Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a ...Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.展开更多
The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission.The clustering technique employed to group...The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission.The clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head.The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network.The proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and Artificial Bee Colony(ABC)algorithm for the better identification of cluster head.The performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies.展开更多
针对历史轨迹加噪发布干扰轨迹时数据集的冗余问题和轨迹形状相似带来的隐私泄露风险,提出轨迹数据先约简后泛化再进行差分隐私加噪的基于改进萤火虫群优化求解的干扰轨迹发布保护机制(IGSO-SDTP)。首先,基于位置显著点约简历史轨迹数据...针对历史轨迹加噪发布干扰轨迹时数据集的冗余问题和轨迹形状相似带来的隐私泄露风险,提出轨迹数据先约简后泛化再进行差分隐私加噪的基于改进萤火虫群优化求解的干扰轨迹发布保护机制(IGSO-SDTP)。首先,基于位置显著点约简历史轨迹数据集;其次,结合k⁃匿名和差分隐私对简化后的轨迹数据集分别进行泛化和加噪;最后,设计了兼顾距离误差和轨迹相似性的加权距离,并以加权距离为评价指标,基于改进萤火虫群优化(IGSO)算法求解加权距离小的干扰轨迹。在多个数据集上的实验结果表明,与RD(Differential privacy for Raw trajectory data)、SDTP(Trajectory Protection of Simplification and Differential privacy)、LIC(Linear Index Clustering algorithm)、DPKTS(Differential Privacy based on K-means Trajectory shape Similarity)相比,IGSO-SDTP方法得到的加权距离分别降低了21.94%、9.15%、14.25%、10.55%,说明所提方法发布的干扰轨迹可用性和稳定性更好。展开更多
文摘Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.
文摘The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission.The clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head.The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network.The proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and Artificial Bee Colony(ABC)algorithm for the better identification of cluster head.The performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies.
文摘针对历史轨迹加噪发布干扰轨迹时数据集的冗余问题和轨迹形状相似带来的隐私泄露风险,提出轨迹数据先约简后泛化再进行差分隐私加噪的基于改进萤火虫群优化求解的干扰轨迹发布保护机制(IGSO-SDTP)。首先,基于位置显著点约简历史轨迹数据集;其次,结合k⁃匿名和差分隐私对简化后的轨迹数据集分别进行泛化和加噪;最后,设计了兼顾距离误差和轨迹相似性的加权距离,并以加权距离为评价指标,基于改进萤火虫群优化(IGSO)算法求解加权距离小的干扰轨迹。在多个数据集上的实验结果表明,与RD(Differential privacy for Raw trajectory data)、SDTP(Trajectory Protection of Simplification and Differential privacy)、LIC(Linear Index Clustering algorithm)、DPKTS(Differential Privacy based on K-means Trajectory shape Similarity)相比,IGSO-SDTP方法得到的加权距离分别降低了21.94%、9.15%、14.25%、10.55%,说明所提方法发布的干扰轨迹可用性和稳定性更好。
