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3rd International Conference on Electronic Information engineering and computer Communication, EIECC 2023

作者： Yi, Yi Huang, Zixuan School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350359961

Satellite communication plays a crucial role in the world due to its global coverage, flexibility, and reliability. As one of the key technologies, beam hopping technology can effectively improve the available capacity of the system and enhance the flexibility of satellite resource scheduling. However, most of the research on resource allocation technology for beam hopping satellites primarily focused on the theoretical level which presents challenges such as high algorithm complexity and low real-Time performance. Therefore, further research is necessary to explore practical applications. In this paper, based on the actual satellite communication scenario, the model of the bouncing beam satellite communication system is analyzed first. Then, based on the DVBRCS2 protocol standard, we propose a backward-link scheduling algorithm that supports channel state adaptation. Finally, two calculation schemes of resource allocation are designed to match different traffic requirements. Simulation results show that compared with the traditional algorithm, the proposed method can meet the system capacity with lower algorithm complexity. Moreover, it successfully meets various service requirements and holds significant engineering implications. © 2023 IEEE.

关键词： beam-hopping communication satelite DVB-RCS2 Resource allocation

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Sustainable Clustering Neural Networks

Sustainable Clustering Neural Networks

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Huashun Li Jingzhao Li Wei Chen Zechao Liu School of Electrical and Information Engineering Anhui University of Science and Technology Hunan China School of Computer Science Anhui University of Science and Technology Hunan China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

In the modern field of power and renewable energy, advancements in data analysis technology are key to driving innovation and sustainable development. However, existing clustering algorithms and neural networks are inefficient and inaccurate when processing large-scale, high-dimensional data, and they consume significant computational resources. To address these issues, this paper proposes a Sustainable Clustering Neural Network (SCNN). By introducing Gaussian units based on joint Gaussian distribution and combining them with a max-pooling layer, we propose a universal clustering module with a competitive update mechanism. This module includes two sets of trainable parameters, allowing for flexible adjustment of the shape and position of the Gaussian distribution to fit different data distribution characteristics. During clustering, feature selection and dimensionality reduction are achieved through the max-pooling layer, effectively enhancing the model's clustering performance. The competitive update mechanism further promotes competition among Gaussian units, enabling each unit to focus more on specific clusters, thereby improving the accuracy and stability of the clustering results. Experiments on the MNIST and Fashion-MNIST datasets achieved clustering accuracies of 93.38% and 72.83%, respectively, demonstrating strong competitiveness compared to existing methods. The code is available at: https://***/ZS520L/HGL-CAE.

关键词： Dimensionality reduction Renewable energy sources Technological innovation Accuracy Shape Neural networks Gaussian distribution Feature extraction Stability analysis Sustainable development

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Disturbance Rejection Target Tracking Control for Snake Robots in the Presence of Obstacles 3

Disturbance Rejection Target Tracking Control for Snake Robo...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Li, Ningwei Chu, Yiming Ren, Shanjun Wang, Chaoli Wang, Gang Li, Peng Institude of Machine Intelligence University of Shanghai for Science and Technology Shanghai China School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798331540319

In this paper, a collision-free target tracking control approach utilizing guided vector field guidance is proposed. In particular, a synthesized vector field is designed to direct the snake robot toward the target while simultaneously navigating around multiple obstacles. In addition, a saturated guidance law is designed to compute a reference angular velocity to prevent damage to the robot joints. A dynamics control law is designed based on a reduced-order structure-modified expansion state observer to compensate for external disturbances and realize the robust target tracking and tracing control of the snake robot. Simulation results show the effectiveness of the proposed guidance and control method. © 2024 IEEE.

关键词： Disturbance rejection

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DSA-MFNet: Deep-shallow Attention based Multi-frame Fusion Network for EEG motor imagery classification

DSA-MFNet: Deep-shallow Attention based Multi-frame Fusion N...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Huifu Zhang, Xun Wan, Yuchai Zhang, Xin Beijing Technology and Business University School of Computer Science and Engineering Beijing China

