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Unsupervised feature selection based on generalized regression model with linear discriminant constraints

COMPLEX & intelligent SYSTEMS 2025年第6期11卷 1-11页

作者： Dai, Xiangguang Guan, Mingyu Dai, Facheng Zhang, Wei Zhang, Tingji Che, Hangjun Dai, Xiangqin Chongqing Three Gorges Univ Chongqing Engn Res Ctr Internet Things & Intellige Wanzhou 404100 Peoples R China Coll Elect & Informat Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Unsupervised feature selection (UFS) methods play a crucial role in improving the efficiency of extracting relevant information and reducing computational complexity in the context of big data analysis. Despite notable advancements in the field of unsupervised feature selection for large-scale datasets, many UFS methods still remain redundant and irrelevant features during the feature selection process. To tackle these challenges, we present a novel unsupervised feature selection method that leverages the generalized regression model with linear discriminant constraints to learn discriminant and effective features from the data. Benefited from this, the relationships and patterns within the high-dimensional data are retained in the reduced-dimensional feature space. We reformulate our proposed method as a multi-variable optimization problem that incorporates equality constraints. To efficiently solve this problem, we develop an algorithm that updates each variable alternately. Extensive experiments on six datasets among nine state-of-the-art methods on the clustering task are conducted to demonstrate the effectiveness of the proposed method.

关键词： Unsupervised feature selection Generalized regression model Linear discriminant constraints

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Design of a high-gain N-path filter with harmonic rejection

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ANALOG INTEGRATED circuits AND SIGNAL PROCESSING 2025年第2期123卷 1-11页

作者： Song, Shuxiang Liu, Changping Jiang, Pinqun Cen, Mingcan Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain inspired Comp & Intelligent Guilin Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Integrated Circuits & Microsyst Guilin Peoples R China

This paper presents an enhanced N-path filter architecture addressing the fundamental limitations of conventional designs in wireless communication systems. The proposed solution tackles two critical challenges: insufficient harmonic rejection and signal loss. By integrating a high-gain low-noise amplifier (LNA) at the input stage and implementing a novel transconductance amplifier-based weighted sinusoidal signal fitting technique, the filter achieves superior performance metrics. Implemented in SMIC 180 nm CMOS technology, the design demonstrates frequency tunability from 800 MHz to 1.2 GHz. Simulation results show third and fifth harmonic rejection ratios of 61 dB and 67 dB, respectively. The filter exhibits a gain of 21 dB with a noise figure of 8-9 dB and an IIP3 of -\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}6.2 dBm. These results represent a significant advancement over traditional N-path filter implementations, offering promising prospects for practical wireless applications.

关键词： N-path filter Harmonic rejection Low noise amplifier

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Robust Mixed-order Graph Learning for incomplete multi-view clustering

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informatION FUSION 2025年 115卷

作者： Guo, Wei Che, Hangjun Leung, Man-Fai Jin, Long Wen, Shiping Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Anglia Ruskin Univ Fac Sci & Engn Cambridge CB1 1PT England Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia

Incomplete multi-view clustering (IMVC) aims to address the clustering problem of multi-view data with partially missing samples and has received widespread attention in recent years. Most existing IMVC methods still have the following issues that require to be further addressed. They focus solely on the first-order correlation information among samples, neglecting the more intricate high-order connections. Additionally, these methods always overlook the noise or inaccuracies in the self-representation matrix. To address above issues, a novel method named Robust Mixed-order Graph Learning (RMoGL) is proposed for IMVC. Specifically, to enhance the robustness to noise, the self-representation matrices are separated into clean graphs and noise graphs. To capture complex high-order relationships among samples, the dynamic high-order similarity graphs are innovatively constructed from the recovered data. The clean graphs are endowed with mixed-order information and tend towards to obtain a consensus graph via a self-weighted manner. An efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the proposed RMoGL, and superior performance is demonstrated by compared with nine state-of-the-art methods across eight datasets. The source code of this work is available at https://***/guowei1314/RMoGL.

