Few-Shot Object Detection Based on the Transformer and High-Resolution Network

Now object detection based on deep learning tries different strategies.It uses fewer data training networks to achieve the effect of large dataset training.However,the existing methods usually do not achieve the balance between network parameters and training data.It makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection results.To improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the image.Channels and spatial attention are used to make the network focus on features that are more useful to the object.In addition,the recently popular transformer is used to fuse the features of the existing object.This compensates for the previous network failure by making full use of existing object features.Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

A Review on Deterministic Lateral Displacement for Particle Separation and Detection

The separation and detection of particles in suspension are essential for a wide spectrum of applications including medical diagnostics.In this field,microfluidic deterministic lateral displacement(DLD)holds a promise due to the ability of continuous separation of particles by size,shape,deformability,and electrical properties with high resolution.DLD is a passive microfluidic separation technique that has been widely implemented for various bioparticle separations from blood cells to exosomes.DLD techniques have been previously reviewed in 2014.Since then,the field has matured as several physics of DLD have been updated,new phenomena have been discovered,and various designs have been presented to achieve a higher separation performance and throughput.Furthermore,some recent progress has shown new clinical applications and ability to use the DLD arrays as a platform for biomolecules detection.This review provides a thorough discussion on the recent progress in DLD with the topics based on the fundamental studies on DLD models and applications for particle separation and detection.Furthermore,current challenges and potential solutions of DLD are also discussed.We believe that a comprehensive understanding on DLD techniques could significantly contribute toward the advancements in the field for various applications.In particular,the rapid,low-cost,and high-throughput particle separation and detection with DLD have a tremendous impact for point-of-care diagnostics.

New Damage Detection Index Based on Mid-span Displacement and Its Application to Simply Supported Beam

This study presents a new idea of using only mid-span displacement measurement for damage detection of simply supported beams. Equivalent element concept is introduced at the beginning. In order to relate the damage detectability by means of mid-span displacement measurement with the damage-caused local stiffness change, a novel index termed as symmetrical mid-span displacement difference index (SMDDI) is proposed. The proposed method based on SMDDI is sensitive to tiny damage and comparatively small quantities of measurements are required during the application process. Another significant attraction of this method is putting aside the knowledge a-priori of the intact state. An example using simulated data has been conducted to examine the suitability of this method and assess its comparative advantages over the previous modal method.

High-resolution bottom detection algorithm for a multibeam echo-sounder system with a U-shaped array

High-resolution approaches such as multiple signal classification and estimation of signal parameters via rotational invariance techniques（ESPRIT） are currently employed widely in multibeam echo-sounder（MBES）systems for sea floor bathymetry,where a uniform line array is also required.However,due to the requirements in terms of the system coverage/resolution and installation space constraints,an MBES system usually employs a receiving array with a special shape,which means that high-resolution algorithms cannot be applied directly.In addition,the short-term stationary echo signals make it difficult to estimate the covariance matrix required by the high-resolution approaches,which further increases the complexity when applying the high-resolution algorithms in the MBES systems.The ESPRIT with multiple-angle subarray beamforming is employed to reduce the requirements in terms of the signal-to-noise ratio,number of snapshots,and computational effort.The simulations show that the new processing method can provide better fine-structure resolution.Then a highresolution bottom detection（HRBD） algorithm is developed by combining the new processing method with virtual array transformation.The application of the HRBD algorithm to a U-shaped array is also discuss.The computer simulations and experimental data processing results verify the effectiveness of the proposed algorithm.

Information Detection of Seismic Debris Flow by UAV High-resolution Image Based on Transfer Learning

A large number of debris flow disasters(called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning(TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network(CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.

Damage detection of frames using the increment of lateral displacement change

The method proposed in this paper is based on the fact that the damage in different types of structural members has distinctive influence on the structural stiffness. The intrinsic mechanical property of the structure is tapped and fully utilized for damage detection. The simplified model of the flexibility of frames treats the individual storeys as springs in series and the frame as an equivalent column. It fully considers the main deformation of all beams and columns in the frame. The deformation property of the simplified model accorded well with that of the actual frame model. The obtained increment of lateral displacement change (IOLDC) at the storey level was found to be very sensitive to the local damage in the frame. A damage detection method is pro- posed using the IOLDCs as the damage identification parameters. Numerical examples demonstrate the potential applicability of this method.

