结合MNF变换和Canny算子的遥感影像变化检测

针对大面积区域的多时相遥感影像变化检测的需求，提出了一种基于最小噪声分离（MNF）的Canny边缘检测提取影像变化信息的检测方法。对多时相影像采用多种变换组合成具有多维波段信息的影像，采用最小噪声分离法分离噪声并得到单波段差异图，通过Canny边缘检测法计算梯度幅值，采用高低双阈值法细化边缘，从而提取差异图变化边缘，有效突出了变化信息。以1995年和2003年加扎勒河的两期遥感影像为例，利用两时相影像进行土地覆被变化检测。实验结果表明，该方法适用于监测大面积区域内地物的突变情况。在数据基础上进行最小噪声分离可以有效解决传统Canny边缘检测提取边缘时造成的伪边缘现象，同时采用高低双阈值法有效去除伪边缘点，从而获得更加精确、直观的变化检测效果，在自然地理变化监测、地理国情灾害监测等有很好的应用价值。     

Mechanically Robust,Ultraelastic Hierarchical Foam with Tunable Properties via 3D Printing

A mechanically robust, ultraelastic foam with controlled multiscale architectures and tunable mechanical/conductive performance is fabricated via 3D printing. Hierarchical porosity, including both macro‐ and microscaled pores, are produced by the combination of direct ink writing (DIW), acid etching, and phase inversion. The thixotropic inks in DIW are formulated by a simple one‐pot process to disperse duo nanoparticles (nanoclay and silica nanoparticles) in a polyurethane suspension. The resulting lightweight foam exhibits tailorable mechanical strength, unprecedented elasticity (standing over 1000 compression cycles), and remarkable robustness (rapidly and fully recover after a load more than 20 000 times of its own weight). Surface coating of carbon nanotubes yields a conductive elastic foam that can be used as piezoresistivity sensor with high sensitivity. For the first time, this strategy achieves 3D printing of elastic foam with controlled multilevel 3D structures and mechanical/conductive properties. Moreover, the facile ink preparation method can be utilized to fabricate foams of various materials with desirable performance via 3D printing.     

Fabrication of a novel ZnO–CoO/rGO nanocomposite for nonenzymatic detection of glucose and hydrogen peroxide

Herein, a novel nanocomposite of binary ZnO–CoO nanoparticles loaded on the graphene nanosheets (ZnO–CoO/rGO) has been successfully constructed via a facile, economical and two–step process. The obtained ZnO–CoO/rGO hybrids with high electrical conductivity and abundant active sites, could be modified on a glassy carbon electrode to detect glucose and H₂O₂ multi–functionally. The fabricated biosensor exhibits wide linear range for glucose (10 μM to 11.205 mM) and H₂O₂ (25 μM to 11.1 mM), and their corresponding sensitivity are 168.7 μA mM^?1 cm^?2 and 183.3 μA mM^?1 cm^?2. The limits of detection are 1.3 μM and 0.44 μM for the oxidation of glucose and the reduction of H₂O₂, respectively. Furthermore, remarkable selectivity, long–term stability and outstanding reproducibility of the non–enzyme biosensor prove that ZnO–CoO/rGO hybrids are the promising candidate in practical applications.     

Electromagnetic silicon MEMS resonator

We have fabricated a silicon MEMS resonator aiming at multimodal sensors, and evaluated vibration characteristics by electromagnetic drive and induced electromotive force detection. The resonance frequency of the driving voltage of 0.6 Vpp shows torsional vibration of approximately 87 kHz, and the resonance frequency is shifted toward the low‐frequency side as the driving voltage increases. Resonance characteristics due to temperature change and film stress were evaluated.     

Enhancing upconversion of Nd3+ through Yb3+-mediated energy cycling towards temperature sensing

Photon upconversion of lanthanides has been a powerful means to convert low-energy photons into high-energy ones. However, in contrast to the mostly investigated lanthanide ions, it has remained a challenge for the efficient upconversion of Nd³⁺ due to the deleterious concentration quenching effect. Here we report an efficient strategy to enhance the upconversion of Nd³⁺ through the Yb³⁺-mediated energy cycling in a core-shell-shell nanostructure. Both Nd³⁺ and Yb³⁺ are confined in the interlayer, and the presence of Yb³⁺ in the Nd-sublattice provides a more matched energy for the upconversion transitions occurring at the intermediate state of Nd³⁺ towards much better population at its emissive levels. Moreover, this design also minimizes the possible cross-relaxation processes at both intermediate level and the emissive levels of Nd³⁺ which are the primary factors limiting the upconversion performance for the Nd³⁺-doped materials. Such energy cycling-enhanced upconversion shows promise in temperature sensing.     

