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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68510 条记录，以下是21-30 订阅

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Research on modeling and feature extraction of maritime vessel targets based on micro-doppler effect characteristics 5

Research on modeling and feature extraction of maritime vess...

引用

5th international conference on Signal Processing and computer science, SPCS 2024

作者： Cai, Fan Jiang, Yuesong Zhu, Tongbo School of Electronic and Electrical Engineering Minnan University of Science and Technology Quanzhou362700 China School of Electronic and Information Engineering Beihang University Beijing100000 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

The micro-Doppler effect captures the fine motion characteristics of maritime targets, serving as a crucial feature for distinguishing between sea clutter and targets, thereby enhancing radar target detection and recognition capabilities. In this study, a long-term radar echo model for micro-motion targets in sea clutter is established using maritime surveillance radar. By analyzing the morphological differences between constant-velocity targets and micro-motion clutter in Short-Time Fourier Transform (STFT) spectrograms, a sliding window cancellation method based on STFT spectrograms is proposed to remove micro-motion clutter. This method effectively separates target echoes from micro-motion clutter, facilitating the extraction of micro-motion target features. Both simulation and experimental results validate the effectiveness of the proposed method, laying a theoretical foundation for advancing maritime vessel detection and recognition. © 2025 SPIE.

关键词： Surveillance radar

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Data Calibration of Binocular Vision Pulse Acquisition System Based on CNN+GRU 24

Data Calibration of Binocular Vision Pulse Acquisition Syste...

引用

8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Lin, Dongmei Gan, Yuanlong Wang, Xiangling Chen, Fuming Chou, Yongxin College of Electrical and Information Engineering Lanzhou University of Technology Gansu Lanzhou China Medical Support Center The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army Gansu Lanzhou China School of Electrical and Automation Engineering Changshu Institute of Technology Jiangsu Changshu China

ISBN: (纸本)9798400710094

To address potential calibration errors that may occur during the acquisition of pulse images based on binocular vision, as well as inaccuracies in three-dimensional(3D) reconstruction, a data calibration model based on Convolutional Neural Networks and Gated Recurrent Units (CNN+GRU) is proposed as a method of calibrating the pulse wave data acquired by the binocular vision pulse acquisition system. The CNN is used to retrieve the error characteristics of the pulse wave and the GRU is used to learn the time-dependent characteristics of the monitoring data and the occurrence of data drift. The precise data produced by the laser profile sensor serves as a reference for calibration. Finally, the fusion module is used to collect the calibration data. The results of both the calibration and ablation experiments show that the calibration model and its component modules are effective. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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A Novel Hybrid Model for Predicting the Remaining Driving Range of Electric Vehicle 5

A Novel Hybrid Model for Predicting the Remaining Driving Ra...

引用

5th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Li, Lianpo Wang, Lin Yazhou Bay Institute of Deepsea SCI-TECH Shanghai Jiao Tong University Sanya572024 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798331533694

Accurately predicting the remaining driving range of electric vehicles car can help users plan their trips and arrange charging time reasonably, thus improving travel efficiency. In this study, a novel hybrid model combining Kolmogorov-Arnold Network (KAN) and Long Short-Term Memory Network (LSTM) is designed. The vehicle driving data of the last 10 minutes is input to predict the remaining mileage of the vehicle online. The vehicle driving data input to the model includes two types of data: monotonic data and dynamic data. The KAN module is used to decompose the function mapping relationship and extract the monotonic data features. The LSTM is used to analyze the time series relationship and extract the dynamic data features. Finally, the features extracted by the two modules are spliced and the remaining mileage is output after weighted fusion. This study assesses the accuracy of model predictions by employing Mean Absolute Error (MAE) and R2 as metrics to compare predicted outcomes with actual values. Compared with using KAN and LSTM alone, the hybrid LSTM-KAN model achieves the best prediction results and can be applied to battery management systems. © 2025 IEEE.

关键词： Hybrid vehicles

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Adapting Existing Industrial Machines to the Internet of Things Paradigm 5th

Adapting Existing Industrial Machines to the Internet of Thi...