ISBN: (纸本)9798350337488

One of the most prevalent brain-computer interface (BCI) paradigms is the Electroencephalogram (EEG) motor imagery (MI). It has found extensive applications in numerous fields. While there have been significant strides in achieving high MI classification performance, certain challenges still persist:Effective utilization of the time-varying spatial and temporal features from multi-channel brain signals remains elusive. Fully leveraging the interactive information embedded within finite-length MI-EEG samples is still an open question. In this study, we introduce the Deep-Shallow Attention-Based Multi-Frame Fusion Network (DSA-MFNet) tailored for EEG-based motor imagery classification. The architecture of DSA-MFNet encompasses both a Deep-Shallow Attention (DSA) module and a Multi-Frame Fusion (MF) module. In detail, the DSA module integrates a deep-shallow convolution block, which extracts both intricate deep spatial-temporal features and surface-level features. Subsequently, an attention block emphasizes the most salient features in MI-EEG data. Meanwhile, the MF module delves into the interactions amongst multiple frames in the MI-EEG data, shedding light on the unique characteristics of time-varying EEG *** model sets a new benchmark by outperforming the leading techniques, achieving an accuracy of 86.6% on the BCI Competition IV-2a dataset for subject-dependent modes. For the sake of transparency and to foster further research, we will be making our code and trained models available on GitHub. © 2023 IEEE.

关键词： attention EEG fusion motor imagery

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Broad Learning and Convolutional Neural Network aided Principal Component Analysis for Detecting Faults in High-speed Trains

Broad Learning and Convolutional Neural Network aided Princi...

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2023 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Zhou, Mingyue Zhao, Hongyang Cheng, Chao School of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781510667662

Traction systems provide the traction power of high-speed trains. Because the complex operation mechanism of train under actual working conditions and the measured data are nonlinear and non-Gaussian, and the sampling frequency of the sensor is high in actual working conditions. Directly using a neural network or the multivariate statistical method is challenging to obtain the ideal fault detection (FD) result. Therefore, this paper proposes a data-driven method based on broad learning system (BLS) and convolutional neural network (CNN) assisted principal component analysis (PCA). Two neural networks are used to enhance the robustness of the algorithm, so that the proposed method has better fault detection ability in nonlinear and non-Gaussian systems. The advantage of this method is that it does not require the establishment of a complex high-speed train data model. Instead, by processing the collected data, the proposed algorithm can ensure good fault detection capabilities. Finally, the effectiveness and feasibility of the proposed method are verified on the simulation platform of traction drive control system (TDCS). © 2023 SPIE.

关键词： Principal component analysis

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Plant Disease Diagnosis and Image Classification Using Deep Learning

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computers, Materials & Continua 2022年第5期71卷 2125-2140页

作者： Rahul Sharma Amar Singh Kavita N.Z.Jhanjhi Mehedi Masud Emad Sami Jaha Sahil Verma School of Computer Applications Lovely Professional University Jalandhar144411India Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Faculty of Computer and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the *** farming employs technology to improve *** and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop *** disease experts are not available in remote areas thus there is a requirement of automatic low-cost,approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert’s *** learning-based computer vision techniques like Convolutional Neural Network(CNN)and traditional machine learning-based image classification approaches are being applied to identify plant *** this paper,the CNN model is proposed for the classification of rice and potato plant leaf *** leaves are diagnosed with bacterial blight,blast,brown spot and tungro *** leaf images are classified into three classes:healthy leaves,early blight and late blight *** leaf dataset with 5932 images and 1500 potato leaf images are used in the *** proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58%accuracy and potato leaves with 97.66%*** results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Decision Tree and Random Forest.

关键词： Plant diseases detection CNN image classification deep learning in agriculture

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Front Light Module for Reflective LCDs

Front Light Module for Reflective LCDs

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International Conference on Display technology, 2024

作者： Li, Xiangjun Li, Kejing Lv, Guoqiang Wang, Zi Feng, Qibin School of Instrument Science and Opto-Electronics Engineering Hefei University of Technology AnHui Hefei China Special Display and Imaging Technology Innovation Center of Anhui Province National Engineering Laboratory of Special Display Technology Academy of Photoelectric Technology Hefei University of Technology AnHui Hefei China

With the requirements of green development, reflective LCDs are gaining more and more applications. When reflective LCDs are used in dark environment, the display luminance cannot meet the requirement that make front light modules necessary. As the fact that front light module does not have optical films as used in backlight module, the light guide plate is directly observed by human eyes, which makes the performance of the light guide plate have profound influence on the final display. This paper firstly establishes a simulation model of the front light module. The spherical and conical dots are taken as examples to explore the influence of dot shape and parameter changes on the display performance. A kind of analysis and evaluation method of the front light module by comprehensively considering the factors of luminance, uniformity and stray light is put forward in the paper. The simulation results show that in the case of R=25μm, the spherical dots have higher comprehensive evaluation parameters at R:H=3:1, and the conical dots have higher comprehensive evaluation parameters at R:H=1:1. The analysis and evaluation method proposed in this paper can effectively guide the development of front light guides, shorten the development time and reduce the development risk. © 2024 John Wiley and Sons Inc. All rights reserved.

