Time Parameter Based Low-Energy Data Encryption Method for Mobile Applications

Various mobile devices and applications are now used in daily life.These devices require high-speed data processing,low energy consumption,low communication latency,and secure data transmission,especially in 5G and 6G mobile networks.High-security cryptography guarantees that essential data can be transmitted securely;however,it increases energy consumption and reduces data processing speed.Therefore,this study proposes a low-energy data encryption(LEDE)algorithm based on the Advanced Encryption Standard(AES)for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things(IoT)devices.In the proposed LEDE algorithm,the system time parameter is employed to create a dynamic S-Box to replace the static S-Box of AES.Tests indicated that six-round LEDE encryption achieves the same security level as 10-round conventional AES encryption.This reduction in encryption time results in the LEDE algorithm having a 67.4%lower energy consumption and 43.9%shorter encryption time than conventional AES;thus,the proposed LEDE algorithm can improve the performance and the energy consumption of IoT edge devices.

A tentative model for the explanation of B?th law using the order parameter of seismicity in natural time

Using the order parameter of seismicity defined in natural time, we suggest a simple model for the expla- nation of Bath law, according to which a mainshock differs in magnitude from its largest aftershock by approximately 1.2 regardless of the mainshock magnitude. In addition, the validity of Bath law is studied in the Global Centroid Moment Tensor catalogue by using two different aftershock definitions. It is found that the mean of this difference, when considering all the pairs mainshock-largest aftershock, does not markedly differ from 1.2 and the corresponding distributions do not depend on the mainshock＇s magnitude threshold in a statistically significant manner. Finally, the analysis of the cumulative distribution functions provides evidence in favour of the proposed model.

Parameterized time-frequency analysis to separate multi-radar signals

Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The intercepted signal is difficult to separate with conventional parameters because of severe overlapping in both time and frequency domains. On the contrary, time-frequency analysis maps the 1D signal into a 2D time-frequency plane, which provides a better insight into the signal than traditional methods. Particularly, the parameterized time-frequency analysis (PTFA) shows great potential in processing such non stationary signals. Five procedures for the PTFA are proposed to separate the overlapped multi-radar signal, including initiation, instantaneous frequency estimation with PTFA, signal demodulation, signal separation with adaptive filter and signal recovery. The proposed method is verified with both simulated and real signals, which shows good performance in the application on multi-radar signal separation.

LMS-LIKE ESTIMATION FOR TIME VARYING PARAMETERS

An LMS-like algorithm is applied for estimating the time-varying parameter theta-n in the linear model y(n) = phi-n-tau-theta-n + upsilon-n, which is general in the sense that none of the probabilistic properties such as stationarity, Markov property, independence and ergodicity is imposed on any of the processes {y(n)}, {phi-n}, {theta-n} and {upsilon-n}. It is shown that the alpha-th moment of the estimation error is of order of the alpha-th moment of the observation noise and the parameter variation w(n) change in equivalence theta-n - theta-n-1.

TIME DOMAIN PARAMETERS IDENTIFICATION OF FOUNDATION-STRUCTURE INTERACTION SYSTEM

The time domain parameter laenuncauon memoa oi me iounuauon-structure interaction system is presented. On the basis of building the computation mode and the motion equation of the foundation-structure interaction system, the system parameter identification method was established by using the extended Kalman filter （EKF） technique and taking the unknown parameters in the system as the augment state variables. And the time parameter identification process of the foundation-structure interaction system was implemented by using the data of the layer foundation-storehouse interaction system model test on the large vibration platform. The computation result shows that the established parameter identification method can induce good parameter estimation.

非线性铣削时滞系统最优控制

针对铣刀铣削过程中的时滞现象,为减小颤振,以执行器施加的平衡力为决策变量,以平衡力和铣削颤振位移为性能指标,构建了时滞系统最优控制模型。首先,通过时间尺度变换,将不固定终端时刻转化为固定终端时刻;其次,利用控制参数法,将其转化为参数优化问题;再次,使用协态方法,得到了性能指标和约束关于优化变量的梯度;最后,构造了序列二次规划(SQP)优化算法求解近似优化问题。

过载和攻击时间约束下的非线性最优制导方法

考虑导弹过载受限条件下,对以期望时间为攻击目标的非线性最优制导问题进行了研究。首先,建立了非线性最优制导问题的理论模型,基于庞特里亚金极大值原理和饱和函数方法建立了最优轨迹的最优性条件。其次,根据最优性条件和哈密尔顿轨迹参数化方法,建立了最优轨迹的参数化微分方程组,使得通过数值积分即可生成从飞行状态到最优制导指令映射关系的数据集。然后,通过前馈神经网络对上述映射关系进行近似,实现了非线性最优制导指令的毫秒量级实时生成。最后,通过数值仿真验证了所提非线性最优制导指令生成方法的有效性。

Time variant multi-objective linear fractional interval-valued transportation problem

This paper studies a time-variant multi-objective linear fractional transportation problem. In reality, transported goods should reach in destinations within a specific time. Considering the importance of time, a time-variant multi-objective linear fractional transportation problem is formulated here. We take into account the parameters as cost, supply and demand are interval valued that involved in the proposed model, so we treat the model as a multi-objective linear fractional interval transportation problem. To solve the formulated model, we first convert it into a deterministic form using a new transformation technique and then apply fuzzy programming to solve it. The applicability of our proposed method is shown by considering two numerical examples. At last, conclusions and future research directions regarding our study is included.

