bluuebunny/crossref_metadata_2025 huggingface dataset:
Benchmark Analysis of Plastic Strain-Based Lifetime Estimation Fatigue Models in Aspect of SMD Component Standoff Height
Authors: Bakonyi Antal , Zelei Ambrus
Published: October 2024

Thermomechanical fatigue is one of the most common cause of the failure in microelectronic technology in the solder joints. The lifetime prediction for microelectronic components is a very important area in nowadays automotive industry, because the lifetime estimation fatigue models in the literature differ in their results by orders of magnitude. However, developing an accurate lifetime estimation methodology for microelectronic components is not straightforward, because the failure mechanism of the solder joints under cyclic thermomechanical load is not fully understood. In addition, there are numerous tolerances and uncertainties during the designing and manufacturing processes, such as component size, copper pad area, solder material volume or the formed standoff height of the component from the copper pad. These parameters can hugely affect the lifetime of the solder joint. In this paper a benchmark analysis based on finite element method were carried out with four plastic strain-based fatigue models to understand the impact of the standoff height to the estimated lifetimes. Three CAD models were created with identical parameters, except the standoff height of the components. Creating the solder geometries for the 3D models, Surface Evolver software were used. The result shows that the fatigue models give the same tendencies varying the standoff height values. However, changing the standoff height increases the differences between models, even if they are tuned so that the estimated lifetime matches for a certain standoff height.
The Effect of the Friction Coefficient Between the Steel-Concrete Connection on the Horizontal Load Capacity
Authors: Gosztola Dániel , Szép János , Movahedi Rad Majid
Published: October 2024

This article presents experimental tests and numerical modeling of steel-composite connections. The study considers the interaction and friction coefficient between the steel structure and concrete, as well as between the steel studs and concrete. The numerical model underwent validation through an iterative process, considering various friction coefficients. The friction coefficient between the structure and the concrete exerted the most significant impact on the load capacity. A new finite element model has been developed, with various friction coefficients between steel structure and concrete, and in this model, the concrete was also subjected to different vertical compression loads. A total of 25 numerical tests have been conducted, using various configurations of vertical load and friction coefficient parameters. It was observed that higher friction coefficients increase the impact of vertical forces on the horizontal load capacity. The friction coefficient can be increased through technological interventions, such as surface roughening techniques or the introduction of intermediate materials designed to elevate it.
Optimization Design of Composite Laminated Structure Based on the Good-Point Set and a New Niche Techniques
Authors: Ma Sen , Wei Xiongzhi , Zhao Chao
Published: October 2024

Unlike traditional material structures, the design of composite structure requires consideration of more parameters, and involves a more complex design space. By utilizing superior intelligent optimization algorithms, it is possible for us to get a better design effectively. In this paper, a Suboptimal individuals Retaining Differential Evolution algorithm (SNDE for short) is proposed according to the characteristics of optimization design of composite laminated structure. In SNDE, Good-point set technique is introduced to initialize the individuals which aims to improve distribution of the initial population; and in order to overcome the problem of individuals concentrating and diversity deteriorating, a new niche mechanism for enhancing the diversity of population is proposed. An optimization design example of composite drive shaft was conducted to check the performance of different algorithms and it was seen that the SNDE achieved the optimal result.
Development of Injection Moulded Electromagnetic Pulse Shielded Polymer Cover
Authors: Dogossy Gábor , Ronkay Ferenc
Published: October 2024

Technology for an electromagnetic pulse shielded injection moulded product was developed, using in-mould metal mesh and a 3D printed spacer made of the same type of acrylonitrile-butadiene-styrene as the injection moulded polymer. The functional and mechanical properties of the manufactured flat and dumbbel specimens were tested. The metal mesh increased the stiffness of the plastic, decreased its Charpy impact strength, and did not significantly affect its tensile strength. Computed tomography scans confirmed that delamination and microcracking occur during the failure due to different elongation of the plastic and metal. The attenuation at 1 GHz was 16-19 dB, depending on the metal mesh used. The shielding properties were affected by the fact that the high pressure at the mould filling during the injection moulding process distorted the geometry of the metal mesh.
Performance of Calcined Brick Soils on Mechanical Properties of Blended Cement
Authors: Pommer Vojtěch , Sellnerová Kateřina , Keppert Martin , Vejmelková Eva , Koňáková Dana
Published: October 2024

Nowadays, there are more and more attempts to replace cement with some kind of suitable pozzolana. Thermally activated clays have become the favoured materials in this field. However, the investigated clays are generally pure kaolinite or kaolinitic clays. This article is aimed at the possibility of thermal activation of brick soils, more specifically of those materials with only about 30-65% of clay minerals. Brick soils from three different localities have been examined. With the help of simultaneous thermal analysis, four temperatures for thermal activation were established, namely 550°C, 600°C, 650°C, and 800°C. Mechanical properties revealed the promising performance of brick soils calcined at 650°C. Especially, the one with a high calcite content reached even higher compressive strength than calcined kaolinitic clay. The pozzolanic activity of activated brick soils was confirmed by measuring the heat flows in the mixture with lime.
Comparative Study of Model Order Reduction Methods via Truss Vibrations
Authors: Horvath Peter
Published: October 2024

