River vector的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括價格和評價等資訊懶人包

Implementations and Applications of Machine Learning

為了解決River vector的問題，作者 這樣論述：

This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book's GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wir

eless mesh network and power systems optimization; computer vision; image and facial recognition; protein prediction; data mining; and data discovery. Numerous state-of-the-art machine learning techniques are employed (with detailed explanations), including biologically-inspired optimization (geneti

c and other evolutionary algorithms, swarm intelligence); Viola Jones face detection; Gaussian mixture modeling; support vector machines; deep convolutional neural networks with performance enhancement techniques (including network design, learning rate optimization, data augmentation, transfer lear

ning); spiking neural networks and timing dependent plasticity; frequent itemset mining; binary classification; and dynamic programming. This book provides valuable information on effective, cutting-edge techniques, and approaches for students, researchers, practitioners, and teachers in the field o

f machine learning. Saad Subair was born on the banks of the river Nile, a few kilometers away from the capital Khartoum He is a Professor of Bioinformatics and Computer Science at the College of Computer Studies, International University of Africa (IUA), Khartoum, Sudan. Prof. Subair obtained a B

Sc from the University of Khartoum, PGD, MSc (Computer Science) and PhD (Bioinformatics) from UTM, Malaysia, and an MSc in Genetics from UPM, Malaysia He is an author and/or contributing author to several books, articles, and scientific papers published in USA, Germany, Malaysia, India, and Arabia.

He has been Keynote Speaker in numerous regional conferences. Prof. Subair is a member of scientific and academic committees in multiple universities in the Gulf region including Princess Nourah bint Abdulrahman University at Riyadh, KSA. Prof Subair has trained hundreds of students in the fields of

machine learning and bioinformatics, and has supervised and/or advised several research students who have achieved further successes in the UK and USA.Christopher P. Thron is Associate Professor of Mathematics at Texas A&M University of Central Texas. Previously he spent a dozen years in the semico

nductor industry as a systems engineer for NEC, Motorola, Freescale, and NXP. Besides consulting for various companies, Dr. Thron has obtained 3 Fulbright visiting professor fellowships and 3 U.S. Air Force faculty fellowships. He has conducted workshops and seminars in mathematical/machine learning

software in several countries in Africa, as well as for the U.S. Army’s Operational Test Command. Dr. Thron’s research focuses on mathematical and statistical modeling in multiple areas of industrial mathematics, including operations research, signal processing, epidemiology, public health, and phy

sics. He holds nine U.S. patents, and has published over 30 peer-reviewed articles.

River vector進入發燒排行的影片

微型電網分層控制策略研究

為了解決River vector的問題，作者張泉泉 這樣論述：

微型電網(Microgrid)作為一種高效利用可再生能源分散式發電(Distributed Generation)的方法，可被用於解決偏遠地區的發電問題或為關鍵負荷提供不間斷供電。為了保證微型電網的可靠性和經濟運行，首要任務是維持系統電壓/頻率穩定和實現分散式發電單元之間功率的精確分配。微型電網通常運行於中低壓電力系統中，其線路阻抗主要呈現電阻電感性，傳統的P-f/Q-U下垂控制(Droop Control)性能不佳，雖然可通過採用虛擬複阻抗(Virtual Complex Impedance)的方法，使線路阻抗中的電阻分量被虛擬負電阻抵消。但由於存在線路阻抗參數漂移和估計誤差等問題，若虛擬

負電阻設計不當會導致系統不穩定。本文根據中低壓微型電網的線路參數特點，採用P-U/Q-f下垂控制，並且在控制迴路中引入由虛擬負電感和虛擬電阻組成的虛擬複阻抗，其中虛擬負電感用於減小系統阻抗中電感分量引起的功率耦合(Power Coupling)，虛擬電阻用於增強系統中的電阻分量，並且調整阻抗匹配度以提高功率分配精度。然而此作法功率分配仍然會受到系統線路阻抗參數的影響。此外，下垂控制結合虛擬阻抗方法易引起電壓偏差問題。因此本文研究了一種新型的基於虛擬複阻抗的穩壓均流控制方法，在不受線路阻抗參數變化影響的情況下實現精確的功率分配，並且提高電壓品質。本研究同時建立基於所提出方法的微型電網系統小信號模

