一种基于特征匹配的目标识别跟踪方法

针对复杂背景下的动态目标跟踪问题,提出了一种基于边缘检测,综合多图像特征与伺服机构位置信息进行匹配的目标识别跟踪方法。利用SUSAN算法检测边缘,提取出单帧图像中的可疑目标,依次选用灰度、目标几何、伺服机构位置信息和边界不变矩信息匹配,完成目标的识别。采用kalman预测滤波对脱靶量滞后时间进行补偿,选用目标空间位置进行多步预测,引导伺服机构跟踪。外场实验表明,该方法能有效她匹配识别出目标,并保持连续稳定的跟踪。     

动态特征选择算法对恶意行为检测的优化研究

针对互联网中存在的恶意行为,特别是社交网络应用中的在线恶意行为,通常使用基于用户多维特征的聚类分析算法进行检测。提出一种动态特征选择算法(DFSA),使用具有特征加权熵的模糊C均值目标函数,首先为参数构建一个学习模式,自动计算每个特征权重,并剔除权重小于阈值的特征,动态选择重要的特征,迭代地更新隶属函数、簇中心和特征权重直到最优化为止,最后识别出具有高精度的恶意用户行为簇。仿真结果表明,对比SDAFS算法、ELAFC算法和NADMB算法,DFSA算法在Rand指数、Jaccard指数和归一化互信息量3个主要性能指标上均有改善。     

记忆合金在弹箭引信安全系统中的应用研究

研究利用Ti—Ni记忆合金的记忆效应，设计制作了引信安保机构原理样机，并对样机进行了解除保险可靠性试验、勤务处理安全性试验和气体炮试验。经一系列试验研究证明，记忆合金在弹箭引信安全系统中做保险件，具有保险、延期解除保险和驱动负载的综合能力。为引信多样化的安全设计提供了一种新的途径。     

基于改进BOF算法的压板状态识别方法研究

随着电网规模的扩张以及二次技术的发展,二次屏柜的压板数量急剧上升.二次压板一旦出现保护误动或拒动,将严重威胁到电网、设备与人身安全.设计了一种基于改进图像检索算法的继电保护压板状态识别系统对压板退投状态进行识别.首先,对压板图像利用背景差分法及光流法进行前景提取;然后,采用图片增强处理、边缘检测等一系列技术识别开关的投...     

A cognitive robot architecture based on tactile and visual information

In this paper, we propose an architecture for a cognitive robot based on tactile and visual information. Visual information contains various features such as location and area of various colored regions. Most of these features are irrelevant for object recognition to achieve the given task. In the architecture, tactile information plays a key role in the selection of visual features and discritization of selected features. In order to find appropriate visual features we use a correlation coefficient between the values of the features and action series. Then the ChiMerge algorithm is employed to discritize the value of the selected feature into a small number of intervals. Consequently, quantization of a state space to accomplish the given task is achieved. An appropriate behavior to the given task is acquired by using this state space with reinforcement learning algorithm. We give experimental results of computer simulation to show the validity of our method.     

基于Leap Motion的动态手势识别方法

伴随虚拟现实（Virtual Reality,VR）技术的发展,以及人们对人机交互性能和体验感的要求提高,手势识别作为影响虚拟现实中交互操作的重要技术之一,其精确度急需提升^[1].针对当前手势识别方法在一些动作类似的手势识别中表现欠佳的问题,提出了一种多特征动态手势识别方法.该方法首先使用体感控制器Leap Motion追踪动态手势获取数据,然后在特征提取过程中增加对位移向量角度和拐点判定计数的提取,接着进行动态手势隐马尔科夫模型（Hidden Markov Model,HMM）的训练,最后根据待测手势与模型的匹配率进行识别.从实验结果中得出,该多特征识别方法能够提升相似手势的识别率.     

Using genetic programming for context-sensitive feature scoring in classification problems

Feature scoring is an avenue to feature selection that provides a measure of usefulness for the individual features of a classification task. Features are ranked based on their scores and selection is performed by choosing a small group of high-ranked features. Most existing feature scoring/ranking methods focus on the relevance of a single feature to the class labels regardless of the role of other features (context-insensitive). The paper proposes a genetic programming (GP)-based method to see how a set of features can contribute towards discriminating different classes. The features receive score in the context of other features participating in a GP program. The scoring mechanism is based on the frequency of appearance of each feature in a collection of GP programs and the fitness of those programs. Our results show that the proposed feature ranking method can detect important features of a problem. A variety of different classifiers restricted to just a few of these high-ranked features work well. The proposed scoring-ranking mechanism can also shrink the search space of size O(2 ⁿ ) of subsets of features to a search space of size O(n) in which there are points that are very likely to improve the classification performance.     

基于深度学习的采摘机器人目标识别定位算法

为了提升采摘机器人对果实的识别准确率以及定位定精度,提出一种基于深度学习Faster-RCNN框架的采摘机器人目标识别和定位算法.首先采用卷积神经网络VGG16模型提取输入图像的特性信息,并利用区域提议网络RPN生成含有目标的候选框,通过引入自适应候选框数的方法有效提升了算法性能,然后利用多任务损失函数对目标进行分类识...     

An Improved Method for Load Taxonomy Using Sample Shifting Technique and Signature Analysis

This paper illustrates an improved method of classification of electrical appliances, particularly for domestic loads, to construct load taxonomy on the basis of their signature analysis. Each electrical load is characterized by its own distinct signature and hence load signature analysis is useful in monitoring the health of the equipment, power quality, in determining individual energy usage etc. type of services. On the other hand, load taxonomy classifies these loads in several clusters on the basis of some features extracted from their signatures. In traditional methods of construction of load taxonomy, different signature patterns based on power metrics, V-I trajectories, Eigen vectors, etc. In this proposed method, with the adoption of sample shifting technique the required number of feature extraction is reduced to a lower value to find out various signature patterns than those are required in traditional load taxonomies. Moreover, a better taxonomy, having well separated groups of loads is achieved with lower number of extracted features.     

基于几何特征的飞机部位识别跟踪算法

针对复杂天气情况下形状可辨的飞机,提出了一种对飞机部位的识别跟踪算法。该算法利用飞机的几何特征,通过数学形态学的原理,在存有噪声干扰的运动图像中简单快速有效地识别出所需目标的重要部位,自动获取其跟踪模板,通过相关匹配技术实现对飞机重要部位的稳定跟踪。给出了应用该算法的具体步骤,并通过实验证明了该算法的有效性。