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Unsupervised Low-Light Image Enhancement Based on Explicit Denoising and Knowledge Distillation

Computers, Materials & Continua 2025年第2期82卷 2537-2554页

作者： Wenkai Zhang Hao Zhang Xianming Liu Xiaoyu Guo Xinzhe Wang Shuiwang Li College of Computer Science and Engineering Guilin University of TechnologyGuilin541006China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of TechnologyGuilin541004China

Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled Retine (DDR) method, an unsupervised approach that integrates denoising priors into a Retinex-based training framework. By explicitly incorporating denoising, the DDR method effectively addresses the challenges of noise and artifacts in low-light images, thereby enhancing the performance of the Retinex framework. The model achieved a PSNR of 19.82 dB on the LOL dataset, which is comparable to the performance of supervised methods. Furthermore, by applying knowledge distillation, the DDR method optimizes the model for real-time processing of low-light images, achieving a processing speed of 199.7 fps without incurring additional computational costs. While the DDR method has demonstrated superior performance in terms of image quality and processing speed, there is still room for improvement in terms of robustness across different color spaces and under highly resource-constrained conditions. Future research will focus on enhancing the model’s generalizability and adaptability to address these challenges. Our rigorous testing on public datasets further substantiates the DDR method’s state-of-the-art performance in both image quality and processing speed.

关键词： Deep learning low-light image enhancement real-time processing knowledge distillation

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Contact Force Optimization to Enhance Fault-tolerant Motion Stability of a Hexapod Robot

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Journal of Bionic Engineering 2024年第5期21卷 2199-2214页

作者： Bo You Shangdong Shi Chen Chen Jiayu Li Nan Li Liang Ding The Key Laboratory of Intelligent Technology for Cutting and Manufacturing Ministry of Education and the Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin University of Science and TechnologyHarbin150080China The State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin150001China

This paper presents a novel method for optimizing the contact force of a hexapod robot to enhance its dynamic motion stability when one of its legs *** proposed approach aims to improve the Force Angle Stability Margin(FASM)and adapt the foot trajectory through contact force optimization to achieve safe and stable motion on various *** foot force optimization approach is designed to optimize the FASM,a factor rarely considered in existing contact force optimization *** formulating the problem of enhancing the motion stability of the hexapod robot as a Quadratic Programming(QP)optimization problem,this approach can effectively address this *** of a hexapod robot using a fault-tolerant gait,along with real field experiments,were conducted to validate the effectiveness and feasibility of the contact force optimization *** results demonstrate that this approach can be used to design a motion controller for a hexapod robot with a significantly improved motion *** summary,the proposed contact force optimization method offers a promising solution for enhancing the motion stability of hexapod robots with single leg failures and has the potential to significantly advance the development of fault-tolerant hexapod robots for various applications.

关键词： Hexapod robot Fault-tolerant Stability margin Contact force optimization

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Multi-modal Bionic Motion Analysis of A Cpg-controlled Pneumatic Soft Robot

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Journal of Bionic Engineering 2024年第5期21卷 2247-2257页

作者： Yu Zhang Peiyu Huang Dongjie Li Jiangyu Zhou Yu Li Bo You Yanhe Zhu Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin University of Science and TechnologyHarbin150080China State Key Laboratory of Robotics and System Harbin Institute of Technology(HIT)Harbin150001China

This paper designs a soft robot with a multi-chamber,multi-airbag mimicking soft biological structure,where the airbags of the same chamber are interconnected with each *** upper and lower chambers are separated by an intermediate layer(thin plate),which is extended and widened to achieve robot movement and *** applying pressure to the different chambers of the soft robot,it is possible to produce a variety of bionic movements of the inchworm and *** to the strong nonlinearity and infinite number of degrees of freedom properties of the material,it is impossible to obtain the analytical solution of the bending morphology and pressure of the soft robot ***,a method to establish a mathematical model of soft robot deformation based on the classical stacked plate theory is proposed,and a chain composite model of soft robot bending motion is established based on the large-deflection modeling *** paper proposes a method to generate a multi-mode soft robot motion control based on the Central Pattern Generator(CPG)using a single controller,which achieves the switching of sine wave-like patterns,half-wave-like patterns,and dragging patterns by adjust-ing frequency,amplitude and period of ***,a pneumatic control platform is built for the validation of the theoretical model and different experimental models of the motion of the *** comparation of the different motion modes of the soft robot under similar non-load and load conditions.

