Continuous Frame Research Articles

Unveiling the Efficiency of the Moment Distribution Method: A Geometric Series Perspective

The moment distribution method, originally introduced by Hardy Cross in his influential publication “Analysis of Continuous Frames by Distributing Fixed‐End Moments” (published in Transactions of the ASCE, 1932;96(1793):1–10), is widely recognized as a fundamental aspect of both structural engineering and global education. This iterative technique aims to improve solution accuracy at each step of distribution and has stood the test of time. In our study, we delve into the mathematical foundation of this method and make a crucial discovery: the terms generated during iterations follow a geometric series pattern. By leveraging this property, we propose a streamlined formula for efficiently calculating the sum of all the terms produced in each step of distribution iteration. Through comparative analysis with alternative methods, we highlight the method’s superior precision and robustness in structural analysis. Our investigation primarily focuses on demonstrating the geometric series nature of the terms generated through iterative moment distribution, providing specific theorems as references. Ultimately, our objective is to present concise, closed‐form mathematical solutions that showcase the practical effectiveness of this method in various applications within the field of structural engineering.

A Complex-Former Tracker With Dynamic Polar Spatio-Temporal Encoding.

Recently, the excellent performance of transformer has attracted the attention of the visual community. Visual transformer models usually reshape images into sequence format and encode them sequentially. However, it is difficult to explicitly represent the relative relationship in distance and direction of visual data with typical 2-D spatial structures. Also, the temporal motion properties of consecutive frames are hardly exploited when it comes to dynamic video tasks like tracking. Therefore, we propose a novel dynamic polar spatio-temporal encoding for video scenes. We use spiral functions in polar space to fully exploit the spatial dependences of distance and direction in real scenes. We then design a dynamic relative encoding mode for continuous frames to capture the continuous spatio-temporal motion characteristics among video frames. Finally, we construct a complex-former framework with the proposed encoding applied to video-tracking tasks, where the complex fusion mode (CFM) realizes the effective fusion of scenes and positions for consecutive frames. The theoretical analysis demonstrates the feasibility and effectiveness of our proposed method. The experimental results on multiple datasets validate that our method can improve tracker performance in various video scenarios.

Perturbation of continuous frames on quaternionic Hilbert spaces

Perturbation of continuous frames on quaternionic Hilbert spaces

Pose estimation via structure-depth information from monocular endoscopy images sequence.

Image-based endoscopy pose estimation has been shown to significantly improve the visualization and accuracy of minimally invasive surgery (MIS). This paper proposes a method for pose estimation based on structure-depth information from a monocular endoscopy image sequence. Firstly, the initial frame location is constrained using the image structure difference (ISD) network. Secondly, endoscopy image depth information is used to estimate the pose of sequence frames. Finally, adaptive boundary constraints are used to optimize continuous frame endoscopy pose estimation, resulting in more accurate intraoperative endoscopy pose estimation. Evaluations were conducted on publicly available datasets, with the pose estimation error in bronchoscopy and colonoscopy datasets reaching 1.43 mm and 3.64 mm, respectively. These results meet the real-time requirements of various scenarios, demonstrating the capability of this method to generate reliable pose estimation results for endoscopy images and its meaningful applications in clinical practice. This method enables accurate localization of endoscopy images during surgery, assisting physicians in performing safer and more effective procedures.

