The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward...The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.展开更多
Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio...Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain.In this paper,DSM is investigated using two mapping algorithms:Look-Up Table Order(LUTO)and Permutation Method(PM).Then,the bit error rate(BER)performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments.The results show that PM has a lower BER than the LUTO mapping algorithm,and the latter has lower complexity than the former.展开更多
Being as unique nonlinear components of block ciphers,substitution boxes(S-boxes) directly affect the security of the cryptographic systems.It is important and difficult to design cryptographically strong S-boxes th...Being as unique nonlinear components of block ciphers,substitution boxes(S-boxes) directly affect the security of the cryptographic systems.It is important and difficult to design cryptographically strong S-boxes that simultaneously meet with multiple cryptographic criteria such as bijection,non-linearity,strict avalanche criterion(SAC),bits independence criterion(BIC),differential probability(DP) and linear probability(LP).To deal with this problem,a chaotic S-box based on the artificial bee colony algorithm(CSABC) is designed.It uses the S-boxes generated by the six-dimensional compound hyperchaotic map as the initial individuals and employs ABC to improve their performance.In addition,it considers the nonlinearity and differential uniformity as the fitness functions.A series of experiments have been conducted to compare multiple cryptographic criteria of this algorithm with other algorithms.Simulation results show that the new algorithm has cryptographically strong S-box while meeting multiple cryptographic criteria.展开更多
In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accele...In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.展开更多
SITAN算法作为重要的匹配定位算法之一,广泛应用于地形、重力、地磁匹配定位等领域。首先,针对传统SITAN算法无法及时修正航向误差的局限,提出了多初始位置点扇形搜索方法;其次,针对传统SITAN平行滤波器离散搜索的缺点,构建了连续解析的...SITAN算法作为重要的匹配定位算法之一,广泛应用于地形、重力、地磁匹配定位等领域。首先,针对传统SITAN算法无法及时修正航向误差的局限,提出了多初始位置点扇形搜索方法;其次,针对传统SITAN平行滤波器离散搜索的缺点,构建了连续解析的SWRS模型。最后,采用拟牛顿BFGS非线性寻优方法对新构建的SWRS模型进行寻优解算,设计了基于连续解析形式重力基准图的SITAN算法。在2′×2′卫星测高反演重力异常数据库的基础上设计了对比仿真实验。仿真实验结果表明,在INS指示航迹定位误差为9.764 n mile、航向误差为3°及重力量测均方根误差为3 mGal实验条件下,所设计的SITAN算法匹配定位精度为1.695 n mile,优于传统SITAN算法的定位精度2.827 n mile。展开更多
广义频分复用(Generalized Frequency Division Multiplexing,GFDM)系统的灵活调制特性使其应用于低轨(Low Earth Orbit,LEO)卫星通信系统中可以带来诸多增益,但多个调制符号的叠加,导致GFDM的高峰均功率比(Peak-to-Average Power Ratio...广义频分复用(Generalized Frequency Division Multiplexing,GFDM)系统的灵活调制特性使其应用于低轨(Low Earth Orbit,LEO)卫星通信系统中可以带来诸多增益,但多个调制符号的叠加,导致GFDM的高峰均功率比(Peak-to-Average Power Ratio,PAPR)问题。针对此问题,在分析GFDM系统PAPR产生的原因后,对传统选择性映射(Conventional Selective Mapping,C-SLM)算法做出改进,利用Logistic混沌序列代替传统相位因子,解决边带信息冗余问题,采用线性重组的方式,进一步生成更多的备选信号,得到基于Logistic混沌序列的低复杂度选择性映射(Low Complexity SLM Based on Logistic Chaotic Sequences,LC-SLM)算法。在利用LC-SLM算法破坏符号间的相位一致性后,为解决单一算法对GFDM系统PAPR抑制不足的问题,将LC-SLM算法与指数压扩算法结合,提出一种低复杂度选择性映射联合指数压扩(LC-SLM with Exponential Companding,LC-SLM-EC)算法,使系统的PAPR进一步降低。实验结果表明,LC-SLM算法与生成相同数量备选信号的C-SLM算法相比,在大幅降低运算复杂度的前提下具有相近的PAPR性能,提出的LC-SLM-EC算法能够在不提升复杂度的基础上,使PAPR抑制效果更优。展开更多
This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow an...This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.展开更多
基金funded by the State Grid Science and Technology Project“Research on Key Technologies for Prediction and Early Warning of Large-Scale Offshore Wind Power Ramp Events Based on Meteorological Data Enhancement”(4000-202318098A-1-1-ZN).
