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.

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.

Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

Multi-task Learning of Semantic Segmentation and Height Estimation for Multi-modal Remote Sensing Images

Deep learning based methods have been successfully applied to semantic segmentation of optical remote sensing images.However,as more and more remote sensing data is available,it is a new challenge to comprehensively utilize multi-modal remote sensing data to break through the performance bottleneck of single-modal interpretation.In addition,semantic segmentation and height estimation in remote sensing data are two tasks with strong correlation,but existing methods usually study individual tasks separately,which leads to high computational resource overhead.To this end,we propose a Multi-Task learning framework for Multi-Modal remote sensing images(MM_MT).Specifically,we design a Cross-Modal Feature Fusion(CMFF)method,which aggregates complementary information of different modalities to improve the accuracy of semantic segmentation and height estimation.Besides,a dual-stream multi-task learning method is introduced for Joint Semantic Segmentation and Height Estimation(JSSHE),extracting common features in a shared network to save time and resources,and then learning task-specific features in two task branches.Experimental results on the public multi-modal remote sensing image dataset Potsdam show that compared to training two tasks independently,multi-task learning saves 20%of training time and achieves competitive performance with mIoU of 83.02%for semantic segmentation and accuracy of 95.26%for height estimation.

The Hub Status and Transportation Network of Kribi Port

In this paper, a study on four African ports was taken out that all have the ca-pability to become a hub port that can serve the central African region. The paper sort to determine which port was most suitable and port indexing was the method that was used to evaluate these ports. The ports evaluated were the port of Kribi, the port of Bata, the port of Libreville and the port of Pointe-Noire. There were other models that were also used which included linear regression and linear programming which all contributed to providing the final results of the port with the most suitable potential to serve as a hub port and meaningful results were obtained. The final results showed that the port of Pointe-Noire was the most suitable port to serve the central African region as a hub port.

Optimal Allocation of Public Transport Hub Based on Load Loss Value and the Economy of Distribution Network

The rapid development of electric buses has brought a surge in the number of bus hubs and their charging and discharging capacities.Therefore,the location and construction scale of bus hubs will greatly affect the operation costs and benefits of an urban distribution network in the future.Through the scientific and reasonable planning of public transport hubs on the premise of meeting the needs of basic public transport services,it can reduce the negative impact of electric bus charging loads upon the power grids.Furthermore,it can use its flexible operation characteristics to provide flexible support for the distribution network.In this paper,taking the impact of public transport hub on the reliability of distribution network as the starting point,a three-level programming optimization model based on the value and economy of distribution network load loss is proposed.Through the upper model,several planning schemes can be generated,which provides boundary conditions for the expansion of middle-level optimization.The normal operation dispatching scheme of public transport hub obtained from the middle-level optimization results provides boundary conditions for the development of lower level optimization.Through the lower level optimization,the expected load loss of the whole distribution system including bus hub under the planning scheme given by the upper level can be obtained.The effectiveness of the model is verified by an IEEE-33 bus example.

基于HubGLasso注意力机制的脑网络分类研究

脑网络分类有助于脑疾病的早期诊断,也有益于理解脑疾病发病机理,具有重要的研究与应用价值。其中,卷积神经网络应用广泛,可以提取脑网络的拓扑特征,是脑网络分类中的一个前沿热点。然而,现有方法未考虑脑网络中Hub节点对脑功能的重要贡献,这可能会导致特征提取不充分,限制了它们的分类性能。为此,该文提出了一种基于HubGLasso注意力机制的卷积神经网络模型,用于进行脑网络分类任务。该方法包含了一种新的卷积层结构,首先利用GLasso模型去除脑网络中的冗余信息,然后引入Hub约束与注意力机制,使其能够提取与异常Hub结构相关的重要特征,并用于脑疾病诊断。实验结果表明,该方法在包含1112个被试的真实自闭症数据集上取得了68.67%的准确率,显著优于目前已有方法,证明了其应用价值。更进一步,通过对训练后的模型进行特征分析,能够得到与脑疾病相关的脑区信息与Hub节点结构信息,为脑疾病病理机制的研究提供了全新的视角。

