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International Conference on Intelligent Transportation

作者： Xiaolin Luo Dongming Wang Wei Wang Yindong Ji Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of California Riverside CA USA Traffic Control Technology Co. Ltd Beijing China Department of Automation Tsinghua University Beijing China School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Virtual coupling technologies have been widely studied in recent years, due to their merits of flexibility and efficiency. However, to operate a virtually coupled train set (VCTS) as a single train, the synchronous operation is still a critical problem to be addressed, especially in arriving. To this end, we propose a distributed coordinated optimal control (DCOC) approach integrated with distributed optimization and model predictive control. First, we formulate a globally coordinated optimal control problem for VCTS, where the states of units in VCTS are coupled in the cost and constraints. Then, we design a distributed algorithm to solve the global problem, where local problems are decoupled into ones with fewer dimensions. Next, we employ distributed model predictive control to guarantee the satisfaction of safety constraints before the convergence of the distributed algorithm. Finally, experimental results demonstrate that our approach can achieve stable and synchronous operation for VCTS and save by 90% computation time than existing centralized approaches.

关键词： Optimal control Robustness Safety Topology Synchronization Distributed algorithms Optimization Intelligent transportation systems Predictive control Convergence

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Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation

arXiv

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arXiv 2023年

作者： Li, Shilei Shi, Dawei Lou, Yunjiang Zou, Wulin Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China Xeno Dynamics Control Department Xeno Dynamics Co. Ltd Shenzhen518055 China

Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance simultaneously, and is optimal for a linear system with Gaussian noises. Unfortunately, The noise in the disturbance channel typically exhibits a heavy-tailed distribution because the nominal disturbance dynamics usually do not align with the practical ones. To handle this issue, we propose a generalized multi-kernel maximum correntropy Kalman filter for disturbance estimation, which is less conservative by adopting different kernel bandwidths for different channels and exhibits excellent performance both with and without external disturbance. The convergence of the fixed point iteration and the complexity of the proposed algorithm are given. Simulations on a robotic manipulator reveal that the proposed algorithm is very efficient in disturbance estimation with moderate algorithm complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Kalman filters

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Create Your World: Lifelong Text-to-Image Diffusion

arXiv

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arXiv 2023年

作者： Sun, Gan Liang, Wenqi Dong, Jiahua Li, Jun Ding, Zhengming Cong, Yang State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China The School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu210094 China The Department of Computer Science Tulane University New OrleansLA70118 United States The College of Automation Science and Engineering South China University of Technology Guangzhou510640 China University of Chinese Academy of Sciences Beijing100049 China

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc. We in this work study the problem of synthesizing instantiations of a user's own concepts in a never-ending manner, i.e., create your world, where the new concepts from user are quickly learned with a few examples. To achieve this goal, we propose a Lifelong text-to-image Diffusion Model (L2DM), which intends to overcome knowledge "catastrophic forgetting" for the past encountered concepts, and semantic "catastrophic neglecting" for one or more concepts in the text prompt. In respect of knowledge "catastrophic forgetting", our L2DM framework devises a task-aware memory enhancement module and a elastic-concept distillation module, which could respectively safeguard the knowledge of both prior concepts and each past personalized concept. When generating images with a user text prompt, the solution to semantic "catastrophic neglecting" is that a concept attention artist module can alleviate the semantic neglecting from concept aspect, and an orthogonal attention module can reduce the semantic binding from attribute aspect. To the end, our model can generate more faithful image across a range of continual text prompts in terms of both qualitative and quantitative metrics, when comparing with the related state-of-the-art models. The code will be released at https://***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Integration of Convolutional Neural Networks for Real-Time Monitoring of Soil Health in Precision Agriculture

Integration of Convolutional Neural Networks for Real-Time M...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Tummapudi Sunil Krishnagandhi Pachiappan S. Senthilrajan Y. Nagendar Renato R. Maaliw C. Pavin Department of CSE Malla Reddy Engineering College Malkajgiri Hyderabad India Electrical and Electronics Engineering Nandha Engineering College Erode Tamil Nadu India Department of Robotics& Automation Rajalakshmi Engineering College Chennai Tamil Nadu India Department of School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of CSE Southern Luzon State University Lucban Quezon Philippines Biomedical Engineer YAR Tech Services Hyderabad India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Convolutional Neural Networks (CNNs) represent a revolutionary breakthrough in improving crop productivity and sustainability when integrated into real-time monitoring systems for soil health in precision agriculture. CNNs are used in a novel way to analyze soil parameters directly from high-resolution photos taken by in-field sensors, such as moisture content, $\mathbf{p H}$ levels, and nutrient concentration. To forecast soil health measures, a CNN model was trained and validated using a collection of soil samples. The model achieved an accuracy rate of approximately $\mathbf{9 2 \%}$. The CNN-based technique shortens the time needed for soil health evaluation by almost $80 \%$ when compared to conventional approaches that involve manual sampling and laboratory analysis, allowing for quick corrective actions in the field. Additionally, more accurate and timely soil health monitoring was found to boost crop output prediction accuracy by $15 \%$. An up to $20 \%$ decrease in the amount of resources used, especially water and fertilizers, can be achieved by using real-time data processing to make educated decisions about irrigation, fertilizing, and pest management. The CNN model offers scalability and adaptability across a range of crop varieties and soil conditions, and it can be easily integrated into current agricultural IoT frameworks. All things considered, using CNNs for soil health monitoring offers an economical and effective way to improve the accuracy and output of contemporary farming methods.