关键词： Liquid crystals

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A Study of Crew Allocation Based on a Novel Heuristic Algorithm and Simulated Annealing Algorithm 2

A Study of Crew Allocation Based on a Novel Heuristic Algori...

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2nd IEEE International Conference on Image Processing and computer Applications, ICIPCA 2024

作者： Wang, Hong Xia, Jun Song, Yuqin School of Management Science and Engineering Anhui University of Technology Ma'anshan China Anhui Jianghuai Automobile Group Co. Ltd. Hefei China

ISBN: (纸本)9798350360240

This paper addresses the optimization of flight crew assignments by introducing a mathematical optimization model aimed at maximizing the allocation of flight crew to flights while minimizing overall occurrences and the use of substitute qualifications. The proposed approach involves route-based clustering, where flight routes are aggregated around hub airports to serve as the fundamental unit for matching flight crew. This transforms the problem of flight crew assignment into a route-based matching scenario. A mathematical programming model is established to minimize the number of unfulfilled crew assignments, flight occurrences, and substitute qualification usage, while also prioritizing these objectives. To enhance computational efficiency, a simulated annealing algorithm and a novel heuristic strategy are employed for solving. Results demonstrate the superiority of the new heuristic strategy over simulated annealing. Specifically, for dataset A, 202 flights were successfully scheduled with no crew occurrences or substitute qualification usage. For dataset B, 13235 flights were successfully arranged with zero crew occurrences or substitute qualification usage, and the program execution time was 0.32s. © 2024 IEEE.

关键词： Simulated annealing

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A Hybrid Approach for Detection in Foggy Environments for Self Driving Cars: YOLO and MSRCR Techniques 4

A Hybrid Approach for Detection in Foggy Environments for Se...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Mahaadev, Maharaj Ghosh, Debjani Dogra, Madhavik Gupta, Gunesh School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350305258

A self-driving car is a topic deserving of intense attention from both research & development folks and industry experts as self-driving cars have latent potential to revolutionize transportation systems around the whole car-driving world. Safety and reliability of the automobile are the topics most important to people when they talk about self-driving cars, which heavily rely on accurate and robust object detection in a myriad of environmental conditions. So, an approach for improving the capability of self-driving cars for object detection being driven in smoggy conditions is put forth. Detecting objects & vehicles on the road is a core part of driverless car technology as it requires high accuracy and real-time processing to ensure safety in various driving scenarios. This research proposes an approach that has been improved and is related to the YOLO (You Only Look Once) algorithm to understand the presence of vehicles & objects on the road when the weather is foggy. Our approach involves integrating & incorporating one component for dehazing into the YOLO model to improve restoring of image info which we achieved with the help of a technology called MSRCR (Multi-Scale Retinex with Colour Restoration). We have trained the updated scenario using augmented data processed with MSRCR to improve its stability and performance. We conducted extensive evaluations on a publicly available dataset and the results clearly indicate that our enhanced YOLO model outperforms conventional YOLO in detecting vehicles in foggy weather conditions. Our findings highlight the latent possibility of mixing multiple technologies to improve object detection for self-driving cars which could improve vehicle safety and vehicle reliability of autonomous vehicles for users in the future. Our approach can be further extended to other applications that require accurate and robust object detection in extreme conditions such as in robotics, surveillance systems, security systems & satellite image

关键词： Foggy conditions MSRCR Satellite imagery Self-driving cars YOLO

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Autoencoder Image Compression for High Compression and Acceptable Quality 21

Autoencoder Image Compression for High Compression and Accep...

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21st OITS International Conference on Information technology, OCIT 2023

作者： Tyagi, Arnav Aditya, Harshvardhan Khandelwal, Rishabh Singh, Jagendra Pal, Yogesh Jaiswal, Garima Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350358230

This This article introduces a novel approach to image compression through the utilization of autoencoders, a class of neural networks adept at learning to distill an image's essential attributes and compactly represent them. The proposed technique entails training an autoencoder on a substantial image dataset and subsequently employing it to compress new images by encoding them into a lower-dimensional representation, which can be later decoded to reconstruct the original image. The paper conducts a comprehensive exploration of the method's compression effectiveness and image fidelity, offering comparative assessments with established compression methodologies. The findings underscore the approach's ability to achieve significant compression ratios while upholding commendable image quality, even surpassing established techniques in specific scenarios. Moreover, the study addresses the constraints and trade-offs associated with employing autoencoders for image compression, emphasizing the delicate balance between compression rate and image quality. It also outlines potential avenues for future research and development. In summary, this conference article not only underscores the utility of autoencoders for image compression but also accentuates their potential to enhance image storage and transmission efficiency across a range of applications. © 2023 IEEE.

关键词： Image compression

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