The paper examines the accuracy of various model reduction techniques via modal analysis of a truss structure. Besides the widely used Craig-Bampton (CB) method in commercial software, three additional methods – modal truncation augmentation (MTA), system equivalent reduction expansion process (SEREP), and the Rubin method – are briefly introduced. Subsequently, the results provided by these methods are investigated through the comparison of natural frequencies, mode shapes, and frequency response functions. The study reveals that generally, the Rubin method provides the most accurate results, except in higher frequency ranges where its error suddenly spikes. With the MTA method, accuracy can be improved at low frequencies, but the error gradually increases. In the case of SEREP, the error remains approximately constant, resulting in larger errors at low frequencies and smaller errors at high frequencies compared to other methods.
Wave Propagation in Composite Metal Foams Investigated by Finite Element Methods in Two Dimensional Case
Authors: Kovács Márió , Pere Balázs
Published: October 2024

The presented work is based on the wave propagation properties of composite metal foams in a two-dimensional model with a focus on the energy absorption. The energy flux method was used for the study and it was shown that composite metal foams have a significant energy absorption capacity. Hence, they can be used with high efficiency, for example, as a sound insulation layer. A pair of materials commonly used in syntactic metal foams, iron shell and aluminium matrix material, was used in the finite element model. Damping is included in the calculations to avoid oscillations.
Numerical Modeling of Multi-Pass Arc Welding Processes: Integration with Experimental Validation for Distortion Analysis and Characterization
Authors: Grubits Péter , Cucuzza Rafaelle , Gosztola Dániel , Domaneschi Marco , Szép János , Movahedi Rad Majid
Published: October 2024

The increasing significance of numerical simulations in the welding industry arises from their capacity to improve manufacturing conditions, ensuring greater effectiveness and precision. The utilization of the finite element method (FEM) enables comprehensive and focused calculations of mechanical and material structural alterations induced by the welding process. Acquiring knowledge of these parameters not only serves to augment the quality of the manufacturing process but also yields consequential benefits, such as reducing adverse effects like base plate distortion. Consequently, enhancing structural performance and prolonging lifespan becomes achievable, aligning with overarching sustainability goals. To accomplish this objective, this paper involves the numerical simulation of a welding process based on experimental tests, with a focus on investigating the deformations caused by the heat generated during welding as the primary parameter of interest. Advanced modeling techniques are employed to assess the results as part of a comprehensive thermo-mechanical analysis framework, examining and characterizing the impact of the temperature distribution. In the finite element analysis (FEA), a total of 12 welding cycles were systematically modeled to align with experimental conditions, incorporating cooling intervals and preheating considerations. The outcomes of this research exemplify the potential of numerical simulation in the welding industry, demonstrating a diverse range of results achieved through FEA to enhance the quality of structures.
Development of FEM Model to Simulate Radiosonde Collisions
Authors: Hegyi Norbert , Lajber Kristóf , Jósvai János
Published: October 2024

This paper deals with safety requirements for radiosondes and their possible finite element method (FEM) simulations. This article proposes a soft outer layer, which exhibits the use of a Polyurethane foam (PUR foam) layer on radiosondes. Analysis and tests of normal and PUR foam layer coated radiosonde’s collisions with test targets were done. FEM models were created to model the collisions of radiosondes with virtual targets at drop tests in different scenarios. To compare and verify the virtual results, specified real-life drop test measurements were carried out. The measured data was analyzed and showed an average accuracy of 8% in force measurements, and 2.22% accuracy in duration of the tests. Based on these virtual and real-life tests it was demonstrated, that an FEM model can simulate the end result of using PUR foam layers on radiosondes. It also demonstrated that the applicable PUR foam layer increases a safer collision also increasing the chance of maintaining functionality of the radiosondes.
Effect of Process Parameters on Force and Energy Parameters of Cross Wedge Rolling Shaft Parts
Authors: Han Li , Ma Jian , Yin Xiuxian , Bai Liyan
Published: October 2024

The force and energy parameter is important to measure the life of the rolling mill. In this paper, we build the finite element model of the cross wedge rolling by DEFORM software and investigate the effects of rolling speed, diameter ratio of roller and billet and friction coefficient on the force and energy parameters of rolling mill in the forming process of shafts. The results are that as the rolling speed increases, the rolling time decreases, the values of the force and energy parameters increase, and the maximum values of transverse force, axial force and rolling torque increase greatly. As the diameter ratio of roller and billet increases, the rolling time decreases, the values of the force and energy parameters change nonlinearly, the rolling torque and axial force decrease, and the lateral force and radial force increase. As the friction coefficient increases, the axial force decreases, the lateral force, radial force and rolling torque increase. The research results are of great significance for rational selection of process parameters, improvement of cross-rolling product production efficiency, and improvement of mill and die life.
Design and Experimental Study of Fluidlastic Isolation System Mounting Frame
Authors: Dai Zhixiong , Qian Feng , Feng Zhizhuang , Yuan Gaofei
Published: October 2024

