Locating the position of objects in non-line-of-sight based on time delay estimation

Non-line-of-sight imaging detection is to detect hidden objects by indirect light and intermediary surface（diffuser）.It has very important significance in indirect access to an object or dangerous object detection, such as medical treatment and rescue. An approach to locating the positions of hidden objects is proposed based on time delay estimation. The time delays between the received signals and the source signal can be obtained by correlation analysis, and then the positions of hidden objects will be located. Compared with earlier systems and methods, the proposed approach has some modifications and provides significant improvements, such as quick data acquisition, simple system structure and low cost, and can locate the positions of hidden objects as well： this technology lays a good foundation for developing a practical system that can be used in real applications.

The use of “Objective Correlative” Theory in T.S. Eliot’s The Love Song of J.Alfred Prufrock

“Objective Correlative”theory was first proposed by T.S. Eliot, who holds that people’s emotion can find expression in a series of objective correlative. In his poem The Love Song of J.Alfred Prufrock, the use of objective correlative fully reflects and reveals modern people’s loneliness, futility and alienation.

Alerting effects of light in healthy individuals: a systematic review and meta-analysis

Light plays an essential role in psychobiological and psychophysiological processes,such as alertness.The alerting effect is influenced by light characteristics and the timing of interventions.This meta-analysis is the first to systematically review the effect of light intervention on alertness and to discuss the optimal protocol for light intervention.In this meta-analysis,registered at PROSPERO(Registration ID:CRD42020181485),we conducted a systematic search of the Web of Science,PubMed,and PsycINFO databases for studies published in English prior to August 2021.The outcomes included both subjective and objective alertness.Subgroup analyses considered a variety of factors,such as wavelength,correlated color temperature(CCT),light illuminance,and timing of interventions(daytime,night-time,or all day).Twenty-seven crossover studies and two parallel-group studies were included in this meta-analysis,with a total of 1210 healthy participants(636(52%)male,mean age 25.62 years).The results revealed that light intervention had a positive effect on both subjective alertness(standardized mean difference(SMD)=-0.28,95%confidence interval(CI):-0.49 to-0.06,P=0.01)and objective alertness in healthy subjects(SMD=-0.34,95%CI:-0.68 to-0.01,P=0.04).The subgroup analysis revealed that cold light was better than warm light in improving subjective alertness(SMD=-0.37,95%CI:-0.65 to-0.10,P=0.007,I2=26%)and objective alertness(SMD=-0.36,95%CI:-0.66 to-0.07,P=0.02,I2=0).Both daytime(SMD=-0.22,95%CI:-0.37 to-0.07,P=0.005,I2=74%)and night-time(SMD=-0.32,95%CI:-0.61 to-0.02,P=0.04,I2=0)light exposure improved subjective alertness.The results of this meta-analysis and systematic review indicate that light exposure is associated with significant improvement in subjective and objective alertness.In addition,light exposure with a higher CCT was more effective in improving alertness than light exposure with a lower CCT.Our results also suggest that both daytime and night-time light exposure can improve subjective alertness.

The Writing Techniques of T.S Eliot——Analysis of The Love Song of J.Alfred Prufrock

During T.S. Eliot's(1888-1965)whole life he left us a lot of fortune, and The Love Song of J. Alfred Prufrock is considered as one of Eliot's finest and most important works. A lot of scholars and critics have done different researches on this poem.The author of this paper tries to analyze one of these poems from the perspective of T.S. Eliot's poetics.

SPATIAL CORRELATION DESCRIPTION OF DEFORMATION OBJECT BASED ON FUZZY CLUSTERING AND GEOLOGICAL ANALYSIS

The methods of deformation analysis and modeling at single point are realized easily now,but available approaches do not make full use of the information from monitoring points and can not reveal integrated deformation regularity of a deformable body.This paper presents a fuzzy clusetering method to analyze the correlative relations of multiple points in space,and then the spatial model for a practical dangerous rockmass in the area of Three Gorges,Yangtze River is established,in which the correlation of six points in space is analyzed by geological investigation and fuzzy set theory.