RFI Detection for Multichannel HRWS SAR System Based on Spatial Cross Correlation

Multichannel high-resolution and wide-swath(HRWS)imaging is an advanced digital beamforming technique for future synthetic aperture radar(SAR)systems.However,radio frequency interference(RFI)is a critical concern for HRWS SAR missions,which distorts measure-ments and produces image artifacts.In this paper,the spatial cross-correlation coefficients of multichannel HRWS SAR signals are investigated for RFI detection.It is found when the two channels are correlated,RFI-polluted areas present lower coherence values than non-polluted areas in the same scenarios,which makes previous methods fail.Further,this paper studies the case of two fully decorrelated channels to maximize the coherence difference among RFI and target echoes,and RFI detection is realized by exploiting the anomaly value of coherence.Experimental results of real air-borne multichannel SAR data demonstrate that the RFI can be detected successfully.

A novel absolute displacement detecting magnetic sensor

A novel magnetic grating based on calibration was proposed.Two tracks,look-up track and index track,were used to realize absolute output.Magnets of look-up track were magnetized according to N-S-N-S,and magnetic field was sensed by 6 linear Hall sensors.Three signals whose phase shift is 120° were obtained through difference,and the offset of magnetic head in a signal period could be obtained by look-up table;Magnets of index track were magnetized according to pseudorandom binary sequence.Hall sensors were used to get the absolute offset of the signal period to which the magnetic head is belonged.The magnetic grating was calibrated using a higher resolution optical grating:output of optical grating and signals from magnetic grating were sampled at the same time and transmitted to computer,the relation between them could be got and stored in MCU for looking-up.The displacement was got according to Hall signals while in working state.A magnetic grating prototype was made,and it could realize absolute detecting in 2048 mm and the resolution could achieve to 0.001 mm.Its structure is simple,cost is very low and it is suitable for mass production.

Detecting the Position of the Moving-iron Solenoid by Non-displacement Sensor Based on Parameter Identification of Flux Linkage Characteristics

Currently, most researches use signals, such as the coil current or voltage of solenoid, to identify parameters; typically, parameter identification method based on variation rate of coil current is applied for position estimation. The problem exists in these researches that the detected signals are prone to interference and difficult to obtain. This paper proposes a new method for detecting the core position by using flux characteristic quantity, which adds a new group of secondary winding to the coil of the ordinary switching electromagnet. On the basis of electromagnetic coupling theory analysis and simulation research of the magnetic field regarding the primary and secondary winding coils, and in accordance with the fact that under PWM control mode varying core position and operating current of windings produce different characteristic of flux increment of the secondary winding. The flux increment of the electromagnet winding can be obtained by conducting time domain integration for the induced voltage signal of the extracted secondary winding, and the core position from the two-dimensional fitting curve of the operating winding current and flux-linkage characteristic quantity of solenoid are calculated. The detecting and testing system of solenoid core position is developed based on the theoretical research. The testing results show that the flux characteristic quantity of switching electromagnet magnetic circuit is able to effectively show the core position and thus to accomplish the non-displacement transducer detection of the said core position of the switching electromagnet. This paper proposes a new method for detecting the core position by using flux characteristic quantity, which provides a new theory and method for switch solenoid to control the proportional valve.

A Real-Time System for Monitoring Distant Dynamic Displacement of Structures

把技术基于数字图象处理，一个即时系统被开发监视并且检测设计结构的动态排水量。由用一个自我规划的软件处理图画，在某个坐标系统的一个目标的即时坐标能也被获得，并且推进动态排水量数据和目标罐头的曲线被完成。数据的也就是说自动的聚会并且即时处理能被这个系统同时执行。首先，为这个系统，一种未触动过的监视技术被采用，罐头它监视或检测目标几到几百米分开；第二，它有灵活安装状况和亚毫米度的好监视精确；第三，它是合适的为动态、伪动态、静态大工程结构监视。通过大桥的几测试和应用程序，系统的好可靠性和优势被证明。

Aspect in Topography to Enhance Fine-detailed Landform Element Extraction on High-resolution DEM

The value of the high-resolution data lies in the high-precision information discovery.The fine-detailed landform element extraction is thus the basis of high-fidelity application of the high-resolution digital elevation models(DEMs).However,the results of landform element extraction generated by classical methods might be ungrounded on high-resolution DEMs.This paper presents our research on using the aspect to reinforce the applicability and robustness of the classical approaches in landform element extraction.First,according to the research of pattern recognition,we assume that aspect-enhanced landform representation is robust to rotation,scaling and affine variance.To testify the role of aspect,we respectively integrated the aspect into three classical approaches:mean curvaturebased fuzzy classification,elevation-based feature descriptor,and object-based segmentation.In the experiment,based on four types of high-resolution DEMs(1 m,2 m,4 m and 8 m),we compare each classical approaches and their corresponding aspect-enhanced approaches based on extracting the rims of two craters having different landforms,and the ridgelines and valleylines of a region covered by few vegetables and man-made buildings.In comparison to the results generated by curvature-based fuzzy classification,the aspect enhanced curvature-based fuzzy classification can effectively filter a number of noises outperforms the curvature-based one.Otherwise,the aspect-enhanced feature descriptor can detect more landform elements than the elevation-based feature descriptor.Moreover,the aspect-based segmentation can detect the main structure of landform,while the boundaries segmented by classical approaches are messing and meaningless.The systematic experiments on meter-level resolution DEMs proved that the aspect in topography could effectively to improve the classical method-system,including fuzzy-based classification,feature descriptors-based detection and object-based segmentation.The value of aspect is significantly great to be worthy of attentions in landform representation.