无人车载网络高可靠HUB的设计

为满足无人车技术的不断发展及日趋复杂的车载网络带来的高带宽和高可靠性的使用要求,设计一种车载网络集线器(HUB)。采用时间触发协议(TTP),设计时间触发以太网(TTE)加星型TTP总线的架构。TTE提高数据带宽,在物理层与驱动设备采用点对点的连接方式代替总线型通信方式,提高可靠性,解决总线型通信方式一点断开,整体通信失效的问题。针对HUB的设计做详细阐述,通过实验验证了设计的可行性和可靠性,对车载网络的优化提供了可行的解决途径。

基于加权基因共表达网络和癌症基因组图谱临床数据分析并鉴定肝细胞癌的Hub基因研究

背景 肝细胞癌(HCC)是全球常见的癌症相关死亡的第三大原因,约占所有原发性肝癌病例的90%,其复发率和死亡率较高,目前发生的分子机制仍不清楚。目的 探索HCC潜在的分子机制,发掘新的生物标志物。方法 从TCGA数据库下载RNA-seq表达数据和临床相关信息,通过差异基因表达分析正常肝脏组织与HCC组织的差异基因;对差异表达基因进行富集分析;基于TCGA中HCC的基因表达数据概况,使用WGCNA R包建立共表达网络,进行加权基因共表达网络分析(WGCNA),选择具有临床意义的模块,并筛选候选Hub基因;进一步分析候选Hub基因在HCC组织和正常肝脏组织显著差异表达、与HCC患者总体生存期和无病生存期是否显著相关,最终确定Hub基因;通过人类蛋白质图谱数据库对Hub基因蛋白表达进行验证。结果 本研究的基因表达数据来自50个正常肝脏组织样本和373个HCC组织样本。通过差异基因表达分析发现7 230个在HCC和正常肝脏组织之间差异表达的基因(HCC中3 691个上调基因和3 539个下调基因)。富集分析表明,上调的差异表达基因主要参与细胞周期调控和有丝分裂过程;下调的差异表达基因主要参与小分子代谢和有机酸代谢等过程。WGCNA确定了19个与HCC患者临床特征相关基因模块,通过分析模块与临床特征之间的关系,筛选出青色模块和紫色模块。青色模块基因中同时与患者总生存期和无病生存期强烈相关的前两个基因为VPS45和FAM189B;紫色模块基因中同时与患者总生存期和无病生存期强烈相关的前两个基因分别为CLEC1B和FCN3,因此将VPS45、FAM189B、CLEC1B和FCN3确定为最终的Hub基因。人类蛋白质图谱数据库免疫组织化学染色显示:VPS45和FAM189B在HCC组织中的表达高于正常肝脏组织,FCN3在HCC组织中的表达低于正常肝脏组织,CLEC1B在HCC组织和正常肝脏组织中表达差异不明显。结论 初步确定VPS45、FAM189B、CLEC1B和FCN3可能是HCC的新型潜在生物标志物,这些Hub基因可能为HCC的靶向治疗提供理论基础。

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.

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.

Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

Functional investigation and two-sample Mendelian randomization study of primary biliary cholangitis hub genes

BACKGROUND The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis(PBC)and finding relevant biomarkers for diagnosis and therapeutic evaluation.AIM To determine PBC-associated hub genes and assess their clinical utility for disease prediction.METHODS PBC expression data were obtained from the Gene Expression Omnibus database.Overlapping genes from differential expression analysis and weighted gene coexpression network analysis(WGCNA)were identified as key genes for PBC.Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes.Hub genes were identified in protein-protein interaction(PPI)networks using the Degree algorithm in Cytoscape software.The relationship between hub genes and immune cells was investigated.Finally,a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC.RESULTS We identified 71 overlapping key genes using differential expression analysis and WGCNA.These genes were primarily enriched in pathways related to cytokinecytokine receptor interaction,and Th1,Th2,and Th17 cell differentiation.We utilized Cytoscape software and identified five hub genes(CD247,IL10,CCL5,CCL3,and STAT3)in PPI networks.These hub genes showed a strong correlation with immune cell infiltration in PBC.However,inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk.CONCLUSION Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment,thereby offering significant clinical utility.

