Continuous Frame Research Articles

Coorbit Theory of Warped Time-Frequency Systems in Rd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {R}}^d$$\\end{document}

Warped time-frequency systems have recently been introduced as a class of structured continuous frames for functions on the real line. Herein, we generalize this framework to the setting of functions of arbitrary dimensionality. After showing that the basic properties of warped time-frequency representations carry over to higher dimensions, we determine conditions on the warping function which guarantee that the associated Gramian is well-localized, so that associated families of coorbit spaces can be constructed. We then show that discrete Banach frame decompositions for these coorbit spaces can be obtained by sampling the continuous warped time-frequency systems. In particular, this implies that sparsity of a given function f in the discrete warped time-frequency dictionary is equivalent to membership of f in the coorbit space. We put special emphasis on the case of radial warping functions, for which the relevant assumptions simplify considerably.

Research on bridge safety early warning method based on strain energy theory and health monitoring data

Bridges are technology-intensive and heavily invested permanent infrastructure. After completion and opening to traffic, bridge structures are easy to be affected by factors such as traffic load and atmospheric environment. Therefore, it is necessary to do safety warning and evaluation of bridges, especially the abnormal behavior in the early stages of bridge operation. In this research paper, a large-span continuous rigid frame bridge installed with the health monitoring system (HMS), of which a large amount of health monitoring data are collected by the HMS, is used as an example, and then a bridge safety early warning method is proposed when the bridge is during early operating period. First of all, the research finding that the internal stress of the prototype bridge obeys normal distribution through statistical analysis is used; next, we deduce that the strain energy inside the prototype bridge is subject to the Non-central Chi-square distribution combined with the strain energy and statistical theories; in the end, the key probability density distribution function of strain energy and its parameters are derived by using the key stress distribution function of the high performance concrete C50 strength grade used in the prototype bridge. The method recommended in this paper is conducive to the formulation of bridge preventive maintenance strategies.

High-Efficiency Finite Element Model Updating of Bridge Structure Using a Novel Physics-Guided Neural Network

An accurate finite element model (FEM) plays a critical role in the structural damage identification. However, due to the existence of the uncertainties, such as material properties and modeling errors, it always exists some gaps between the analytical FEM and experimental structure. While an artificial neural network (ANN)-based model updating methods have been widely adopted to narrow the gap and obtain a baseline FEM, it still faces inaccurate results and fails to meet the physical law. In this regard, the study proposes a novel physics-based loss function inspired by modal sensitivity analysis and incorporates it into the residual neural network, thereby forming a novel physics-guided neural network (PGNN) method. The mapping relationship between the input of structural responses and output of model updating variables is constrained to retain its physical meaning by guiding the training process instead of pure data association, which aims to improve the accuracy of the ANN-based method and achieve accurate and high-efficiency model updating. An experimental example of a continuous rigid frame bridge is adopted to verify the feasibility of the proposed method. Additionally, other common model updating methods, including moth-flame optimization and regularization method, are used to make a comparison. The noise-robustness of the proposed method is investigated as well. Compared to the existing method, the results illustrate that the proposed PGNN method can achieve better model updating and good noise-robustness under high uncertainties, which means the introduction of the physics-based loss function significantly enhances the parameters updating ability of the neural network. The proposed method exhibits high efficiency and promising potential for large-scale bridge structure model updating.

Development and validation of machine-learning models for monitoring individual behaviors in group-housed broiler chickens