关键词： Soft robot Bionic multi-modal movement Laminated plate theory CPG control

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Learning Class-Aware Local Representations for Few-Shot Remote Sensing Scene Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13225-13237页

作者： Wang, Liu Zhuo, Li Zhang, Hui Li, Jiafeng Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

Few-Shot Remote Sensing Scene Classification (FSRSSC) aims at recognizing novel categories with only a few labeled samples. Local representations can retain richer details than image-level feature vectors and adapt well to the characteristics of spatially scattered objects in remote sensing images. However, the existing local representation-based few-shot learning works generally overlook the role of class-semantic information. To address this issue, in this paper, a Class-aware Local Representation Learning (CLRL) method is proposed for FSRSSC. It generates class-aware support local representations by calibrating the local features with class-level global features to alleviate the problem of ignoring the class-semantic information. The Image-to-Class (I2C) metric is used to measure the similarities between query samples and support samples. Moreover, a novel Local Representation-based Self-supervised Learning (LRSL) strategy is proposed. The main idea of LRSL is to maximize the relationship consistency between the query local representations and different views of support local representations. A novel consistency loss is further proposed as the objective function of LRSL to supervise the model training. Different from the existing image-level feature vector based self-supervised learning tasks, the proposed LRSL forces the model to capture the structure information of local representations. A robust model can be trained by jointly optimizing LRSL tasks and FSRSSC tasks. The experimental results on the NWPU-RESISC45, WHU-RS19 and UC Merced datasets show the superiority of our proposed method. © 2008-2012 IEEE.

关键词： Self-supervised learning

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Entropy regulation in LaNbO_(4)-based fergusonite to implement high-temperature phase transition and promising dielectric properties

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Journal of Advanced Ceramics 2023年第5期12卷 1067-1080页

作者： Deqin Chen Na Yan Xuefeng Cao Fengrong Li Laijun Liu Qinghua Shen Huanfu Zhou Chunchun Li Guangxi University Key Laboratory of Non-ferrous Metal Oxide Electronic Functional Materials and Devices College of Material Science and EngineeringGuilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of TechnologyGuilin 541004China

High-entropy effect is a novel design strategy to optimize properties and explore novel *** this work,(La_(1/5)Nd_(1/5)Sm_(1/5)Ho_(1/5)Y_(1/5))NbO_(4)(5RNO)high-entropy microwave dielectric ceramics were successfully prepared in the sintering temperature(S.T.)range of 1210–1290℃via a solid-phase reaction route,and medium-entropy(La_(1/3)Nd_(1/3)Sm_(1/3))NbO_(4) and(La_(1/4)Nd_(1/4)Sm_(1/4)Ho_(1/4))NbO_(4)(3RNO and 4RNO)ceramics were *** effects of the entropy(S)on crystal structure,phase transition,and dielectric performance were *** entropy increase yields a significant increase in a phase transition temperature(from monoclinic fergusonite to tetragonal scheelite structure).Optimal microwave dielectric properties were achieved in the high-entropy ceramics(5RNO)at the sintering temperature of 1270℃for 4 h with a relative density of 98.2%and microwave dielectric properties of dielectric permittirity(ε_(r))=19.48,quality factor(Q×f)=47,770 GHz,and resonant frequency temperature coefficient(τ_(f))=–13.50 ppm/℃.This work opens an avenue for the exploration of novel microwave dielectric material and property optimization via entropy engineering.

关键词： high-entropy ceramics microwave dielectric property ion disorder far-infrared

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Online Fault-Tolerant Tracking Control With Adaptive Critic for Nonaffine Nonlinear systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 215-227页

作者： Ding Wang Lingzhi Hu Xiaoli Li Junfei Qiao IEEE the School of Information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protection and Beijing Institute of Artificial Intelligence Beijing University of Technology

In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator ***, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov *** tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.