RepDDNet:a fast and accurate deforestation detection model with high-resolution remote sensing image

Forest is the largest carbon reservoir and carbon absorber on earth.Thus,mapping forest cover change accurately is of great significance to achieving the global carbon neutrality goal.Accurate forest change information could be acquired by deep learning methods using high-resolution remote sensing images.However,deforestation detection based on deep learning on a large-scale region with high-resolution images required huge computational resources.Therefore,there was an urgent need for a fast and accurate deforestation detection model.In this study,we proposed an interesting but effective re-parameterization deforestation detection model,named RepDDNet.Unlike other existing models designed for deforestation detection,the main feature of RepDDNet was its decoupling feature,which means that it allowed the multi-branch structure in the training stages to be converted into a plain structure in the inference stage,thus the computation efficiency can be significantly improved in the inference stage while maintaining the accuracy unchanged.A large-scale experiment was carried out in Ankang city with 2-meter high-resolution remote sensing images(the total area of it was over 20,000 square kilometers),and the result indicated that the model computation efficiency could be improved by nearly 30%compared with the model without re-parameterization.Additionally,compared with other lightweight models,RepDDNet also displayed a trade-off between accuracy and computation efficiency.

高精度磁栅式隧道磁阻微位移传感器

利用隧道磁阻(TMR)效应设计了一种结构相对简单的磁栅式高精度微位移传感器。首先,介绍了磁栅式隧道磁阻位移传感器检测原理,凭借隧道磁阻传感器高灵敏度的特点检测磁栅敏感轴上方的空间磁场,接着利用COMSOL有限元仿真软件对磁栅结构进行仿真设计,验证了磁栅的磁场分布特性。通过搭建四桥路检测电路能够使TMR传感器有效抑制外部磁场干扰以及温度对TMR传感器的影响;得益于90°移相电路模块的加入,得到两路相位相差90°的标准正余弦信号,解决了传统的由于空间非正交引起的相位和幅值等误差问题,为超高倍数的细分插值技术奠定了基础,最后结合细分因子为9600的插值电路,理论上可实现208 nm的高分辨率位移检测,在20 mm量程范围内能达到0.15%的位移测量精度。

确定性侧向位移芯片在儿童横纹肌肉瘤循环肿瘤细胞检测方法的可靠性研究

目的采用一种高效的循环肿瘤细胞(CTCs)捕获鉴定方式扩展CTCs的液体活检的临床应用。探究CTCs与横纹肌肉瘤(RMS)患儿临床特征的关系。方法将横纹肌肉瘤TE761细胞系混悬到外周血中,使用确定性侧向位移芯片(DLD-ΔChip),依据细胞尺寸快速无损分离富集肿瘤细胞,之后采用免疫荧光标记(DAPI+/CD45-/MyoD1+),在显微镜下鉴定TE761系细胞。检测该项技术对于肿瘤细胞的分离富集效率,以及检测RMS患儿外周血中CTCs数目并发掘与患者临床状况的相关性。结果DLD-ΔChip对于TE761系细胞的捕获率为94.0%,白细胞去除率为99.99%,红细胞去除率为100%。经DLD-ΔChip分离后的TE761系细胞活性为94.0%,与分离前的细胞增殖无统计学差异(t=2.21,P>0.05)。使用DLD-ΔChip测量RMS患儿外周血CTCs,RMS中危组和高危组外周血中的CTCs存在相关关系(U=289.00,P<0.05)。结论DLD-ΔChip分离富集CTCs效率高,分离后的CTCs活性高。RMS患儿中的CTCs在患者临床危险度分级、判断预后方面有良好的应用前景。