Monocular endoscopy images depth estimation with multi-scale residual fusion

BackgroundMonocular depth estimation plays a fundamental role in clinical endoscopy surgery. However, the coherent illumination, smooth surfaces, and texture-less nature of endoscopy images present significant challenges to traditional depth estimation methods. Existing approaches struggle to accurately perceive depth in such settings. MethodTo overcome these challenges, this paper proposes a novel multi-scale residual fusion method for estimating the depth of monocular endoscopy images. Specifically, we address the issue of coherent illumination by leveraging image frequency domain component space transformation, thereby enhancing the stability of the scene's light source. Moreover, we employ an image radiation intensity attenuation model to estimate the initial depth map. Finally, to refine the accuracy of depth estimation, we utilize a multi-scale residual fusion optimization technique. ResultsTo evaluate the performance of our proposed method, extensive experiments were conducted on public datasets. The structural similarity measures for continuous frames in three distinct clinical data scenes reached impressive values of 0.94, 0.82, and 0.84, respectively. These results demonstrate the effectiveness of our approach in capturing the intricate details of endoscopy images. Furthermore, the depth estimation accuracy achieved remarkable levels of 89.3 % and 91.2 % for the two models’ data, respectively, underscoring the robustness of our method. ConclusionsOverall, the promising results obtained on public datasets highlight the significant potential of our method for clinical applications, facilitating reliable depth estimation and enhancing the quality of endoscopy surgical procedures.

Design and Performance Evaluation of the Curved Hengjiang Avenue Pedestrian Bridge: Y-shaped Piers and Mixed Decks

Urban pedestrian bridges should contribute to the improvement of the overall image and function of the place where the footbridge is located. In order to establish a complete pedestrian access area of a Green Belt Park, an elegant pedestrian landscape bridge with Y-shaped piers and mixed decks was designed and built, providing an open and diverse viewing experience. Accelerated bridge construction technologies were adopted to build the footbridge. Finite-element models, including the full bridge model and local model, were established and a continuous rigid frame and continuous girder composite bridge system was proposed after scheme comparison. Preliminary statics and dynamics analysis, as well as detailed calculations for the local stresses of Y-shaped piers and the shear lag effect of the deck, were carried out aiming to demonstrate its safety and suitability.

A Study of the Mechanical Properties of Polyester Fiber Concrete Continuous Rigid Frame Bridge during Construction

This study investigates the mechanical performance of a polyester fiber concrete continuous rigid frame bridge during construction and the spatial stress distribution of the 0# block box girder, with a focus on the backdrop of the bridge in Pipa Zhou, Jiangxi Province. Stress monitoring at critical cross-sections during bridge construction was combined with FE simulations to analyze the stress and alignment deviation variations along the cantilevered construction process of the bridges. Subsequently, after validating the accuracy of the whole bridge model, the actual internal force of the box girder cross-section was extracted to act on the 0# block box girder solid model, and the spatial force of the 0# block box girder under the state of maximal cantilever and the completed bridge was further investigated. The results indicate that during cantilever construction, the top, and bottom plates of the box girder were subjected to compression, with the bottom plates having relatively low compression stress close to the critical values for compression and tension. Attention should be paid to controlling tensile stress application. After reaching a quarter of the bridge’s span in construction, the alignment deviation of the main beam increases, necessitating enhanced monitoring and adjustments of the main beam elevation. Furthermore, FE analysis shows that under maximum cantilever and the completed bridge states, the stress variations of the top and bottom plates of the 0# block box girder remain consistent, with the top plate stress varying by no more than 2.5 MPa and the bottom plate stress varying by approximately 1 MPa. Moreover, the 0# block box girder shrinkage cracks were mainly located in the bottom and web plate, and the number of cracks in the 0# block box girder with polyester fibers was reduced compared to the cracks in the ordinary concrete box girder.

Two topologies on the set of minimal prime elements of a continuous frame

In this paper, we discuss the properties of the hull-kernel topology and the inverse topology on the set [Formula: see text] of minimal prime elements of a continuous frame [Formula: see text]. We prove that the set [Formula: see text] endowed with the hull-kernel topology and the inverse topology, respectively, is a [Formula: see text]-space. Moreover, we obtain that there is a bijective correspondence between the set [Formula: see text] of all maximal Scott open filters of [Formula: see text] and the set [Formula: see text] when [Formula: see text] is a stably continuous frame. By considering the bijective correspondence between the sets [Formula: see text] and [Formula: see text], we propose some sufficient conditions for the topological spaces [Formula: see text] endowed with the hull-kernel topology and the inverse topology, respectively, to be sober, Hausdorff, compact, extremely disconnected and zero-dimensional spaces, respectively, when [Formula: see text] is a stably continuous frame.

