By identifying and responding to any malicious behavior that could endanger the system,the Intrusion Detection System(IDS)is crucial for preserving the security of the Industrial Internet of Things(IIoT)network.The be...By identifying and responding to any malicious behavior that could endanger the system,the Intrusion Detection System(IDS)is crucial for preserving the security of the Industrial Internet of Things(IIoT)network.The benefit of anomaly-based IDS is that they are able to recognize zeroday attacks due to the fact that they do not rely on a signature database to identify abnormal activity.In order to improve control over datasets and the process,this study proposes using an automated machine learning(AutoML)technique to automate the machine learning processes for IDS.Our groundbreaking architecture,known as AID4I,makes use of automatic machine learning methods for intrusion detection.Through automation of preprocessing,feature selection,model selection,and hyperparameter tuning,the objective is to identify an appropriate machine learning model for intrusion detection.Experimental studies demonstrate that the AID4I framework successfully proposes a suitablemodel.The integrity,security,and confidentiality of data transmitted across the IIoT network can be ensured by automating machine learning processes in the IDS to enhance its capacity to identify and stop threatening activities.With a comprehensive solution that takes advantage of the latest advances in automated machine learning methods to improve network security,AID4I is a powerful and effective instrument for intrusion detection.In preprocessing module,three distinct imputation methods are utilized to handle missing data,ensuring the robustness of the intrusion detection system in the presence of incomplete information.Feature selection module adopts a hybrid approach that combines Shapley values and genetic algorithm.The Parameter Optimization module encompasses a diverse set of 14 classification methods,allowing for thorough exploration and optimization of the parameters associated with each algorithm.By carefully tuning these parameters,the framework enhances its adaptability and accuracy in identifying potential intrusions.Experimental results demonstrate tha展开更多
Healthy forest is the vital resource to regulate climate at a regional and global level. Forest fire has been regarded as one of the major reasons for the loss of forest and degradation of the environment. Global warm...Healthy forest is the vital resource to regulate climate at a regional and global level. Forest fire has been regarded as one of the major reasons for the loss of forest and degradation of the environment. Global warming is increasing its intensity at an alarming rate. Real-time fire detection is a necessity to avoid large scale losses. Remote sensing is a quick and cheap technique for detecting and monitoring forest fires on a large scale. Advance Very Radiometer Resolution (AVHRR) has been used already for a long period for fire detection. The use of Moderate Resolution Imaging Radio Spectrometer (MODIS) for fire detection has recently preceded AVHRR and a large number of fire products are being developed. MODIS based forest fire detection and monitoring system can solve the problem of real-time forest fire monitoring. The system facilitates data acquisition, processing, reporting and feedback on the fire location information in an automated manner. It provides location information at 1 × 1 kilometer resolution on the active fires which are present during the satellite overpass twice a day. The users are provided with the information on SMS alert with fire location details, email notification, and online visualization of fire locations on website automatically. The whole processes are automated and provide better accuracy for fire detection.展开更多
With the advancement of technology in recent years, effective fault diagnosis became a necessity to verify the performance and ensure the quality of complex systems. In this paper, an original verification methodology...With the advancement of technology in recent years, effective fault diagnosis became a necessity to verify the performance and ensure the quality of complex systems. In this paper, an original verification methodology for complex consumer electronic devices is presented. Verification of the system which consists of hardware （integrated circuit） and corresponding software within a flat panel TV set is in the focus. Proposed methodology provides reliable functional failure detection using the concept of black box testing. Further, the approach is fully automated, improving the reliability and speed of failure detection. The methodology effectiveness has been experimentally evaluated and the analysis results have been reported.展开更多
A new on line titration method and device based on a new technique continuous flow titration is described. By the means of electronically controlled switching of a solenoid valve, the main component of the system, t...A new on line titration method and device based on a new technique continuous flow titration is described. By the means of electronically controlled switching of a solenoid valve, the main component of the system, the equivalent point of the titration is easily determined. Several kinds of mixing tools were examined, whereby a self made mixing chamber with minimum volume gave best results and was therefore used in the device. The error of the titration is within 0.2% and the relative standard deviation (RSD) is below 1.2%. The results show no difference compared with a commercial device, meanwhile the new on line titration system is cheaper and fully automated and thus easy to hand and less solvent consumption.展开更多
