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Fault feature enhancement of gearbox in combined machining center by using adaptive cascade stochastic resonance

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Science China(Technological Sciences) 2011年第12期54卷 3203-3210页

作者： LI Bing LI JiMeng HE ZhengJia State Key Laboratory for Manufacturing Systems Engineering Xi＇an Jiaotong University Xi＇an 710049 China

The difficulty to select the best system parameters restricts the engineering application of stochastic resonance （SR）. An adaptive cascade stochastic resonance （ACSR） is proposed in the present study. The proposed method introduces correlation theory into SR, and uses correlation coefficient of the input signals and noise as a weight to construct the weighted signal-to-noise ratio （WSNR） index. The influence of high frequency noise is alleviated and the signal-to-noise ratio index used in traditional SR is improved accordingly. The ACSR with WSNR can obtain optimal parameters adaptively. And it is not necessary to predict the exact frequency of the target signal. In addition, through the secondary utilization of noise, ACSR makes the signal output waveforrn smoother and the fluctuation period more obvious. Simulation example and engineering application of gearbox fault diagnosis demonstrate the effectiveness and feasibility of the proposed method.

关键词： stochastic resonance adaptive weighted signal-to-noise ratio feature enhancement combined machining center

Traffic Scene Captioning with Multi-Stage feature enhancement

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Computers, Materials & Continua 2023年第9期76卷 2901-2920页

作者： Dehai Zhang Yu Ma Qing Liu Haoxing Wang Anquan Ren Jiashu Liang School of Software Yunnan UniversityKunming650091China

Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images,ensuring road safety while providing an important decision-making function for sustainable *** order to provide a comprehensive and reasonable description of complex traffic scenes,a traffic scene semantic captioningmodel withmulti-stage feature enhancement is proposed in this *** general,the model follows an encoder-decoder ***,multilevel granularity visual features are used for feature enhancement during the encoding process,which enables the model to learn more detailed content in the traffic scene ***,the scene knowledge graph is applied to the decoding process,and the semantic features provided by the scene knowledge graph are used to enhance the features learned by the decoder again,so that themodel can learn the attributes of objects in the traffic scene and the relationships between objects to generate more reasonable *** paper reports extensive experiments on the challenging MS-COCO dataset,evaluated by five standard automatic evaluation metrics,and the results show that the proposed model has improved significantly in all metrics compared with the state-of-the-art methods,especially achieving a score of 129.0 on the CIDEr-D evaluation metric,which also indicates that the proposed model can effectively provide a more reasonable and comprehensive description of the traffic scene.

关键词： Traffic scene captioning sustainable transportation feature enhancement encoder-decoder structure multi-level granularity scene knowledge graph

EFECL:feature encoding enhancement with contrastive learning for indoor 3D object detection

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Computational Visual Media 2023年第4期9卷 875-892页

作者： Yao Duan Renjiao Yi Yuanming Gao Kai Xu Chenyang Zhu School of Computing National University of Defense TechnologyChangsha 410000China

Good proposal initials are critical for 3D object detection ***,due to the significant geometry variation of indoor scenes,incomplete and noisy proposals are inevitable in most *** feature information among these“bad”proposals may mislead the *** learning provides a feasible way for representing proposals,which can align complete and incomplete/noisy proposals in feature *** aligned feature space can help us build robust 3D representation even if bad proposals are ***,we devise a new contrast learning framework for indoor 3D object detection,called EFECL,that learns robust 3D representations by contrastive learning of proposals on two different ***,we optimize both instance-level and category-level contrasts to align features by capturing instance-specific characteristics and semantic-aware common ***,we propose an enhanced feature aggregation module to extract more general and informative features for contrastive *** on ScanNet V2 and SUN RGB-D benchmarks demonstrate the generalizability and effectiveness of our method,and our method can achieve 12.3%and 7.3%improvements on both datasets over the benchmark *** code and models are publicly available at https://***/YaraDuan/EFECL.

关键词： indoor scene object detection contrastive learning feature enhancement

Contrastive Learning Based on feature enhancement for Multi-modal Fake News Detection

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Contrastive Learning Based on Feature Enhancement for Multi-...

