Outliers significantly impact the accuracy of geometric model fitting. Previous approaches to handling outliers have involved threshold selection and scale estimation. However, many scale estimators assume that the inlier distribution follows a Gaussian model, which often does not accurately represent cases in geometric model fitting. Outliers, defined as points with large residuals to all true models, exhibit similar characteristics to high values in quantized residual preferences, thus causing outliers to cluster away from inliers in quantized residual preference space. In this paper, we leverage this consensus among outliers in quantized residual preference space by extending energy minimization to combine model error and spatial smoothness for outlier detection. The outlier detection process based on energy minimization follows an alternate sampling and labeling framework. Subsequently, an ordinary energy minimization method is employed to optimize inlier labels, thereby following the alternate sampling and labeling framework. Experimental results demonstrate that the energy minimization-based outlier detection method effectively identifies most outliers in the data. Additionally, the proposed energy minimization-based inlier segmentation accurately segments inliers into different models. Overall, the performance of the proposed method surpasses that of most state-of-the-art methods.

The nonlinear output frequency response functions (NOFRFs) are extensions of the frequency response function (FRF) in linear systems to weakly nonlinear systems, have been applied in nonlinear system analysis and structural damage detection. The NOFRFs of the nonlinear auto- regressive with exogenous input (NARX) model can be obtained by solving a series of difference equations in a method called generalized associated linear equations (GALEs). In the present study, an experimental study was carried out on a beam structure with cracks. The main method is to use a data-driven method to establish the NARX model of the beam under different damage states, and obtain the NOFRF of the beam under different damage states by computing the GALEs of the system. Finally, the beam structure in different damage states was evaluated based on the NOFRFs. And it was shown that the NOFRFs can be used to represent the dynamics of different systems. It is shown that NOFRFs can be used to represent the state characteristics of different system dynamics, which can be well applied to damage detection of engineering structures.

In this article, we consider the problem of adaptive fixed-time tracking control for a class of multiagent systems (MASs) with mismatched uncertainty. Unlike the existing methodologies that only implement the practical finite-/fixed-time stability for MASs, a newly adaptive consensus control criterion is developed to reach fixed-time stability, where the controller design includes a series of newly Lyavonov functions and modified tuning functions. Radial basis function neural networks are employed to deal with the unknown functions in each agent, and the direct adaptive strategy solves the obstacle of “explosion of complexity.” Under the performance-oriented controller, the error of the MASs converges to a predetermined interval within a fixed time. Two simulations illustrate the results obtained.

When the Preisach operator, a commonly used hysteresis model, is coupled with uncertain unparametrizable nonlinear dynamics of systems, its tracking control problem in particular with the demands for prescribed tracking accuracy and finite convergence time is challenging, and has not yet been solved in the existing literature. In this study, we focus on the problem, and develop a fixed-time adaptive fuzzy control scheme as a solution to it, based upon a novel decomposition of the Preisach model, the design of a robust control framework, and the integration of a direct adaptive fuzzy control approach. With our scheme, it can be rigorously proved that the tracking error goes to a predefined interval around zero in a bounded convergence time, and all signals in the closed-loop system are bounded. Besides theoretical analysis, the obtained results are also confirmed by experimental tests based on a real-life piezoactuated positioner.

作为水泥工业的重要生产设备,回转窑的运行状态决定了企业的生产效益.主要针对回转窑托轮振动信号为媒介对其故障识别做出研究,将奇异谱分解等信号分解方法应用于回转窑托轮振动信号的分解和重构,使用分解得到的托轮、筒体谐波幅值和重构信号的时域特征构建特征向量输入到分类模型中.针对特征维数过高可能造成的分类准确性差异,使用主成分分析方法对特征集进行降维处理,最后采用降维后的特征集建立故障分类模型对样本数据进行故障识别分类,结果表明.奇异谱分解能更好地实现信号分解与重构,降维特征集训练出的分类器能更准确地识别出回转窑的故障类型.

