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IEEE conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Wang, Ying Wang, Yueyue Park, Hyo Yol Lee, Myoungwon Liu, Wei Jing, Meifang Samsung Res China Beijing SRC B Beijing Peoples R China Samsung Elect Air Tech design Grp Network Business Suwon South Korea

ISBN: (纸本)9798350310900

In FDD massive MIMO (mMIMO) systems, although assisted with Rel.16 eType ii feedback, Channel State Information (CSI) accuracy is limited by overhead, quantization error and user mobility. In this paper, we propose Multimodal Hetero-frequency Channel Extrapolation (M-HCE) based CSI reconstruction, where channel model with enhanced Levy-Ito decomposition path structure and Karhuen-Loeve expansion are introduced. Downlink channel is reconstructed from the multimodal transformation of uplink Sounding Reference Signal (SRS) channel and CSI feedback, by exploring both partial downlink-uplink reciprocity between hetero-frequency channels and reported downlink CSI. The performance is verified in commercial evaluation platform, which shows 23%-66% throughput gain compared with eType ii CSI for user mobility 10-60 km/h and typical FDD operating bands.

关键词： hetero-frequency channel extrapolation partial reciprocity Rel.16 eType ii CSI reconstruction

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Comparison Between Perturb & Observe, Fuzzy Logic MPPT Technique, and The Artificial Neural network Techniques at Different Temperature Conditions 12

Comparison Between Perturb & Observe, Fuzzy Logic MPPT Techn...

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12th IFAC Symposium on control of Power and Energy systems (CPES)

作者： El Hamzaoui, Fatima Zahra Abderrahime, Taouni Taoufiq, Belhoussine Drissi Ain Chock Hassan II Univ Fac Sci Lab Elect & Ind Engn Informat Proc & Logist LGEIT Casablanca Morocco

Photovoltaic (PV) system performance is influenced by environmental factors such as irradiation, temperature, and shading effects, which we cannot control. However, to address this aspect, maximum power point tracking (MPPT) algorithms come into play. They keep an eye on the working point and modify it in real-time to comply with the Maximum Power Point (MPP), which assists in reducing the impact of environmental fluctuations. This, in turn, results in steady and ideal power generation from the PV system, increasing its dependability and efficiency regardless of the unpredictable climate conditions. This research compares three commonly used approaches for tracking the MPP in PV systems: the perturb and observe (P&O) method, fuzzy logic control (FLC), and Artificial Neural networks (ANN). This comparison is based on a simulation using MATLAB/Simulink to carry out an in-depth analysis of these three methods. This simulation enables a complete examination of all the three methods, considering parameters like complexity, stability, and efficiency. This study intends to give a comprehensive review of the performance and applicability of the P&O method, fuzzy logic control, and ANN in PV systems by assessing these parameters. The present research adds useful insights to current information by thoroughly examining three popular MPPT approaches with simulations in MATLAB/Simulink The findings can help researchers and professionals choose the best strategy for their PV systems based on their specific requirements. Furthermore, the comparison study highlights prospective possibilities for future research and MPPT technique improvements. Copyright (c) 2024 The Authors.

关键词： Photovoltaic system temperature irradiation DC-DC boost converter MPPT Techniques P&O ANN FL methods stability efficiency MATLAB/Simulink

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ADVERSARIAL DEFECT SYNTHESIS FOR INDUSTRIAL PRODUCTS IN LOW DATA REGIME 30

ADVERSARIAL DEFECT SYNTHESIS FOR INDUSTRIAL PRODUCTS IN LOW ...

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30th IEEE International conference on Image Processing (ICIP)

作者： Coscia, Pasquale Genovese, Angelo Scotti, Fabio Piuri, Vincenzo Univ Milan Dept Comp Sci Milan Italy

ISBN: (纸本)9781728198354

Synthetic defect generation is an important aid for advanced manufacturing and production processes. Industrial scenarios rely on automated image-based quality control methods to avoid time-consuming manual inspections and promptly identify products not complying with specific quality standards. However, these methods show poor performance in the case of ill-posed low-data training regimes, and the lack of defective samples, due to operational costs or privacy policies, strongly limits their large-scale applicability. To overcome these limitations, we propose an innovative architecture based on an unpaired image-to-image (I2I) translation model to guide a transformation from a defect-free to a defective domain for common industrial products and propose simultaneously localizing their synthesized defects through a segmentation mask. As a performance evaluation, we measure image similarity and variability using standard metrics employed for generative models. Finally, we demonstrate that inspection networks, trained on synthesized samples, improve their accuracy in spotting real defective products.

关键词： Synthetic defect generation generative adversarial network defective mask residual network

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Research on vehicle autonomous driving based on convolutional neural network and embedded system in computer vision 4

Research on vehicle autonomous driving based on convolutiona...

