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Improving NLOS/LOS Classification Accuracy in Urban Canyon Based on Channel-Independent Patch Transformer with Temporal information

Improving NLOS/LOS Classification Accuracy in Urban Canyon B...

引用

2024 International Technical Meeting of The Institute of Navigation, ITM 2024

作者： Chen, Jiajun Wang, Jingran Zheng, Shaolong Liu, Yujie Li, Zhenni Xie, Shengli Wang, Qianming School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education China

ISBN: (纸本)9780936406367

The global navigation satellite systems (GNSS) positioning performance is significantly degraded due to the blocking of direct signals and errors caused by reflected signals in urban canyons. Recent studies have used deep learning or machine learning methods to distinguish the line-of-sight(LOS) and non-line-of-sight(NLOS) signals to solve multi-path problems. However, these approaches still face challenges. The visibility of satellites is relatively stable in a short period, but the features of their signals change in varying degrees. Existing methods focus on the visibility identification of satellites at a single epoch, which fails to capture the effect of the temporal features of satellite signals on visibility and ignores the complex associations between satellites at multiple continual epochs. To address the above challenge, this paper develops a novel channel-independent patch transformer neural network with temporal information, for improving the prediction of GNSS satellite visibility. Firstly, to capture the influence of individual satellite features on the classifications of NLOS signals, we adopt the concept of independent channels to disentangle the various satellite features. In this way, we construct temporal variations for each feature and then independently assess the effect of these feature variations on satellite visibility. Secondly, to account for the association of multiple continuous epochs satellites, we partition the constructed temporal window feature sequences into a collection of sub-sequences level patches. This patch-level structural design preserves the semantic associations of multiple epochs satellites while also maintaining the ability to focus across a sufficient number of epochs. Finally, based on the idea of channel independence and patch, we develop a novel channel-independent patch transformer (CIPT) neural network with temporal information for predicting satellite visibility, which can not only learn the effect of individual f

关键词： Satellites

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Automatic Recognition of Whale and Dolphin Whistles Based on Visual Feature Extraction Approach 2

Automatic Recognition of Whale and Dolphin Whistles Based on...

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2nd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Wu, Wei Feng, Huibin Junejo, Naveed Ur Rehman Qiu, Yuquan Yan, Jiaquan College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China University of Lahore Department of Computer Engineering Lahore Pakistan

ISBN: (纸本)9781665442763

Automatic recognition of marine mammals plays a significant role in the protection of endangered marine species, generally utilizing their vocalizations. In this paper, we propose a novel visual feature extraction approach for whale and dolphin signals incorporating variational mode decomposition (VMD) and Hilbert transform (VMD-HT) to construct the high-resolution time-frequency signatures and using gray level co-occurrence matrix (GLCM) to extort image features. Firstly, the objected signals are decomposed by VMD algorithm into multiple intrinsic mode functions (IMFs), which can capture local frequency features precisely. Secondly, the time-frequency signature of the objected signal with high resolution is constructed by linear superposition operation of each IMF time-frequency image depicted through Hilbert transform (HT). Thirdly, the image features are extracted using GLCM. Eventually, support vector machine (SVM) is adopted to validate the classification performance of the proposed method. The experimental results verify that the resolution improvement of time-frequency image attains the correct recognition rate and the proposed method accuracy is higher than the compared method about 11.11% © 2022 IEEE.

关键词： Variational mode decomposition

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Phase Retrieval by Tensor Total Least Squares

Phase Retrieval by Tensor Total Least Squares

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jiani Liu Ce Zhu Yang Chen Xiaolin Huang Yipeng Liu School of Information and Communication Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China Department of Automation The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

Phase retrieval seeks to reconstruct a series of image sequences from measurements that only capture their magnitudes. Current approaches either flatten and stack the image sequences, disregarding their multidimensional structural information, or fail to account for errors within the sensing vectors/tensors. To address these two issues simultaneously, we propose a unified framework for the phase retrieval problem, namely tensor total least squares (TTLS). Specifically, we set up a tensor representation for image sequences and the corresponding measurement model, and for the first time employ the advanced tensor ring network to effectively explore the inherent multidimensional structure for more accurate estimation. Moreover, in addition to the additive noise, the multiplicative errors within the sensing tensor can be also well-corrected, leading to a more robust estimation. Experimental results on both simulated data and real videos demonstrate the superiority of the proposed method.

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Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning 13

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncer...

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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Zhao, Tingting Li, Zuoyong Quan, Li Zeng, Kun Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Computer Science and Big Data Fuzhou University Fuzhou350121 China

ISBN: (纸本)9798350319156

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance. © 2023 IEEE.

关键词： Textures

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Security-Aware Reinforcement Learning under Linear Temporal Logic Specifications

Security-Aware Reinforcement Learning under Linear Temporal ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bohan Cui keyi Zhu Shaoyuan Li Xiang Yin Department of Automation Shanghai Jiao Tong University Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China

In this paper, we investigate the problem of reinforcement learning under linear temporal logic (LTL) specifications for Markov decision processes (MDPs) with security constraints. We consider an outside passive intruder (observer) that can observe the external output behavior of the system through an output projection. We assume that the secret of the system is a subset of the initial states. The security constraint requires that the observer can never infer for sure that the agent was initiated from a secret state. Our objective is to learn a control policy that achieves the LTL task while ensuring security. To solve the problem of shaping the reward for reinforcement learning, we propose an approach based on the initial-state estimator and the limit deterministic Büchi automata. We illustrate the proposed approach by a case study of mobile robot example.

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Streaming-Data-Driven Distributionally Robust Joint Operation of Multi-Microgrids and Off-Site Hydrogen Refueling Stations under Uncertainties 11

Streaming-Data-Driven Distributionally Robust Joint Operatio...