Animals' individual behavior is commonly monitored by live or video observation by a person. This can be labor intensive and inconsistent. An alternative is the use of machine learning-based computer vision systems. The objectives of this study were to 1) develop and optimize machine learning model frameworks for detecting, tracking and classifying individual behaviors of group-housed broiler chickens in continuous video recordings; and 2) use an independent dataset to evaluate the performance of the developed machine leaning model framework for individual poultry behaviors differentiation. Forty-two video recordings from 4 different pens (total video duration = 1,620 min) were used to develop and train multiple models for detecting and tracking individual birds and classifying 4 behaviors: feeding, drinking, active, and inactive. The optimal model framework was used to continuously analyze an external set of 11 videos (duration = 326 min), and the second-by-second behavior of each individual broiler was extracted for the comparison of human observation. After comparison of model performance, the YOLOv5l, out of 5 detection models, was selected for detecting individual broilers in a pen; the osnet_x0_25_msmt17, out of 4 tracking algorithms, was selected to track each detected bird in continuous frames; and the Gradient Boosting Classifier, out of 12 machine learning classifiers, was selected to classify the 4 behaviors. Most of the models were able to keep previously assigned individual identifications of the chickens in limited amounts of time, but lost the identities throughout an examination period (≥4 min). The final framework was able to accurately predict feeding (accuracy = 0.895) and drinking time (accuracy = 0.9) but subpar for active (accuracy = 0.545) and inactive time (accuracy = 0.505). The algorithms employed by the machine learning models were able to accurately detect feeding and drinking behavior but still need to be improved for maintaining individual identities of the chickens and identifying active and inactive behaviors.

VividWav2Lip: High-Fidelity Facial Animation Generation Based on Speech-Driven Lip Synchronization

Speech-driven lip synchronization is a crucial technology for generating realistic facial animations, with broad application prospects in virtual reality, education, training, and other fields. However, existing methods still face challenges in generating high-fidelity facial animations, particularly in addressing lip jitter and facial motion instability issues in continuous frame sequences. This study presents VividWav2Lip, an improved speech-driven lip synchronization model. Our model incorporates three key innovations: a cross-attention mechanism for enhanced audio-visual feature fusion, an optimized network structure with Squeeze-and-Excitation (SE) residual blocks, and the integration of the CodeFormer facial restoration network for post-processing. Extensive experiments were conducted on a diverse dataset comprising multiple languages and facial types. Quantitative evaluations demonstrate that VividWav2Lip outperforms the baseline Wav2Lip model by 5% in lip sync accuracy and image generation quality, with even more significant improvements over other mainstream methods. In subjective assessments, 85% of participants perceived VividWav2Lip-generated animations as more realistic compared to those produced by existing techniques. Additional experiments reveal our model’s robust cross-lingual performance, maintaining consistent quality even for languages not included in the training set. This study not only advances the theoretical foundations of audio-driven lip synchronization but also offers a practical solution for high-fidelity, multilingual dynamic face generation, with potential applications spanning virtual assistants, video dubbing, and personalized content creation.

Behavior recognition of cage-free multi-broilers based on spatiotemporal feature learning

Poultry behavior indicates their health, welfare, and production performance. Timely access to broilers' behavioral information can improve their welfare and reduce disease spread. Most behaviors require a period of observation before they can be accurately judged. However, the existing approaches for multi-object behavior recognition were mostly developed based on a single-frame image and ignored the temporal features in videos, which led to misrecognition. This study proposed an end-to-end method for recognizing multiple simultaneous behavioral events of cage-free broilers in videos by Broiler Behavior Recognition System (BBRS) based on spatiotemporal feature learning. The BBRS consisted of 3 main components: the improved YOLOv8s detector, the Bytetrack tracker, and the 3D-ResNet50-TSAM model. The basic network YOLOv8s was improved with MPDIoU to identify multiple broilers in the same frame of videos. The Bytetrack tracker was used to track each identified broiler and acquire its image sequence of 32 continuous frames as input for the 3D-ResNet50-TSAM model. To accurately recognize behavior of each tracked broiler, the 3D-ResNet50-TSAM model integrated a temporal-spatial attention module for learning the spatiotemporal features from its image sequence and enhancing inference ability in the case of its image sequence less than 32 continuous frames due to its tracker ID switching. Each component of BBRS was trained and tested with the rearing density of 7 to 8 birds/m2. The results demonstrated that the mAP@0.5 of the improved YOLOv8s detector was 99.50%. The Bytetrack tracker achieved a mean MOTA of 93.89% at different levels of occlusion. The Accuracy, Precision, Recall, and F1score of the 3D-ResNet50-TSAM model were 97.84, 97.72, 97.65, and 97.68%, respectively. The BBRS showed satisfactory inference ability with an Accuracy of 93.98% when 26 continuous frames of the tracked broiler were received by the 3D-ResNet50-TSAM model. This study provides an efficient tool for automatically and accurately recognizing behaviors of cage-free multi-broilers in videos. The code will be released on GitHub (https://github.com/CoderYLH/BBRS) as soon as the study is published.

