针对太赫兹探测器的研究现状,分析研究了现有太赫兹探测器的优缺点,并对热释电型太赫兹探测器的热释电材料和吸收材料进行研究,提出了一种基于铌镁钛酸铅(PMNT)晶片的新型太赫兹探测器的设计和制作方法。用PMNT晶片作为热释电材料,碳纳...针对太赫兹探测器的研究现状,分析研究了现有太赫兹探测器的优缺点,并对热释电型太赫兹探测器的热释电材料和吸收材料进行研究,提出了一种基于铌镁钛酸铅(PMNT)晶片的新型太赫兹探测器的设计和制作方法。用PMNT晶片作为热释电材料,碳纳米管作为吸收材料,使用精密减薄抛光工艺和溅射电极工艺等工艺技术,完成了新型热释电太赫兹探测器的设计与制作。并利用该探测器设计了一款太赫兹功率计,经测试,该功率计在0.1~30 THz宽频段、0.5~100 m W大功率动态范围内,功率测量准确度达到了±10%,综合指标达到国际同类产品先进水平,应用效果良好。展开更多
With the rapid development of the Internet,network security and data privacy are increasingly valued.Although classical Network Intrusion Detection System(NIDS)based on Deep Learning(DL)models can provide good detecti...With the rapid development of the Internet,network security and data privacy are increasingly valued.Although classical Network Intrusion Detection System(NIDS)based on Deep Learning(DL)models can provide good detection accuracy,but collecting samples for centralized training brings the huge risk of data privacy leakage.Furthermore,the training of supervised deep learning models requires a large number of labeled samples,which is usually cumbersome.The“black-box”problem also makes the DL models of NIDS untrustworthy.In this paper,we propose a trusted Federated Learning(FL)Traffic IDS method called FL-TIDS to address the above-mentioned problems.In FL-TIDS,we design an unsupervised intrusion detection model based on autoencoders that alleviates the reliance on marked samples.At the same time,we use FL for model training to protect data privacy.In addition,we design an improved SHAP interpretable method based on chi-square test to perform interpretable analysis of the trained model.We conducted several experiments to evaluate the proposed FL-TIDS.We first determine experimentally the structure and the number of neurons of the unsupervised AE model.Secondly,we evaluated the proposed method using the UNSW-NB15 and CICIDS2017 datasets.The exper-imental results show that the unsupervised AE model has better performance than the other 7 intrusion detection models in terms of precision,recall and f1-score.Then,federated learning is used to train the intrusion detection model.The experimental results indicate that the model is more accurate than the local learning model.Finally,we use an improved SHAP explainability method based on Chi-square test to analyze the explainability.The analysis results show that the identification characteristics of the model are consistent with the attack characteristics,and the model is reliable.展开更多
目的探究弥散峰度成像参数对阿尔兹海默病的诊断价值。方法选取2019年4月至2020年12月绍兴市第七人民医院诊治的阿尔兹海默病患者50例为观察组,另外选取50例健康体检的人员为对照组。所有患者均进行弥散峰度成像技术,观察平均弥散率(ave...目的探究弥散峰度成像参数对阿尔兹海默病的诊断价值。方法选取2019年4月至2020年12月绍兴市第七人民医院诊治的阿尔兹海默病患者50例为观察组,另外选取50例健康体检的人员为对照组。所有患者均进行弥散峰度成像技术,观察平均弥散率(average diffusion rate,MD)、平均峰度(meankurtosis,MK)、参数各向异性分数(fraction of the parameter anisotropy,FA)参数。分析不同分组间及不同部位间的弥散峰度成像参数的差异,并比较其与患者认知功能情况。结果阿尔兹海默病患者海马、顶叶白质、额叶白质的MK、FA水平低于健康对照人群,MD高于健康对照人群(P<0.05)。重度阿尔兹海默病患者海马、顶叶白质、额叶白质的MK、FA水平较低于轻度组和中度组,MD高于轻度组和中度组(P<0.05)。阿尔兹海默病患者认知功能评分低于健康对照人群(P<0.05)。结论弥散峰度成像参数与疾病严重程度具有一定的相关性,且其在海马、顶叶白质、额叶白质中的表达对阿尔兹海默病患者具有诊断价值。展开更多
Recently,various privacy-preserving schemes have been proposed to resolve privacy issues in federated learning(FL).However,most of them ignore the fact that anomalous users holding low-quality data may reduce the accu...Recently,various privacy-preserving schemes have been proposed to resolve privacy issues in federated learning(FL).However,most of them ignore the fact that anomalous users holding low-quality data may reduce the accuracy of trained models.Although some existing works manage to solve this problem,they either lack privacy protection for users’sensitive information or introduce a two-cloud model that is difficult to find in reality.A reliable and privacy-preserving FL scheme named reliable and privacy-preserving federated learning(RPPFL)based on a single-cloud model is proposed.Specifically,inspired by the truth discovery technique,we design an approach to identify the user’s reliability and thereby decrease the impact of anomalous users.In addition,an additively homomorphic cryptosystem is utilized to provide comprehensive privacy preservation(user’s local gradient privacy and reliability privacy).We give rigorous theoretical analysis to show the security of RPPFL.Based on open datasets,we conduct extensive experiments to demonstrate that RPPEL compares favorably with existing works in terms of efficiency and accuracy.展开更多
文摘针对太赫兹探测器的研究现状,分析研究了现有太赫兹探测器的优缺点,并对热释电型太赫兹探测器的热释电材料和吸收材料进行研究,提出了一种基于铌镁钛酸铅(PMNT)晶片的新型太赫兹探测器的设计和制作方法。用PMNT晶片作为热释电材料,碳纳米管作为吸收材料,使用精密减薄抛光工艺和溅射电极工艺等工艺技术,完成了新型热释电太赫兹探测器的设计与制作。并利用该探测器设计了一款太赫兹功率计,经测试,该功率计在0.1~30 THz宽频段、0.5~100 m W大功率动态范围内,功率测量准确度达到了±10%,综合指标达到国际同类产品先进水平,应用效果良好。
基金supported by National Natural Science Fundation of China under Grant 61972208National Natural Science Fundation(General Program)of China under Grant 61972211+2 种基金National Key Research and Development Project of China under Grant 2020YFB1804700Future Network Innovation Research and Application Projects under Grant No.2021FNA020062021 Jiangsu Postgraduate Research Innovation Plan under Grant No.KYCX210794.
