• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.44-51
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• 1999

This paper presents a massively parallel computational model for the efficient integration of speech and natural language understanding. The phoneme model is based on continuous Hidden Markov Model with context dependent phonemes, and the language model is based on a knowledge base approach. To construct the knowledge base, we adopt a hierarchically-structured semantic network and a memory-based parsing technique that employs parallel marker-passing as an inference mechanism. Our parallel speech recognition algorithm is implemented in a multi-Transputer system using distributed-memory MIMD multiprocessors. Experimental results show that the parallel speech recognition system performs better in recognition accuracy than a word network-based speech recognition system. The recognition accuracy is further improved by applying code-phoneme statistics. Besides, speedup experiments demonstrate the possibility of constructing a realtime parallel speech recognition system.

• Atmosphere
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• v.22 no.1
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• pp.109-115
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• 2012

We parallelized the CFD_NIMR model, which is a numerical meteorological model, for best performance on both of distributed and shared memory parallel computers. This hybrid parallelization uses MPI (Message Passing Interface) to apply horizontal 2-dimensional sub-domain out of the 3-dimensional computing domain for distributed memory system, as well as uses OpenMP (Open Multi-Processing) to apply vertical 1-dimensional sub-domain for utilizing advantage of shared memory structure. We validated the parallel model with the original sequential model, and the parallel CFD_NIMR model shows efficient speedup on the distributed and shared memory system.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.570-578
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• 2021

Recently, in the field of engineering and scientific and technical calculations, problems of mathematical modeling, real-time problems, there has been a tendency towards rejection of sequential solutions for single-processor computers. Almost all modern application packages created in the above areas are focused on a parallel or distributed computing environment. This is primarily due to the ever-increasing requirements for the reliability of the results obtained and the accuracy of calculations, and hence the multiply increasing volumes of processed data [2,17,41]. In addition, new methods and algorithms for solving problems appear, the implementation of which on single-processor systems would be simply impossible due to increased requirements for the performance of the computing system. The ubiquity of various types of parallel systems also plays a positive role in this process. Simultaneously with the growing demand for parallel programs and the proliferation of multiprocessor, multicore and cluster technologies, the development of parallel programs is becoming more and more urgent, since program users want to make the most of the capabilities of their modern computing equipment[14,39]. The high complexity of the development of parallel programs, which often does not allow the efficient use of the capabilities of high-performance computers, is a generally accepted fact[23,31].

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1778-1790
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• 2018

Based on the requirements and characteristics of multi-zone autonomous decision-making in modern power system, fully distributed computing methods are needed to optimize the economic dispatch (ED) problem coordination of multi-regional power system on the basis of constructing decomposition and interaction mechanism. In this paper, four fully distributed methods based on alternating direction method of multipliers (ADMM) are used for solving the ED problem in distributed manner. By duplicating variables, the 2-block classical ADMM can be directly used to solve ED problem fully distributed. The second method is employing ADMM to solve the dual problem of ED in fully distributed manner. N-block methods based on ADMM including Alternating Direction Method with Gaussian back substitution (ADM_G) and Exchange ADMM (E_ADMM) are employed also. These two methods all can solve ED problem in distributed manner. However, the former one cannot be carried out in parallel. In this paper, four fully distributed methods solve the ED problem in distributed collaborative manner. And we also discussed the difference of four algorithms from the aspects of algorithm convergence, calculation speed and parameter change. Some simulation results are reported to test the performance of these distributed algorithms in serial and parallel.

