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Underwater target recognition based on adaptive multi-feature fusion network

Multimedia Tools and Applications 2025年第10期84卷 7297-7317页

作者： Pan, Xiaoying Sun, Jia Feng, TianHao Lei, MingZhu Wang, Hao Zhang, WuXia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an710121 China School of Computer Science and Technology Xi’an University of Post & Telecommunications Xi’an710121 China

Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics research. In recent years, deep learning has been widely used in underwater recognition due to its powerful feature learning capabilities. However, most deep learning-based underwater target recognition methods use only the time-frequency domain features of signals, ignoring the frequency domain image features of signals. Additionally, they only use simple summation or splicing strategies to fuse and discriminate the extracted features, which leads to limited improvement in the accuracy of underwater target recognition. To address these issues, this paper proposes an underwater target recognition method based on an adaptive multi-feature fusion network. The method consists of a data pre-processing module, a multi-dimensional feature extraction module, and an adaptive multi-feature fusion module. In the multi-dimensional feature extraction module, the long short time memory network (LSTM) and the one-dimensional convolutional neural network (1DCNN) are used to extract the time-frequency features of the underwater signal. Furthermore, the two-dimensional convolutional neural network (2DCNN) is used to extract the image features in the frequency domain. The adaptive multi-feature fusion module uses an attention mechanism for adaptive weighted fusion to make full use of the learned features. The effectiveness of the proposed method is validated on the ShipEar dataset, and the recognition accuracy reaches 94.92%, which is higher than other existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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AMHF-TP:Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期13卷 127-141页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’anChina Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’anChina Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anChina School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’anChina

Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization *** address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance *** AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence *** with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP *** comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.

关键词： deep learning hypergraph multifunctional therapeutic peptides multi-granularity hierarchical features

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Dynamic graph-based graph attention network for anomaly detection in industrial multivariate time series data

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Gao, Cong Ma, Hongye Pei, Qingqi Chen, Yanping School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China School of Telecommunications Engineering Xidian University Xi’an710071 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China

For industrial big data, anomaly detection for multivariate time series data is of critical strategic significance. However, the complexity of industrial equipment and production processes, combined with the high dimensionality of production data, makes it challenging for traditional anomaly detection methods to effectively capture the complex interdependencies and dynamic evolutionary relationships among multiple variables. Additionally, issues such as unstable data distributions, variability, and data drift exacerbate the challenges faced by anomaly detection methods in industrial data analysis and decision-making. This study presents a dynamic graph-based graph attention network for anomaly detection in a multivariate time series data (D-GATAD) model, introducing an innovative approach to dynamic graph construction. The proposed method seamlessly integrates node content features with graph topological structure information, enabling adaptive construction of dynamic graphs based on the current sensor network structure. This design allows for precise modeling of the complex temporal dependencies between variables. Furthermore, the method incorporates an optimized prediction-based model design that organically combines embedding vectors with node data, thereby significantly enhancing the interpretability of the analytical results. Experimental evaluations demonstrate that the proposed method outperforms existing state-of-the-art models across multiple public benchmark datasets. Notably, on the highly complex WADI dataset, it achieves a 5.12% improvement in the AUC score, underscoring its robustness and effectiveness in industrial anomaly detection. This research offers an innovative and widely applicable solution for industrial data analysis and anomaly detection, with significant implications for practical deployment. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Graph neural networks

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Glare-SNet: Unsupervised Glare Suppression Balance network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Edge-Cloud Cooperation-Driven intelligent Sustainability Evaluation Strategy Based on IoT and CPS for Energy-Intensive Manufacturing Industries

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IEEE Internet of Things Journal 2025年第9期12卷 12287-12297页

作者： Ma, Shuaiyin Huang, Yuming Chen, Yanping Xiao, Qinge Xu, Jun Leng, Jiewu Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing School of Computer Science and Technology Xi’an710121 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Xidian University Advanced Manufacturing Technology Innovation Center Guangzhou Institute of Technology Guangzhou510555 China Guangdong University of Technology Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangzhou510006 China

The advancement of the Industry 5.0 in information technology has led to increased interest in integrating edge-cloud cooperation with Internet of Things (IoT) and cyber-physical system (CPS) designs. This integration effectively reduces service delays and provides real-time analysis feedback to physical spaces, attracting attention from both academia and industry. These advanced technologies enhance production system intelligence, their alignment with circular economy principles for promoting sustainability has been overlooked. To address this gap, this article proposes an intelligent sustainability evaluation strategy driven by edge-cloud cooperation, IoT, and CPS. The proposed approach aims to enhance production sustainability and intelligence through circular economy perspectives. It introduces improved gray relation analysis and deep clustering network techniques to extract meaningful insights from diverse indicators within the evaluation system. By analyzing relationships between different equipment and workshops, it provides an analytical method that enhances production efficiency while reducing energy consumption and resource waste. To further validate the proposed method, an illustrative example using a partner company’s production data demonstrates its accuracy. © 2014 IEEE.