摘要The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant No.62061024the Project of Gansu Province Science and Technology Department under Grant No.22ZD6GA055.
摘要Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain.In this paper,DSM is investigated using two mapping algorithms:Look-Up Table Order(LUTO)and Permutation Method(PM).Then,the bit error rate(BER)performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments.The results show that PM has a lower BER than the LUTO mapping algorithm,and the latter has lower complexity than the former.
基金supported by the National Natural Science Foundation of China(6060309260975042)
摘要Being as unique nonlinear components of block ciphers,substitution boxes(S-boxes) directly affect the security of the cryptographic systems.It is important and difficult to design cryptographically strong S-boxes that simultaneously meet with multiple cryptographic criteria such as bijection,non-linearity,strict avalanche criterion(SAC),bits independence criterion(BIC),differential probability(DP) and linear probability(LP).To deal with this problem,a chaotic S-box based on the artificial bee colony algorithm(CSABC) is designed.It uses the S-boxes generated by the six-dimensional compound hyperchaotic map as the initial individuals and employs ABC to improve their performance.In addition,it considers the nonlinearity and differential uniformity as the fitness functions.A series of experiments have been conducted to compare multiple cryptographic criteria of this algorithm with other algorithms.Simulation results show that the new algorithm has cryptographically strong S-box while meeting multiple cryptographic criteria.
基金Project(61301181) supported by the National Natural Science Foundation of China
摘要In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.
摘要SITAN算法作为重要的匹配定位算法之一,广泛应用于地形、重力、地磁匹配定位等领域。首先,针对传统SITAN算法无法及时修正航向误差的局限,提出了多初始位置点扇形搜索方法;其次,针对传统SITAN平行滤波器离散搜索的缺点,构建了连续解析的SWRS模型。最后,采用拟牛顿BFGS非线性寻优方法对新构建的SWRS模型进行寻优解算,设计了基于连续解析形式重力基准图的SITAN算法。在2′×2′卫星测高反演重力异常数据库的基础上设计了对比仿真实验。仿真实验结果表明,在INS指示航迹定位误差为9.764 n mile、航向误差为3°及重力量测均方根误差为3 mGal实验条件下,所设计的SITAN算法匹配定位精度为1.695 n mile,优于传统SITAN算法的定位精度2.827 n mile。
摘要广义频分复用(Generalized Frequency Division Multiplexing,GFDM)系统的灵活调制特性使其应用于低轨(Low Earth Orbit,LEO)卫星通信系统中可以带来诸多增益,但多个调制符号的叠加,导致GFDM的高峰均功率比(Peak-to-Average Power Ratio,PAPR)问题。针对此问题,在分析GFDM系统PAPR产生的原因后,对传统选择性映射(Conventional Selective Mapping,C-SLM)算法做出改进,利用Logistic混沌序列代替传统相位因子,解决边带信息冗余问题,采用线性重组的方式,进一步生成更多的备选信号,得到基于Logistic混沌序列的低复杂度选择性映射(Low Complexity SLM Based on Logistic Chaotic Sequences,LC-SLM)算法。在利用LC-SLM算法破坏符号间的相位一致性后,为解决单一算法对GFDM系统PAPR抑制不足的问题,将LC-SLM算法与指数压扩算法结合,提出一种低复杂度选择性映射联合指数压扩(LC-SLM with Exponential Companding,LC-SLM-EC)算法,使系统的PAPR进一步降低。实验结果表明,LC-SLM算法与生成相同数量备选信号的C-SLM算法相比,在大幅降低运算复杂度的前提下具有相近的PAPR性能,提出的LC-SLM-EC算法能够在不提升复杂度的基础上,使PAPR抑制效果更优。
摘要This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.