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Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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Improved Synthesis of Nondeterministic Publicly-Known Edit Functions for Opacity Enforcement

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IEEE Transactions on Automatic control 2025年

作者： Liu, Rongjian Hadjicostis, Christoforos N. Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus

In this paper, we investigate the synthesis problem of edit functions for opacity enforcement in systems modelled as partially-observed finite-state automata. For better plausible deniability for the edit functions, in cases where the intruder may learn/know their existence, a synthesis problem for nondeterministic edit functions under a publicly-known setting was introduced in the literature, but not yet fully solved. In light of this, we revisit the publicly-known edit mechanism by proposing a sound and complete approach for solving the synthesis problem of nondeterministic publicly-known edit functions. For practical applications, the edit function is assumed to have limited erasure and insertion ability. Our approach is based on a new labelled finite-state structure, and results in wider applicability than previously-proposed in the literature. © 1963-2012 IEEE.

关键词： Discrete event simulation

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FROST: Fusion and Multimodal 3D Reconstruction of Icy Surfaces for Robotic Exploration

FROST: Fusion and Multimodal 3D Reconstruction of Icy Surfac...

引用

Computational Intelligence on engineering/Cyber Physical Systems Companion (CIES Companion), IEEE Symposium on

作者： Huy Xuan Pham Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (Air Lab) Aarhus University Aarhus C Denmark Automatic Control Group (RAT) Paderborn University Paderborn Germany

ISBN: (数字)9798331508494

ISBN: (纸本)9798331508500

Three-dimensional (3D) reconstruction serves as a cornerstone in various robotic applications, playing critical roles in scene understanding and navigation. Traditionally, LiDAR has been instrumental in generating precise point clouds of the environment, providing essential data for these applications. However, the efficacy of LiDAR sensors is significantly hindered in challenging conditions, such as the presence of water or icy surfaces. The complex interplay between laser beams and icy or non-ideal surfaces can result in signal degradation, distortion, or even complete signal loss, adversely affecting the accuracy and reliability of the 3D reconstruction process. The reflective and refractive properties of ice, along with its variable surface conditions, present challenges that traditional LiDAR sensors struggle to address. This paper proposes a diverse dataset to facilitate a multimodal approach for reconstructing icy surfaces using various sensors. A preliminary study based on this dataset demonstrates the feasibility of combining the geometric surface obtained from consecutive LiDAR scans with reconstructed meshes from visual cameras. The integration of distinct data sources has the potential to improve the robustness of reconstruction algorithms in diverse scenarios. The dataset can be downloaded at https://***/records/13862009.

关键词： Point cloud compression Surface reconstruction Laser radar Three-dimensional displays Navigation Ice Robustness Laser beams Image reconstruction Surface treatment

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Calibration-free method of recovering the time-varying absorbance profile in TDLAS by the Fourier reconstructed spectroscopy

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Yang, Youquan Peng, Zhimin Zheng, Dao Ding, Yanjun Du, Yanjun School of Control and Computer Engineering North China Electric University Beijing102206 China State Key Laboratory of Power Systems Department of Energy and Power Engineering Tsinghua University Beijing100084 China Shanxi Research Institute of Huairou Laboratory Taiyuan030032 China

Traditional scan-direct absorption spectroscopy (scan-DAS) is susceptible to 1/f noise, limiting its ability to realize high signal-to-noise ratio (SNR) measurements. This study proposes a calibration-free and noise-immune method, named fast scan–Fourier reconstructed spectroscopy (FC-FRS), capable of improving the SNR of the single-shot absorbance spectrum and reconstructing the time-varying absorbance without decreasing the time resolution. Two validation experiments with CO2 absorbance varying over time were designed using a shock tube device within the millisecond time duration. In the first validation experiment, CO2 was gradually released during the combustion of hydrocarbons, resulting in the slowly increased CO2 absorbance. The average spectral SNR of FC-FRS reconstructed absorbance at 2100 K reached 73.51, which increased by 22.83 % compared to the original scan-DAS spectrum. The CO2 generation profile extracted from the recovered absorbance agreed well with the prediction of kinetic mechanisms. In the second experiment where the CO2 absorbance was stable behind the reflected shock at 1312 K, the average spectral SNR of the recovered absorbance line behind the reflected shock was 433.81, which is 44 % higher than that of the scan-DAS spectrum. The averaged fitted line strength from the recovered absorbance curve had a relative error of only 1.07 % compared to the HITAN2020 database. Finally, the proposed method was compared with the 2f/1f method of scan-WMS in a static CO measurement experiment, the FC-FRS showed the similar SNR but better stability over time. © 2025 Elsevier Ltd

