Belief exponential divergence for D-S evidence theory and its application in multi-source information fusion

Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this issue,a fusion approach based on a newly defined belief exponential diver-gence and Deng entropy is proposed.First,a belief exponential divergence is proposed as the conflict measurement between evidences.Then,the credibility of each evidence is calculated.Afterwards,the Deng entropy is used to calculate information volume to determine the uncertainty of evidence.Then,the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence.Ultimately,initial evidences are amended and fused using Dempster’s rule of combination.The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic exam-ples.Additionally,the proposed approach is applied to aerial tar-get recognition and iris dataset-based classification to validate its efficacy.Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.

The Fusion of Temporal Sequence with Scene Priori Information in Deep Learning Object Recognition

For some important object recognition applications such as intelligent robots and unmanned driving, images are collected on a consecutive basis and associated among themselves, besides, the scenes have steady prior features. Yet existing technologies do not take full advantage of this information. In order to take object recognition further than existing algorithms in the above application, an object recognition method that fuses temporal sequence with scene priori information is proposed. This method first employs YOLOv3 as the basic algorithm to recognize objects in single-frame images, then the DeepSort algorithm to establish association among potential objects recognized in images of different moments, and finally the confidence fusion method and temporal boundary processing method designed herein to fuse, at the decision level, temporal sequence information with scene priori information. Experiments using public datasets and self-built industrial scene datasets show that due to the expansion of information sources, the quality of single-frame images has less impact on the recognition results, whereby the object recognition is greatly improved. It is presented herein as a widely applicable framework for the fusion of information under multiple classes. All the object recognition algorithms that output object class, location information and recognition confidence at the same time can be integrated into this information fusion framework to improve performance.

A multi-source information fusion method for tool life prediction based on CNN-SVM

For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.

An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy.First,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure.A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features.Finally,the advantage of the deep belief network probability model is used as the fault classifier to identify the faults.The effectiveness of the proposed method was verified by a gearbox test-bed.Experimental results show that,compared with traditional and existing intelligent fault diagnosis methods,the proposed method can obtain representative information and features from the raw data with higher classification accuracy.

Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.

An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile teaching.Therefore,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,respectively.By integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is constructed.According to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple perspectives.Finally,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.

Structural damage detection method based on information fusion technique

Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.

Research on Fine-Grained Recognition Method for Sensitive Information in Social Networks Based on CLIP

With the emergence and development of social networks,people can stay in touch with friends,family,and colleagues more quickly and conveniently,regardless of their location.This ubiquitous digital internet environment has also led to large-scale disclosure of personal privacy.Due to the complexity and subtlety of sensitive information,traditional sensitive information identification technologies cannot thoroughly address the characteristics of each piece of data,thus weakening the deep connections between text and images.In this context,this paper adopts the CLIP model as a modality discriminator.By using comparative learning between sensitive image descriptions and images,the similarity between the images and the sensitive descriptions is obtained to determine whether the images contain sensitive information.This provides the basis for identifying sensitive information using different modalities.Specifically,if the original data does not contain sensitive information,only single-modality text-sensitive information identification is performed;if the original data contains sensitive information,multimodality sensitive information identification is conducted.This approach allows for differentiated processing of each piece of data,thereby achieving more accurate sensitive information identification.The aforementioned modality discriminator can address the limitations of existing sensitive information identification technologies,making the identification of sensitive information from the original data more appropriate and precise.

基于Informer融合模型的油田开发指标预测方法

为解决油田开发指标的预测问题,提出了一种基于物质平衡方程和Informer的融合模型。首先,通过物质平衡方程领域知识建立油田开发产量递减前后的机理模型;其次,将所建机理模型作为约束与Informer模型损失函数进行融合建立符合油田开发物理规律的指标预测模型;最后,采用油田实际生产数据进行实验分析,结果表明相比于纯数据驱动的几种循环结构预测模型,本融合模型在相同数据条件下的预测效果更优。该模型的机理约束部分能引导模型的训练过程,使其收敛速度更快,且波峰波谷处的预测更准确。该融合模型具有更好的预测能力和泛化能力和比较合理的物理可解释性。

