• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1679-1691
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• 2016

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.665-676
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• 2001

The most expensive spatial operation in patial database in a spatial join which computes a combined table of which tuple consists of two tuples of the two tables satisgying 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 requiremetns of interactive users. It is usually appropriate to use parallel processing to improve the performance of spatial join processing. in spatial database the fixed grids which consist of the regularly partitioned cells can be employed the previous works on the spatial joins have not studied the parallel processing of spatial joins using fixed grids. This paper has presented an analytical cost model that estimates the comparative performance of a parallel spatial join algorithm based on the fixed grids in terms of the number of MBR comparisons. disk accesses, and message passing, Several experiments on the synthetic and real datasets show that the proposed analytical model is very accurate. This most model is also expected to used for implementing a very important DBMS component, Called the query processing optimizer.

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.2
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• pp.11-22
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• 1989

The problem of scheduling n-jobs on m-uniform parallel machines is considered, in which each job has a release time, a deadline, and a processing requirement. The job processing requirements are allocated to the machines so that the maximum of the load differences between time periods is minimized. Based on Federgruen's maximum flow network model to find a feasible schedule, a polynomially bounded algorithm is developed. An example to show the effectiveness of our algorithm is presented.

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.13-22
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• 2017

This research proposes and applies a pipelining parallel processing technique to enhance the speed of visualizing the results of real-time simulations. Generally, a simulation with real-time visualization consists of three processes: executing a simulation model, transmitting simulation result, and visualizing simulation result. If we have these processes in serial, the latency from simulation to visualization will be very long, which degrades the speed of visualization of data from real-time simulation. Thus, the main purpose of this research is maximizing performance by adapting pipelining parallel processing technique to the real-time simulation visualization. Also we show that performance is improved by adding multi-threading technique to each process. This paper proposes a theoretical performance model and simulation results of the techniques and then we applied this to an air combat simulation model as a case study. As the result, it shows that the performance is greatly enhanced than the original model's execution time.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.

• Journal of the Korea Society of Computer and Information
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• v.19 no.7
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• pp.9-16
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• 2014

Since the haze removal algorithm using dark channel prior was introduced, many researches for improving processing speed have been addressed even if it presented impressive results. Remarkable one is using median dark channel prior. Although it has been considered as a very attactive method, processing speed is as low as ever. So, a parallel memory model which is suitable for SIMD architecture processing haze removal on high-definition images in high-speed is introduced in this paper. The proposed parallel memory model allows to access n pixels simultaneously. It is also support stride 3, 5, 7, and 11 in order to execute convolution mask operations, e.g., median filter. The proposed parallel memory model can therefore support enough data bandwidth to process the algorithm using median dark channel prior in high-speed.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.4
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• pp.165-172
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• 2023

Heterogeneous computing is a technology that utilizes different types of processors to perform parallel processing. It maximizes task processing and energy efficiency by leveraging various computing resources such as CPUs, GPUs, and FPGAs. On the other hand, edge computing has developed with IoT and 5G technologies. It is a distributed computing that utilizes computing resources close to clients, thereby offloading the central server. It has evolved to intelligent edge computing combined with artificial intelligence. Intelligent edge computing enables total data processing, such as context awareness, prediction, control, and simple processing for the data collected on the edge. If heterogeneous computing can be successfully applied in the edge, it is expected to maximize job processing efficiency while minimizing dependence on the central server. In this paper, experiments were conducted to verify the feasibility of various parallel programming models on high-end and low-end edge devices by using benchmark applications. We analyzed the performance of five parallel programming models on the Raspberry Pi 4 and Jetson Orin Nano as low-end and high-end devices, respectively. In the experiment, OpenACC showed the best performance on the low-end edge device and OpenSYCL on the high-end device due to the stability and optimization of system libraries.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2932-2945
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• 1999

As the Internet and Java technology have been growing up, parallel processing approach to utilize those idle resources connected to the Internet has become quite attractive. In this paper, JICE(Java Internet Computing Environment) was implemented as a parallel computing platform based on the Internet using multithreading and RMI mechanisms provided by Java. The basic model of JICE is constructed as three components, such as a client, a set of workers, and a broker. A worker communicates with other workers via a globally shared memory system. It provides users with master-slave programming model and a collection of library functions. The basic model of JICE is also extended as a multimanaging system. This multimanaging system is evaluated by analysis to show its effectiveness. According to numerical analysis and experiments with several benchmarks, it is shown that the performance of basic model depends on the shared memory reference ratio and user's library is a quite promising.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.4
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• pp.125-135
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• 1993

An Unrelated Parallel Processing with Weighted jobs and Setup times scheduling prolem is studied. We consider a parallel processing in which a group of processors(machines) perform a single operation on jobs of a number of different job types. The processing time of each job depends on both the job and the machine, and each job has a weight. In addition each machine requires significant setup time between processing jobs of different job types. The performance measure is to minimize total weighted flow time in order to meet the job importance and to minimize in-process inventory. We present a 0-1 Mixed Integer Programming model as an optimizing algorithm. We also present a simple heuristic algorithm. Computational results for the optimal and the heuristic algorithm are reported and the results show that the simple heuristic is quite effective and efficient.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.959-968
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• 1999

several papers have reported that parallel heuristics or hybrid approaches combining several heuristics can get better results. However, the parallelization and hybridization of any search methods on the single CPU type computer need enormous computation time. that case, we need more elegant combination method. For this purpose, we propose Fast Model of Parallel Heuristics(FMPH). FMPH is based on the island model of parallel genetic algorithms and takes local search to the elite solution obtained form each island(sub group). In this paper we introduce how can we adapt FMPH to the job-shop scheduling problem notorious as the most difficult NP-hard problem and report the excellent results of several famous benchmark problems.