• Title/Summary/Keyword: Distributed Parameter Systems

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Parameter Estimation of The Distributed System via Improved Block Pulse Coefficients Estimation

  • Kim, Tai-hoon;Shim, Jae-sun
    • 제어로봇시스템학회:학술대회논문집
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    • 2002.10a
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    • pp.61.6-61
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    • 2002
  • In these days, Block Pulse functions are used in a variety of fields such as the analysis and controller design of the systems. In applying the Block Pulse function technique to control and systems science, the integral operation of the Block Pulse series plays important roles. This is because differential equations are always involved in the representations of continuous-time models of dynamic systems, and differential operations are always approximated by the corresponding Block Pulse series through integration operational matrices. In order to apply the Block Pulse function technique to the problems of continuous-time dynamic systems more efficiently, it is necessary to find th...

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Design of a ParamHub for Machine Learning in a Distributed Cloud Environment

  • Su-Yeon Kim;Seok-Jae Moon
    • International Journal of Internet, Broadcasting and Communication
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    • v.16 no.2
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    • pp.161-168
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    • 2024
  • As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

Statically compensated modal approximation of a class of distributed parameters systems

  • Imai, Jun;Wada, Kiyoshi;Sagara, Setsuo
    • 제어로봇시스템학회:학술대회논문집
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    • 1995.10a
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    • pp.416-419
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    • 1995
  • A finite-dimensional approximation technique is developed for a class of spectral systems with input and output operators which are unbounded. A corresponding bounding technique on the frequency-response error is also established for control system design. Our goal is to construct an uncertainty model including a nominal plant and its error bounds so that the results from robust linear control theory can be applied to guarantee a closed loop control performance. We demonstrate by numerical example that these techniques are applicable, with a modest computational burden, to a wide class of distributed parameter system plants.

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Asymptotic Behaviro of Adaptive Systems: Convergence Analysis Without the Barbalat's Lemma

  • Hong, Keum-Shik;Hong, Yong-do
    • 제어로봇시스템학회:학술대회논문집
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    • 1994.10a
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    • pp.277-282
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    • 1994
  • Convergence of the state error e to zero in adaptive systems is shown using the uniqueness of solutions and the existence of a Lyapunov function in which the adaptation laws are constructed. Results in the paper are general, and therefore applicable to any adaptive control of a linear/nonlinear, time-varying or distributed-parameter system. Since the approach taken in the paper does not require the boundedness of the derivative of the state error e for all t .geq. 0, it is particularly useful in the adaptive control of infinite dimensional systems.

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Empirical Performance Evaluation of Communication Libraries for Multi-GPU based Distributed Deep Learning in a Container Environment

  • Choi, HyeonSeong;Kim, Youngrang;Lee, Jaehwan;Kim, Yoonhee
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.3
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    • pp.911-931
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    • 2021
  • Recently, most cloud services use Docker container environment to provide their services. However, there are no researches to evaluate the performance of communication libraries for multi-GPU based distributed deep learning in a Docker container environment. In this paper, we propose an efficient communication architecture for multi-GPU based deep learning in a Docker container environment by evaluating the performances of various communication libraries. We compare the performances of the parameter server architecture and the All-reduce architecture, which are typical distributed deep learning architectures. Further, we analyze the performances of two separate multi-GPU resource allocation policies - allocating a single GPU to each Docker container and allocating multiple GPUs to each Docker container. We also experiment with the scalability of collective communication by increasing the number of GPUs from one to four. Through experiments, we compare OpenMPI and MPICH, which are representative open source MPI libraries, and NCCL, which is NVIDIA's collective communication library for the multi-GPU setting. In the parameter server architecture, we show that using CUDA-aware OpenMPI with multi-GPU per Docker container environment reduces communication latency by up to 75%. Also, we show that using NCCL in All-reduce architecture reduces communication latency by up to 93% compared to other libraries.

Low-Complexity Design of Quantizers for Distributed Systems

  • Kim, Yoon Hak
    • Journal of information and communication convergence engineering
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    • v.16 no.3
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    • pp.142-147
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    • 2018
  • We present a practical design algorithm for quantizers at nodes in distributed systems in which each local measurement is quantized without communication between nodes and transmitted to a fusion node that conducts estimation of the parameter of interest. The benefits of vector quantization (VQ) motivate us to incorporate the VQ strategy into our design and we propose a low-complexity design technique that seeks to assign vector codewords into sets such that each codeword in the sets should be closest to its associated local codeword. In doing so, we introduce new distance metrics to measure the distance between vector codewords and local ones and construct the sets of vector codewords at each node to minimize the average distance, resulting in an efficient and independent encoding of the vector codewords. Through extensive experiments, we show that the proposed algorithm can maintain comparable performance with a substantially reduced design complexity.