Application of Digital Image Correlation and Geodetic Displacement Measuring Methods to Monitor Water Dam Behavior under Dynamic Load

This paper presents experiment results of the measurement conducted at the Roznew Dam power plant. For a course of starting and operating of turbo-plants, downstream face of the dam was monitored in relation to its eventual displacements on direction parallel to the construction axis. For the purpose of the experiment, geodetic measurement techniques and 2D DIC （digital image correlation） method （utilizing photographs of the object recorded with digital camera） were compared with regard to credibility, efficiency and accuracy. The vertical and horizontal displacements were monitored by tachometers measurements. The deformations in x-axis and y-axis on the wall surface was monitored by 2D DIC. It has been noticed that 2D DIC method is a surface method, continuous--not discreet. It allows for continuous observations of surface deformations, which is not possible in case of tachemetric measurements. Despite many advantages, the 2D DIC method lacks unambiguous evaluation of precision and relevance of designated displacements, which is rather significant for possibilities of utilization in technical control of large engineered objects. It should be also marked that the tachometric method is more reliable but is more laborious. Research of this type might comprise additional element for the assessment of the influence of dynamic loads, such as activating turbine water flow, onto the overall condition of the surveyed structure.

Visual Object Tracking Based on Modified LeNet-5 and RCCF

The field of object tracking has recently made significant progress.Particularly,the performance results in both deep learning and correlation filters,based trackers achieved effective tracking performance.Moreover,there are still some difficulties with object tracking for example illumination and deformation(DEF).The precision and accuracy of tracking algorithms suffer from the effects of such occurrences.For this situation,finding a solution is important.This research proposes a new tracking algorithm to handle this problem.The features are extracted by using Modified LeNet-5,and the precision and accuracy are improved by developing the Real-Time Cross-modality Correlation Filtering method(RCCF).In Modified LeNet-5,the visual tracking performance is improved by adjusting the number and size of the convolution kernels in the pooling and convolution layers.The high-level,middle-level,and handcraft features are extracted from the modified LeNet-5 network.The handcraft features are used to determine the specific location of the target because the handcraft features contain more spatial information regarding the visual object.The LeNet features are more suitable for a target appearance change in object tracking.Extensive experiments were conducted by the Object Tracking Benchmarking(OTB)databases like OTB50 and OTB100.The experimental results reveal that the proposed tracker outperforms other state-of-the-art trackers under different problems.The experimental simulation is carried out in python.The overall success rate and precision of the proposed algorithm are 93.8%and 92.5%.The average running frame rate reaches 42 frames per second,which can meet the real-time requirements.

Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion,a tracking algorithm based on multi-time-space perception and instance-specific proposals is proposed to optimize the mathematical model of the correlation filter(CF).Firstly,according to the consistency of the changes between the object frames and the filter frames,the mask matrix is introduced into the objective function of the filter,so as to extract the spatio-temporal information of the object with background awareness.Secondly,the object function of multi-feature fusion is constructed for the object location,which is optimized by the Lagrange method and solved by closed iteration.In the process of filter optimization,the constraints term of time-space perception is designed to enhance the learning ability of the CF to optimize the final track-ing results.Finally,when the tracking results fluctuate,the boundary suppres-sion factor is introduced into the instance-specific proposals to reduce the risk of model drift effectively.The accuracy and success rate of the proposed algorithm are verified by simulation analysis on two popular benchmarks,the object tracking benchmark 2015(OTB2015)and the temple color 128(TC-128).Extensive experimental results illustrate that the optimized appearance model of the proposed algorithm is effective.The distance precision rate and overlap success rate of the proposed algorithm are 0.756 and 0.656 on the OTB2015 benchmark,which are better than the results of other competing algorithms.The results of this study can solve the problem of real-time object tracking in the real traffic environment and provide a specific reference for the detection of traffic abnormalities.

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.

大数据的6种地理学应用范式

随着大数据时代的来临,多源大数据正在兴起,数据驱动研究范式与地理学日益融合。基于个体行为的地理空间大数据可提供对海量个体行为模式的观察,从而实现“由人及地”的社会感知,支持城市管理、交通、公共卫生等不同应用。本文从应用角度,以地理空间大数据为重点,梳理其支持的6种应用范式,按照层次从低到高依次为描述时空分布、识别异常对象、发现普适规律、揭示关联关系、预测未来趋势及优化空间决策。其中,第1个方向是对地理现象和地理要素时空特征的简单刻画;第2~4个方向则注重探寻时空分布特征背后的规律和机理;最后两项,则是在决策层面提供支持。继而,本文指出大数据应用中数据获取、分析方法和应用目标3方面的问题。