The fluidlastic isolation system is a device installed on the main transmission channel of a helicopter to isolate vibration loads from the rotor. In order to study the vibration isolation efficiency of the fluidlastic isolation system at the 4Ω frequency point, taking the civil helicopter H425 as the research object, a test frame for the fluidlastic isolator were designed. The vibration levels of the test frame in the X, Y, and Z directions were experimentally studied before and after installing the fluidlastic isolation system under excitation forces. The experimental results show that at the 4Ω frequency point, the comprehensive vibration isolation efficiency of the fluidlastic isolation system reaches 95.48% in the X direction, 75.08% in the Y direction, and 90.70% in the Z direction. This indicates that this vibration isolation method has good application prospects in the vibration reduction design of helicopters or other mechanical equipment.
Special Wind Tunnel Test of Hypersonic Dynamic Stability Parameters: Internal and External Dynamic Derivative Support Interference
Authors: Han Hao , Liu Xu , You Yi , Hu Cuiping
Published: October 2024

In this paper, the numerical simulation of the tail support interference of the special wind tunnel test with dynamic three-dimensional unsteady N-S equation is carried out. Through a series of simulation research, the form and size of the tail support of the aspirating hypersonic vehicle are given, and the supporting interference research of the dynamic stability parameters of the aspirating hypersonic vehicle is carried out. The support has a slight influence on the roll damper derivative and the yaw cross-derivative obtained by the forced roll motion. The difference between the shape of the struts and the struts is not more than 5%. The struts have little effect on the pitch damper derivative of the forced pitch motion and the yaw damping derivative and the roll cross derivative obtained by the forced yaw motion. It is considered that the tail support of this form design has less dynamic flow field interference to the aspirating hypersonic vehicle and meets the needs of the dynamic wind tunnel test.
Numerical Study of the Geogrid Reinforced Soil Wall Incorporating Strain-Softening Constitutive Soil Model
Authors: Salari Mahdi , Shafaie Vahid , Emamian Seyed Ali , Herczeg Géza , Movahedi Rad Majid
Published: October 2024

This study embarks on a numerical exploration of Geogrid Reinforced Soil Walls (GRSW), employing finite difference analysis to compare two soil constitutive models, highlighting the efficacy of a refined strain-softening model. This innovative approach markedly improves the prediction of GRSW performance, particularly aligning the safety factor more closely with real-world observations. Notably, the strain-softening model demonstrates a superior ability over the perfectly plastic model by significantly reducing the mean overall error in predicting maximum geogrid strain overall from 51% to 30%, reflecting a significant 41% improvement in precision, thereby presenting a significant tool for enhancing geotechnical design practices. The research underlines the potential of this model to elevate the safety and reliability of GRSW constructions, contributing to elevated design standards within the field of geotechnical engineering.
Change in Stiffness of Reinforced Concrete Tunnel Walls and Its Effect Under Fire Load
Authors: Major Zoltán , Koch Edina , Lublóy Éva
Published: October 2024

This article uses the knowledge gained from tunnel fires to address the structural analysis of tunnel walls during fire exposure. The designing at normal temperature and its theoretical background are discussed in the literature. As these books did not yet deal with the issue of fire protection designing, we tried to supplement the existing theoretical knowledge with the knowledge provided by the relevant standards for reinforced concrete tunnel walls. In addition, we have tried to add our own individual ideas to the theory where we felt that there were gaps. The theoretical summary has been compiled in such a way that it can be easily transferred and applied to everyday practice. In this article, we discuss in detail the calculation of the internal forces in tunnel walls during fire exposure. Due to space constraints, the issue of designing at normal temperatures is only touched upon in this article, limiting it to the knowledge available in the literature. Since finite element modelling has become a commonly used technique in tunnel design since the 1970s, we use its potential to investigate the effects of earth pressure and surface loads on the tunnel walls during fire and their changes, using specific software for geotechnical design. In accordance with the limitations of the scope, the determination of the equivalent thickness and the modulus of elasticity of the tunnel wall is also presented in order to determine the internal forces during the fire action.
Shear Buckling Resistance of I-Beams with Partially Stiffened Webs
Authors: Hajdú Gábor , Pasternak Hartmut
Published: October 2024

