Bridge cables, which serve as critical load-bearing components in cable-supported bridges, are highly susceptible to broken wires caused by corrosion and fatigue. Accurate quantitative evaluation of this type of damage is essential to ensure structural safety. This study presents a novel quantitative evaluation method for identifying the number of broken wires by employing the magnetic flux leakage (MFL) signal in three-dimensional (3D) space. The method first acquires the 3D spatial magnetic leakage field and generates 3D arrays of MFL signals for computational analysis. It then extracts two categories of features. The first is the global feature, defined as the L1 norm distance (L1D) between the measured 3D spatial MFL signal and the defect-free reference signal. The second is the local feature, consisting of multi-scale waveform features organized as a feature array. The multi-scale waveform feature arrays are compressed using Multilinear Principal Component Analysis (MPCA) to reduce computational redundancy. The compressed feature arrays are then introduced into the Elastic Net-Constrained Regression (ENCR) model to predict defect width. The number of broken wires is ultimately calculated as the ratio of the total L1D value to the L1D value corresponding to the predicted width of a single wire. For a given broken wire width, the L1D demonstrates linear superposition with respect to the number of broken wires, with goodness-of-fit values exceeding 0.999, enabling proportional quantification. Validation through simulations and experiments on PES7-127 cables (127 wires of 7 mm diameter) confirms the method's effectiveness. The simulations achieved 100 % accuracy in counting broken wires across varying widths and distributions, while experimental results verified 100 % quantification accuracy (within a tolerance of +/- 1 wire) for defects involving <= 4 broken wires. This performance is obtained using a training dataset of only 30 samples. This study provides a practical and efficient approach for bridge cable assessment.

Non-destructive testing (NDT) of cable-supported bridges is crucial for ensuring their safe and reliable operation. However, detecting internal broken wires using traditional Magnetic Flux Leakage (MFL) testing remains a challenge due to the low Signal-to-Background Ratio (SBR). To address this issue, this paper proposes a novel sensor composed of multilayer-arranged Hall elements (MAHE) based on the Radial Differential Magnetic Flux Leakage (RDMFL) principle, aiming for effective detection of broken wires at various depths within the cable. The RDMFL method detects damage by superimposing the differences in magnetic flux density measured at multiple radial points and identifying local peaks in the combined signals. A theoretical expression for the RDMFL signal is derived, and the influence of MAHE sensor structural parameters on both SBR and amplitude enhancement of broken wire signals is analyzed. Finite element simulations are used to verify performance improvements and identify the optimal structural parameters. Experimental results on a cable specimen demonstrated that the MAHE sensor-equipped testing system effectively detected single broken wires positioned in layers 1-7 of a PES7-127 specification cable (comprising 127 steel wires, each with a 7 mm diameter). Meanwhile, a preliminary image of all broken wires within the cable specimen was generated, providing a clear magnetic field visualization of the damages. Finally, the detection capability of the MAHE sensor was analyzed using a Receiver Operating Characteristic (ROC) curve, and the optimized threshold for identifying broken wires was determined.

Guided wave based non-destructive testing technique has been widely used in many engineering structures. Accurate characterization of defects by extracting defect-related features from the guided wave testing signals is very important for the condition evaluation of the tested specimen. In this paper, a topological feature for defect characterization is extracted from the topological domain of the guided wave testing signal. Specifically, the topological feature is extracted from the 2-dimensional phase space of the testing signal based on persistent homology. The steel wire notch experiments show that there is an exponential relationship between the topological feature and the cross sectional area loss ratio of the steel wire, and the goodness of fit is 0.9962. This paper confirmed that the persistent homology is a useful tool to analyze guided wave testing signals to investigate the defect characterization.