动态时空异常感知的相关滤波目标跟踪算法

针对背景感知算法未与目标的时空域特性建立联系,以及无法准确处理遮挡、形变等异常跟踪情况的问题,提出了能够动态感知时空异常的目标跟踪算法。在相关滤波器训练过程中引入动态空间正则项,使其与样本的时空域特性建立联系;结合响应图的峰值唯一性和锐利信息,提出异常感知方法;利用历史滤波器具有不同置信度的特点以及目标在时域中的连续性,通过异常感知方法自适应选择高置信度的历史滤波器作为时间正则化的参考模板,降低滤波器退化的风险。在OTB50、OTB100和TC128测试基准上进行仿真实验,该算法能够适应外观变化、画面杂乱等复杂条件下的跟踪任务,具有较强的鲁棒性和实用性。

基于灰色关联-熵权法的座体注塑工艺参数优化

手持风扇底座具有复杂曲面与多筋结构等复杂的结构特征,这阻碍了材料的流动,可能会使产品产生熔接线、翘曲、体积收缩等缺陷,进而影响产品的表面质量和尺寸精度。为解决上述问题,通过理论算法与有限元仿真相结合开展风扇底座注塑工艺参数的优化研究。首先,根据产品特征确定了型腔数量及浇口位置,初步分析发现产品的缩痕指数相对较大;其次,以翘曲变形和缩痕指数为优化目标,建立中心复合试验方案并开展仿真分析,结合灰色关联与熵权法进行多目标优化,确定了工艺参数对灰色关联度影响的大小程度,依次为熔体温度、模具温度、注射时间、保压压力及保压时间;最后,确定最佳工艺参数组合为熔体温度225℃、模具温度30℃、注射时间5 s、保压压力20 MPa及保压时间30 s。相比初始方案,最佳工艺参数组合下的缩痕指数降低了30.79%,由于加强筋的存在翘曲变形优化前后相差较小。

基于DMOEA-APTC算法的无人机在线航迹规划

为了解决具有时间关联性质的动态多目标优化问题,以无人机在线航迹规划问题为基础,引入时间关联特征概念并建立无人机时间关联动态多目标优化问题模型,提出一种使用自适应预测响应机制和时间关联性优化机制的动态多目标双层优化算法。根据环境变化相关性判断环境变化强弱并启用不同响应机制,快速适应环境变化;优化过程中通过最小二乘法学习历史数据拟合航迹未来预测值,根据预测可靠性自适应选择“仅优化当前”或“同时优化当前与未来”优化模式;使用改进后的切比雪夫分解法对符合偏好的航迹进行决策。实验结果表明:所提算法在复杂飞行环境下降低飞行时长的同时具有更高生存概率,提高了无人机飞行稳定性,更合理有效地处理了在线航迹规划问题。

2D-SiC/SiC复合材料的弹丸冲击损伤及冲击后拉伸性能

连续纤维增韧陶瓷基复合材料以其良好的高温性能而被广泛应用在航空发动机等高温部件处,但服役过程中容易受到异物碎片冲击,这受到了广泛关注。研究采用轻气炮对化学气相渗透(CVI)技术制备的2D-SiC/SiC试样进行弹丸冲击,利用高速摄像机记录冲击过程,使用光学显微镜和CT观察异物损伤(FOD)的表面和内部结构,探讨了2D-SiC/SiC复合材料的高速冲击损伤特性。结果表明,2D-SiC/SiC复合材料的高速冲击损伤形式包括锥形裂纹、层间分层、纤维断裂以及基体压溃等。通过表征损伤发现,试件背部损伤和边缘处分层损伤由反射拉伸波造成,随着冲击速度提升,弹丸与拉伸波共同作用会造成试件穿透,试件边缘分层损伤减弱。对高速冲击后的试件进行准静态拉伸试验,明确了剩余力学性能与冲击速度、弹丸直径的关系,表明剩余拉伸强度是表征冲击损伤程度的有效参数。同时,采用数字图像相关(DIC)方法获得拉伸过程中的应变分布,并结合不同弹丸直径、冲击速度冲击后的剩余拉伸强度,进一步对比探究不同变量对冲击损伤的影响。研究结果表明弹丸直径是高速冲击损伤程度的主要影响因素。

