A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

A New Images Hiding Scheme Based on Chaotic Sequences

We propose a data hidding technique in a still image. This technique is based on chaotic sequence in the transform domain of cover image. We use different chaotic random sequences multiplied by multiple sensitive images, respectively, to spread the spectrum of sensitive images. Multiple sensitive images are hidden in a covert image as a form of noise. The results of theoretical analysis and computer simulation show the new hiding technique have better properties with high security, imperceptibility and capacity for hidden information in comparison with the conventional scheme such as LSB (Least Significance Bit).

Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

In the past few years,deep learning has developed rapidly,and many researchers try to combine their subjects with deep learning.The algorithm based on Recurrent Neural Network(RNN)has been successfully applied in the fields of weather forecasting,stock forecasting,action recognition,etc.because of its excellent performance in processing Spatio-temporal sequence data.Among them,algorithms based on LSTM and GRU have developed most rapidly because of their good design.This paper reviews the RNN-based Spatio-temporal sequence prediction algorithm,introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction,and includes precipitation nowcasting algorithms and traffic flow forecasting algorithms.At the same time,it also compares the advantages and disadvantages,and innovations of each algorithm.The purpose of this article is to give readers a clear understanding of solutions to such problems.Finally,it prospects the future development of RNN in the Spatio-temporal sequence prediction algorithm.

Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.

AN ALGORITHM OF LOCAL PREDICTION FOR CHAOTIC SEQUENCES WITH VARIABLE FRAME LENGTH

According to the issues that the predict errors of chaotic sequences rapidly accumulated in multi-step forecasting which affects the predict accuracy, we proposed a new predict algorithm based on local modeling with variable frame length and interpolation points. The core idea is that, using interpolation method to increase the available sample data, then modeling the chaos dynamics system with least square algorithm which based on the Bernstein polynomial to realize the forecasting. We use the local modeling method, looking for the optimum frame length and interpolation points in every frame to improve the predict peformance. The experimental results show that the proposed algorithm can improve the predictive ability effectively, decreasing the accumulation of iterative errors in multi-step prediction.

Spatio-temporal epidemic type aftershock sequence model for Tangshan aftershock sequence

Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akalke Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.

Two Sequences of Operator Monotone Functions and Strictly Chaotic Order

In this paper we introduce two sequences of operator functions and their dualfunctions： fk（t） = （flogt）k-（t-1）k/log^k＋2t （k = 1,2,...）, gk（t） = （t-1）k-logkt /log^k＋1t （k = 1,2,...） and fk（t）tklog^k＋1t/（tlogt）k-（t-1）^k（k=1,2…）,gk（t）=t^klog^k＋1t/（t-1）^k-log^kt（k=1,2…）defined onWe find that they are all operator monotone functions with respect to the strictly chaoticorder and some ordinary orders among positive invertible operators. Indeed, we extend theresults of the operator monotone function tlogt-t＋1/log^2t which is widely used in the theory of heat transfer of the heat engineering and fluid mechanics[1].

A Class of Chaotic Sequences with Gauss Probability Distribution for Radar Mask Jamming

A simple generation approach for chaotic sequences with Gauss probability distribution is proposed. Theoretical analysis and simulation based on Logistic chaotic model show that the approach is feasible and effective. The distribution characteristics of the novel chaotic sequence are comparable to that of the standard normal distribution. Its mean and variance can be changed to the desired values. The novel sequences have also good randomness. The applications for radar mask jamming are analyzed.

Periodic performance of the chaotic spread spectrum sequence on finite precision

It is well known that the periodic performance of spread spectrum sequence heavily affects the correlative and secure characteristics of communication systems. The chaotic binary sequence is paid more and more attention since it is one kind of applicable spread spectrum sequences. However, there are unavoidable short cyclic problems for chaotic binary sequences in finite precision. The chaotic binary sequence generating methods are studied first. Then the short cyclic behavior of the chaotic sequences is analyzed in detail, which are generated by quantification approaches with finite word-length. At the same time, a chaotic similar function is defined for presenting the cyclic characteristics of the sequences. Based on these efforts, an improved method with scrambling control for generating chaotic binary sequences is proposed. To quantitatively describe the improvement of periodic performance of the sequences, an orthogonal estimator is also defined. Some simulating results are provided. From the theoretical deduction and the experimental results, it is concluded that the proposed method can effectively increase the period and raise the complexity of the chaotic sequences to some extent.