Cooling and Optimization in the Groove of the Outer Rotor HubMotor

The external rotor hub motor adopts direct drive mode,no deceleration drive device,and has a compact structure.Its axial size is smaller than that of a deceleration-driven hub motor,which greatly reduces the weight of the vehicle and increases the cruising range of the vehicle.Because of the limited special working environment and performance requirements,the hub motor has a small internal space and a large heat generation,so it puts forward higher requirements for heat dissipation capacity.For the external rotor hub motor,a new type of in-tank watercooled structure of hub motor was proposed to improve its cooling effect and performance.Firstly,the threedimensional finite element model of the motor is established to analyze the characteristics of motor loss and temperature field distribution.Secondly,the cooling performance of different cooling structures in the tank was studied.Finally,the thermal network model and three-dimensional finite element analysis were used to optimize the water-cooled structure in the tank,and the power density of themotor was improved by improving the cooling performance under the condition of volume limitation of the hub motor.The results show that the cooling effect of the proposed water-cooled structure in the tank is significant under the condition of constant power density.Compared to natural ventilation,the maximum temperature was reduced by 33.13°C and the cooling effect was increased by about 27.7%.

考虑Hub网络拥堵的轴辐式网络优化

在轴辐式网络背景下,枢纽节点通过集散货物,产生规模经济效应来降低成本。但同时也会增加Hub网络拥堵的可能性和成本的增加。首先,为缓解Hub网络拥堵并降低其造成的经济损失成本,提出与轴辐式网络节点流量相关的拥堵成本函数。其次,在保证轴辐式网络节点正常货物运输的前提下,最大限度的平衡货物运输规模经济效应和产生的Hub网络拥堵成本,建立混合整数非线性规划模型,以最小化运输成本、建设成本和拥堵成本为目标。最后,引用实际算例验证Hub网络拥堵对网络优化的影响与模型的合理性。对于未来如何平衡规模经济效应和Hub网络拥堵的轴辐式网络优化有重要的参考价值。

基于产品开发网络Hub节点的工程变更

为了深入研究工程变更的传播机理,在产品开发网络中扩充了Hub节点的内涵,揭示了Hub节点在变更传播中的重要性;为了识别Hub节点,基于设计结构矩阵和图论的有关知识,对产品开发网络的节点进行聚类;提出了以INI指数作为判定Hub节点"重要性"的量化指标;分析了确定Hub节点个数的方法;在此基础上,阐述了从Hub节点出发的工程变更控制策略。以某型号摩托车发动机为例,验证了所提方法的合理性。

基于城际多HUB的应急物流网络协同动力学模型分析

为了提高应急物流在生物反恐体系中的应急物资配送水平,完善城际应急物流系统,根据生物危险源扩散规律,建立了多层次的城际多HUB应急物流网络协同模型,分析了城际多HUB应急物流网络协同状态下,处理危机的流程.从系统动力学角度建立了传染病模型,并讨论了多HUB物流网络协同状态下,应急物资在最短时间内,以最合适的量配送到疫区的方法.研究表明,根据城际多HUB应急物流网络协同性的研究,能够有效控制生物危险源的扩散,提高应急系统多个城市之间的应急管理水平.

关于无标度网络中Hub节点的研究

无标度网络中少量节点的连接数非常大(称为Hub节点),而大量节点的连接数则非常少。通过理论和仿真两方面的研究,发现复杂网络中Hub节点的度值、数量与度分布指数具有直接关系。研究表明,度分布指数等于2是无标度网络中度分布指数的一个临界值。

人类蛋白质互作网络hub蛋白与其结构域关联分析

hub蛋白质作为参与较多互作的"中心蛋白",在实现蛋白质功能和生命活动中发挥着关键作用.而结构域作为蛋白质上的基本功能区域,决定着蛋白质功能及蛋白质互作的情况.互作网络中hub蛋白质和结构域对于蛋白质功能的实现均起到决定性的作用.对蛋白质互作与结构域的关系分析表明,蛋白质互作与结构域之间存在着密切的联系.对人类蛋白质互作网络中的hub蛋白与结构域进行关联分析,探讨hub蛋白及其互作partner与结构域数目之间的关系,并通过hub蛋白质之间的互作对相应结构域的关系进行进一步的论证.

Hub-and-Spoke型运输网络改善方法及其应用

Hub-and-Spoke(本文简称为HS)网络形式被广泛采用于多个领域,其在实践中显现的缺陷也为学术界所重视。本文采用运输车辆空驶吨公里作为评判标准,以"中途点停靠"(pickup stopover)的形式改善HS型运输网络。通过解析法获得HS运输网络结合中途点停靠运输模式的适用条件,并以我国公路快速货运业干线运输组织为对象加以实证分析。相比于运筹学领域优化计算方法而言,这种针对HS运输网络改善方法的可行性论证过程简单而实用。