Vibro-acoustic characteristics of a rail transit viaduct paved with trapezoidal sleeper track

To investigate the effects of a trapezoidal sleeper track (TST) on the vibration and noise of rail transit viaducts, the wheel–rail interaction model was first built according to the vehicle–track coupling dynamics theory. Subsequently, the vibration responses of the viaduct were analyzed using the beam–slab finite element method, and its radiated noise was calculated using the acoustic boundary element method. The modeling method was verified based on the test data of a 3 × 40 m continuous rigid frame bridge. Taking the Jialing River Bridge of Chongqing Rail Transit Line 9 as the background, the control effects of the TST on the vibration and noise of the viaduct and the effective laws of different track parameters on the bridge noise were analyzed. Results show that the simulated and measured values are in good agreement; for example, the modeling methods are reliable; the radiated noise of the viaduct is influenced by the vibration responses under the action of train load, which are jointly determined by dynamic wheel–rail force (external excitation) and displacement admittance (self-vibration characteristics); compared with embedded sleeper track (EST), the TST can reduce the overall vibration acceleration level and sound pressure level of the box girder web plate by 13.9 dB and 7.8 dB, respectively; the stiffness of the damping path is the primary factor influencing the noise of the elevated box girder, followed by the sleeper thickness and the fastener stiffness; taking the bottom plate as an example, the stiffness of the damping pad is reduced by 1 and the overall sound pressure level can be reduced by approximately 3 dB; the sleeper thickness is increased by 1, and the overall sound pressure level can be reduced by 1–2 dB; and the fastener stiffness is reduced by 1, and the noise reduction is within 1 dB.

YH-Pose: Human pose estimation in complex coal mine scenarios

Human pose estimation in continuous video frames captured from complex coal mine scenes is challenging. The video frames in this scene may suffer from problems such as uneven brightness, blurred image details, and excessive noise. Mainstream pose estimation methods have good detection performance for high-quality static images, but their accuracy and prediction rate can be significantly reduced in coal mine scenes. In this work, a yielding human pose estimation framework is proposed, termed YH-Pose. The framework strives to incorporate additional visual evidences from neighboring frames to facilitate the pose estimation of the current frame. Firstly, a human detector is introduced to locate the person’s position in the video frame and extract global features. This can provide a good initialization for the latter keypoint detection, making the training process converge quickly. Secondly, heatmaps are used to encode the joint locations as Gaussian peaks, and a temporal road module (TRM) is designed, which encodes video frames at intervals. The module efficiently fuses spatio-temporal information through frame-rate groups in a hierarchical manner. Lastly, the spatial road module (SRM) learns from fused keypoint context features and predicts the location of the keypoints in the next frame. In addition, a dataset called Colliery-1 is proposed, which derives from underground surveillance video from chinese coal mines and consists of 3600 video clips. The experimental results on the Colliery-1 dataset indicate that the framework achieved an average accuracy of 82% and 80% on the training and test sets, respectively. Moreover, the framework achieved a 94.2% prediction rate for pose estimation. To further evaluate the effectiveness of the proposed method, some comparisons have been made between it and various mainstream methods using different metrics.

