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Research on the Design of Online Teaching System of Basketball Basic Skill Based on computer 4

Research on the Design of Online Teaching System of Basketba...

引用

4th International conference for Emerging Technology, INCET 2023

作者： Wan, Nan Jiangxi Science and Technology Normal University College of Physical Education Nanchang City330038 China

ISBN: (纸本)9798350335750

With the popularization of multimedia teaching and application update, the advantages of this information technology in teaching have become increasingly prominent, and continue to go beyond the simple application model to the independent research and development model. It is very obvious in higher education, and a new type of educational informatization development mode of "application development"is emerging. Practice has proved that the education model of "Internet plus Education"is leading China to the era of education informatization 2.0. To drive the modernization of education with informatization will be the only way to realize the high quality and balanced development of education. The application of computers and other modern tools will help promote the implementation and development of systematic education methods in basketball education, and promote the scientific implementation and management of basketball teaching. Based on this, this paper first analyzes the theoretical basis of online teaching system of basic basketball skills, then studies the design of web-based teaching system of basic basketball skills based on computer, and finally gives the design of online teaching resources of basic basketball skills based on computer technology. The system highlights the characteristics of practical teaching of technical subjects, abandons the performance of online teaching system for light viewing and reference, innovatively proposes the learning mode of timely evaluation, real-time learning and simulation comparison, and effectively improves the students' interest in learning basic basketball skills. Make great use of students' spare time after class to learn technical skills, and provide innovative basis for the reform of technical practice teaching. © 2023 IEEE.

关键词： Students

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Sequence to Sequence Load Recognition Model Based On Sparse Self-attention Transformer 9

Sequence to Sequence Load Recognition Model Based On Sparse ...

引用

9th International conference on Big Data Computing and Communications, BigCom 2023

作者： Dong, Zhihua Guo, Xing Anhui University The School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350331240

Load recognition is a technique for monitoring the status of different appliances from the aggregate measurement signal. Traditional load recognition methods either rely on high frequency sampled data or have significantly degraded performance as the amount of devices increases. Recently, research has shown that deep learning-based load recognition solutions offer better performance. However, the recognition accuracy still needs to be improved. In this paper, we propose a load recognition model based on tranformer, and we introduce a sparse self-attention mechanism to reduce the computational complexity. By conducting experiments on the low-frequency sampled UKDALE dataset, the results show that our proposed method outperforms previous methods, demonstrating the effectiveness of the proposed method. © 2023 IEEE.

关键词： Deep learning

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Research on positionless control of mining high force energy density permanent magnet direct drive integrated machine based on improved sliding mode observer 3

Research on positionless control of mining high force energy...

引用

3rd International conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Wang, Sen Chen, Nan Jia, Zijian Liang, Daili Xiao, Yanhui Yu, Wanhong Wang, Wenshuo Wang, Lei Zhu, Yuqin Zhang, Li Liu, Hao Yu, Zhanyang Du, Jianmin Shenyang Institute of Engineering Department of College of Automation Liaoning Shenyang China China Coal Technology and Engineering Group Shenyang Resreach Institute State Key Laboratory of Coal Mine Safety Technology 113122 China Shenyang University of Technology Liaoning Shenyang China

ISBN: (纸本)9798350331578

In order to realize sensorless control of mine high energy density permanent magnet direct drive integrated machine, improved sliding mode control is used to estimate rotor position and rotational speed. Due to the frequent switching of working conditions in the mine, the control system of the integrated machine is affected to a certain extent. In order to reduce the chattering effect caused by traditional sliding mode control, a sensorless vector control system with double closed-loop speed and current was built by adding phase-locked loop to improve the stability and observation accuracy of the control system. The simulation results show that the system has high state estimation accuracy, stable operation and fast response speed, which can cope with complex working conditions in mine, and is suitable for the mine with high energy density permanent magnet direct drive all-in-one. © 2023 IEEE.

关键词： Position control

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Remote sensing crop classification model integrating 1D convolutional residual blocks and temporal attention 4

Remote sensing crop classification model integrating 1D conv...

引用

4th International conference on Machine Learning and computer Application, ICMLCA 2023

作者： Yuan, Zhengwu Chen, Wang Yang, Aixia Shao, Wen College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Crop type recognition in remote sensing plays a crucial role in agricultural management and resource monitoring. In addressing the challenge of temporal feature extraction from remote sensing time series data, this study introduces a novel temporal extraction module that integrates 1D convolutional residual blocks and temporal attention mechanisms. Our approach effectively captures temporal patterns in crop growth processes and achieves high-precision crop classification in remote sensing imagery. Through experimental validation on the Sentinel2-Agri dataset, our model demonstrates performance improvements in classification accuracy compared to traditional methods, yielding higher accuracy and finer-grained segmentation results. Additionally, ablation studies and exploration of model configurations substantiate the effectiveness and robustness of our approach. This research holds important implications for the advancement of remote sensing-based crop classification. © 2024 SPIE.

