• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.479-491
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• 2008

A "task network" is a type of social network that consists of experts who exchange professional help and advice regarding executing tasks. In this study, we investigate the task network used within the IS department of a national bank in Korea. We identify how this network moderates the influence of computer-mediated communication (CMC) technology on an individual s task performance. Size, density, and centrality were measured as the characteristics of a personal task networks. Size equates to the total number of colleagues who work with a specific member for a certain project. Density is the ratio of the number of actual relationships to the total number of available relationships. Centrality defines whether an individual s position is in the exact center of whole network, and is measured by betweenness centrality, meaning the position one member holds between others in a network. Our findings conclude that the conditions - the larger the size of the task network, the smaller its density and the higher its level of centrality - lead to more benefits of using CMC media. Further, this positive effect of CMC is more noticeable when it provides synchronicity.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.72-75
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• 2004

In this paper, the task assignment strategy considering communication delay and the priority of distributed tasks is proposed for the real-time network system in order to maximize the utilization of the system. For the task assignment strategy, the relationship among priority of tasks in network nodes, the calculation time of each task, and the end-to-end response time including the network delay is formulated firstly. Then, the task assignment strategy using the genetic algorithm is proposed to optimize the utilization of the system considering the LCM(Least Common Multiple) period. The effectiveness of proposed strategy is proven by the simulation for estimating the performance such as the utilization and the response time of the system in case of changing the number of tasks and the number of network nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• Design & Manufacturing
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• v.16 no.3
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• pp.1-8
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• 2022

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.742-749
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• 2020

This paper proposes a method for recognizing both phoneme and emotion using a common neural network and a multi-task training method for the common neural network. The common neural network performs the same function for both recognition tasks, which corresponds to the structure of multi-information recognition of human using a single auditory system. The multi-task training conducts a feature modeling that is commonly applicable to multiple information and provides generalized training, which enables to improve the performance by reducing an overfitting occurred in the conventional individual training for each information. A method for increasing phoneme recognition performance is also proposed that applies weight to the phoneme in the multi-task training. When using the same feature vector and neural network, it is confirmed that the proposed common neural network with multi-task training provides higher performance than the individual one trained for each task.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.601-614
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• 2006

In this paper, a study on task assignment strategies for a complex real-time network system is presented. Firstly, two task assignment strategies are proposed to improve previous strategies. The proposed strategies assign tasks with meeting end-to-end real-time constraints, and also with optimizing system utilization through period modulation of the tasks. Consequently, the strategies aim at the optimizationto optimize of system performance with while still meeting real-time constraints. The proposed task assignment strategies are devised using the genetic algorithmswith heuristic real-time constraints in the generation of new populations. The strategies are differentiated by the optimization method of the two objectives-meeting end-to-end real-time constraints and optimizing system utilization: the first one has sequential genetic algorithm routines for the objectives, and the second one has one multiple objective genetic algorithm routine to find a Pareto solution. Secondly, the performances of the proposed strategies and a well-known existing task assignment strategy using the BnB(Branch and Bound) optimization are compared with one other through some simulation tests. Through the comparison of the simulation results, the most adequate task assignment strategies are proposed for some as system requirements-: the optimization of system utilization, the maximization of running tasktasks, and the minimization of the number of network node nodesnumber for a network system.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.596-599
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• 2018

본 논문은 일정 등록을 위한 대화 시스템 개발에 대한 연구를 수행하였다. 기계는 사용자가 요구하는 일정 등록, 일정 수정 및 일정 삭제 등 다양한 목적에 따라 이에 맞는 API를 호출하게 된다. DSCT 6가 제안한 방법을 활용하여 호출되는 API의 종류에 따라 사람과 기계와의 대화를 task 라 불리는 여러 종류의 소규모 목적 대화로 분류하였다. 그 후 분류된 목적 task를 위해 Memory Network 개발에 대한 연구를 수행하였다. 첫 번째로 분류된 task에 대한 실행 결과 75%, 두 번째 task 88%, 세 번째 task 89%, 마지막 모든 task를 합쳤을 때 90%의 성능을 확인할 수 있었다.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.31-48
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• 2023

The popularization of smart devices and subsequent optimization of their sensing capacity has resulted in a novel mobile crowdsensing (MCS) pattern, which employs smart devices as sensing nodes by recruiting users to develop a sensing network for multiple-task performance. This technique has garnered much scholarly interest in terms of sensing range, cost, and integration. The MCS is prevalent in various fields, including environmental monitoring, noise monitoring, and road monitoring. A complete MCS life cycle entails task allocation, data collection, and data aggregation. Regardless, specific drawbacks remain unresolved in this study despite extensive research on this life cycle. This article mainly summarizes single-task, multi-task allocation, and space-time multi-task allocation at the task allocation stage. Meanwhile, the quality, safety, and efficiency of data collection are discussed at the data collection stage. Edge computing, which provides a novel development idea to derive data from the MCS system, is also highlighted. Furthermore, data aggregation security and quality are summarized at the data aggregation stage. The novel development of multi-modal data aggregation is also outlined following the diversity of data obtained from MCS. Overall, this article summarizes the three aspects of the MCS life cycle, analyzes the issues underlying this study, and offers developmental directions for future scholars' reference.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.511-514
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• 1998

This paper proposes efficient methods that integrate and execute local plan rules of task agents in a multi-agent environment. In these methods, each agent's plan rules are represented in a network structure, and these networks are then collected by a single task agent to build a integrated domain network, which is exploited to achieve the goal. Agent problem solving by using the domain network enables a concurrent execution of plan rules that are sequential in nature.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.671-674
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• 2004

We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.