• Journal of the Korea Society for Simulation
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• v.5 no.2
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• pp.13-24
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• 1996

Workflow is an ordered sequence of interdependent component data activities each of which can be executed on an integrated information system by accessing a remote information system. In our previous research [4], we proposed a distributed CIM Workflow system which consists of a workflow execution model called DAF-Net and an agent-based information systems called AIMIS. Given a component data activity, there needs an interaction protocol among agents which allocates the component data activity to a relevant information systems exist. The objective of this research is to propose and test two protocols: ARR(Asynchronous Request and Response)protocol and NCL(Negotiation with Case based Learning) protocol. To test the effectiveness of the protocols, we applied the PVM(Parallel Virtual Machine) software to simulate the distributed CIM Workflow system. PVM provides a distributed computing environment in which users can run different software processes in different computers while allowing communication among the processes.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.194-199
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• 2019

As the performance of the system increases, more parallelized data is being processed than single processing of data. Today's cpu structure has been developed to leverage multicore, and hence data processing methods are being developed to enable parallel processing. In recent years desktop cpu has increased multicore, data is growing exponentially, and there is also a growing need for data processing as artificial intelligence develops. This neural network of artificial intelligence consists of a matrix, making it advantageous for parallel processing. This paper aims to speed up the processing of the system by using raspberrypi to implement the cluster building and parallel processing system against the backdrop of the foregoing discussion. Raspberrypi is a credit card-sized single computer made by the raspberrypi Foundation in England, developed for education in schools and developing countries. It is cheap and easy to get the information you need because many people use it. Distributed processing systems should be supported by programs that connected multiple computers in parallel and operate on a built-in system. RaspberryPi is connected to switchhub, each connected raspberrypi communicates using the internal network, and internally implements parallel processing using the Message Passing Interface (MPI). Parallel processing programs can be programmed in python and can also use C or Fortran. The system was tested for parallel processing as a result of multiplying the two-dimensional arrangement of 10000 size by 0.1. Tests have shown a reduction in computational time and that parallelism can be reduced to the maximum number of cores in the system. The systems in this paper are manufactured on a Linux-based single computer and are thought to require testing on systems in different environments.

• JSTS:Journal of Semiconductor Technology and Science
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• v.10 no.1
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• pp.49-54
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• 2010

This paper describes a fast circuit simulation technique using the latency insertion method (LIM) with a parallel and distributed leapfrog algorithm. The numerical simulation results on the PC cluster system that uses the cloud computing system are shown. As a result, it is confirmed that our method is very useful and practical.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.119-124
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• 2001

In this pattern recognition on the large volumes of remote sensing satellite images, the inference time is much increased. In the case of the remote sensing data [5] having 4 wavebands, the 778 training patterns are learned. Each land cover pattern is classified by using 159, 900 patterns including the trained patterns. For the fuzzy classification, the 778 fuzzy rules are generated. Each fuzzy rule has 4 fuzzy variables in the condition part. Therefore, high performance parallel fuzzy inference system is needed. In this paper, we propose a novel parallel fuzzy inference system on T3E parallel computer. In this, fuzzy rules are distributed and executed simultaneously. The ONE_To_ALL algorithm is used to broadcast the fuzzy input to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of the fuzzy rules, the parallel fuzzy inference algorithm extracts match parallelism and achieves a good speed factor. This system can be used in a large expert system that ha many inference variables in the condition and the consequent part.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.303-310
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• 2006

The sorting algorithms have been developed to take advantage of distributed computers. But the speedup of parallel sorting algorithms decrease rapidly with increased number of processors due to parallel processing overhead such as context switching time and inter-processor communication cost. In this paper, we propose a parallel sorting method which provides linear speedup of an optimal serial algorithm for a system with a large number of processors. This algorithm may even provide superlinear speedup for a practical system. The algorithm takes advantage of an interconnection network properties and its protocol.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.9
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• pp.1289-1295
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• 2012

An active resonance damper for a DC Distributed Power System(DPS) with parallel loads is presented. Each pulse power load in a DC DPS comprises both a resistive power load and a step-up converter. The step-up converter behave as constant power load(CPL) when tightly regulated and usually cause a negative impedance instability problem. Furthermore, when an input filter is connected to a large constant power load, the instability of DC bus voltage. In this paper, a bidirectional DC/DC converter with a reduced storage capacitor quantitatively are proposed as a active resonance damper, to mitigate the voltage instability on the bus. The validity of the proposed method was confirmed by simulation and experimental works.