A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.

A Multi-mode Electronic Load Sensing Control Scheme with Power Limitation and Pressure Cut-off for Mobile Machinery

In mobile machinery,hydro-mechanical pumps are increasingly replaced by electronically controlled pumps to improve the automation level,but diversified control functions(e.g.,power limitation and pressure cut-off)are integrated into the electronic controller only from the pump level,leading to the potential instability of the overall system.To solve this problem,a multi-mode electrohydraulic load sensing(MELS)control scheme is proposed especially considering the switching stability from the system level,which includes four working modes of flow control,load sensing,power limitation,and pressure control.Depending on the actual working requirements,the switching rules for the different modes and the switching direction(i.e.,the modes can be switched bilaterally or unilaterally)are defined.The priority of different modes is also defined,from high to low:pressure control,power limitation,load sensing,and flow control.When multiple switching rules are satisfied at the same time,the system switches to the control mode with the highest priority.In addition,the switching stability between flow control and pressure control modes is analyzed,and the controller parameters that guarantee the switching stability are obtained.A comparative study is carried out based on a test rig with a 2-ton hydraulic excavator.The results show that the MELS controller can achieve the control functions of proper flow supplement,power limitation,and pressure cut-off,which has good stability performance when switching between different control modes.This research proposes the MELS control method that realizes the stability of multi-mode switching of the hydraulic system of mobile machinery under different working conditions.

Multi-mode Multi-frequency GNSS-IR Combination System for Sea Level Retrieval

With the development of Global Navigation Satellite Systems(GNSS),geodetic GNSS receivers have been utilized to monitor sea levels using GNSS-Interferometry Reflectometry(GNSS-IR)technology.The multi-mode,multi-frequency signals of GPS,GLONASS,Galileo,and Beidou can be used for GNSS-IR sea level retrieval,but combining these retrievals remains problematic.To address this issue,a GNSS-IR sea level retrieval combination system has been developed,which begins by analyzing error sources in GNSS-IR sea level retrieval and establishing and solving the GNSS-IR retrieval equation.This paper focuses on two key points:time window selection and equation stability.The stability of the retrieval combination equations is determined by the condition number of the coefficient matrix within the time window.The impact of ill-conditioned coefficient matrices on the retrieval results is demonstrated using an extreme case of SNR data with only ascending or descending trajectories.After determining the time window and removing ill-conditioned equations,the multi-mode,multi-frequency GNSS-IR retrieval is performed.Results from three International GNSS Service(IGS)stations show that the combination method produces high-precision,high-resolution,and high-reliability sea level retrieval combination sequences.

UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

Flow characteristics and hot workability of a typical low-alloy high-strength steel during multi-pass deformation

Heavy components of low-alloy high-strength(LAHS) steels are generally formed by multi-pass forging. It is necessary to explore the flow characteristics and hot workability of LAHS steels during the multi-pass forging process, which is beneficial to the formulation of actual processing parameters. In the study, the multi-pass hot compression experiments of a typical LAHS steel are carried out at a wide range of deformation temperatures and strain rates. It is found that the work hardening rate of the experimental material depends on deformation parameters and deformation passes, which is ascribed to the impacts of static and dynamic softening behaviors. A new model is established to describe the flow characteristics at various deformation passes. Compared to the classical Arrhenius model and modified Zerilli and Armstrong model, the newly proposed model shows higher prediction accuracy with a confidence level of 0.98565. Furthermore, the connection between power dissipation efficiency(PDE) and deformation parameters is revealed by analyzing the microstructures. The PDE cannot be utilized to reflect the efficiency of energy dissipation for microstructure evolution during the entire deformation process, but only to assess the efficiency of energy dissipation for microstructure evolution in a specific deformation parameter state.As a result, an integrated processing map is proposed to better study the hot workability of the LAHS steel, which considers the effects of instability factor(IF), PDE, and distribution and size of grains. The optimized processing parameters for the multi-pass deformation process are the deformation parameters of 1223–1318 K and 0.01–0.08 s^(-1). Complete dynamic recrystallization occurs within the optimized processing parameters with an average grain size of 18.36–42.3 μm. This study will guide the optimization of the forging process of heavy components.

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

A Comprehensive Survey on Deep Learning Multi-Modal Fusion:Methods,Technologies and Applications

Multi-modal fusion technology gradually become a fundamental task in many fields,such as autonomous driving,smart healthcare,sentiment analysis,and human-computer interaction.It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities.Under complex scenes,multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions.However,achieving outstanding performance is challenging because of equipment performance limitations,missing information,and data noise.This paper comprehensively reviews existing methods based onmulti-modal fusion techniques and completes a detailed and in-depth analysis.According to the data fusion stage,multi-modal fusion has four primary methods:early fusion,deep fusion,late fusion,and hybrid fusion.The paper surveys the three majormulti-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various fields.Finally,it discusses the challenges and explores potential research opportunities.Multi-modal tasks still need intensive study because of data heterogeneity and quality.Preserving complementary information and eliminating redundant information between modalities is critical in multi-modal technology.Invalid data fusion methods may introduce extra noise and lead to worse results.This paper provides a comprehensive and detailed summary in response to these challenges.

Towards trustworthy multi-modal motion prediction:Holistic evaluation and interpretability of outputs

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT Scan: From Concepts toward Clinical Explainability

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.Secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.

Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.

