• Journal of Korean Elementary Science Education
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• v.39 no.1
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• pp.133-144
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• 2020

The purpose of this study is to identify the effect of Teacher Participation-oriented Education Program (TPEP) for Elementary School Teachers to Improve Class Expertise in Science Classes with a focus on visual attention. The participants were two elementary school teachers in Seoul and taught science subjects. The lesson topic applied to this study were 'Structure and Function of Our Body' in the second semester of fifth grade and 'Volcano and Earthquake' in the second semester of fourth grade. The mobile eye tracker SMI's ETG 2w, which is a binocular tracking system was used in this study. In this study, the actual practice time, participant's visual attention, visual intake time average, and visual intake time average were analyzed by class phase. The results of the study are as follows. First, as a result of analyzing the actual class execution time, the actual class execution time was almost in line with the lesson plan after the TPEP application. Second, visual attention in the areas related to teaching and learning activities was high after applying TPEP. Factors affecting the progress of the class and cognitive burdens were identified quantitatively and objectively through visual attention. Third, as a result of analyzing the visual intake time average of participants, there was a statistically significant difference in all classes. Fourth, as a result of analyzing the visual intake time average of participants, the results were statistically significant in the introduction(video), activity 1, activity 2, and activity 3 stages in the lecture type class. The Teacher Participation-oriented Education Program (TPEP) for Elementary School Teachers to Improve Class Expertise in Science Classes can extend elementary science class expertise such as self-class analysis, eye tracking, linguistic, gesture, and class design beyond traditional class analysis and consulting.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1704-1712
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• 2014

The unused Worst-Case Execution Time (WCET) allocated to a real-time task occurs when the actual execution time of the task can be far less than the WCET preassigned to the task for a schedulability test. Any unused WCET allocated to the task can be exploited to reduce the power consumption of battery-powered sensor nodes through real-time power-aware scheduling techniques. From the distribution perspective of the unused WCET, the unused WCET distribution policy is classified into three types: Conservative Unused WCET (CU-WCET), Moderate Unused WCET (MU-WCET), and Aggressive Unused WCET (AU-WCET) distribution policies. We evaluated the performance of real-time power-aware scheduling techniques incorporating each of three unused WCET distribution policies in terms of low power consumption.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.707-716
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• 2011

It is important that desktop grids should be able to aggressively deal with the dynamic properties that arise from the volatility and heterogeneity of resources. Therefore, it is required that task scheduling be able to positively consider the execution behavior that is characterized by an individual resource. In this paper, we implement a log analysis system with REST web services, which can analyze the execution behavior by utilizing the actual log data of desktop grid systems. To verify the log analysis system, we conducted simulations and showed that the resource group-based task scheduling, based on the analysis of the execution behavior, offers a faster turnaround time than the existing one even if few resources are used.

• The Journal of Korean Physical Therapy
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• v.29 no.1
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• pp.27-32
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• 2017

Purpose: Recent electrophysiological studies have shown that the sensorymotor cortex is activated during both actual action excuted by themselves and observation of action performed by other persons. Observation of action based on mirror neuron system can be used as a cognitive intervention to promote motor learning. The purpose of this study was to investigate the brain activity changes during action observation and action execution using EEG. Methods: Thirty healthy volunteers participated and were requested to perform hand action and to observe the video of hand action performed by another person. The EEG activity was evaluated by a method which segregated the time-locked for each condition. To compare the differences between action observation and execution, the Mu suppression and the relative band power were analysed. Results: The results showed significant mu suppression during the action observation and execution, but the differences between the two conditions were not observed. The relative band power showed a significant difference during the action observation and execution, but there were no differences between the two conditions. Conclusion: These results indicate that action execution and observation involve overlapping neural networks in the sensorymotor cortical areas, proposing positive changes on neurophysiology. We are expected to provide information related to the intervention of cognitive rehabilitation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.5
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• pp.309-318
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• 1987

In the on-line security analysis of power system, Automatic Contingency Selection (ACS) is commonly used to reduce the number of contingency cases which will be evaluated in detail. This paper describes a fast and reliable ACS method which adopts DC load flow in conjunction with compensation theorem to improve execution time, and applies severity performance index, divided on each limit level for considering overload rate, to make reliable contingency ranking. The method has been tested in IEEE 25 bus system and KEPCO 130 bus actual power system. The results of these tests verify its superiority to both the execution time and reliability, and illustrate its effectiveness for the practical use.