Novel l_2-l_∞ controller design for LPV discrete time-delay systems

One of the first attempts to derive energy-to-peak performance criteria and state-feedback controller design problem for linear parameter-varying discrete time systems with time delay is provided. Firstly, we present a parameter-dependent l 2-l ∞ performance criterion using a parameter-dependent Lyapunov function. Upon the conditions addressed, an improved parameter-dependent l 2-l ∞ performance criterion is established by the introduction of a slack variable, which exhibits a kind of decoupling between Lyapunov functions and system matrices. This kind of decoupling enables us to obtain more easily tractable conditions for analysis and synthesis problems. Then, the corresponding parameter-dependent state-feedback controller design is investigated upon these performance criteria, with sufficient conditions obtained for the existence of admissible controllers in terms of parameterized linear matrix inequalities. Finally, a numerical example is provided to illustrate the feasibility and advantage of the proposed controller design procedure.

Observer-based Adaptive Iterative Learning Control for Nonlinear Systems with Time-varying Delays

An observer-based adaptive iterative learning control （AILC） scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays. The linear matrix inequality （LMI） method is employed to design the nonlinear observer. The designed controller contains a proportional-integral-derivative （PID） feedback term in time domain. The learning law of unknown constant parameter is differential-difference-type, and the learning law of unknown time-varying parameter is difference-type. It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized. By constructing a Lyapunov-Krasovskii-like composite energy function （CEF）, we prove the boundedness of all closed-loop signals and the convergence of tracking error. A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.

Push-Pull Finite-Time Convergence Distributed Optimization Algorithm

With the widespread application of distributed systems, many problems need to be solved urgently. How to design distributed optimization strategies has become a research hotspot. This article focuses on the solution rate of the distributed convex optimization algorithm. Each agent in the network has its own convex cost function. We consider a gradient-based distributed method and use a push-pull gradient algorithm to minimize the total cost function. Inspired by the current multi-agent consensus cooperation protocol for distributed convex optimization algorithm, a distributed convex optimization algorithm with finite time convergence is proposed and studied. In the end, based on a fixed undirected distributed network topology, a fast convergent distributed cooperative learning method based on a linear parameterized neural network is proposed, which is different from the existing distributed convex optimization algorithms that can achieve exponential convergence. The algorithm can achieve finite-time convergence. The convergence of the algorithm can be guaranteed by the Lyapunov method. The corresponding simulation examples also show the effectiveness of the algorithm intuitively. Compared with other algorithms, this algorithm is competitive.

Ehrenfest Approach to the Adiabatic Invariants and Calculation of the Intervals of Time Entering the Energy Emission Process in Simple Quantum Systems

In the first step, the Ehrenfest reasoning concerning the adiabatic invariance of the angular orbital momentum is applied to the electron motion in the hydrogen atom. It is demonstrated that the time of the energy emission from the quantum level n+1 to level n can be deduced from the orbital angular momentum examined in the hydrogen atom. This time is found precisely equal to the time interval dictated by the Joule-Lenz law governing the electron transition between the levels n+1 and n. In the next step, the mechanical parameters entering the quantum systems are applied in calculating the time intervals characteristic for the electron transitions. This concerns the neighbouring energy levels in the hydrogen atom as well as the Landau levels in the electron gas submitted to the action of a constant magnetic field.

基于网格搜索算法的6-RUS并联机器人时间最优轨迹规划

针对6-RUS并联喷涂机器人再现轨迹不平滑、轨迹规划效率低等问题,提出了基于优化贝塞尔曲线节点位置的6-RUS并联机器人时间最优轨迹规划方法。首先,将预处理的轨迹离散化为网格点,更新节点参数并优化贝塞尔曲线弧长,进一步拟合小线段路径获取最优几何路径;然后,计算不同粗网格点对应的最佳速度以及求解时间,选择合适的粗网格点,进一步以较小步长密化网格点间路径,迭代求解正反向最大速度,搜索路径的最佳速度曲线,获取6-RUS并联机器人的最佳运行时间。最后,在自研的6-RUS并联机器人平台上进行实验。结果表明,在相同示教轨迹条件下,基于所提的改进贝塞尔曲线算法得到的路径长为8.12 m,优于传统贝塞尔曲线算法以及G2CBS算法的结果;同时将改进的时间最优轨迹规划算法(TOPP)用于优化后的示教路径,所提算法的最优速度曲线的求解时间为416.4 ms,与TOPP-RA算法的最优速度曲线的求解时间相比缩短了244.7 ms,而且该算法下最优轨迹规划时间也优于TOPP-RA算法,该方法提高了最佳速度的求解速率,缩短了6-RUS并联机器人轨迹再现时间,提高了工作效率。

