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arXiv 2025年

作者： Guo, Tian Yuan, Hui Liu, Qi Su, Honglei Hamzaoui, Raouf Kwong, Sam School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Ji’nan250061 China School of Electronic Information Qingdao University Qingdao266071 China School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Department of Computing and Decision Science Lingnan University Hong Kong

Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing approaches focus primarily on point-to-point fidelity, often neglecting the importance of perceptual quality as interpreted by the human visual system. To address this issue, we propose a generative adversarial network for point cloud quality enhancement (PCE-GAN), grounded in optimal transport theory, with the goal of simultaneously optimizing both data fidelity and perceptual quality. The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit. The LFE unit uses dynamic graph construction and a graph attention mechanism to efficiently extract local features, placing greater emphasis on points with severe distortion. The GSC unit uses the geometry information of neighboring patches to construct an extended local neighborhood and introduces a transformer-style structure to capture long-range global correlations. The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction. Experimental results show that the proposed method achieves state-of-the-art performance. Specifically, when applying PCE-GAN to the latest geometry-based point cloud compression (G-PCC) test model, it achieves an average BD-rate of -19.2% compared with the PredLift coding configuration and -18.3% compared with the RAHT coding configuration. Subjective comparisons show a significant improvement in texture clarity and color transitions, revealing finer details and more natural color gradients. Copyright © 2025, The Authors. All rights reserved.

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Autonomous On-ramp Merge Strategy Using Deep Reinforcement Learning in Uncertain Highway Environment

Autonomous On-ramp Merge Strategy Using Deep Reinforcement L...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Wu, Sifan Tian, Daxin Zhou, Jianshan Duan, Xuting Sheng, Zhengguo Zhao, Dezong Beijing Key Laboratory For Cooperative Vehicle Infrastructure Systems&Safety Cooperative Control Beijing China Beijing Advanced Innovation Center For Big Data and Brain Computing Beijing China Department of Engineering and Design Richmond United Kingdom University of Sussex Richmond United Kingdom James Watt School of Engineering Glasgow United Kingdom University of Glasgow Glasgow United Kingdom

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

On-ramp merge is a complex traffic scenario in autonomous driving. Because of the uncertainty of the driving environment, most rule-based models cannot solve such a problem. In this study, we design a Deep Reinforcement Learning (DRL) method to solve the issue of ramp merges in uncertain scenarios and modify the structure of the Twin Delayed Deep Deterministic policy gradient algorithm (TD3), using Long Short-Term Memory (LSTM) to select an action based on temporal information. The proposed method is applied in the on-ramp merge and verified in the Simulation of Urban Mobility (SUMO). Results show that the proposed method performs significantly better generalization in uncertain traffic scenarios. © 2022 IEEE.

关键词： Long short-term memory

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Adjusted and controllability Properties of Memristor Rucklidge System

Adjusted and Controllability Properties of Memristor Rucklid...

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2023 IEEE International Conference on Memristive computing and Applications, ICMCA 2023

作者： Zang, Hongyan Huang, Lili Zhang, Shourong Joseph, Annie Anak Fu, Haiyan Lei, Tengfei Qilu Institute of Technology Collaborative Innovation Center of Memristive Computing Application Jinan250200 China Universiti Malaysia Sarawak Faculty of Engineering Kota Samarahan Sarawak Malaysia College of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan250200 China Shandong Vanform High Energy Physics Technology Co. Ltd. Jinan250100 China Universiti Malaysia Sarawak Department of Electrical and Electronics Kota Samarahan Sarawak Malaysia College of Computer and Information Engineering Qilu Institute of Technology Jinan250200 China Jinan250200 China

ISBN: (纸本)9798350316230

There are many models of memristors, but there are no reports on exponential memristors. In this paper, a three- dimension memristor Rucklidge system with exponential memristor is proposed by introducing exponential function and constructing exponential memristor. The dynamic behavior of the system is analyzed by means of Lyapunov exponent and bifurcation diagram. The complex attractor coexistence and amplitude modulation characteristics of the system are found. The internal variable of memristor is introduced into the second two-dimensional equation as a feedback term, and a conditional symmetric system is constructed for the three-dimensional memristor system, which can realize the polarity control of output signal. By discussing the dynamic characteristics of exponential memristor chaotic system and the control mechanism of amplitude modulation, bias and conditional symmetry, the complex dynamic characteristics of exponential function memristor are revealed, which lays a foundation for its application in the field of secure communication. © 2023 IEEE.