关键词： Convolution

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Ultra Low Latency Hardware Optimised Radix-4 FFT for Optical Wireless FPGA Transceivers Via Hermitian Symmetry Characteristics

Ultra Low Latency Hardware Optimised Radix-4 FFT for Optical...

引用

2024 IEEE high performance Extreme Computing conference, HPEC 2024

作者： Codd, Michael McDonald, Ciara Jiang, Yiyue Chen, Chunan Claussen, Holger Leeser, Miriam Dooley, John Maynooth University Department of Electronic Engineering Ireland Northeastern University Department of Electronic Engineering United States Tyndall National Institute Ireland School of Computer Science and Information Technology University College Cork Ireland School of Engineering Trinity College Dublin Ireland

ISBN: (纸本)9798350387131

Future telecommunication networks are expected to deliver exponential performance increases across all domains and with the increased prevalence of real-time IoT devices, greater emphasis is placed on reducing the latency of network links. Traditionally, wireless networking requirements have been fulfilled primarily through use of the RF spectrum, which is rapidly approaching saturation and will eventually very likely become insufficient to meet all future network demands. The optical spectrum however offers enormous amounts of unrestricted and unallocated bandwidth. Efficient high-modulation indoor LED lighting fixtures could potentially integrate with and complement the RF spectrum for short to medium distance low latency applications. The Fast Fourier Transform (FFT) is a ubiquitous operation in many communication network topologies. Typically the FFT is computed via serial methods which are optimised for low resource usage, however these architectures fall short of the Ultra Low Latency (ULL) requirements for optical wireless communication. Fully parallel FFT computations can achieve nanosecond latency and tens of gigasamples of throughput, far surpassing serial methods. However, their prohibitively high resource utilisation has limited their practical use. In this work, we introduce a hardware optimised, fully parallel architecture for optical wireless communication which leverages hermitian symmetry characteristics within real-valued optical signals and properties of the discrete DFT to reduce the footprint of a fully parallel FFT on an FPGA. The final architecture is implemented on an AMD RFSoC 2 × 2 and requires only 3 clock cycles to compute a 256 -point real-valued FFT, a 290 fold reduction compared to an equivalent serial model. The design was tested at 122.88 MHz, resulting in a 24 nanosecond latency, demonstrating its potential for use in optical wireless communication and other high-performance 5G+ networks. © 2024 IEEE.

关键词： Lighting fixtures

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Framework for Improving Authenticity and Traceability in Educational Institution Evaluations in Perú Based on a Blockchain Architecture

Framework for Improving Authenticity and Traceability in Edu...

引用

2023 IEEE International conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Silva, Rodrigo De La Cruz, José Wong, Lenis Universidad Peruana de Ciencias Aplicadas Information Systems Engineering Program Lima Peru

ISBN: (纸本)9798350327816

During emergencies like COVID-19, maintaining educational quality involves integrating non-specialized online. Therefore, a framework is proposed to improve the authenticity and traceability in the assignation of grades in online classrooms of educational institutions in Perú. The proposed framework will impact students and teachers. The former, will be able to have more confidence in their exam grades, which will avoid request to the professors to review them. The latter will trace their students learning in an organized way, which will allow them to assess them continuously. The framework consists of three phases: (1) design of the blockchain architecture, (2) design of the web application architecture and (3) implementation of the web application. It was validated with the application of two experiments made with the support of twenty Peruvian students and a survey conducted with three experts. The validation results show that the proposed framework reduces the time required for the request of study certificates by 99.96% and the survey results by the experts showed that 'Traceability','Authenticity', 'performance', and 'Usability' obtained an average rating of 4.83 (very high), 4.7 (very high), 4 (high), and 4 (high) respectively. © 2023 IEEE.

关键词： Architecture Authenticity Blockchain Education Evaluation Framework Traceability Web applications

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Label Semantics-Filtered Enhanced Prototypical Network for Few-Shot Named Entity Recognition 21

Label Semantics-Filtered Enhanced Prototypical Network for F...

引用

21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Wu, Xucheng Lian, Jia Gao, Hui Wang, Youbo School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan College of Architectural Technology Chengdu China

ISBN: (纸本)9798331519254

In the task of Named Entity Recognition (NER), few-shot learning has garnered significant attention due to its potential in scenarios with limited labeled data. Existing research often relies on label names to enhance semantic representation, but the information provided by label names is limited, which affects classification performance. To address this issue, this paper proposes a Label Semantics-filtered Enhanced Prototypical Network (LSE-PN), which integrates rich label annotation information to improve the model's knowledge representation. The model employs a BERT encoder to extract semantic features from both text and labels, and optimizes category differentiation through a semantic fusion module. The prototypical network learns intermediate representations from the support set to achieve category clustering, with the model matching the nearest labels and prototype centers based on distance metrics. Experimental results indicate that LSE-PN outperforms existing methods across multiple few-shot NER benchmark tests. Our model is highly effective in cross-domain scenarios. © 2024 IEEE.