目的分析自动乳腺全容积扫描(automated breast volume scanner,ABVS)正交三切面观察乳腺肿瘤边缘征象的应用价值。方法回顾性分析2017年4月至2019年5月于陕西省肿瘤医院行乳腺外科手术并经术后病理证实的女性乳腺肿瘤患者206例共计292...目的分析自动乳腺全容积扫描(automated breast volume scanner,ABVS)正交三切面观察乳腺肿瘤边缘征象的应用价值。方法回顾性分析2017年4月至2019年5月于陕西省肿瘤医院行乳腺外科手术并经术后病理证实的女性乳腺肿瘤患者206例共计292个病灶。所有患者术前均行ABVS检查,比较ABVS正交3个不同切面病灶边缘征象对乳腺良恶性肿瘤的诊断效能,并对正交三切面评价乳腺良恶性肿瘤的总体诊断效能进行分析。结果292个病灶中,良性148个,恶性144个。以外科术后病理结果为“金标准”,冠状面毛刺征诊断乳腺癌的敏感度为68.05%,与横切面的17.36%和矢状面的13.19%相比,差异均有统计学意义(χ2=5.63、41.19,P均<0.05)。冠状面边缘成角诊断乳腺癌的敏感度为42.36%,与横切面的26.38%和矢状面的22.22%相比,差异均有统计学意义(χ2=16.00、21.73,P均<0.001)。ABVS正交3个切面的病灶微小分叶和边缘模糊征象诊断乳腺癌的敏感度比较,差异均无统计学意义(P均>0.05)。ABVS正交三切面边缘微小分叶、毛刺征、边缘成角和边缘模糊诊断乳腺癌的敏感度、特异度、阳性预测值、阴性预测值和准确性分别为81.94%、86.48%、85.50%、83.11%、84.24%;68.75%、96.62%、95.19%、76.06%、82.87%;47.22%、95.27%、91.89%、64.97%、71.57%;54.16%、57.43%、55.31%、56.29%、55.82%。结论ABVS冠状面毛刺征和边缘成角评价乳腺良恶性肿瘤具有优势,诊断敏感度优于矢状面与横切面。与其他边缘征象相比,微小分叶的综合评价效能具有稳定性,是诊断恶性结节敏感度和准确性最佳的表面特征。展开更多
Based on analyzing the techniques and architecture of existing network Intrusion Detection System (IDS), and probing into the fundament of Immune System (IS), a novel immune model is presented and applied to network I...Based on analyzing the techniques and architecture of existing network Intrusion Detection System (IDS), and probing into the fundament of Immune System (IS), a novel immune model is presented and applied to network IDS, which is helpful to design an effective IDS. Besides, this paper suggests a scheme to represent the self profile of network. And an automated self profile extraction algorithm is provided to extract self profile from packets. The experimental results prove validity of the scheme and algorithm, which is the foundation of the immune model.展开更多
文摘By identifying and responding to any malicious behavior that could endanger the system,the Intrusion Detection System(IDS)is crucial for preserving the security of the Industrial Internet of Things(IIoT)network.The benefit of anomaly-based IDS is that they are able to recognize zeroday attacks due to the fact that they do not rely on a signature database to identify abnormal activity.In order to improve control over datasets and the process,this study proposes using an automated machine learning(AutoML)technique to automate the machine learning processes for IDS.Our groundbreaking architecture,known as AID4I,makes use of automatic machine learning methods for intrusion detection.Through automation of preprocessing,feature selection,model selection,and hyperparameter tuning,the objective is to identify an appropriate machine learning model for intrusion detection.Experimental studies demonstrate that the AID4I framework successfully proposes a suitablemodel.The integrity,security,and confidentiality of data transmitted across the IIoT network can be ensured by automating machine learning processes in the IDS to enhance its capacity to identify and stop threatening activities.With a comprehensive solution that takes advantage of the latest advances in automated machine learning methods to improve network security,AID4I is a powerful and effective instrument for intrusion detection.In preprocessing module,three distinct imputation methods are utilized to handle missing data,ensuring the robustness of the intrusion detection system in the presence of incomplete information.Feature selection module adopts a hybrid approach that combines Shapley values and genetic algorithm.The Parameter Optimization module encompasses a diverse set of 14 classification methods,allowing for thorough exploration and optimization of the parameters associated with each algorithm.By carefully tuning these parameters,the framework enhances its adaptability and accuracy in identifying potential intrusions.Experimental results demonstrate tha
文摘Healthy forest is the vital resource to regulate climate at a regional and global level. Forest fire has been regarded as one of the major reasons for the loss of forest and degradation of the environment. Global warming is increasing its intensity at an alarming rate. Real-time fire detection is a necessity to avoid large scale losses. Remote sensing is a quick and cheap technique for detecting and monitoring forest fires on a large scale. Advance Very Radiometer Resolution (AVHRR) has been used already for a long period for fire detection. The use of Moderate Resolution Imaging Radio Spectrometer (MODIS) for fire detection has recently preceded AVHRR and a large number of fire products are being developed. MODIS based forest fire detection and monitoring system can solve the problem of real-time forest fire monitoring. The system facilitates data acquisition, processing, reporting and feedback on the fire location information in an automated manner. It provides location information at 1 × 1 kilometer resolution on the active fires which are present during the satellite overpass twice a day. The users are provided with the information on SMS alert with fire location details, email notification, and online visualization of fire locations on website automatically. The whole processes are automated and provide better accuracy for fire detection.