关键词： Memristors

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Soil-Available Potassium Content Measurement Based on Nernst Informed Neural Network With Electrical Signal

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年

作者： Sun, Mingchun Zhao, Rongqiang Liu, Hongfang Jin, Jing Liu, Jie Harbin Institute of Technology Faculty of Computing Harbin150001 China National Key Laboratory of Smart Farm Technologies and Systems Harbin150001 China Heilongjiang Academy of Agriculture Science Jiamusi154000 China Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

Soil-available potassium content is essential for agricultural crop management. The electrochemical measurement method exhibits advantages of high speed, low cost, and simple operation. However, it encounters challenges, such as poor signal reproducibility, limited sample accessibility, and restrictions on measurement accuracy. To address these issues, the Nernst informed neural network (NINN)-based electrochemical measurement of soil-available potassium content is proposed. The framework for the electrochemical soil-available potassium content measurement is established. The proposed method is developed to exploit the nonlinear relationship between the electrical signal and the soil-available potassium content end-to-end with a single potassium ions-selective electrode. The NINN in the proposed method is developed to improve the measurement accuracy on the dataset with a limited number of samples by integrating Nernst mechanism into the network. The potassium-content dynamic estimation in the proposed method is developed to overcome the uncertainties of the dilution factor and influence of interfering ions. The practical measurement exhibits the mean absolute percentage error in a low range and the time within 10 min. Experimental results demonstrate that the proposed method improves the accuracy compared to purely data-driven models and reduces the electrochemical measurement costs. Furthermore, the proposed method also exhibits the superiority in terms of measurement speed;operational complexity compared to the commercial products relied on spectroscopy. The proposed method can contribute to the advancement of the measurement of soil nutrients. © 2023 IEEE.

关键词： Crops

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Multi-Target Tracking Using a Swarm of UAVs by Q-learning Algorithm

Multi-Target Tracking Using a Swarm of UAVs by Q-learning Al...

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Sensor Signal Processing for Defence (SSPD)

作者： Seyed Ahmad Soleymani Shidrokh Goudarzi Xingchi Liu Lyudmila Mihaylova Wenwu Wang Pei Xiao Centre for Vision Speech and Signal Processing (CVSSP) The University of Surrey UK School of Computing and Engineering University of West London UK Department of Automatic Control and Systems Engineering The University of Sheffield UK Institute for Communication Systems (5GIC) The University of Surrey UK

This paper proposes a scheme for multiple un-manned aerial vehicles (UAVs) to track multiple targets in challenging 3-D environments while avoiding obstacle collisions. The scheme relies on Received-Signal-Strength-Indicator (RSSI) measurements to estimate and track target positions and uses a Q-Learning (QL) algorithm to enhance the intelligence of UAVs for autonomous navigation and obstacle avoidance. Considering the limitation of UAVs in their power and computing capacity, a global reward function is used to determine the optimal actions for the joint control of energy consumption, computation time, and tracking accuracy. Extensive simulations demonstrate the effectiveness of the proposed scheme, achieving accurate and efficient target tracking with low energy consumption.

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Fatigue Load Prediction of Large Wind Turbine by Big Data and Deep Learning

Fatigue Load Prediction of Large Wind Turbine by Big Data an...

