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Hybrid particle swarm optimization with adaptive learning strategy

Soft Computing 2024年第17-18期28卷 9759-9784页

作者： Wang, Lanyu Tian, Dongping Gou, Xiaorui Shi, Zhongzhi School of Mathematics and Information Sciences Baoji University of Arts and Science Shaanxi Baoji721007 China Institute of Computer Software Baoji University of Arts and Sciences Shaanxi Baoji721007 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Particle swarm optimization (PSO) is a meta-heuristic algorithm that can be used to solve different real-world optimization problems. However, PSO suffers from some intrinsic drawbacks, including falling into local optimal and slow convergence in the latter evolution process. To deal with these issues, a hybrid PSO with adaptive learning strategy (abbreviated as HPSO-ALS) is proposed in this paper. To start with, the chaotic opposition-learning method is employed to generate the uniformly distributed particles so as to enhance the quality of the initial swarm. Next, a self-logical mapping-based nonlinear decreasing inertia weight is formulated to trade off the global search and local search abilities. Meanwhile, different adaptive position updating strategies are leveraged to achieve better convergence according to different evolution status. In addition, inspired by the cask effect, the global worst particle (gworst) is introduced into the velocity updating to make full use of the potential information of the particles. Finally, extensive experiments on sixteen benchmark functions and CEC’13 test suite have verified the promising performance of HPSO-ALS proposed in this paper. Alternatively, HPSO-ALS is also applied to the classical traveling salesman problem (TSP) and the experimental results have further validated the effectiveness and efficiency of our proposal. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Traveling salesman problem

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Evidential Open Set Recognition for Imbalanced Medical Images via Multi-level Data Augmentation

Evidential Open Set Recognition for Imbalanced Medical Image...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Rui-Wei Xu, Yiqian Zhang, Ying Feng, Rui Shanghai Maritime University College of Information Engineering China Fudan University Academy for Engineering and Technology China Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9798350386226

Due to the exstence of common and rare diseases, the complex clinical scenario often poses the challenging class imbalanced open set recognition problem. Unfortunately, most existing approaches are ill-suited for such situations with limited and imbalanced data during training and the possibility of encountering unseen classes during test. In this work, we propose a novel Multi-level mixup-based Evidential Open Set Recognition (ME-OSR) approach to more explicitly and effectively address the open set recognition for class-imbalanced medical images. Briefly, we first extract disentangled discriminative and background image features. Then, based on the original images and extracted features, we propose to sample and conduct Multi-level Open Mixup (MOM) for a more balanced open set data augmentation. It includes extensive intra- and interclass mixup operations in both image and feature spaces, which can augment rare classes with different feature combinations and generate potential pseudo-unknown class examples in the open set to boost the model training. Based on the augmented data and their extracted discriminative features, we propose a Regularized Evidential Deep Learning (REDL) classifier to work with the augmented data to achieve open set recognition with prediction uncertainty estimation and unknown example rejection. Through comparative experiments and ablation studies on several representative medical datasets, we showed that our proposed method outperforms other state-of-the-arts on four popular medical OSR datasets. © 2024 IEEE.

关键词： Medical imaging

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Meibomian glands segmentation in infrared images with limited annotation

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International Journal of Ophthalmology(English edition) 2024年第3期17卷 401-407页

作者： Jia-Wen Lin Ling-Jie Lin Feng Lu Tai-Chen Lai Jing Zou Lin-Ling Guo Zhi-Ming Lin Li Li College of Computer and Data Science Fuzhou UniversityFuzhou 350108Fujian ProvinceChina Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou UniversityFuzhou 350108Fujian ProvinceChina Shengli Clinical Medical College of Fujian Medical University Fuzhou 350001Fujian ProvinceChina Department of Ophthalmology Fujian Provincial Hospital South BranchFujian Provincial HospitalFuzhou 350001Fujian ProvinceChina

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble *** viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,***,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for *** model maintains an exceptionally high level of segmentation accuracy while substantially reducing training *** holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

关键词： infrared meibomian glands images meibomian gland dysfunction meibomian glands segmentation weak supervision scribbled annotation

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Reduced State DRE for Clipping and Quantization Noise Elimination in Optical Interconnection with Low-resolution DAC 49

Reduced State DRE for Clipping and Quantization Noise Elimin...

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49th European Conference on Optical Communications, ECOC 2023

作者： Yin, Mingzhu Ni, Weihao Li, Fan Yi, Xingwen Li, Zhaohui Lu, Chao School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Sun Yat-Sen University Guangzhou510275 China Zhuhai519000 China

ISBN: (纸本)9781839539954

Reduced state DRE for clipping-And-quantization noise elimination is studied with 3-bit DAC. Experiment-results indicate that 99.2% computational complexity reduction can be achieved for 40Gbaud PAM-8 signal, maintaining below 0.2dB ROP penalty at the 1×10-2 threshold, using 5-Taps CIR-supported DRE with 5 soft-quantization possibilities. © The Institution of Engineering & technology 2023.

关键词： Optical interconnects

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Prediction of GDP Carbon Emission Based on Grey Model and Neural Network 4th

Prediction of GDP Carbon Emission Based on Grey Model and Ne...