文摘With the advancement of technology in recent years, effective fault diagnosis became a necessity to verify the performance and ensure the quality of complex systems. In this paper, an original verification methodology for complex consumer electronic devices is presented. Verification of the system which consists of hardware （integrated circuit） and corresponding software within a flat panel TV set is in the focus. Proposed methodology provides reliable functional failure detection using the concept of black box testing. Further, the approach is fully automated, improving the reliability and speed of failure detection. The methodology effectiveness has been experimentally evaluated and the analysis results have been reported.
文摘A new on line titration method and device based on a new technique continuous flow titration is described. By the means of electronically controlled switching of a solenoid valve, the main component of the system, the equivalent point of the titration is easily determined. Several kinds of mixing tools were examined, whereby a self made mixing chamber with minimum volume gave best results and was therefore used in the device. The error of the titration is within 0.2% and the relative standard deviation (RSD) is below 1.2%. The results show no difference compared with a commercial device, meanwhile the new on line titration system is cheaper and fully automated and thus easy to hand and less solvent consumption.
文摘目的分析自动乳腺全容积扫描(automated breast volume scanner,ABVS)正交三切面观察乳腺肿瘤边缘征象的应用价值。方法回顾性分析2017年4月至2019年5月于陕西省肿瘤医院行乳腺外科手术并经术后病理证实的女性乳腺肿瘤患者206例共计292个病灶。所有患者术前均行ABVS检查,比较ABVS正交3个不同切面病灶边缘征象对乳腺良恶性肿瘤的诊断效能,并对正交三切面评价乳腺良恶性肿瘤的总体诊断效能进行分析。结果292个病灶中,良性148个,恶性144个。以外科术后病理结果为“金标准”,冠状面毛刺征诊断乳腺癌的敏感度为68.05%,与横切面的17.36%和矢状面的13.19%相比,差异均有统计学意义(χ2=5.63、41.19,P均<0.05)。冠状面边缘成角诊断乳腺癌的敏感度为42.36%,与横切面的26.38%和矢状面的22.22%相比,差异均有统计学意义(χ2=16.00、21.73,P均<0.001)。ABVS正交3个切面的病灶微小分叶和边缘模糊征象诊断乳腺癌的敏感度比较,差异均无统计学意义(P均>0.05)。ABVS正交三切面边缘微小分叶、毛刺征、边缘成角和边缘模糊诊断乳腺癌的敏感度、特异度、阳性预测值、阴性预测值和准确性分别为81.94%、86.48%、85.50%、83.11%、84.24%;68.75%、96.62%、95.19%、76.06%、82.87%;47.22%、95.27%、91.89%、64.97%、71.57%;54.16%、57.43%、55.31%、56.29%、55.82%。结论ABVS冠状面毛刺征和边缘成角评价乳腺良恶性肿瘤具有优势,诊断敏感度优于矢状面与横切面。与其他边缘征象相比,微小分叶的综合评价效能具有稳定性,是诊断恶性结节敏感度和准确性最佳的表面特征。
基金the National Natural Science Foundation of China(69983005)and the Research Fund for the Doctoral Program of Higher Education(RFDP1999048602)
文摘Based on analyzing the techniques and architecture of existing network Intrusion Detection System (IDS), and probing into the fundament of Immune System (IS), a novel immune model is presented and applied to network IDS, which is helpful to design an effective IDS. Besides, this paper suggests a scheme to represent the self profile of network. And an automated self profile extraction algorithm is provided to extract self profile from packets. The experimental results prove validity of the scheme and algorithm, which is the foundation of the immune model.