Assessment of Distributed and Dynamic Potential of Photovoltaic Systems in Urban Areas (태양광 발전 시스템의 시공간적 잠재성 평가 소프트웨어 개발)

  • Choi, Yosoon
    • 한국신재생에너지학회:학술대회논문집
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    • 2011.11a
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    • pp.59.2-59.2
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    • 2011
  • This study presents a new method for coupling ArcGIS (popular GIS software) with TRaNsient SYstems Simulation (TRNSYS, reference software for researchers and engineers around the world) to use capabilities of the 4 and 5-parameter PV array performance models within the ArcGIS environment. Using the validated and industry-proven solar energy simulation models implemented in TRNSYS and other built-in ArcGIS functionalities, dynamic characteristics of distributed PV potential in terms of hourly, daily or monthly power outputs can be investigated with considerations of diverse options in selecting and mounting PV panels. In addition, the proposed method allows users to complete entire procedures in a single framework (i.e., a preliminary site survey using 3D building models, shading analyses to investigate usable rooftop areas with considerations of different sizes and shapes of buildings, dynamic energy simulation to examine the performances of various PV systems, visualization of the simulation results to understand spatially and temporally distributed patterns of PV potential). Therefore tedious tasks for data conversion among multiple softwares can be significantly reduced or eliminated. While the programming environment of TRNSYS is proprietary, the redistributable executable, simulation kernel and simulation engine of TRNSYS can be freely distributed to end-users. Therefore, GIS users who do not have a license of TRNSYS can also use the functionalities of solar energy simulation models within ArcGIS.

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On the convergence Rate Improvement of Mathematical Decomposition Technique on distributed Optimal Power Flow (수화적 분할 기법을 이요한 분산처리 최적조류계산의 수렴속도 향상에 관한 연구)

  • Hur, Don;Park, Jong-Keun;Kim, Balho-H.
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.50 no.3
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    • pp.120-130
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    • 2001
  • We present an approach to parallelizing optimal power flow that is suitable for distributed implementation and is applicable to very large interconnected power systems. This approach can be used by utilities to optimize economy interchange without disclosing details of their operating costs to competitors. Recently, it is becoming necessary to incorporate contingency constraints into the formulation, and more rapid updates of telemetered data and faster solution time are becoming important to better track changes in the system. This concern led to a research to develop an efficient algorithm for a distributed optimal power flow based on the Auxiliary Problem Principle and to study the convergence rate improvement of the distributed algorithm. The objective of this paper is to find a set of control parameters with which the Auxiliary Problem Principle (Algorithm - APP) can be best implemented in solving optimal power flow problems. We employed several IEEE Reliability Test Systems, and Korea Power System to demonstrate the alternative parameter sets.

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Low Complexity Zero-Forcing Beamforming for Distributed Massive MIMO Systems in Large Public Venues

  • Li, Haoming;Leung, Victor C.M.
    • Journal of Communications and Networks
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    • v.15 no.4
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    • pp.370-382
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    • 2013
  • Distributed massive MIMO systems, which have high bandwidth efficiency and can accommodate a tremendous amount of traffic using algorithms such as zero-forcing beam forming (ZFBF), may be deployed in large public venues with the antennas mounted under-floor. In this case the channel gain matrix H can be modeled as a multi-banded matrix, in which off-diagonal entries decay both exponentially due to heavy human penetration loss and polynomially due to free space propagation loss. To enable practical implementation of such systems, we present a multi-banded matrix inversion algorithm that substantially reduces the complexity of ZFBF by keeping the most significant entries in H and the precoding matrix W. We introduce a parameter p to control the sparsity of H and W and thus achieve the tradeoff between the computational complexity and the system throughput. The proposed algorithm includes dense and sparse precoding versions, providing quadratic and linear complexity, respectively, relative to the number of antennas. We present analysis and numerical evaluations to show that the signal-to-interference ratio (SIR) increases linearly with p in dense precoding. In sparse precoding, we demonstrate the necessity of using directional antennas by both analysis and simulations. When the directional antenna gain increases, the resulting SIR increment in sparse precoding increases linearly with p, while the SIR of dense precoding is much less sensitive to changes in p.

Frequency Analysis on Parametric Resonance of Periodically Non-stationary Systems with Distributed Parameters (주기 비안정 연속계의 파라메터공진에 관한 주파수 해석)

  • Lee, Yong-Kwan;Chechurin, S.L.
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2004.11a
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    • pp.295-299
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    • 2004
  • 본 논문에서는 주기 비안정 연속계의 해석을 위한 주파수 방법이 제안된다. 비안정시스템의 안정화를 위한 기존의 주파수 해석법을 일부 수정하여 연속계를 포함한 비안정 시스템에 적합하도록 수정하였으며, 직류모터와 동기발전기로 구성되어 있는 전기-기계 시스템에 적용하여 유용성을 보였다. 복잡한 비안정 연속계의 문제를 각 요소별 주파수 응답을 분리하고 조합하는 작업들을 통하여 쉽게 풀 수 있음을 보였다. 모터-발전기로 구성되어있는 전기-기계 시스템에서 발전기의 상호유도인덕턴스의 시간에 따른 주기적 변화와 장선(long electrical line)의 부하가 시스템의 불안정성을 야기함을 보였다.

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