Corrosion of steel wires in cables is a frequent occurrence and is a major factor affecting the durability and safety of cables. Magnetostrictive guided waves have been employed to detect steel wire corrosion. However, due to the complexity of the geometry of the corrosion area, it is difficult to interpret the magnetostrictive guided wave testing signals. Hence, extracting features from testing signals to characterize the corrosion condition is a great challenge. This study proposes a topological feature for corrosion characterization of steel wire, which is extracted from the two-dimensional point cloud with optimal geometry structure of the testing signal based on persistent homology theory. Corrosion experiments were carried out on 30 galvanized steel wires. The experimental results indicate that the topological feature increases roughly linearly with the corrosion depth. Furthermore, compared to other features obtained from the time domain, frequency domain, and joint time–frequency domain, this topological feature can reflect the progression of corrosion more accurately.

Multi-wire cables are widely used in suspension bridges and cable-stayed bridges as primary load bearing structural elements. Broken Wires in cables can lead to catastrophic accidents such as bridge collapse. Magnetostrictive guided wave testing technology has been employed to detect the broken wire defects in multi-wire cables, and the defect size is estimated by analyzing the defect echo signals. However, there are many studies on the guided wave testing for the seven-wire steel strands but fewer for the bridge cables which have a large number of wires. Moreover, the relationship between the guided wave testing signal features and the defect size of multi-wire structures is imprecise, which means the defect size estimated by the features may deviate significantly from the real defect size. In this paper, large-scale topological features are extracted by using persistent homology from the dynamical reconstruction topology of the guided wave testing signals to characterize broken wire defects in the bridge cable. The broken wire experiments were performed on a 61-wire cable. The experimental results show a good linear relationship (the goodness of fit 0.9946) between the large-scale topological features and the number of broken wires in the cable. It indicates that it is feasible to extract topological features from the topological domain of the testing signals to characterize the broken wire defects of bridge cables.

对于不锈钢管道、容器等保温包覆层较厚的情况(提离距离超过50 mm),从拓扑学角度分析信号特征,提出基于持续同调特征条形码的不锈钢板脉冲涡流大提离测量方法.分析结果表明:在待测板厚大于18 mm的情况下,该方法对60～140 mm提离距离测量的相对误差为±6.00％.

钢丝腐蚀退化是桥梁拉索承载能力下降的主要原因,定期腐蚀检测对拉索的寿命非常重要.为模拟拉索实际腐蚀过程和检测思路,选取制造完好的平行钢丝拉索截段倾斜摆放,在不剥开拉索外护套HDPE的条件下,设计一套均匀滴加NaCl溶液吊瓶装置,通过小孔滴加NaCl溶液进行98 d加速腐蚀,使用磁致伸缩导波检测仪器定期跟踪采集拉索腐蚀区域和端部信号.分析对比腐蚀前后信号发现,腐蚀区域反射系数逐渐增大,导波能量在腐蚀钢丝中逐渐衰减,端部回波信号逐渐减小.拉索腐蚀试验的导波信号特性规律能为实桥在役拉索腐蚀检测提供数据依据和可靠支撑.

混合式教学模式采用“线上+线下”相结合的方式,合理划分课程内容,利用现有网络平台丰富的课程资源,以及智能移动终端的普及带给学习的便利性,提高教学效率和学习效果.将该教学模式应用于通信工程专业必修课程数字电子技术中,通过对比传统教学模式2016级与混合式教学模式2017级学生学习成绩,表明混合式教学模式在培养学生对所学知识的综合应用能力方面更有效果.

针对脉冲涡流检测信号易受提离高度影响导致测厚困难的不足,提出了一种基于Laplace小波特征频率的金属构件脉冲涡流信号处理测厚方法.首先在详细小波分析脉冲涡流信号波形特性的基础上,选用单边振荡指数衰减曲线Laplace小波虚部为基函数,采用分段Laplace小波相关滤波法获取信号相关系数、频率以及阻尼比随时间的变化序列;然后以相关系数大于某一阈值的频率序列均值为表征壁厚的特征频率,并使用幂函数拟合特征频率与试件壁厚的关系;最后分析壁厚、提离高度对测厚结果的影响.研究表明,对于12～ 30 mm壁厚304不锈钢试件,该方法在提离高度0～120 mm范围内壁厚测量结果误差在8％以内.