• The Journal of Bigdata
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• v.3 no.1
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• pp.47-54
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• 2018

Previous studies of business process analysis have analyzed various factors such as task, customer service, operator convenience, and execution time prediction. To accurately analyze these factors, it is effective to utilize actual historical data recorded in information systems. Process mining is a technique for analyzing various elements of a business process from event log data. In this case study, process mining was applied to the transaction data of a purchase agency to analyze the business process of their procurement process, the execution time, and the operators.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.3
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• pp.127-137
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• 2010

As large-scale computational applications in various scientific domains have been utilized over many integrated sets of grid computing resources, the difficulty of their execution management and control has been increased. It is beneficial to refer job history generated from many application executions, in order to identify application‘s characteristics and to decide selection policies of grid resource meaningfully. In this paper, we apply a statistical technique, Plackett-Burman design with fold-over (PBDF), for analyzing grid environments and execution history of applications. PBDF design identifies main factors in grid environments and applications, ranks based on how much they affect to their execution time. The effective factors are used for selecting reference job profiles and then preferable resource based on the reference profiles is chosen. An application is performed on the selected resource and its execution result is added to job history. Factor's credit is adjusted according to the actual execution time. For a proof-of-concept, we analyzed job history from an aerospace research grid system to get characteristics of grid resource and applications. We built JARS algorithm and simulated the algorithm with the analyzed job history. The simulation result shows good reliability and considerable performance in grid environment with frequently crashed resources.

• The Journal of Korean Physical Therapy
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• v.32 no.2
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• pp.70-74
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• 2020

Purpose: This study examined whether 1) the motor inhibition response as cognitive-behavioral component is learning though a stop signal task using stop-signal paradigm, and 2) whether there is a difference in the learning degree according to imagery training and actual practice training. Methods: Twenty young adults (males: 9, females: 11) volunteered to participate in this study, and were divided randomly into motor imagery training (IT, n=10) and practice training (PT, n=10) groups. The PT group performed an actual practice stop-signal task, while the IT group performed imagery training, which showed a stop-signal task on a monitor of a personal computer. The non-signal reaction time and stop-signal reaction time of both groups were assessed during the stop-signal task. Results: In the non-signal reaction time, there were no significant intra-group and inter-group differences between pre- and post-intervention in both groups (p>0.05). The stop-signal reaction time showed a significant difference in the PT group in the intra-group analysis (p<0.05). On the other hand, there was no significant intra-group difference in the IT group and inter-group difference between pre- and post-intervention (p>0.05). Conclusion: These results showed that the motor inhibition response could be learned through a stop-signal task. Moreover, these findings suggest that actual practice is a more effective method for learning the motor inhibition response.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.37-45
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• 2010

A system which needs timely accuracy has to design and to verify correctly about execution-time for reliability. Accordingly, it is necessary for timing analysis tools, and much previous research worked. In timing analysis tool, there are two methods. One is a static analysis, and the other is a measurement based analysis. A static analysis is able to spend time less than a measurement based analysis method, but has low reliability of analysis result caused by hard to estimate time of I/O caused by various hardware. A measurement based analysis can be close analysis to real result, but it is hard to adapt to actual application, and spend a lot of time to get result of analysis. As such, this paper present a software monitoring architecture to supply reliability of static analysis process. In a presented architecture, it can select target as needed measurement through static analysis, and reuse result of measurement exist. Therefore, The architecture can reduce overload of time and performance for measurement, and improve the reliability which is the worst problem of static analysis.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.69-75
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• 2022

We propose energy-efficient scheduling considering real-time constraints and energy efficiency in smart mobile with heterogeneous multi-core structure. Recently, high-performance applications such as VR, AR, and 3D game require real-time and high-level processings. The big.LITTLE architecture is applied to smart mobiles devices for high performance and high energy efficiency. However, there is a problem that the energy saving effect is reduced because LITTLE cores are not properly utilized. This paper proposes a heterogeneous multi-core assignment technique that improves real-time performance and high energy efficiency with big.LITTLE architecture. Our proposed method optimizes the energy consumption and the execution time by predicting the actual task execution time using SVM (Support Vector Machine). Experiments on an off-the-shelf smartphone show that the proposed method reduces energy consumption while ensuring the similar execution time to legacy schemes.