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Electrical, Automation and Computer engineering (ICEACE), International Conference on

作者： Jiayue Wu Qinmin Yang Nanlin Jin Low Carbon Electrical Power and Energy Technology xXi'an Jiaotong-Liverpool University Suzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China

This paper introduces a fatigue load prediction framework for the nacelle of wind turbines utilizing operational data sourced from the supervisory control and data acquisition (SCADA) system of a Wind Farm. The primary objective revolves around achieving precise fatigue load predictions for the nacelle, forecasting 15 minutes in advance. Firstly, a series of data preprocessing steps were implemented on the wind turbine data procured from the SCADA system to enhance the model's generalization capability and robustness, including 3-Sigma outlier detection, sample point selection, as well as filtering and de-noising techniques. Then, the gate recurrent unit (GRU) was tuned and trained as the optimal model for long time-series forecasting of SCADA parameter channels. Subsequently, the support vector regression (SVR) model, refined with grid search methodology, was employed to establish the intricate relationship between SCADA parameters and fatigue loads. Finally, the predicted SCADA parameter values were fed into the optimized SVR model to derive fatigue load prediction results for wind turbines. Experimental results indicated that the proposed model framework can effectively predict fatigue loads on the nacelle of wind turbines utilizing the SCADA data in real scenarios.

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A Simple Discretization Scheme for Gain Matrix Conditioning

arXiv

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arXiv 2022年

作者： O'Connor, Daniel L. Siang, Lim C. Elnawawi, Shams Control Consulting Inc. Great FallsMT United States Department of Process Control Engineering Refinery BurnabyBC Canada College of Computing Georgia Institute of Technology AtlantaGA United States

In industrial model predictive controllers (MPCs), models generated from regression-based system identification methods typically contain small or even physically non-existent degrees of freedom. control issues can arise when the steady-state optimizer uses these small degrees of freedom to calculate targets for plant operation due to matrix ill-conditioning. Mathematical techniques like Relative Gain Array (RGA) and Singular Value Decomposition (SVD) are helpful for analyzing controller gain interactions and identifying conditioning issues, which can be corrected relatively easily in small models. However, these techniques are difficult and tedious to apply for larger, more complex models. This paper describes a novel, non-iterative, RGA-based, binning technique for discretizing the gain matrix and quickly solving 2×2 conditioning issues for any model size, while guaranteeing gain adjustments below a certain threshold. Higher order interactions are also discussed. © 2022, CC BY.

关键词： Singular value decomposition

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Real-time Semantic Segmentation of Solar Photovoltaic Arrays for Autonomous UAV Flights 43

Real-time Semantic Segmentation of Solar Photovoltaic Arrays...

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43rd Chinese control Conference, CCC 2024

作者： Li, Nanyan Chen, Hao Sun, Zhigang Gao, Jing Yi, Dewei Liu, Cunjia Su, Jinya Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Cse Ministry of Education Nanjing210096 China State Grid Jiangsu Integrated Energy Service CO. Ltd Nanjing210096 China Jiangsu Sudian Industry Management CO. Ltd Nanjing210096 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom Loughborough University Department of Aeronautical and Automotive Engineering LoughboroughLE11 3TU United Kingdom

ISBN: (纸本)9789887581581

This paper considers the real-time semantic segmentation of solar PhotoVoltaic (PV) arrays using down-facing cameras onboard Unmanned Aerial Vehicles (UAVs) to support autonomous navigation for ultra-low altitude industrial inspection. Various semantic segmentation algorithms, including SegNet, UNet, Deeplabv3+, and their respective variants, are compared to identify the most suitable one that strikes a balance between segmentation and real-time performance. Subsequently, each video frame is processed by the selected segmentation network to create a solar array mask. This mask is then optimally fitted to diverse solar array shapes to determine angular and horizontal deviations between the image center and the solar array center-line. The algorithms are validated on a self-constructed dataset of 594 solar PV array UAV images, encompassing varied environmental conditions and solar farm layouts. The top-performing network, lightweight vgg 16bn-Unet, achieves 99.4% overall accuracy and 98.2% Intersection over Union (IoU), while the most balanced network, mobilev3-Deeplabv3+, attains 98.9% accuracy and 96.5% IoU, with a 27.59 fps (using an NVIDIA GeForce MX150 GPU) suitable for real-time UAV flights. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Semantic Segmentation

