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.

基于全扇区统一占空比函数的Ⅰ型NPC三电平SVPWM载波化算法

Ⅰ型中性点钳位(neutral point clamped, NPC)三电平变流器作为中大容量新能源发电系统的核心设备,其调制策略直接影响变流器的能量转换效率、谐波特性和运行控制特性等。鉴于Ⅰ型三电平变流器的传统空间矢量脉宽调制(space vector pulse width modulation, SVPWM)策略在实施过程中包含大量三角函数运算,基于三相静止坐标系进行调制算法的大小扇区判断和矢量作用时间计算等,并通过归纳整理,按照单调制波双载波的载波化思路,获得了适用于全扇区的三相统一的占空比函数。借助该占空比函数,变流器的数字控制器仅需通过简单的关系运算和数值计算,便可获得变流器功率管的控制信号,省去了大量三角函数计算,节约了硬件资源。此外,将SVPWM与正弦波脉宽调制(sinusoidal pulse width modulation, SPWM)+3次谐波注入调制算法进行对比,从调制波层面,揭示了这2种调制算法之间的内在差异。最后,通过仿真验证了该算法的有效性。

一种基于FPGA的SVPWM硬件架构及其计算速度优化

为了提高七段式两电平SVPWM算法的调制速度并减少逻辑资源的使用量,提出了一种基于FPGA的SVPWM硬件架构。在该硬件架构输入参考电压后,首先,进行基于Clarke逆变换的坐标变换,通过一系列加法运算构建出含有三相占空比的3组中间变量,同时通过2个异或运算从上述硬件布线中得到简化后的2 bit扇区判断条件;然后,根据简化后的2 bit扇区判断条件从以上3组中间变量中筛选出三相占空比,并进行钳位保护,按照自然采样法输出PWM。以上过程形成一个整体,在FPGA中只需3次触发,便能在2个时钟周期内完成从参考电压输入到三相PWM输出的整个过程,有效提高了计算速度。此外,还给出了该硬件架构在不同的FPGA平台下的资源使用情况,与其他方法相比,LUT使用量由至少500个缩减至300个左右,逻辑资源使用量降低。通过仿真与实物试验,验证了所提硬件架构的有效性。

基于虚拟电压矢量的双三相直线磁通切换永磁电机容错SVPWM策略

高可靠性是电梯驱动系统的首要指标。针对双三相直线磁通切换永磁(dual-three-phase linear flux switching permanent magnet,DTP-LFSPM)电机单相开路故障,提出一种基于虚拟电压矢量的容错空间矢量脉宽调制策略。首先,基于单相开路故障下的五维变换矩阵计算得到故障后的32个空间电压矢量分布。然后,筛选部分空间电压矢量,以z子空间电压幅值为零且每个扇区产生对称的脉宽调制波形为目标,重构得到12个虚拟电压矢量,相邻的2个虚拟电压矢量都由3个基本电压矢量组成,并用于合成参考电压矢量,不仅可以减少单相开路故障容错控制时的电流谐波,而且具有计算简单,易于硬件实现等优点。最后,搭建DTP-LFSPM电机驱动控制系统仿真与实验平台,仿真和实验测试结果证明所提容错策略的正确性和有效性。

抑制级联H 桥变换器共模电压的简化SVPWM 策略

为解决传统空间矢量调制策略难以拓展至高电平数级联H桥多电平变换器,以及抑制级联H桥多电平变换器调制策略实现过程中产生的共模电压的问题,本文提出一种简化空间矢量调制策略。该策略基于α'β'坐标系,通过判断空间矢量所处区域,得出其边界开关状态,再引入修正参数K对边界开关状态进行修正,以此提高策略的快速性并完成共模电压抑制。仿真和实验结果表明该策略实现简单,可以将级联H桥多电平变换器共模电压抑制在最小变化范围内。

基于SVPWM补偿优化的三电平NPC并网逆变器容错控制

为保证并网系统中三电平中点箝位(neutral point clamped,NPC)型并网逆变器单相桥臂短路或断路故障后持续运行,提出一种基于空间矢量脉宽调制(space vector pulse width modulation,SVPWM)的优化补偿型低共模电压容错控制策略。首先,通过分析故障后八开关三相逆变器(eight switch three phase inverters,ESTPI)拓扑开关状态对应的共模电压大小,确定参考电压矢量合成规则;然后,通过一个基波周期内中点电流情况分析中点电位波动机理,进而对空间矢量合成进行调节补偿;最后,设计低通滤波器和滞环控制器进一步对补偿进行优化调整,保证并网电流质量的同时有效抑制了直流母线中点电位偏移。仿真结果表明,该容错控制策略能够实现三电平NPC并网逆变器单相桥臂故障后并网系统的稳定可靠运行,每个基波周期有三分之一时间的共模电压得到改善,优化补偿后的并网电流质量显著提高,且在并网电流突变时具备良好的控制特性。

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.

基于FPGA的永磁同步电机SVPWM的数字电路设计

针对永磁同步电机矢量控制算法中的空间矢量脉冲宽度调制(Space Vector Pulse Width Modulation,SVPWM)在现场可编程门阵列(Field-Programmable Gate Array,FPGA)中不能直接实现正余弦计算、无理数乘法的问题进行优化设计。设计了基于查表法和一次线性补偿的正余弦数值计算、有符号数的√3乘法计算模块,并使用Cyclone IV FPGA的硬件乘法器资源,对反Park变换、扇区判断和三相作用时间的计算进行了时序约束,对输入电机编码器数值、旋转坐标系的V_d和V_q参数经过进行计算,输出带死区功能的三相七段式PWM控制信号。在FPGA中实现SVPWM算法精度为3.0×10^(-5),计算时间为1.6μs,对比高性能STM32F429微处理器有一定优势,满足了永磁同步电机的矢量控制要求。

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.

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.

基于FPGA的改进型SVPWM算法仿真与实现

在传统的电机控制算法中,三相空间矢量脉宽调制(SVPWM)通常都是在数字信号处理器(DSP)中实现的,但DSP的串行执行特点在一些高性能控制场合或者特殊应用领域会限制算法性能的提升。为此,提出一种基于现场可编程门阵列(FPGA)的改进型SVPWM算法。通过对传统七段式SVPWM算法进行简化处理,避免了复杂的三角函数和乘除运算,以适应FPGA的运算特点。在Matlab/Simulink中搭建了该算法和逆变器的模型,并使用Verilog HDL硬件描述语言进行硬件实现。仿真波形和实验结果均证实了所提算法的正确性和有效性。该算法的实现为后期永磁同步电机的矢量闭环控制应用提供了必要基础。

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.

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-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

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.

永磁同步电机SVPWM矢量控制仿真研究

研究永磁同步电机的SVPWM矢量控制策略。首先分析了永磁同步电机数学模型,给出了SVPWM矢量控制算法,然后运用MATLAB/Simulink软件搭建SVPWM控制永磁同步电机仿真模型,得到电机的速度、转矩、电流仿真运行曲线,分析仿真结果表明SVPWM控制下的永磁同步电机具有良好的动态和稳态性能。

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.