可伸缩的增量连续k近邻查询处理

针对基于TPR树(time-parameterized R-tree)索引的大量并发CKNN(continuous k-nearest neighbor)查询处理,提出了一种可伸缩的增量连续k近邻查询处理(scalable processing of incremental continuous k-nearest neighbor queries,简称SI-CNN)框架,通过引入搜索区域进行预裁剪以减少查询更新所需要的TPR树节点访问代价,并引入了增量结果表以保存候选对象,批量地更新查询结果集,具有良好的可伸缩性.基于SI-CNN框架提出了一种增量更新的SI-CNN查询处理算法,能够基于上次查询结果增量的更新查询,支持查询集合中加入或删除查询和移动对象数据集的插入、删除等动态更新操作.实验结果与分析表明,基于SI-CNN框架的SI-CNN算法可以很好地支持大量并发的CKNN查询处理,具有良好的实用价值.

翼伞系统最优归航轨迹设计的敏感度分析方法

本文对三自由度翼伞系统归航轨迹优化问题进行了研究,采用控制变量参数化与时间尺度变换相结合的优化算法对翼伞系统的最优控制问题进行数值求解.该方法是基于灵敏度分析的优化算法,将控制量以及控制量转换时间转化为一系列参数优化问题同时进行求解.仿真结果表明,相对于基于两端边值优化算法而言,灵敏度分析法只需要正向积分进行求解,因而具有计算简单、耗时短等优点,其控制效果良好,距离偏差和方向偏差均满足实际需求,有效地提高了翼伞系统的着陆精度,验证了该优化算法的可行性.

基于反应力向量灵敏度的模型参数化方法

模型参数化是建立识别方程的关键。本文在虚拟结构向量转换方法的基础上提出了“基于反应力向量灵敏度的模型参数化方法”。该方法首先将反应力向量对单元物理参数作一阶展开 ,对线性参数系统来说 ,即为反应力向量灵敏度与单元物理参数的乘积 ;其次 ,在线性参数系统的假定下 ,对一个单元中包含多个单元物理参数的情况提出了一个简便的求反应力向量灵敏度的算法。利用该方法可直接由常规的有限元代码获得反应力向量灵敏度矩阵 ,而无需编制专门的有限元代码集成分析代码。文中通过算例阐述了该方法的应用。

支持高效更新的移动对象索引研究

基于TPR-树(Time parameterized R-tree,TPR-树)提出的LUTPR-树采用基于主存的更新方法,更新对象的删除操作在内存中完成,故避免了TPR-树中对象更新所导致的耗时的查找操作。同时,LUTPR-树能有效利用主存资源。实验结果表明,与TPR-树相比,LUTPR-树以牺牲很小部分查询性能为代价,大大提高了更新效率,更加适用于更新密集型应用。

周期时变时滞非线性参数化系统的自适应学习控制

针对一阶未知非线性参数化周期时变时滞系统,设计了一种自适应学习控制方案。假设未知时变参数,时变时滞和参考信号的共同周期是已知的,通过重构系统方程,将包含时变时滞在内的所有未知时变项合并成为一个周期时变向量,采用周期自适应律估计该向量.通过构造一个Lyapunov—Krasovskii型复合能量函数证明了所有信号有界并且跟踪误差收敛.结果被推广到一类含有混合参数的高阶非线性系统.通过两个仿真例子说明本文所提出的控制算法的有效性.

旋转机械转速波动检测及故障诊断

针对旋转机械运行转速波动导致信息获取及诊断决策困难问题,以非平稳信号的瞬时频率估计为切入点,结合快速路径优化算法进行非平稳信号的瞬时频率估计,以实现转速波动信息的准确获取。基于参数化时频分析理论的短时阶比原理,依据估计的振动信号瞬时频率变化函数构造匹配旋转算子,将转速波动信号的时频特征进行旋转,获得各时频窗内的短时阶次谱,进而完整保留转速波动信号的瞬态信息。仿真及实验结果表明,该方法可以准确提取出旋转机械转速波动工况下的状态信息及故障特征,为后续故障精密诊断奠定基础。

基于参数化时频分析的进动锥裙目标瞬时微多普勒频率提取方法

微动目标散射点的微多普勒对目标运动、结构参数的估计具有重要意义。该文针对表面光滑的锥裙目标,首先依据进动锥裙目标的等效散射点模型,推导出散射点的理论微多普勒曲线表达式。结合进动调制的锥裙目标微多普勒曲线为多阶正弦级数叠加的先验信息,提出一种基于参数化时频分析的进动锥裙目标微多普勒曲线提取方法。针对多分量信号组成的锥裙目标回波,该方法利用相干信号单距离多普勒干涉(CSRDI)方法估计锥旋频率,进而利用参数化时频分析估计散射点的微多普勒曲线,之后利用带阻滤波器分离估计得到的散射点回波信号。基于电磁仿真数据验证了所提方法的有效性。