Numerical model for time-dependent cracking and deflection of large-span concrete bridge structures

A new computational framework is proposed to analyse the simultaneous occurrence of multiple physical processes affecting the long-term behavior of large-scale concrete structures. Relying on the additivity of small strain, the framework comprehensively takes the nonlinearity of concrete creep, cyclic creep induced by traffic load, concrete shrinkage, concrete cracking, normal-strength steel rebars and prestressing strands relaxation into account, and other physical processes are further imagined. The algorithm is implemented with Abaqus/Standard with the aid of several user-supplied subroutines. The implicit time integration scheme is deemed appropriate for large-scale structures. The framework is systematically validated by several classical experimental results. Finally, a three-dimensional study of a three-span continuous rigid frame bridge with 270 m main span is performed. Comparison between the experimental and numerical results further reveals the contributions of various potential influencing factors. The proposed model shows promise for predicting the time-dependent performance of large-scale reinforcement concrete (RC) structures.

Intelligent toy tracking trajectory design based on mobile cloud terminal deployment and depth-first search algorithm.

The popularization of intelligent toys enriches the lives of the general public. To provide the public with a better toy experience, we propose the intelligent toy tracking method by the mobile cloud terminal deployment and depth-first search algorithm. Firstly, we construct a toy detection model via Transformer, which realizes the positioning of toys in the image through the refined region adaptive boundary representation. Then, using these detected continuous frames, we improve the toy tracking based on a depth-first search. Long-short-term memory (LSTM) constructs the continuous frame tracking structure, and the depth-first search mechanism is embedded to realize the accurate tracking of multiple targets in continuous frames. Finally, to realize the terminal marginalization of the proposed method, this chapter proposes a lightweight model deployment method based on mobile cloud terminals to realize the maintenance of the optimal machine state of intelligent toys. The experiment proves that our proposed target method can reach the world-leading level and obtain the mAP value of 0.858. Our tracking method can also perform excellently with a MOTA value of 0.916.

The Freudenthal and other compactifications of continuous frames

The N-star compactifications of frames are the frame-theoretic counterpart of the N-point compactifications of locally compact Hausdorff spaces. A π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pi $$\\end{document}-compactification of a frame L is a compactification constructed using a special type of a basis called a π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pi $$\\end{document}-compact basis; the Freudenthal compactification is the largest π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pi $$\\end{document}-compactification of a rim-compact frame. As one of the main results, we show that the Freudenthal compactification of a regular continuous frame is the least upper bound for the set of all N-star compactifications. A compactification whose right adjoint preserves disjoint binary joins is called perfect. We establish a class of frames for which N-star compactifications are always perfect. For the class of zero-dimensional frames, we construct a compactification which is isomorphic to the Banaschewski compactification and the Freudenthal compactification; in some special case, this compactification is isomorphic to the Stone–Čech compactification.

Simulation of artificial intelligence robots in dance action recognition and interaction process based on machine vision

Artificial intelligence robots based on machine vision have shown great potential in dance action recognition and interaction. In order to perform action recognition, a large amount of dance action video data was collected as the basis for training and testing the model. In motion recognition, various computer vision techniques are used to extract motion features from images or videos to capture key information of dance movements. Machine learning technology was adopted to construct an action classification model. By using the data collected in the early stage and the extracted features, an efficient and accurate classifier has been studied and trained. It can classify the input dance actions into different categories, segment continuous video frames into different action segments, and ensure accurate differentiation of different dance actions during the recognition process, avoiding misjudgment or confusion. By accurately capturing and analyzing the posture information of the human body, we can better understand and simulate dance movements, and enhance the performance and realism of artificial intelligence robots in dance interactions. The experimental results show that the system can accurately recognize various dance movements and achieve good interaction with users.