回转窑中心线变化一般是缓慢的,窑故障的发生也具有隐蔽性和不确定性,当故障严重时会影响回转窑的正常生产,许多企业通常到回转窑运转出现故障后才发现.本文提出一种回转窑中心线的在线监测方法,此方法在检测得到窑中心线的基础上,通过监测回转窑轮带表面三点的位置,实现回转窑中心线的在线监测,使企业自己能够及时获取回转窑中心线的运行状态,有利于及时发现回转窑的早期问题,避免大型故障的发生.

针对回转窑内部高温监控布线难、成本高、不易维护等问题,设计了一套基于LoRa技术通信的回转窑温度采集系统.该系统由多个采集节点和监控中心组成,利用LoRa技术进行数据的无线传输.采集节点以低功耗的STM32L1系列单片机为核心,通过热电偶传感器采集回转窑内部温度,数据经MCP9600温度转换器处理后,由LoRa无线通信模块无线发出.回转窑温度数据经LoRa网络上传至LoRaWAN网关,并由网关通过RS485通信方式上传至上位机监控中心.系统采用星型组网方式,实现了对回转窑温度参数采集、显示、报警、存储等功能,具有一定的研究、实用、推广价值.

针对水泥回转窑有线监控布线繁杂、成本高、维护和管理难度大等问题,设计了基于ZigBee和云端的全覆盖回转窑托轮振动监测系统.系统由若干个架设在回转窑各档托轮上的终端节点和一个协调器构成,终端与协调器都使用了低功耗、低成本的ZigBee处理器CC2530.托轮振动数据由高精度低功耗的接触式位移传感器采集,终端节点将数据经由ZigBee网络发送给协调器.协调器通过串口将多个终端节点的数据打包发送给4G模块,再由4G模块将数据发送到指定云端服务器.监测人员可以通过连接云端服务器的电脑端和移动端应用实时监测托轮振动数据,早期诊断窑运行故障.该系统具有结构简单、部署方便、无需布线等优点,有效解决了单一 ZigBee网络传输距离限制的问题,达到了预期设计效果,将在回转窑运行状态监测方面具有广泛工程应用前景.

In microgrids with different voltage levels, the difference in line parameters leads to the fact that the output power of the inverter cannot be divided accurately. The traditional droop control cannot solve this problem well. To this end, this paper proposes adaptive voltage compensation and recovery strategy to power sharing control for multi-voltage level microgrid. Firstly, by introducing virtual impedance, the total impedance of inverter system at high/low voltage levels is inductive/resistive. Secondly, by comparing and analyzing the influence of virtual impedance parameters on the total output impedance of the system, the appropriate control parameters are selected. Then, an adaptive voltage compensation module is introduced in the improved droop control, so as to eliminate the deviation between the system output voltages and achieve accurate equalization of reactive/active power. On the other hand, a voltage recovery mechanism is introduced to prevent the inverter output voltage from exceeding the rated range. Finally, the effectiveness of the proposed strategy is verified by experiments.

As an important part of face recognition, facial image segmentation has become a focus of human feature detection. In this paper, the AdaBoost algorithm and the Gabor texture analysis algorithm are used to segment an image containing multiple faces, which effectively reduces the false detection rate of facial image segmentation. In facial image segmentation, the image containing face information is first analyzed for texture using the Gabor algorithm, and appropriate thresholds are set with different thresholds of skin-like areas, where skin-like areas in the image’s background information are removed. Then, the AdaBoost algorithm is used to detect face regions, and finally, the detected face regions are segmented. Experiments show that this method can quickly and accurately segment faces in an image and effectively reduce the rate of missed and false detections.