关键词： Adaptive critic control neural network (NN) nonaffine nonlinear systems online fault-tolerant tracking design uniform ultimate boundedness

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A Tutorial on Quantized Feedback Control

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 5-17页

作者： Minyue Fu IEEE the School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Control Theory and Intelligent Systems and Centre for Control Science and TechnologySouthern University of Science and TechnologyShenzhen 518055China

In this tutorial paper, we explore the field of quantized feedback control, which has gained significant attention due to the growing prevalence of networked control systems. These systems require the transmission of feedback information, such as measurements and control signals, over digital networks, presenting novel challenges in estimation and control design. Our examination encompasses various topics, including the minimal information needed for effective feedback control, the design of quantizers, strategies for quantized control design and estimation,achieving consensus control with quantized data, and the pursuit of high-precision tracking using quantized measurements.

关键词： Consensus control high-precision control networked control quantized estimation quantized feedback control robust control

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High-Order Fully Actuated system Models for Strict-Feedback systems With Increasing Dimensions

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2451-2462页

作者： Xiang Xu Guang-Ren Duan Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and TechnologyShenzhen 518055 the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China

This paper mainly addresses control problems of strict-feedback systems(SFSs)with increasing *** with the commonly-considered SFSs where the subsystems have the same dimension,we aim to handle more complex cases,i.e.,the subsystems in the considered SFSs are assumed to have increasing *** transforming the systems into highorder fully-actuated system(HOFAS)models,the stabilizing controllers can be directly *** first-order SFSs,secondorder and high-order SFSs are also considered.

关键词： Increasing dimensions high-order fully-actuated system nonlinear systems strict-feedback systems

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Hierarchical Learning-Based Integrated Robust Optimal Control for Nonlinear systems

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3119-3129页

作者： Zhang, Jiacheng Wang, Jingjing Han, Honggui Hou, Ying Huang, Yanting Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

The optimal control of nonlinear systems is crucial to improve system performance. However, the uncertainties of cost functions and systems dynamics make it difficult to solve the optimal control laws. To cope with this problem, a hierarchical learning-based integrated robust optimal control (HL-IROC) method is proposed in this article. The merits of the proposed HL-IROC method are three aspects: First, a hierarchical learning-based integrated optimal control (HL-IOC) scheme, contains a system dynamic learning layer and a cost function learning layer, is designed to transform the original optimal control problem into an integrated optimization problem with control laws as decision variables. Then, the relationships between cost functions and control laws are captured to overcome the difficulties brought by uncertainties in the optimal control process. Second, a global-local cooperative robust evolutionary optimization (GL-CREO) algorithm is proposed to obtain the optimal control laws. Then, a global-local robust searching strategy is employed to deal with two types of uncertainties for improving the robustness of control laws. Third, the convergence analysis of HL-IOC and GL-CREO is discussed in theory. In the experiments, the effectiveness of HL-IROC is illustrated with a nonlinear system and a wastewater treatment process. © 2013 IEEE.

关键词： Optimal control systems

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Application-Oriented Cloud Workload Prediction:A Survey and New Perspectives

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Tsinghua Science and technology 2025年第1期30卷 34-54页

作者： Binbin Feng Zhijun Ding Key Laboratory of Embedded System and Service Computing Ministry of EducationDepartment of Computer Science and TechnologyTongji UniversityShanghai 201804China Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji UniversityDepartment of Computer Science and TechnologyTongji UniversityShanghai 201804China Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Workload prediction is critical in enabling proactive resource management of cloud *** workload prediction is valuable for cloud users and providers as it can effectively guide many practices,such as performance assurance,cost reduction,and energy consumption ***,cloud workload prediction is highly challenging due to the complexity and dynamics of workloads,and various solutions have been proposed to enhance the prediction *** paper aims to provide an in-depth understanding and categorization of existing solutions through extensive literature *** existing surveys,for the first time,we comprehensively sort out and analyze the development landscape of workload prediction from a new perspective,i.e.,application-oriented rather than prediction methodologies per ***,we first introduce the basic features of workload prediction,and then analyze and categorize existing efforts based on two significant characteristics of cloud applications:variability and ***,we also investigate how workload prediction is applied to resource ***,open research opportunities in workload prediction are highlighted to foster further advancements.

关键词： cloud computing workload prediction resource management artificial intelligence for IT operations(AIOps)

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