关键词： Incomplete multi-view clustering High-order relationships Similarity matrix Consensus graph Self-weighted manner

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A Formal Scheme of Fault Injection on Coherent Integrated Photonic Neural Networks

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 2025年第3期31卷

作者： Su, Ye Jiang, Xiao Xu, Fang Ye, Yichen Chen, Zhuang Lu, Simi Liu, Weichen Xie, Yiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Inst Engn Sch Comp & Internet Things Chongqing 400056 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639815 Singapore Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Chin Myanmar Chongqing Key Lab Nonlinear Circuits & intelligent Chongqing 400715 Peoples R China Univ Chongqing Key Lab Networks & Cloud Comp Secur Chongqing 400715 Peoples R China

Based on Mach-Zehnder interferometers (MZIs) coherent integrated photonic neural networks (PNNs) may provide a promising solution for the realization of deep learning with low power consumption, low latency, and ultra-high speed. Adversarial attacks have been widely confirmed to be a serious threat to deep learning. This has led to a large amount of studies in this direction of the electronic domain, including input attacks and inject faults for weights. In this paper, focusing on the phases in the linear operation unit of PNNs, a phase gradient attack (PGA) scheme based on the phase gradient sorting of the MZI-arrays and injecting disturbances along the gradient direction is proposed for the first time. The simulation results indicate that even with weak-intensity PGA, it is almost impossible for PNNs to perform the classification inference. Furthermore, taking into account the effects of fabrication-process variations (FPV) and thermal crosstalk in MZI-arrays that lead to tuning phase deviation in practical application, we systematically analyzed the validity of proposed scheme on the PNNs with phase uncertainties. Specifically, we tested the impact of injecting faults by compressing the number of attacked phase angles to 3, 5, and 7, respectively. The experiment results show that injection attack based using PGA on PNNs trained with Gaussian datasets would reduce classification accuracy to 27.97%, 15.47%, and 8.91% for corresponding cases.

关键词： Matrix decomposition Electronics packaging Circuit faults Photonics Phase shifters Optical network units Optical interferometry Training Optical modulation Optical imaging Mach-Zehnder interferometer (MZI) coherent integrated photonic neural networks phases uncertainty phase gradient attack (PGA)

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HYBRID ALGORITHM FOR INDUSTRIAL MICRO-GRID DEMAND RESPONSE WITH ASSEMBLY PROCESS BASED ON DISTRIBUTIONALLY ROBUST CHANCE CONSTRAINT

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第1期21卷 613-635页

作者： Wu, Qian Song, Qiankun He, Xing Chongqing Jiaotong Univ Sch Econ & Management Chongqing 400074 Peoples R China Chongqing Jiaotong Univ Sch Math & Stat Chongqing 400074 Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Sch Elect & Informat Engn Chongqing 400715 Peoples R China

This paper considers an industrial isolated micro-grid demand response problem, and introduces a non-convex mixed integer programming formulation that accounts for assembly process, PV uncertainty, reserve of conventional generator, and battery degradation cost. Both constraints on the probability of meeting power demand and minimizing the expected cost of doing so are considered at first. Then, the reformulation based on the Wasserstein metric is used to convert the distributionally robust chance constraint and expected cost into constraints with zero-one variables. Meanwhile, a hybrid algorithm combining improved constrained differential evolution and Benders dual decomposition is proposed, which significantly improves solution quality and computation time when compared with a recently proposed state-of-the-art constrained differential evolution combining generalized Benders decomposition. Finally, simulation studies are conducted using historical data from the Desert Knowledge Australia Centre, shedding some light on the scheduling plan of the manufacturing process and the necessary trade-offs that must be made between power balance and expected cost. The necessity of PVs and batteries for industrial isolated micro-grid is proved, while the superiority of the proposed hybrid algorithm in both solution quality and computation time is visually demonstrated.

