Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative ...Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative selection algorithm is presented.In this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are deployed.The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set.Results show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly.展开更多
Self-biting disease occurred in most farmed fur animals in the world. The mechanism and rapid detection method of this disease has not been reported. We applied bulked sergeant analysis （BSA） in combination with RAP...Self-biting disease occurred in most farmed fur animals in the world. The mechanism and rapid detection method of this disease has not been reported. We applied bulked sergeant analysis （BSA） in combination with RAPD method to analyze a molecular genetic marker linked with self-biting trait in mink group. The molecular marker was converted into sequence-characterized amplified regions （SCAR） marker for rapid detection of this disease. A single RAPD marker A8 amplified a specific band of 263bp in self-biting minks, which was designated as SRA8-250, and non-specific band of 315bp in both self-biting and healthy minks. The sequences of the bands exhibited 75% and 88% similarity to Canis familiarizes major histocompatibility complex （MHC） class II region and Macaca mulatta MHC class I region, respectively. A SCAR marker SCAR-A8 was designed for the specific fragment SRA8-250 and validated in 30 self-biting minks and 30 healthy minks. Positive amplification of SCAR-A8 was detected in 24 self-biting minks and 12 healthy minks. χ2 test showed significant difference （p〈0.01） in the detection rate between the two groups. This indicated that SRA8-250 can be used as a positive marker to detect self-biting disease in minks. Furthermore, the finding that self-biting disease links with MHC genes has significant implications for the mechanism of the disease.展开更多
Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the m...Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the monitoring range and the communication range, and proposes an optimal deployment with self-healing movement algorithm for closed or semi-closed area with irregular shape, which can not only satisfy both coverage and connectivity by using as few nodes as possible, but also compensate the failure of nodes by mobility in WSNs. We compute the maximum efficient range of several neighbor sensors based on the dif-ferent relationships between monitoring range and communication range with consideration of the complex boundary or obstacles in the region, and combine it with the Euclidean Minimum Spanning Tree (EMST) algorithm to ensure the coverage and communication of Region of Interest (ROI). Besides, we calculate the location of dead nodes by Geometry Algorithm, and move the higher priority nodes to replace them by an-other Improved Virtual Force Algorithm (IVFA). Eventually, simulation results based-on MATLAB are presented, which do show that this optimal deployment with self-healing movement algorithm can ensure the coverage and communication of an entire region by requiring the least number of nodes and effectively compensate the loss of the networks.展开更多
Self-driving tour is one of the most important ways for people to travel, and network travel notes actually reflect the traveling information of self-driving tourists. In this paper, with the network travel notes of s...Self-driving tour is one of the most important ways for people to travel, and network travel notes actually reflect the traveling information of self-driving tourists. In this paper, with the network travel notes of self-driving tourists as the research object, methods such as text analysis and visualization were adopted to study behavior patterns of self-driving tourists, tourism experience,time-space migration, and distribution of tourism resources in Inner Mongolia, from the multiple dimensions of mobile drivers,perceived dimensions, and spatial migration. The results showed that: ① self-driving tourists had a variety of motivations for traveling, in which love for nature dominated; ② self-driving tour destinations were mainly Hulunbuir,Ordos,and Alxa League;③ spatial migration was characterized by obvious seasonal fluctuations. The research on the behavior of self-driving tourists in Inner Mongolia is an important part for the study of the connection between tourism resources and market connection in Inner Mongolia, and is of significance for guiding the theory, practice and policy formulation of self-driving tours in Inner Mongolia.展开更多
基金Sponsored by the National Natural Science Foundation of China (Grant No. 60671049)the Subject Chief Foundation of Harbin (Grant No.2003AFXXJ013)+1 种基金the Education Department Research Foundation of Heilongjiang Province(Grant No. 10541044, 1151G012)the Postdoctor Foundation of Heilongjiang Province(Grant No.LBH-Z05092)
文摘Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative selection algorithm is presented.In this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are deployed.The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set.Results show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly.
文摘Self-biting disease occurred in most farmed fur animals in the world. The mechanism and rapid detection method of this disease has not been reported. We applied bulked sergeant analysis （BSA） in combination with RAPD method to analyze a molecular genetic marker linked with self-biting trait in mink group. The molecular marker was converted into sequence-characterized amplified regions （SCAR） marker for rapid detection of this disease. A single RAPD marker A8 amplified a specific band of 263bp in self-biting minks, which was designated as SRA8-250, and non-specific band of 315bp in both self-biting and healthy minks. The sequences of the bands exhibited 75% and 88% similarity to Canis familiarizes major histocompatibility complex （MHC） class II region and Macaca mulatta MHC class I region, respectively. A SCAR marker SCAR-A8 was designed for the specific fragment SRA8-250 and validated in 30 self-biting minks and 30 healthy minks. Positive amplification of SCAR-A8 was detected in 24 self-biting minks and 12 healthy minks. χ2 test showed significant difference （p〈0.01） in the detection rate between the two groups. This indicated that SRA8-250 can be used as a positive marker to detect self-biting disease in minks. Furthermore, the finding that self-biting disease links with MHC genes has significant implications for the mechanism of the disease.
文摘Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the monitoring range and the communication range, and proposes an optimal deployment with self-healing movement algorithm for closed or semi-closed area with irregular shape, which can not only satisfy both coverage and connectivity by using as few nodes as possible, but also compensate the failure of nodes by mobility in WSNs. We compute the maximum efficient range of several neighbor sensors based on the dif-ferent relationships between monitoring range and communication range with consideration of the complex boundary or obstacles in the region, and combine it with the Euclidean Minimum Spanning Tree (EMST) algorithm to ensure the coverage and communication of Region of Interest (ROI). Besides, we calculate the location of dead nodes by Geometry Algorithm, and move the higher priority nodes to replace them by an-other Improved Virtual Force Algorithm (IVFA). Eventually, simulation results based-on MATLAB are presented, which do show that this optimal deployment with self-healing movement algorithm can ensure the coverage and communication of an entire region by requiring the least number of nodes and effectively compensate the loss of the networks.
基金Sponsored by Scientific Research Projects of Colleges and Universities in the Inner Mongolia Autonomous Region(NJSY018)
文摘Self-driving tour is one of the most important ways for people to travel, and network travel notes actually reflect the traveling information of self-driving tourists. In this paper, with the network travel notes of self-driving tourists as the research object, methods such as text analysis and visualization were adopted to study behavior patterns of self-driving tourists, tourism experience,time-space migration, and distribution of tourism resources in Inner Mongolia, from the multiple dimensions of mobile drivers,perceived dimensions, and spatial migration. The results showed that: ① self-driving tourists had a variety of motivations for traveling, in which love for nature dominated; ② self-driving tour destinations were mainly Hulunbuir,Ordos,and Alxa League;③ spatial migration was characterized by obvious seasonal fluctuations. The research on the behavior of self-driving tourists in Inner Mongolia is an important part for the study of the connection between tourism resources and market connection in Inner Mongolia, and is of significance for guiding the theory, practice and policy formulation of self-driving tours in Inner Mongolia.
基金supported by National Natural Science Foundation of China(No.51467008)Gansu Provincial Department of Education Industry Support Program(No.2021CYZC-32)。
