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Multi-objective configuration optimization method based on antlion algorithm for customer-oriented modular process

Journal of intelligent and Fuzzy systems 2024年第3-4期47卷 265-277页

作者： Zhang, Qianqian Fan, Bo Sun, Lifan Information Engineering College Henan University of Science and Technology Luoyang China Henan Key Laboratory of Robot and Intelligent Systems Henan University of Science and Technology Luoyang China

Process parameter configuration needs to respond quickly in the customized manufacturing environment. A multi-objective optimization method based on antlion algorithm for product process configuration design is proposed. The weight mapping mechanism of customer demands is established to clarify the relationship between customer demands and manufacturing process, which also simplifies the process attribute analysis of customized products. The parameters to be optimized in the customization process are analyzed: product performance, process cost and production time. Based on the objective function including these parameters, a multi-objective optimization model of modular process reconstruction is established. The proposed model and algorithm can optimize these parameters in parallel and realize the rapid configuration of product modular process. In the application case of this paper, the proposed method is suitable for the whole process modular configuration of copper plate and strip. The feasibility and effectiveness of the model and algorithm in the proposed configuration scheme are verified. © 2024-IOS Press. All rights reserved.

关键词： Multiobjective optimization

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A text dependent copy-paste plagiarism and text-rewriting plagiarism model

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Journal of intelligent and Fuzzy systems 2025年第6期48卷 849-858页

作者： Guo, Qingkai Huang, Guimin Wang, Yabing Zhou, Ya School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin China

Plagiarism is common in English writing exams. Researchers classify plagiarism into copy-paste and text-rewriting plagiarism, but existing models need help with problems such as the single way of checking and unsatisfactory results. Aiming at the copy-paste problem in English writing plagiarism, this paper proposes a digital fingerprint model based on the N-Gram window jumping mechanism. The model incorporates a sliding window and an improved matching tool to solve the problems of excessive fingerprint density and low checking efficiency in text extraction. Meanwhile, the model adds a Fisher-Yates shuffle algorithm with a salt parameter to crack the hash collision in text matching. The experiments show that the model can detect copy-paste plagiarism in English composition. For text rewriting plagiarism, this paper designs a TextCNN-BiGRU-based model, which combines TextCNN and BiGRU so that the extracted text semantic information considers the text’s local and contextual features. The experiments show that the model improves the accuracy by 1.9 percentage points and the F1 value by 1.2 percentage points on the MRPC dataset compared with other models. © 2025 – IOS Press. All rights reserved.

关键词： copy and paste Plagiarism checking text rewriting

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Efficient On-board Remote Sensing Scene Classification Using FPGA With Ternary Weight 2

Efficient On-board Remote Sensing Scene Classification Using...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Qi, Guijie Qiao, Tingting Yu, Jingchi Xie, Yizhuang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Remote Sensing Scene Classification (RSSC) is essential for applications such as environmental monitoring and catastrophe management, which often have stringent time constraints requiring real-time processing. On-board processing significantly enhances real-time performance but is challenged by limited computational resources and strict power requirements. To address these challenges, this study proposes an FPGA-based accelerator designed for Ternary Weight Networks (TWNs), which use ternary values {+1, 0, -1} for weights. By adopting TWNs, the multiplication operations in convolution are eliminated, resulting in a significant reduction in computing and power needs. Experimental results show that TWNs significantly reduce network parameters while retaining classification accuracy comparable to Full-precision Weight Networks (FPWNs). The FPGA-based accelerator achieves an energy-efficiency ratio of 434.11 GOP/W, outperforming most existing CNN accelerators, hence meeting the requirements for on-board RSSC. © 2024 IEEE.

关键词： Image thinning

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Advanced Scale-aware Attention for Enhanced Tiny Object Detection in Remote Sensing 2

Advanced Scale-aware Attention for Enhanced Tiny Object Dete...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Qiao, Tingting Chen, He Chai, Bingqian Liu, Wenchao Qi, Guijie Xie, Yizhuang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Recent advancements in remote sensing object detection have progressed, but distinguishing objects from complex backgrounds remains difficult, especially for tiny objects. To address this, we introduce the Advanced Scale-Aware Attention (ASA) module, which retains key channel information while minimizing computational cost. This module reorganizes channels into the batch dimension and splits them into multiple sub-features. The ASA module contains three branches: the first encodes weights based on scale and channel relationships. The second and third branches use different convolution kernel sizes to capture features across various receptive fields. These branches collaborate to enhance pixel-level relationships, improving foreground-background contrast. On the AI-TOD dataset, our method improved AP50 by 8.6% over state-of-the-art approaches. © 2024 IEEE.

