The putative Saginaw impact structure,Michigan, Lake Huron,in the light of gravity aspects derived from recent EIGEN 6C4 gravity field model

The hypothetical impact structure in the Saginaw Bay (Michigan, USA, Lake Huron) has been tested by the gravity data derived from the recent gravity field model EIGEN 6C4 (expanded to degree and order 2190, with ground resolution of ~9?km). The following gravity field aspects were used: the gravity disturbances/anomalies, second derivatives of the disturbing potential (Marussi tensor), two of three gravity invariants, their specific ratio (known as 2D factor), the strike angles, and the virtual deformations. These gravity aspects are sensitive in various ways to the underground density contrasts. For the Saginaw Bay area, we confirm that we do not see any typical impact crater in terms of gravity disturbance or the radial second order derivative, possibly because of the thick layer of the ice located at the place and time of the impact. But the “combed” strike angles (one type of the gravity aspects we use) disclose a trace of high pressure to the SE/S/SW of the Bay and may be due to an impacting body. Thus, we provide circumstantial evidence of the Younger Dryas impact hypothesis.     

新型中央空调控制系统设计

针对中央空调控制系统中控制不灵活、数据采集的实时性以及功耗大等一系列问题,利用窄带物联网技术,设计了一种新型的中央空调控制系统。首先,对目前中央空调控制系统的现状进行了研究,又结合模块化设计思想,给出了系统的总体方案设计,包括中央空调控制器和云端人机交互软件。然后,以STM32F103为微控制器、窄带物联网(NB-IoT)作为无线通信技术,设计了中央空调控制器,给出了其设计原理、主要接口电路、软件流程图以及系统通信协议。以C#作为软件平台,设计了云端人机交互软件。最后,进行了系统测试。测试表明,系统可采集室内温湿度、功率电量、空调状态等参数并上传至云服务器,也可手动实现对中央空调的实时控制,达到了良好的效果,同时也验证了NB-IoT的低功耗、多连接以及覆盖广的特点。基于NB-IoT的中央空调控制系统对智慧城市和智慧楼宇的发展和建设具有参考意义。     

基于扫描点云的建筑物特征剖面快速获取

三维激光扫描技术可以快速而精确地获取既有建筑的完整点云数据,但要从点云中获得修缮设计及结构健康评估所需要的特征信息仍然是一个极为耗时耗力的工作。为改善目前纯手工交互的作业现状,提出了一种从点云中快速获取建筑特征剖面数据的算法,阐述了其基本思想与作业流程,并研发了基于该算法的具有较高自动化水平的剖面图制作软件工具。实际应用证明,软件在面对海量点云数据处理时,能够显著提高作业效率及提升成果制作质量。     

Exponential Synchronization of Memristive Chaotic Recurrent Neural Networks Via Alternate Output Feedback Control

This paper considers the global exponential synchronization problem of two memristive chaotic recurrent neural networks with time‐varying delays using periodically alternate output feedback control. First, the periodically alternate output feedback control rule is designed for the global exponential synchronization of two memristive chaotic recurrent neural networks. Then, according to the Lyapunov stability theory, we construct an appropriate Lyapunov‐Krasovskii functional to derive several new sufficient conditions guaranteeing exponential synchronization of two memristive chaotic recurrent neural networks under periodically alternate output feedback control. Compared with existing results on synchronization conditions on the basis of linear matrix inequalities of memristive chaotic recurrent neural networks, the derived results complement, extend earlier related results, and are also easy to validate in this paper. An illustrative example is provided to illustrate the effectiveness of the synchronization criteria.     

Hierarchical search strategy in particle filter framework to track infrared target

A target of interest may exhibit significant appearance variations because of its complex maneuvers, ego-motion of the camera platform, etc. Currently, target tracking in forward-looking infrared (FLIR) sequences is still a challenging problem in the field of computer vision. Although many efforts have been devoted, there are still some issues to be addressed. First, state particles generated by prior information cannot approximate the probability density function well when the target state changes obviously. Second, plenty of particles have to be employed to obtain satisfying estimation of target state which will cause heavy computational burden in turn. In this paper, a hierarchical search strategy (HS tracker) is proposed to track infrared target in the particle filter framework, and there are two observation models employed to locate the target robustly. In the first stage, a saliency map leads the redistributed state particles to cover the salient areas that can provide a rough prediction of the target areas. In the second stage, sparse representation is employed to search for a subset of true ones from all the target candidates; thus, only efficient state particles are used to estimate the target state. The proposed method is tested on numerous FLIR sequences from the US army aviation and missile command database, and experimental results demonstrate the excellent performance.

