针对无线传感网数据不确定性处理有效性策略研究,分析了传感数据不确定的多样性和层次性,设计了sensor Agent、cluster and analyzer Agent和sink and decision-maker Agent三类Agent,探讨了传感数据不确定性类与多智能Agent分层对应关...针对无线传感网数据不确定性处理有效性策略研究,分析了传感数据不确定的多样性和层次性,设计了sensor Agent、cluster and analyzer Agent和sink and decision-maker Agent三类Agent,探讨了传感数据不确定性类与多智能Agent分层对应关系。具体定义了Agent的局部不确定数据处理和通信两种调和组成模块,以及无线传感网与粗糙集技术的智能特性组合,进而提出了传感不确定性分层多智能Agent调和模型。最后,给出了相应实现算法及实例分析,结果表明该智能模型分层调和机制具有化解各类复杂传感数据不确定性的灵活性与实用性。展开更多
The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is conside...The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.展开更多
This study introduced an automatic authentication technique for checking the genuineness of a vehicle. The rough set-based technique was used to handle the uncertainty arisen from artifacts in the acquired images impr...This study introduced an automatic authentication technique for checking the genuineness of a vehicle. The rough set-based technique was used to handle the uncertainty arisen from artifacts in the acquired images imprinted on a vehicle. However, it has been proved to be NP-hard to find all reductions and the minimal reduction, and generally different heuristic algorithms were used to find a set of reductions and the Ganssian distribution was used to describe the uncertainty to achieve the minimal reduction. On the basis of inductive logic programming, the technique can distinguish between two similar images, as is superior to the conventional pattern-recognition technique being merely capable of classifier. Furthermore, it can avoid some failures of the technique based on the correlation coefficient to authenticate binary image. The experiments show an accuracy rate close to 93. 2%.展开更多
文摘针对无线传感网数据不确定性处理有效性策略研究,分析了传感数据不确定的多样性和层次性,设计了sensor Agent、cluster and analyzer Agent和sink and decision-maker Agent三类Agent,探讨了传感数据不确定性类与多智能Agent分层对应关系。具体定义了Agent的局部不确定数据处理和通信两种调和组成模块,以及无线传感网与粗糙集技术的智能特性组合,进而提出了传感不确定性分层多智能Agent调和模型。最后,给出了相应实现算法及实例分析,结果表明该智能模型分层调和机制具有化解各类复杂传感数据不确定性的灵活性与实用性。
基金supported by proposal No.OSD/BCUD/392/197 Board of Colleges and University Development,Savitribai Phule Pune University,Pune
文摘The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.
基金Sponsored by the National High Technology Research and Development Program of China (Grant No. 2003AA1Z2610)
文摘This study introduced an automatic authentication technique for checking the genuineness of a vehicle. The rough set-based technique was used to handle the uncertainty arisen from artifacts in the acquired images imprinted on a vehicle. However, it has been proved to be NP-hard to find all reductions and the minimal reduction, and generally different heuristic algorithms were used to find a set of reductions and the Ganssian distribution was used to describe the uncertainty to achieve the minimal reduction. On the basis of inductive logic programming, the technique can distinguish between two similar images, as is superior to the conventional pattern-recognition technique being merely capable of classifier. Furthermore, it can avoid some failures of the technique based on the correlation coefficient to authenticate binary image. The experiments show an accuracy rate close to 93. 2%.
