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检索条件"任意字段=Conference on Signal Processing, Sensor Fusion, and Target Recognition X"

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fusion feature multi-objective classification algorithm based on adaptive scale feature residual network 16

Fusion feature multi-objective classification algorithm base...

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Hua, Chenyang xie, Renhong Li, Peng Fang, Gan Zhao, xianya Zhou, Maoyuan School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9781510689251

With the development of science and technology, radar has been widely used in both civil and military fields, and radar target classification and recognition is an important research direction in radar signal processing field. The traditional classification and recognition technology does not utilize all the information of echo, and the recognition rate will decrease obviously when the environment is too complicated and the SNR is low. To solve the above problems, this paper proposes three recognition algorithms. It is a fusion feature multi-objective classification algorithm based on multi-scale feature residual network (MKRN), a fusion feature multi-objective classification algorithm based on adaptive scale feature residual network (AKRN) and a fusion feature multi-objective classification algorithm based on adaptive scale feature multi-level branch residual network (AKSRN). These three algorithms introduce residual structure to avoid network degradation caused by the deepening of network depth, and use convolution kernel of different sizes to enhance the ability of the network to extract multi-scale feature information, which improves the accuracy of network classification and recognition. © 2025 SPIE.

关键词： Radar signal processing

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Multi-sensor fusion based in-process monitoring technology for vertical milling cutter damage and breakage detection 4

Multi-sensor fusion based in-process monitoring technology f...

引用

4th International conference on Advanced Manufacturing Technology and Electronic Information, AMTEI 2024

作者： Dong, Chunping Zhang, xi School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

With the development of signal processing technology, statistical analysis, time domain analysis, frequency domain analysis and time-frequency domain analysis based on sensor signals enrich the scope of feature extraction. The development of modern manufacturing technology makes the monitoring mode transition from the traditional single sensor to the multi-sensor fusion monitoring mode. Aiming at the failure of tool breakage and tool breakage, this paper proposes an on-line monitoring method of milling tool breakage and tool breakage based on the fusion of spindle load and acoustic emission signal. The eigenvalues of acoustic emission signal and spindle load signal are fused to establish the feature vector, and the tool status decision model based on support vector machine is established. Compared with the single signal monitoring method, the tool status recognition accuracy of the signal fusion method proposed in this paper is significantly improved, which overcomes the problems of low accuracy and poor fault tolerance of tool status recognition of single signal. The kernel parameters and penalty factors of SVM are optimized by grid search, genetic algorithm and particle swarm optimization algorithm respectively. The research shows that the support vector machine decision model optimized by genetic algorithm has the best effect on the classification of milling cutter breakage and cutter breakage, and the fusion of the two sensor signals also improves the accuracy of the detection of breakage and cutter breakage. The feasibility of this method is verified by two sets of actual cutting experiments. © 2025 SPIE.

关键词： Milling cutters

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Human Action recognition Method based on Spatiotemporal Decoupling Multi-Scale Feature fusion Network 16

Human Action Recognition Method based on Spatiotemporal Deco...

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： xie, Jigang Wan, Kai Tian, Cheng Li, Junhuai Hu, Shuai Wang, Huaijun China Unicom Western Innovation Research Institute Co. Ltd. China Xi'an University of Technology China

ISBN: (纸本)9781510689251

Aiming at the problems of incomplete expression ability of a single neural network, ignoring the spatiotemporal characteristics of time series data and slow reasoning speed and low accuracy of the traditional network model in wearable behavior recognition tasks, this paper proposes a human behavior recognition method based on spatiotemporal decoupling multi-scale feature fusion network. Firstly, the Multi-Dimensione Convolutional Neural Network (MDCNN) is used to extract the different scale features of multi-channel sensor data, and the shallow and deep features of the signal are extracted at the same time. The complementary information between various levels of features is fully utilized to extract the features that represent the action information. Then, through the Decoupling Temporal-Spatially Transformer (DTST) based on the Transformer, the network is decoupled into time decoupling and space decoupling structures, to accurately extract the spatiotemporal features in the inertial signal. Finally, the Softmax classifier is used for action classification and recognition. The experimental results show that the proposed method can effectively utilize the consistency and complementarity between multi-sensor data, fuse multi-scale spatiotemporal features, and improve the reasoning performance and recognition accuracy in human behavior recognition. The average accuracy rate on the UCI-MHEALTH dataset is 96.83 %, and the average F1 value is 96.81 %. © 2025 SPIE.

关键词： Convolutional neural networks

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Dual-band Image Sequence Registration based on Description of Velocity and Curvature Distribution 16

Dual-band Image Sequence Registration based on Description o...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Zhao, Bingqing Tang, Shanjun Li, Chuchen Yang, Beier Yang, Qingyu Shanghai Electro-Mechanical Engineering Institute Shanghai201109 China

ISBN: (纸本)9781510689251

The composite seeker based on multi-band imaging detection is the development focus of future precision-guided weapons. A certain seeker adopts a recognition and tracking strategy based on the fusion of long-wave infrared and visible light images. The quality of the fused image is closely related to the image registration accuracy. In order to solve the problem that the feature description of infrared and visible dual-band images is not identical due to large difference in grayscale texture information, we propose a registration algorithm based on description of velocity and curvature distribution. In particular, we first extract the infrared and visible light target foreground, and use the created velocity distribution description to complete the FAST corner point matching on the foreground contour. All the matched point pairs are used to achieve coarse contour registration. Then, we use the created curvature distribution description to complete the CSS corner point matching of the target foreground. Finally, a real-time updated matching reservoir is built to calculate the fine registration matrix. The experimental results of multiple sets of actual infrared/visible light image sequences show that the proposed algorithm can achieve better feature description identity and better registration accuracy than the other algorithms. © 2025 SPIE.