Hybrid chaotic sequence for QS-CDMA system with RAKE receiver

A class of the hybrid chaotic sequences is presented. The generator of the sequences is given and realized by the digital method. The hybrid chaotic sequences exhibit good random properties that are very important for the performance of QS-CDMA system with RAKE receiver. The performance of the system is analyzed when the hybrid chaotic sequences are used as spreading codes in a QS-CDMA system with RAKE receiver and compared with those obtained for m-se-quences and logistic sequences. The results show that the hybrid chaotic sequences are a class of very promising spreading codes for QS-CDMA system.

Comparative analysis between chaotic sequence and PN sequence in spread spectrum system

This paper introduces two kinds of sequences used in spread spectrum communication system： pseudo-noise （PN） se-quence generated by linear feedback shift register（LFSR）, such as m-sequence and Gold sequence, and the chaotic sequence generated by the determination of the nonlinear system. The characteristics of PN sequence and chaotic sequence are analyzed by simulation, and the advantages and disadvantages of different sequences in spread spectrum communication system are ob-tained. The advantages of sequence can be used to improve signal transmission in a spread spectrum communication system.

Multi-scale regionalization based mining of spatio-temporal teleconnection patterns between anomalous sea and land climate events

Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-dependency in this kind of pattern is still not well handled by existing work. Therefore, in this study, the multi-scale regionalization is embedded into the spatio-temporal teleconnection pattern mining between anomalous sea and land climatic events. A modified scale-space clustering algorithm is first developed to group climate sequences into multi-scale climate zones. Then, scale variance analysis method is employed to identify climate zones at characteristic scales, indicating the main characteristics of geographical phenomena. Finally, by using the climate zones identified at characteristic scales, a time association rule mining algorithm based on sliding time windows is employed to discover spatio-temporal teleconnection patterns. Experiments on sea surface temperature, sea level pressure, land precipitation and land temperature datasets show that many patterns obtained by the multi-scale approach are coincident with prior knowledge, indicating that this method is effective and reasonable. In addition, some unknown teleconnection patterns discovered from the multi-scale approach can be further used to guide the prediction of land climate.

Analysis of quaternary digital chaotic sequence performance based on chaotic matrix

With good randomness and high sensitivity to initial values,chaotic sequences have been extensively used in secure communication.Real chaotic sequences are highly sensitive to initial values.It is an analog quantity in the domain of attraction,which is not conducive to the transmission of digital signals.In order to improve the stability,real chaotic sequences can be quantized into digital chaotic sequences.According to the relationship between the information rate and the symbol rate,the symbol rate of binary sequence is the same as the information rate.The information rate can be doubled by quantizing a real-valued sequence into a quaternary sequence.The chaotic sequence has weak periodicity.Moreover,the periodicity of binary digital chaotic sequences is much weaker than that of quaternary chaotic sequences.Compared with the multi-dimensional chaotic map,the one-dimensional chaotic map has small key space and low security.In this paper,a new real-valued chaotic sequence is generated based on the chaotic matrix method constructed by Logistic map and Kent map.Two quantization methods are used to digitize the real-valued chaotic sequence to obtain the quaternary digital chaotic sequence.Moreover,the randomness,the time series complexity and the correlation of the new quaternary chaotic sequence are compared and studied.The simulation results demonstrate that the quaternary digital chaotic sequence obtained by the chaotic matrix has good randomness and correlation.

Design of Digital Hybrid Chaotic Sequence Generator

The feasibility of the hybrid chaotic sequences as the spreading codes in code divided multiple access(CDMA) system is analyzed. The design and realization of the digital hybrid chaotic sequence generator by very high speed integrated circuit hardware description language(VHDL) are described. A valid hazard canceledl method is presented. Computer simulations show that the stable digital sequence waveforms can be produced. The correlations of the digital hybrid chaotic sequences are compared with those of m-sequences. The results show that the correlations of the digital hybrid chaotic sequences are almost as good as those of m-sequences. The works in this paper explored a road for the practical applications of chaos.

Video Transmission Secrecy Improvement Based on Fractional Order Hyper Chaotic System

In the Digital World scenario,the confidentiality of information in video transmission plays an important role.Chaotic systems have been shown to be effective for video signal encryption.To improve video transmission secrecy,compressive encryption method is proposed to accomplish compression and encryption based on fractional order hyper chaotic system that incorporates Compressive Sensing(CS),pixel level,bit level scrambling and nucleotide Sequences operations.The measurement matrix generates by the fractional order hyper chaotic system strengthens the efficiency of the encryption process.To avoid plain text attack,the CS measurement is scrambled to its pixel level,bit level scrambling decreases the similarity between the adjacent measurements and the nucleotide sequence operations are done on the scrambled bits,increasing the encryption.Two stages are comprised in the reconstruction technique,the first stage uses the intra-frame similarity and offers robust preliminary retrieval for each frame,and the second stage iteratively improves the efficiency of reconstruction by integrating inter frame Multi Hypothesis(MH)estimation and weighted residual sparsity modeling.In each iteration,the residual coefficient weights are modified using a mathematical approach based on the MH predictions,and the Split Bregman iteration algorithm is defined to resolve weighted l1 regularization.Experimental findings show that the proposed algorithm provides good compression of video coupled with an efficient encryption method that is resistant to multiple attacks.

