中央空调系统是一个高度非线性的复杂系统,其控制系统的传感器一旦发生故障会导致中央空调机组的运行偏离正常状态,不仅造成能耗浪费而且还引发安全事故。针对中央空调故障监测的问题,本文提出了一种基于SVDD(Support vector data descr...中央空调系统是一个高度非线性的复杂系统,其控制系统的传感器一旦发生故障会导致中央空调机组的运行偏离正常状态,不仅造成能耗浪费而且还引发安全事故。针对中央空调故障监测的问题,本文提出了一种基于SVDD(Support vector data description)在线实时故障监测方法,利用中央空调机组正常的运行数据,首先对数据进行标准化处理,消除变量间的量纲关系,然后建立SVDD模型。通过引入不同程度故障,分析该方法的监测效率。结果表明,基于SVDD的中央空调故障监测效果明显,但对不同变量的不同程度的故障,故障检测的准确率存在一定的差别。展开更多
针对现有通信辐射源个体识别研究在遇到开集问题时识别性能不高的问题,提出了一种基于堆栈去噪自编码器和支持向量描述(Support Vector Data Description,SVDD)的开集识别方法。该方法通过堆栈去噪自编码器实现降噪和特征压缩提取,将特...针对现有通信辐射源个体识别研究在遇到开集问题时识别性能不高的问题,提出了一种基于堆栈去噪自编码器和支持向量描述(Support Vector Data Description,SVDD)的开集识别方法。该方法通过堆栈去噪自编码器实现降噪和特征压缩提取,将特征输入SVDD进行通信辐射源个体开集识别实验。结果表明,在不同开放度下,该方法可以将未知通信辐射源个体和已知通信辐射源个体以高准确率区分出来,进而将开集识别转为闭集识别。同时,对已知通信辐射源个体识别有很好的识别准确率和抗噪声能力。展开更多
COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in 2019.Discovering the infected people is the most important factor in the fight against the disease.T...COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in 2019.Discovering the infected people is the most important factor in the fight against the disease.The gold-standard test to diagnose COVID-19 is polymerase chain reaction(PCR),but it takes 5–6 h and,in the early stages of infection,may produce false-negative results.Examining Computed Tomography(CT)images to diagnose patients infected with COVID-19 has become an urgent necessity.In this study,we propose a residual attention deep support vector data description SVDD(RADSVDD)approach to diagnose COVID-19.It is a novel approach combining residual attention with deep support vector data description(DSVDD)to classify the CT images.To the best of our knowledge,we are the first to combine residual attention with DSVDD in general,and specifically in the diagnosis of COVID-19.Combining attention with DSVDD naively may cause model collapse.Attention in the proposed RADSVDD guides the network during training and enables quick learning,residual connectivity prevents vanishing gradients.Our approach consists of three models,each model is devoted to recognizing one certain disease and classifying other diseases as anomalies.These models learn in an end-to-end fashion.The proposed approach attained high performance in classifying CT images into intact,COVID-19,and non-COVID-19 pneumonia.To evaluate the proposed approach,we created a dataset from published datasets and had it assessed by an experienced radiologist.The proposed approach achieved high performance,with the normal model attained sensitivity(0.96–0.98),specificity(0.97–0.99),F1-score(0.97–0.98),and area under the receiver operator curve(AUC)0.99;the COVID-19 model attained sensitivity(0.97–0.98),specificity(0.97–0.99),F1-score(0.97–0.99),and AUC 0.99;and the non-COVID pneumoniamodel attained sensitivity(0.97–1),specificity(0.98–0.99),F1-score(0.97–0.99),and AUC 0.99.展开更多
Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic relationships.It is possible to offer the explainable basis of decision-maki...Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic relationships.It is possible to offer the explainable basis of decision-making for inference results.Through the causality of risk factors that have an ambiguous association in big medical data,it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease status.In addition,the technique makes it possible to accurately predict disease risk for anomaly detection.Vision transformer for anomaly detection from image data makes classification through MLP.Unfortunately,in MLP,a vector value depends on patch sequence information,and thus a weight changes.This should solve the problem that there is a difference in the result value according to the change in the weight.In addition,since the deep learning model is a black box model,there is a problem that it is difficult to interpret the results determined by the model.Therefore,there is a need for an explainablemethod for the part where the disease exists.To solve the problem,this study proposes explainable anomaly detection using vision transformerbasedDeep Support Vector Data Description(SVDD).The proposed method applies the SVDD to solve the problem of MLP in which a result value is different depending on a weight change that is influenced by patch sequence information used in the vision transformer.In order to draw the explainability of model results,it visualizes normal parts through Grad-CAM.In health data,both medical staff and patients are able to identify abnormal parts easily.In addition,it is possible to improve the reliability of models and medical staff.For performance evaluation normal/abnormal classification accuracy and f-measure are evaluated,according to whether to apply SVDD.Evaluation Results The results of classification by applying the proposed SVDD are evaluated excellently.Therefore,through the proposed method,it is possible to improve the reliability of decision-making by identifying the location of the disease and deriving consistent results.展开更多
