恶意网址URL检测一直是信息安全防御技术领域的研究热点之一。针对传统恶意URL检测技术无法自主探测未知URL,并且缺乏适应大数据时代发展的能力等问题,设计并实现了一种基于大数据技术,结合决策树算法与黑白名单技术的恶意URL检测模型。该模型基于Spark分布式计算框架,利用已知URL训练集提取特征、训练决策树分类模型,然后用已有分类模型对黑白名单无法检测出的URL进行分类预测,达到检测目的。实验证明,构建的检测模型具有很好的检测效果和稳定性。

Many Human-Body-Fall-Dow n Detection models are faced with problems like lower adaptability and higher w rong-detection rate in different detection scenes，and targeting on these shortcomings this research proposes a Human-Body-Fall-Dow n Detection Model based on the human skeleton keypoints and the LSTM neural network． This model detects the skeleton keypoints of the continuous multi-frame human body by Alphapose，and then divides the coordinate sequences of the skeleton keypoints into X and Y coordinate sequence，and then inputs them respectively into LSTM neural network to extract the time-order character; the last step is to input the LSTM hidden layer output vector into a full connection layer to obtain the results． This research uses public data set M uHAViMAS and Le2i to execute this experiment，and compares itself with many other detection models． The results show that this model has relatively high detection accuracy in multiple scenes，multiple view s，and multiple poses of falling．

该文件中代码通过C++和opencv的DNN模块调用darknet训练的yolo检测模型，实现目标检测。

使用yolov4训练自己的目标检测模型，具体教程参考https://blog.csdn.net/weixin_43220532/article/details/115906274

yolov3 裂痕检测模型 权重 自己训练的模型 感兴趣可以拿去用 有任何问题可以私聊我

yolov5s目标检测模型代码，使用方法见https://blog.csdn.net/weixin_43220532/article/details/115725433

DNN，深度学习使用到的配置文件, 基于OpenCV3.4.3，caffemodel

变电站指针式仪表目标检测训练模型（已训练好），训练了500张图片，可用于检测指针式仪表

找了好久，费尽心血才找到

matlab中搭建的三相电力系统谐波检测仿真模型，谐波分析，谐波补偿，利用pq方法。对于谐波检测方法，选择了目前主流的检测算法，模型已经过数据校验，与实验结果吻合良好。