“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。

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.

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.

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。

基于SiO_(2)气凝胶的消防机器人一体化防火隔热结构设计优化

围绕消防机器人工作时机体外表面热防护问题,建立以SiO_(2)气凝胶为隔热材料的一体化热防护结构。通过设置热防护结构传热模型,利用ANSYS软件模拟计算一体化结构在不同工况下的温度分布,以此分析防火隔热性能和传热机理;并对不同的隔热材料进行比较,使用最优拉丁超立方方法对一体化热防护结构进行敏感性分析,在满足约束的前提下选用遗传算法对结构进行厚度优化设计。结果表明:以SiO_(2)气凝胶作为隔热材料相比于其他隔热材料,防火隔热性能更好。其与隔热性能比较接近的玻璃纤维相比,同一厚度下隔热能力提升57%,同时面密度和厚度优化分别增加了9.1%和8.3%,且机体结构冷面温度最大值降低56.5%,一体化热防护结构防火隔热性能提升明显。

CCUS封存环境CO 2泄漏监测数据融合算法研究

为进一步探究如何提升CCUS封存CO_(2)项目的运行质量,以提升数据监测水平的角度入手,结合实际项目特征与需求,首先整合传感器技术、数据通信技术等,搭建面向CCUS封存环境下的CO_(2)泄漏监测数据系统;而后为提升系统运行质量,设计卡尔曼滤波数据融合算法,以最限度消除数据测量误差,实现更为准确的数据监测。从现场实验测试效果来看,建立的数据监测系统能够实现更为准确的监测,预计其在后续工作中也具有潜在应用价值。

基于SUFI-2算法的SWAT模型在修水流域径流模拟中的应用

为在江西修水流域高沙水文站以上汇水区域构建SWAT分布式水文模型,采用SUFI-2算法进行模型参数敏感性分析、率定、验证及不确定性分析,对修水流域1984~2003年月径流量过程进行模拟。结果表明,敏感度最强的前三个参数依次为土壤饱和导水率、土壤蒸发补偿系数和径流曲线数;率定期与验证期的纳什系数ENS值和相关系数R2值均高于0.90,月径流量模拟值与实测流量过程线拟合程度较好;95PPU置信区间的模拟值和实测值较为一致;不确定性区间较小,验证期小于率定期;实证了采用SUFI-2算法的SWAT模型在修水流域月径流模拟中具有一定程度的适宜性。

基于SWAT模型和SUFI-2算法的石羊河流域月径流分布式模拟

[目的]揭示石羊河流域径流量对土地利用变化的响应机制,为旱区水资源的合理配置和优化利用提供理论依据。[方法]利用石羊河流域DEM、气象、土壤、1995与2000年2期土地利用等数据,结合RS和GIS技术,构建SWAT分布式水文模型。基于SWAT模型及SUFI-2算法,对石羊河流域1980—2009年的月径流过程进行模拟。[结果]流域内1995与2000年土地利用均以耕地、林地、城乡工矿居民用地面积显著增加,草地、水域、未利用地减少为主;影响SWAT模型模拟效果前3位的参数分别是CN2,Alpha_Bf,Timp;校正期和验证期月流量的模拟值和实测值拟合较好,其Nash Suttcliffe系数Ens和相关系数R2均大于0.8,相对误差|Re|均小于10%。[结论]采用SUFI-2算法的SWAT模型在石羊河流域月径流模拟中具有一定程度的适宜性。

基于SWAT模型和SUFI-2算法的渭河流域月径流分布式模拟

在渭河流域构建分布式水文模型SWAT,并采用SUFI-2算法进行参数敏感性分析以及参数率定、模型验证与不确定性分析,对渭河流域1961—2008年的月径流过程进行模拟.研究结果表明28个跟水文相关的参数对径流均有不同程度的相关性,如CN2、ALPHA_BF、SLSUBBSN、SOL_BD、HRU_SLP、SOL_K、SOL_AWC以及SMFMX等最为敏感.林家村、魏家堡、咸阳、临潼和华县水文站的月径流模拟与实测过程线总体拟合程度较好,率定期和验证期多数站的Φ和效率系数ENS值均高于0.6(0.7),所有站的确定性系数R2值均高于0.7(0.8).R-factor均较小,但林家村和魏家堡站在率定期的P-factor也很小,表明在这2个站的实际不确定性较大.可能的原因是水库运行以及农业用水考虑的不足.总体上,渭河流域SWAT模型月径流模拟效果较好.

基于SUFI-2算法和SWAT模型的妫水河流域水文模拟及参数不确定性分析

敏感性分析和不确定性分析是分布式水文模型参数校准和模型应用的基础。本文以妫水河流域为例,基于SWAT模型和SUFI-2算法进行模型参数敏感性分析,结合手动调参和自动率定,通过SUFI-2的P因子和R因子进行模型不确定性分析,构建妫水河流域分布式水文模型。本次妫水河流域月尺度模拟中:率定期,确定系数R2=0.59,效率系数NSE=0.56;验证期,确定系数R2=0.82,效率系数NSE=0.80;P因子均大于0.5,R因子均小于0.5。结果表明,妫水河流域SWAT模型水文模拟效果较好。