引用

5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Bargavi, Manju Bhatt, Rahul K, Rama Krishna Agarwal, Trapty Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Adapting current business machines to the internet of things (Iota) paradigm offers many possibilities for commercial procedures to grow to be greater efficient, dependable, and cost-powerful. This paper offers a high-level review of ways existing business machines can be adapted to the Iota paradigm. by utilizing mounted communication protocols and rising technology which includes business net of things (Eliot) packages, commercial machines may be geared up with the potential to talk and interact with different gadgets within the network, creating a unified device of linked additives, which may be used to enhance procedure manage. Sooner or later, extending the scope of these structures via superior analytics and system getting to know can free up new possibilities in predictive preservation and wise automation that may further improve the adapted industrial machines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive analytics

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Disturbance Observer-based H∞ Control of TCP/AQM System 24

Disturbance Observer-based H∞ Control of TCP/AQM System

引用

2nd international conference on Frontiers of Intelligent Manufacturing and automation, CFIMA 2024

作者： Liu, Huahao Ma, Pengfei Hou, Linlin Sun, Haibin School of Computer Science Qufu Normal University Shandong China College of Engineering Qufu Normal University Shandong China

ISBN: (纸本)9798400710681

In this issue, the disturbance observer-based H∞ control is studied for TCP/AQM system with multiple disturbances. The T-S fuzzy system containing two different kinds of disturbances is adopted to describe the TCP/AQM system. The disturbance observer is designed to obtain the estimation of the external disturbances. Then the state feedback controller is constructed using disturbance estimation. By virtue of Lyapunov-Krasovskii functional theory, the exponential stability with H∞ performance is analyzed. The sufficient conditions for the solvable gains of the disturbance observer and controller are obtained by using linear matrix inequalities. Finally, simulation experiments demonstrate the effectiveness of the proposed control scheme. © 2024 Copyright held by the owner/author(s).

关键词： State feedback

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DarijaGenie: Learning Moroccan Arabic Through a Multimodal Chatbot 8th

DarijaGenie: Learning Moroccan Arabic Through a Multimodal C...

引用

8th international conference on Arabic Language Processing

作者： El Alaoui, Hamza Cavalli-Sforza, Violetta Al Akhawayn Univ Ifrane 53003 Morocco

ISBN: (纸本)9783031791635;9783031791642

The rapid advancement of digital technologies has significantly influenced the landscape of language learning, introducing innovative tools and methodologies to enhance educational experiences. Among these developments, the integration of artificial intelligence and chatbot technology in language education offers a unique approach to teaching less commonly taught languages. This paper introduces "DarijaGenie," an early version of a task-based, multimodal chatbot system designed to facilitate the learning of Moroccan Arabic. Characterized by its spoken nature, non-standard structure, and the scarcity of its resources, Darija presents distinct challenges for learners. DarijaGenie aims to address these challenges by leveraging natural language processing and artificial intelligence to simulate real-life scenarios, providing users with an interactive platform for practical language learning.

关键词： Chatbots Task-based Language Learning Natural Language Processing computer Assisted Language Learning Second Language Learning

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Handwritten digit recognition based on SVM 24

Handwritten digit recognition based on SVM

引用

2nd international conference on Frontiers of Intelligent Manufacturing and automation, CFIMA 2024

作者： Tang, Wendi Fei, Li Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798400710681

Handwritten digit recognition (HDR) is one of the most classic application scenarios in machine learning and is often employed to validate algorithms and techniques. The task poses multiple challenges due to variations in writing styles and angles. Traditional feature extraction methods are intricate, and machine learning algorithms have difficulty handling complex nonlinear relationships and rely on features. Deep learning algorithms require a considerable amount of data and computing resources, and there exist problems such as gradient disappearance, explosion, and poor interpretability. In this paper, an algorithm model consisting of an encoder, a decoder, and an image classifier is proposed. Features are extracted through convolutional neural networks, image representation is optimized, and multi-classification support vector machines are utilized for classification. In light of the lack of challenge in MNIST datasets, variational autoencoders are utilized to generate numerically extended datasets in diverse styles. Experiments demonstrate that the proposed method outperforms the classical CNN algorithm by nearly 10% on EMNIST datasets, and the accuracy of the generated datasets is 99.4%. At the same time, the influence of different kernel functions on the performance of the model is also explored. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Multi-level Attention-enhanced Learning for Fine-Grained Visual Classification 24

Multi-level Attention-enhanced Learning for Fine-Grained Vis...