Deep Bimodal Fusion Approach for Apparent Personality Analysis

Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research concludespreliminary results to get immense knowledge from visual and Audio(sound) modality. To overcome the deficiency, we proposed the Deep BimodalFusion (DBF) approach to predict five traits of personality-agreeableness,extraversion, openness, conscientiousness and neuroticism. In the proposedframework, regarding visual modality, the modified convolution neural networks(CNN), more specifically Descriptor Aggregator Model (DAN) areused to attain significant visual modality. The proposed model extracts audiorepresentations for greater efficiency to construct the long short-termmemory(LSTM) for the audio modality. Moreover, employing modality-based neuralnetworks allows this framework to independently determine the traits beforecombining them with weighted fusion to achieve a conclusive prediction of thegiven traits. The proposed approach attains the optimal mean accuracy score,which is 0.9183. It is achieved based on the average of five personality traitsand is thus better than previously proposed frameworks.

Enhancing train position perception through Al-driven multi-source information fusion

This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.

基于注意力机制与多任务的肺部疾病诊断方法

肺部疾病存在种类多、病灶区域小的特点,现有数据集也存在数据量小的问题,导致模型效果不理想。为提高诊断效果,提出一种基于多任务联合注意力机制的肺部诊断网络(ASNet)。基于U-Net构建多任务诊断网络,在原有病灶分割任务基础上加入病理分类任务,加强任务之间的联系,以分割任务为辅,提升分类任务准确率;提出多尺度挤压激励模块,加强空间和通道之间的信息融合;引入一种轴向注意力机制,强调全局上下文信息和位置信息缓解由于医疗数据匮乏引起的欠拟合问题;设计自适应多任务混合损失函数,实现分割和分类任务损失权重的均衡。在自建数据集上的实验结果表明,提出网络在病灶分割任务上的Dice系数、特异性(SP)、灵敏度(SE)、HD距离和准确率的平均结果分别为81.1%、99.0%、84.1%、24.6 mm和97.5%,优于SAUNet++、SwinUnet等其他先进分割网络;在病理分类任务上比MobileNetV2网络在精确率、召回率和准确率指标上分别提升了2.0、1.8和1.7百分点,明显提升了在分类和分割上的精度,对小目标病灶分割效果更佳,其在合理的参数量下更适用于协助肺部疾病诊断。

船载HFSWR船只目标多维样本库构建方案设计

深度学习方法在船载高频地波雷达(high-frequency surface wave radar,HFSWR)船只目标检测中得到应用,然而其成功的关键在于构建精准全面的目标样本库。当前基于距离-多普勒(range-Doppler,RD)谱检测结果的单一维度样本库不完备,限制了模型的学习效果。为解决该问题,文章设计了一种船载HFSWR船只目标多维样本库构建方案,包含3个维度:①基于RD谱的检测结果结合自动识别系统(automatic identification system,AIS)验证RD维度;②基于多帧航迹段识别RT(range-time)/DT(Doppler-time)维度;③基于时频TF(time-frequency)分析检测TF维度。通过对3个维度的匹配和重合目标筛除以构建样本集。最后,利用实测数据构建的目标样本库对基于U-Net的目标检测网络进行训练和测试,结果表明该样本库可满足基于深度学习的目标检测模型训练需求。

Effective short text classification via the fusion of hybrid features for IoT social data

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

基于Deep Web检索的查询结果处理技术的应用

针对当前Deep Web信息检索中Web数据库返回的查询结果页面内容多样、形式各异、有效信息难以提取等不足,将信息抽取与数据融合技术加以改进,提出了对查询结果页面进行处理的技术。该技术通过对HTML页面解析、信息过滤、分块、剪枝、提取抽取规则,实现了有效信息的自动抽取。通过建立合并规则、去重规则、清洗规则,实现了数据的有效融合,并最终以统一的模式进行存储。最后,通过相关项目应用,验证了该技术的有效性和实用性。

