• 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.

• Physical Therapy Korea
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• v.19 no.3
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• pp.61-71
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• 2012

In daily activities, people often perform two or more tasks simultaneously. This is referred to as dual-tasking or multi-tasking. The purpose of this study was to examine the effects of performing dual tasks while using a mobile phone on static and dynamic postural stability. Twenty-four subjects were asked to stand on a force plate and then instructed to perform a balance task only (BT), a balance task while listening to music (BTL), a balance task while talking on the mobile phone (BTT), and a balance task while sending text messages (BTS). We used the BioRescue$^{(R)}$ to measure postural sway and limit of stability for static and dynamic postural stability. Also the star excursion balance test (SEBT) was used to measure dynamic postural stability. A one-way ANOVA with repeated measures was used to compare the effects of the BT, BTL, BTT, and BTS. The Bonferroni's post hoc test was used to determine the differences among four tasks. Carrying out the BTS significantly decreased the limit of stability compared with carrying out the BT, BTL, and BTT (p<.05). In limit of stability, total surface area of BTT was more significantly decreased than that of BT and total surface area of BTS was more decreased than that of BT, BTL and BTT (p<.05). In the SEBT, the BTS displayed significantly smaller reach distance values compared with the BT or BTL (p<.05). These findings suggest that performing the balance task while sending text message on the mobile phone decreases dynamic postural stability, whereas performing the same task while listening to music using the mobile phone does not. Therefore, it requires more attention to maintain dynamic balance while sending text messages.

• Industrial Engineering and Management Systems
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• v.4 no.2
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• pp.176-183
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• 2005

The theory of signal detection has been applied to a wide range of practical situation for a long time, including sonar detection, air traffic control and so on. In general, in this theory, sensitivity parametric index d' and bias parametric index $\beta$ are used to evaluated the performance of vigilance. These indices use observer's response "hit" and "false alarm" to explain and evaluate vigilance, but not considering reaction time. However, the reaction time of detecting should be considered in measuring vigilance in some supervisory tasks such as unlimited monitoring tasks (e.g., supervisors in nuclear plant). There are some researchers have used the segments of reaction time to generate a pair of probabilities of hit and false alarm probabilities and plot the receiver operating characteristic curve. The purpose of this study was to develop a quantitative vigilance-measuring model by fuzzy sets, which combined the concepts of hit, false alarm and reaction time. The model extends two-values logic to multi-values logic by membership functions of fuzzy sets. A simulated experiment of monitoring task in nuclear plant was carried out. Results indicated that the new vigilance-measuring model is more efficient than traditional indices; the characteristics of vigilance would be realized more clearly in unlimited monitoring task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5546-5559
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• 2019

Facial action units (AUs) have recently drawn increased attention because they can be used to recognize facial expressions. A variety of methods have been designed for frontal-view AU detection, but few have been able to handle multi-view face images. In this paper we propose a method for multi-view facial AU detection using a fused multilayer, multi-task, and multi-label deep learning network. The network can complete two tasks: AU detection and facial view detection. AU detection is a multi-label problem and facial view detection is a single-label problem. A residual network and multilayer fusion are applied to obtain more representative features. Our method is effective and performs well. The F1 score on FERA 2017 is 13.1% higher than the baseline. The facial view recognition accuracy is 0.991. This shows that our multi-task, multi-label model could achieve good performance on the two tasks.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.69-83
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• 1995

In a large number of literature on of inspection tasks, one of the most consistent findings is the existence of large and consistent differences among inspectors. It is possible that the individual difference is described by the difference of cognitive skills, because cognitive skills are required more than manual skills in inspection tasks. Therefore, a set of cognitive factors in human information processing may underly human performance in inspection tasks. In this study, a cognitive skill was described as the relative importance of the cognitive factors involved. A hierarchical task analysis and a fuzzy hierarchical analysis were used to represent how the importance of cognitive factors contribute to inspection performance. An experiment was conducted using the computer simulations of PCB inspection tasks. The results revealed that the subject group with better performance showed the importance weights of cognitive factors in the following rank; (attention, perception, judgement, classification, recognition)<(detection)$\ll$(memory). The results of the experiment can serve as a selection criterion for efficient inspection performance and the information of skilled learning for an inspection training program. The usefullness of a hierarchical task analysis and a fuzzy hierarchical task analysis for the analysis of cognitive tasks are also confirmed.

• Journal of the Korean Society of Safety
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• v.24 no.4
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• pp.104-110
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• 2009

In this driving simulation study, the impairing effects of various types of electronic devices usage(i. e., destination search by using in-vehicle navigation system, TV watching and dialing cellular phone) during driving on front-to-rear-end collision avoidance were investigated. Percentage of collisions, driving speeds when the drivers collided, and initial reaction time for collision avoidance were analyzed and compared as the dependent measures. The results indicated that (1) any types of electronic devices usage during driving induced more serious collision-related impairment than control condition where no additional task was required, and (2) in general, destination search task appeared to impair drivers collision avoidance performance more than the other task requirements in terms of percentage of collisions and initial reaction time for collision avoidance, but TV watching induced most serious collision impact. These results suggested that any types of electronic device usage could distract drivers attention from the primary task of driving, and be resulted in serious outcome in potentially risky situation of front-to-rear-end collision. In particular, mandatory use of eye-hand coordination and receiving feedback seemed to one of essential factor leading the drivers visual attentional distraction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3603-3618
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• 2022

The offloading method is important when there are multiple mobile nodes and multiple edge servers. In the environment, those mobile nodes connect with edge servers with different bandwidths, thus taking different time and energy for offloading tasks. Considering the system load of edge servers and the attributes (the number of instructions, the size of files, deadlines, and so on) of tasks, the energy-aware offloading problem becomes difficult under our mobile edge environment (MCE). Most of the past work mainly offloads tasks by judging where the job consumes less energy. But sometimes, one task needs more energy because the preferred edge servers have been overloaded. Those methods always do not pay attention to the influence of the scheduling on the future tasks. In this paper, first, we try to execute the job locally when the job costs a lower energy consumption executed on the MD. We suppose that every task is submitted to the mobile server which has the highest bandwidth efficiency. Bandwidth efficiency is defined by the sending ratio, the receiving ratio, and their related power consumption. We sort the task in the descending order of the ratio between the energy consumption executed on the mobile server node and on the MD. Then, we give a "suffrage" definition for the energy consumption executed on different mobile servers for offloading tasks. The task selects the mobile server with the largest suffrage. Simulations show that our method reduces the execution time and the related energy consumption, while keeping a lower value in the number of uncompleted tasks.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.27 no.2
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• pp.155-161
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• 2017

When a military vehicle is driven on a cross-country road, the occupants are exposed to vibration at a body resonance. In case that the exposure continues for too long period, the attention ability of the occupant could be affected by the vibration exposure. In the study, it was experimentally tried to find if there is a correlation between degradation of attention and vibration exposure. Two kinds of test among various psychological attention tests were employed, which were selected with considering a situation of carrying out military mission on vehicle. At the result, the searching test for controlled attention showed significant degradation in the accuracy and performance time in case of exposure at the vibration. And the attention degradation appeared to be greater when the vibration exposure increases. The dual task test for divided attention showed the difference between vibration and non-vibration condition, but showed it is insignificant for the attention to degrade by increasing exposure time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3976-3990
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• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.16-37
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• 2022

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.