提出一种应用于帧率提升系统的,根据内插帧各匹配块的均值SAD为检测依据的场景切换检测算法,解决场景切换时ME/MC算法因匹配失误产生严重块效应的问题.将当前内插帧的均值SADcurr与前面已生成的内插帧的均值SADpre相除,所得商与经验阈值进行比较,若大于经验阈值则判定发生场景切换;否则,没有场景切换.帧率提升系统根据判定结果选择不同的算法产生内插帧.实验结果表明;与传统的检测算法相比,本文算法在场景切换检测的准确率上有明显的提升.

High-strength steel wire is the basic unit of the cable, and the status of the wire directly determines the cable carrying capacity. Based on the study of the wire broken detection signal by using magnetostrictive guided wave testing, 150 steel wires were classified by artificial accelerated corrosion method. 30 steel wires are used as a group on a special bracket and regularly pour dilute hydrochloric acid to accelerate corrosion. According to the depth of corrosive pits, the corrosion of steel wire was divided into five grades. The signal characteristics of steel wire before and after corrosion were tested by using magnetostrictive waveguide nondestructive testing instrument. The above experiments show that perfect wire background signal reflection coefficient is below 2%(97% probability). So the detection signal can be sensitive to feedback the wire corrosion or not. However, it is difficult to evaluate corrosion degree grading by pits depth or weight loss. The non-destructive testing signal by using magnetostrictive guided wave is very complicated because of the size of pits, shape, orientation, surface distribution, weight loss and other factors. In conclusion, the research ideas and conclusions on the corrosion grading of steel wire and the non-destructive testing of magnetostrictive guided wave have a good effect on the corrosion evaluation of steel wire and its cable.

"大创新"时代,伴随着大学生就业形势日趋严峻和新生代大学生自我意识的增强,大学生创新创业成为必然.虽然大学生创新创业面临着诸多挑战与困难,但在"互联网+"环境下,大学生创新创业具有前景好、模式灵活、风险小、商品多样化、资源获取渠道通畅、门槛低等优势.本文从政府、高校、大学生自主创业者三方面提出一些具体措施来推进大学生创新创业.

This paper proposes a motion estimation algorithm based on time-recursive motion vector to reduce calculation amount. It also presents an improved VOD matching function (IVOD) which can make accurate matching under illumination intensity change scene. The motion estimation algorithm is applied to the Frame Rate Up-conversion system and the experimental results show that the proposed algorithm maintains satisfactory visual quality and PSNR. And IVOD matching function can overcome the effects of illumination intensity change to find the best matching block.

A new Frame Rate Up-Conversion(FRUC) approach based on triangulation mesh is proposed.The algorithm constructs a triangular mesh based on the identified nodes of the current frame,which is extracted by implementing the redundant wavelet transform.It gets the motion vectors of the identified nodes by estimating displacement of such nodes from the previous frame with respect to the current frame in the triangular mesh.The new frame is rendered based on the reference frames and the estimated displacement of nodes.Experimental results show that the proposed algorithm provides better performance than conventional motion-compensated interpolation approach.

Based on the classic Three Step Search(TSS) algorithm,a more efficient motion estimation method,Double Tracing Three Step algorithm(DTTSS) is proposed.After the first step,DTTSS selects two best match points as start points in the next step for double tracking.With relatively high search speed,DTTSS can avoid the local optimum problem in the TSS.This algorithm is compared with Full Search(FS),Local Full Search(LFS),Three Step Search(TSS) in Motion Estimation and Motion Compensation(ME/MC) frame rate up conversion system.Experimental results show the effectiveness in the average number of search points of each block,the accuracy,the visual effect of the interpolated frame and its PSNR.

Since there are many errors in an optical 4f system,the recorded images contain a large number of noises.This article discusses the general image restoration method based on neural network.combined with the characteristics of optical 4f system,a restoration method based on whole image is proposed,which takes effective use of the structure of the system error information.The result shows that the new method is effective in image restoration of 4f optical system.