Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

Canny-Gauss通用域图像隐写算法

自适应图像隐写算法是一种以图像为载体,通过手工设计嵌入失真代价,指导隐写码在图像载体中嵌入秘密消息的信息隐藏算法.长期以来,这类算法将秘密消息尽可能隐藏在图像纹理更深更复杂的位置以对抗基于富特征的隐写分析检测.然而,伴随着深度学习在隐写分析领域的快速发展,人工设计的自适应算法受到严重挑战.此外,基于加性失真的隐写编码在嵌入消息时,复杂纹理向边界聚集所产生的统计异常问题也亟待解决.因此,本文总结了各类人工失真代价的优势和不足,归纳出当前自适应算法在空域的设计范式,并结合UNIWARD在各嵌入域的转换规则,提出基于嵌入失真代价ρ的通用域隐写转换公式.然后,从隐写嵌入失真代价与图像纹理稀疏关系的角度出发,以Canny算子划分纹理、Gauss模糊缩放轮廓、AutoML搜索阈值的方式,提出了一种通用域隐写算法Canny Gauss.实验结果表明,本文所提通用域隐写转换公式能够有效应用于现有主流算法.同时,在UNIWARD所有可行嵌入域中,本文所提算法表达出更高嵌入失真代价稳定性和隐写隐蔽性,在第三方权重加持下的深度隐写分析表现与UNIWARD相比至少提升2.6%、最高提升14.6%.这为自适应隐写算法的通用域设计,以及抵抗基于纹理特征的深度隐写分析检测提供了新思路.

非理想条件下基于SAADR-PI的MMC型电力电子变压器CCS-MPC控制策略

基于模块化多电平换流器(modular multilevel converter,MMC)的电力电子变压器(power electronic transformer,PET)在非理想工况下易发生故障及扰动,严重影响系统电能质量。针对传统控制方法在非理想工况下存在的动稳态性能差等问题,在MMC-PET整流级提出了基于自适应自抗扰比例积分控制器的连续控制集模型预测控制策略。首先,设计了自适应自抗扰比例积分控制器用于电压外环,解决了电压外环信号跟踪及扰动抑制能力差等问题。其次,电流内环使用连续控制集模型预测控制方法以提高系统的响应速度及稳态性能,引入改进型载波移相调制策略解决桥臂电流畸变问题。最后,在网侧负载突变、网压不平衡、输出级负载投入等非理想工况下对MMC-PET系统进行对比仿真和实验,验证了所提控制策略的优越性。

Fuzzy stochastic damage mechanics(FSDM) based on fuzzy auto-adaptive control theory

In order to fully interpret and describe damage mechanics, the origin and development of fuzzy stochastic damage mechanics were introduced based on the analysis of the harmony of damage, probability, and fuzzy membership in the interval of [0,1]. In a complete normed linear space, it was proven that a generalized damage field can be simulated through β probability distribution. Three kinds of fuzzy behaviors of damage variables were formulated and explained through analysis of the generalized uncertainty of damage variables and the establishment of a fuzzy functional expression. Corresponding fuzzy mapping distributions, namely, the half-depressed distribution, swing distribution, and combined swing distribution, which can simulate varying fuzzy evolution in diverse stochastic damage situations, were set up. Furthermore, through demonstration of the generalized probabilistic characteristics of damage variables, the cumulative distribution function and probability density function of fuzzy stochastic damage variables, which show β probability distribution, were modified according to the expansion principle. The three-dimensional fuzzy stochastic damage mechanical behaviors of the Longtan rolled-concrete dam were examined with the self-developed fuzzy stochastic damage finite element program. The statistical correlation and non-normality of random field parameters were considered comprehensively in the fuzzy stochastic damage model described in this paper. The results show that an initial damage field based on the comprehensive statistical evaluation helps to avoid many difficulties in the establishment of experiments and numerical algorithms for damage mechanics analysis.

Dynamics and adaptive control of a dual-arm space robot with closed-loop constraints and uncertain inertial parameters

A dynamics-based adaptive control approach is proposed for a planar dual-arm space robot in the presence of closed-loop constraints and uncertain inertial parameters of the payload. The controller is capable of controlling the po- sition and attitude of both the satellite base and the payload grasped by the manipulator end effectors. The equations of motion in reduced-order form for the constrained system are derived by incorporating the constraint equations in terms of accelerations into Kane＇s equations of the unconstrained system. Model analysis shows that the resulting equations perfectly meet the requirement of adaptive controller design. Consequently, by using an indirect approach, an adaptive control scheme is proposed to accomplish position/attitude trajectory tracking control with the uncertain parameters be- ing estimated on-line. The actuator redundancy due to the closed-loop constraints is utilized to minimize a weighted norm of the joint torques. Global asymptotic stability is proven by using Lyapunov＇s method, and simulation results are also presented to demonstrate the effectiveness of the proposed approach.

