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arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

arXiv

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

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Reconfigurable topological modes in acoustic non-Hermitian crystals

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Physical Review B 2023年第20期107卷 L201108-L201108页

作者： Haiyan Fan He Gao Tuo Liu Shuowei An Xianghong Kong Guoqiang Xu Jie Zhu Cheng-Wei Qiu Zhongqing Su Department of Mechanical Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong SAR China Department of Electrical and Computer Engineering National University of Singapore Singapore 116083 Singapore Key Laboratory of Noise and Vibration Research Institute of Acoustics Chinese Academy of Sciences Beijing 100190 China Institute of Acoustics School of Physics Science and Engineering Tongji University Shanghai 200092 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai 201210 China

Non-Hermiticity, usually represented in the context of gain and loss, gives rise to many exotic topological phenomena and offers more opportunities to steering topological functions. In modern acoustics, it has been widely perceived that the topological mode will be altered if topological phase changes. Our work shows otherwise in non-Hermitian acoustic crystals. We experimentally demonstrate an acoustic quadrupole topological insulator, whose topological corner, edge, and bulk modes could be arbitrarily engineered at any desired positions with its topological phase maintained. These non-Hermiticity-controlled topological modes bestow a bulk structure with unique features beyond the classical bulky state, offering a reconfigurable and versatile approach to manipulating topological phenomena. This non-Hermitian scheme can be readily generalized to other topological systems in various dimensions, such as the three-dimensional photonic/phononic lattices, which offer advanced and externally controllable recipes for manipulating topological phenomena.

关键词： Acoustic metamaterials Metamaterials Topological defects Non-Hermitean systems

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Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

Regional Medium-Term Hourly Electricity Demand Forecasting B...

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IEEE General Meeting Power& Energy Society

作者： Hongfei Sun Dongliang Duan Hongming Zhang Seong Choi Jie Rockey Luo Liuqing Yang Center of Power Systems Engineering National Renewable Energy Laboratory (NREL) Golden CO Department of Electrical and Computer Engineering University of Wyoming Laramie WY Chief Modeling Engineer with NREL Lower Colorado River Authority in Texas Department of Electrical and Computer Engineering Colorado State University Fort Collins CO Internet of Things Thrust and Intelligent Transportation Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people’s daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

arXiv

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arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects

arXiv

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arXiv 2024年

作者： Jiang, Xiaoheng Yan, Feng Lu, Yang Wang, Ke Guo, Shuai Zhang, Tianzhu Pang, Yanwei Niu, Jianwei Xu, Mingliang The School of Computer and Artificial Intelligence Zhengzhou University Engineering Research Center of Intelligent Swarm Systems Ministry of Education National Supercomputing Center in Zhengzhou Zhengzhou450001 China The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China The School of Information Science and Technology University of Science and Technology of China Hefei230026 China The School of Electrical Automation and Information Engineering Tianjin University Tianjin300072 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Hangzhou Innovation Institute of Beihang University Hangzhou310051 China

Surface defect inspection plays an important role in the process of industrial manufacture and production. Though Convolutional Neural Network (CNN) based defect inspection methods have made huge leaps, they still confront a lot of challenges such as defect scale variation, complex background, low contrast, and so on. To address these issues, we propose a joint attention-guided feature fusion network (JAFFNet) for saliency detection of surface defects based on the encoder-decoder network. JAFFNet mainly incorporates a joint attention-guided feature fusion (JAFF) module into decoding stages to adaptively fuse low-level and high-level features. The JAFF module learns to emphasize defect features and suppress background noise during feature fusion, which is beneficial for detecting low-contrast defects. In addition, JAFFNet introduces a dense receptive field (DRF) module following the encoder to capture features with rich context information, which helps detect defects of different scales. The JAFF module mainly utilizes a learned joint channel-spatial attention map provided by high-level semantic features to guide feature fusion. The attention map makes the model pay more attention to defect features. The DRF module utilizes a sequence of multi-receptive-field (MRF) units with each taking as inputs all the preceding MRF feature maps and the original input. The obtained DRF features capture rich context information with a large range of receptive fields. Extensive experiments conducted on SD-saliency-900, Magnetic tile, and DAGM 2007 indicate that our method achieves promising performance in comparison with other state-of-the-art methods. Meanwhile, our method reaches a real-time defect detection speed of 66 FPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Angel's girl for blind painters: An efficient painting navigation system validated by multimodal evaluation approach

arXiv

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arXiv 2021年

作者： Liu, Hang Hu, Menghan Chen, Yuzhen Li, Qingli Zhai, Guangtao Yang, Simon X. Zhang, Xiao-Ping Yang, Xiaokang The Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University The Key Laboratory of Articial Intelligence Ministry of Education The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph The Department of Electrical Computer and Biomedical Engineering Ryerson University

