• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.3
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• pp.31-37
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• 2003

With the paradigm shifting from the principal of manufacturing efficiency to business globalism and rapid adaptation to its environments, more and more enterprises are being virtually organized as manufacturing network of different units in web. The formation of these enterprise called as Virtual Enterprise(VE) is becoming a growing trend as enterprises concentrating on core competence and economic benefit. 13us paper proposes multi-agent based task assignment system for VE, which attempts to address the selection of individually managed partners and the task assignment to them A case example is presented to illustrate how the proposed system can assign the task to partners.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.3
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• pp.264-276
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• 2000

Recent advances of technologies make easy sharing various information and utilizing system resources on the Internet. Especially, code migration using applets of Java supports the distribution of programs on the web environment, and also browsers executing the applets guarantee the reliability of a migrated codes. In this paper, we describe the design and implementation of a web-based parallel processing system, which distributes migratable codes of a large job, makes the distributed codes to execute in parallel, and controls and gathers the results of each execution. The hosts participate in the computation reside on the Internet, spreaded out geographically, and the heterogeneity and the variability among them are severe. Thus, task allocation considering the performance differences and the adaptability to the severe variability are necessary. We present an adaptive task allocation algorithm applied to our system and the performance evaluation.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.347-360
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• 2001

The most expensive spatial operation in spatial databases is a spatial join which computes a combined table of which tuple consists of two tuples of the two tables satisfying a spatial predicate. Although the execution time of sequential processing of a spatial join has been so far considerably improved, the response time is not tolerable because of not meeting the requirements of interactive users. It is usually appropriate to use parallel processing to improve the performance of spatial join processing. However, as the number of processors increases, the efficiency of each processor decreases rapidly because of the disk bottleneck and the overhead of message passing. This paper proposes the method of task allocation to soften the disk bottleneck caused by accessing the shared disk at the same time, and to minimize message passing among processors. In order to evaluate the performance of the proposed method in terms of the number of disk accesses and message passing, we conduct experiments on the two kinds of parallel spatial join algorithms. The experimental tests on the MIMD parallel machine with shared disks show that the proposed semi-dynamic task allocation method outperforms the static and dynamic task allocation methods.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.699-708
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• 2006

Bus splitting technique reduces bus energy by placing modules with frequent communications closely and using necessary bus segments in communications. But, previous bus splitting techniques can not be used in MPSoC platform, because it uses cache coherency protocol and all processors should be able to see the bus transactions. In this paper, we propose a bus splitting technique for MPSoC platform to reduce bus energy. The proposed technique divides a bus into several bus segments, some for private memory and others for shared memory. So, it minimizes the bus energy consumed in private memory accesses without producing cache coherency problem. We also propose a task allocation technique considering cache coherency protocol. It allocates tasks into processors according to the numbers of bus transactions and cache coherence protocol, and reduces the bus energy consumption during shared memory references. The experimental results from simulations say the bus splitting technique reduces maximal 83% of the bus energy consumption by private memory accesses. Also they show the task allocation technique reduces maximal 30% of bus energy consumed in shared memory references. We can expect the bus splitting technique and the task allocation technique can be used in multiprocessor platforms to reduce bus energy without interference with cache coherency protocol.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.50
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• pp.23-32
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• 1999

Reliability and maintainability allocation in the analysis of the system's design, with the objective of planning and installing the individual components in such a way that the system performance is achieved. This paper has been made to solve an important task in reliability management of manufacturing systems within the general objective being to increase productivity while maintaining costs low. Thus, the purpose of this paper is to provide an analytical approach to determine an optimal reliability and maintainability allocation, trading off among system performance and parts investment costs. Two important considerations will be addressed in this regard : (ⅰ) determine the reliability and maintainability allocation of parts which maximizes a given production index, having fixed the total cost of investments ; and (ⅱ) determine the reliability and maintainability allocation which minimizes the total cost of investments, having fixed a minimum acceptable level of productivity. The procedure proposed in this paper is able to provide to managers and designers useful indications on the reliability and maintainability characteristics of parts in series -parallel systems. And this heuristic model is a decision support tool for contractors who are involved in large scale design projects such as ship and aircraft design. Numerical examples prove that an approximate expression of the average throughput rate is sufficiently accurate to be used in a numerical optimization method.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• Journal of the Ergonomics Society of Korea
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• v.32 no.1
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• pp.59-68
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• 2013