利用常规方法检测网络数据流异常存在检测效率低的问题,为此提出基于改进支持向量数据描述(Support Vector Data Description,SVDD)算法的网络数据流异常检测方法。首先,选取一对一的构造方法将通信网络异常流量数据分为两个类别;其次,...利用常规方法检测网络数据流异常存在检测效率低的问题,为此提出基于改进支持向量数据描述(Support Vector Data Description,SVDD)算法的网络数据流异常检测方法。首先,选取一对一的构造方法将通信网络异常流量数据分为两个类别;其次,根据数据流的处理标准和需求,采用聚类分析技术构建监测模型;最后,通过改进SVDD流量异常检测模型对经过聚类特征提取的数据进行识别和检测。实验结果表明,该方法的检测准确率均高于97.5%,检测耗时较短,优于对照组。展开更多
【目的】为保证烘丝过程安全稳定运行,研究滚筒叶丝干燥过程异常工况检测具有重大价值。【方法】本文提出基于自动编码器(Auto encoder,AE)和支持向量数据描述(Support vector data description,SVDD)的AE-SVDD算法。首先,使用深度学习...【目的】为保证烘丝过程安全稳定运行,研究滚筒叶丝干燥过程异常工况检测具有重大价值。【方法】本文提出基于自动编码器(Auto encoder,AE)和支持向量数据描述(Support vector data description,SVDD)的AE-SVDD算法。首先,使用深度学习自动编码器提取数据深层特征,构建重构误差,利用重构误差训练SVDD分类模型得到超球体半径阈值,建立检测率指标。通过工业实际生产案例进行模型验证,并应用PCA、SVDD算法分别建立异常检测模型作对比实验。【结果】基于AE-SVDD的算法模型检测率可提高约63%,并能预测4~8min后即将发生的质量异常,明显优于其他算法模型。【结论】与传统方法相比,AE-SVDD异常工况检测方法不仅显著提高了检测率,而且具有良好的异常工况预警作用,有助于及时发现、控制滚筒叶丝干燥过程潜在异常工况,降低质量异常的产生几率。展开更多
针对支持向量数据描述(Support Vector Data Description,SVDD)在线学习时的支持向量数量随样本规模的扩大呈线性增加,进而导致模型更新时间呈非线性增长的问题,提出一种基于支持向量约减的支持向量数据描述(R-SVDD)在线学习方法。该算...针对支持向量数据描述(Support Vector Data Description,SVDD)在线学习时的支持向量数量随样本规模的扩大呈线性增加,进而导致模型更新时间呈非线性增长的问题,提出一种基于支持向量约减的支持向量数据描述(R-SVDD)在线学习方法。该算法通过执行支持向量约减,控制在线学习时的支持向量数量,从而使其具有比其他SVDD算法更快速且更稳定的模型更新时间,适合大规模数据的分类处理。首先阐述了支持向量约减的原理;进而给出了在线R-SVDD算法。在单分类和多分类数据集上的实验结果表明,R-SVDD算法相较于SVDD算法,能够在保持分类精度的基础上拥有更快的学习速度。展开更多
文摘中央空调系统是一个高度非线性的复杂系统,其控制系统的传感器一旦发生故障会导致中央空调机组的运行偏离正常状态,不仅造成能耗浪费而且还引发安全事故。针对中央空调故障监测的问题,本文提出了一种基于SVDD(Support vector data description)在线实时故障监测方法,利用中央空调机组正常的运行数据,首先对数据进行标准化处理,消除变量间的量纲关系,然后建立SVDD模型。通过引入不同程度故障,分析该方法的监测效率。结果表明,基于SVDD的中央空调故障监测效果明显,但对不同变量的不同程度的故障,故障检测的准确率存在一定的差别。
文摘针对现有通信辐射源个体识别研究在遇到开集问题时识别性能不高的问题,提出了一种基于堆栈去噪自编码器和支持向量描述(Support Vector Data Description,SVDD)的开集识别方法。该方法通过堆栈去噪自编码器实现降噪和特征压缩提取,将特征输入SVDD进行通信辐射源个体开集识别实验。结果表明,在不同开放度下,该方法可以将未知通信辐射源个体和已知通信辐射源个体以高准确率区分出来,进而将开集识别转为闭集识别。同时,对已知通信辐射源个体识别有很好的识别准确率和抗噪声能力。
文摘COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in 2019.Discovering the infected people is the most important factor in the fight against the disease.The gold-standard test to diagnose COVID-19 is polymerase chain reaction(PCR),but it takes 5–6 h and,in the early stages of infection,may produce false-negative results.Examining Computed Tomography(CT)images to diagnose patients infected with COVID-19 has become an urgent necessity.In this study,we propose a residual attention deep support vector data description SVDD(RADSVDD)approach to diagnose COVID-19.It is a novel approach combining residual attention with deep support vector data description(DSVDD)to classify the CT images.To the best of our knowledge,we are the first to combine residual attention with DSVDD in general,and specifically in the diagnosis of COVID-19.Combining attention with DSVDD naively may cause model collapse.Attention in the proposed RADSVDD guides the network during training and enables quick learning,residual connectivity prevents vanishing gradients.Our approach consists of three models,each model is devoted to recognizing one certain disease and classifying other diseases as anomalies.These models learn in an end-to-end fashion.The proposed approach attained high performance in classifying CT images into intact,COVID-19,and non-COVID-19 pneumonia.To evaluate the proposed approach,we created a dataset from published datasets and had it assessed by an experienced radiologist.The proposed approach achieved high performance,with the normal model attained sensitivity(0.96–0.98),specificity(0.97–0.99),F1-score(0.97–0.98),and area under the receiver operator curve(AUC)0.99;the COVID-19 model attained sensitivity(0.97–0.98),specificity(0.97–0.99),F1-score(0.97–0.99),and AUC 0.99;and the non-COVID pneumoniamodel attained sensitivity(0.97–1),specificity(0.98–0.99),F1-score(0.97–0.99),and AUC 0.99.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2020R1A6A1A03040583).