关键词： Image fusion

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signal processing, sensor fusion, and target recognition x

Signal processing, sensor fusion, and target recognition X

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signal processing, sensor fusion, and target recognition x

The proceedings contains 57 papers from the conference of signal processing, sensor fusion, and target recognition x. Topics discussed include multiple-model nonlinear filtering for low-signal ground target applications;multiple-model tracking with feedback;application of fuzzy logic to target tracking in a cluttered environment;feature detection and fusion for intelligent compression;optical path tracking in high-scintillation scenarios;force detection and identification system;and fluctuation-based technique for beamforming and autodetection.

关键词： signal processing

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Text-guided Multimodal fusion for the Multimodal Emotion and Intent Joint Understanding

Text-guided Multimodal Fusion for the Multimodal Emotion and...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yu Zhang Bin Chen Hongfei Ye Zijian Gao Tianjiao Wan Long Lan Kele xu University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Emotion and Intent Joint Understanding in Multi-modal Conversation is a challenging task in the field of affective computing, aiming to decode the semantic information manifested in the multimodal conversational while simultaneously inferring the emotions and intents of the utterance. To address this challenge, we propose the Text-guided Multimodal Emotion-Intent Joint recognition method. By leveraging the text modality to guide the fusion process, it effectively reduces the noise introduced by other modalities. To strengthen the text modality’s guiding role, we use large language models (LLMs) for multi-turn targeted data augmentation and oversampling strategies to address data imbalance. Our approach achieved first place in Track 1 (English) of the ICASSP 2025 MEIJU Challenge, demonstrating its effectiveness in practical applications.

关键词： Emotion recognition target tracking Large language models Noise Semantics Machine learning Interference Data augmentation Robustness Speech processing

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Fundamentals of the Dempster-Shafer Theory

Fundamentals of the Dempster-Shafer Theory

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conference on signal processing, sensor fusion, and target recognition xxI

作者： Peri, Joseph S. J. Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (纸本)9780819490704

In this paper, I discuss the basic notions of the Dempster Shafer theory. Using a simple engineering example, I highlight sources of confusion in the Dempster Shafer literature, and some questions that arise in the course of applying the Dempster Shafer algorithm Finally, I discuss the measure theoretic foundation that reveals the intimate connections between the Dempster Shafer theory and Probability theory.

关键词： Probability theory sensors Algorithms Applied physics Current controlled current source Head Mathematics sensor fusion signal processing target recognition

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Hierarchical multifeature integration for automatic target recognition

Hierarchical multifeature integration for automatic target r...

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conference on signal processing, sensor fusion, and target recognition VIII

作者： Shah, S Aggarwal, JK Wayne State Univ Comp Vis Lab Dept Elect & Comp Engn Detroit MI 48202 USA

ISBN: (纸本)081943194X

This paper presents a methodology for object recognition in complex scenes by learning multiple feature object representations in second generation Forward Looking InfraRed (FLIR) images. A hierarchical recognition framework is developed which solves the recognition task by performing classification using decisions at the lower levels and the input features. The system uses new algorithms for detection and segmentation of objects and a Bayesian formulation for combining multiple object features for improved discrimination. Experimental results on a large database of FLIR images is presented to validate the robustness of the system, and its applicability to FLIR imagery obtained from real scenes.

关键词： target recognition sensor fusion Bayesian classification

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Tactical Multi sensor fusion (TMSF) an SAIC white paper

Tactical Multi Sensor Fusion (TMSF) an SAIC white paper

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conference on signal processing, sensor fusion, and target recognition VIII

作者： Henderson, B Short, B Stewart, C SAIC Sterling VA 20164 USA

ISBN: (纸本)081943194X

A software application for correlating and fusing information products from multiple dissimilar sensors is presented. The Tactical Multi sensor fusion (TMSF) system is a C++ object oriented application implementing data correlation and fusion algorithms which provide decision aids for identifying, locating, and determining the status of processing equipment within suspected weapons of mass destruction (WMD) sites. The TMSF system also provides valuable information for assessing weapon delivery accuracy and effectiveness.

关键词： sensors Data fusion Weapons of mass destruction Data modeling Error analysis Interfaces Weapons Image fusion sensor fusion Data processing

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Bayesian cluster detection and tracking using a generalized Cheeseman approach

Bayesian cluster detection and tracking using a generalized ...

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conference on signal processing, sensor fusion, and target recognition xII

作者： Mahler, R Lockheed Martin Tactical Syst Eagan MN USA

ISBN: (纸本)0819449563

Cluster tracking is the problem of detecting and tracking clustered formations of large numbers of targets, without necessarily being obligated to track each and every individual target. We address this problem by generalizing to the dynamic case a static Bayesian finite-mixture data-clustering approach due to P. Cheeseman. After summarizing Cheeseman's approach, we show that it implicitly draws on random set theory. Making this connection explicit allows us to incorporate it into a multitarget recursive Bayes filter, thereby leading to a rigorous Bayesian foundation for finite-mixture cluster tracking. A computational approach is proposed, based on an approximate, multitarget first-order moment filter ("cluster PHD" filter).

关键词： Digital filtering target detection Electronic filtering Filtering (signal processing) Data modeling Statistical modeling Detection and tracking algorithms sensor fusion signal processing target recognition

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