This paper presents the results of laboratory tests and numerical analysis for plate girders with partial height end stiffeners. Design rules for rigid end posts and non-rigid end posts are given in EN 1993-1-5:2006 but the shear resistance of partially stiffened webs is not mentioned. The use of non-tightly fitted stiffeners is hazardous due to insufficient investigations of the real behaviour. To discover this problem parametric studies on beams with different web slenderness are examined with the usage of advanced numerical simulations. The accuracy of the EN 1993-1-5:2006 standard design rule for partially stiffened beams is also examined. The results of the numerical simulations shows that the shear buckling capacity of girders is lower than the design recommendation where the height of the web stiffener is 25% of the web height. For girders with full height stiffeners the EN 1993-1-5:2006 standard leads conservative results.
Calculation Possibilities of the Local Fire Effect for the Examination of Bridge Superstructures
Authors: Major Zoltán , Szép János , Lublóy Éva
Published: October 2024

For the examination of bridge superstructures, there is no single standard method in the literature for the design of structures, and possibility for their diagnosis after fire damage. Designers often overcome this problem by examining the fire curves used for tunnel fires, as the materials feeding the combustion are considered to be very similar. In contrast, in some articles, the use of localised fires in design and control is suggested by the authors. This is a standard method used in Eurocode. To solve the problem, two methods must be applied. Heskestad’s method describes the case where the flame does not reach the superstructure of the bridge, while Hasemi’s method describes the case where the flame does. Heskestad’s method is presented in the standard in a way that can be used by practising engineers. The great advantage of Hasemi’s method is that it can quantify the effects of several localised fires, each one separate from the other. This feature is very useful for the fire design of, for example, covered car parks and bridge structures. In such a test, the total heat flow on the lower plane of the slab or superstructure can be interpreted as the sum of the heat flows from each local fire. The standard, on the other hand, it does not provide additional assistance to designers in solving the problem. That is way, he should be able to determine the temperature of the structural element, a method which is not provided for in the standard. This problem leads to a fourth degree equation, which again leaves the designer on his own to solve. There is no formula for solving the fourth degree equation. In this case, it becomes more useful to find a sufficiently accurate approximate solution (Newton’s method) rather than an exact solution. In this article, we present in detail the possibilities of solving the local fire effect in order to enable practising engineers to use it.
Analytical Study of Steel-Polypropylene Hybrid Fibre-Reinforced Concrete Deep Beams with Different Shear Span-to-Depth Ratios
Authors: Ibrahim Sarah Khaleel , Movahedi Rad Majid
Published: October 2024

This study explores the performance of steel-polypropylene hybrid fibre-reinforced concrete deep beams across various shear span-to-depth ratio values. Combining the ductility of steel fibres with the crack control properties of polypropylene fibres, the research includes experimental work and numerical calibration using ABAQUS. Various parameters, including load locations and shear span-to-depth ratios (0.5 < a/d < 1.5), are considered alongside different steel-polypropylene fibre ratios. The results indicate that reducing the shear span-to-depth ratio enhances the shear strength of the deep beams and reduces deflection values. Additionally, the steel-polypropylene fibres influence the overall behaviour, affecting the load-deflection behaviour and damage patterns highlighting insights for designing efficient deep beams in practical applications.
Assessing Local Site-Specific Response Spectra Based on Site Data in Győr
Authors: Labar Benjamin , Kegyes-Brassai Orsolya
Published: October 2024

It is essential to understand seismic ground motion in order to understand how dynamically a structure responds to earthquakes. Due to variations in seismic loading, strong ground vibrations can damage structures to varying degrees. The different essential traits of powerful ground motions help explain this ground diversity during moderate to large earthquakes. This study mainly focuses on the comparison between ground motion parameters such as the Peak Ground Acceleration (PGA), and local site spectra considering the design response spectrum and site-specific response spectra of varying soil profiles in Győr. Multichannel Analysis of Surface Waves (MASW) data from eleven different places in Győr were considered and analyzed using the 1-dimensional response analysis software, STRATA, and a detailed comparison was carried out between the different site locations in terms of PGA, and local site spectra. The result revealed the sites with the highest amplifications based on peak ground values of acceleration, velocity, and displacements. With 1-dimensional STRATA software, peak ground acceleration profiles, and response spectrum results are obtained and compared to Eurocode 8 standards.
Conservative Method for the Calculation of Thermal Forces in Reinforced Concrete Tunnel Wall During Fire
Authors: Major Zoltán , Szép János , Lublóy Éva
Published: October 2024

This article builds on the author’s existing knowledge of the heating of reinforced concrete tunnel linings and deals with the structural analysis of tunnel linings under fire exposure. Due to space constraints, the issue of designing for earth pressure and surface loads is not address. Since the analysis of thermal stresses due to restrained deformations is insufficient in the available literature, we complement the existing theoretical knowledge with the knowledge provided by the relevant standards for reinforced concrete tunnel linings. In addition, we add our own individual reflections to the theory where we have identified gaps. In determining the additional stresses due to inhibited thermal expansion, we use a numerical model based on our own ideas. The reason for this is that the heating of reinforced concrete tunnel lining during fire is extremely uneven and it is almost impossible to take this into account in the finite element programs commonly used. The other important reason is that this uneven temperature change that causes the colder zones of the wall to inhibit deformation. Thus, a solution implemented in MS Excel environment is presented, which allows an approximate accurate determination of the force effects due to inhibited deformation. The solution used and the values proposed are based on the authors’ individual ideas and are not the result of an accepted professional consensus. In all cases where more precise data are required, it is recommended that a more accurate test be carried out. Advanced finite element modelling can provide the necessary support for designers and experts.
A Review on the Seepage Strength of Unsaturated Soils
Authors: Yu Xinmin , Liu Zhiwei , Xiao Yurong , Wang Dehong
Published: October 2024

