• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.121-135
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• 2007

Constraint directed scheduling techniques, representing problem constraints explicitly and constructing schedules by constrained heuristic search, have been successfully applied to real world scheduling problems that require satisfying a wide variety of constraints. However, there has been little basic research on the representation and optimization of the objective value of a schedule in the constraint directed scheduling literature. In particular, the cost objective is very crucial for enterprise decision making to analyze the effects of alternative business plans not only from operational shop floor scheduling but also through strategic resource planning. This paper aims to explicitly represent and optimize the total cost of a schedule including the tardiness and inventory costs while satisfying non-relaxable constraints such as resource capacity and temporal constraints. Within the cost based scheduling framework, a cost propagation algorithm is presented to update cost information throughout temporal constraints within the same job.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.267-268
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• 2007

A dynamic range compression algorithm using Markov random field (MRF) modeling to display high dynamic range (HDR) images on low dynamic range (LDR) devices is proposed in this work. The proposed algorithm separates foreground objects from the background using the edge information, and then compresses the color differences across the edges based on the MRF modeling. By minimizing a cost function using belief propagation, the proposed algorithm can provide an effective LDR image. Simulation results show that the proposed algorithm provides good results.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.117-129
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• 2008

The cost-based scheduling work has been done in both the Operations Research (OR) and Artificial Intelligence (AI) literature. To deal with more realistic problems, AI-based heuristic scheduling approach with non-regular performance measures has been studied. However, there has been little research effort to develop a full inter-job cost propagation algorithm (CPA) for different jobs having multiple downstream and upstream activities. Without such a CPA, decision-making in scheduling heuristics relies upon local, incomplete cost information, resulting in poor schedule performance from the overall cost minimizing objective. For such a purpose, we need two types of CPAs : intra-job CPA and inter-job CPA. Whenever there is a change in cost information of an activity in a job in the process of scheduling, the intra-job CPA updates cost curves of other activities connected through temporal constraints within the same job. The inter-job CPA extends cost propagation into other jobs connected through precedence relationships. By utilizing the cost information provided by CPAs, we propose cost-based scheduling heuristics that attempt to minimize the total schedule cost. This paper develops inter-job CPAs that create and update cost curves of each activity in each search state, and propagate cost information throughout a whole network of temporal constraints. Also we propose various cost-based scheduling heuristics that attempt to minimize the total schedule cost by utilizing the cost propagation algorithm.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1542-1551
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• 2006

In the Grid Database, some replicas will have more requests from the clients than others. A fast consistency algorithm has been presented to satisfy the high demand nodes in a shorter period of time. But it has poor performance in multiple regions of high demand for forming the island of locally consistent replicas. Then, a leader election method is proposed, whereas it needs much additional cost for periodic leader election, information storage, and message passing, Also, false leader can be created. In this paper, we propose a tree-based algorithm for replica update propagation. Leader replicas with high demand are considered as the roots of trees which are interconnected. All the other replicas are sorted and considered as nodes of the trees. Once an update occurs at any replica, it need be transmitted to the leader replicas first. Every node that receives the update propagates it to its children in the tree. The update propagation is optimized by cost reduction for fixed propagation schedule. And it is also flexible for the dynamic model in which the demand conditions change with time.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.673-680
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• 2002

This paper proposes a method for detecting edge using an evolutionary programming and a momentum back-propagation algorithm. The evolutionary programming does not perform crossover operation as to consider reduction of capability of algorithm and calculation cost, but uses selection operator and mutation operator. The momentum back-propagation algorithm uses assistant to weight of learning step when weight is changed at learning step. Because learning rate o is settled as less in last back-propagation algorithm the momentum back-propagation algorithm discard the problem that learning is slow as relative reduction because change rate of weight at each learning step. The method using EP-MBP is batter than GA-BP method in both learning time and detection rate and showed the decreasing learning time and effective edge detection, in consequence.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.54-63
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• 2007

The purpose of this study is to present a new energy minimization method for image completion with hierarchical approach. The goal of image completion is to fill in missing part in a possibly large region of an image so that a visually plausible outcome is obtained. An exemplar-based Markov Random Field Modeling(MRF) is proposed in this paper. This model can deal with following problems; detection of global features, flexibility on environmental changes, reduction of computational cost, and generic extension to other related domains such as image inpainting. We use the Priority Belief Propagation(Priority-BP) which is a kind of Belief propagation(BP) algorithms for the optimization of MRF. We propose the hierarchical Priority-BP that reduces the number of nodes in MRF and to apply hierarchical propagation of messages for image completion. We show that our approach which uses hierarchical Priority-BP algorithm in image completion works well on a number of examples.

• Journal of Internet Computing and Services
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• v.4 no.6
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• pp.87-93
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• 2003

Data Replication makes distributed system and database system improved in availability and performance. But it is difficult to keep strict consistency in data update. Existing algorithms keep strict consistency, but have problems of cost a great deal and delay time. It is to introduce hybrid update propagation algorithm which permits eager update for original copy and propagates lazy update for other copies. Hybrid update propagation algorithm also permits group update in ownership. This algorithm menages replica version in order to control group update consistency. Consistency and performance is improved by combining eager update and lazy update with group ownership. Simulation shows improvement of transaction throughput and response time depending on application and execution environment.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.301-311
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• 2000

For effective part modifications which is necessary in the design process frequently, variational geometric modeling with constraint management being used in a wide. Most variational geometric modeling methods, however, manage just the constraints about sketch elements used for generation of primitives. Thus, not only constraint propagation but also re-build of various modeling operations stored in the modeling history is necessary iota part geometry modifications. Especially, re-build of high-cost Boolean operations is apt to deteriorate overall modeling efficiency abruptly. Therefore, in this paper we proposed an algorithm that can handle all geometric entities of the part directly. For this purpose, we introduced eight type geometric constraints to the various geometric calculations about all geometric entities in sweepings and Boolean operations as well as the existing constraints of the sketch elements. The algorithm has a merit of rapid part geometric modifications through only constraint propagation without rebuild of modeling operations which are necessary in the existing variational geometric modeling method.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.257-267
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• 2004

Most algorithms for handling distributed deadlock problem in the generalized request model use the diffusing computation technique where propagation of probes and backward propagation of replies carrying dependency information between processes are both required to detect deadlock Since fast deadlock detection is critical, we propose an algorithm that lets probes rather than replies carry the information required for deadlock detection. This helps to remove the backward propagation of replies and reduce the time cost for deadlock detection to almost half of that of the existing algorithms. Moreover, the proposed algorithm is extended to deal with concurrent executions, which achieves further improvement of deadlock detection time, whereas the current algorithms deal only with a single execution. We compare the performance of the proposed algorithm with that of the other algorithms through simulation experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.459-461
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• 2022

One of the big differences between Network-on-Chip (NoC) and the existing parallel processing system based on an off-chip network is that data packet routing is performed using a centralized control scheme. In such an environment, the best-effort packet routing problem becomes a real-time assignment problem in which data packet arriving time and processing time is the cost. In this paper, the Hungarian algorithm, a representative computational complexity reduction algorithm for the linear algebraic equation of the allocation problem, is implemented in the form of a hardware accelerator. As a result of logic synthesis using the TSMC 0.18um standard cell library, the area of the circuit designed through case analysis for the cost distribution is reduced by about 16% and the propagation delay of it is reduced by about 52%, compared to the circuit implementing the original operation sequence of the Hungarian algorithm.