为了诊断回转窑工作故障和评估窑运行状况,有效提取窑筒体故障的特征信号极为重要.通过分析故障状态下窑筒体与托轮之间受力关系,建立托轮振动模型,得出窑故障与托轮位移振动的关联关系.针对现有窑筒体故障特征信息提取方法的不足,提出基于小波包分解的特征频率提取方法,对实际采集的数据进行小波包分解和提取特征频段进行重构.对重构后的数据进行Hilbert分析表明,采用小波包分解方法在托轮位移信号中提取2个窑故障特征频率,即筒体工作频率(KH)与托轮工作频率(RH),并以KH和RH的能量密度作为评估参数来分别反映筒体弯曲和各托轮超载受力的故障程度.通过对实测回转窑托轮信号进行处理,表明所提出方法有效,从而为后续研究回转窑运行故障的在线监测提供了新思路.

回转窑筒体表面形变会导致回转窑筒体中心线偏移,从而会引发一系列设备故障,影响企业的正常生产.目前,国内外通行的回转窑故障检测技术多采用静态的事后检测方法,成本高、操作困难.文章提出了一种回转窑中心线动态检测的方法,并用间接法测量动态回转窑中心线.通过测量托轮与轮带直径、托轮位置,并根据其几何位置关系,推导出回转窑各档筒体的中心位置;通过分析该检测方法的检测结果,对回转窑轮带与托轮直径、托轮轴心位置的测量方法作了详细介绍.该方法检测成本低、精度高、操作难度小、方便且安全,适用于检测各类回转窑的中心线偏移情况,提高检测效率,有利于及时发现回转窑的早期问题,避免出现大故障.

The anisotropy compression deformation behaviour of Mg-2Zn-Al-0.2Ca-0.2Mn-0.2Gd (wt.%) magnesium bar with bimodal structure were studied by compressing parallel and perpendicular to extrusion direction. EBSD technique was used to analyse the deformation behaviour, microstructure and texture evolution. The results show that Mg-2Zn-1Al-0.2Ca-0.2Mn-0.2Gd (wt.%) bar with special bimodal grain size but comparable texture characteristics with c-axes perpendicular to extrusion direction for both fine and coarse grains. The stress-strain curves and strain hardening rate response were significantly affected by the loading directions and initial texture. The anisotropy mechanical properties occurred due to different deformation behaviour. The dominant deformation mechanisms are basal slip in fine grains and extension twinning in coarse grains at initial deformation, however, the activation of prismatic slip and the unfavourable orientations for basal slip for sample compressing parallel extrusion direction contributes to the yield anisotropy. As the deformation continuing, twinning growth and basal slip dominant deformation. When loading perpendicular to extrusion direction, prismatic slip in fine grains also contributes to deformation to some extent. The c-axes of both samples turn to parallel to loading direction after compression.

Most SLAM (Simultaneous Localization and Mapping) algorithms are based on the assumption that the scene is static. However, in practice, most real scenes usually contain moving objects. In this letter, we introduce DymSLAM, a dynamic stereo visual SLAM system being capable of reconstructing a 4D (3D + time) dynamic scene with rigid moving objects. Unlike previous attempts that have considered moving objects as outliers and ignored them, DymSLAM obtains the 6DoF motion trajectory and 3D models about the dynamic objects. We segment motion models of different moving objects by a multi-motion segmentation approach and obtain the accurate masks of moving objects. Besides ego-motion, our system can obtain the 4D (3D + time) model and 6DoF trajectory of the moving object in the global reference frame while simultaneously reconstructing the dense map of the static background. Meanwhile, DymSLAM does not rely on semantic cues or prior knowledge and is suitable for unknown rigid objects. We conducted experiments in a real-world indoor environment where both the camera and the objects were moving in a wide range. The results proved that our proposed method is a state-of-the-art SLAM system for use in this dynamic environment.

