• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1000-1010
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• 2019

Image semantic segmentation and dehazing are key tasks in the computer vision. In recent years, researches in both tasks have achieved substantial improvements in performance with the development of Convolutional Neural Network (CNN). However, most of the previous works for semantic segmentation assume the images are captured in clear weather and show degraded performance under hazy images with low contrast and faded color. Meanwhile, dehazing aims to recover clear image given observed hazy image, which is an ill-posed problem and can be alleviated with additional information about the image. In this work, we propose a deep multi-task network for simultaneous semantic segmentation and dehazing. The proposed network takes single haze image as input and predicts dense semantic segmentation map and clear image. The visual information getting refined during the dehazing process can help the recognition task of semantic segmentation. On the other hand, semantic features obtained during the semantic segmentation process can provide cues for color priors for objects, which can help dehazing process. Experimental results demonstrate the effectiveness of the proposed multi-task approach, showing improved performance compared to the separate networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1661-1678
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• 2016

Clustering is one of the key technologies in vehicular networks. Constructing and maintaining stable clusters is a challenging task in high mobility environments. DWCM (Distributed and Weighted Clustering based on Mobility Metrics) is proposed in this paper based on the d-hop dominating set of the network. Each vehicle is assigned a priority that describes the cluster relationship. The cluster structure is determined according to the d-hop dominating set, where the vehicles in the d-hop dominating set act as the cluster head nodes. In addition, cluster maintenance handles the cluster structure changes caused by node mobility. The rationality of the proposed algorithm is proven. Simulation results in the NS-2 and VanetMobiSim integrated environment demonstrate the performance advantages.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2646-2659
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• 2017

Equipped with the advantages of flexible access control and fine-grained authentication, attribute based signcryption is diffusely designed for security preservation in many scenarios. However, realizing efficient key evolution and reducing the calculation costs are two challenges which should be given full consideration in attribute based cryptosystem. In this paper, we present a key-policy attribute based signcryption scheme (KP-ABSC) with delegated computation and efficient key updating. In our scheme, an access structure is embedded into user's private key, while ciphertexts corresponds a target attribute set. Only the two are matched can a user decrypt and verify the ciphertexts. When the access privileges have to be altered or key exposure happens, the system will evolve into the next time slice to preserve the forward security. What's more, data receivers can delegate most of the de-signcryption task to data server, which can reduce the calculation on client's side. By performance analysis, our scheme is shown to be secure and more efficient, which makes it a promising method for data protection in data outsourcing systems.

• Journal of Korean Academy of Nursing Administration
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• v.11 no.1
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• pp.1-12
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• 2005

Purpose: The purpose was to analyze the effects of internal marketing activity factors on nurse's turnover intention, nursing task performance and nursing productivity. Methods: The subjects were 355 nurses who were working at the 3 universities hospital over 1 year. The instruments were used of internal marketing activity factors(Lee, 2001), turnover intension(Lee, 1995), nursing task performance(Park, 1988) and nursing productivity(McNeese-Smith, 1996). Results: The mean score of internal marketing activity factors was 2.79, education and training 2.97, individualization 2.93, communication 2.87, promotion 2.76, work environment 2.63, reward system 2.62, and management vision for employee 2.61. The turnover intention was 3.12, nursing task performance 3.49, and nursing productivity 3.38. The internal marketing activity factors were negatively correlated with turnover intention(r=-0.37, p<0.0001), and positively correlated with nursing task performance(r=0.29, p<0.0001) and nursing productivity(r=0.30, p<0.0001). The key predictor of turnover intension was reward system, education and training, communication, and salary. They explained 35.0% of the total variance. In nursing task performance, communication, management vision for employee, salary and unit explained 26.0% of the total variance. In nursing productivity, communication, reward, education and training, salary, and position explained 24.0%. Conclusions: To increase nurse's nursing task performance and nursing productivity and to decrease turnover intention, it is necessary to concentrate on improving communication and reward system in the internal marketing activity factors. Through these activities, the effectiveness of internal marketing strategies will be enhanced and finally, nursing organizational outcome will be increased.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4103-4121
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• 2018