文摘Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic relationships.It is possible to offer the explainable basis of decision-making for inference results.Through the causality of risk factors that have an ambiguous association in big medical data,it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease status.In addition,the technique makes it possible to accurately predict disease risk for anomaly detection.Vision transformer for anomaly detection from image data makes classification through MLP.Unfortunately,in MLP,a vector value depends on patch sequence information,and thus a weight changes.This should solve the problem that there is a difference in the result value according to the change in the weight.In addition,since the deep learning model is a black box model,there is a problem that it is difficult to interpret the results determined by the model.Therefore,there is a need for an explainablemethod for the part where the disease exists.To solve the problem,this study proposes explainable anomaly detection using vision transformerbasedDeep Support Vector Data Description(SVDD).The proposed method applies the SVDD to solve the problem of MLP in which a result value is different depending on a weight change that is influenced by patch sequence information used in the vision transformer.In order to draw the explainability of model results,it visualizes normal parts through Grad-CAM.In health data,both medical staff and patients are able to identify abnormal parts easily.In addition,it is possible to improve the reliability of models and medical staff.For performance evaluation normal/abnormal classification accuracy and f-measure are evaluated,according to whether to apply SVDD.Evaluation Results The results of classification by applying the proposed SVDD are evaluated excellently.Therefore,through the proposed method,it is possible to improve the reliability of decision-making by identifying the location of the disease and deriving consistent results.
文摘利用常规方法检测网络数据流异常存在检测效率低的问题,为此提出基于改进支持向量数据描述(Support Vector Data Description,SVDD)算法的网络数据流异常检测方法。首先,选取一对一的构造方法将通信网络异常流量数据分为两个类别;其次,根据数据流的处理标准和需求,采用聚类分析技术构建监测模型;最后,通过改进SVDD流量异常检测模型对经过聚类特征提取的数据进行识别和检测。实验结果表明,该方法的检测准确率均高于97.5%,检测耗时较短,优于对照组。
文摘【目的】为保证烘丝过程安全稳定运行,研究滚筒叶丝干燥过程异常工况检测具有重大价值。【方法】本文提出基于自动编码器(Auto encoder,AE)和支持向量数据描述(Support vector data description,SVDD)的AE-SVDD算法。首先,使用深度学习自动编码器提取数据深层特征,构建重构误差,利用重构误差训练SVDD分类模型得到超球体半径阈值,建立检测率指标。通过工业实际生产案例进行模型验证,并应用PCA、SVDD算法分别建立异常检测模型作对比实验。【结果】基于AE-SVDD的算法模型检测率可提高约63%,并能预测4~8min后即将发生的质量异常,明显优于其他算法模型。【结论】与传统方法相比,AE-SVDD异常工况检测方法不仅显著提高了检测率,而且具有良好的异常工况预警作用,有助于及时发现、控制滚筒叶丝干燥过程潜在异常工况,降低质量异常的产生几率。
文摘针对支持向量数据描述(Support Vector Data Description,SVDD)在线学习时的支持向量数量随样本规模的扩大呈线性增加,进而导致模型更新时间呈非线性增长的问题,提出一种基于支持向量约减的支持向量数据描述(R-SVDD)在线学习方法。该算法通过执行支持向量约减,控制在线学习时的支持向量数量,从而使其具有比其他SVDD算法更快速且更稳定的模型更新时间,适合大规模数据的分类处理。首先阐述了支持向量约减的原理;进而给出了在线R-SVDD算法。在单分类和多分类数据集上的实验结果表明,R-SVDD算法相较于SVDD算法,能够在保持分类精度的基础上拥有更快的学习速度。