型(Small-Signal Model)，用於分析系統的穩定性和動態性能，同時為控制器參數的設計提供理論依據。分析結果表明，所提出方法對線路阻抗參數漂移和估計誤差具有強健性，並且使系統具有較大的穩定裕度和較快的動態響應速度。再者，本文針對微型電網併聯逆變器的有功功率分配和電壓偏差問題探討，基於全域滑動模式控制(Total Sliding-Mode Control)技術重新設計功率-電壓下垂控制器和內迴路電壓調節器。首先，針對功率-電壓下垂控制回路，定義有功功率與公共耦合點(Point-of-Common-Coupling)電壓幅值之間的下垂控制關係誤差。然後通過採用全域滑動模式控制以獲得新的

下垂控制關係，從而同時實現有功功率分配和電壓幅值恢復。由於全域滑動模式控制方案可為系統提供快速的動態性能和強健性，高精度的暫態有功功率分配也可在不受線路阻抗影響的情況下被實現。更進一步，本文針對微型電網提出基於自我調整模糊類神經網路(Adaptive Fuzzy Neural Network)的分散式二級控制(Distributed Secondary Control)方案，以實現電壓/頻率恢復和最優功率分配。首先，建立微型電網動態系統模型，該模型由逆變器介面分散式電源模型和微型電網電力網絡模型組成，其中分散式電源模型可通過具有最優有功功率分配方案的初級控制器的動態模型來表示。微型電網電力網絡

模型由潮流動態模型和負荷模型組成。然後定義基於一致性演算法的誤差函數，並提出基於模型的全域滑動模式控制技術來處理同步和跟蹤問題。為達到無須詳細動態控制設計，本文設計自我調整模糊類神經網路方案來模擬全域滑動模式控制律，以繼承其快速動態響應性能和強健性。同時，所提出的自我調整模糊類神經網路控制方法可以解決全域滑動模式控制對微型電網動態模型精確資訊的依賴。藉由投影演算法(Project Algorithm)和李雅普諾夫穩定性(Lyapunov Stability)定理，推導模糊類神經網路的參數自我調整調節律，以保證基於自我調整模糊類神經網路的分散式二級控制系統的穩定性。本文所提出方法的有效性和優越性

將通過數值模擬和實驗進行驗證。

OpenCL Programming Guide

A PHP Error was encountered

Severity: Warning

Message: file_put_contents(/var/www/html/prints/public/images/books_new/F01/238/81/F012381279.jpg): failed to open stream: Permission denied

Filename: helpers/global_helper.php

Line Number: 140

Backtrace:

File: /var/www/html/prints/application/helpers/global_helper.php
Line: 140
Function: file_put_contents

File: /var/www/html/prints/application/views/article_v2.php
Line: 248
Function: coverWebp_online

File: /var/www/html/prints/application/controllers/Pages.php
Line: 662
Function: view

File: /var/www/html/prints/public/index.php
Line: 319
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: getimagesize(/var/www/html/prints/public/images/books_new/F01/238/81/F012381279.jpg): failed to open stream: No such file or directory

Filename: helpers/global_helper.php

Line Number: 62

Backtrace:

File: /var/www/html/prints/application/helpers/global_helper.php
Line: 62
Function: getimagesize

File: /var/www/html/prints/application/helpers/global_helper.php
Line: 142
Function: coverWebp

File: /var/www/html/prints/application/views/article_v2.php
Line: 248
Function: coverWebp_online

File: /var/www/html/prints/application/controllers/Pages.php
Line: 662
Function: view

File: /var/www/html/prints/public/index.php
Line: 319
Function: require_once

A PHP Error was encountered

Severity: Notice

Message: Trying to access array offset on value of type bool

Filename: helpers/global_helper.php

Line Number: 64

Backtrace:

File: /var/www/html/prints/application/helpers/global_helper.php
Line: 64
Function: _error_handler

File: /var/www/html/prints/application/helpers/global_helper.php
Line: 142
Function: coverWebp

File: /var/www/html/prints/application/views/article_v2.php
Line: 248
Function: coverWebp_online

File: /var/www/html/prints/application/controllers/Pages.php
Line: 662
Function: view

File: /var/www/html/prints/public/index.php
Line: 319
Function: require_once

A PHP Error was encountered

Severity: Notice

Message: Trying to access array offset on value of type bool

Filename: helpers/global_helper.php

Line Number: 66

Backtrace:

File: /var/www/html/prints/application/helpers/global_helper.php
Line: 66
Function: _error_handler

File: /var/www/html/prints/application/helpers/global_helper.php
Line: 142
Function: coverWebp

File: /var/www/html/prints/application/views/article_v2.php
Line: 248
Function: coverWebp_online

File: /var/www/html/prints/application/controllers/Pages.php
Line: 662
Function: view

File: /var/www/html/prints/public/index.php
Line: 319
Function: require_once

A PHP Error was encountered

Severity: Notice

Message: Trying to access array offset on value of type bool

Filename: helpers/global_helper.php

Line Number: 68

Backtrace:

File: /var/www/html/prints/application/helpers/global_helper.php
Line: 68
Function: _error_handler

File: /var/www/html/prints/application/helpers/global_helper.php
Line: 142
Function: coverWebp

File: /var/www/html/prints/application/views/article_v2.php
Line: 248
Function: coverWebp_online

File: /var/www/html/prints/application/controllers/Pages.php
Line: 662
Function: view

File: /var/www/html/prints/public/index.php
Line: 319
Function: require_once

為了解決River vector的問題，作者Munshi, Aaftab/ Gaster, Benedict R./ Mattson, Timothy G./ Fung, 這樣論述：

Using the new OpenCL (Open Computing Language) standard, you can write applications that access all available programming resources: CPUs, GPUs, and other processors such as DSPs and the Cell/B.E. processor. Already implemented by Apple, AMD, Intel, IBM, NVIDIA, and other leaders, OpenCL has outstan

ding potential for PCs, servers, handheld/embedded devices, high performance computing, and even cloud systems. This is the first comprehensive, authoritative, and practical guide to OpenCL 1.1 specifically for working developers and software architects. Written by five leading OpenCL authorities,

OpenCL Programming Guide covers the entire specification. It reviews key use cases, shows how OpenCL can express a wide range of parallel algorithms, and offers complete reference material on both the API and OpenCL C programming language. Through complete case studies and downloadable code examples

, the authors show how to write complex parallel programs that decompose workloads across many different devices. They also present all the essentials of OpenCL software performance optimization, including probing and adapting to hardware. Coverage includesUnderstanding OpenCL's architecture, concep

ts, terminology, goals, and rationale Programming with OpenCL C and the runtime API Using buffers, sub-buffers, images, samplers, and events Sharing and synchronizing data with OpenGL and Microsoft's Direct3D Simplifying development with the C++ Wrapper API Using OpenCL Embedded Profiles to support

devices ranging from cellphones to supercomputer nodes Case studies dealing with physics simulation; image and signal processing, such as image histograms, edge detection filters, Fast Fourier Transforms, and optical flow; math libraries, such as matrix multiplication and high-performance sparse mat

rix multiplication; and more Source code for this book is available at https: //code.google.com/p/opencl-book-samples/ Aaftab Munshi is the spec editor for the OpenGL ES 1.1, OpenGL ES 2.0, and OpenCL specifications and coauthor of the book OpenGL ES 2.0 Programming Guide (with Dan Ginsburg and D

ave Shreiner, published by Addison-Wesley, 2008). He currently works at Apple. Benedict R. Gaster is a software architect working on programming models for next-generation heterogeneous processors, in particular looking at high-level abstractions for parallel programming on the emerging class of pro

cessors that contain both CPUs and accelerators such as GPUs. Benedict has contributed extensively to the OpenCL’s design and has represented AMD at the Khronos Group open standard consortium. Benedict has a Ph.D. in computer science for his work on type systems for extensible records and variants.

He has been working at AMD since 2008.Timothy G. Mattson is an old-fashioned parallel programmer, having started in the mid-eighties with the Caltech Cosmic Cube and continuing to the present. Along the way, he has worked with most classes of parallel computers (vector supercomputers, SMP, VLIW, NUM

A, MPP, clusters, and many-core processors). Tim has published extensively, including the books Patterns for Parallel Programming (with Beverly Sanders and Berna Massingill, published by Addison-Wesley, 2004) and An Introduction to Concurrency in Programming Languages (with Matthew J. Sottile and Cr

aig E. Rasmussen, published by CRC Press, 2009). Tim has a Ph.D. in chemistry for his work on molecular scattering theory. He has been working at Intel since 1993.James Fung has been developing computer vision on the GPU as it progressed from graphics to general-purpose computation. James has a Ph.D

. in electrical and computer engineering from the University of Toronto and numerous IEEE and ACM publications in the areas of parallel GPU Computer Vision and Mediated Reality. He is currently a Developer Technology Engineer at NVIDIA, where he examines computer vision and image processing on graph

ics hardware.Dan Ginsburg currently works at Children’s Hospital Boston as a Principal Software Architect in the Fetal-Neonatal Neuroimaging and Development Science Center, where he uses OpenCL for accelerating neuroimaging algorithms. Previously, he worked for Still River Systems developing GPU-acc

elerated image registration software for the Monarch 250 proton beam radiotherapy system. Dan was also Senior Member of Technical Staff at AMD, where he worked for over eight years in a variety of roles, including developing OpenGL drivers, creating desktop and hand-held 3D demos, and leading the de

velopment of handheld GPU developer tools. Dan holds a B.S. in computer science from Worcester Polytechnic Institute and an M.B.A. from Bentley University.