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4th International conference on Green Communication, network, and Internet of Things, CNIoT 2024

作者： Huang, David New York University IndianaPA15701 United States

ISBN: (纸本)9781510685710

With the rapid development of autonomous driving technology, the autonomous driving ability of vehicles has become an important research direction in intelligent transportation systems. Convolutional Neural network (CNN) has become one of the core technologies in autonomous driving vision systems due to its excellent performance in image recognition and classification. Embedded systems also play an important role in the realization of autonomous vehicle driving due to their high efficiency and real-time nature. In this study, a vehicle autonomous driving scheme based on Convolutional Neural network (CNN) and embedded system is proposed. Firstly, the convolutional neural network was used to perform real-time image processing and feature extraction on the road scene. Specifically, through the superposition of multi-layer convolutional layers and pooling layers, features such as edges, textures, and objects in the image are extracted layer by layer, so as to achieve efficient recognition of complex road environments. In this process, the convolutional layer is used to extract local features, while the pooling layer is used to reduce dimensionality and prevent overfitting to ensure the robustness and efficiency of the model. Secondly, an embedded system was designed and optimized, and the trained CNN model was deployed on the system to ensure real-time processing power and efficient energy consumption management. The design of the embedded system focuses on the optimal allocation of hardware resources and the effective control of energy consumption to meet the real-time operation needs of vehicles under different road conditions. Specifically, the embedded system uses high-performance processors and low-power hardware modules to ensure fast inference and real-time decision-making capabilities of CNN models. In addition, the overall performance and reliability of the system are further improved through the co-design of software and hardware. Combining the above two techn

关键词： Convolutional neural networks

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Optimization Method for Model Predictive control Based on BiLSTM 9

Optimization Method for Model Predictive Control Based on Bi...

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9th Asia-Pacific conference on Intelligent Robot systems (ACIRS)

作者： Chen, Zehua Tang, Xinhua Huang, Yuheng Pan, Shuguo Southeast Univ Sch Instrument Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9798350352573

Model predictive control (MPC) has been widely employed in autonomous driving control, offering advantages including rolling optimization. However, the performance of MPC can be significantly compromised due to the inaccurate system model of the vehicle. In response, a novel MPC model optimization algorithm based on BiLSTM has been proposed to address the issue of inaccurate system models. Firstly, MPC-based pathtracking data is collected under varying speeds, paths, and wheelbases. A BiLSTM network is then trained using this dataset and integrated with a MPC controller. This integration allows for the update of the wheelbase parameters of the kinematic model, thereby improving the accuracy of the vehicle prediction model. The proposed method is validated through simulation experiments. The results demonstrate that the path tracking accuracy of the MPC combined with a BiLSTM method is higher than that of MPC and linear quadratic regulator (LQR). Concurrently, this approach yields a considerable enhancement in path tracking precision, with an improvement of 37.53%.

关键词： model predictive control BiLSTM path tracking

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M-FANet: Multi-Feature Attention Convolutional Neural network for Motor Imagery Decoding

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IEEE TRANSACTIONS ON NEURAL systems AND REHABILITATION ENGINEERING 2024年 32卷 401-411页

作者： Qin, Yiyang Yang, Banghua Ke, Sixiong Liu, Peng Rong, Fenqi Xia, Xinxing Shanghai Univ Sch Mech & Elect Engn & Automat Shanghai 200444 Peoples R China Shanghai Univ Res Ctr Brain Comp Engn Sch Mechatron Engn & Automat Sch Med Shanghai 200444 Peoples R China Minist Educ Engn Res Ctr Tradit Chinese Med Intelligent Rehabi Shanghai 201203 Peoples R China

Motor imagery (MI) decoding methods are pivotal in advancing rehabilitation and motor control research. Effective extraction of spectral-spatial-temporal features is crucial for MI decoding from limited and low signal-to-noise ratio electroencephalogram (EEG) signal samples based on brain-computer interface (BCI). In this paper, we propose a lightweight Multi-Feature Attention Neural network (M-FANet) for feature extraction and selection of multi-feature data. M-FANet employs several unique attention modules to eliminate redundant information in the frequency domain, enhance local spatial feature extraction and calibrate feature maps. We introduce a training method called Regularized Dropout (R-Drop) to address training-inference inconsistency caused by dropout and improve the model's generalization capability. We conduct extensive experiments on the BCI Competition IV 2a (BCIC-IV-2a) dataset and the 2019 World robot conference contest-BCI Robot Contest MI (WBCIC-MI) dataset. M-FANet achieves superior performance compared to state-of-the-art MI decoding methods, with 79.28% 4-class classification accuracy (kappa: 0.7259) on the BCIC-IV-2a dataset and 77.86% 3-class classification accuracy (kappa: 0.6650) on the WBCIC-MI dataset. The application of multi-feature attention modules and R-Drop in our lightweight model significantly enhances its performance, validated through comprehensive ablation experiments and visualizations.

关键词： Brain-computer interface motor imagery convolutional neural networks multi-feature attention

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Output Feedback Fault-Tolerant Attitude control for Quadrotor Based on Neural network Approximation

Output Feedback Fault-Tolerant Attitude Control for Quadroto...

