• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.91-100
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes a service prediction-based job scheduling model and present its scheduling algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts the next processing time of each processing component and distributes a job to a processing component with minimum processing time. This paper implements the job scheduling model on the DEVS modeling and simulation environment and evaluates its efficiency and reliability. Empirical results, which are compared to conventional scheduling policies, show the usefulness of service prediction-based job scheduling.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.05a
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• pp.29-33
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes the service prediction-based job scheduling model and present its algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts a processing time of each processing component and distributes a job to processing component with minimum processing time. This paper implements the job scheduling model on the DEVSJAVA modeling and simulation environment and simulates with a case study to evaluate its efficiency and reliability Empirical results, which are compared to the conventional scheduling policies such as the random scheduling and the round-robin scheduling, show the usefulness of service prediction-based job scheduling.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.344-350
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• 2006

The software GPS receivers based on the SDR technology provide the ability to easily adapt the other signal processing algorithms without changing or modifying the hardware of the GPS receiver. However, it is difficult to implement the software GPS receivers using a commercial processor because of the heavy computational burden for processing the GPS signals in real-time. This paper proposes an efficient GPS signal processing scheme to reduce the computational burden for processing the GPS signals in the software GPS receiver, which uses a fundamental notion compressing the replica signals and the encoded look-up table method to generate correlation values between GPS signals and replica signals. In this paper, it is explained that the computational burden of the proposed scheme is much smaller than that of the typical GPS signal processing scheme. Finally, the processing time of the proposed scheme is compared with that of the typical scheme, and the improvement in the aspect of the computational burden is also shown.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.1-14
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• 2024

Time-series forecasting is extensively used in the actual world. Recent research has shown that Transformers with a self-attention mechanism at their core exhibit better performance when dealing with such problems. However, most of the existing Transformer models used for time series prediction use the traditional encoder-decoder architecture, which is complex and leads to low model processing efficiency, thus limiting the ability to mine deep time dependencies by increasing model depth. Secondly, the secondary computational complexity of the self-attention mechanism also increases computational overhead and reduces processing efficiency. To address these issues, the paper designs an efficient multi-layer attention-based time-series forecasting model. This model has the following characteristics: (i) It abandons the traditional encoder-decoder based Transformer architecture and constructs a time series prediction model based on multi-layer attention mechanism, improving the model's ability to mine deep time dependencies. (ii) A cross attention module based on cross attention mechanism was designed to enhance information exchange between historical and predictive sequences. (iii) Applying a recently proposed sparse attention mechanism to our model reduces computational overhead and improves processing efficiency. Experiments on multiple datasets have shown that our model can significantly increase the performance of current advanced Transformer methods in time series forecasting, including LogTrans, Reformer, and Informer.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.231-238
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• 2017

Detecting small infrared targets from the low-SCR images at a long distance is very hard. The previous Local Contrast Method (LCM) algorithm based on the human visual system shows a superior performance of detecting small targets by a background suppression technique through local contrast measure. However, its slow processing speed due to the heavy multi-scale processing overhead is not suitable to a variety of real-time applications. This paper presents a lightweight real-time small target detection algorithm, called by the Improved Selective Local Contrast Method (ISLCM), to reduce the scale-invariant computational overhead. The proposed ISLCM applies the improved local contrast measure to the predicted selective region so that it may have a comparable detection performance as the previous LCM while guaranteeing low scale-invariant computational load by exploiting both adaptive scale estimation and small target feature feasibility. Experimental results show that the proposed algorithm can reduce its computational overhead considerably while maintaining its detection performance compared with the previous LCM.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.343-344
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• 2012

With the increase of sensitive data and their secure transmission and storage, the use of encryption techniques has become widespread. The performance of encoding majorly depends on the computational time, so a system with less computational time suits more appropriate as compared to its contrary part. Double Random Phase Encoding (DRPE) is an algorithm with many sub functions which consumes more time when executed serially; the computation time can be significantly reduced by implementing important functions in a parallel fashion on Graphics Processing Unit (GPU). Computing convolution using Fast Fourier transform in DRPE is the most important part of the algorithm and it is shown in the paper that by performing this portion in GPU reduced the execution time of the process by substantial amount and can be compared with MATALB for performance analysis. NVIDIA graphic card GeForce 310 is used with CUDA C as a programming language.

• Korean Journal of Computational Design and Engineering
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• v.9 no.4
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• pp.294-305
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• 2004

Highly detailed geometric models are rapidly becoming commonplace in computer graphics and other applications. These complex models, which is often represented as complex1 triangle meshes, mainly suffer from the vast memory requirement for real-time manipulation of arbitrary geometric shapes without loss of data. Various techniques have been devised to challenge these problems in views of geometric processing, not a representation scheme. This paper proposes the new mesh structure for the compact representation and the efficient handling of the highly complex models. To verify the compactness and the efficiency, the memory requirement of our representation is first investigated and compared with other existing representations. And then we analyze the time complexity of our data structure by the most critical operation, that is, the enumeration of the so-called one-ring neighborhood of a vertex. Finally, we evaluate some elementary modeling functions such as mesh smoothing, simplification, and subdivision, which is to demonstrate the effectiveness and robustness of our mesh structure in the context of the geometric modeling and processing.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.327-332
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• 2007

An engineering design problem involving Multi-Disciplinary Analysis(MDA) generally requires a large amounts of CPU time for the entire design process, and therefore Multiple Processing System (MPS) are essential to reduce the completion time. However, when applying conventional parallel processing techniques, all of the CAE S/W required for the MDA should be installed on all the servers making up NIPS because of characteristic of MDA and it would be a great expense in CAE S/W licenses. To solve this problem, we propose a Weight-based Multiqueue Load Balancing algorithm for a heterogeneous MPS where performance of servers and CAE S/W installed on each server are different of each other. To validate the performance, a computational experiments comparing the First Come First Serve algorithm and our proposed algorithm was accomplished.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.73-80
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• 2018

Certification revocation list(CRL) is needed for excluding compromised, faulty, illegitimate vehicle nodes and preventing the use of compromised cryptographic materials in vehicular communications. It should be distributed to vehicles resource-efficiently and CRL computational load of vehicles should not impact on life-critical applications with delay sensitive nature such as the pre-crash sensing that affords under 50msec latency. However, in the existing scheme, when a vehicle receives CRL, the vehicle calculates linkage values from linkage seeds, which results in heavy computational load. This paper proposes, a new CRL distribution scheme is proposed, which minimizes the time for CRL processing of vehicles. In the proposed scheme, the linkage value calculation procedure is performed by road-side unit(RSU) instead of the vehicle, and then the extracted linkage values are relayed to the vehicle transparently. The simulation results show that the proposed scheme reduces the CRL computational load dramatically, which would minimize impact on life-critical applications' operations with low latency.

• Management Science and Financial Engineering
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• v.21 no.1
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• pp.1-9
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• 2015

We consider an m-machine flow shop scheduling problem to minimize the latest completion time, where processing times are uncertain. Processing time uncertainty is described through a finite set of processing time vectors. The objective is to minimize maximum deviation from optimality for all scenarios. Since this problem is known to be NP-hard, we consider it with an ordered property. We discuss optimality properties and develop a pseudo-polynomial time approach for the problem with a fixed number of machines and scenarios. Furthermore, we find two special structures for processing time uncertainty that keep the problem NP-hard, even for two machines and two scenarios. Finally, we investigate a special structure for uncertain processing times that makes the problem polynomially solvable.