A Tilt-correction Adaptive Optical System for the Solar Telescope of Nanjing University

A tilt-correction adaptive optical system installed on the 430 mm Solar Telescope of Nanjing University has been put in operation. It consists of a tip-tilt mirror, a correlation tracker and an imaging CCD camera. An absolute difference algorithm is used for detecting image motion in the correlation tracker. The sampling frequency of the system is 419 Hz. We give a description of the system's configuration, an analysis of its performance and a report of our observational results. A residual jitter of 0.14 arcsec has been achieved. The error rejection bandwidth of the system can be adjusted in the range 5-28 Hz according to the beacon size and the strength of atmospheric turbulence.

Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neurofuzzy interface system （ANFIS） to the modeling of gap acceptance behavior of rightturning vehicles at limited priority Tintersections （in India, vehicles are driven on the left side of a road）. Field data are collected using video cameras at four Tintersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver＇s decision （accepted/rejected）. ANFIS models are developed by using 80 % of the extracted data （total data observations for major road right turning vehicles are 722 and 1,066 for minor road right turning vehicles） and remaining are used for model vali dation. Four different combinations of input variables are considered for major and minor road right turnings sepa rately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. Themodels developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.

High signal-to-noise ratio sensing with Shack–Hartmann wavefront sensor based on auto gain control of electron multiplying CCD

High signal-to-noise ratio can be achieved with the electron multiplying charge-coupled-device（EMCCD） applied in the Shack–Hartmann wavefront sensor（S–H WFS） in adaptive optics（AO）.However,when the brightness of the target changes in a large scale,the fixed electron multiplying（EM） gain will not be suited to the sensing limitation.Therefore an auto-gain-control method based on the brightness of light-spots array in S–H WFS is proposed in this paper.The control value is the average of the maximum signals of every light spot in an array,which has been demonstrated to be kept stable even under the influence of some noise and turbulence,and sensitive enough to the change of target brightness.A goal value is needed in the control process and it is predetermined based on the characters of EMCCD.Simulations and experiments have demonstrated that this auto-gain-control method is valid and robust,the sensing SNR reaches the maximum for the corresponding signal level,and especially is greatly improved for those dim targets from 6 to 4 magnitude in the visual band.

Simulation of a ground-layer adaptive optics system for the Kunlun Dark Universe Survey Telescope

Ground Layer Adaptive Optics （GLAO） is a recently developed technique extensively applied to ground-based telescopes, which mainly compensates for the wavefront errors induced by ground-layer turbulence to get an appropriate point spread function in a wide field of view. The compensation results mainly depend on the turbu-lence distribution. The atmospheric turbulence at Dome A in the Antarctic is mainly distributed below 15 meters, which is an ideal site for applications of GLAO. The GLAO system has been simulated for the Kunlun Dark Universe Survey Telescope, which will be set up at Dome A, and uses a rotating mirror to generate several laser guide stars and a wavefront sensor with a wide field of view to sequentially measure the wavefronts from different laser guide stars. The system is simulated on a computer and parameters of the system are given, which provide detailed information about the design of a practical GLAO system.

An Improved Binary Wolf Pack Algorithm Based on Adaptive Step Length and Improved Update Strategy for 0-1 Knapsack Problems

Binary wolf pack algorithm (BWPA) is a kind of intelligence algorithm which can solve combination optimization problems in discrete spaces.Based on BWPA, an improved binary wolf pack algorithm (AIBWPA) can be proposed by adopting adaptive step length and improved update strategy of wolf pack. AIBWPA is applied to 10 classic 0-1 knapsack problems and compared with BWPA, DPSO, which proves that AIBWPA has higher optimization accuracy and better computational robustness. AIBWPA makes the parameters simple, protects the population diversity and enhances the global convergence.

Investigation on the Structure of Chute-feed and Card Auto-leveller and Their Selection of Technological Processes

Based on the structure of chute - feed and autoleveHer, an analysis of their working principle and the verification of their practical production results have been carried out. Finally, the future investigation direetiom of chute - feed and card autuleveller are put forward.

基于深度SSDAE网络的刀具磨损状态识别

针对刀具磨损状态识别过程中采集数据量大、干扰信号复杂且需人为选择特征参数的问题,为提高刀具磨损状态识别模型的鲁棒性与泛化性,提出了一种数据驱动下深度堆叠稀疏降噪自编码(stacking sparse denoising auto-encoder,简称SSDAE)网络的刀具磨损状态识别方法,实现隐藏在数据中深层次的数据特征自动挖掘。首先,将原始振动信号分解为一系列固有模态分量(intrinsic mode function,简称IMF),并采用皮尔逊相关系数法选取了最优固有模态来组合一个新的信号;其次,采用SSDAE网络自适应提取特征后对刀具磨损阶段进行了状态识别,识别精度达到98%;最后,对网络模型进行实验验证,并与最常用的刀具磨损状态识别方法进行了对比。实验结果表明,所提出的方法能够很好地处理非平稳振动信号,对不同刀具磨损阶段状态的识别效果良好,并具有较好的泛化性能和可靠性。

