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An ε-domination based two-archive 2 algorithm for many-objective optimization

Journal of Systems engineering and Electronics 2022年第1期33卷 156-169页

作者： WU Tianwei AN Siguang HAN Jianqiang SHENTU Nanying College of Mechanical and Electrical Engineering China Jiliang UniversityHangzhou 310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jilang UniversityHangzhou 310018China

The two-archive 2 algorithm(Two_Arch2) is a manyobjective evolutionary algorithm for balancing the convergence,diversity,and complexity using diversity archive(DA) and convergence archive(CA).However,the individuals in DA are selected based on the traditional Pareto dominance which decreases the selection pressure in the high-dimensional *** traditional algorithm even cannot converge due to the weak selection ***,Two_Arch2 adopts DA as the output of the algorithm which is hard to maintain diversity and coverage of the final solutions synchronously and increase the complexity of the *** increase the evolutionary pressure of the algorithm and improve distribution and convergence of the final solutions,an ε-domination based Two_Arch2 algorithm(ε-Two_Arch2) for many-objective problems(MaOPs) is proposed in this *** ε-Two_Arch2,to decrease the computational complexity and speed up the convergence,a novel evolutionary framework with a fast update strategy is proposed;to increase the selection pressure,ε-domination is assigned to update the individuals in DA;to guarantee the uniform distribution of the solution,a boundary protection strategy based on I_(ε+) indicator is designated as two steps selection strategies to update individuals in *** evaluate the performance of the proposed algorithm,a series of benchmark functions with different numbers of objectives is *** results demonstrate that the proposed method is competitive with the state-of-the-art multi-objective evolutionary algorithms and the efficiency of the algorithm is significantly improved compared with Two_Arch2.

关键词： many-objective optimization ε-domination boundary protection strategy two-archive algorithm

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First-principles study on the interfacial bonding strength and segregation at Mg/MgZn_(2)matrix interface

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Journal of Magnesium and Alloys 2024年第10期12卷 4053-4062页

作者： Longke Bao Peng Du Shengkun Xi Cuiping Wang Kaihong Zheng Rongpei Shi Guoqiang Xie Xingjun Liu State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhen 518055PR China College of Materials Science and Engineering and Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055PR China College of Materials Science and Engineering Xiamen UniversityXiamen 361005PR China Institute of New Materials Guangdong Academy of SciencesGuangzhou 510650PR China

To understand the interface characteristics between the precipitateβ2'and the Mg matrix,and thus guide the development of new Mg-Zn alloys,we investigated the atomic interface structure,work of adhesion(Wad),and interfacial energy(γ)of Mg(0001)/β2’(MgZn_(2))(0001)interface,as well as the effect of segregation behavior of the introduced transition metal atoms(3d,4d and 5d)on interfacial bonding *** calculated works of adhesion and interfacial energies dementated that the Zn2-terminated MT+HCP configuration is the most stable structure for all considered *** the Zn2-MT+HCP interface as the research object,estimated segregated energies(Eseg)reveal that added transition metal atoms prefer to segregate at Mg-I and Mg-II *** predicted Wad and charge density difference results reveal that the segregation of alloying additives employed may all strengthen Mg(0001)/MgZn_(2)(0001)interface,with the enhancement effect of Os,Re,Tc,W,and Ru at the Mg-II site being the most pronounced.

关键词： Mg/MgZn_(2)interface Work of adhesion Interfacial energy Interface segregation

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Application of a neural network model with multimodal fusion for fluorescence spectroscopy

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Nuclear Science and Techniques 2024年第10期35卷 135-148页

作者： Lin Tang Shuang Zhou Kai-Bo Shi Hong-Tao Shen Lei You College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Vanadium and Titanium Resource Comprehensive Utilization Key Laboratory of Sichuan Province Panzhihua UniversityPanzhihua 617000China Key Laboratory of Interior Layout optimization and Security Institutions of Higher Education of Sichuan ProvinceChengdu Normal UniversityChengdu 611130China

In energy-dispersive X-ray fluorescence spectroscopy,the estimation of the pulse amplitude determines the accuracy of the spectrum *** error generated by the amplitude estimation of the pulse output distorted by the measurement system leads to false peaks in the measured *** eliminate these false peaks and achieve an accurate estimation of the distorted pulse amplitude,a composite neural network model is proposed,which embeds long and short-term memory(LSTM)into the UNet *** UNet network realizes the fusion of pulse sequence features and the LSTM model realizes pulse amplitude *** model is trained using simulated pulse datasets with different amplitudes and distortion *** the pulse height estimation,the average relative error of the trained model on the test set was approximately 0.64%,which is 27.37% lower than that of the traditional trapezoidal shaping *** processing of a standard iron source further validated the pulse height estimation performance of the UNet-LSTM *** estimating the amplitude of the distorted pulses using the model,the false peak area was reduced by approximately 91% over the full spectrum and was corrected to the characteristic peak region of interest(ROI).The corrected peak area accounted for approximately 1.32%of the characteristic peak ROI *** results indicate that the model can accurately estimate the height of distorted pulses and has substantial corrective effects on false peaks.