混沌密码理论研究与应用新进展

混沌密码研究主要包括混沌序列密码、混沌分组密码和混沌公钥密码。混沌序列密码具有软硬件实现简单、加解密处理速度快、没有或只有有限的错误传播等特点。混沌序列密码的设计方面研究成果极为丰硕,但其安全性分析一般停留在统计性验证分析阶段,而对可证明安全性或其他有条件安全性的证明和分析很少,因此证明混沌序列密码的安全性已成为该领域亟需填补的空白。混沌分组密码的研究颇为成熟,但其整体结构一般受限于Feistel结构、SPN结构、Lai-Massey结构及其变形结构。因此,构建突破传统结构的创新性密码架构成为混沌分组密码亟待解决的关键问题之一。混沌公钥密码的研究相对薄弱,利用混沌系统特有的密码特性和公钥密码系统相结合的混沌公钥密码算法不仅具有理论研究意义,而且具有实际应用价值。混沌系统也被广泛应用于通信传输、语音加密、图像加密和扩频通信等保密通信领域。电、光混沌系统实现简便,其计算复杂性不会随着密钥空间的增大而明显增大,能够为无线通信、物理层链路安全、应用层数据安全等不同场景提供安全保护。

动作时序优化振动信号混沌吸引子特征的断路器操动状态辨识方法

高压断路器分合闸操作过程包括掣子脱扣、弹簧释能、动静触头碰撞等动作,机械振动信号作为能量传递的表现,蕴含着丰富的运行状态信息。该文基于断路器关键动作节点的时序,提出一种动作时序优化振动信号混沌吸引子特征的断路器操动状态辨识方法。首先,由高速相机捕捉断路器操动过程主轴拐臂运动图像,利用目标跟踪算法获取动触头关键运动节点时序指标;然后,依据时序指标划分操动过程,通过对不同阶段的振动信号相空间重构,提取相空间吸引子几何和属性特征;最后,利用智能分类算法进行故障辨识,实验结果验证该方法在大幅节省时间开销的同时,有效提高了分类辨识的准确率达96.9%。该文方法在断路器状态监测和带电测试中具有广阔应用前景。

基于三维超混沌映射的图像加密及其FPGA实现

随着信息技术的不断发展,确保信息安全已成为重要问题,而图像作为被广泛使用的多媒体工具,时常面临着泄露的风险,针对该问题提出一种基于三维超混沌映射的图像加密算法。该算法首先构造一个离散三维超混沌系统,此系统相较于传统的混沌系统拥有更好的混沌特性,所生成的序列具有更强的随机性。然后在此序列的基础上对图片进行置乱和扩散:在置乱阶段,通过混沌序列对原始图片的像素点进行索引排序,实现像素置乱;在扩散阶段,使用处理后的混沌序列对置乱图像的像素值进行分层异或,实现像素扩散。软件仿真和性能分析表明,该算法具有较高的安全性和较强的抗干扰能力,最后通过FPGA硬件平台完成图像加密。整个算法包括软件仿真和硬件实现两个部分,能够有效提升学生将理论和实践相结合的能力,增强学生对非线性理论的理解。

动态层簇式无线传感网络信息加密算法

与传统的无线网络基于某种确定的网络拓扑结构组织不同,动态层簇式无线传感网络(Dynamic Layer Clustered Wireless Sensor Networks,DLCN)则是通过动态层次化的方式进行组织,导致DLCN在层次簇、簇首节点选取等诸多细节,实现较为复杂,其信息加密复杂度增加,安全性和平衡性问题一直是一个难点。提出针对动态层簇式无线传感网络的信息加密算法。分析动态层簇式无线传感网络结构,计算网络节点权重跳数以及传输时延,选取最优簇首,通过Logistic混沌映射对混沌序列展开计算,结合Merlay状态机的迁移传输,输出子密钥,实现了传感信息的加密。仿真结果表明:所提算法在DLCN数据加密过程中的密文位数始终在60左右、平均密钥扩展用时在2.5 ms以下、且二值比数值更接近于1,具备较高的安全性和平衡性。