空间自适应的被动声源定位系统的设计

传统的声源定位技术分为时延估计和空间定位两步,本文将几种经典的时延估计和空间定位方法加以整合与改进,提高了系统的实时性与抗干扰能力,创新性地提出并实现了根据声源位置变化而自适应调整麦克风阵列形态的定位系统,在不降低实时性的同时,有效地降低了定位误差。本文从定位精度、实时性和抗干扰能力三个方面对系统性能进行评估,实验证明:理想条件下,该声音定位系统的不确定度在4cm以内;对噪声和混响有较好的抵抗能力,在恶劣环境下不确定度不超过6cm;单次定位耗时0.5s,可以对运动声源或脉冲声源进行定位,有较强的实时性,可以适用于大部分应用场景。

自动固相微萃取(SPM E)GC-MS、GC-MS-MS法检测环境水中有机磷杀虫剂的研究

固相微萃取(solid-phase microextraction,SPME)是20世纪90年代发展起来的一种样品前处理技术,与传统的液-液提取、液-固提取相比,SPME更适用于提取、浓缩液态或气态的挥发性和半挥发性物质,SPME技术可将采样、萃取、浓缩和样本引入集中于一个步骤完成,尤其随着自动SPME与GC-MS等联用技术的日益完善,使SPME技术优点得到更充分的发挥.……

综合孵化系统自适应机制研究——基于CAS刺激-反应模型

为深入研究创投与孵化器的有效合作问题,提出了综合孵化器系统的概念。结合当前复杂性研究成果,阐述了综合孵化器系统的概念内涵,从标识机制、内部模型、积木机制探讨了综合孵化系统的复杂适应性特征,基于复杂适应系统(CAS)刺激-反应模型构建了综合孵化器系统主体行为的基本模式,并从学习、协调、进化三方面分析了其自适应机制,得出了综合孵化器系统可以通过学习创新、协调多主体间资源分配和涌现进化来适应环境的结论,为综合孵化器系统的运行构建了理论基础。

微地震信号的变速FK滤波-自适应极化滤波方法

针对微地震信号的特点,研究了变速FK滤波-自适应极化滤波方法.在设计FK滤波因子时,通过计算相邻道滑动时窗内信号的互相关系数,确定时窗内信号的视速度范围,实现变速FK滤波;对时窗内的信号进行二维FFT变换时,分别在时窗的纵横向上补零充位,消除信号在二维变换时造成的时空域的混跌和泄漏;在设计自适应极化滤波因子时,针对复杂波场中波的偏振方向的不确定性问题,本文通过计算相部道信号偏振投影的最大互相关系数,确定波的跟踪分量;把波的跟踪分量作为极化滤波因子里的期望方向,改进常规滤波因子,实现自适应极化滤波.对理论模型和实际资料的处理结果表明,该方法理论正确,实际资料的处理效果也得到了明显的改善.

基于AR模型的脑-机接口问题研究

在脑一机接口的研究中分类识别技术占有重要地位。将脑电信号中事件去同步化/相同步化现象作为特征信息,深入讨论基于AR模型的自适应算法(AAR)和多变量参数AAR模型算法(MVAAR)在脑电信号特征提取中的应用。结合三种分类器,对这两种算法进行了比较,实验证明两种方法的实验效果都很好,但是MVAAR算法比AAR算法能够达到更高的分类正确率,其阶次一般选取也比较低,数据仿真吻合度高,具有更强的通用性。

电液伺服系统的扩展自耦PD控制

针对具有高度非线性和参数不确定性的3阶电液伺服系统,基于自耦PID控制理论提出了一种扩展自耦PD控制方法。通过将电液伺服系统内部动态与外部不确定因素定义为总扰动,使3阶非线性系统等价映射为3阶线性扰动系统,形成一个在总扰动激励下的受控误差系统,并以自适应速度因子为基础设计了扩展自耦PD控制器,理论分析了扩展自耦PD闭环控制系统具有良好的鲁棒稳定性和抗扰动鲁棒性。仿真实验表明:该控制方法不仅动态响应速度快,且稳态控制精度高,因此在电液伺服系统领域具有良好的实际应用前景。

欠驱动飞行器横侧向通道的自耦PD控制方法

针对欠驱动高超声速飞行器横侧向通道的姿态控制问题,提出一种基于自耦PD(proportional-differential)控制理论的控制方法.该方法将欠驱动飞行器横侧向姿态模型转换为由速度倾斜角与侧滑角两个通道组成的二阶动态模型,并在速度倾斜角通道中引入一个侧滑角虚拟指令,再对速度倾斜角与侧滑角两个通道的内部动态与外部扰动分别定义两个总扰动,从而将非线性欠驱动扰动系统等价映射为虚拟全驱动线性扰动系统.使用最小速度因子及其自适应速度因子来设计外环速度倾斜角的自耦PD控制器,以便获得侧滑角虚拟指令,并根据该虚拟指令来设计内环侧滑角的自耦PD控制器,从而获得副翼偏角的控制力.分析了自耦PD控制系统的鲁棒稳定性和抗扰动鲁棒性.仿真结果表明,本文设计的自耦PD控制器不仅具有良好的抗扰动鲁棒性,而且具有良好的动态品质和稳态性能.