• Journal of Information Processing Systems
• /
• v.7 no.4
• /
• pp.707-716
• /
• 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 Information Systems
• /
• v.16 no.1
• /
• pp.179-200
• /
• 2007

In this research, we study how task and KMS fit influences on KMS performance in large corporations during its practical use. Based on the task-technology fit theory and information system success model, we developed a research model by considering the characteristics of KMS for supporting tasks. We try to verify how individual traits, task traits, and KMS Units affect task-KMS fit and how task KMS fit influences on KMS performance. We surveyed 212 employees who were using KMS and working for the large-sized manufacturing firms. We analyzed the collected data from LISREL 8.54 for Windows, and found the following significant results. First user satisfaction is increased when KMS provides knowledge to help to perform task rather than KMS' functionality. Second, user satisfaction is increased when KMS is suitable for performing task Hence, we verified task-KMS fit is an antecedent of user satisfaction. Third, task-KMS fit and user satisfaction have significant impacts on KMS performance. And user satisfaction affected more heavily on KMS performance than task-KMS fit did. As a result, we realized an individual performance can be improved when task KMS fit is high and, consequently, user satisfaction is increased. Forth while the usefulness of task-KMS fit is demonstrated, causal factors such as individual traits, task traits, and KMS traits significantly affect task-KMS fit. Formalization and knowledge trait we significant in enhancing user satisfaction, but KMS self-efficacy, autonomy, md system trait are not. These results indicate that task-KMS fit variable is useful as a measure of KMS performance as well as that of user satisfaction. Based on these results, we conclude that when KMS supports task activity, performance can be significantly improved by coordinating the task with KMS.

• Journal of Korean Academy of Nursing
• /
• v.36 no.5
• /
• pp.770-781
• /
• 2006

Purpose: This study was designed to create a job description for Korean geriatric care managers and examine performance frequency, importance, and difficulty of task elements. Method: The sample consisted of 38 geriatric care managers and professors who performed duties related to geriatric care management at community based-facilities in Korea. A survey method was used, and the questionnaire included frequency, importance, and difficulty of task elements in job descriptions using the DACUM method. Using SPSS WIN 10.0, descriptive statistics such as frequency distribution, means, and standard deviation were conducted to examine the subject's general characteristics, frequency, importance, and difficulties of task performance. Result: The Job description of geriatric care managers revealed 10 duties, 34 tasks, and 105 task elements. On all ten duties, the average performance frequency, importance, and difficulty was 2.55, 2.21 and 2.43 respectively. Conclusion: The job description of geriatric care managers includes duty, task, and task elements and the definition of a completed job. Thus we recommend a data based trial to confirm and validate the information gathered.

• Journal of The Korean Society of Integrative Medicine
• /
• v.8 no.4
• /
• pp.59-66
• /
• 2020

Purpose : This study aimed to present normative data for older Korean adults completing the Yonsei dual task cognitive screening test (Y-DuCog) and identify changes in cognitive function on the Montreal Cognitive Assessment - Korean (MoCA-K) with age. Methods : From May 2019 to August 2019, 195 healthy adults aged ≥60 years participated in this study. All participants completed the Y-DuCog to assess their dual-task performance and the MoCA-K to assess their cognitive function. Participants were divided into three groups based on their age: 60~69 years, 70~79 years, and ≥80 years. Results : The results of the Y-DuCog showed that dual-task performance time, effect, and correct response rate decreased significantly with age (p<.001). Scores from the three groups showed differences on all items (p<.001). Cognitive function on the MoCA-K also decreased significantly with age (mean score ± standard deviation [SD]; 27.33 ± 2.61 in subjects aged 60~69 years; 24.82 ± 3.20 in subjects aged 70~79 years; and 22.10 ± 4.91 in subjects aged ≥80 years; p<.001). Conclusions : Occupational therapists should be aware of the decline in cognitive function and dual-task performance time, effect, and correct response rate in older adults and consider interventions to treat this decline. Further studies are needed with larger groups of participants to examine factors, such as sex and education, that may impact dual-task performance and cognitive function.

• Journal of the Korean Society of Physical Medicine
• /
• v.12 no.1
• /
• pp.121-129
• /
• 2017

PURPOSE: This study was conducted to determine the effect of dual-task training (based on the International Classification of Functioning, Disability, and Health; ICF) on walking ability and self-efficacy in individuals with chronic stroke. METHODS: 22 chronic stroke patients participated in this study. Participants were randomly allocated into either the single-task group (n=11) or the dual-task group (n=11). Both groups had physical training three a week for 4 weeks, and at a three-week follow-up. Outcome measures included the 10m walking test (10MWT), figure of 8 walk test (F8WT), dynamic gait index (DGI), and Self-efficacy scale. All data were analyzed using SPSS 18.0 for Windows. Between-group and within-group comparison were analyzed by using the Mann-Whitney U test and Wilcoxon singed-rank test respectively. RESULTS: In the dual-task group, the 10MWT, time and steps of F8WT, DGI, and self-efficacy showed significant differences between pre- and post-test (p<.05). The Changes between the pre- and post-test values of 10MWT (p<.05), DGI (p<.05), and self-efficacy scale (p<.05) showed significant differences between the dual-task group and single-task group. CONCLUSION: Participants reported improved walking ability and self-efficacy, suggesting that dual-task training holds promise in the rehabilitation of walking in chronic stroke patients. This study showed that ICF-based on a dual-task protocol contiributes to motor learning after chronic stroke.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.1
• /
• pp.27-38
• /
• 2023