关键词： Object recognition

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SC-DVNet: A Dual-Branch Recognition Network for Synthetic Aperture Radar Images Based on Scattering Features 2

SC-DVNet: A Dual-Branch Recognition Network for Synthetic Ap...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Chen, Fan Shi, Hao He, Jingfei Yin, Yifei Meng, Fanyu Chen, Liang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Ship classification using Synthetic Aperture Radar (SAR) images is a crucial component in marine monitoring. With the rise of deep neural networks (DNN), the abstract feature maps obtained by CNN-based methods have significantly improved classification performance. However, most current DNN-based methods are predominantly inherited from optical imagery, designed only for SAR intensity images, which may present challenges for further improving recognition performance. Therefore, this paper proposes using the point Scattering Center (SC) model for scattering feature extraction combined with graph convolutional networks for ship classification tasks. Comprehensive ablation experiments were carried out to demonstrate the efficacy of the proposed approach. Finally, experimental results on OpenSARShip1.0 dataset verify that the performance of the suggested approach notably outperforms DNN-driven techniques. © 2024 IEEE.

关键词： Deep neural networks

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Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

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Chinese Journal of Electronics 2024年第3期33卷 814-822页

作者： Junqi LIU Tao WEN Guo XIE Yuan CAO Clive Roberts Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology The School of Electronic and Information Engineering Beijing Jiaotong University Birmingham Centre for Railway Research and Education University of Birmingham

The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and *** have studies considered diverse noises along the *** fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple *** decomposes condition monitoring signals after coarse-grained processing in varying *** this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single *** the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the *** adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关键词： Railway point machines Fault diagnosis Multi-time-scale Variational mode decomposition

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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Audio-guided self-supervised learning for disentangled visual speech representations

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Frontiers of Computer Science 2024年第6期18卷 277-279页

作者： Dalu FENG Shuang YANG Shiguang SHAN Xilin CHEN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

1 *** visual speech representations from talking face videos is an important problem for several speech-related tasks,such as lip reading,talking face generation,and audiovisual speech separation[1,2].The key difficulty lies in tackling speech-irrelevant factors presented in the videos,such as lighting,resolution,viewpoints,and head motion.

关键词： such dis visual

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Selective Visual Relationship Detection with Adaptive Context Learning 17

Selective Visual Relationship Detection with Adaptive Contex...

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17th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Tu, Jiangwei Liu, Feng Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing China

ISBN: (纸本)9798331507398

Visual relation detection aims to describe the relationships between objects in a scene by using the form of a triplet . Existing methods not only suffer from the huge number of combinations of triples, but also make the context of most of the object pairs in a scene non-discriminative due to simply using the union box of the object pair. In this work, we propose a novel two-stage network. In the first stage, the multi-modal feature fusion strategy is used to complete the correlation detection of object pairs, which reduces the number of triples and enables the model to selectively focus on the relationship between strongly correlated objects in the scene. The object-directed context module (OCM) and global message passing (GMP) introduced in the second stage to adaptively learning the higher-order context of each object pair in the scenario. We evaluate our method on two widely used datasets: Visual Relationship Detection (VRD) and Visual Genome(VG) datasets. Experimental results verify the effectiveness of our method, e.g., the top 100 phrases detection recall improves from 40.01% to 41.21% on the VRD dataset. © 2024 IEEE.

关键词： Contrastive Learning

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DBST:a lightweight block cipher based on dynamic S-box

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Frontiers of Computer Science 2023年第3期17卷 177-185页

作者： Liuyan YAN Lang LI Ying GUO College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China

IoT devices have been widely used with the advent of *** devices contain a large amount of private data during *** is primely important for ensuring their ***,we proposed a lightweight block cipher based on dynamic S-box named *** is introduced for devices with limited hardware resources and high throughput *** is a 128-bit block cipher supporting 64-bit key,which is based on a new generalized Feistel variant *** retains the consistency and significantly boosts the diffusion of the traditional Feistel *** SubColumns of round function is implemented by combining bit-slice technology with *** S-box is dynamically associated with the *** has been demonstrated that DBST has a good avalanche effect,low hardware area,and high *** S-box has been proven to have fewer differential features than RECTANGLE *** security analysis of DBST reveals that it can against impossible differential attack,differential attack,linear attack,and other types of attacks.

关键词： internet of things 5G dynamic S-box bit-slice technology lightweight block cipher

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