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IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation

arXiv

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arXiv 2023年

作者： Narayanan, Shankara Sikha, O.K. Benitez, Raul Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore641112 India Department of Automatic Control Universitat Politècnica de Catalunya Av. d’Eduard Maristany Barcelona08019 Spain Barcelona08034 Spain

Skin lesion segmentation plays a crucial role in the computer-aided diagnosis of melanoma. Deep Learning models have shown promise in accurately segmenting skin lesions, but their widespread adoption in real-life clinical settings is hindered by their inherent black-box nature. In domains as critical as healthcare, interpretability is not merely a feature but a fundamental requirement for model adoption. This paper proposes IARS SegNet an advanced segmentation framework built upon the SegNet baseline model. Our approach incorporates three critical components: Skip connections, residual convolutions, and a global attention mechanism onto the baseline Segnet architecture. These elements play a pivotal role in accentuating the significance of clinically relevant regions, particularly the contours of skin lesions. The inclusion of skip connections enhances the model’s capacity to learn intricate contour details, while the use of residual convolutions allows for the construction of a deeper model while preserving essential image features. The global attention mechanism further contributes by extracting refined feature maps from each convolutional and deconvolutional block, thereby elevating the model’s interpretability. This enhancement highlights critical regions, fosters better understanding, and leads to more accurate skin lesion segmentation for melanoma diagnosis. This study primarily focuses on the interpretation of performance improvements in the base model resulting from the integration of each of these three components. To comprehensively assess the performance gain achieved with each addition, we employ two sets of evaluation metrics, quantifying performance based on both regions and contours. The results underscore the superior segmentation capabilities of the proposed architecture compared to the SegNet and U-Net models. Notably, it provides interpretable results, particularly when applied to the PH2 dataset. Copyright © 2023, The Authors. All rights reserved

关键词： Convolution

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Influence of the variables describing brain signals on the performance of the Naive Bayesian Classifier

Influence of the variables describing brain signals on the p...

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2022 Progress in Applied Electrical engineering, PAEE 2022

作者： Kawala-Sterniuk, Aleksandra Zygarlicki, Jaroslaw Lysiak, Adam Grochowicz, Barbara Pelc, Mariusz Bauer, Waldemar Baczkowicz, Dawid Krecisz, Krzysztof Dylag, Katarzyna A. Wieczorek, Wiktoria Kahankova, Radana Martinek, Radek Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole Poland University of Greenwich School of Computing and Mathematical Sciences London United Kingdom Agh University of Science and Technology Department of Automatic Control and Robotics Krakow Poland Opole University of Technology Faculty of Physical Education and Physiotherapy Opole Poland St. Louis Children Hospital Krakow Poland Jagiellonian University in Krakow-Collegium Medicum Department of Pathophysiology Krakow Poland Jagiellonian University in Krakow-Collegium Medicum Department of Bioinformatics and Telemedicine Krakow Poland Vsb - Technical University Ostrava Department of Cybernetics and Biomedical Engineering-FEECS Ostrava-Poruba Czech Republic

ISBN: (纸本)9781665455152

In this paper various variables describing brain signals such as near-infrared spectroscopy (NIRS) and electroencephalography (EEG) on the Naive Bayesian Classifier were presented. Analysis of biomedical signals is a very challenging task - therefore the authors of this work decided to analyse two types of brain signals - EEG and NIRS, which have different features, character, nature and are differently prone to various artifacts occurrence. The applied classifiers can be applied for various features extraction and pattern comparisons, which is useful in diagnostic processes. © 2022 IEEE.

关键词： Near infrared spectroscopy

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