Study on the Closing Sequence of Asymmetric High Pier Multi-Span Continuous Rigid Frame Bridge

In order to study the influence of different closing schemes on the non-symmetrical high-pier multi-span continuous rigid frame bridge, this paper uses Midas/Civil finite element software to simulate the vertical displacement of main beam, stress of main beam, horizontal displacement of pier top and bending moment of pier bottom under four different closing schemes. The results show that the vertical displacement of the main beam is small when scheme 3 is adopted. The stress of the main beam and the bending moment of the bottom of the pier are smaller after adopting the scheme 4 closing scheme. The maximum horizontal displacement of the top pier is smaller after the closure of scheme 1 and Scheme 3. After comprehensive consideration, it is suggested that the closure of Hegou Bridge should be carried out in scheme 4.

Multiparameter Mechanical Phenotyping for Accurate Cell Identification Using High-Throughput Microfluidic Deformability Cytometry.

Mechanical phenotyping has been widely employed for single-cell analysis over recent years. However, most previous works on characterizing the cellular mechanical properties measured only a single parameter from one image. In this paper, the quasi-real-time multiparameter analysis of cell mechanical properties was realized using high-throughput adjustable deformability cytometry. We first extracted 12 deformability parameters from the cell contours. Then, the machine learning for cell identification was performed to preliminarily verify the rationality of multiparameter mechanical phenotyping. The experiments on characterizing cells after cytoskeletal modification verified that multiple parameters extracted from the cell contours contributed to an identification accuracy of over 80%. Through continuous frame analysis of the cell deformation process, we found that temporal variation and an average level of parameters were correlated with cell type. To achieve quasi-real-time and high-precision multiplex-type cell detection, we constructed a back propagation (BP) neural network model to complete the fast identification of four cell lines. The multiparameter detection method based on time series achieved cell detection with an accuracy of over 90%. To solve the challenges of cell rarity and data lacking for clinical samples, based on the developed BP neural network model, the transfer learning method was used for the identification of three different clinical samples, and finally, a high identification accuracy of approximately 95% was achieved.

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection.

Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures.

Research on the Method of Prestressing Tendon Layout for Large-Span Prestressed Components Continuous Rigid Frame Bridge Based on “Zero Bending Moment Dead Load Theory”

Based on the theory of zero bending moment under constant load, various optimization methods exist for the top beam of large-span continuous rigid frame bridges. These include achieving zero bending moment at the root of the cantilever beam, at the control stage section, and through the zero deflection method. This study aims to explore the methods and effects of optimizing roof beam design using the constant load zero bending moment method and the “three group bundle method”. Using finite element modeling, the total number and eccentricity of prestressed tendons required for each suspended pouring block are determined. Additionally, the “three group beam matching method” is employed to adjust the steel beam, adhering to the design concept of “large cantilever beam matching and small cantilever beam matching”, to achieve a reasonable configuration of the top plate beam. Through specific engineering examples, the results demonstrate that utilizing the constant load zero moment method and the “three group bundle method” can significantly enhance the structural performance and economy of large-span continuous rigid frame bridges. Moreover, it offers practical operability, providing an important reference basis for similar project designs.

A two-stream sign language recognition network based on keyframe extraction method

Sign language recognition (SLR) tasks are typically performed on a large number of continuous frames of sign language videos, making it challenging to utilize information efficiently and accurately from such a vast amount of data. Firstly, to efficiently utilize the information, it is necessary to select key information from numerous video frames to summarize the video. Therefore, this paper proposes a new method for key frame extraction that efficiently summarizes video information and addresses the issues of key frame clustering and data imbalance present in previous methods. Secondly, for more accurate information utilization, it is essential to learn the correct sign language expression features from a large dataset. Hence, we establish the Full-hand two-stream network to focus on the most crucial hand features in sign language expressions. The Full stream employs attention blocks to extract deep-level information and establishes temporal dependencies using global attention module. The Hand stream utilizes hand attention to focus on hand-specific feature information. Ultimately, our approach achieves state-of-the-art results on the CSL–500 Dataset and competitive results on the LSA64 Dataset. This paper validates the effectiveness of keyframe extraction methods and two-stream networks on the AUTSL dataset.