基于稳健随机分隔森林的 GNSS位移序列粗差实时检测方法

受GNSS硬件设备、通讯链路以及观测环境等因素影响,GNSS位移监测数据往往包含粗差,无法反映真实的变形特征。针对该问题,本文提出将稳健随机分割森林(robust random cut forest,RRCF)算法应用于GNSS位移监测数据粗差实时检测。仿真数据处理结果表明,RRCF算法粗差实时检测的准确率、精确率与召回率分别优于95%、98%、96%。地质灾害位移监测数据处理结果表明,GNSS位移监测数据发生异常突变时,RRCF方法检测结果与实际异常值情况吻合且误判率较低。总体而言,RRCF算法对GNSS位移监测数据异常实时检测的准确率和可用性均较好。

基于改进边缘信息的ZNCC位移提取算法研究

为了解决ZNCC模板匹配算法计算量大、定位精度低等问题,提出了一种基于改进边缘信息的ZNCC位移提取算法。首先,通过曲面拟合法提高基于ZNCC位移提取算法的亚像素精度。然后利用滤波处理后的Sobel算子边缘检测算法提取视频每一帧中的重要信息特征,创建新的模板,并进行ZNCC相关值计算。为减少匹配点和运算量,根据信息点数设定不同的步长。最后,采用基于改进边缘信息的ZNCC位移提取算法提取实验视频中运动对象的位移波形图进行分析验证。实验结果显示,测量速度上提高了77.46%,测量误差为0.234 4%,证明了基于改进边缘信息的ZNCC位移提取算法不仅保留了原算法的搜寻精度与稳定性优势,并在速度上取得了显著提高。

既有铁路桥梁桩基检测与病害加固处治

某既有铁路桥梁因水害导致墩身位移,为快速无损进行桩基完整性检测,采用基于桩侧切割小平台作为激发点的瞬态激振的双速度法,共检测5根角桩,判定3根桩无缺陷反射,2根桩局部有缺陷异常反射。对其中2根无缺陷桩和1根有缺陷桩进行钻孔验证,3根桩芯样结果与检测结果一致。针对墩身位移病害,分两阶段进行加固处治,第一阶段主要采取山体侧清方卸载、设置环形截水沟、临空侧设置钢板桩及填土反压等应急抢险措施,阻止病害程度进一步加剧;第二阶段综合采取增设仰斜孔排水、锚杆框架梁、锚固桩、扩大承台及加桩等永久整治措施。经两阶段加固处治后线路恢复运营,桩身应变、墩身及轨道位移监测数据均正常。

基于北斗卫星定位的电力线路异常位移检测研究

由于电力线路所处环境较为复杂,导致电力线路位移位置定位以及异常位移检测难度加大,因此提出基于北斗卫星定位的电力线路异常位移检测方法。根据电力线路异常位移检测原理,使用卡尔曼滤波器估计当前北斗卫星状态,采用基于北斗广域四重差分该正数方法对北斗卫星定位进行优化处理,得到精准的电力线路位移位置,将其与位置检测初始数据进行比较后,得出电力线路异常位移检测结果。实验结果表明,该方法估计北斗卫星不同方向上的SEP数值在90.59%-93.14%之间,定位能力较强,可有效检测电力线路异常位移,实际应用效果更好。

GNSS实时动态定位在短期滑坡位移检测中的应用

全球卫星导航系统(GNSS)是滑坡检测最有效的手段之一。本文针对目前利用GNSS进行短期滑坡检测存在的不稳定、容易受到粗差和观测噪声影响的问题,给出了一种基于GNSS动态定位时间序列分割的短期位移检测方法,进行了重大位移检测仿真实验和小位移检测仿真实验研究。其中,重大位移检测到的三种位移的误差百分比分别为12.8%、6.1%和5.9%,小位移检测到的三种位移的误差百分比分别为2.5%、9%和4%。实验结果表明本文所设计的两个仿真实验中的各突变均能被准确检测,检测结果可达亚厘米精度。通过本文所给方法中由当前历元与GNSS动态学定位得到的前历元坐标构成的检测窗口和基于时间序列分割的统计方法,减少了局部扰动对短期位移检测的影响。

起重机制动下滑位移量的检测方法研究

针对目前起重机制动下滑位移量的检测测量方法存在的问题,本文提出一种基于加速度改进的检测测量方法。首先,对每个方向的加速度传感器进行标定;其次,将加速度与偏转角进行融合互补滤波降低风载荷对制动加速度的影响;然后,通过Kalman滤波的方法降低钢丝绳抖动干扰信号;最后,采用改进后的方法进行划分,在时域内再对加速度进行积分,避免直接在频域内进行傅里叶变换引起截断问题。对制动匀速下降过程中加速度瞬时变化量进行精准识别和双重积分,获得精确的制动下滑位移量。实验结果表明,该方法在作业环境中检测测量起重机制动下滑位移量的准确性高,抗干扰能力强。