关键词： UNet Long-and short-term memory Pulse distortion Pulse height estimation Fluorescent spectroscopy

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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Computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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knowledge Enhanced Coreference Resolution via Gated Attention

Knowledge Enhanced Coreference Resolution via Gated Attentio...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： He, Kai Mao, Bing Zhou, Xiangyu Li, Yufei Gong, Tieliang Li, Chen Wu, Jialun Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China

ISBN: (纸本)9781665468190

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge. © 2022 IEEE.

关键词： Model structures

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T2R-GAN: A CGAN-based model for rural thematic road extraction 27th

T2R-GAN: A CGAN-based model for rural thematic road extracti...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ni, Zixiang Zhai, Weixin College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural Affairs Beijing100083 China

ISBN: (纸本)9783031783883

Rural roads extracted from agricultural machinery trajectories have significant research value. Due to the intricate network of rural roads and the large difference in the density of agricultural machinery trajectories, traditional road extraction methods struggle to perform effectively on rural roads with complex topology and on agricultural machinery trajectories with obscure road features. Therefore, this paper proposes a CGAN-based model named T2R-GAN (Trajectory to Road-GAN) for rural thematic road extraction, which learns the trajectory-to-road feature mapping through continuous adversarial training between the ELAU-Net generator and the PatchGAN discriminator to adapt to trajectories of various densities. ELAU-Net is an efficient network that utilizes an encoder-decoder structure and ELA modules to enhance the capture of obscure road features between sparse trajectories. To enhance model performance and reduce the risk of overfitting, bilateral hinge loss is designed to train our model to enhances the discriminator’s discriminative ability to facilitate the generator to generate more realistic roads improve the generalization of the model. To verify the effectiveness of T2R-GAN in extracting roads from agricultural trajectory data, this paper selects the real agricultural trajectory data from Henan Province, China in June 2021 as the dataset for verification. The experimental results show that the proposed method achieves 79.23% F1score, which is 5.53% higher than the previous state-of-art. The proposed T2R-GAN provides a novel and effective approach for extracting rural thematic roads from agricultural machinery trajectories for the first time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Phase spectrum based automatic ship detection in synthetic aperture radar images

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Journal of Ocean engineering and Science 2021年第2期6卷 185-195页

作者： Miaohui Zhang Baojun Qiao Ming Xin Bo Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifengChina Institute of Data and Knowledge Engineering Henan UniversityKaifengChina School of Computer Science and Engineering Beihang UniversityBeijingChina

This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase *** proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea *** order to eliminate the influence of land regions in SAR images,a novel land removing method is *** removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these ***,an appropriate land segmentation threshold is accordingly ***,an automatic ship detector based on phase spectrum is *** proposed detector is free from various idealized assumptions and can accurately detect ships in SAR *** results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.

关键词： Ship detection saliency detection phase spectrum sea-land segmentation

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation 17

Multi-scale cyclical similarity prototype refinement for few...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Ou, Yingfeng Yang, Xing Zhang, Jian Jian, Caiqing Wang, Lihui College of Computer Science and Technology Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data Engineering Research Center of Text Computing Ministry of Education Guiyang China

ISBN: (纸本)9798350387384

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%. © 2024 IEEE.

关键词： Ultrasonic imaging

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IF-Font: Ideographic Description Sequence-Following Font Generation 38

IF-Font: Ideographic Description Sequence-Following Font Gen...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xinping Ke, Xiao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style features of glyphs, combining the content features of the source glyph with the style features of the reference glyph to generate new glyphs. However, the disentanglement is challenging due to the complexity of glyphs, often resulting in glyphs that are influenced by the style of the source glyph and prone to artifacts. We propose IF-Font, a novel paradigm which incorporates Ideographic Description Sequence (IDS) instead of the source glyph to control the semantics of generated glyphs. To achieve this, we quantize the reference glyphs into tokens, and model the token distribution of target glyphs using corresponding IDS and reference tokens. The proposed method excels in synthesizing glyphs with neat and correct strokes, and enables the creation of new glyphs based on provided IDS. Extensive experiments demonstrate that our method greatly outperforms state-of-the-art methods in both one-shot and few-shot settings, particularly when the target styles differ significantly from the training font styles. The code is available at https://***/Stareven233/IF-Font. © 2024 Neural information processing systems foundation. All rights reserved.

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Prescribed Performance-Based Finite-Time Neural Adaptive Backstepping Control for the Chaotic PMSM

Prescribed Performance-Based Finite-Time Neural Adaptive Bac...

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2022 International Conference on Mechanical and Electronics engineering, ICMEE 2022

作者： Wu, Fengbin Zhang, Junxing Li, Shaobo Li, Shaoyang Wu, Xiao Wang, Shuai College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Mechanical Engineering Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Mechanical Engineering Guizhou University Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9781665492201

This paper presents a prescribed performance-based finite-time neural adaptive backstepping control scheme for the chaotic permanent magnet synchronous motor (PMSM). Specifically, an error transformation coupled with a prescribed performance function is introduced to guarantee that the tracking error keeps within a defined bound. The finite-time stability theory and backstepping framework are further combined to design finite-time adaptive laws and controllers. Then, it is shown that all signals are ultimately bounded in finite time and the tracking error can converge to a defined region in finite time. Finally, simulation results are presented to verify the feasibility of the proposed controller. © 2022 IEEE.

关键词： Backstepping

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