Improving balance using augmented visual orientation cues: a proof of concept

Falls are a major health concern. Existing augmented reality (AR) and virtual reality solutions for fall prevention aim to improve balance in dedicated training sessions. We propose a novel AR prototype as an assistive wearable device to improve balance and prevent falls in daily life. We use a custom head-mounted display toolkit to present augmented visual orientation cues in the peripheral field of view. The cues provide a continuous space-stationary visual reference frame for balance control using the user’s tracked head position. In a proof of concept study, users performed a series of balance trials to test the effect of the displayed visual cues on body sway. Our results showed that body sway can be reduced with our device, indicating improved balance. We also showed that superimposed movements of the visual reference in forward-backward or sideways directions induce respective sway responses. This indicates a direction-specific balance integration of the displayed cues. Based on our findings, we conclude that artificially generated visual orientation cues using AR can improve balance and could possibly reduce fall risk.

Graphic Statics for Continuous Beams and Frames: A Review of the Fixed-points Method

ABSTRACT Graphic statics not only applies to statically determinate systems but also extends to indeterminate systems. This paper reviews a historical graphical method tailored for continuous beams and frames: the fixed-points method. Despite its current obscurity, the fixed-points method played a crucial role in the repertoire of graphic statics and enjoyed considerable popularity during the late 19th and early 20th centuries. Our review outlines its historical evolution, explains its principles and techniques, and illuminates Robert Maillart’s applications of this method in the Simme bridge in Garstatt and the Weissensteinstrasse Overpass in Bern.

Investigation of the Mechanical Features of Steel–Concrete Composite Girder Rigid Frame Bridges with V-Shaped Piers during Construction Stages

Steel–concrete composite girder rigid frame bridges with V-shaped piers are a new type of bridge structure. Based on the traditional composite continuous beam bridge, part or all of the vertical piers are changed into V-shaped piers. This special structure makes them have the mechanical characteristics of both composite continuous beams and V-shaped piers. In this paper, the finite element model of the first steel–concrete composite continuous beam V-pier rigid frame bridge in China is established by simulation software, the construction process of the bridge is simulated, and the stress and deflection of the bridge in each construction stage are studied. At the same time, the stress of the completed bridge model considering the construction stage is compared with that of the completed bridge model without considering the construction stage. It is found that the stress difference between the two concrete slabs is as high as 2.7 MPa. The results show that the stress state of the bridge is greatly affected by the construction process. This study can provide guidance for the design and construction of such bridges, which is of great significance.

Duals of Continuous Frames in Hilbert C∗-Modules

The concept of frame is an exciting, dynamic, and fast-paced subject with applications in numerous fields of mathematics and engineering. The purpose of this paper is to introduce equivalent ∗-continuous frames and to present ordinary duals of constructed ∗-continuous frames by an adjointable and invertible operator. Also, we establish some properties.

Visual Odometry Based on Improved Oriented Features from Accelerated Segment Test and Rotated Binary Robust Independent Elementary Features

To address the problem of system instability during vehicle low-speed driving, we propose improving the visual odometer using ORB (Oriented FAST and Rotated BRIEF) features. The homogeneity of ORB features leads to poor corner point properties of some feature points. When the environmental texture lacks richness, it leads to poor matching performance and low matching accuracy of the feature points. We solve the problem of the corner point properties of feature points using weight calculation for regions with different textures. When the vehicle speed is too low, the continuous frames captured by the camera will overlap significantly, causing large fluctuations in the system error. We use motion model estimation to solve this problem. Meanwhile, experimental validation using the KITTI dataset achieves good results.