Objective: The purpose of this paper is to introduce a task complexity model combining task design aspects and complexity dimensions and to explain an approach to identifying and organizing task complexity factors based on the model. Background: Task complexity is a critical concept in describing and predicting human performance in complex systems such as nuclear power plants(NPPs). In order to understand the nature of task complexity, task complexity factors need to be identified and organized in a systematic manner. Although several methods have been suggested for identifying and organizing task complexity factors, it is rare to find an analytical approach based on a theoretically sound model. Method: This study regarded a task as a system to be designed. Three levels of design ion, which are functional, behavioral, and structural level of a task, characterize the design aspects of a task. The behavioral aspect is further classified into five cognitive processing activity types(information collection, information analysis, decision and action selection, action implementation, and action feedback). The complexity dimensions describe a task complexity from different perspectives that are size, variety, and order/organization. Combining the design aspects and complexity dimensions of a task, we developed a model from which meaningful task complexity factors can be identified and organized in an analytic way. Results: A model consisting of two facets, each of which is respectively concerned with design aspects and complexity dimensions, were proposed. Additionally, twenty-one task complexity factors were identified and organized based on the model. Conclusion: The model and approach introduced in this paper can be effectively used for examining human performance and human-system interface design issues in NPPs. Application: The model and approach introduced in this paper could be used for several human factors problems, including task allocation and design of information aiding, in NPPs and extended to other types of complex systems such as air traffic control systems as well.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.987-992
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• 2016

This study introduces an example environment where wireless devices are mobile, devices use dynamic voltage scaling, devices and tasks are heterogeneous, tasks have deadline, and the computation and communication power is dynamically changed for energy saving. For this type of environment, the efficient system-level energy management and resource management for task completion can be an essential part of the operation and design of such systems. Therefore, the resources are assigned to tasks and the tasks may be scheduled to maximize a goal which is to minimize energy usage while trying to complete as many tasks as possible by their deadlines. This paper also introduces mobility of nodes and variable transmission power for communication which complicates the resource management/task scheduling problem further.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.337-345
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• 2009

Deregulation in power industry has made the reactive power ancillary service management a critical task to power system operators from both technical and economic perspectives. Reactive power management in power systems is a complex combinatorial optimization problem involving nonlinear functions with multiple local minima and nonlinear constraints. This paper proposes a practical market-based reactive power ancillary service management scheme to tackle the challenge. In this paper a new model for voltage security and reactive power management is presented. The proposed model minimizes reactive support cost as an economic aspect and insures the voltage security as a technical constraint. For modeling validation study, two optimization algorithm, a genetic algorithm (GA) and particle swarm optimization (PSO) method are used to solve the problem of optimum allocation of reactive power in power systems under open market environment and the results are compared. As a case study, the IEEE-30 bus power system is used. Results show that the algorithm is well competent for optimal allocation of reactive power under practical constraints and price based conditions.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.155-160
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• 2011

In distributed source localization where sensors transmit measurements to a fusion node, we address the sensor selection problem where the goal is to find the best set of sensors that maximizes localization accuracy when quantization of sensor measurements is taken into account. Since sensor selection depends heavily upon rate assigned to each sensor, joint optimization of rate allocation and sensor selection is required to achieve the best solution. We show that this task could be accomplished by solving the problem of allocating rates to each sensor so as to minimize the error in estimating the position of a source. Then we solve this rate allocation problem by using the generalized BFOS algorithm. Our experiments demonstrate that the best set of sensors obtained from the proposed sensor selection algorithm leads to significant improvements in localization performance with respect to the set of sensors determined from a sensor selection process based on unquantized measurements.