COVID19 Classification Using CT Images via Ensembles of Deep Learning Models

The recent COVID-19 pandemic caused by the novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has had a significant impact on human life and the economy around the world.A reverse transcription polymerase chain reaction(RT-PCR)test is used to screen for this disease,but its low sensitivity means that it is not sufficient for early detection and treatment.As RT-PCR is a time-consuming procedure,there is interest in the introduction of automated techniques for diagnosis.Deep learning has a key role to play in the field of medical imaging.The most important issue in this area is the choice of key features.Here,we propose a set of deep learning features based on a system for automated classification of computed tomography(CT)images to identify COVID-19.Initially,this method was used to prepare a database of three classes:Pneumonia,COVID19,and Healthy.The dataset consisted of 6000 CT images refined by a hybrid contrast stretching approach.In the next step,two advanced deep learning models(ResNet50 and DarkNet53)were fine-tuned and trained through transfer learning.The features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization approach.For each deep model,the Rao-1 algorithm and the PSO algorithm were combined in the hybrid approach.Later,the selected features were merged using the new minimum parallel distance non-redundant(PMDNR)approach.The final fused vector was finally classified using the extreme machine classifier.The experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%.Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework.

Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential applications.Most of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ASR.However,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene images.In addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local features.To tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent information.The main highlights of this study include two aspects.On the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene images.Specifically,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local features.On the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during training.Comprehensive experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

Diagnosing gastrointestinal cancer by classical means is a hazardous procedure.Years have witnessed several computerized solutions for stomach disease detection and classification.However,the existing techniques faced challenges,such as irrelevant feature extraction,high similarity among different disease symptoms,and the least-important features from a single source.This paper designed a new deep learning-based architecture based on the fusion of two models,Residual blocks and Auto Encoder.First,the Hyper-Kvasir dataset was employed to evaluate the proposed work.The research selected a pre-trained convolutional neural network(CNN)model and improved it with several residual blocks.This process aims to improve the learning capability of deep models and lessen the number of parameters.Besides,this article designed an Auto-Encoder-based network consisting of five convolutional layers in the encoder stage and five in the decoder phase.The research selected the global average pooling and convolutional layers for the feature extraction optimized by a hybrid Marine Predator optimization and Slime Mould optimization algorithm.These features of both models are fused using a novel fusion technique that is later classified using the Artificial Neural Network classifier.The experiment worked on the HyperKvasir dataset,which consists of 23 stomach-infected classes.At last,the proposed method obtained an improved accuracy of 93.90%on this dataset.Comparison is also conducted with some recent techniques and shows that the proposed method’s accuracy is improved.

Image fusion methods in high-speed railway scenes:A survey

Image fusion refers to extracting meaningful information from images of different sources or modalities,and then fusing them to generate more informative images that are beneficial for subsequent applications.In recent years,the growing data and computing resources have promoted the development of deep learning,and image fusion technology has continued to spawn new deep learning fusion methods based on traditional fusion methods.However,high-speed railroads,as an important part of life,have their unique industry characteristics of image data,which leads to different image fusion techniques with different fusion effects in high-speed railway scenes.This research work first introduces the mainstream technology classification of image fusion,further describes the downstream tasks that image fusion techniques may combine within high-speed railway scenes,and introduces the evaluation metrics of image fusion,followed by a series of subjective and objective experiments to completely evaluate the performance level of each image fusion method in different traffic scenes,and finally provides some possible future image fusion in the field of rail transportation of research.

基于CNN-LSTM混合驱动的焊接成形质量监测

焊接成形质量监测对于现代制造业至关重要,现有的质量识别方法大多基于单一传感器,识别精度难以进一步提升,面对复杂条件下的抗干扰能力较弱.针对单一传感器识别技术存在的不足,多源信息融合技术能够发挥不同类型传感器的自身优势,实现对焊接过程更为全面且准确的监测.在进行多信息融合过程中,深度学习模型的特征挖掘机制仍然欠缺解释,不同信息的互补性仍未明晰,为此,提出一种基于多源信息混合驱动的CNN-LSTM焊接成形质量监测模型.结果表明,通过融合图像和电压信号实现了99.72%的平均识别准确率,可视化结果还展示了不同信息之间的互补优势.