Fatigue Safety Assessment of Concrete Continuous Rigid Frame Bridge Based on Rain Flow Counting Method and Health Monitoring Data

The fatigue of concrete structures will gradually appear after being subjected to alternating loads for a long time, and the accidents caused by fatigue failure of bridge structures also appear from time to time. Aiming at the problem of degradation of long-span continuous rigid frame bridges due to fatigue and environmental effects, this paper suggests a method to analyze the fatigue degradation mechanism of this type of bridge, which combines long-term in-site monitoring data collected by the health monitoring system (HMS) and fatigue theory. In the paper, the authors mainly carry out the research work in the following aspects: First of all, a long-span continuous rigid frame bridge installed with HMS is used as an example, and a large amount of health monitoring data have been acquired, which can provide efficient information for fatigue in terms of equivalent stress range and cumulative number of stress cycles; next, for calculating the cumulative fatigue damage of the bridge structure, fatigue stress spectrum got by rain flow counting method, S-N curves and damage criteria are used for fatigue damage analysis. Moreover, it was considered a linear accumulation damage through the Palmgren-Miner rule for the counting of stress cycles. The health monitoring data are adopted to obtain fatigue stress data and the rain flow counting method is used to count the amplitude varying fatigue stress. The proposed fatigue reliability approach in the paper can estimate the fatigue damage degree and its evolution law of bridge structures well, and also can help bridge engineers do the assessment of future service duration.

Research on Fatigued-Driving Detection Method by Integrating Lightweight YOLOv5s and Facial 3D Keypoints.

In response to the problem of high computational and parameter requirements of fatigued-driving detection models, as well as weak facial-feature keypoint extraction capability, this paper proposes a lightweight and real-time fatigued-driving detection model based on an improved YOLOv5s and Attention Mesh 3D keypoint extraction method. The main strategies are as follows: (1) Using Shufflenetv2_BD to reconstruct the Backbone network to reduce parameter complexity and computational load. (2) Introducing and improving the fusion method of the Cross-scale Aggregation Module (CAM) between the Backbone and Neck networks to reduce information loss in shallow features of closed-eyes and closed-mouth categories. (3) Building a lightweight Context Information Fusion Module by combining the Efficient Multi-Scale Module (EAM) and Depthwise Over-Parameterized Convolution (DoConv) to enhance the Neck network's ability to extract facial features. (4) Redefining the loss function using Wise-IoU (WIoU) to accelerate model convergence. Finally, the fatigued-driving detection model is constructed by combining the classification detection results with the thresholds of continuous closed-eye frames, continuous yawning frames, and PERCLOS (Percentage of Eyelid Closure over the Pupil over Time) of eyes and mouth. Under the premise that the number of parameters and the size of the baseline model are reduced by 58% and 56.3%, respectively, and the floating point computation is only 5.9 GFLOPs, the average accuracy of the baseline model is increased by 1%, and the Fatigued-recognition rate is 96.3%, which proves that the proposed algorithm can achieve accurate and stable real-time detection while lightweight. It provides strong support for the lightweight deployment of vehicle terminals.

Static Load Test Detection of Continuous Rigid Frame Railway Bridge

The quality of the continuous rigid frame railway bridge is related to the safety of train operation, so it is necessary to test its stiffness, strength and other indicators. Static load test is a common technique for bridge inspection. This article summarizes the purpose of static load test of continuous rigid frame railway bridge, required equipment, operation methods, etc., and lists examples to analyze the operation process and precautions of static load test, hoping to provide reference information for relevant personnel.

Sensitivity Analysis of Structural Parameters of Unequal-Span Continuous Rigid Frame Bridge with Corrugated Steel Webs

To investigate the effect of structural parameters of bridges with unequal spans on the bridge alignment, the finite element model simulating the full-scale bridge was developed, considering the construction process. For ease of finite element modeling and investigation, the section of composite beam with corrugated steel web was first converted into the section composed of the same material. For this purpose, an equivalent method of replacing corrugated steel webs with concrete webs was proposed based on theoretical derivation. After equivalent replacement, the influences of material bulk density, internal prestress, pipe friction coefficient, and pipe deviation coefficient on the main beam at the maximum cantilever stage were analyzed, and the influences of external prestress on the main beam after bridge construction were analyzed. The results show that the most sensitive parameter to structural response is bulk density, subsequently the external prestress, internal prestress, pipe friction coefficient, and pipe deviation coefficient. Among them, the bulk density, internal prestress, and external prestress are all sensitive parameters, while pipe friction coefficient and pipe deviation coefficient are non-sensitive parameters.