基于LoRa技术通信的采煤机姿态感知系统设计

为实现采煤机滚筒智能调高与远程控制记忆截割煤等功能,需获取采煤机精确姿态信息。设计一种基于LoRa技术通信的采煤机姿态感知系统。考虑井下工作条件恶劣、通信干扰因素多等特点,该系统采用抗振倾角传感器精确获取采煤机姿态信息,利用LoRa技术并结合滤波算法来提高系统的通信距离,实现采煤机滚筒智能调高和记忆截割煤的远程控制。通过在黄陵1#煤矿井下进行实际测试,结果表明,该系统在井下恶劣环境中获取的采煤机姿态信息精确,传输距离可达140 m,可以满足工作面远程控制需求。     

Compressive behavior and energy absorption of polymeric lattice structures made by additive manufacturing

Frontiers of Mechanical Engineering - Lattice structures have numerous outstanding characteristics, such as light weight, high strength, excellent shock resistance, and highly efficient heat...     

喷丸对25CrNi2MoV钢滚动接触疲劳性能的影响

目的提高25CrNi2MoV钢的滚动接触疲劳性能。方法对25CrNi2MoV钢进行表面喷丸处理,并采用3D形貌仪、光学显微镜、显微硬度仪、X射线应力分析仪与滚动接触疲劳试验机等仪器,对试样表面形貌、表面显微组织、显微硬度、表面残余压应力与滚动接触疲劳性能等进行测试分析。结果与未处理试样相比,经喷丸处理后,试样表面形貌由磨削加工槽型向酒窝状的弹坑转变,表面粗糙度增大,表面显微硬度由503HV0.2增大到577HV0.2,增加了14.7%,表面残余压应力由-90.0 MPa增大到-758.0 MPa。当喷丸强度为0.445 mmA时,试样具有最好的滚动接触疲劳寿命,其额定寿命(L10)、中值寿命(L50)、特征寿命(L63.2)分别为4.973×10^6次、6.578×10^6次和6.945×10^6次,分别是未处理试样对应寿命的11.1倍、7.3倍和7.0倍,试样滚动接触疲劳失效形式主要为疲劳剥落。当喷丸强度为0.596 mmA时,试样表面出现微裂纹,导致滚动接触疲劳寿命降低,此时试样疲劳失效形式主要为点蚀与疲劳剥落。未处理试样疲劳失效形式主要为分层。结论喷丸处理能细化试样表层晶粒组织,增大试样表面粗糙度、表面硬度与表面残余压应力。合适强度的喷丸处理可以抑制试样表面与次表面裂纹的萌生与扩展,显著提高滚动接触疲劳性能。     

基于仿生结构的Al-Ti-Ti2AlNb层状复合材料的组织与力学性能

受生物材料的韧脆复合结构的启发，本文将Al箔、Ti箔和Ti2AlNb箔材堆叠排列，采真空热压烧结技术制备了层级结构的Al-Ti-Ti2AlNb层状复合材料。利用SEM、XRD等技术表征了材料的微观结构，并测试了抗弯与抗压性能。研究发现，本文设计的层状结构复合了Ti、TiAl系列间化合物、Ti2AlNb等多种材料，层级结构明显，界面清晰。抗弯强度与抗压强度分别为1231±71MPa和1341±63MPa，相比同类材料具有显著的优势。分析认为，多层级结构的存在对裂纹的扩展有显著地阻碍作用；相比常规的二元TiAl层状材料，Ti2AlNb层的存在显著提高了力学性能。     

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a data-driven soft sensor, which can supplement the physical sensor. Artificial Neural Networks have long been used for time series forecasting given their ability to recognize patterns in the data. Long Short-Term Memory (LSTM) neural networks are equipped with memory gates to help them learn time dependencies in a data series and have been proven to outperform other type of networks in predicting water levels in urban drainage systems. When used for soft sensing, neural networks typically receive antecedent observations as input, as these are good predictors of the current value. However, the antecedent observations may be missing due to transmission errors or deemed anomalous due to errors that are not easily explained. This study quantifies and compares the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. We applied these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark. We observed that i) LSTM predictions generally displayed large variability across training runs, which may be reduced by improving the selection of hyperparameters (non-trainable parameters); ii) when the most recent observations were known, adding information on the past did not improve the prediction accuracy; iii) when gaps were introduced in the antecedent water depth observations, LSTM networks were capable of compensating for the missing information with the other available input features (time of the day and rainfall intensity); iv) LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors. Therefore, we concluded that LSTM neural network may be trained to act as soft sensors in urban drainage systems even when observations from the physical sensors are missing.