two

交通分布中的无约束重力模型，matlab程序(A matlab program of unconstrained gravity model in traffic distribution.)

pca 轴承故障诊断程序，有数据，有运行结果(pca Bearing fault diagnosis program, data, have the operation results)

光纤光栅振动传感器的悬臂梁参数优化，包括四个变量，显示频率变化和灵敏度(optimization of FBG sensor)

backwardfoeward load flow for distribution network

说明：  盒维数MATLAB计算程序。%根据计盒维数原理编写了求一维曲线分形维数的matlab程序 function D=FractalDim(y,cellmax) %求输入一维信号的计盒分形维数 %y是一维信号 %cellmax:方格子的最大边长,可以取2的偶数次幂次(1,2,4,8...),取大于数据长度的偶数 %D是y的计盒维数（一般情况下D>=1）,D=lim(log(N(e))/log(k/e)), (box dimension MATLAB program. % Under the box dimension of the principle of preparing for a peacekeeping curve fractal dimension of Matlab procedures function FractalDim D = (y, cellmax)% for the importation of one-dimensional signal box fractal dimension% y Victoria is a signal llmax : square of the largest side length, the two can take several dual Power (1, 2,4,8 ...), data from more than even the length of the% D y is the box dimension (Dgt under normal circumstances; = 1), D = lim (log (N (e))/log (k/e)) ,)

Local edge detection for image processing: sauvola & niblack

AlphaControl v8.41 FullSource〖含全部源码和DEMO，支持D5-XE4〗

m file matlab traitement d image

system identification and DMC control of a debutanizer plant

：该文把局部三值模式(Local Ternary Patterns, LTP)纹理特征引入Mean Shift 跟踪算法，提出了基于多 特征的Mean Shift 人脸跟踪算法以解决Mean shift 跟踪算法的鲁棒性问题。通过对LTP 纹理特征的分析、研究， 提出了一个LTP 关键纹理模型，既增强了目标的关键纹理信息，又简化了LTP 纹理模型。在此基础上，提出一 种基于LTP 关键纹理特征和肤色特征的Mean Shift 人脸跟踪算法，有效地解决了Mean Shift 算法的鲁棒性问题。 为进一步提高对快速运动目标的跟踪速度和跟踪性能，该文引入了卡尔曼滤波器对目标进行预测。实验结果表明， 该文的算法在目标定位的准确性和跟踪性能上比Mean Shift 算法均有明显的提高。(: In this paper, the texture characteristics of the local ternary patterns Local Ternary Patterns (LTP) Mean Shift tracking algorithm proposed Mean Shift face tracking algorithm based on multiple features in order to solve the Mean shift tracking algorithm robustness. Texture characteristics of LTP analysis, research, a key LTP texture model, not only enhanced the key goal of texture information, but also simplifies the the LTP texture model. On this basis, based on the LTP key texture features and color characteristics Mean Shift face tracking algorithm, effectively solved the robustness of the Mean Shift algorithm. To further enhance the fast-moving target tracking speed and tracking performance, this paper introduces the Kalman filter to predict the target. The experimental results show that the algorithm of the text in the target positioning accuracy and tracking performance than the Mean Shift algorithm to significantly improve.)