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2024 International Annual conference on Complex systems and Intelligent Science, CSIS-IAC 2024

作者： Qi, Shu-Chang Gao, Chuang Jiang, Meng-Yi Li, Bo-Ning School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan China

ISBN: (纸本)9798331504755

This paper explores the control of a quadrotor in the presence of attitude tracking and actuator failure, and introduces the Nussbaum function in the design of the backstepping controller in order to eliminate the influence of the unknown parameters, thus ensuring the stability and performance of the quadrotor. In order to estimate the unmeasurable states in the attitude tracking of the quadrotor, a novel design of a state observer for the quadrotor is also proposed and the use of neural networks to approximate the unknowns in the process. The proposed control scheme ensures that all signals of the quadrotor are bounded and the tracking errors converge to a small range around zero. Finally, numerical example results are also given to further demonstrate the effectiveness of the proposed control scheme. © 2024 IEEE.

关键词： Backstepping

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Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered control 5

Toward Multi-Agent Reinforcement Learning for Distributed Ev...

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5th Annual conference on Learning for Dynamics and control

作者： Kesper, Lukas Trimpe, Sebastian Baumann, Dominik Rhein Westfal TH Aachen Inst Data Sci Mech Engn Aachen Germany Aalto Univ Dept Elect Engn & Automat Espoo Finland Uppsala Univ Dept Informat Technol Uppsala Sweden

Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches require precise mathematical models of the system dynamics, which may not always be available. Model-free learning of communication and control policies provides an alternative. Nevertheless, existing methods typically consider single-agent settings. This paper proposes a model-free reinforcement learning algorithm that jointly learns resource-aware communication and control policies for distributed multi-agent systems from data. We evaluate the algorithm in a high-dimensional and nonlinear simulation example and discuss promising avenues for further research.

关键词： Event-Triggered Communication Hierarchical Reinforcement Learning networked control systems Multi-Agent Learning

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Driving behavior analysis and classification by vehicle OBD data using machine learning

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JOURNAL OF SUPERCOMPUTING 2023年第16期79卷 18800-18819页

作者： Kumar, Raman Jain, Anuj Lovely Profess Univ Phagwara India

The transportation industry's focus on improving performance and reducing costs has driven the integration of IoT and machine learning technologies. The correlation between driving style and behavior with fuel consumption and emissions has highlighted the need to classify different driver's driving patterns. In response, vehicles now come equipped with sensors that gather a wide range of operational data. The proposed technique collects critical vehicle performance data, including speed, motor RPM, paddle position, determined motor load, and over 50 other parameters through the OBD interface. The OBD-ii diagnostics protocol, the primary diagnostic process used by technicians, can acquire this information via the car's communication port. OBD-ii protocol is used to acquire real-time data linked to the vehicle's operation. This data are used to collect engine operation-related characteristics and assist with fault detection. The proposed method uses machine learning techniques, such as SVM, AdaBoost, and Random Forest, to classify driver's behavior based on ten categories that include fuel consumption, steering stability, velocity stability, and braking patterns. The solution offers an effective means to study driving behavior and recommend corrective actions for efficient and safe driving. The proposed model offers a classification of ten driver classes based on fuel consumption, steering stability, velocity stability, and braking patterns. This research work uses data extracted from the engine's internal sensors via the OBD-ii protocol, eliminating the need for additional sensors. The collected data are used to build a model that classifies driver's behavior and can be used to provide feedback to improve driving habits. Key driving events, such as high-speed braking, rapid acceleration, deceleration, and turning, are used to characterize individual drivers. Visualization techniques, such as line plots and correlation matrices, are used to compare drivers' performanc

关键词： Society of automotive engineers (SAE) On-board diagnostic (OBD) Internet of things (IoT) Engine control unit (ECU) Analysis of driving behavior (ADB) Body control unit (BCU) Keyword protocol (KWP) Data acquisition systems (DAS) controller area network (CAN) Information and communication technology (ICT)

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Design and Implementation of Real-Time Wireless Sensor network Protocols with Relay Nodes and Localization Methods for Efficient System performance

Design and Implementation of Real-Time Wireless Sensor Netwo...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Balan, Gunapriya Pandey, Garima Sirisha, N. Ziara, Sajjad Thethi, H. Pal Alekhya, Juttiga New Horizon College of Engineering Department of Electrical and Electronics Engineering Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Phagwara India Griet Department of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798331527495

In this research paper we focus on the design and implementation of reliable real time wireless sensor network (WSN) protocols that make use of relay node and proper localization techniques to enhance the performance of the system in real time applications. However, Wireless Sensor networks (WSNs) offer a very important application in environmental monitoring, industrial control, and smart cities. Maintaining efficient communication protocols and accurate real time localization of sensor nodes, especially in environments with dynamic topologies, is one of the key challenges. This paper presents a robust WSN protocol with relay nodes which achieve both energy efficiency and communication efficiency. Additionally, it examines specialized localization algorithms for use in real time systems that offer enhanced localization precision without sacrificing system performance. Results show the reliability, scalability and energy efficiency of WSNs if utilized in real time applications can be highly improved using the optimized protocols that are coupled with localization techniques. © 2025 IEEE.

关键词： Wireless sensor networks

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