Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge

Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinforce-ment learning(DRL)theory and an improved Multi-Agent Deep Deterministic Policy Gradient(MADDPG-D2)algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA.The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm,and considers the introduction of a double noise mechanism to increase the action exploration space in the early stage of the algorithm,and the introduction of a double experience pool to improve the data utilization rate;at the same time,in order to accelerate the training speed and efficiency of the agents,and to solve the cold-start problem of the training,the a priori knowledge technology is applied to the training of the algorithm.Finally,the MADDPG-D2 algorithm is compared and analyzed based on the digital battlefield of ground and air confrontation.The experimental results show that the agents trained by the MADDPG-D2 algorithm have higher win rates and average rewards,can utilize the resources more reasonably,and better solve the problem of the traditional single agent algorithms facing the difficulty of solving the problem in the high-dimensional decision space.The MADDPG-D2 algorithm based on multi-agent architecture proposed in this paper has certain superiority and rationality in DTA.

“哨兵-2”卫星影像及C2RCC算法在太湖冬春季水色分析中的应用

利用太湖2019—2022年冬春季“哨兵-2”卫星10 m分辨率的MSI影像和SNAP软件C2RCC(Case 2 Regional CoastColour)模块,以适合浑浊、富营养程度较高、光学复杂的“二类水体”的C2X-nets为模式,开展太湖叶绿素a、总悬浮物浓度、透明度等水色参数反演。结果表明:冬春季太湖各湖区叶绿素a浓度处于较低水平,但4月中旬起有明显抬升;太湖西部沿岸区、南部沿岸区和湖心区总悬浮物浓度较高,表明这些湖区易受风浪影响,造成沉积物再悬浮;太湖东部沿岸区、北部湖湾及东太湖遥感反演的水体清澈度较高,可能与这些湖区冬春季沉水植物菹草的分布有关,菹草生长对水质有较好的净化效果。总体来看,基于C2X-nets模式的C2RCC反演算法从较高分辨率卫星遥感影像中提取的水色信息,能有效反映太湖冬春季水色指标的空间分布特征,可为湖体生态状况及动态变化的长期监测评估提供有价值的参考。

基于Sentinel-2影像的巴尔托洛冰川冰面湖研究

冰面湖是冰川的重要组成部分,是冰川消融的指示器,不仅对全球气候变化响应迅速,而且对了解和掌握区域水资源信息意义重大。本文基于Sentinel-2遥感数据,利用随机森林算法,对巴尔托洛冰川冰面湖进行识别提取,并基于提取结果分析研究区冰面湖的空间分布特征,以及冰面湖面积、数量与冰川高程的关系。本文冰面湖提取的准确率达96.07%,完整率达92.18%,错误率为11.59%;识别出巴尔托洛冰川冰面湖567个,面积为249.46~37134 m^(2);冰面湖多分布在距冰川末端3~26 km处,其中海拔3800~4300 m之间冰面湖数量最多,面积普遍较大,平均面积为1922 m^(2);随着高程的升高,冰面湖的数量和面积逐渐减少,在高程5300 m以上冰面湖数量仅为15个,平均面积为356 m^(2);高程升高导致冰面温度降低,是冰面湖数量和面积骤减的主要原因。

基2-FFT输入分级截断算法在频域合成孔径超声成像中的研究

为提高超声频域成像算法的计算速度,提出一种应用于超声频域成像算法的基2-FFT输入分级截断算法。首先,借助于COMSOL多物理场仿真软件,建立钢件中含有孔缝缺陷的有限元模型进行声场仿真。仿真结果得到关于缺陷的回波信号,并通过PSM算法对频域内声场进行重建,得到成像区域的聚焦图像,和原始仿真信号的B扫图像相比效果更加直观且成像质量更好,验证了PSM算法的可行性。然后为了避免超声频域成像算法中二维傅里叶变换的冗余计算,进一步提出了支持任意非0值输入的基2-FFT输入分级截断算法。实验结果证明,基2-FFT输入分级截断算法比标准基2-FFT算法快27%,超声频域算法成像速度提高13%。

基于改进INFO-Bi-LSTM模型的SO_(2)排放质量浓度预测

针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。

响应面法和人工神经网络对亚临界CO_(2)萃取红花籽油的建模与优化

本文旨在寻找有效建模方法以预测亚临界CO_(2)萃取红花籽油的萃取率,优化其萃取工艺条件。以单因素实验为基础,采用Box-Behnken试验设计,研究了萃取压力、分离温度、萃取时间对红花籽油萃取率的影响,并采用响应面法(RSM)和人工神经网络(ANN)两种方法分别对同一实验进行建模分析,通过RSM数值优化、人工神经网络和遗传算法结合(ANN-GA)两种方法优化其工艺条件。结果表明,RSM与ANN两种模型均能较为精准预测,但通过两种模型的决定系数(R^(2))、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)、均方根误差(RMSE)值比较,得出ANN模型(R^(2)=0.9966)的预测效果较优于RSM模型(R^(2)=0.9950)。ANN-GA确定的最佳萃取条件及萃取率分别为:萃取压力19.04 MPa、分离温度55.50℃、萃取时间134.98 min、萃取率23.52%。综上,RSM和ANN两种方法均可用于亚临界CO_(2)萃取带壳红花籽油的建模与优化,但ANN的预测准确度及拟合能力更为优秀。

