基于改进SVDD算法的池塘水质数据流异常检测  

Abnormal detection of aquaculture water quality data stream using an improved SVDD in pond

作　　者：施珮 匡亮[3] 唐玥 袁永明 余晓栋 Shi Pei;Kuang Liang;Tang Yue;Yuan Yongming;Yu Xiaodong(Binjiang College,Nanjing University of Information Science&Technology,Wuxi 214105;Jiangsu Internet of Things Device Super Convergence and Security Research Center,Wuxi 214105;School of IoT Engineering,Jiangsu Vocational College of Information Technology,Wuxi 214153,China;Freshwater Fisheries Research Center,Chinese Academy of Fishery Sciences,Wuxi 214081,China)

机构地区：[1]南京信息工程大学滨江学院,无锡214105 [2]江苏省物联网设备超融合与安全工程研究中心,无锡214105 [3]江苏信息职业技术学院物联网工程学院,无锡214153 [4]中国水产科学研究院淡水渔业研究中心,无锡214081

出　　处：《农业工程学报》2021年第24期249-256,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：江苏省高校自然科学基金面上项目(21KJB520020);南京信息工程大学滨江学院人才启动经费资助(2021r038);国家自然科学基金项目(62072216);现代农业产业技术体系专项(CARS-46)。

摘　　要：无线传感器网络获取的水质数据流具有高复杂性、非平稳性、非线性等特点,为了提高传感数据流的异常检测能力,保障水质监测数据流的有效性,该研究提出一种基于改进支持向量数据描述(SupportVectorDataDescription,SVDD)水质数据流异常检测方法。首先应用马氏距离改进Parzen-Window高斯窗函数,避免数据在分类过程中产生干扰。再利用改进的Parzen-Window获取训练数据的分布密度估计,并结合模糊隶属度函数,对传统SVDD算法进行密度补偿,构建改进的SVDD异常检测模型,从而降低有噪正常样本的干扰性,提高算法的分类精度。最后,选择密度补偿支持向量数据描述(Density Weighted Support Vector Data Description,D-SVDD)、传统SVDD和FastFood算法,在不同试验池塘的多个测试数据集中进行对比试验。结果表明,改进SVDD算法具有较高的检测性能,该算法在3口池塘的最高异常检测正确率TPR(True Positive Rate)值达到99.83%,最高检测准确率Accuracy达到99.83%,明显优于D-SVDD和传统SVDD算法,且最低运行时间仅1.34 s。结果可为水质数据流异常监测提供技术支持。An anomaly detection of the data stream has been one of the most critical subjects for the monitoring of water quality in aquaculture. The data stream of water quality collected by wireless sensor network is normally difficult to be detected accurately, due to the characteristics of high complexity, instability, and nonlinearity. The traditional support vector data description(SVDD) presents a relatively low recognition on a small number of abnormal samples under the condition of data imbalance. The noise samples have also a great interference to the anomaly detection, leading to the specific features that cannot be captured completely. In this study, an improved support vector data description(improved SVDD) was proposed to strengthen the detection capability of the sensor data stream. First, a mahalanobis distance was applied to enhance the Gaussian function of Parzen-Window, thus avoiding data interference in the process of classification. Then, the improved Parzen-Window function was utilized to realize the density estimation of training data. As such, the data classification was completed to extract the distribution of training data. In this case, the new ISVDD model was constructed to combine the fuzzy membership function. Thus, the interference of the model from the noise samples was significantly reduced to improve the classification accuracy. Finally, the abnormal detection effect of SVDD different kernel functions was compared to determine the optimal kernel function, according to the performances. The density-weighted support vector data description(D-SVDD),traditional support vector data description(improved SVDD), and the FastFood were selected to verify the performance in different testing datasets of three ponds. The D-SVDD was used to verify the superiority of the fuzzy membership function during improvement operation. The traditional SVDD was used to verify the detection precision of improved SVDD. The Fast Food was taken to verify the running efficiency. All detections were tested several times

关 键 词：水产养殖 水质 数据流 密度补偿 支持向量数据描述 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] TP212[自动化与计算机技术—计算机科学与技术]