• Smart Structures and Systems
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• v.31 no.4
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• pp.335-349
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• 2023

Bridges constantly undergo deterioration and damage, the most common ones being concrete damage and exposed rebar. Periodic inspection of bridges to identify damages can aid in their quick remediation. Likewise, identifying components can provide context for damage assessment and help gauge a bridge's state of interaction with its surroundings. Current inspection techniques rely on manual site visits, which can be time-consuming and costly. More recently, robotic inspection assisted by autonomous data analytics based on Computer Vision (CV) and Artificial Intelligence (AI) has been viewed as a suitable alternative to manual inspection because of its efficiency and accuracy. To aid research in this avenue, this study performs a comparative assessment of different architectures, loss functions, and ensembling strategies for the autonomous segmentation of bridge components and damages. The experiments lead to several interesting discoveries. Nested Reg-UNet architecture is found to outperform five other state-of-the-art architectures in both damage and component segmentation tasks. The architecture is built by combining a Nested UNet style dense configuration with a pretrained RegNet encoder. In terms of the mean Intersection over Union (mIoU) metric, the Nested Reg-UNet architecture provides an improvement of 2.86% on the damage segmentation task and 1.66% on the component segmentation task compared to the state-of-the-art UNet architecture. Furthermore, it is demonstrated that incorporating the Lovasz-Softmax loss function to counter class imbalance can boost performance by 3.44% in the component segmentation task over the most employed alternative, weighted Cross Entropy (wCE). Finally, weighted softmax ensembling is found to be quite effective when used synchronously with the Nested Reg-UNet architecture by providing mIoU improvement of 0.74% in the component segmentation task and 1.14% in the damage segmentation task over a single-architecture baseline. Overall, the best mIoU of 92.50% for the component segmentation task and 84.19% for the damage segmentation task validate the feasibility of these techniques for autonomous bridge component and damage segmentation using RGB images.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.904-914
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• 2008

An action-selection-mechanism(ASM) has been proposed to work as a fully connected finite state machine to deal with sequential behaviors as well as to allow a state in the task program to migrate to any state in the task, in which a primitive node in association with a state and its transitional conditions can be easily inserted/deleted. Also, such a primitive node can be learned by a shortest path-finding-based reinforcement learning technique. Specifically, we define a behavioral motivation as having state-dependent value as a primitive node for action selection, and then sequentially construct a network of behavioral motivations in such a way that the value of a parent node is allowed to flow into a child node by a releasing mechanism. A vertical path in a network represents a behavioral sequence. Here, such a tree for our proposed ASM can be newly generated and/or updated whenever a new behavior sequence is learned. To show the validity of our proposed ASM, experimental results of a mobile robot performing the task of pushing- a- box-in to- a-goal(PBIG) will be illustrated.

• Educational Technology International
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• v.20 no.2
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• pp.137-168
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• 2019

This study examined the effects of information volume and distribution on learners' cognitive load and recall in a mobile augmented reality (AR) environment. Information volume refers to the degree of information users are provided in a learning task, while information distribution indicates the way in which information is distributed, either in a virtual or real format. Sixteen undergraduate students participated in the study, which employed a 2 × 3 randomized block factorial design with repeated measures. Information volume and distribution were independent variables, and factors in learners' cognitive load (mental effort, perceived ease of use, and perceived task difficulty) and recall test scores were the dependent variables. Information volume had significant main effects on perceived ease of use and task difficulty, and recall test scores, while information distribution had significant main effects on perceived task difficulty and test scores. A detailed discussion and implications are provided.

• Journal of Korean Elementary Science Education
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• v.40 no.3
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• pp.343-365
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• 2021

The purpose of this study was to clarify the effect of learner variables on triggering state curiosity and state anxiety in confronting scientific task situation for fifth to sixth grades of elementary school. Science curiosity, interest, need for cognition, science self-concept, science anxiety, prior knowledge, and perceived difficulty were selected as learner variables that are expected to affect state curiosity and state anxiety. As a result of this study, the variables that had a significant influence on evoking state curiosity in confronting scientific task situation were in the order of interest, need for cognition, science curiosity, and prior knowledge, and all of these variables had a positive effect. In addition, the variables that significantly affect on evoking state anxiety in confronting scientific task situation were in the order of science anxiety, perceived difficulty, need for cognition, science self-concept, and prior knowledge. Of these, only prior knowledge had a negative effect on evoking state anxiety, and the other variables had a positive effect. The results of this study are expected to broaden the comprehension of students' emotional states in science education, and provide a theoretical foundation for the studies of state curiosity and state anxiety in science learning.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.371-377
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• 2012

Objective: The aim of this study is to investigate effect of driver's cognitive distraction on driver's physiological state and driving performance, and then to determine parameters appropriate for detecting the cognitive distraction. Background: Driver distraction is a major cause of traffic accidents and poses a serious threat to traffic safety due to ever increasing use of in-vehicle information systems and mobile phones during driving. Cognitive distraction, among four different types of distractions, prevents a driver from processing traffic information correctly and adapting to change in surround vehicle behavior in time. However, the cognitive distraction is more difficult to detect because it normally does not involve significant change in driver behavior. Method: A full-scale driving simulator was used to create virtual driving environment and situations. Participants in the experiment drove the driving simulator in three different conditions: attentive driving with no secondary task, driving and conducting secondary task of adding numbers, and driving and conducting secondary task of conversing with an experimenter. Parameters related with driver's physiological state and driving performance were measured and analyzed for their change. Results: The experiment results show that driver's cognitive distraction, induced by secondary task of addition and conversation during driving, increased driver's cognitive workload, and indeed brought change in driver's physiological state and degraded driving performance. Conclusion: The galvanic skin response, pupil size, steering reversal rate, and driver reaction time are shown to be statistically significant for detecting cognitive distraction. The appropriate combination of these parameters will be used to detect the cognitive distraction and estimate risk of traffic accidents in real-time for a driver distraction warning system.

