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A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

Digital Communications and Networks 2024年第2期10卷 481-497页

作者： Sheng Hao Huyin Zhang Fei Yang Chenghao Li Jing Wang School of Computer Science Central China Normal UniversityWuhan430079PR China Hubei Key Laboratory of intelligent Robot(Wuhan Institute of Technology) Wuhan430079PR China School of Computer Science Wuhan UniversityWuhan430079China School of Information Science and Engineering Shandong Normal UniversityJinan250000China School of Computer Science Hubei University of TechnologyWuhan430079China

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware *** research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)*** designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network *** solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 ***,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,*** on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC *** the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into ***,we prove the proposed analytical model has the ***,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

关键词： Hybrid power-line/Visible light communication (HPVC)networks MAC protocol CSMA/CA IEEE 802.15.7 IEEE 1901 Performance analysis

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Air quality index prediction model integrating multi-head self-attention mechanism 5

Air quality index prediction model integrating multi-head se...

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2023 5th International Conference on Information Science, Electrical, and Automation Engineering, ISEAE 2023

作者： Yao, Lantao Liu, Lizhi Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China School of Computer Science and Engineering Artificial Intelligence Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9781510667440

Aiming at the problem of insufficient accuracy of existing models in the process of air quality index prediction, an air quality index (AQI) prediction model (CALSTM) incorporating a multi-head self-attention mechanism is proposed based on Long Short-Term Memory network (LSTM). The model effectively extracts low-dimensional features of air pollutant concentrations and meteorological data related to the air quality index through Convolutional Neural Network (CNN) and uses LSTM to fully reflect the long-term historical process in the input time series. To improve the acquisition ability of the global information of the model context, the multi-head self-attention mechanism is used to extract the hidden information of the time series at different levels and improve the model's generalization ability. In addition, to further improve the prediction accuracy of the model, an AQI time series difference method is proposed based on the data correlation analysis. The experimental results show that the CALSTM using the AQI time series difference method has achieved an effect of 16.27% on MAPE, and on the indicators of MAE, MSE, RMSE, and R2, they are 6.99, 114.79, 10.71, and 0.9586, respectively. Compared with LSTM has achieved better prediction results. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Time series

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PM2.5 concentration prediction based on CNN-GRU model fused with Luong attention 5

PM2.5 concentration prediction based on CNN-GRU model fused ...

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2023 5th International Conference on Information Science, Electrical, and Automation Engineering, ISEAE 2023

作者： Wang, Zhen Liu, Lizhi Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China School of Computer Science and Engineering Artificial Intelligence Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9781510667440

In order to improve the accuracy of PM2.5 concentration prediction, a CNN-GRU deep learning model based on fusion of Luong Attention is proposed. Firstly, the correlation between various air pollutants and meteorological factors and PM2.5 concentration is comprehensively analyzed, and the high correlation data is formed into a feature set. Secondly, the feature set is input into CNN for feature dimensioning, and then the output results of each time step are extracted through GRU. Finally, by introducing the Luong attention mechanism, the attention scores of the hidden states at each position of the output sequence are calculated, and the context vector is weighted to highlight the input step information that plays a key role in the prediction of PM2.5 concentration. The results show that using the CNN-GRU model with attention mechanism to predict the PM2.5 concentration in the next 24 hours, compared with the machine model and other deep learning models, RMSE and MAE have a certain decline, and have a higher generalization ability. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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PM2.5 concentration prediction model based on SA-LSTM 23

PM2.5 concentration prediction model based on SA-LSTM

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Proceedings of the 2023 International Conference on Artificial Intelligence, Systems and Network Security

作者： Zhen Wang Lizhi Liu Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan Institute of Technology China

ISBN: (纸本)9798400716966

PM2.5 concentration is one of the important indicators for measuring air quality. In order to enhance the precision of PM2.5 concentration forecasting, a short-term prediction model for PM2.5 concentration based on SA-LSTM (Self Attention Long Short Term Memory) is proposed. LSTM stores and transmits the update information of the previous time step through cell state and calculates the output vectors of each time step in the temporal input. Self Attention extracts the internal dependency relationships between multiple output vectors and weights them to obtain a key output vector that integrates global input information. The experiment selected air quality monitoring data from the Yangshoujing Avenue Automatic Air Quality Monitoring Station in Yidu City, Yichang City from January 1, 2019 to December 31, 2020. The results showed that using the SA-LSTM model to predict the PM2.5 concentration in the next hour reduced RMSE and MAE by 18% and 11% respectively, and increased R2 by 3.78% compared to the benchmark LSTM model.