引用

8th international conference on computer science and Artificial Intelligence, CSAI 2024

作者： Ding, Kaifeng Yang, Caolin Yang, Chengzhuan Zheng, Zhonglong School of Computer Science and Technology School of Artificial Intelligence Zhejiang Normal University ZheJiang Jinhua China

ISBN: (纸本)9798400718182

Due to significant intra-class variations and subtle inter-class differences, fine-grained images often pose challenges. In this paper, we propose a multi-level attention-enhanced network framework to address this issue. First, we employ convolutional features to crop the original images to focus on the main part and mitigate the interference of backgrounds on classification. Second, we design a scale-adaptive local discriminative region generation module based on the main part’s images, effectively selecting locally detailed and rich image patches. It is worth noting that our proposed method only utilizes image-level labels. Finally, we extensively tested our method on three benchmark datasets, including the CUB-200-2011, Stanford Cars, and FGVC Aircraft. The experimental results demonstrate that our method achieves comparable or better classification performance than previous state-of-the-art fine-grained visual classification methods. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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GRU-Based Winner Subcarrier Detection in Frequency Domain Contention 24

GRU-Based Winner Subcarrier Detection in Frequency Domain Co...

引用

7th international conference on Information science and Systems, ICISS 2024

作者： Yao, Shumin Zhao, Qinglin Xu, Zezhou Feng, Li Li, Guanghui Tian, Liwei Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology China Department of Broadband Communication Pengcheng Laboratory China School of Artificial Intelligence and Computer Science Jiangnan University China Department of Computer Guangdong University of Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798400717567

Single-carrier frequency domain contention (S-FDC) is an efficient wireless contention mechanism based on orthogonal frequency-division multiplexing (OFDM). In each round of S-FDC, each node randomly selects and signals on one of the OFDM subcarriers while listening to all subcarriers simultaneously. Each node independently detects the activated subcarrier with the smallest index and regards it as the winner subcarrier. Accurately detecting the winner subcarrier is crucial to ensuring the functionality of the network. However, it is extremely difficult in practice due to power leakage problems that arise from frequency asynchronization among nodes. In this paper, we propose a gated-recurrent-unit (GRU)-based model that incorporates S-FDC’s unique features as domain knowledge to enable accurate winner subcarrier detection. Extensive simulation experiments verify that our DL model is effective and outperforms state of the art. This research highlights the potential of DL in improving wireless communication systems. © 2024 Copyright held by the owner/author(s).

关键词： Frequency domain analysis

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Deep Learning Model for Stock Prediction Incorporating Sentiment Factors 24

Deep Learning Model for Stock Prediction Incorporating Senti...

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8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Xiao, Pengfei Guan, Rong Lin, Zhen Mao, Jian You, Zhining School of Computer Technology Jimei University Fujian Xiamen China

ISBN: (纸本)9798400710094

Deep learning was a machine learning method based on artificial neural networks that enabled the learning and abstraction of features through multi-level non-linear models, thus facilitating the resolution of complex pattern recognition and prediction problems. Stock forecasting was the analysis of historical data and market trends with the objective of predicting future stock price trends and associated potential risks. Conventional stock prediction techniques based on deep learning typically relied on structured information, such as stock price time series and fundamental data, as a foundation for analysis and prediction. It had been observed that there was a close connection between stock market returns and investor sentiment, which demonstrated a positive correlation. However, prediction through sentiment analysis was not currently widely available. In light of this, this study introduced sentiment factors into the training of three models, namely LSTM, GRU, and Informer. The results demonstrate that the prediction accuracy of the model with additional sentiment factors is significantly enhanced compared to the model using only stock price data. Furthermore, the Informer-Sentiment model exhibits the most promising prediction effect among the three models incorporating sentiment factors. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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