关键词： Industrial isolated micro-grid demand response manufacturing assembling process distributionally robust optimization Benders dual decomposition differential evolution

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Auto-Weighted Multi-View Deep Non-Negative Matrix Factorization With Multi-Kernel Learning

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IEEE TRANSACTIONS ON SIGNAL AND informatION PROCESSING OVER NETWORKS 2025年 11卷 23-34页

作者： Yang, Xuanhao Che, Hangjun Leung, Man-Fai Liu, Cheng Wen, Shiping Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Anglia Ruskin Univ Fac Sci & Engn Sch Comp & Informat Sci Cambridge CB1 1PT England Shantou Univ Dept Comp Sci Shantou 515063 Guangdong Peoples R China Univ Technol Sydney Australian Artificial Intelligence Inst Fac Engn & Informat Technol Sydney NSW 2007 Australia

Deep matrix factorization (DMF) has the capability to discover hierarchical structures within raw data by factorizing matrices layer by layer, allowing it to utilize latent information for superior clustering performance. However, DMF-based approaches face limitations when dealing with complex and nonlinear raw data. To address this issue, Auto-weighted Multi-view Deep Nonnegative Matrix Factorization with Multi-kernel Learning (MvMKDNMF) is proposed by incorporating multi-kernel learning into deep nonnegative matrix factorization. Specifically, samples are mapped into the kernel space which is a convex combination of several predefined kernels, free from selecting kernels manually. Furthermore, to preserve the local manifold structure of samples, a graph regularization is embedded in each view and the weights are assigned adaptively to different views. An alternate iteration algorithm is designed to solve the proposed model, and the convergence and computational complexity are also analyzed. Comparative experiments are conducted across nine multi-view datasets against seven state-of-the-art clustering methods showing the superior performances of the proposed MvMKDNMF.

关键词： Kernel Data models Matrix decomposition Vectors Manifolds information processing Clustering algorithms Adaptation models Optimization Computational modeling Multi-view clustering deep matrix factorization multi-kernel learning

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Research on image captioning using dilated convolution ResNet and attention mechanism

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MULTIMEDIA SYSTEMS 2025年第1期31卷 1-13页

作者： Li, Haisheng Yuan, Rongrong Li, Qiuyi Hu, Cong Guangxi Normal Univ Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin 541004 Guangxi Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Nonlinear Circuits & Opt Commun Guilin 541004 Guangxi Peoples R China Guangxi Normal Univ Sch Elect & Informat Engn Guilin 541004 Guangxi Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Guangxi Peoples R China

Image captioning, which refers to generating a textual description of the image content from a given image, has been recognized as a key problem in visual-to-linguistic tasks. In this work, we introduce dilated convolution to increase the perceptual field, which can better capture an image's details and contextual information and extract richer image features. A sparse multilayer perceptron is introduced and combined with an attention mechanism to enhance the extraction of detailed features and attention to essential feature regions, thus improving the network's expressive ability and feature selection. Furthermore, the residual squeeze-and-excitation module is added to help the model better understand the image content, thus improving the accuracy of the image captioning task. However, the main challenge is achieving high accuracy in capturing both local and global image features simultaneously while maintaining model efficiency and reducing computational costs. The experimental results on the Flickr8k and Flickr30k datasets show that our proposed method has improved the generation accuracy and diversity, which can better capture image features and improve the accuracy of generated captions.

关键词： Image captioning Dilated convolution Sparse multilayer perceptron Attention mechanisms

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Linear Convergence of Asynchronous Gradient Push Algorithm for Distributed Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 2147-2159页

作者： Li, Huaqing Cheng, Huqiang Lu, Qingguo Wang, Zheng Huang, Tingwen Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

This article focuses on multiagent distributed asynchronous optimization over directed networks where each agent can only access its individual local function, and the aggregate aim is to minimize the cumulative sum of all local functions. Considering the asynchrony among the agents, we develop an algorithm in which agents compute and communicate individually, without any form of synchronized coordination. Agents perform their local updates by local communication with their immediate neighbors, and this may involve the use of stale information. Since asynchrony naturally leads to latency or packet loss, an asynchronous robust gradient tracking mechanism is developed to guarantee estimating the average of agents' gradients precisely. Moreover, it employs uncoordinated step-sizes which are more flexible and general than constant or decaying step-size. When the global objective is strongly convex and the local objectives have Lipschitz-continuous gradients, we prove that each agent executing the asynchronous algorithm linearly converges to the consensus optimal point at an O(lambda(k)) rate, where lambda is an element of (0, 1) is convergence factor and k represents the iteration number, with a step satisfying a tight explicit upper bound. Numerical experiments demonstrate that our algorithm has better advantages over the state-of-the-art asynchronous algorithms.