• Korean Journal of Cognitive Science
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• v.23 no.1
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• pp.25-46
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• 2012

When performing a monotonous task, one often experiences an urge to quit. This urge may vary depending on how long one has performed the task (a temporal factor) and on which particular component of the task one is carrying out (an event-based factor). Using the Stroop task and a working memory task, we examined changes in the urge to quit as a function of basic temporal (repetition) and event-based (cognitive conflict) factors. Consistent with the law of least work and recent theorizing, for the memory task, urges to quit were greater following difficult trials; for the Stroop task, urges to quit were greater following incongruent than congruent trials, but only during early/novice phases of performance, when responding is inefficient. This is a demonstration of an avoidance response toward cognitive conflict. Regarding temporal sources of quitting, urges to quit were greater for late task stages than early stages. These basic findings may illuminate the nature of the more 'hot' motivational struggles involving the delay of gratification.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.104-113
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• 2019

Object manipulation in cluttered environments remains an open hard problem. In cluttered environments, grasping objects often fails for various reasons. This paper proposes a novel task and motion planning scheme to grasp objects obstructed by other objects in cluttered environments. Task and motion planning (TAMP) aims to generate a sequence of task-level actions where its feasibility is verified in the motion space. The proposed scheme contains an open-loop consisting of three distinct phases: 1) Generation of a task-level skeleton plan with pose references, 2) Instantiation of pose references by motion-level search, and 3) Re-planning task based on the updated state description. By conducting experiments with simulated robots, we show the high efficiency of our scheme.

• Science of Emotion and Sensibility
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• v.12 no.4
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• pp.373-380
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• 2009

This study tried to investigate how three levels of arousal affect performance of a 3-back task. Ten university male (age $25.7{\pm}1.5$) and ten female (age $24.5{\pm}1.8$) students participated in this experiment. Using pictures selected from a group test, three levels of arousal, i.e. tensed, neutral, and relaxed emotions, were induced. Each subject was run through the procedure three times, once for each arousal level. The procedure consisted of six phases for each arousal condition Rest 1(2 min), Picture 1(presenting emotion arousing photos for 2 min), 3-back task 1(2 min), Picture 2(presenting emotion arousing photos for 2 min), 3-back task 2(2 min), and Rest 2(2 min). Skin conductance level(SCL) of electrothermal activity was also measured during all phases of the experiment. The accuracy rate of 3-back task performance was the highest at a neutral emotional state, followed by relaxed and then tensed emotional state. There was no difference in reaction time(RT) among the three levels of arousal. SCL was the highest at a tensed emotional state, followed by neutral emotional state and then relaxed emotional state. Based on the results, it could be inferred that tension, induced by stimuli unrelated to cognitive tasks, decreases the ability to perform cognitive tasks.

• East Asian mathematical journal
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• v.16 no.2
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• pp.251-266
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• 2000

The problem of solution uniqueness to the task of determining unknown parameters of mathematical models from input-output observations is studied. This problem is known as structural identifiability problem. We offer a new approach for testing structural identifiability of linear state space models. The approach compares favorably with numerous methods proposed by other authors for two main reasons. First, it is formulated in obvious mathematical form. Secondly, the method does not involve unfeasible symbolic computations and thus allows to test identifiability of large-scale models. In case of non-identifiability, when there is a set of solutions to the task, we offer a method of computing functions of the unknown parameters which can be determined uniquely from input-output observations and later used as new parameters of the model. Such functions are called parametric functions capable of estimation. To develop the method of computation of these functions we use Lie group transformation theory. Illustrative example is given to demonstrate applicability of presented methods.

• Physical Therapy Korea
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• v.10 no.3
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• pp.1-15
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• 2003

The purpose of this study was to assess the effect of visual block (eyes open or closed), mental task type, and participation in an exercise program on static balance in the elderly. The subjects were 34 elderly (>65 years old) residents of a social welfare institute in Gyeonggi-do. We measured the following variables. Berg balance scale, mini mental state examination, balance performance monitor (sway area, path, and maximal sway velocity), age, weight, height and whether the subject participated in an exercise program. Scores for the Berg balance scale and mini mental state examination were evaluated to assess static balance ability either alone (single task paradigm) or while performing a mental task (dual task paradigm). Static balance variables that were measured included sway area, path, and maximal sway velocity. Each test was repeated three times. Multiple regressions analysis was used to examine the effect of each variable on static balance ability. For the dual task paradigm, static balance was affected by whether the subject participated in an exercise program. The Berg balance scale score for subjects with their eyes open was affected by whether they participated in an exercise program, while this variable in addition to the height and weight of subjects were determining variables in subjects with their eyes closed. As a result, whether subjects participated in an exercise program affected their static balance irrespective of whether their eyes open or closed. Therefore, regular exercise is recommended for elderly people and further research is needed to examine the relationship between static and dynamic balance while performing mental tasks such as cognition and attention.