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Clustering of Ionospheric Irregularities based on Spatiotemporal ROTI Keogram Images

Clustering of Ionospheric Irregularities based on Spatiotemp...

International Conference on Electrical engineering/Electronics, Computer, Telecommunications and information technology, ECTI-CON

作者： Gleb Mutasov Lin Min Min Myint Pornchai Supnithi Jirapoom Budtho Napat Tongkasem Michi Nishioka School of Engineering King Mongkut's Institute Technology of Ladkrabang Bangkok Thailand National Institute of Information and Communication Technology Tokyo Japan

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

Ionospheric irregularities associated with Equatorial plasma bubbles (EPB) can significantly impact navigation and communication systems. Therefore, their occurrences need to be studied and predicted. To solve the prediction problem, it is necessary to identify types of spatiotemporal characteristics as reference points for the predictive model. This work employs unsupervised machine learning algorithms to identify types of ionospheric irregularities due to EPB using the rate of total electron content index (ROTI) keograms. Two machine learning methods: two models, the Gaussian mixture model (GMM), and k-means, are considered. Comparative analysis is performed, and the optimal number of clusters is estimated using one classical, k-means and one additional - repeatability score, introduced in this work metric. The optimal GMM model successfully classifies three types of irregularity patterns offering valuable insights for the development of an effective EPB prediction model and enhancing our understanding of ionospheric behavior.

关键词： Measurement Machine learning algorithms Navigation Machine learning Predictive models Feature extraction Prediction algorithms

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BAGAN: Semantic Segmentation of Oil Spills in Optical Remote Sensing Images Based on Bottleneck Attention Module and Generative Adversarial Networks

BAGAN: Semantic Segmentation of Oil Spills in Optical Remote...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Zhihui Zhang Li Guo Xinzhe Wang Jianchao Fan School of Information Science and Engineering Dalian Polytechnic University Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

To address the serious threat that marine oil spills pose to the ecological environment, satellite remote sensing technology for monitoring these spills is especially important. Optical satellite remote sensing, with its benefits of high spatial and temporal resolution, high spectral resolution, and autonomous operation, can effectively track and monitor marine oil spill incidents. However, the acquisition of optical satellite data is highly susceptible to climate conditions, making it difficult to obtain sufficient data for efficient information extraction. Therefore, this article proposes a bottleneck attention generative adversarial network (BAGAN) that can accurately extract oil spill areas from visible remote sensing data. The addition of data preprocessing and bottleneck attention module (BAM) in the model has improved the accuracy of oil spill segmentation. Through the optical remote sensing data of GF-1 satellite in the Bohai Bay from 2022 to 2023, the average accuracy reached 89.11%, verifying that this model is superior to other models in semantic segmentation.

关键词： Satellites Oils Biological system modeling Semantic segmentation Optical imaging Data models Optical sensors

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Continuous Head Motion Recognition Method for Millimeter-Wave Radar

Continuous Head Motion Recognition Method for Millimeter-Wav...

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Sensors and information technology (ICSI), International Conference on

作者： Yuqing Bu Qi Zhang Fugang Wu Yuan Wang Zhuxuan Tian Shisheng Guo Institute of System Engineering China Academy of Engineering Physics Mianyang China The School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

In this paper, a continuous head motion recognition method of millimeter wave radar is studied. Firstly, a combination of standard convolution and depthwise separable convolution is used to extract features, which reduces the number of parameters and the size of the model while ensuring the accuracy. Then the Coordinate Attention mechanism is combined to filter the features, so that the network pays more attention to the important features. The feature dimension is reduced by using max pooling instead of full connection layer, and the parameter number and computation cost are reduced. The LSTM network is then used to train the time series data. Finally, the proposed method is verified by the measured data, and its effectiveness is proved.

关键词： Attention mechanisms Accuracy Convolution Millimeter wave measurements Time series analysis Millimeter wave radar Feature extraction Sensors Long short term memory Standards

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An Improved Keypoint Detection Method for Radar Point Cloud Registration in Urban Environments

An Improved Keypoint Detection Method for Radar Point Cloud ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yongqiang Wang Di Zhang Lihua Ni Qun Wan School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

The advancement of autonomous driving technology has driven a surge of applications in urban environments, where the precision of the registration of keypoint-based radar point clouds plays a crucial role in determining the overall performance of these applications. However, numerous inaccurate keypoints would be detected in the presence of multipath, beam spread, and noise, leading to the reduction of registration accuracy of the radar point cloud. To deal with this problem, we propose an improved keypoint detection method, where a novel candidate keypoint selection strategy and a threshold selection strategy are designed. The results of experiments based on a public radar dataset demonstrate that the proposed method can effectively reduce the number of inaccurate keypoints. Moreover, it achieves high precision on radar point cloud registration compared to the state-of-the-art method.

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Accurate Association of Radar-Vision Measurements Based on Weighted Euclidean Distance

Accurate Association of Radar-Vision Measurements Based on W...

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International Conference on control, Automation and information Sciences (ICCAIS)

作者： Hang Bai Hongfu Li Wei Yi Zhiyuan Zou School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

Associating radar measurements with vision measurements is essential for radar-vision fusion. Most radar-vision association methods rely on Euclidean distance. However, these methods have overlooked measurement errors, leading to a significant amount of erroneous associations. To address the limitations of existing methods, we propose an association method based on weighted Euclidean distance between radar measurements and vision measurements. This paper analyzes the measurement errors of radar and vision and uses this analysis as the basis for calculating weighted Euclidean distance, to make weighted Euclidean distance sensitive to measurement errors and reduce the occurrence of association mistakes caused by such inaccuracies. The obtained weighted Euclidean distance is then used as a cost matrix to perform association using the Hungarian algorithm. The proposed method is validated through simulations, and the experiments demonstrate that our approach achieves higher accuracy.

