영향을 미칠 것으로 예상되는 학생의 과학호기심 수준을 공변량으로 하여 2(흥미 낮음 vs 흥미 높음)×2(난이도 쉬움 vs 난이도 어려움) ANCOVA를 실시하였다. 분석 결과, 과제의 내용에 대한 흥미가 높은 학생이 과학상태호기심도 높게 유발되었다. 한편, 학생이 인식하는 과제난이도에 따라 유발되는 과학상태호기심 수준은 차이가 없었다. 그리고 과제의 내용에 대한 흥미가 낮은 학생은 과제를 쉽다고 인식할 경우 과학상태호기심이 높게 유발되었으나, 흥미가 높은 학생은 과제를 어렵다고 인식할 경우 과학상태호기심이 높게 유발되었다. 본 연구는 해당 과제의 내용에 대한 흥미가 과학상태호기심 유발에 영향을 주며, 과제난이도 수준에 따라 흥미가 과학상태호기심 유발에 미치는 영향이 달라진다는 것을 실증적으로 검증하였다는 점에서 의미가 있다.

• Safety and Health at Work
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• 제10권3호
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• pp.321-326
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• 2019

Background: Performing cognitive tasks and muscular fatigue have been shown to increase muscle activity of the lower extremity during quiet standing. A common intervention to reduce muscular fatigue is to provide a softer shoe-surface interface. However, little is known regarding how muscle activity is affected by softer shoe-surface interfaces during static standing. The purpose of this study was to assess lower extremity muscular activity during erect standing on three different standing surfaces, before and after an acute workload and during cognitive tasks. Methods: Surface electromyography was collected on ankle dorsiflexors and plantarflexors, and knee flexors and extensors of fifteen male participants. Dependent electromyography variables of mean, peak, root mean square, and cocontraction index were calculated and analyzed with a $2{\times}2{\times}3$ within-subject repeated measures analysis of variance. Results: Pre-workload muscle activity did not differ between surfaces and cognitive task conditions. However, greater muscle activity during post-workload balance assessment was found, specifically during the cognitive task. Cognitive task errors did not differ between surface and workload. Conclusions: The cognitive task after workload increased lower extremity muscular activity compared to quite standing, irrespective of the surface condition, suggesting an increased demand was placed on the postural control system as the result of both fatigue and cognitive task.