不要提吃饭
发表于 2025-3-21 19:14:53
书目名称Knowledge Discovery from Sensor Data影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0543866<br><br> <br><br>书目名称Knowledge Discovery from Sensor Data影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0543866<br><br> <br><br>书目名称Knowledge Discovery from Sensor Data网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0543866<br><br> <br><br>书目名称Knowledge Discovery from Sensor Data网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0543866<br><br> <br><br>书目名称Knowledge Discovery from Sensor Data被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0543866<br><br> <br><br>书目名称Knowledge Discovery from Sensor Data被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0543866<br><br> <br><br>书目名称Knowledge Discovery from Sensor Data年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0543866<br><br> <br><br>书目名称Knowledge Discovery from Sensor Data年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0543866<br><br> <br><br>书目名称Knowledge Discovery from Sensor Data读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0543866<br><br> <br><br>书目名称Knowledge Discovery from Sensor Data读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0543866<br><br> <br><br>
Maximize
发表于 2025-3-21 23:39:40
Spatiotemporal Neighborhood Discovery for Sensor Data, discretize temporal intervals. These methods were tested on real life datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b)highway sensor network data archive. We have found encouraging results which are validated by real life phenomenon.
发牢骚
发表于 2025-3-22 00:34:36
Unsupervised Plan Detection with Factor Graphs,levant locations. Instead, we introduce 2 unsupervised methods to simultaneously estimate model parameters and hidden values within a Factor graph representing agent transitions over time. We evaluate our approach by applying it to goal prediction in a GPS dataset tracking 1074 ships over 5 days in the English channel.
interpose
发表于 2025-3-22 04:53:27
Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set,n or simple thresholding techniques to identify these anomalies. We describe the application of probabilistic modeling and unsupervised learning techniques to this data set and illustrate how these approaches can successfully detect underlying systematic patterns even in the presence of substantial noise and missing data.
欲望小妹
发表于 2025-3-22 09:12:28
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耕种
发表于 2025-3-22 13:05:32
Data Mining for Diagnostic Debugging in Sensor Networks: Preliminary Evidence and Lessons Learned,osis in the face of non-reproducible behavior, high interactive complexity, and resource constraints. Several examples are given to finding bugs in real sensor network code using the tools developed, demonstrating the efficacy of the approach.
解决
发表于 2025-3-22 17:27:05
An Adaptive Sensor Mining Framework for Pervasive Computing Applications,nt in pervasive computing applications, such as the startup triggers and temporal information. In this paper, we present a description of our mining framework and validate the approach using data collected in the CASAS smart home testbed.
野蛮
发表于 2025-3-23 00:21:18
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柏树
发表于 2025-3-23 02:16:11
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STELL
发表于 2025-3-23 08:06:44
Pari Delir Haghighi,Brett Gillick,Shonali Krishnaswamy,Mohamed Medhat Gaber,Arkady Zaslavsky