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Deep Parallel Structure Network for Multi-Scale Target Detection in Remote Sensing Images

Deep Parallel Structure Network for Multi-Scale Target Detec...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zelin Liu Xu Zhou Yin Zhang Jifang Pei Weibo Huo Yulin Huang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

This paper constructs a detection network that combines convolutional neural network and Transformer in parallel to address the challenges of multi-scale target detection in remote sensing images. The network utilizes global and local information interaction to further improve the effectiveness of multi-scale object detection tasks. Additionally, the network introduces both top-down and bottom-up pathways to fuse multi-scale information, and employs coordinate attention mechanism to perform feature selection. The proposed network is compared with some existing networks on the LEVIR remote sensing image dataset, and the results show that the proposed network achieves higher average detection accuracy in terms of multi-scale target detection, particularly for small objects.

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A 16.5 mW 4.0 GHz Bandwidth VGA Based on Capacitance Neutralization Technique in 65 nm CMOS

A 16.5 mW 4.0 GHz Bandwidth VGA Based on Capacitance Neutral...

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2023 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2023

作者： Chen, Yaxi Wu, Rui Liang, Xingdong Aerospace Information Research Institute Chinese Academy of Sciences National Key Laboratory of Microwave Imaging Technology Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350344288

This paper presents a wideband variable gain amplifier (VGA) based on capacitance neutralization technique for high-speed communications and high-resolution radar systems. The negative capacitance path proposed in this paper effectively compensates the output capacitance of the VGA, thus achieving the bandwidth extension. Furthermore, it ensures a flat gain over a wide bandwidth at different gain settings. The VGA is designed in a standard 65nm CMOS process. The simulation results demonstrate that the VGA achieves a -3dB bandwidth of more than 4.0 GHz with a wide gain range from -2.8 to 49.4 dB. Moreover, it consumes 16.5 mW at a supply voltage of 1 V and has a core circuit area of 0.006mm2. © 2023 IEEE.

关键词： Variable gain amplifiers

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Integrated Multimodal Fault Diagnosis of Industrial Gearboxes Using Vibration Signals and Infrared Thermal Imaging

Integrated Multimodal Fault Diagnosis of Industrial Gearboxe...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Xunqi Cao Yan Li Kun Yu Yongchao Zhang School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Mechanical Engineering and Automation Northeastern University Shenyang China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

With the increasing demand for precision in fault diagnosis of gearboxes in complex systems, diagnostic methods based solely on vibration signal characteristics have shown limitations. Therefore, multimodal information fusion technology has become a new trend in the field of gearbox fault diagnosis. This article innovatively proposes a multimodal diagnostic method that combines vibration signals with thermal images, aiming to use thermal imaging technology to intuitively capture the thermal changes during gearbox operation and integrate vibration signals to comprehensively reflect fault characteristics. By introducing Convolutional Neural Networks (CNN) for feature depth extraction, this method effectively improves the parsing ability of fault information, thereby enhancing the accuracy and reliability of fault diagnosis. Experimental data confirms that compared to diagnostic methods using a single signal source, the method proposed in this paper performs well in fault detection of helical gearboxes, significantly improving diagnostic accuracy and precision.

关键词： Fault diagnosis Vibrations Accuracy Soft sensors Imaging Feature extraction Data models Convolutional neural networks Data mining Thermal stability

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Application of neural-network hybrid models in estimating the infection functions of nonlinear epidemic models

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International Journal of Biomathematics 2024年第6期17卷 141-159页

作者： Chentong Li Changsheng Zhou Junmin Liu Yao Rong Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangdong Academy of Science Guangzhou Guangdong 510070P.R.China School of Mathematics and Information Science Guangzhou University Guangzhou Guangdong 510006P.R.China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an Shaanxi 710049P.R.China College of Engineering Physics Shenzhen Technology University Shenzhen Guangdong 518118P.R.China

Hybrid neural network models are effective in analyzing time-series data by combining the strengths of neural networks and differential equation *** most studies have focused on linear hybrid models,few have examined nonlinear *** work explores the potential of a hybrid nonlinear epidemic neural network in predicting the correct infection function of an epidemic *** design a novel loss function by combining bifurcation theory and mean-squared error loss to ensure the trainability of the hybrid ***,we identify unique existence conditions that support ordinary differential equations for estimating the correct infection ***,numerical experiments using the Runge-Kutta method confirm our proposed model's soundness both on our synthetic data and the real COVID-19 data.

关键词： Differential equations epidemic model hybrid model neural network

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Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management 3

Cloud-Based IoT Solutions for Smart Grids: Advancing Smart T...

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3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Antonius Alijoyo, Franciskus Vidhate, Deepak A. Kaur, Chamandeep Nivedan, V. Rani, V. Kanpur Balakumar, A. School of Business and Information Technology STMIK LIKMI Bandung Indonesia Dr. Vithalrao Vikhe Patil College of Engineering Department of Information Technology Maharashtra Ahmednagar India Jazan University Department of Computer Science Jazan Saudi Arabia Sri Ramakrishna College of Arts & Science Department of Electronics and Communication System Coimbatore India Hindustan Institute of Technology and Science Chennai India K.Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350390728

The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future. © 2024 IEEE.

关键词： Cloud platforms

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