Thermal imaging spatial noise removal via deep image prior and step-variable total variation regularization

The imaging quality of an uncooled long wave infrared camera suffers severely from time-drifted spatial noises if not regulated by a thermoelectric cooler (TEC). These noises can be categorized within the frequency domain as either fixed pattern noise (FPN) or low-frequency noise (LPN). The time-drifted FPN is primarily governed by the temperature fluctuations of the focal plane array (FPA), whereas the LPN predominantly originates from non-uniform background radiations. Most existing methods have been proposed to address the FPN or LPN independently rather than from a unified perspective. In this paper, a self-supervised convolution neural network (CNN) is proposed to remove these two types of noise simultaneously. Because of the combination of the deep image prior (DIP) architecture and the total variation (TV) regularizer, this method is named TV-DIP. With the assumption that the spatial noises are constant in a short period of time, the spatial noise pattern is extracted directly from multiple continuous raw frames without ground-truth noise-free images. In this way, the noise pattern can be updated periodically during running time, without requiring the use of a mechanical shutter. Besides, the original TV regularizer is extended with a variable step, which shows a significant enhancement on the LPN denoising task. Furthermore, both simulation and experimental results have demonstrated that this approach achieves better performance in terms of the removal of spatial noise, the preservation of detail, and the suppression of artifacts. The source code and dataset are available on the website: https://github.com/brunolinux/TV-DIP

Visual perception-oriented quality assessment for high dynamic range stereoscopic omnidirectional video system

High dynamic range stereoscopic omnidirectional video (HSOV) system provides users with an immersive visual experience, but the distortions generated in the process of visual content generation, coding, and visualization also brings challenges to visual quality assessment. This paper proposes a visual perception-oriented quality metric for HSOV system. Considering the computational efficiency, for frames within each second, several key frames are selected so that features reflecting distortions can be extracted from the viewports and equi-angular cubemap (EAC) projection surfaces of these frames. In viewport feature extraction module, the sum and difference maps of the left and right views of the key frames are used to simulate the binocular perception. Considering that distortions will affect the structure, color and statistical properties of the image, the gradient, local binary pattern and statistical features are extracted, respectively. To reduce projection distortion, EAC projection is used to instead of other projections normally used in quality assessment, and the Sigma domain feature extraction and color feature extraction are performed on EAC projection map. In temporal feature extraction module, wavelet decomposition is used to perform temporal domain bandpass filtering on partial continuous frames to calculate the naturalness features. Finally, the above features of the frames at different scales are pooled through random forest model to predicate the quality score of HSOV. Experimental results on the NBU-HSOVD dataset show that the proposed metric is superior to some blind image/video quality assessment metrics and has higher consistency with the human visual perception.

Parametric families of continuous belief functions based on generalized Gaussian random fuzzy numbers

The theory of epistemic random fuzzy sets is a general theory of uncertainty encompassing both possibility theory and the Dempster-Shafer theory of belief functions as special cases. Within this framework, Gaussian random fuzzy numbers have recently been introduced as a practical model of uncertainty about real variables. However, the limited flexibility of this model does not allow it to represent all kinds of beliefs encountered in applications. In this paper, it is extended in two ways. First, we study one-to-one transformations of random fuzzy sets and show that such transformations commute with combination. This property allows us to define parametric families of easily combinable random fuzzy numbers and vectors on different frames based on the Gaussian model. We then go one step further by studying mixtures of random fuzzy variables, which provide a very flexible model making it possible to construct belief functions on continuous frames with arbitrary complexity. To demonstrate the applicability and practical interest of these models, two applications are studied: the elicitation of expert beliefs about numerical quantities, and generalized Bayesian inference with weak prior information represented by random fuzzy numbers.