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4th EAI International Conference on Robotic Sensor Networks, 2020

作者： Zhang, Feng Zhao, Huihuang Ramasamy, Manimaran College of Computer Science and Technology Hengyang Normal University Hengyang China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China

ISBN: (纸本)9783030704506

In order to improve the prediction accuracy of Gross Domestic Product (GDP) carbon emission, combining the advantages of grey prediction and neural network, a discrete grey neural network prediction model named DGMBP is established. This model not only has the advantages of less data sample and larger development coefficient range of GM 1,1 model but also combines the advantages of neural network to deal with nonlinear data sample. Taking the GDP carbon emission data of China, India, the United States and the European Union as an example, the prediction results obtained are stable, and the accuracy of the prediction data is over 95%, which is much higher than that obtained by the linear regression equation and the logical regression equation. Finally, the carbon emission of GDP in 2020–2030 is predicted by using this model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Shapley Value-based Contrastive Alignment for Multimodal information Extraction 24

Shapley Value-based Contrastive Alignment for Multimodal Inf...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Luo, Wen Xia, Yu Tianshu, Shen Li, Sujian State Key Laboratory of Multimedia Information Processing Peking University Beijing China School of Computer Science Peking University Beijing China Wangxuan Institute of Computer Technology Peking University Beijing China

ISBN: (纸本)9798400706868

The rise of social media and the exponential growth of multimodal communication necessitates advanced techniques for Multimodal information Extraction (MIE). However, existing methodologies primarily rely on direct Image-Text interactions, a paradigm that often faces significant challenges due to semantic and modality gaps between images and text. In this paper, we introduce a new paradigm of Image-Context-Text interaction, where large multimodal models (LMMs) are utilized to generate descriptive textual context to bridge these gaps. In line with this paradigm, we propose a novel Shapley Value-based Contrastive Alignment (Shap-CA) method, which aligns both context-text and context-image pairs. Shap-CA initially applies the Shapley value concept from cooperative game theory to assess the individual contribution of each element in the set of contexts, texts and images towards total semantic and modality overlaps. Following this quantitative evaluation, a contrastive learning strategy is employed to enhance the interactive contribution within context-text/image pairs, while minimizing the influence across these pairs. Furthermore, we design an adaptive fusion module for selective cross-modal fusion. Extensive experiments across four MIE datasets demonstrate that our method significantly outperforms existing state-of-the-art methods. © 2024 ACM.

关键词： Contrastive Learning

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Note on a New Construction of Kantorovich Form q-Bernstein Operators Related to Shape Parameter λ

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computer Modeling in Engineering & Sciences 2022年第3期130卷 1479-1493页

作者： Qingbo Cai Resat Aslan Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingSchool of Mathematics and Computer ScienceQuanzhou Normal UniversityQuanzhou362000China Department of Mathematics Faculty of Sciences and ArtsHarran UniversitySanlıurfa63300Turkey

The main purpose of this paper is to introduce some approximation properties of a Kantorovich kind q-Bernstein operators related to B′ezier basis functions with shape parameterλ∈[−1,1].Firstly,we compute some basic results such as moments and central moments,and derive the Korovkin type approximation theorem for these ***,we estimate the order of convergence in terms of the usual modulus of continuity,for the functions belong to Lipschitz-type class and Peetre’s K-functional,***,with the aid of Maple software,we present the comparison of the convergence of these newly defined operators to the certain function with some graphical illustrations and error estimation table.

关键词： q-calculus (λ,q)-Bernstein polynomials order of convergence Lipschitz-type function Peetre’s K-functional

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Neural Network-based Pre-Distorter with Long Memory Length for PAM-8 IM/DD Transmission

Neural Network-based Pre-Distorter with Long Memory Length f...

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Chen, Zhiwei Wang, Wei Zou, Dongdong Ni, Weihao Deng, Mincong Li, Fan Sun Yat-Sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Guangzhou510275 China Zhuhai519000 China

Neural network-assisted pre-distortion method is proposed to generate pattern errors with memory length of more than 5 symbols in PAM-8 IM/DD transmission utilizing limited samples as training sequence, which enables ... 详细信息

ISBN: (纸本)9781665462136

关键词： Pulse amplitude modulation

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Low Power Nonlinear MLSE with Optimized Transition Reservation Mechanism and LUT in Trellis for PAM-4 IM/DD System

Low Power Nonlinear MLSE with Optimized Transition Reservati...

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Ni, Weihao Wang, Wei Chen, Zhiwei Li, Fan Li, Zhaohui Sun Yat-Sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Guangzhou510275 China Zhuhai519000 China

A low power nonlinear MLSE is proposed, which can compress the transitions in the trellis adaptively. Compared to LUT aided conventional MLSE, up to 90% power consumption can be reduced with neglectable receiver sensi... 详细信息

ISBN: (纸本)9781665462136

关键词： Pulse amplitude modulation

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Noise-tolerant fixed-time leader-follower consensus controller design for multi-agent systems via fuzzy-neural-network

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Neural Computing and Applications 2025年 1-23页

作者： Dai, Jianhua Tan, Ping Xiao, Lin Wang, Zidong He, Yongjun Zuo, Qiuyue Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing MOE-LCSM Hunan Normal University Hunan Changsha410081 China Department of Computer Science Brunel University London UxbridgeUB8 3PH United Kingdom

The leader-follower consensus control problem in multi-agent systems (MASs) is critical and has received significant attention. However, the simultaneous achievement of fixed-time stability and robustness is often challenging in MASs due to their inherent complexity and uncertainty. This paper developed a controller based on the proposed noise-tolerant fixed-time fuzzy neural network (NF-FNN) model to realize the leader-follower consensus of MASs. Specifically, the introduction of an integral error term makes the NF-FNN model have powerful noise tolerance, and a fuzzy gain parameter generated by the Takagi-Sugeno fuzzy logic system makes the NF-FNN model have fuzzy adaptiveness. In addition, a new partition-sign-by-power activation function is developed to ensure fixed-time stability of the NF-FNN model. Theoretical analysis and comparative simulations confirm the superb swift stability and excellent noise tolerance of controllers based on the NF-FNN model for achieving the leader-follower consensus of MASs, as compared with existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Fuzzy neural networks

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