In modern engineering, seepage in unsaturated soils is the most common problem and a subject of extensive research at home and abroad. This paper introduces the current research status of seepage in unsaturated soils from three aspects: current seepage theory, experimental research and simulation research, and discusses the existing analysis and experimental methods. At present, the existing seepage theory mainly uses the functional permeability coefficient to describe the seepage characteristics of unsaturated soils; the most reliable method for the seepage law test is still to carry out the test directly for the specific soil condition; the numerical analysis of seepage in unsaturated soils should pay attention to the influence of the gas phase. Finally, the shortcomings of the research direction and the trend of further research are analysed to provide some references for practical engineering.
Computational Wear Prediction in Total Knee Replacements as a Function of Replacement Size
Authors: Fekete Gusztáv , Máté Márton , Popa-Müller Izolda , Wang Hai-Qiao , Dilna Natalia , Nemoga Karol
Published: October 2024

Wear is the third most important factor that restricts the longevity of total knee replacements (TKRs). Wear is particularly influenced by load, local kinematics between the contact surfaces and presumably by the geometry of the contact surfaces. This article investigated, by means of multibody models, how wear in total knee replacements is affected by the size of a TKR or by TKR-related geometric parameters during gait motion. As a result, it has been established that wear rate increases linearly as a function of TKR size, while the impact of TKR-related geometric parameters can be described by linear or quadratic functions. One can conclude that the newly introduced dimensionless parameters can provide guidelines to effectively minimize wear in TKRs.
Finite Element Analysis of Impact Forces in Running: Evaluating Footwear Strike Patterns, and Load Transfer
Authors: Senapati Debiprasad , Rao C Lakshmana , Pandey Manoj
Published: October 2024

Impact or sudden force is a prevalent occurrence in numerous sports, significantly affecting athletes’ performance and increasing injury risks. Runners, in particular, face injuries due to repeated forceful impacts on their feet during running and jumping. The landing impact characteristics, including peak impact force, loading rate, and center of pressure, are pivotal and influenced by factors such as shoe type, foot shape, landing posture, speed of motion, physical condition of runner, and ground conditions. This research introduces a three-dimensional finite element model to simulate the interaction between the ground and footwear. The primary focus is evaluating maximum impact loads during different strikes (heel, toe, and midfoot) and exploring load transfer efficiency. Employing numerical analysis, the study exclusively assesses Polyurethane rubber (PU rubber) as the material of interest. Results indicate that midfoot strikes experience higher reaction forces than toe and heel strikes, with a noticeable decrease in acceleration through the material thickness. This study contributes valuable insights into comprehending and addressing challenges related to impacts in sports, particularly in the domain of running mechanics.
AI for Declarative Processes: Representation, Mining, Synthesis
Authors: Montali Marco
Published: October 2024

The integration of business process management (BPM) with artificial intelligence (AI) is driving unprecedented advancements in the creation of trustworthy, intelligent information systems. On the one hand, BPM poses unconventional, relevant questions about processes and the event data produced during their execution. On the other hand, AI offers established techniques that must be adapted and further enhanced to address these questions effectively. This synergy is particularly impactful in the case of flexible processes, which are best represented using a declarative approach – emphasising the temporal constraints that must be respected by the process, instead of explicitly detailing all the acceptable flows of activities. In this article, we overview how automated reasoning and learning techniques for temporal logics on finite traces provide robust foundations for representing, mining, and synthesising declarative processes. We cover established results as well as frontier research in this area.
Actively Learning from Machine Learning Models with Queries and Counterexamples (Extended Abstract)
Authors: Ozaki Ana
Published: October 2024

We consider the exact and probably approximately correct (PAC) learning frameworks from computational learning theory and discuss opportunities and challenges for applying notions developed within these frameworks to extract information from black-box machine learning models, in particular, from language models. We discuss recent works that consider algorithms designed for the exact and PAC frameworks to extract information in the format of automata, Horn theories, and ontologies from machine learning models and possible applications of these approaches for understanding the models, studying biases, and knowledge acquisition.
I-Adapt: Using IoU Adapter to Improve Pseudo Labels in Cross-Domain Object Detection
Authors: Zhang Qifeng , Chen Changjian , Liu Zhizhong , Tang Zhuo
Published: October 2024