关键词： Precision agriculture Irrigation Accuracy Soil Predictive models Real-time systems Data models Convolutional neural networks Monitoring Farming

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Improving Prediction of GNSS Satellite Visibility in Urban Canyon Based on Graph Transformer 36

Improving Prediction of GNSS Satellite Visibility in Urban C...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Zheng, Shaolong Li, Zhenni Wang, Qianming Xie, Kan Liu, Ming Xie, Shengli Polycarpou, Marios 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 Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Rsearch and Innovation Center of Excellence Nicosia Cyprus

ISBN: (纸本)9780936406350

Signals from global navigation satellite systems (GNSS) suffer from serious multipath errors in urban areas caused by building blockages and reflections. The use of deep neural networks offer great potential for predicting and eliminating complex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first is the inability to exploit effectively the irregular GNSS dataset because of inconsistent numbers of visible satellites in different epochs. The second is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network for predicting satellite visibility that effectively learns environment representations from the irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from the irregular GNSS measurements, a sky satellite graph is constructed as the input to a graph neural network by using the satellites captured in the same epoch, which can represent the spatial relationships between the satellites and enhance the model to enable learning of satellite-related features sufficiently well. To improve generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites for extracting the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, we develop a novel graph transformer neural network (GTNN) for predicting satellite visibility, which can not only handle irregular GNSS measurements but also learn an environment representation via graph attention. Comparative experiments were carried out on real-world GNSS meas

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Demonstrating Autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot

Demonstrating Autonomous 3D Path Planning on a Novel Scalabl...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Eric Sihite Filip Slezak Ioannis Mandralis Adarsh Salagame Milad Ramezani Arash Kalantari Alireza Ramezani Morteza Gharib The Department of Aerospace Engineering California Institute of Technology Pasadena USA Department of Electrical and Computer Engineering The Silicon Synapse Labs Northeastern University Boston USA The Robotics and Autonomous Systems DATA61 CSIRO Brisbane QLD Australia The Jet Propulsion Laboratory Pasadena USA

Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and walk. This capability is extremely beneficial for expanding the animal's habitat range and they can choose the most energy efficient mode of locomotion in a given environment. The robotic biomimicry of this multi-modal locomotion capability can be very challenging but offer the same advantages. However, the expanded range of locomotion also increases the complexity of performing localization and path planning. In this work, we present our morphing multi-modal robot, which is capable of ground and aerial locomotion, and the implementation of readily available SLAM and path planning solutions to navigate a complex indoor environment.

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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Inner-approximate Reachability Computation via Zonotopic Boundary Analysis

arXiv

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arXiv 2024年

作者： Ren, Dejin Liang, Zhen Wu, Chenyu Ding, Jianqiang Wu, Taoran Xue, Bai Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Computer Science and Technology National University of Defense Technology Changsha China Department of Electrical Engineering and Automation Aalto University Espoo Finland

Inner-approximate reachability analysis involves calculating subsets of reachable sets, known as inner-approximations. This analysis is crucial in the fields of dynamic systems analysis and control theory as it provides a reliable estimation of the set of states that a system can reach from given initial states at a specific time instant. In this paper, we study the inner-approximate reachability analysis problem based on the set-boundary reachability method for systems modelled by ordinary differential equations, in which the computed inner-approximations are represented with zonotopes. The set-boundary reachability method computes an inner-approximation by excluding states reached from the initial set’s boundary. The effectiveness of this method is highly dependent on the efficient extraction of the exact boundary of the initial set. To address this, we propose methods leveraging boundary and tiling matrices that can efficiently extract and refine the exact boundary of the initial set represented by zonotopes. Additionally, we enhance the exclusion strategy by contracting the outer-approximations in a flexible way, which allows for the computation of less conservative inner-approximations. To evaluate the proposed method, we compare it with state-of-the-art methods against a series of benchmarks. The numerical results demonstrate that our method is not only efficient but also accurate in computing inner-approximations. Copyright © 2024, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Privacy Preservation for Cloud-Edge-Collaborative Energy Management System Using Post-Quantum Homomorphic Encryption

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IEEE Transactions on Smart Grid 2025年

作者： Jiang, Cheng Li, Xue Du, Dajun Qian, Hong Wen, Mi Wu, Lei Findeisen, Rolf Shanghai University of Electric Power Shanghai Key Laboratory of Power Station Automation Technology College of Automation Engineering Shanghai200090 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States TU Darmstadt Control and Cyber-Physical Systems Laboratory Darmstadt64227 Germany

Cloud-based energy management systems (EMS) in smart grids face privacy challenges, as existing methods based on traditional homomorphic encryption support limited operations and are vulnerable to quantum attacks. We propose a privacy preservation method for smart grid energy management system, leveraging edge-assisted computing and ring learning with errors (RLWE) homomorphic encryption. Our approach enables both addition and multiplication operations on encrypted smart grid data while defending against quantum threats. We demonstrate the methods security under the RLWE hardness assumption and validate its effectiveness through an experimental platform simulating smart grid environments. This work addresses computational limitations in current privacy-preserving techniques, enhancing data security for evolving smart grid technologies © 2010-2012 IEEE.

关键词： Power management (telecommunication)

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