In this paper, we propose a conversational AI agent based on continual learning that can continuously learn and grow with new data over time. A continual learning-based conversational AI agent consists of three main components: Task manager, User attribute extraction, and Auto-growing knowledge graph. When a task manager finds new data during a conversation with a user, it creates a new task with previously learned knowledge. The user attribute extraction model extracts the user's characteristics from the new task, and the auto-growing knowledge graph continuously learns the new external knowledge. Unlike the existing conversational AI agents that learned based on a limited dataset, our proposed method enables conversations based on continuous user attribute learning and knowledge learning. A conversational AI agent with continual learning technology can respond personally as conversations with users accumulate. And it can respond to new knowledge continuously. This paper validate the possibility of our proposed method through experiments on performance changes in dialogue generation models over time.

• Proceedings of the Korea Society for Simulation Conference
• /
• 1994.10a
• /
• pp.32-32
• /
• 1994

In this example, a message passing based multicomputer system with general interdonnedtion network is considered. After multicomputer systems are developed with morm-hole routing network, topologies of interconecting network are not major considertion for process management and resource sharing. Tehre is an independeent operating system kernel oneach node. It communicates with other kernels using message passingmechanism. Based on this architecture, the problem is how mech does performance degradation will occur in the case of processor sharing on multicomputer systems. Processor sharing between application programs is veryimprotant decision on system performance. In almost cases, application programs running on massively parallel computer systems are not so much user-interactive. Thus, the main performance index is system throughput. Each application program has various communication patterns. and the sharing of processors causes serious performance degradation in hte worst case such that one processor is shared by two processes and another processes are waiting the messages from those processes. As a result, considering this problem is improtant since it gives the reason whether the system allows processor sharingor not. Input data has many parameters in this simulation . It contains the number of threads per task , communication patterns between threads, data generation and also defects in random inupt data. Many parallel aplication programs has its specific communication patterns, and there are computation and communication phases. Therefore, this phase informatin cannot be obtained random input data. If we get trace data from some real applications. we can simulate the problem more realistic . On the other hand, simualtion results will be waseteful unless sufficient trace data with varisous communication patterns is gathered. In this project , random input data are used for simulation . Only controllable data are the number of threads of each task and mapping strategy. First, each task runs independently. After that , each task shres one and more processors with other tasks. As more processors are shared , there will be performance degradation . Form this degradation rate , we can know the overhead of processor sharing . Process scheduling policy can affects the results of simulation . For process scheduling, priority queue and FIFO queue are implemented to support round-robin scheduling and priority scheduling.

• Journal of Korean language education
• /
• v.29 no.3
• /
• pp.255-292
• /
• 2018

Most grading scales of Korean language proficiency tests are based on existing grading scales that are not empirically verified. The purpose of this study is to develop an empirically verified scale descriptor. The 'Performance data-driven approach' that is suggested by Fulcher (1987) was used to develop the detailed description of characteristics for each level of performance. This study is focused on the functional phase of speech samples analysis (coding data) to create explanatory categories of discourse skills into which individual observations of speech phenomena can be scored. The speech samples that were collected through this study demonstrated stages of speech that can be a foundation of a grading scale. The data used in the study was collected from 23 native speakers of Korean. Speech samples were recorded from simulated speaking tests using the 'REFUSAL' task, and transcribed for analysis. The transcript was analyzed using discourse analysis. The result showed that the 'REFUSAL' task needs to go through four functional phases in actual communication. Furthermore, this study found specific and detailed explanatory categories of discourse competence based on the actual native speaker's speech data. Such findings are expected to contribute to the development of more valid and reliable speaking assessment.

• Smart Structures and Systems
• /
• v.31 no.4
• /
• pp.383-392
• /
• 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%.

• Journal of Information Technology Applications and Management
• /
• v.29 no.5
• /
• pp.13-25
• /
• 2022

The purpose of this study is to explore the factors that influence the purchase of AI speakers in China. We integrate the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-technology fit (TTF) model into one model and put forward assumptions. According to the characteristics of AI speakers, we selected 6 independent variables, such as Performance Expectation, Effort Expectation, Social Influence, Facilitating Conditions, Task and Technology-characteristics. The final impact on purchase behavior is evaluated through Task-technology fit and purchase intention. After counting 478 samples, through SPSS22.0 and AMOS analysis, hypotheses have been proved by strong experimental data, except facilitating conditions. These results also imply that improving the technical level of AI speakers and enhancing consumers' purchasing intention are the central line of marketing. Based on this, we put forward several suggestions to marketers, including strengthening the research and development of AI speaker technology, and building a circle of friends of AI speakers.