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Design and Realization of a Multi-DoF robotic Head for Affective Humanoid Facial Expression Imitation 16th

Design and Realization of a Multi-DoF Robotic Head for Affec...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Li, Jiayan Lv, Honghao Zhang, Nan Wu, Haiteng Yang, Geng State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Key Laboratory of Intelligent Robot for Operation and Maintenance of Zhejiang Province Hangzhou Shenhao Technology Hangzhou China

ISBN: (纸本)9789819964826

As the elderly population continues to expand, there is a corresponding rise in the demand for service robotics in home environments, with a particular focus on humanoid service robots. This paper presents the design of a multi-DoF robotic head based on the Facial Action Coding System (FACS). The head comprises 26 servo motors that drive the independent Action Units on the face, enabling the imitation of facial expressions following the FACS theory. Additionally, a camera is embedded in the eye for visual perception and interaction, while a 6-DOF Stewart platform driven by six servo motors is integrated as the neck part, ensuring the head’s flexibility of movement. Finite element simulation experiments are conducted to validate the robotic facial expression imitation performance of 6 basic expressions including happy, sad, surprise, fear, anger and disgust. The optimized load parameters of the corresponding action units are obtained through simulation, allowing for effective facial expression imitation. The proposed robotic head with facial expression imitation functionality has potential utility for improving human-robot interaction in homecare scenarios and the service quality of the homecare robots. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Eye movements

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Enable Intuitive and Immersive Teleoperation: Design, Modeling and Control of a Novel Wearable Exoskeleton 16th

Enable Intuitive and Immersive Teleoperation: Design, Modeli...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Wang, Ruohan Cui, Xi Lv, Honghao Zhang, Guangyao Wu, Haiteng Yang, Geng State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Key Laboratory of Intelligent Robot for Operation and Maintenance of Zhejiang Province Hangzhou Shenhao Technology Hangzhou China

ISBN: (纸本)9789819964857

Haptic devices with human motion capture function enable natural and flexible teleoperation within unstructured environment. As one of the typical types among haptic devices, wearable exoskeleton allows intuitive and immersive teleoperation. For such a portable device, weight and motion compatibility have a great influence on the potential control performance. This paper presents the development of WIE, the wearable intelligent equipment, a 5-DOF portable exoskeleton that is lightweight and accessible. The mechanism of WIE is designed to suit the majority of human using the adjustable links. Kinematic analysis of it shows a high motion compatibility with human upper limb in the workspace. Furthermore, an incremental motion mapping method is adopted in the joint space to control the slave robot. The pick-and-place experiment is conducted to evaluate the performance of WIE as well as the remote-control strategy. The results indicate that the robot manipulator presents a good following behavior according to the human motions captured by WIE. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Remote control

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Air Quality Index Prediction Model Integrating Spatiotemporal Information 5

Air Quality Index Prediction Model Integrating Spatiotempora...

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5th International Academic Exchange Conference on Science and technology Innovation, IAECST 2023

作者： Yao, Lantao Liu, Lizhi Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan430205 China Wuhan Institute of Technology School of Computer Science & Engineering Artificial Intelligence Wuhan430205 China

ISBN: (纸本)9798350357738

Aiming at the problem that traditional air quality prediction research ignores spatial information, this paper proposes an air quality index prediction model (SGCN-GRU) that integrates spatiotemporal information based on gated convolution (Gated-Conv), graph convolution (GCN), and gated recurrent unit (GRU). The model first calculates the geodesic distance between each other based on the longitude and latitude coordinates of each monitoring station in the input data and establishes a graph structure. The reciprocal of the geodesic distance is used as the edge weight in the graph structure, and then the data is input into the two-layer Gated-Conv and a layer of Cheb Graph-Conv is used to obtain spatial information in a spatial graph convolution block composed of a 'sandwich' structure, and then input the obtained feature map into GRU to capture the long-term dependencies existing in it, and finally obtain it through the output layer final prediction result. Experimental results show that SGCN-GRU achieves an effect of 14.67% on MAPE in one-hour AQI prediction. Compared with other benchmark models, SGCN-GRU performs best in multi-hour predictions. © 2023 IEEE.