Domain adaptation has been extensively explored in object detection. Through the utilization of self-training and the decoupling of adversarial feature learning from the training of the detector, current methods make detectors more transferable and ensure their discriminability. However, the presence of low-quality pseudo labels during self-training introduces noises to the training phase and thus degrades the model performance. To tackle this challenge, we introduce an I-adapt framework, whose IoU Adapter accurately predicts the Intersection over Union (IoU) between predicted boxes and their corresponding ground-truth boxes in both source and target domains. This enables an effective measure for the pseudo-label quality. Based on this measure, we propose a re-weighting strategy, which enforces the detector to focus on learning from high-quality pseudo labels. We achieve state-of-the-art (SOTA) performance in several cross-domain object detection tasks, proving the effectiveness of I-adapt.
Learning A Closed-Loop Bidirectional Scale-Recurrent Network for Image Deraining
Authors: Huang Peizhou , Zhong Zixuan , Wang Pengjie , Wang Xiangyu , Chen Xiang
Published: October 2024

Recent years have witnessed significant advances in image deraining tasks due to the emergence of numerous effective Transformers and multi-layer perceptron (MLP) models. However, these models still rely on unidirectional information flow and fail to fully exploit the potentially useful information from multiple image scales, thus limiting the robustness of the models in complex rainy scenes. To this end, we develop an effective closed-loop bidirectional scale-recurrent network (called CBS-Net) for image deraining, which organically integrates both Transformer and MLP models to jointly explore multi-scale rain representations. Specifically, we introduce a sparse Transformer block within the intra-scale branch to adaptively capture the most useful content-aware features. Furthermore, we construct a dimensional MLP block within the inter-scale branch to dynamically modulate spatial-aware features from different scales. To ensure more accurate bidirectional estimations in our scale-recurrent network, a simple yet effective feedback propagation block is embedded to perform coarse-to-fine and fine-to-coarse information communication. Extensive experimental results show that our approach achieves state-of-the-art performance on multiple benchmark datasets, demonstrating its effectiveness and scalability.
Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers
Authors: Li Mengke , Li Da , Yang Guoqing , Cheung Yiu-ming , Huang Hui
Published: October 2024

Pre-trained large-scale models have exhibited remarkable efficacy in computer vision, particularly for 2D image analysis. However, when it comes to 3D point clouds, the constrained accessibility of data, in contrast to the vast repositories of images, poses a challenge for the development of 3D pre-trained models. This paper therefore attempts to directly leverage pre-trained models with 2D prior knowledge to accomplish the tasks for 3D point cloud analysis. Accordingly, we propose the Adaptive PointFormer (APF), which fine-tunes pre-trained 2D models with only a modest number of parameters to directly process point clouds, obviating the need for mapping to images. Specifically, we convert raw point clouds into point embeddings for aligning dimensions with image tokens. Given the inherent disorder in point clouds, in contrast to the structured nature of images, we then sequence the point embeddings to optimize the utilization of 2D attention priors. To calibrate attention across 3D and 2D domains and reduce computational overhead, a trainable PointFormer with a limited number of parameters is subsequently concatenated to a frozen pre-trained image model. Extensive experiments on various benchmarks demonstrate the effectiveness of the proposed APF. The source code and more details are available at https://vcc.tech/research/2024/PointFormer.
An Animation-Based Augmentation Approach for Action Recognition from Discontinuous Video
Authors: Song Xingyu , Li Zhan , Chen Shi , Cai Xin-Qiang , Demachi Kazuyuki
Published: October 2024

Action recognition, an essential component of computer vision, plays a pivotal role in multiple applications. Despite significant improvements brought by Convolutional Neural Networks (CNNs), these models suffer performance declines when trained with discontinuous video frames, which is a frequent scenario in real-world settings. This decline primarily results from the loss of temporal continuity, which is crucial for understanding the semantics of human actions. To overcome this issue, we introduce the 4A (Action Animation-based Augmentation Approach) pipeline, which employs a series of sophisticated techniques: starting with 2D human pose estimation from RGB videos, followed by Quaternion-based Graph Convolution Network for joint orientation and trajectory prediction, and Dynamic Skeletal Interpolation for creating smoother, diversified actions using game engine technology. This innovative approach generates realistic animations in varied game environments, viewed from multiple viewpoints. In this way, our method effectively bridges the domain gap between virtual and real-world data. In experimental evaluations, the 4A pipeline achieves comparable or even superior performance to traditional training approaches using real-world data, while requiring only 10% of the original data volume. Additionally, our approach demonstrates enhanced performance on In-the-wild videos, marking a significant advancement in the field of action recognition. The full version of this paper, along with the code and data, can be found at [41].
Quater-GCN: Enhancing 3D Human Pose Estimation with Orientation and Semi-Supervised Training
Authors: Song Xingyu , Li Zhan , Chen Shi , Demachi Kazuyuki
Published: October 2024

