• Journal of Korean Academy of Nursing Administration
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• v.13 no.2
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• pp.156-166
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• 2007

Purpose: The purpose of the present study was to examine the causal relationships among hospital nursing organizational characteristics (organizational climate, workload), individual characteristics (experience, education) and outcome variables (job satisfaction, job stress, task performance) by constructing and testing a conceptual framework. Method: Five large general hospitals located in Seoul were selected to participated. The total sample of 245 registered nurses represents a response rate of 94 percent. Data for this study was collected from January to February in 2006 by questionnaire. Path analyses with LISREL program were used to test the fit of the proposed model to the data and to examine the causal relationships among variables. Result: Both the proposed model and the modified model fit the data excellently. The model revealed relatively high explanatory power of work stress (40%), job satisfaction (46%) and task performance (27%) by predicted variables. In predicting work stress, job satisfaction and task performance, the finding of this study clearly demonstrate organizational climate might be the most important variable. Conclusion: Based on the findings of the study, it was suggested that desirable organizational climate was needed to increase the nurses' mental and physical health as well as qualified task performance.

• Journal of the military operations research society of Korea
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• v.29 no.1
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• pp.57-75
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• 2003

Policy-based networking offers a network manager the ability to manage the network in a holistic and dynamic fashion rather than force a network manager to manage the network by dealing with each device individually. Policy-based networking is focusing on users and applications instead of emphasizing devices and interfaces. An important part of the policy-based networking is to simplify the task of administration and management for different disciplines. The Defense Information System(DIS) of today are complex and heterogeneous systems. Operational needs are not a trivial task and Quality of Service(QoS) is not generally guaranteed. So, important data may be missed or congested by trivial data. Policy-based networking provide a way to support QoS and simplify the management of multiple devices deploying complex technologies. This paper suggest implementation of policy-based networking in DIS to improvement of performance, and implementation is progressed step by step. Especially this paper is focusing on the providing QoS with policy-based networking using Lightweight Directory Access Protocol(LDAP) Server.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.193-195
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• 2003

The analysis of EEG data is an important task in the domain of Brain Computer Interface (BCI). In general, this task is extremely difficult because EEG data is very noisy and contains many artifacts and consists of mixtures of several brain waves. The P300 component of the evoked potential is a relatively evident signal which has a large positive wave that occurs around 300 msec after a task-relevant stimulus. Thus automatic detection of P300 is useful in BCI. To this end, in this paper we employ a method of reference-based independent component analysis (ICA) which overcomes the ordering ambiguity in the conventional ICA. We show here. that ICA incorporating with prior knowledge is useful in the task of automatic P300 detection.

• Journal of Korean Academy of Nursing Administration
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• v.17 no.1
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• pp.106-114
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• 2011

Purpose: This study was done to examine effects of task performance style, communication ability and their interaction on problem-solving ability and nursing competency of nursing students participating in a nursing management practicum. Methods: The study was a non-equivalent control group non-synchronized design. Participants were 56 fourth year nursing students (25 in the cooperative task group and 31 in the individual task group) and data were collected from March to September 2010. Additionally, two groups were classified based on communication ability of students and four groups were classified by their task performance style and communication ability. Problem-solving ability and nursing competency were measured pre- and post-test and compared between groups. Data were analyzed using SPSS Windows 17.0 program. Results: Neither problem-solving ability and nursing competency were statistically significantly different according to task performance style. Nursing competency was statistically significantly higher in the high communication group compared to the low communication group. Problem-solving ability was significantly different among the four groups classified by task performance style and communication ability. Conclusion: Nursing educators may need to improve students' communication ability to improve nursing competency and also assign different tasks based on communication ability of nursing students to improve problem-solving ability.

• Journal of Environmental Health Sciences
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• v.37 no.1
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• pp.73-79
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• 2011

The growing interest in nanotechnology has resulted in increasing concern and a number of published environmental and workplace measurements for assessing occupational exposure to engineered nanomaterials. However, the amount of previous exposure data remains limited. Furthermore the data available was collected with extensive variation in terms of exposure measurement strategy, which limits the ability to pool the data in the future. In response, this paper reviewed several pertinent issues related to exposure measurement strategy to suggest a harmonized measurement strategy which would make exposure data more useful in the future, e.g. correlation between exposure metrics, relationship between activity and exposure, task-based or shift-based assessment, background concentration, limitation of personal exposure monitoring and other determinants of exposure/modeling. An improved sampling strategy for nanomaterial exposure assessment should be considered in order to maximize the use of the data from various real time monitoring instruments.

• ETRI Journal
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• v.42 no.6
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• pp.899-911
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• 2020

The data in tables are accurate and rich in information, which facilitates the performance of information extraction and question answering (QA) tasks. TableQA, which is based on tables, solves problems by understanding the table structure and searching for answers to questions. In this paper, we introduce both novice and intermediate Korean TableQA tasks that involve deducing the answer to a question from structured tabular data and using it to build a question answering pair. To solve Korean TableQA tasks, we use S3-NET, which has shown a good performance in machine reading comprehension (MRC), and propose a method of converting structured tabular data into a record format suitable for MRC. Our experimental results show that the proposed method outperforms a baseline in both the novice task (exact match (EM) 96.48% and F1 97.06%) and intermediate task (EM 99.30% and F1 99.55%).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1877-1891
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• 2022

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.175-180
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• 2024

This paper investigates the effect of a deep learning-based system on data labeling task productivity by individuals with developmental disabilities. It was found that interventions, particularly those using AI, significantly improved productivity compared to self-serving task. AI interventions were notably more effective than job coach-led approaches. This research underscores the positive role of AI in enhancing task efficiency for those with developmental disabilities. This study is the first to apply AI technology to the data labeling tasks of individuals with developmental disabilities and highlighting deep learning's potential in vocational training and productivity enhancement for this group.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.392-395
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• 1995

This paper presents a time efficient method for determining a sequence of locations of a mobile manipulator that facilitates tracking of continuous path in cluttered environment. Given the task trajectory in the form of octree data structure, the algorithm performs characterization of task space and subsequent multistage optimization process to determine task feasible locations of the robot. Firstly, the collision free portion of the trajectory is determined and classified according to uniqueness domains of the inverse kinematics solutions. Then by implementing the extent of task feasible subspace into an optimization criteria, a multistage optimization problem is formulated to determines the task feasible locations of the mobile manipulator. The effectiveness of the proposed method is shown through a simulation study performed for a 3-d.o.f. manipulator with generic kinematic structure.

• Korean Journal of Child Studies
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• v.30 no.1
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• pp.127-148
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• 2009

This study investigated the effects on task performance of dyad collaboration based on young children's cognitive and social ability. The 108 5-year-old subjects were assigned to a collaborative experimental group, a comparison group working individually in sorting, writing, and making a puzzle, or a control group. Data from before and after measurements on sorting and perspective taking tasks were analyzed by t-test and ANOVA. Results showed that (1) Children working in dyad collaboration obtained significantly more improvement in their performance on both tasks than those working individually. (2) Dyads composed of a child with high level social skill but low level intelligence and a child with low levels of both showed most improvement in performance on the perspective taking task.