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

核燃料棒芯块^(235)U丰度检测方法研究

核燃料棒中UO_(2)芯块的^(235)U丰度检测是保证核反应堆正常运行的重要环节,根据铀样品能谱谱形,通过迭代拟合算法精确选取目标信号的能量范围,减少了测量精度受UO_(2)芯块年龄的影响,扩大了目标信号能量选取范围,基于小波变换法,过滤无关频率信号,提高了突变信号的识别精度,进一步提高了检测速度。通过模拟存在异常丰度芯块燃料棒检测验证了方法的可行性。

基于Sentinel-1/2数据的洪水淹没范围提取模型

遥感是监测洪水淹没范围、掌握洪涝灾情演变的重要手段,而光学影像在洪水发生时往往有较多缺失,全天候的SAR影像在提取水体时精度略低。为快速、精准提取洪水淹没范围,构建了一种综合利用Sentinel-2光学影像和Sentinel-1雷达影像数据的洪水淹没范围提取模型,采用一种自适应阈值分割算法即大津算法(OTSU)分别对两种数据以及该模型进行了水体范围提取试验,并以河北省保定市为例进行了应用分析。结果显示:云量较少的Sentinel-2影像水体提取效果最好,总体精度(OA)达到95.6%;所构建的模型在引入部分可用Sentinel-2数据后,OA达到95%,相比单独使用Sentinel-1数据OA和Kappa系数分别提升1.2%和2.4%。该模型搭载于Google Earth Engine平台,能实现快速、准确、低成本的地表水体空间范围连续输出,不受限于云雾且比单独使用Sentinel-1影像的提取精度更高,在云覆盖严重导致Sentinel-2数据缺少的情况下,该模型可作为洪水淹没范围提取方法的一种选择。

基于传感器阵列及神经网络算法的NH_(3)和NO_(2)混合气体体积分数识别

针对电阻型气体传感器具有的交叉敏感性,开发了基于WO_(3)传感器阵列及神经网络算法的NH_(3),NO_(2)混合气体体积分数预测技术。采用火焰合成法合成了La掺杂的WO_(3)敏感材料并制备了气体传感器,与商用MQ—137电阻型气体传感器组成阵列。通过提取特征值、神经网络训练,构建传感器阵列输出与气体体积分数的映射模型,并使用该模型由传感器阵列的响应结果对NH_(3),NO_(2)混合气体进行体积分数预测。实验结果表明:经训练后的神经网络能对NH_(3),NO_(2)混合气体中各组分体积分数进行有效预测,平均预测误差分别为3.64%和2.48%。本文所开发的传感器阵列及神经网络算法有效避免了电阻型传感器选择性差的局限,实现了对NH_(3)和NO_(2)混合气体的高效识别和体积分数测量。

An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more prominent.The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big data.However,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability.In addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced.Based on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data usability.Specifically,we construct a new information loss function based on the information quantity theory.Considering that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss function.In addition,to reduce information loss,we improve K-anonymity in two ways.First,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering algorithm.In addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass.Meanwhile,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information loss.Finally,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.

火山SO_(2)排放速率反演

SO_(2)紫外相机因在时间分辨率、空间分辨率、探测灵敏度以及探测精度等诸多方面均具有显著优势而成功应用于火山活动监测及其动力学研究。为解决紫外相机反演SO_(2)排放速率容易受烟羽湍流及图像低对比度影响等问题,提出了融入神经网络的光流算法。首先,基于大气紫外辐射传输特性,阐述了SO_(2)紫外相机的工作机理及SO_(2)浓度图像的反演方法;其次,将神经网络融入光流算法,实现了火山烟羽图像中SO_(2)排放速率的精确反演;最后,与传统光流法进行对比,论证了神经网络光流算法的科学性及优越性与精确性。实验结果表明:在图像低对比度及烟羽湍流效应的双重影响下,神经网络光流法可以把边缘反演的误差从94%降低至5%,显著提高了SO_(2)排放速率反演的精确性。

An Efficient and Provably Secure SM2 Key-Insulated Signature Scheme for Industrial Internet of Things

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce overheads.However,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be addressed.The SM2 digital signature algorithm can provide an authentication mechanism for data to solve such problems.Unfortunately,it still suffers from the problem of key exposure.In order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 algorithm.This scheme boasts strong key insulation and secure keyupdates.Our scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud environments.Finally,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