In this paper, a novel quaternion broad learning system is proposed in this paper for tremor estimation and elimination in teleoperation. In the new proposed QBLS, the architecture can be divided into three layers, including quaternion feature layer, enhancement layer and the output layer. In quaternion feature layer, a quaternion-value auto-encoder (QAE) based on the quaternion algebra is proposed and employed to extract the unsupervised features in quaternion domain. Moreover, the enhancement nodes are mapped to improve the system’s regression ability in enhancement layer. In the output layer, the nodes of feature layer and enhancement layer are concatenated to map the output of QBLS. The weight parameters of output layer can be calculated by the minimum norm least squares solutions. In addition, the semi-physical simulation experiment is completed and the new proposed QBLS has been compared with some existing methods. Finally, the effectiveness and efficiency of QBLS are demonstrated by experimental results.

This paper proposes a new three-domain fuzzy wavelet broad learning system (TDFW-BLS) for tremor estimation in tele-operation. In the feature layer of our novel method, feature nodes can be mapped by a three-domain fuzzy wavelet sub-systems (3DFWs) and the k-means method is applied to determine the parameters in the TDFWs. The architecture of the new proposed system maps the input of different dimensions to different groups feature nodes by 3DFWS. In the enhancement layer, feature nodes are mapped to enhancement nodes. Moreover, feature nodes and increment nodes can be concatenated into a matrix to map the total output of the novel system by the full connection layer. Finally, the semi-physical simulation experiment is designed to demonstrate the effectiveness of the novel TDFW-BLS. Meanwhile, it is compared with some existing methods and the results have shown superior performance.

回转窑筒体的弯曲变形和中心点在水平面偏移都会造成同档支承处两个托轮的受力不均,定性分析了支承处筒体截面的不同故障对托轮挠度的影响,以某水泥回转窑为对象,测得各个托轮的挠度变化信号,根据其频谱,分析出回转窑支承处筒体的弯曲变形和中心点在水平面的偏移情况,并通过与其他两种测量方法所测结果对比,验证了以托轮挠度变化为参数的回转窑筒体故障识别方法简单而有效,为回转窑故障早期预测及托轮调整提供了参考依据.

The recent progress of an ongoing project utilizing a ducted-fan propulsion system to improve a humanoid robot’s ability to step over large ditches is reported. A novel method (GAS) based on the genetic algorithm with smoothness constraint can effectively minimize the thrust by optimizing the robot’s posture during 3D stepping. The significant advantage of the method is that it can realize the continuity and smoothness of the thrust and pelvis trajectories. The method enables the landing point of the robot’s swing foot to be not only in the forward but also in a side direction. The methods were evaluated by simulation and by being applied on a prototype robot, JetHR1. By keeping a quasistatic balance, the robot could step over a ditch with a span of 450 mm (as much as 97% of the length of the robot’s leg) in 3D stepping.

The preference analysis approach is widely used in solving multimodel fitting problems, but it does not combine the potential spatial correlation in the data. In this paper, we propose a novel method for introducing spatial information to optimize the results of preference analysis through energy minimization. The method consists of two steps: 1) T-linkage is performed based on localized window sampling to obtain the initial clustering results; and 2) the alternate sampling and clustering framework is applied until the energy is stable. The main contribution is that we introduce the spatial information of the data points in the preference analysis by minimizing an energy function, in order to make the inlier preferences more distinguishable and improve the clustering. The experimental results confirm that the proposed method compares favorably with the current state of the art.

With the development of computer vision, visual odometry is adopted by more and more mobile robots. However, we found that not only its own pose, but the poses of other moving objects are also crucial for the decision of the robot. In addition, the visual odometry will be greatly disturbed when a significant moving object appears. In this letter, a stereo-based multi-motion visual odometry method is proposed to acquire the poses of the robot and other moving objects. In order to obtain the poses simultaneously, a continuous motion segmentation module and a coordinate conversion module are applied to the traditional visual odometry pipeline. As a result, poses of all moving objects can be acquired and transformed into the ground coordinate system. The experimental results show that the proposed multi-motion visual odometry can effectively eliminate the influence of moving objects on the visual odometry, as well as achieve 10 cm in position and 3{\deg} in orientation RMSE (Root Mean Square Error) of each moving object.