Study on the pier bottom self-centering seismic isolation structure of the high-pier continuous rigid frame bridges

Four seismic isolation specimens were designed, fabricated, and subjected to physical loading tests to enhance the seismic performance of a high-pier continuous rigid-frame bridge. A finite element model of the seismic isolation structure of the high-pier was established using numerical simulation software. The entire bridge model was established based on the multi-scale modeling method to investigate the seismic response of the high-pier continuous rigid frame bridge under different seismic waves. The results indicate that the seismic isolation pier has self-centering and energy dissipation capabilities. The seismic isolation pier has satisfactory energy dissipation capacity and ductility when the ratio of the long-axis and short-axis radii is 1.5. The results of the finite-element simulation corresponded well with the experimental results. Significant damage occurred to the concrete at the bottom and corner of the pier, and the reinforcement did not reach its yield strength throughout the loading process. Therefore, measures must be implemented to ensure the seismic performance of the structure. The pier bottom self-centering seismic isolation structure effectively absorbs shocks, as evidenced by reduced absolute acceleration and relative displacement at the pier top, decreased shear force and bending moment at the pier bottom, and minimized girder displacement. It was recommended that the ratio of the long-axis and short-axis radii be adopted as 1.5–2.0 and that the pier height be selected as 60 m–100 m. These results provide a reference for the research and applications of this type of structure.

Real-Time Bucket Pose Estimation Based on Deep Neural Network and Registration Using Onboard 3D Sensor.

Real-time and accurate bucket pose estimation plays a vital role in improving the intelligence level of mining excavators, as the bucket is a crucial component of the excavator. Existing methods for bucket pose estimation are realized by installing multiple non-visual sensors. However, these sensors suffer from cumulative errors caused by loose connections and short service lives caused by strong vibrations. In this paper, we propose a method for bucket pose estimation based on deep neural network and registration to solve the large registration error problem caused by occlusion. Specifically, we optimize the Point Transformer network for bucket point cloud semantic segmentation, significantly improving the segmentation accuracy. We employ point cloud preprocessing and continuous frame registration to reduce the registration distance and accelerate the Fast Iterative Closest Point algorithm, enabling real-time pose estimation. By achieving precise semantic segmentation and faster registration, we effectively address the problem of intermittent pose estimation caused by occlusion. We collected our own dataset for training and testing, and the experimental results are compared with other relevant studies, validating the accuracy and effectiveness of the proposed method.

Microstructure and mechanical properties of highly biomimetic brick-and-mortar structure Ti6Al4V/Al3Ti metal-intermetallic laminate composite

The brick-and-mortar structure of shell nacre allows for the simultaneous enhancement of strength and plasticity through the synergistic effect of multiple toughening mechanisms at different scales. In this work, a novel nacre-like brick-and-mortar structure Ti6Al4V/Al3Ti metal-intermetallic laminate (BMS-MIL) composite was successfully fabricated via vacuum hot pressing sintering method. The BMS-MIL composite is mainly composed of Al3Ti and Ti phases, with a small amount of Al2Ti and AlTi phases. Al3Ti is in the shape of polygonal platelet, and a 5–8 μm diffusion layer composed of Al2Ti and AlTi is distributed on the surface of the platelets. Ti6Al4V connects the platelets and is present in a continuous network frame structure. Compared with the layered structure Ti6Al4V/Al3Ti metal-intermetallic laminate (LS-MIL) composite, the fracture strain of BMS-MIL composite increases by 125.1% while maintaining high strength under load perpendicular to layer, and the compressive strength of BMS-MIL composite increases by 26.3% under load parallel to layer, respectively. Material isotropy in compressive properties has been significantly improved. In bending tests, the strength of the BMS-MIL composite is 1.9 times that of the LS-MIL composite. The brick-and-mortar structure’s excellent strengthening and toughening effect benefits from the enhancement of its crack constraint.