关键词： Absorption spectroscopy

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A Survey on Network Management for xANET: Evolution, Challenges, Future Directions

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IEEE Communications Surveys and Tutorials 2025年

作者： Li, Tong Yang, Chungang Wang, Yao Cai, Lin Anpalagan, Alagan Han, Zhu Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China National Key Laboratory of Multi-domain Data Collaborative Processing and Control Xi'an710068 China Hangzhou Institute of Technology of Xidian University Hangzhou311231 China University of Victoria Department of Electrical and Computer Engineering VictoriaBCV8P 5C2 Canada Ryerson University Department of Electrical and Computer Engineering TorontoONM5B 2K3 Canada University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446701 Korea Republic of

The x ad hoc network (xANET) termed as a family of ad hoc networks, including mobile ad hoc network (MANET), vehicular ad hoc network (VANET), flying ad hoc network (FANET) and satellite ad hoc network (SANET), has found a wide range of applications in providing ubiquitous wireless services. Despite its broad utility, the dynamic nature and lack of a centralized controller pose significant challenges to effective and flexible network management for xANET. Conventional network management protocols face challenges such as scalability, security vulnerabilities, configuration complexity, robustness, and performance resilience. Recent efforts have presented different approaches, focusing on policy-based network management (PBNM) and intent-driven network management (IDNM). However, there is no comprehensive survey to clarify their concepts and classifications. This paper presents a survey of the network management evolution for xANET, covering configuration-based, policy-based and the latest intent-driven approaches. We first introduce the characteristics and applications of xANET. Meanwhile, we investigate the network management concepts and challenges. Moreover, we survey the evolution of management protocols for xANET, including simple network management protocol (SNMP), PBNM, and IDNM. Then, we follow the detailed network management of xANET from configuration-based to policy-based and intent-driven approaches. Through comparative analysis, it is found that IDNM employs a more intelligent management protocol, demonstrating higher efficiency and flexibility in handling complex tasks and dynamic network management. This makes it better suited to addressing the challenges of xANET management. Finally, we summarize the remaining challenges and possible future research directions. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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Accelerating Stable Matching between Workers and Spatial-Temporal Tasks for Dynamic MCS: A Stagewise Service Trading Approach

arXiv

引用

arXiv 2025年

作者： Qi, Houyi Liwang, Minghui Wang, Xianbin Fu, Liqun Hong, Yiguang Li, Li Cheng, Zhipeng School of Informatics Xiamen University Fujian China Department of Control Science and Engineering Canada Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Jiangsu China

Designing proper incentives in mobile crowdsensing (MCS) networks represents a critical mechanism in engaging distributed mobile users (workers) to contribute heterogeneous data for diverse applications (tasks). We develop a novel stagewise trading framework to reach efficient and stable matching between tasks and workers, upon considering the diversity of tasks and the dynamism of MCS networks. This framework integrates futures and spot trading stages, where in the former, we propose futures trading-driven stable matching and pre-path-planning (FT-SMP3) for long-term task-worker assignment and pre-planning of workers’ paths based on historical statistics and risk analysis. While in the latter, we investigate spot trading-driven DQN path planning and onsite worker recruitment (ST-DP2WR) mechanism to enhance workers’ and tasks’ practical utilities by facilitating temporary worker recruitment. We prove that our proposed mechanisms support crucial properties such as stability, individual rationality, competitive equilibrium, and weak Pareto optimality theoretically. Also, comprehensive evaluations confirm the satisfaction of these properties in practical network settings, demonstrating our commendable performance in terms of service quality, running time, and decision-making overheads. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk analysis

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Reinforcement Learning-Enhanced Active Disturbance Rejection control for Mobile Robot Trajectory Tracking