关键词： Forecasting

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DETECTING MACHINE-GENERATED TEXTS BY MULTI-POPULATION AWARE OPTIMIZATION FOR MAXIMUM MEAN DISCREPANCY 12

DETECTING MACHINE-GENERATED TEXTS BY MULTI-POPULATION AWARE ...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Shuhai Song, Yiliao Yang, Jiahao Li, Yuanqing Han, Bo Tan, Mingkui South China University of Technology China Pazhou Laboratory China The University of Adelaide Australia Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong

Large language models (LLMs) such as ChatGPT have exhibited remarkable performance in generating human-like texts. However, machine-generated texts (MGTs) may carry critical risks, such as plagiarism issues, misleading information, or hallucination issues. Therefore, it is very urgent and important to detect MGTs in many situations. Unfortunately, it is challenging to distinguish MGTs and human-written texts because the distributional discrepancy between them is often very subtle due to the remarkable performance of LLMs. In this paper, we seek to exploit maximum mean discrepancy (MMD) to address this issue in the sense that MMD can well identify distributional discrepancies. However, directly training a detector with MMD using diverse MGTs will incur a significantly increased variance of MMD since MGTs may contain multiple text populations due to various LLMs. This will severely impair MMD's ability to measure the difference between two samples. To tackle this, we propose a novel multi-population aware optimization method for MMD called MMD-MP, which can avoid variance increases and thus improve the stability to measure the distributional discrepancy. Relying on MMD-MP, we develop two methods for paragraph-based and sentence-based detection, respectively. Extensive experiments on various LLMs, e.g., GPT2 and ChatGPT, show superior detection performance of our MMD-MP. The source code is available at https://***/ZSHsh98/MMD-MP. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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Motion Blur Image Restoration Based on Generative Adversarial Networks

Motion Blur Image Restoration Based on Generative Adversaria...

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2022 International Symposium on Artificial Intelligence Control and Application technology, AICAT 2022

作者： Wang, Feng Cai, Qiong Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9781510656710

During the image capture process, it is difficult to maintain relative stillness between the camera and the subject, resulting in blurred images. In order to solve this problem, based on the generative adversarial network, this paper introduces a residual module to solve the gradient dispersion problem generated during the training process. The multi-scale theory is introduced to process the image to better extract the detailed information of the image. Finally, the experiment shows that the scheme has achieved good results. © 2022 SPIE.

关键词： Generative adversarial networks

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Indoor object segmentation based on YOLACT++

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International Journal of Wireless and Mobile Computing 2023年第2期24卷 127-133页

作者： Sun, Ying Zhao, Zichen Tao, Bo Liu, Xin Yun, Juntong Liu, Ying Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Wuhan University of Science and Technology Hubei Wuhan China

intelligent Manufacturing originates from the research of artificial intelligence, which can not only reduce operating costs, but also improve product quality, and has become the way to cultivate new advantages in forging international competition in manufacturing. With the transformation of traditional manufacturing to intelligent manufacturing, the course of intelligent manufacturing should also be different from the traditional teaching mode, but through the survey, it has been found that the course of intelligent manufacturing still has a single teaching mode, insufficient innovative guidance for students and insufficient combination of theory and practice. In order to solve the issues mentioned above, the Project-Analysis-Evaluation (PAE) structure is proposed in this paper, and it is combined with constructivist theory and analysed through the study of indoor object instance segmentation detection. The YOLACT++ algorithm is pre-trained on SUNRGBD dataset and applied to indoor environment detection in this paper. © 2023 Inderscience Enterprises Ltd.

关键词： Operating costs

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