基于数据驱动660 MW循环流化床锅炉多目标燃烧优化

为降低某电厂循环流化床锅炉污染物排放,同时提高锅炉燃烧运行经济性,本文采用数据驱动技术实现循环流化床锅炉多目标燃烧优化。基于改进粒子群优化长短期记忆神经网络建立循环流化床锅炉NO_(x)/SO_(2)排放数学模型和锅炉排烟温度数学模型,以相对误差为预测性评估指标以确定最佳网络参数;其次,基于改进粒子群优化长短期记忆神经网络(IPSO-LSTM)、长短期记忆神经网络(LSTM)、广义回归神经网络(GRNN)和反向传播神经网络(BPNN)分别构建NO_(x)/SO_(2)排放数学模型和锅炉排烟温度数学模型,通过比较预测性评估指标,证明本文构建预测模型有效性;最后,基于非支配排序遗传算法(NSGA-Ⅱ)获取不同运行工况下循环流化床锅炉燃烧优化调整方案,以降低NO_(x)/SO_(2)排放浓度,同时维持排烟温度稳定性。结果表明:相比优化前,优化后NO_(x)排放浓度平均降低了10.58%,SO_(2)排放浓度平均降低了25.81%,最大降低了650 mg/m^(3),且排烟温度平均降低0.14%。

多工况输气管道泄漏声波信号自适应去噪

为实现极低信噪比下管道泄漏声波信号去噪,基于多通道信号的相关性,提出使用相关系数矩阵筛选变分模态分解所得模态分量。针对不同工况的泄漏信号,提出不依赖真值的去噪质量评价指标,将其作为多目标灰狼优化算法目标函数,基于Pareto前沿获取变分模态分解的最佳模态数K和惩罚因子η,实现多工况自适应去噪。搭建了输气管道泄漏多工况实验平台,在不同工况、不同输入信号信噪比(-8~4 dB)下验证所提方法的去噪效果。结果表明,该方法能有效抑制噪声,-8 dB时去噪信号信噪比提升2.84 dB以上。对比基于单目标优化的去噪方法,-8 dB下新方法的信噪比和相关系数分别提高了3.65 dB和31.26%。

基于多基因遗传规划的注塑过程参数关联预测方法

鉴于传统注塑过程参数选择与设置依赖于人工经验,且过程参数与输出参数之间存在着非线性、强耦合的关系,文中提出了一种基于多基因遗传规划的注塑过程参数关联预测方法。该方法基于数据生成初始多基因树种群,随后迭代进行选择,高、低级树交叉及两类树突变操作,以获得最适应数据的模型,并演化出一个能清晰描述过程参数与输出参数关联的代数方程,确定控制输出的关键参数。在设置选择指标时,针对模型方程的复杂性会影响到实际使用效率的问题,引入复杂性维度作为评估指标进行多目标选择。以注塑过程为例,试验结果表明了所提方法的有效性。此外,参数和灵敏度分析验证了模型的鲁棒性,揭示了参数间隐藏的非线性关系,对后续注塑过程参数设置及优化提供了依据。

基于Transformer的目标跟踪与分割统一算法

采用相关滤波的判别式目标跟踪算法因具有较好的跟踪效果得到广泛关注,但该类方法使用的矩形框估计法通常只能得到目标正矩形框,难以获得目标更加精细的状态信息,如旋转矩形框、目标轮廓、掩码信息等。为解决上述问题,提出一种基于Transformer的单目标跟踪与分割统一算法T-TS,利用Transformer的注意力机制优势对目标精确定位,通过得到的目标定位编码信息引导目标分割网络对目标进行前、背景分割,获得目标精细掩码,再对掩码进行形态学处理,优化得到目标的最佳旋转矩形框及其轮廓。在跟踪数据集VOT2018和分割数据集DAVIS上进行实验,结果显示,T-TS算法与孪生网络类算法相比具有更高的鲁棒性,与相关滤波类算法相比具有更高的跟踪精度,其在VOT2018上期望平均重叠率指标达到0.463,在视频分割任务上也实现了较好结果,DAVIS2016和DAVIS2017上Jaccard指标分别达到77.3和65.3,运行速度达到34 frame/s。实验结果表明,该算法能够准确得到旋转矩形框,对目标进行精准预测,有效解决目标旋转、形变等问题。

改进KCF的尺度自适应目标跟踪算法研究

针对KCF跟踪算法在目标跟踪过程中存在目标尺度变化时检测精度低、目标遮挡时跟踪容易丢失等问题,提出了SMAKCF(Scale-Adaptive Multiple-Feature Anti-Occlusion KCF)跟踪算法,该算法同时优化了KCF算法中的尺度响应、特征选择及模板更新策略,融合HOG特征及CN特征,加入尺度估计滤波器并利用APCE判据改进位置滤波器的更新方式,同时引入了一个检测模块对不可靠跟踪结果进行重检测。在Visual Tracker Benchmark的50个测试视频序列上进行实验来评估算法的性能,实验表明,SMAKCF算法能够有效地解决目标的尺度变化及遮挡问题,提高跟踪算法在长时目标跟踪过程中的性能。