关键词： Zero-shot learning

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ZenNet-SA: An Efficient Lightweight Neural Network with Shuffle Attention for Facial Expression Recognition 24

ZenNet-SA: An Efficient Lightweight Neural Network with Shuf...

引用

24th International conference on Control, Automation and Systems, ICCAS 2024

作者： Nguyen, Trang Nabi, Hamza Ghulam Han, Ji-Hyeong Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

ISBN: (纸本)9788993215380

Facial expression recognition (FER) is an important task in computer vision, having practical applications in areas such as human-computer interaction, education, healthcare, and online monitoring. Although significant progress has been made possible by deep learning, real-world deployment of the FER system still needs to be improved, particularly with limited resources such as embedded or mobile systems. From this challenging FER task, we propose a lightweight network that combines a lightweight high performance backbone by applying ZenNAS [1] and a distillation feature part after backbone to refine features. Extensive experimental results demonstrate that the proposed ZenNet-SA achieves state-of-the-art results on three FER benchmark datasets, RAF-DB (88.53%) as well as AffectNet 7 class (65.23%) and 8 class (61.32%) in the light-weight models group with a model size less than 1M and computation costs approximately 450M Flops. Our results demonstrate a notable improvement in efficiency, with a decrease of approximately 97% in FLOPS and 91% in the number of parameters compared to POSTER V2 in terms of computational and resource costs. © 2024 ICROS.

关键词： Deep learning

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Style-Aware Modality Fusion Learning for Visible-Infrared Person Re-Identification 21

Style-Aware Modality Fusion Learning for Visible-Infrared Pe...

引用

21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Hangyu, Guo The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

The goal of Visible-Infrared Person Re-Identification (VI-ReID) is to match the visible and infrared images of the same individual. However, significant cross-modal discrepancies hinder performance improvements in VI-ReID. Current approaches primarily concentrate on extracting shared features across modalities to mitigate these discrepancies. Nevertheless, pedestrian images captured by cameras with different spectral ranges exhibit substantial style variations, which limit the discriminative capacity of feature representations. To tackle this problem, this paper introduces a style-aware modality fusion learning (SAMFL) model to effectively bridge cross-modal differences. We design a progressive style fusion module (PSFM) to gradually eliminate cross-modal style differences. Additionally, we introduce a dynamic semantic fusion module (DSFM) that further fuses heterogeneous image features based on style fusion, automatically generating an auxiliary modality and effectively guiding the construction of modality-invariant representations. Furthermore, this paper introduces a feature consistency loss to maintain the distribution consistency of features for the same identity across different modalities. Extensive experiments on three VI-ReID benchmarks reveal that the proposed method exceeds existing state-of-the-art approaches. © 2024 IEEE.

关键词： Image enhancement

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Early Stage Diabetes Identification Using Machine Learning Ensemble Techniques 20

Early Stage Diabetes Identification Using Machine Learning E...

引用

20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Saboor, Abdus Li, Jian Ping Hussain, Abid Ul Haq, Amin Haq, Abdul School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Mohi-Ud-Din Islamic University Computer Science Department AJ and Kashmir Pakistan

ISBN: (纸本)9798350318982

Diabetes or Diabetes mellitus (DM) is an illness in which patients build a high level of blood sugar due to metabolic disorders. Diabetes occurs due to the excessiveness of glucose in the human body. The proposed approach utilizes machine learning technique such as ensemble learning technique known as stacking to enhance the predictive accuracy of a classification model. The methodology involves will used dataset downloaded from kaggle website, which contain 08 attributes and 768 records. We will divide the dataset into train and test dataset. We will train the multiple diverse base models, by using various algorithms, such as Random Forest, Ada Boost and SVM will use as a base models are then engaged in generating predictions for a validation dataset. Then we will incorporate methods in ensemble-ML (such as Bagging, Boosting, and Stacking) to enhance the predictive capability. Subsequently, a meta-model exemplified by Random Forest, will train from base model predictions. The outputs of the base models used input for the mata-model, the ensemble stacking classifier creates a more reliable model. The effectiveness of this ensemble or stacking model will demonstrate through evaluating its performance on a testing dataset, resulting in an accuracy score that validates its potential to enhance predictive outcomes compared to individual models. The approach serves as a practical strategy will force the strengths of Ensemble Learning Diabetes Diagnosis (ELDD) model and achieve superior predictive performance and will predict the diabetics is present or not in both male and female. performance of our model will measure by using performance matrices such as Accuracy, Precision, recall, F1-Score. The propose model (ELDD) will be easily incorporated in E-healthcare systems for diagnosis of Diabetics to ensure proper treatment and recovery. © 2023 IEEE.

关键词： Decision trees

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