     

Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory

Extracting reliable features from vibration signals is a key problem in machinery fault recognition. This study proposes a novel sparse wavelet reconstruction residual (SWRR) feature for rolling element bearing diagnosis based on wavelet packet transform (WPT) and sparse representation theory. WPT has obtained huge success in machine fault diagnosis, which demonstrates its potential for extracting discriminative features. Sparse representation is an increasingly popular algorithm in signal processing and can find concise, high-level representations of signals that well matches the structure of analyzed data by using a learned dictionary. If sparse coding is conducted with a discriminative dictionary for different type signals, the pattern laying in each class will drive the generation of a unique residual. Inspired by this, sparse representation is introduced to help the feature extraction from WPT-based results in a novel manner: (1) learn a dictionary for each fault-related WPT subband; (2) solve the coefficients of each subband for different classes using the learned dictionaries and (3) calculate the reconstruction residual to form the SWRR feature. The effectiveness and advantages of the SWRR feature are confirmed by the practical fault pattern recognition of two bearing cases.     

Simulation of liquid micro-jet in free expanding high-speed co-flowing gas streams

We present the development of an experimentally validated computational fluid dynamics model for liquid micro jets. Such jets are produced by focusing hydrodynamic momentum from a co-flowing sheath of gas on a liquid stream in a nozzle. The numerical model based on laminar two-phase, Newtonian, compressible Navier–Stokes equations is solved with finite volume method, where the phase interface is treated by the volume of fluid approach. A mixture model of the two-phase system is solved in axisymmetry using?~?300,000 finite volumes, while ensuring mesh independence with the finite volumes of the size 0.25 µm in the vicinity of the jet and drops. The numerical model is evaluated by comparing jet diameters and jet lengths obtained experimentally and from scaling analysis. They are not affected by the strong temperature and viscosity changes in the focusing gas while expanding at nozzle outlet. A range of gas and liquid-operating parameters is investigated numerically to understand their influence on the jet performance. The study is performed for gas and liquid Reynolds numbers in the range 17–1222 and 110–215, and Weber numbers in the range 3–320, respectively. A reasonably good agreement between experimental and scaling results is found for the range of operating parameters never tackled before. This study provides a basis for further computational designs as well as adjustments of the operating conditions for specific liquids and gases.     

Fabrication of buried microfluidic channels with observation windows using femtosecond laser photoablation and parylene-C coating

We developed an advanced method for fabricating microfluidic structures comprising channels and inputs/outputs buried within a silicon wafer based on single level lithography. We etched trenches into a silicon substrate, covered these trenches with parylene-C, and selectively opened their bottoms using femtosecond laser photoablation, forming channels and inputs/outputs by isotropic etching of silicon by xenon difluoride vapors. We subsequently sealed the channels with a second parylene-C layer. Unlike in previously published works, this entire process is conducted at ambient temperature to allow for integration with complementary metal oxide semiconductor devices for smart readout electronics. We also demonstrated a method of chip cryo-cleaving with parylene presence that allows for monitoring of the process development. We also created an observation window for in situ visualization inside the opaque silicon substrate by forming a hole in the parylene layer at the silicon backside and with local silicon removal by xenon difluoride vapor etching. We verified the microfluidic chip performance by forming a segmented flow of a fluorescein solution in an oil stream. This proposed technique provides opportunities for forming simple microfluidic systems with buried channels at ambient temperature.     

基于深度神经网络的正交频分复用波形外辐射源雷达参考信号重构

针对正交频分复用(OFDM)波形外辐射源雷达的参考信号获取问题,基于“解调-再调制”的重构方法结合了波形优势,能获得更为纯净的参考信号。该文在此基础上提出一种联合OFDM解调、信道估计、信道均衡和星座点逆映射的深度神经网络(DNN)重构方法,建立了基于DNN的参考信号重构方案,通过网络学习自适应深度挖掘从时域接收符号到传输码元之间的映射关系、隐式地估计信道响应,从而提高解调精度和重构性能。该文首先研究了仿真数据集的获取问题、DNN的搭建和训练问题,接着对基于DNN方法在导频数目减少、循环前缀的移除、存在符号定时偏差、存在载波频偏、对高峰值平均功率比信号进行时域加窗滤波等情况下的参考信号重构性能进行了仿真分析,仿真结果表明该方法对参考信号重构的有效性。