文摘With the rapid development of the Internet,network security and data privacy are increasingly valued.Although classical Network Intrusion Detection System(NIDS)based on Deep Learning(DL)models can provide good detection accuracy,but collecting samples for centralized training brings the huge risk of data privacy leakage.Furthermore,the training of supervised deep learning models requires a large number of labeled samples,which is usually cumbersome.The“black-box”problem also makes the DL models of NIDS untrustworthy.In this paper,we propose a trusted Federated Learning(FL)Traffic IDS method called FL-TIDS to address the above-mentioned problems.In FL-TIDS,we design an unsupervised intrusion detection model based on autoencoders that alleviates the reliance on marked samples.At the same time,we use FL for model training to protect data privacy.In addition,we design an improved SHAP interpretable method based on chi-square test to perform interpretable analysis of the trained model.We conducted several experiments to evaluate the proposed FL-TIDS.We first determine experimentally the structure and the number of neurons of the unsupervised AE model.Secondly,we evaluated the proposed method using the UNSW-NB15 and CICIDS2017 datasets.The exper-imental results show that the unsupervised AE model has better performance than the other 7 intrusion detection models in terms of precision,recall and f1-score.Then,federated learning is used to train the intrusion detection model.The experimental results indicate that the model is more accurate than the local learning model.Finally,we use an improved SHAP explainability method based on Chi-square test to analyze the explainability.The analysis results show that the identification characteristics of the model are consistent with the attack characteristics,and the model is reliable.
文摘目的探究弥散峰度成像参数对阿尔兹海默病的诊断价值。方法选取2019年4月至2020年12月绍兴市第七人民医院诊治的阿尔兹海默病患者50例为观察组,另外选取50例健康体检的人员为对照组。所有患者均进行弥散峰度成像技术,观察平均弥散率(average diffusion rate,MD)、平均峰度(meankurtosis,MK)、参数各向异性分数(fraction of the parameter anisotropy,FA)参数。分析不同分组间及不同部位间的弥散峰度成像参数的差异,并比较其与患者认知功能情况。结果阿尔兹海默病患者海马、顶叶白质、额叶白质的MK、FA水平低于健康对照人群,MD高于健康对照人群(P<0.05)。重度阿尔兹海默病患者海马、顶叶白质、额叶白质的MK、FA水平较低于轻度组和中度组,MD高于轻度组和中度组(P<0.05)。阿尔兹海默病患者认知功能评分低于健康对照人群(P<0.05)。结论弥散峰度成像参数与疾病严重程度具有一定的相关性,且其在海马、顶叶白质、额叶白质中的表达对阿尔兹海默病患者具有诊断价值。
基金supported in part by the Fundamental Research Funds for Central Universities under Grant No.2022RC006in part by the National Nat⁃ural Science Foundation of China under Grant Nos.62201029 and 62202051+2 种基金in part by the BIT Research and Innovation Promoting Project under Grant No.2022YCXZ031in part by the Shandong Provincial Key Research and Development Program under Grant No.2021CXGC010106in part by the China Postdoctoral Science Foundation under Grant Nos.2021M700435,2021TQ0042,2021TQ0041,BX20220029 and 2022M710007.
文摘Recently,various privacy-preserving schemes have been proposed to resolve privacy issues in federated learning(FL).However,most of them ignore the fact that anomalous users holding low-quality data may reduce the accuracy of trained models.Although some existing works manage to solve this problem,they either lack privacy protection for users’sensitive information or introduce a two-cloud model that is difficult to find in reality.A reliable and privacy-preserving FL scheme named reliable and privacy-preserving federated learning(RPPFL)based on a single-cloud model is proposed.Specifically,inspired by the truth discovery technique,we design an approach to identify the user’s reliability and thereby decrease the impact of anomalous users.In addition,an additively homomorphic cryptosystem is utilized to provide comprehensive privacy preservation(user’s local gradient privacy and reliability privacy).We give rigorous theoretical analysis to show the security of RPPFL.Based on open datasets,we conduct extensive experiments to demonstrate that RPPEL compares favorably with existing works in terms of efficiency and accuracy.