Quality of Service (QoS) awareness is recognized as a key point for the success of Internet of Things (IOT).Realizing the full potential of the Internet of Things requires, a real-time task scheduling algorithm must be designed to meet the QoS need. In order to schedule tasks with diverse QoS requirements in cloud environment efficiently, we propose a task scheduling strategy based on dynamic priority and load balancing (DPLB) in this paper. The dynamic priority consisted of task value density and the urgency of the task execution, the priority is increased over time to insure that each task can be implemented in time. The scheduling decision variable is composed of time attractiveness considered earliest completion time (ECT) and load brightness considered load status information which by obtain from each virtual machine by topic-based publish/subscribe mechanism. Then sorting tasks by priority and first schedule the task with highest priority to the virtual machine in feasible VMs group which satisfy the QoS requirements of task with maximal. Finally, after this patch tasks are scheduled over, the task migration manager will start work to reduce the load balancing degree.The experimental results show that, compared with the Min-Min, Max-Min, WRR, GAs, and HBB-LB algorithm, the DPLB is more effective, it reduces the Makespan, balances the load of VMs, augments the success completed ratio of tasks before deadline and raises the profit of cloud service per second.

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.17-30
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• 2007

In recent years, pair programming has become a widely used approach for development of information systems. According to a worldwide survey, 35 percent of 104 development projects reported using pair programming. However, previous studies have shown rather mixed results in terms of the effectiveness of pair programming, comparing to individual or independent programming. This paper, therefore, uses a lab setting to control some of the variables that appear to have caused conflicting results in earlier studies. Writing SQL Queries for given problem statements is selected as the task the subjects to solve. One key issue addressed is the distribution of work load among the pair programmers and the independent programmers. Another is communication among co-workers as would occur in a real-world system development environment. The results of this study indicate there is no significant difference in task performance pair programming and independent programming.

• ETRI Journal
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• v.45 no.4
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• pp.627-635
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• 2023

In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0-1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility.

• Journal of Korean Academy of Nursing
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• v.51 no.6
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• pp.758-768
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• 2021

Purpose: This study aimed to conduct a job analysis of nurse carecoordinators and to identify the frequency, importance and difficulty of each task of their job. Methods: A committee for developing a curriculum (DACUM) was formed and members of the committee defined nurse care coordinators' jobs and enumerated the duties, tasks and task elements by applying the DACUM technique. Then nurse care coordinators enrolled in the pilot project evaluated the frequency, importance and difficulty of each task. Results: From the job descriptions of nurse care coordinators, we identified 12 duties and 42 tasks. Each task comprised 1~5 task elements. Among tasks, 'assess the patient's general health status' was carried out most frequently. Nurse care coordinators perceived that 'check vital signs' and 'strengthen patient competence to promote health behaviors' were more important than all other tasks. The most difficult task was 'develop professionalism as a nurse care coordinator'. Conclusion: The nurse care coordinators' roles developed in this study will serve as the key guidelines for human resource management of care coordinators. Further, job specifications for nurse care coordinators need to be developed, which is necessary for designing education and training programs. We also need to integrate primary health care as an essential component in nursing education.

• Journal of Aerospace System Engineering
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• v.17 no.2
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• pp.78-85
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• 2023

For Multi-UAV systems, a task allocation could be a key factor to determine the capability to perform a task. In this paper, we proposed a task allocation method based on genetic algorithm for minimizing makespan and satisfying various constraints. To obtain the optimal solution of the task allocation problem, a huge calculation effort is necessary. Therefore, a genetic algorithm-based method could be an alternative to get the answer. Many types of UAVs, tasks, and constraints in real worlds are introduced and considered when tasks are assigned. The proposed method can build the task sequence of each UAV and calculate waiting time before beginning tasks related to constraints. After initial task allocation with a genetic algorithm, waiting time is added to satisfy constraints. Multiple numerical simulation results validated the performance of this mission planning method with minimized makespan.

• Steel and Composite Structures
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• v.21 no.6
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• pp.1183-1210
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• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.