3D human pose estimation is a vital task in computer vision, involving the prediction of human joint positions from images or videos to reconstruct a skeleton of a human in three-dimensional space. This technology is pivotal in various fields, including animation, security, human-computer interaction, and automotive safety, where it promotes both technological progress and enhanced human well-being. The advent of deep learning significantly advances the performance of 3D pose estimation by incorporating temporal information for predicting the spatial positions of human joints. However, traditional methods often fall short as they primarily focus on the spatial coordinates of joints and overlook the orientation and rotation of the connecting bones, which are crucial for a comprehensive understanding of human pose in 3D space. To address these limitations, we introduce Quater-GCN (Q-GCN), a directed graph convolutional network tailored to enhance pose estimation by orientation. Q-GCN excels by not only capturing the spatial dependencies among node joints through their coordinates but also integrating the dynamic context of bone rotations in 2D space. This approach enables a more sophisticated representation of human poses by also regressing the orientation of each bone in 3D space, moving beyond mere coordinate prediction. Furthermore, we complement our model with a semi-supervised training strategy that leverages unlabeled data, addressing the challenge of limited orientation ground truth data. Through comprehensive evaluations, Q-GCN has demonstrated outstanding performance against current state-of-the-art methods. The full version of this paper, along with the code and data, can be found at [39].
SaccadeMOT: Enhancing Object Detection and Tracking in Gigapixel Images via Scale-Aware Density Estimation
Authors: Li Wenxi , Zhang Ruxin , Lin Haozhe , Guo Yuchen , Ma Chao , Yang Xiaokang
Published: October 2024

The proliferation of gigapixel imaging has ushered in unprecedented challenges in object detection and tracking due to the intense computational demands. Previous deep learning approaches, often tailored for megapixel images, fall short in addressing the unique complexities presented by the gigapixel level. To bridge this gap, we introduce SaccadeMOT, a novel architecture designed for efficient gigapixel-level multi-object tracking. Based on our observations of density map regression in crowd counting and small object detection in object detection tasks, we propose a novel gigapixel detection paradigm that combines the strengths of both approaches. Firstly, the “saccade” stage swiftly identifies regions likely containing objects, followed by the “gaze” stage that refines the detection within these areas. This strategic region selection is complemented by a robust tracking mechanism that combines head and body tracking, enhancing accuracy in environments with potential occlusions. Validated on the PANDA dataset, SaccadeMOT not only demonstrates an 13× speed improvement over existing state-of-the-art tracker BotSORT but also exhibits promising applications in gigapixel-level pathology analysis, particularly in Whole Slide Imaging (WSI). This approach sets a new benchmark for handling super high-resolution images, offering significant advancements in both the speed and precision of object tracking technologies.
CPNet: 3D Semantic Relation and Geometry Context Prior Network for Multi-Organ Segmentation
Authors: Ji Yuzhu , Sun Mingshan , Zhang Yiqun , Zhang Haijun
Published: October 2024

Automatic multi-organ segmentation of the abdominal region is a critical yet challenging task in computer-aided medical image analysis. Recent advances in CNN- and Transformer-based encoder-decoder models tend to implicitly learn context features by using enhanced effective receptive fields to capture local and global range dependencies. However, due to the complex anatomical structure, those models cannot recover the anatomical topology properly and result in broken organs with inaccurate semantic labels. Therefore, in this paper, by considering the anatomy priors of multi-organs, we propose a Context Prior Network, namely CPNet, which integrates the 3D context semantic relations and geometry priors as explicit anatomical constraints. Specifically, a Semantic Relation Prior Propagation (SRPP) module is designed to propagate the semantic relations between voxels progressively. Moreover, a Multiple Context Prior Prediction (MCPP) module is adopted to preserve the accurate shape and topology by recovering 3D contours and surface normal. Experimental results demonstrate our proposed model outperforms state-of-the-art models for multi-organ segmentation on Abdomen CT and MRI datasets, especially for recovering organs with correct semantic labels and anatomical structures.
Semantic Augmentation on Motion Manifold for Single-Stream Unsupervised Action Recognition
Authors: Zhao Haichuan , Du Peng , Ru Xudong , Wu Zhongke , Wang Xingce
Published: October 2024

Unsupervised learning-based action recognition methods have shown immense potential by leveraging vast amounts of unlabeled data, yielding competitive performance in action recognition tasks. To capture semantic information in action, many efforts have focused on multi-stream fusion techniques, which makes the models heavier and less flexible to train. However, existing single-stream methods struggle to provide rich semantic information through data augmentation. To address these challenges, we propose a novel joint-level semantic augmentation method based on a constructed motion manifold, which achieves significant performance gains with virtually no additional training costs. Our approach introduces random perturbations on the motion manifold constructed based on evolutionary metrics. We propose several semantic augmentation strategies, including directional semantic perturbations, magnitude semantic perturbations, and initial pose perturbations. To pay greater attention to samples with significant semantic differences, we introduce the Semantic Adaptive Weighted Loss (SAWL) based on motion manifold distance. SAWL encourages the model to pay more attention to these samples, leading to the learning of an embedding space with semantic invariance. Extensive experiments were conducted on three large-scale datasets, i.e., NTU-60, NTU-120, and PKU-MMD II. The results demonstrate that our single-stream approach, empowered by joint-level semantic augmentation, achieves state-of-the-art (SOTA) performance among single-stream methods and competitive performance among multi-stream methods.
Complex-Valued Gabor-Attention Residual Fusion Network for Iris Recognition
Authors: Li Zhuoru , Xiao Jian , Bai Xiaowei , Wang Xiaodong , Li Yingxi , Fang Zhenyu , Xie Liang , Yan Ye , Yin Erwei
Published: October 2024