Infrared Small Target Detection Algorithm Based on ISTD-CenterNet

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作者： Guangchao Wu Boyue Wang Xiaoyan Li Qian Wang Beijing University of Technology

The rapid spread of fake news on social media has a great impact on people's lives and social stability. Existing multimodal fake news detection methods are very concerned about whether the cross-modal information matches. In order to better understand the relationship between different modalities, this paper proposes the Contrastive Learning Based on feature enhancement model(CONLFE), which consists of three core modules: the High-quality feature Extraction based on CLIP module, the feature Interaction Based on Transformer module and the Cross-modal Contrastive Learning module. Firstly,the pre-train CLIP model is used to extract richer semantic features from text and images. Then, the attention mechanism in transformer is used to process and optimize the interaction between enhanced uni-modal features. Additionally, by learning matched and mismatched text-image pairs in real news, the representation of different modalities is aligned to a certain *** method effectively improves the accuracy and efficiency of multimodal fake news detection.

关键词： Multi-modal fake news detection Contrastive learning feature enhancement

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Computers, Materials & Continua 2023年第12期77卷 3511-3531页

作者： Ning Li Shucai Huang Daozhi Wei Air and Missile Defense College Air Force Engineering UniversityXi’an710051China

This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet(ISTD-CenterNet)network for detecting small infrared targets in complex *** method eliminates the need for an anchor frame,addressing the issues of low accuracy and slow *** is used as the framework for feature extraction,and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects.A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire infrared ***,an improved sensory field enhancement module is designed to leverage semantic information in low-resolution feature maps,and a convolutional attention mechanism module is used to increase network stability and convergence *** experiments conducted on the infrared small target data set *** experiments show that compared to the benchmark network CenterNet-HRNet,the proposed ISTD-CenterNet improves the recall by 22.85%and the detection accuracy by 13.36%.Compared to the state-of-the-art YOLOv5small,the ISTD-CenterNet recall is improved by 5.88%,the detection precision is improved by 2.33%,and the detection frame rate is 48.94 frames/sec,which realizes the accurate real-time detection of small infrared targets.

关键词： Infrared small target detection CenterNet data enhancement feature enhancement attention mechanism

Detecting XSS with Random Forest and Multi-Channel feature Extraction

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Computers, Materials & Continua 2024年第7期80卷 843-874页

作者： Qiurong Qin Yueqin Li Yajie Mi Jinhui Shen Kexin Wu Zhenzhao Wang Smart City College Beijing Union UniversityBeijing100101China

In the era of the Internet,widely used web applications have become the target of hacker attacks because they contain a large amount of personal *** these vulnerabilities,stealing private data through crosssite scripting(XSS)attacks is one of the most commonly used attacks by ***,deep learning-based XSS attack detection methods have good application prospects;however,they suffer from problems such as being prone to overfitting,a high false alarm rate,and low *** address these issues,we propose a multi-stage feature extraction and fusion model for XSS detection based on Random Forest feature *** model utilizes RandomForests to capture the intrinsic structure and patterns of the data by extracting leaf node indices as features,which are subsequentlymergedwith the original data features to forma feature setwith richer information *** feature extraction is conducted through three parallel *** I utilizes parallel onedimensional convolutional layers(1Dconvolutional layers)with different convolutional kernel sizes to extract local features at different scales and performmulti-scale feature fusion;Channel II employsmaximum one-dimensional pooling layers(max 1D pooling layers)of various sizes to extract key features from the data;and Channel III extracts global information bi-directionally using a Bi-Directional Long-Short TermMemory Network(Bi-LSTM)and incorporates a multi-head attention mechanism to enhance global ***,effective classification and prediction of XSS are performed by fusing the features of the three *** test the effectiveness of the model,we conduct experiments on six *** achieve an accuracy of 100%on the UNSW-NB15 dataset and 99.99%on the CICIDS2017 dataset,which is higher than that of the existing models.

