Cooperative spectrum monitoring with multiple sensors has been deemed as an efficient mechanism for improving the monitoring accuracy and enlarging the monitoring area in wireless sensor networks.However,there exists ...Cooperative spectrum monitoring with multiple sensors has been deemed as an efficient mechanism for improving the monitoring accuracy and enlarging the monitoring area in wireless sensor networks.However,there exists redundancy among the spectrum data collected by a sensor node within a data collection period,which may reduce the data uploading efficiency.In this paper,we investigate the inter-data commonality detection which describes how much two data have in common.We define common segment set and divide it into six categories firstly,then a method to measure a common segment set is conducted by extracting commonality between two files.Moreover,the existing algorithms fail in finding a good common segment set,so Common Data Measurement(CDM)algorithm that can identify a good common segment set based on inter-data commonality detection is proposed.Theoretical analysis proves that CDM algorithm achieves a good measurement for the commonality between two strings.In addition,we conduct an synthetic dataset which are produced randomly.Numerical results shows that CDM algorithm can get better performance in measuring commonality between two binary files compared with Greedy-String-Tiling(GST)algorithm and simple greedy algorithm.展开更多
This paper presents a novel laser⁃induced fluorescence(LIF)Lidar system for marine oil spilling detection.A bifurcated Y⁃type optical fiber and an optical collimating lens compose a coaxial configuration transceiver f...This paper presents a novel laser⁃induced fluorescence(LIF)Lidar system for marine oil spilling detection.A bifurcated Y⁃type optical fiber and an optical collimating lens compose a coaxial configuration transceiver for this LIF⁃Lidar system.This LIF⁃Lidar system was further applied to measure the excitation spectra from floating oil slicks with different thicknesses on top of seawater at different distances.The system presents several advantages such as compact structure,stable optical path,and convenient operation,which offers a wide application prospect in ocean exploration.展开更多
With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks ...With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks have improved industrial productivity and convenience,they are highly dependent on nonstandard protocol stacks and open-source-based,poorly validated software,resulting in several security vulnerabilities.How-ever,conventional AI-based software vulnerability discovery technologies cannot be applied to IoT because they require excessive memory and com-puting power.This study developed a technique for optimizing training data size to detect software vulnerabilities rapidly while maintaining learning accuracy.Experimental results using a software vulnerability classification dataset showed that different optimal data sizes did not affect the learning performance of the learning models.Moreover,the minimal data size required to train a model without performance degradation could be determined in advance.For example,the random forest model saved 85.18%of memory and improved latency by 97.82%while maintaining a learning accuracy similar to that achieved when using 100%of data,despite using only 1%.展开更多
基金supported in part by the National Natural Science Foundation of China(No.61901328)the China Postdoctoral Science Foundation (No. 2019M653558)+1 种基金the Fundamental Research Funds for the Central Universities (No. CJT150101)the Key project of National Natural Science Foundation of China (No. 61631015)
文摘Cooperative spectrum monitoring with multiple sensors has been deemed as an efficient mechanism for improving the monitoring accuracy and enlarging the monitoring area in wireless sensor networks.However,there exists redundancy among the spectrum data collected by a sensor node within a data collection period,which may reduce the data uploading efficiency.In this paper,we investigate the inter-data commonality detection which describes how much two data have in common.We define common segment set and divide it into six categories firstly,then a method to measure a common segment set is conducted by extracting commonality between two files.Moreover,the existing algorithms fail in finding a good common segment set,so Common Data Measurement(CDM)algorithm that can identify a good common segment set based on inter-data commonality detection is proposed.Theoretical analysis proves that CDM algorithm achieves a good measurement for the commonality between two strings.In addition,we conduct an synthetic dataset which are produced randomly.Numerical results shows that CDM algorithm can get better performance in measuring commonality between two binary files compared with Greedy-String-Tiling(GST)algorithm and simple greedy algorithm.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61605033)the Natural Science Foundation of Shandong Province(Grant No.ZR2016FQ24)+1 种基金the Taishan Blue Industry Leadership Program,Project of Shandong Province(Grant No.[2015]1363)the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201719).
文摘This paper presents a novel laser⁃induced fluorescence(LIF)Lidar system for marine oil spilling detection.A bifurcated Y⁃type optical fiber and an optical collimating lens compose a coaxial configuration transceiver for this LIF⁃Lidar system.This LIF⁃Lidar system was further applied to measure the excitation spectra from floating oil slicks with different thicknesses on top of seawater at different distances.The system presents several advantages such as compact structure,stable optical path,and convenient operation,which offers a wide application prospect in ocean exploration.
基金supported by a National Research Foundation of Korea (NRF)grant funded by the Ministry of Science and ICT (MSIT) (No.2020R1F1A1061107)the Korea Institute for Advancement of Technology (KIAT)grant funded by the Korean Government (MOTIE) (P0008703,The Competency Development Program for Industry Specialists)the MSIT under the ICAN (ICT Challenge and Advanced Network of HRD)program (No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning and Evaluation (IITP).
文摘With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks have improved industrial productivity and convenience,they are highly dependent on nonstandard protocol stacks and open-source-based,poorly validated software,resulting in several security vulnerabilities.How-ever,conventional AI-based software vulnerability discovery technologies cannot be applied to IoT because they require excessive memory and com-puting power.This study developed a technique for optimizing training data size to detect software vulnerabilities rapidly while maintaining learning accuracy.Experimental results using a software vulnerability classification dataset showed that different optimal data sizes did not affect the learning performance of the learning models.Moreover,the minimal data size required to train a model without performance degradation could be determined in advance.For example,the random forest model saved 85.18%of memory and improved latency by 97.82%while maintaining a learning accuracy similar to that achieved when using 100%of data,despite using only 1%.