微型電網併聯多模組變流器智慧型控制策略研究

為了解決River vector的問題，作者楊艷 這樣論述：

逆變器是微型電網系統中的重要電力電子介面，可將分佈式發電系統與當地負載連接構成微型電網系統，或者與公共大電網連接實現併網運行。隨著分佈式能源發電規模的擴大，考慮電力電子開關的應力以及系統冗餘性能，通常將多個小容量逆變器模組併聯以建立大容量的微電網系統。此外，介面逆變器也通過併聯運行方式將微型電網系統中不同的分佈式能源接至公共連接點。研究智慧型控制方法以提高微型電網系統中併聯逆變器模組的控制性能及優化微型電網輸出電力品質，對於提高分佈式能源接入微型電網的滲透率顯得相對重要。為了提高微型電網孤島運行模式下併聯逆變器模組在不同負載及不同運行狀況下的動態性能及供電可靠性，本文設計基於主-從電流均衡控

制策略下的併聯逆變器模组自適應模糊類神經網路模擬滑動模式控制(Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control, AFNNISMC)，將併聯逆變器模组視為主體，構建完整的數學模型以保證其系統級的穩定性，並在此基礎上，首先設計全域滑動模式控制(Total Sliding-Mode Control, TSMC)和具有自適應觀測器的全域滑動模式控制架構。為了提高系統的強健性、克服傳統全域滑動模式控制對系統詳細動力學模型的依賴，及消除由全域滑動模式控制引起的控制抖動現象，本文使用四層模糊類神經網路(Fuzzy Neural Net

work, FNN)來模擬全域滑動模式控制律，根據里亞普諾夫穩定理論(Lyapunov Stability Theorem)和投影算法(Projection Algorithm)，利用模糊神經網路與全域滑動模式控制律之間的近似誤差，設計網路參數的線上自適應調整律，以保證網路參數的收斂性和控制系統的穩定性。因此，即使系統存在不確定性的情況下，也可以保證併聯逆變器模組輸出高品質的電能，以及併聯逆變器模組之間高精度電流均衡性能。此外，當單一逆變器從併聯系統斷開或重新接入時，所提出的 AFNNISMC 可以保證併聯系統的不斷電運行，從而提高微型電網系統的冗餘度和操作靈活性。進一步，藉由數值模擬和實驗結

果，驗證所提出自適應模糊神類經網路模擬滑動模式控制的可行性和有效性。此外，亦與傳統的適應性全域滑動模式控制(Adaptive TSMC, ATSMC)和比例積分控制(Proportional-Integral Control, PIC)架構進行性能比較，驗證所提出的自適應模糊類神經網路模擬滑動模式控制的優越性。考慮到固定結構的模糊神類經網路難以兼顧計算負擔及控制性能，本文進一步研究 一 種 自 組 織 結 構 模 糊 類 神 經 網 路 模 擬 滑 動 模 式 控 制 (Self-Constructing Fuzzy-Neural-Network-Imitating Sliding-Mode

Control, SFNNISMC)，用於執行主-從電流均衡控制策略下的微型電網併聯逆變器模組的併網電流跟蹤控制，所設計的模糊類神經網絡同時具有結構和參數自學習能力。本文所提出自組織結構模糊類神經網路(Self-Constructing Fuzzy Neural Network, SFNN)中，輸入層的初始節點由併網逆變器模組的數目決定，而隸屬函數層的規則由動態規則生成機制依據當前的暫態輸入從無到有自動生成。同時，本結構還引入了動態派翠(Petri)網路實現規則刪減機制，派翠網路使用於重新激活與新接入的從逆變器相對應的規則，只有被派翠網路激活的規則相關的網路參數才會被線上更新，而不是所有的網路

參數皆更新，從而減輕參數學習過程的計算負擔。此外，利用里亞普諾夫穩定理論和投影算法設計網路參數的線上學習律，保證網路參數及併網電流跟蹤誤差的收斂性。藉由數值模擬展示所提出的自組織結構模糊類神經網路模擬滑動模式控制在併聯逆變器模組不同運行狀況下規則演化的過程。本文亦利用兩個逆變器模組併聯的實驗平臺，亦與傳統的比例積分控制(PIC)、滑動模式控制(Sliding-Mode Control, SMC)及固定結構的自適應模糊神經網路模擬滑動模式控制(AFNNISMC)進行對比實驗，進一步驗證所提出的自組織結構模糊類神經網路模擬滑動模式控制方案的優越性。