Formulation of Work-Study Combined and Result-Oriented Integrated Curriculum Standards

According to the Annex Technical Regulations for Integrated Curriculum Development(Trial)in Document No.30 of the General Office of the Ministry of Human Resources and Social Security(2012),this paper studies the formulation of the curriculum standards for the integration of Chinese medicinal materials production.We focus on the formulation ideas of the curriculum standards for the integration of Chinese medicinal materials production,the formulation process of the curriculum standards for the integration of Chinese medicinal materials production,including the description of typical work tasks,the determination of curriculum objectives,the analysis of study content,the description of referential study tasks,teaching implementation suggestions,assessment and evaluation suggestions,which can provide a reference for the development and research of other related integrated courses.

Energy-Efficient Multi-Mode Clusters Maintenance(M^2CM) for Hierarchical Wireless Sensor Networks

How to energy-efficiently maintain the topology of wireless sensor networks(WSNs) is still a difficult problem because of their numerous nodes,highly dynamic nature,varied application scenarios and limited resources.An energy-efficient multi-mode clusters maintenance(M2CM) method is proposed based on localized and event-driven mechanism in this work,which is different from the conventional clusters maintenance model with always periodically re-clustered among the whole network style based on time-trigger for hierarchical WSNs.M2 CM can meet such demands of clusters maintenance as adaptive local maintenance for the damaged clusters according to its changes in time and space field.,the triggers of M2 CM include such events as nodes' residual energy being under the threshold,the load imbalance of cluster head,joining in or exiting from any cluster for new node or disable one,etc.Based on neighboring relationship of the damaged clusters,one can start a single cluster(inner-cluster) maintenance or clusters(inter-cluster) maintenance program to meet diverse demands in the topology management of hierarchical WSNs.The experiment results based on NS2 simulation show that the proposed method can significantly save energy used in maintaining a damaged network,effectively narrow down the influenced area of clusters maintenance,and increase transmitted data and prolong lifetime of network compared to the traditional schemes.

Design and optimization of fluid lubricated bearings operated with extreme working performances——a comprehensive review

Fluid lubricated bearings have been widely adopted as support components for high-end equipment in metrology,semiconductor devices,aviation,strategic defense,ultraprecision manufacturing,medical treatment,and power generation.In all these applications,the equipment must deliver extreme working performances such as ultraprecise movement,ultrahigh rotation speed,ultraheavy bearing loads,ultrahigh environmental temperatures,strong radiation resistance,and high vacuum operation,which have challenged the design and optimization of reliable fluid lubricated bearings.Breakthrough of any related bottlenecks will promote the development course of high-end equipment.To promote the advancement of high-end equipment,this paper reviews the design and optimization of fluid lubricated bearings operated at typical extreme working performances,targeting the realization of extreme working performances,current challenges and solutions,underlying deficiencies,and promising developmental directions.This paper can guide the selection of suitable fluid lubricated bearings and optimize their structures to meet their required working performances.

Exploring the Factors Associated with 12-Month Non-Return to Work among Motorcyclists Involved in Road Accidents

Introduction: Motorcyclists bear a disproportionate burden of morbidity and mortality from road accidents. In addition, the consequences of these accidents affect the ability of victims to return to work. This study aimed to determine the prevalence and factors associated with non-return to work among surviving motorcyclists involved in road accidents 12 months after the event. Materials and Methods: It was a cross-sectional study conducted using data from a cohort of motorcyclists involved in accidents and recruited in five hospitals in Benin from July 2019 to January 2020. The dependent variable was non-return to work 12 months after the accident (yes vs no). The independent variables were categorized into two groups: baseline and 12-month follow-up variables. Logistic regression was used to determine the factors associated with non-return to work at 12 months among the participants. Results: Among the 362 participants, 55 (15.19%, 95% CI = 11.84 - 19.29) had not returned to work 12 months after the accident. Risk factors for non-return to work identified were: smoking (aOR = 4.41, 95% CI = 1.44 - 13.56, p = 0.010), hospitalization (aOR = 2.87, 95% CI = 1.14 - 7.24, p Conclusion: The prevalence of non-return to work at 12 months was high among surviving motorcyclists involved in road accidents in Benin. Integrated support for patients based on identified risk factors should effectively improve their return to work.

A Review on Technologies for the Use of CO2 as a Working Fluid in Refrigeration and Power Cycles

The use of carbon dioxide as a working fluid has been the subject of extensive studies in recent years, particularly in the field of refrigeration where it is at the heart of research to replace CFC and HCFC. Its thermodynamic properties make it a fluid of choice in the efficient use of energy at low and medium temperatures in engine cycles. However, the performance of transcritical CO2 cycles weakens under high temperature and pressure conditions, especially in refrigeration systems;On the other hand, this disadvantage becomes rather interesting in engine cycles where CO2 can be used as an alternative to the organic working fluid in small and medium-sized electrical systems for low quality or waste heat sources. In order to improve the performance of systems operating with CO2 in the field of refrigeration and electricity production, research has made it possible to develop several concepts, of which this article deals with a review of the state of the art, followed by analyzes in-depth and critical of the various developments to the most recent modifications in these fields. Detailed discussions on the performance and technical characteristics of the different evolutions are also highlighted as well as the factors affecting the overall performance of the systems studied. Finally, perspectives on the future development of the use of CO2 in these different cycles are presented.