关键词： Circular economy

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Medical Image Segmentation Based on 3d Pdc with Swin Transformer

SSRN

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SSRN 2025年

作者： Fan, Lin Ding, Xiaojia Wang, Zhongmin Wang, Hai Zhang, Rong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Information Science and Technology Northwest University Shaanxi Xi’an710121 China

3D medical image segmentation is vital for disease diagnosis and effective treatment strategies. Despite the advancements in Convolutional Neural networks (CNN), their fixed receptive fields constrain global context modeling, leading to suboptimal performance, particularly with complex shapes and multi-scale variations. Transformer enhances the global modeling capability through self-attention mechanism. However, challenges persist in capturing fine-grained local features and extracting edge features. To overcome these issues, In this study, a dual-path encoder is proposed, which consists of a 3D PDC and a Swin Transformer and is combined with a CNN for the decoder. The 3D PDC module extracts local features by calculating the differences between neighboring pixels. To improve the ability of capturing edge structures, three 3D PDC variants (3D CPDC, 3D APDC, and 3D RPDC) are proposed to optimize the processing ability of complex edges, multi-directional edges, and multi-scale structures, respectively. After performance evaluation, the selected CARV combinations (3D CPDC, 3D APDC, 3D RPDC, and 3D ordinary convolution) achieve a compromise between high segmentation accuracy and low computational cost. The Swin Transformer encoder captures global context using a hierarchical shift-window mechanism, while the CNN decoder fuses features progressively to produce precise pixel-level segmentation. The proposed method exceeds the performance of existing state-of-the-art techniques, as shown by experiments conducted on the BTCV, FLARE21, and AMOS22 datasets. © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Spatial-Temporal Semantic Feature Interaction network for Semantic Change Detection in Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Zhang, Yuhang Zhang, Wuxia Ding, Songtao Wu, Siyuan Lu, Xiaoqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an 710121 China Xi'an University of Technology College of Computer Science and Engineering Shaanxi Xi'an 710048 China Fuzhou University College of Physics and Information Engineering Fuzhou 350002 China

Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU) corresponding to changed areas in RSI. The “from-to” information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multi-level features, leading to challenges in extracting LCLU features in complex changing scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multi-Level Feature Interaction module (TML-FI), and a Multi-Level Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multi-level features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multi-scale features, thus improving the model's capability to interpret complex changing scenes. Finally, ML-FFD integrates the multi-level features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods. © 2008-2012 IEEE.

关键词： Multiscale analysis Remote sensing Semantic change detection Transformer

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Multimodal Sentiment analysis of Graphic Texts Based on Multicategorical Relative Fusion 6

Multimodal Sentiment Analysis of Graphic Texts Based on Mult...

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Zhai, Sheping Gao, Mingyang Yang, Rui Kang, Xinnian Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Unlike the traditional unimodal sentiment analysis, multimodal sentiment analysis can jointly process features between different modalities. Existing multimodal sentiment analysis methods simply extract features from text and images ignoring the influence of emoticons that may be contained in text and scene information in images on sentiment polarity judgment. At the same time, the unimodal feature fusion stage does not fully fuse the features of each modality resulting in low accuracy of sentiment analysis. Therefore, this paper proposes a multimodal sentiment analysis model based on multicategorical relative fusion of images and texts. which separates text content and emoji in the word embedding stage in the feature extraction stage, the traditional emoji sentiment dictionary is queried to obtain their sentiment scores and attention mechanism is introduced to extract scene features and object features of images respectively;in the feature fusion stage, the emoji sentiment scores. mixed image. The final multimodal features are obtained by fusion the emoji sentiment scores. mixed image features and text features with layer normalization. The experiments on the dataset Yelp show that the improvement of the model in the feature extraction and feature fusion stages effectively improves the accuracy and F1 value of multimodal sentiment analysis in which the accuracy increases by 4.7 percentage points on the New York dataset and the F1 value reaches 85.7% on the San Francisco dataset. © 2023 ACM.

关键词： Sentiment analysis

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Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

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Computers, Materials & Continua 2023年第4期75卷 2101-2117页

作者： Yanping Chen Xuyang Bai Xiaomin Jin Zhongmin Wang Fengwei Wang Li Ling School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anShaanxi710121China ZTE Corporation Shenzhen51805China

Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.

关键词： Edge computing task scheduling blockchain task caching industrial security

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Application study of oil pipe entity recognition combined with Deep Reinforcement Learning: Named Entity Recognition combined with Deep Reinforcement Learning 6

Application study of oil pipe entity recognition combined wi...

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Guan, Wei Liu, Shijie Ma, Li Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9798400707674

Aiming at the problems of inaccurate extraction of entity feature information and mislabeling of recognition results in the construction of knowledge graphs in the field of oil pipe failure using traditional methods, this paper proposes a research method of BERT-BiLSTM-CRF named entity recognition combined with deep reinforcement learning. The method firstly uses the BERT (Bidirectional encoder representations from transformers) pre-training model to obtain the word vectors of the input sequence semantics;secondly, the trained word vectors are fed into the bi-directional long short- term memory network (BiLSTM) to further obtain contextual features and output the maximum probability sequence annotation results according to the labeling rules of conditional random fields (CRF) and sequence decoding ability;finally, the BERT-BiLSTM-CRF model is supplemented with the deep reinforcement learning-based labeling Finally, the BERT-BiLSTM-CRF-DRL model is added to the BERT-BiLSTM-CRF model to form the framework of named entity recognition model in oil pipe failure domain. The model is compared with other 2 named entity recognition models on a self-built oil pipe data-set, and the experimental results show that the accuracy, recall and F1 values of the model reach 90.5%, 92.7% and 91.9%, respectively, and the entity recognition is better than the other 2 models. © 2023 ACM.

关键词： Long short-term memory

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