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11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Li, Longyan Ning, Chao Qiu, Haifeng Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China School of Electrical & Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798350399660

This paper proposes a novel online-learning-enabled hierarchical distributionally robust energy management framework for multi-microgrids (MMGs) with off-site hydrogen refueling stations (HRSs). The proposed framework integrates day-ahead scheduling, intra-day rescheduling and real-time control. The day-ahead scheduling is implemented via a central energy management system for optimal joint scheduling of the MMG systems and off-site HRSs, then the intra-day rescheduling is carried out to optimize the real-time operation and to generate the reference for local controllers. To adequately characterize the statistical properties of renewable and load uncertainties from data streams, we leverage a Dirichlet process mixture model to construct an online-update ambiguity set, which encodes multimodality and local moment information. Based upon this ambiguity set, an online-learning distributionally robust model predictive control (OL-DRMPC) is developed to enhance the tracking performance of energy storage references and to reduce conservatism while guaranteeing probabilistic operational constraints. Its effectiveness is verified in a case study. © 2022 IEEE.

关键词： Real time control

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An OpenPLC-based Active Real-time Anomaly Detection Framework for Industrial control systems

An OpenPLC-based Active Real-time Anomaly Detection Framewor...

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2022 Chinese Automation Congress, CAC 2022

作者： Zheng, Chengxu Wang, Xiaopeng Luo, Xiaoyu Fang, Chongrong He, Jianping Shanghai Jiao Tong University The Key Laboratory of System Control and Information Processing Ministry of Education of China And Shanghai Engineering Research Center of Intelligent Control and Management The Dept. of Automation Shanghai China NSFOCUS Technologies Group Co. Ltd Beijing China

ISBN: (纸本)9781665465335

In recent years, the design of anomaly detectors has attracted a tremendous surge of interest due to security issues in industrial control systems (ICS). Restricted by hardware resources, most anomaly detectors can only be deployed at the remote monitoring ends, far away from the control sites, which brings potential threats to anomaly detection. In this paper, we propose an active real-time anomaly detection framework deployed in the controller of OpenPLC, which is a standardized open-source PLC and has high scalability. Specifically, we add adaptive active noises to control signals, and then identify a linear dynamic system model of the plant offline and implement it in the controller. Finally, we design two filters to process the estimated residuals based on the obtained model and use χ2 detector for anomaly detection. Extensive experiments are conducted on an industrial control virtual platform to show the effectiveness of the proposed detection framework. © 2022 IEEE.

关键词： Anomaly detection

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Research on Rolling Bearing Fault Diagnosis Based on Classification Generative Adversarial Network

Research on Rolling Bearing Fault Diagnosis Based on Classif...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Kang, Ma Hongjun, Wang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science and Technology Cooperation Base for Intelligent Sensing and Control of High-End Equipment Beijing100192 China Beijing Key Laboratory of Measurement and Control of Electromechanical System Beijing100192 China

ISBN: (纸本)9783031494123

In practical engineering, it is usually difficult to label monitoring data, and the fault diagnosis accuracy is not high in strong noise environment. To solve the above problems, this paper uses Cwt-CatGAN method to process signals and diagnose faults. In this method, CWT is first used to convert one-dimensional original vibration signals into two-dimensional time–frequency graphs as the input of classification generation admixture network. Then, the CatGAN model is obtained through the adversarial training process, and fake samples similar to the extraction distribution of CWT are generated. Finally, the input samples are clustered into certain categories. In addition, the performance of the Cwt-CatGAN method is verified by using the classical rotating machine data set, the experiment is compared with the unsupervised fault diagnosis model CatAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Trajectory Optimization for Multi-Lane Platoon Formation with Undefined Configurations

Trajectory Optimization for Multi-Lane Platoon Formation wit...

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International Conference on Intelligent Transportation

作者： Siwen Yang Yunwen Xu Dewei Li Chen Zhang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

Many studies have demonstrated the potential benefits of connected and autonomous vehicles (CAVs) platooning to energy consumption and traffic efficiency. However, how to implement platoon formation, which assigns and regulates vehicles scattered in different lanes to platoons, while reducing vehicle delay and time consumption remains an open issue. Besides, most existing studies require a predetermined platoon configuration, which decreases maneuverability. This paper proposes a multilane platoon formation strategy with undefined configurations. The vehicle sequence in newly formed platoons along with the longitudinal trajectories of all vehicles before changing lanes is optimized in a nonlinear programming model. The multi-objective function considers the average velocity of all CAVs and the time consumption of formation. A Fibonacci search algorithm with an embedded mixed integer linear programming (MILP) model is developed to obtain solutions efficiently. The numerical experiments indicate the superiority of the proposed method in optimizing the vehicles' trajectories and improving multi-lane platoon formation efficiency.

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86-Gb/s Optical Chaos Communication over 100-km Fiber Transmission Using Semiconductor Lasers 49

86-Gb/s Optical Chaos Communication over 100-km Fiber Transm...

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49th European Conference on Optical Communications, ECOC 2023

作者： Mao, Xiaoxin Fan, Xiaoqi Wang, Longsheng Fu, Songnian Wang, Yuncai Wang, Anbang Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province Taiyuan030024 China College of Optoelectronics Taiyuan University of Technology Taiyuan030024 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangzhou510006 China

An optical-chaos secure communication of 86-Gb/s 16-Ary QAM signal over 100-km fiber transmission is experimentally demonstrated under the 20%-overhead SD-FEC BER threshold of 2.0×10-2 by using wideband chaos syn... 详细信息

ISBN: (纸本)9781839539954

关键词： Semiconductor lasers

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