Reinforcement Learning-Enhanced Active Disturbance Rejection...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： H. Usama M. Nasir R. Fareh J. Ghommam S. Khadraoui M. Bettayeb Research Institute of Sciences and Engineering University of Sharjah Sharjah UAE Department of Electrical Engineering University of Sharjah Sharjah UAE Department of Electrical and Computer Engineering Sultan Qaboos University Oman Control and Energy Management Laboratory (CEM Lab) Ecole National d'Ingenieur de Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

In recent years, the application of robotics has significantly advanced many fields, providing robust performance and efficiency in complex tasks without the need for human intervention. Robot control is a key area of robotics that has received a lot of interest and technological development. Active Disturbance Rejection controllers (ADRCs) are widely adopted due to several features, including their ability to maintain robust performance and effectively reject disturbances. The manual tuning of controller parameters is time-consuming and highlights the need for automation. This paper presents an innovative approach to tune the parameters of ADRC using Reinforcement Learning (RL) based on Deep Deterministic Policy Gradient (DDPG) for a Differential Drive Mobile Robot (DDMR). The RL agent learns the ideal parameters of the ADRC through real-time, iterative interactions with the simulated environment, improving ADRC's performance without manual tuning. Simulation results demonstrate the effectiveness of the proposed idea in improving the trajectory tracking performance. Combining RL and ADRC provides a promising automated controller tuning solution, opening the door to more intelligent and adaptive robotic systems. 10.0pt.

关键词： Robust control Trajectory tracking Simulation Reinforcement learning Manuals Scattering parameters Mobile robots Artificial intelligence State estimation Tuning

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MedFILIP: Medical Fine-grained Language-Image Pre-training

arXiv

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arXiv 2025年

作者： Liang, Xinjie Li, Xiangyu Li, Fanding Jiang, Jie Dong, Qing Wang, Wei Wang, Kuanquan Dong, Suyu Luo, Gongning Li, Shuo School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Thoracic Surgery at No. 4 Affiliated Hospital Harbin Medical University Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of computer and control engineering Northeast Forestry University Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH United States

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestX-ray14, VinBigData, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69%. The code is available in https://***/ PerceptionComputingLab/MedFILIP. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Transforming Healthcare Diagnostics With Tensorized Attention and Continual Learning on Multi-Modal Data

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Shabaz, Mohammad Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Algamdi, Shabbab Ali Li, Yang Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Model Institute of Engineering and Technology J and K Jammu India Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia Prince Sattam bin Abdulaziz University College of Computer Science and Engineering Department of Software Engineering Al Kharj Saudi Arabia Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China

Analyzing multi-modal medical data in the setting of uncertain healthcare situations continues to be a major topic in medical image analysis and healthcare big data. Traditional machine learning algorithms are severely hampered by inaccurate data fusion, a lack of adaptability to changing patient data, and challenges managing uncertainty. These difficulties are made worse by complicated medical images and diverse data sources, which results in less accurate diagnosis and worse-than-ideal healthcare choices. To tackle these urgent problems, this paper suggests two new approaches: Continual Learning using Progressive Neural Networks (PNNs) and Tensorized Attention Mechanism for Data Fusion. The Tensorized Attention Mechanism improves multi-modal data fusion by using dynamic, task-specific attention to improve feature alignment across modalities, and the PNNs framework uses continual learning, memory augmentation, and domain adaptation to ensure robust learning under data uncertainty. We test these methods on a variety of multi-modal datasets, such as MIMIC-IV, CheXpert, MOST, OAI, and Heart Murmur, which offer a comprehensive representation of medical data from clinical reports, chest X-rays, heart murmurs, and other heterogeneous data sources. Our experimental results show notable improvements in diagnostic performance, with notable results like a CFI of 0.10, a KR score of 90.4%, and an MMC score of 0.097, indicating superior generalization and robustness across domains. Healthcare AI applications could be revolutionized by the use of specialized losses, such as Conditional Variational Autoencoder (CVAE), Adversarial Contrastive Learning (ACL), Reciprocal Regularization, and domain adaptation losses, which are essential for preventing forgetting and guaranteeing learning stability across shifting data streams. © 1975-2011 IEEE.

关键词： Sensor data fusion

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