observable distribution grid are investigated. A distribution grid is observable if the state of the grid can be fully determined. For the simulations, the modified 34-bus IEEE test feeder is used. The measurements needed for the state estimation are generated by the ladder iterative technique. Two methods for the state estimation are analyzed: WEighted Least Squares and Extended Kalman Filter. Both estimators try to find the most probable state based on the available measurements. The result is that the Kalman filter mostly needs less iterations and calculation time. The disadvantage of the Kalman filter is that it needs some foreknowlegde about the state.
標(biāo)簽： distribution observable grid investigated
上傳時(shí)間： 2014-12-08
上傳用戶：ls530720646
利用5階精度的WENO方法求解歐拉方程，里面有源代碼和說明文檔，具體可參考論文Efficient Implementation of WEighted ENO Schemes。
上傳時(shí)間： 2017-03-25
上傳用戶：小眼睛LSL
NN Functions a program in Lisp to demonstrate working of an artificial neuron. (Enter an input vector X and weight vector W. Calculate WEighted sum XW. Transform this using signal or activation functions like logistic, threshold, hyperbolic-tangent, linear, exponential, sigmoid or some other functions (syntax provided) and display the output).
標(biāo)簽： demonstrate artificial Functions program
上傳時(shí)間： 2013-12-30
上傳用戶：hfmm633
ADIAL Basis Function (RBF) networks were introduced into the neural network literature by Broomhead and Lowe [1], which are motivated by observation on the local response in biologic neurons. Due to their better approximation capabilities, simpler network structures and faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a WEighted sum of hidden units’ outputs.
標(biāo)簽： introduced literature Broomhead Function
上傳時(shí)間： 2017-08-08
上傳用戶：lingzhichao
Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a WEighted average of a large neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually.
標(biāo)簽： Super-resolution Multi-scale Dictionary Single Image for
上傳時(shí)間： 2019-03-28
上傳用戶：fullout
基于TMS320F28335的超聲波流量計(jì)硬件原理圖+軟件源碼本文以TMS320F28335 處理器為核心，設(shè)計(jì)一種用于管道流量測(cè)量的超聲波流量計(jì)。系統(tǒng)硬件由核心板，超聲波發(fā)射和接收電路，切換電路，超聲換能器，基于ADS805 的高速信號(hào)采集電路，人機(jī)交互以及電源等模塊構(gòu)成。采用時(shí)差法進(jìn)行管道流量測(cè)量，時(shí)差測(cè)量采用SCOT 加權(quán)的廣義互相關(guān)時(shí)延估計(jì)算法。本論文設(shè)計(jì)的超聲波流量計(jì)具有測(cè)量速度快、準(zhǔn)確性好、成本低等優(yōu)點(diǎn)。關(guān)鍵字：C2000，超聲波，流量，廣義互相關(guān)算法AbstractA kind of ultrasonic flowmeter using for the pipe flow measurement is designed based onTMS320F28335 in this paper. The system hardware consists of the following parts: the core board,ultrasonic signal transmitter and receiver circuits, switch circuit, ultrasonic transducer, signalacquisition circuit based on ADS805, human-computer interaction module and power supplymodule, etc. The system use the method of time difference for pipeline flow measurement and thetime difference is calculated by the time-delay algorithm of SCOT WEighted generalizedcross-correlation. The ultrasonic flowmeter has the features of high testing speed, high precisionand low cost, etc.Keywords: C2000,Ultrasonic, Flow, Generalized Cross-Correlation Algorithm
標(biāo)簽： tms320f28335 超聲波流量計(jì)
上傳時(shí)間： 2022-05-06
上傳用戶：
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