load initial_track s; % y:initial data,s:data with noiseT=0.1;
% yp denotes the sample value of position% yv denotes the sample value of velocity% Y=[yp(n);yv(n)];% error deviation caused by the random acceleration % known dataY=zeros(2,200);Y0=[0;1];Y(:,1)=Y0;A=[1 T 0 1]; B=[1/2*(T)^2 T]';H=[1 0];
C0=[0 0 0 1];C=[C0 zeros(2,2*199)];Q=(0.25)^2; R=(0.25)^2;
為了提高直接轉矩控制（DTC）系統定子磁鏈估計精度，降低電流、電壓測量的隨機誤差，提出了一種基于擴展卡爾曼濾波（EKF）實現異步電機轉子位置和速度估計的方法。擴展卡爾曼濾波器是建立在基于旋轉坐標系下由定子電流、電壓、轉子轉速和其它電機參量所構成的電機模型上，將定子電流、定子磁鏈、轉速和轉子角位置作為狀態變量，定子電壓為輸入變量，定子電流為輸出變量，通過對磁鏈和轉速的閉環控制提高定子磁鏈的估計精度，實現了異步電機的無速度傳感器直接轉矩控制策略，仿真結果驗證了該方法的可行性,提高了直接轉矩的控制性能。
Abstract:
In order to improve the Direct Torque Control（DTC） system of stator flux estimation accuracy and reduce the current， voltage measurement of random error， a novel method to estimate the speed and rotor position of asynchronous motor based on extended Kalman filter was introduced. EKF was based on d-p axis motor and other motor parameters （state vector: stator current， stator flux linkage， rotor angular speed and position； input: stator voltage； output: staror current). EKF was designed for stator flux and rotor speed estimation in close-loop control. It can improve the estimated accuracy of stator flux. It is possible to estimate the speed and rotor position and implement asynchronous motor drives without position and speed sensors. The simulation results show it is efficient and improves the control performance.
在集成電路內建自測試的過程中，電路的測試功耗通常顯著高于正常模式產生的功耗，因此低功耗內建自測試技術已成為當前的一個研究熱點。為了減少被測電路內部節點的開關翻轉活動率，研究了一種隨機單輸入跳變（Random Single Input Change，RSIC）測試向量生成器的設計方案，利用VHDL語言描述了內建自測試結構中的測試向量生成模塊，進行了計算機模擬仿真并用FPGA（EP1C6Q240C8）加以硬件實現。實驗結果證實了這種內建自測試原理電路的正確性和有效性。
“抓住它”小遊戲，a applet that plays a game called Catch the Crearure. have the crature appear at a ramdom lacation for a random durarion. the goal is to catch the creature by pressing the moouse button while the mouce pointer is on the creature
-- KeYl0gByMe --
Il s agit d un petit keylogger tout simple.
Il logs tout les types de touches.
Le fichier logs se met ?la racine du disque dur principal.
Le fichier en question se nomme : stsvc.txt
Cr閍teur : benozor77.
Courriel : webmaster[arobase]hackologie.tk
Site Web: http://www.hackologie.tk
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irc.coolsmile:6667 >>> #hackplanet sous le pseudo AbUsE.
Although there has been a lot of AVL tree libraries available now, nearly all of them are meant to work in the random access memory(RAM). Some of them do provide some mechanism for dumping the whole tree into a file and loading it back to the memory in order to make data in that tree persistent. It serves well when there s just small amount of data. When the tree is somewhat bigger, the dumping/loading process could take a lengthy time and makes your mission-critical program less efficient. How about an AVL tree that can directly use the disk for data storage ? If there s something like that, we won t need to read through the whole tree in order to pick up just a little bit imformation(a node), but read only the sectors that are neccssary for locating a certain node and the sectors in which that node lies. This is my initial motivation for writing a storage-media independent AVL Tree. However, as you step forth, you would find that it not only works fine with disks but also fine with memorys, too.