关键词： Random forest feature enhancement three-channel parallelism XSS detection

BDPartNet: feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

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Computers, Materials & Continua 2024年第6期79卷 4621-4639页

作者： Xuejie Wang Jianxun Zhang Ye Tao Xiaoli Yuan Yifan Guo Department of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Liangjiang Institute of Artificial Intelligence Chongqing University of TechnologyChongqing400054China

While single-modal visible light images or infrared images provide limited information,infrared light captures significant thermal radiation data,whereas visible light excels in presenting detailed texture ***-bining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations,resulting in high-quality images with enhanced contrast and rich texture *** capabilities hold promising applications in advanced visual tasks including target detection,instance segmentation,military surveillance,pedestrian detection,among *** paper introduces a novel approach,a dual-branch decomposition fusion network based on AutoEncoder(AE),which decomposes multi-modal features into intensity and texture information for enhanced *** contrast enhancement module(CEM)and texture detail enhancement module(DEM)are devised to process the decomposed images,followed by image fusion through the *** proposed loss function ensures effective retention of key information from the source images of both *** comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture *** the qualitative results,we can see the advantages of fusion details and local *** the quantitative experiments,entropy(EN),mutual information(MI),structural similarity(SSIM)and other results have improved and exceeded the SOTA(State of the Art)model as a whole.

关键词： Deep learning feature enhancement computer vision

DFE-GCN: Dual feature Enhanced Graph Convolutional Network for Controversy Detection

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Computers, Materials & Continua 2023年第10期77卷 893-909页

作者： Chengfei Hua Wenzhong Yang Liejun Wang Fuyuan Wei KeZiErBieKe HaiLaTi Yuanyuan Liao College of Software Xinjiang UniversityUrumqi830000China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Xinjiang UniversityUrumqi830000China Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region Xinjiang UniversityUrumqi830000China

With the development of social media and the prevalence of mobile devices,an increasing number of people tend to use social media platforms to express their opinions and attitudes,leading to many online *** online controversies can severely threaten social stability,making automatic detection of controversies particularly *** controversy detection methods currently focus on mining features from text semantics and propagation ***,these methods have two drawbacks:1)limited ability to capture structural features and failure to learn deeper structural features,and 2)neglecting the influence of topic information and ineffective utilization of topic *** light of these phenomena,this paper proposes a social media controversy detection method called Dual feature Enhanced Graph Convolutional Network(DFE-GCN).This method explores structural information at different scales from global and local perspectives to capture deeper structural features,enhancing the expressive power of structural ***,to strengthen the influence of topic information,this paper utilizes attention mechanisms to enhance topic features after each graph convolutional layer,effectively using topic *** validated our method on two different public datasets,and the experimental results demonstrate that our method achieves state-of-the-art performance compared to baseline *** the Weibo and Reddit datasets,the accuracy is improved by 5.92%and 3.32%,respectively,and the F1 score is improved by 1.99%and 2.17%,demonstrating the positive impact of enhanced structural features and topic features on controversy detection.

关键词： Controversy detection graph convolutional network feature enhancement social media

Underwater Sea Cucumber Target Detection Based on Edge-Enhanced Scaling YOLOv4

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Journal of Beijing Institute of Technology 2023年第3期32卷 328-340页

作者： Ziting Zhang Hang Zhang Yue Wang Tonghai Liu Yuxiang He Yunchen Tian College of Computer and Information Engineering Tianjin Agricultural UniversityTianjin 300380China

Sea cucumber detection is widely recognized as the key to automatic *** underwater light environment is complex and easily obscured by mud,sand,reefs,and other underwater *** date,research on sea cucumber detection has mostly concentrated on the distinction between prospective objects and the ***,the key to proper distinction is the effective extraction of sea cucumber feature *** this study,the edge-enhanced scaling You Only Look Once-v4(YOLOv4)(ESYv4)was proposed for sea cucumber *** emphasizing the target features in a way that reduced the impact of different hues and brightness values underwater on the misjudgment of sea cucumbers,a bidirectional cascade network(BDCN)was used to extract the overall edge greyscale image in the image and add up the original RGB image as the detected ***,the YOLOv4 model for backbone detection is scaled,and the number of parameters is reduced to 48%of the original number of *** results of 783images indicated that the detection precision of positive sea cucumber samples reached *** improvement reflects that the algorithm is more effective to improve the edge feature information of the *** thus contributes to the automatic multi-objective detection of underwater sea cucumbers.

关键词： sea cucumber edge extraction feature enhancement edge-enhanced scaling You Only Look Once-v4(YOLOv4)(ESYv4) model scaling