关键词： Convergence Optimization Protocols System recovery Linear programming Electronic mail Delays Vectors Synchronization Fasteners Asynchronous algorithm delay distributed optimization linear convergence

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Convergence Analysis of Distributed Generalized Nash Equilibria Seeking Algorithm With Asynchrony and Delays

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第1期70卷 642-648页

作者： Li, Huaqing Ran, Liang Zheng, Lifeng Li, Zhe Hu, Jinhui Li, Jun Huang, Tingwen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Cent South Univ Dept Automat Changsha 410083 Peoples R China City Univ Hong Kong Dept Biomed Engn Kowloon Hong Kong Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

This article considers a class of noncooperative games in which the feasible decision sets of all players are coupled together by a coupled inequality constraint. Adopting the variational inequality formulation of the game, we first introduce a new local edge-based equilibrium condition with full information. Considering challenges when communication delays occur, we then devise an asynchronous distributed algorithm to seek a generalized Nash equilibrium. This asynchronous scheme arbitrarily activates one player to start new computations independently at different iteration instants, which means that the picked player can use the involved outdated information from itself and its neighbors to perform new updates. In theoretical aspect, we provide explicit conditions on algorithm parameters, for instance, the step-sizes to establish a sublinear convergence rate for the synchronous version. Next, the asynchronous algorithm guarantees almost sure convergence in expectation under the same step-size conditions and some standard assumptions. Finally, the viability and performance of the proposed algorithm are demonstrated by numerical studies on the Cournot competition.

关键词： Games Convergence Vectors Heuristic algorithms Delays Standards Distributed algorithms Asynchronous distributed algorithm delay communication generalized Nash equilibria (GNE) noncooperative games operator splitting

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Constraint-Projection-Based Actor-Critic Algorithm for Dynamic Distributed Economic Dispatch With Nonconvex Nonsmooth Cost

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 1102-1114页

作者： Li, Jun Li, Huaqing Xia, Dawen Huang, Tingwen Zheng, Lifeng Ran, Liang Ji, Lianghao Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Kaili Univ Coll Big Data Engn Kaili 556011 Peoples R China Kaili Univ Coll Microelect & Artificial Intelligence Kaili 556011 Peoples R China Shenzhen Univ Adv Technol Sch Comp Sci & Control Engn Shenzhen 518107 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

Out of consideration for the dynamic demand of energy dispatching with local affine constraints and globally shared nonconvex nonsmooth hybrid cost arising from highly prevalent intermittent renewable energy sources, load effects, and multiple fuel resources, the constraint-projection-based actor-critic algorithm is investigated to solve the dynamic distributed economic dispatch problem in microgrids. To characterize the generation power against the fuel cost, we denote various fuel choices by segmented nonconvex costs. To help address the lack of prior knowledge of the system model, we propose a novel distributed actor-critic algorithm to find the feasible power. The algorithm combines the actor policy network with the multiplier splitting approach and projected gradient method, maps the action's policy distribution to a feasible power interval, and finally realizes real-time dispatching in continuous action space by approximating the evaluation function from the critic network. The convergence of the algorithm is theoretically analyzed. The effectiveness of the approach is verified by several simulations.

关键词： Heuristic algorithms Costs Fuels Cost function Power system dynamics Microgrids Generators Dispatching Renewable energy sources Real-time systems Constraint-projection-based actor-critic (CPAC) distributed economic dispatch (DED) forward-backward method reinforcement learning (RL)

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