Iris recognition has gained significant attention in identity verification due to the unique, stable texture patterns in iris. Successfully extracting these patterns is essential for quick and precise identification. Although deep learning methods have automated the iris recognition, they predominantly rely on real-valued networks that overlook the complex-valued representation of iris texture. This means they cannot effectively process phase and amplitude information, and fail to integrate domain-specific knowledge of iris, thereby not fully capturing the intricate details of the iris texture. Inspired by classical manual methods that efficiently harness the complex-valued representation of the iris to extract both amplitude and phase information. We integrate Gabor filters with complex-valued neural networks, propose a Complex-Valued Gabor-Attention Residual Fusion Network (GRFN) tailored for iris recognition, aiming to comprehensively capture the iris texture’s multi-scale and multi-orientation phase and amplitude features. The GRFN incorporates adaptive Gabor Complex-Valued Convolution Kernels (GCVK) to introduce a Gabor attention mechanism focused on iris biometric characteristics. Furthermore, we propose a novel residual feature fusion approach that selects and merges local and global features across multiple directions and scales, mitigating model degradation and enhancing the network’s ability to extract iris texture features effectively. Extensive experiments show that the proposed network outperforms the state-of-the-art performance on two benchmark datasets.
BOB-YOLO: Balancing Optimization Binarized YOLO via Module-Wise Latency
Authors: Liu Xinyu , Zhou Wenqiang , Yu Zhendong , Yang Jiaming , Wang Tao , Tang Chenwei , Lv Jiancheng
Published: October 2024

When it comes to object detection tasks, YOLO stands out for its impressive speed and efficiency. Nonetheless, deploying YOLO on resource-constrained devices remains a challenge due to its substantial model size and memory requirements. The direct application of conventional binary quantization strategies to YOLO can result in significant accuracy degradation. A prevalent solution is to introduce floating-point shortcuts. However, the increased computational demand and parameter complexity associated with these shortcuts limit their practical deployment on hardware platforms for optimal acceleration. To solve this problem, we propose a binary neural network (BNN) for object detection called BOB-YOLO to achieve a balanced performance in terms of computational speed, model size, and detection accuracy. Our BOB-YOLO fully leverages module-wise latency (MWL) to supervise the latency of floating-point shortcut branches by that of 1-bit trunk branches. This supervision maximizes the information carried by the floating-point data flow in shortcuts while maintaining latency within the limits set by the 1-bit convolution branch, thereby improving parallel computational efficiency. We also introduce the Roofline Model to address these limitations by considering both computational complexity and parameter compression, ensuring high computational intensity. Additionally, we propose a performance evaluation metric Pd, which provides an intuitive description of the trade-off between speed and accuracy, aligning closely with the practical requirements of binary quantization strategies. Extensive experiments on the VOC and COCO datasets demonstrate the significant advantages of our method over state-of-the-art BNN methods.
MIM-HD: Making Smaller Masked Autoencoder Better with Efficient Distillation
Authors: Zhang Zherui , Wang Changwei , Xu Rongtao , Xu Wenhao , Xu Shibiao , Guo Li , Zhang Jiguang , Teng Xiaoqiang , Xu Wenbo
Published: October 2024

Self-supervised learning and knowledge distillation intersect to achieve exceptional performance on downstream tasks across diverse network capacities. This paper introduces MIM-HD, which implements enhancements for masked image modeling (MIM) distillation, in two key aspects. First, a vision transformer head-level relation adaptive distillation approach is proposed, allowing the student to dynamically draw multi-source knowledge from the teacher based on its evolving state, compatible with scenarios where teacher-student transformer block head count differs. Second, to address the overemphasis on the encoder and neglect of the decoder role in maintaining representation consistency in previous MIM distillations, a dual-view decoding strategy for latent visual representations is introduced, reusing the teacher’s decoder to alleviate MIM burdens on smaller networks. MIM-HD effectiveness is demonstrated through evaluations on ADE20K (mIoU) and ImageNet-1K (Acc), achieving +1.4% and +0.5% improved performance, respectively, compared to state-of-the-art methods, with substantial advantages on smaller pre-training datasets. Moreover, MIM-HD achieves superior efficiency, reducing pre-training epochs from 300 to 100.