基于GEE和Sentinel-2影像的杭州城市湿地精细化分类研究

基于Google Earth Engine(GEE)云平台,使用Sentinel-2卫星影像数据,采用随机森林算法面向杭州城市湿地类型进行遥感分类研究.研究结果表明:在进行湿地类型精细化分类时,相比于使用单一特征信息分类,多特征信息的组合可以显著提高分类精度,最优特征组合包括传统光谱特征、红边光谱特征、变换特征、纹理特征和地形特征,其总体精度为81.2%,Kappa系数为0.75;不同特征信息对于不同湿地类型的提取具有重要作用,传统光谱特征对于滩地和养殖池的识别非常有利,而红边光谱特征和变换特征分别对草本沼泽和湖泊的识别更为有效.此外,红边光谱特征和纹理特征的组合有利于运河/水渠的识别.通过特征对比实验可为湿地精细化分类的案例研究提供参考,湿地分类结果可为杭州城市湿地信息的遥感识别提供数据支撑.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

基于遗传算法的重型特种车辆主动悬架H_(2)/H_(∞)控制研究

重型特种车辆的传统被动悬架对不同路况适应能力差,恶劣路况下车辆性能下降明显,无法满足重型特种车辆在不同路况下行驶平顺性和操纵稳定性的需求.针对这一问题,研究建立了三轴重型特种车辆整车9自由度主动悬架模型,设计了H_(2)/H_(∞)鲁棒控制器,并在满足H_(∞)约束条件下采用遗传算法对H_(2)性能指标进行优化,获取了全状态最优控制律,同时对连续随机路面和凸块路面下主、被动悬架性能进行分析.结果表明:在连续随机路面下,相较于被动悬架,H_(2)/H_(∞)控制主动悬架在时域和频域范围内均具有良好的性能,能够提高车辆的行驶平顺性和操纵稳定性;在凸块路面下,主动悬架能够更快地衰减来自不平路面的振动,并使左前轮胎动载荷的最大值降低了23.5%,提升了轮胎的接地性.H_(2)/H_(∞)控制主动悬架对不同路况具有良好的适应能力,能够满足不同路况下重型特种车辆行驶平顺性和操纵稳定性的提升需要.

基于改进核极限学习机和集成算法的脱硫出口SO_(2)浓度预测

脱硫出口SO_(2)浓度的准确预测对实现脱硫系统经济运行具有重要意义,针对脱硫出口SO_(2)浓度影响因素众多,难以准确预测这一问题,提出了基于龙格库塔优化的核极限学习机(KELM)和改进AdaBoost集成算法相结合的预测模型。首先采用核极限学习机作为弱预测器,利用AdaBoost集成算法组合构建强预测器,通过调整脱硫系统不同工况下运行数据权重,建立了一种基于AdaBoost集成算法的出口SO_(2)浓度预测模型。为进一步提升模型学习性能和预测精度,通过引入惩罚系数和先验知识参数改进AdaBoost算法的损失函数,运用龙格库塔算法对KELM的正则系数C和核参数S进行寻优,克服初始参数设置对模型稳定性和预测精度的影响。最后利用电厂运行数据进行仿真实验,结果表明,所建立的出口SO_(2)浓度集成模型预测性能优越、准确度高,能够为脱硫系统优化控制提供技术支持。

基于“2-4”模型和Apriori算法的商业综合体火灾成因分析

为深入分析商业综合体火灾事故成因,揭示事故致因因素之间的关联规则,运用事故致因“2-4”模型和Apriori算法,对40起商业综合体火灾事故原因进行分析,研究结果表明:在组织文化、管理体系、个体能力和个体动作4个层面中,安全主体责任的认识(100%)、安全管理制度未执行(80%)、无安全培训(70%)等因素是火灾事故的主要原因;通过Apriori算法挖掘出277条关联规则,并从高支持度、高置信度和高提升度3个维度进行深入分析,发现员工参与度与防火意识薄弱、安全管理知识匮乏与报警意识薄弱且应急响应不及时等因素之间存在显著关联。基于分析结果,提出加强安全责任意识、完善管理制度执行、强化安全培训等针对性建议,为商业综合体火灾预防提供理论依据和实践指导。

基于O_(2)分子1.27μm气辉反演临近空间温度廓线的新方法

1.27μm波段O_(2)(a^(1)Δg)气辉的辐射强度高、自吸收效应弱,是反演临近空间大气温度的理想目标源。基于O_(2)分子气辉光谱理论以及“剥洋葱”算法,利用扫描成像大气吸收光谱仪(SCIAMACHY)的近红外临边观测数据,成功反演50~100 km区域的大气温度廓线。与SABER、ACE-FTS及激光雷达的观测结果对比表明,在55~85 km的切线高度范围内温度测量误差优于±10 K,而在55 km以下与85 km以上空间区域,由于受到自吸收效应、大气散射以及OH气辉的光谱污染等干扰,温度反演结果出现显著偏差。