For people who ardently love painting but unfortunately have visual impairments, holding a paintbrush to create a work is a very difficult task. People in this special group are eager to pick up the paintbrush, like Leonardo da Vinci, to create and make full use of their own talents. Therefore, to maximally bridge this gap, we propose a painting navigation system to assist blind people in painting and artistic creation. The proposed system is composed of cognitive system and guidance system. The system adopts drawing board positioning based on QR code, brush navigation based on target detection and bush real-time positioning. Meanwhile, this paper uses human-computer interaction on the basis of voice and a simple but efficient position information coding rule. In addition, we design a criterion to efficiently judge whether the brush reaches the target or not. According to the experimental results, the thermal curves extracted from the faces of testers show that it is relatively well accepted by blindfolded and even blind testers. With the prompt frequency of 1s, the painting navigation system performs best with the completion degree of 89% ± 8.37% and overflow degree of 347% ± 162.14%. Meanwhile, the excellent and good types of brush tip trajectory account for 74%, and the relative movement distance is 4.21 ± 2.51. This work demonstrates that it is practicable for the blind people to feel the world through the brush in their hands. In the future, we plan to deploy "Angle's Eyes" on the phone to make it more portable. The demo video of the proposed painting navigation system is available at: https://***/10.6084/***.9760004.v1. © 2021, CC BY.

关键词： Cognitive systems

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Classifying Cover Crop Residue from RGB Images: A Simple SVM versus a SVM Ensemble

Classifying Cover Crop Residue from RGB Images: A Simple SVM...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Parth C. Upadhyay Lokesh Karanam John A. Lory Guilherme N. DeSouza Vision-Guided and Intelligent Robotics (ViGIR) Laboratory Department of Electrical Engineering and Computer Science (EECS) Division of Plant Sciences University of Missouri Columbia MO

ISBN: (纸本)9781728190495

Plant-derived crop residue on soil surface provides many important advantages including preventing erosion and conserving soil moisture. In that sense, making accurate determination on the percent of crop residue cover using RGB images can be a fundamental tool in protecting the soil. In our research, we approach the determination of such percentages as a classification problem, and in this paper, we compare two of these approaches. Both approaches relied on support vector machines (SVM) as the classifier of choice, and the same set of features, which were selected in our previous studies on the same topic. In this paper we developed a SVM ensemble with a hierarchical structure and compared it against a single, multi-class SVM classifier. In the SVM ensemble framework, four two-class SVMs and one five-class SVM were combined in sequence to better separate adjacent levels of residue cover. The rationale of the ensemble was to allow each of the two-class SVMs to find the hyperplanes that maximize the margin between the corresponding two consecutive classes. Then, based on the distance of the samples to these hyperplanes, probabilistic estimates of the data-point belonging to the class were computed and added as extra inputs for the last SVM. In order to enhance the performance of the ensemble, other considerations such as the use of Grid Search method for optimizing the hyperparameters were employed in the tuning of the SVMs. Numerical experiments were conducted over a dataset of 4,400 images, which were collected from 88 locations in 40 row crop fields in five Missouri counties between mid-April and early July in 2018 and 2019. The images were collected using a camera mounted on a tripod, with a spatial resolution of 0.014 cm pixel- 1 GSD (Ground Sampling Distance). The experiments highlighted the better performance of the proposed hierarchical ensemble classifier, which achieved a cross-validation accuracy of 86.3 % vs an accuracy of 80.4 % for the single SVM, whil

关键词： Support vector machines Measurement Training Search methods Soil moisture Crops Probabilistic logic

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Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models

arXiv

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arXiv 2024年

作者： Zhang, Yu Wen, Long Yao, Xiangtong Bing, Zhenshan Kong, Linghuan He, Wei Knoll, Alois The Department of Informatics Technical University of Munich Germany The Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China The School of Intelligence Science and Technology University of Science and Technology Beijing Beijing100083 China The Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing100083 China

This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a forgetting factor to refine a variational sparse GP algorithm, thus enhancing its adaptability. Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model’s online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from newly samples, in conjunction with the learned hyperparameters. In the second phase, we propose a safety filter based on high order control barrier functions (HOCBFs), synergized with the previously trained learning model. By leveraging the compound kernel from the first phase, we effectively address the inherent limitations of GPs in handling high-dimensional problems for real-time applications. The derived controller ensures a rigorous lower bound on the probability of satisfying the safety specification. Finally, the efficacy of our proposed algorithm is demonstrated through real-time obstacle avoidance experiments executed using both simulation platform and a real-world 7-DOF robot. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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Rfid-based warehouse management system prototyping using a heterogeneous team of robots 23rd

Rfid-based warehouse management system prototyping using a h...

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23rd International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2020

作者： Khazetdinov, Artur Aleksandrov, Andrey Zakiev, Aufar Magid, Evgeni Hsia, Kuo-Hsien Laboratory of Intelligent Robotic Systems Intelligent Robotics Department Higher Institute of Information Technology and Intelligent Systems Kazan Federal University 35 Kremlevskya street Kazan420008 Russia Department of Electrical Engineering National Yunlin University of Science and Technology Tainan City74448 Taiwan

ISBN: (纸本)9781916449046

Robotic warehouse management is a promising field of research with a high practical applicability. Typically, warehouses are strictly organized and controlled environments with a high potential for rapid robot deployments. To provide system robustness every robotic deployment should be tested in advance using simulations. Such virtual testing environment should correspond well with a real world warehouse environment and be consistent with international and national standards. In this paper, we present a warehouse Gazebo simulation that was constructed in accordance with Government Standards of Russian Federation and international standards. We implemented a simplified Radio Frequency IDentification (RFID) transmitter-receiver mechanism within the Gazebo simulator and tested it in a virtual warehouse environment using a heterogeneous team of TIAGo Base mobile robot and PX4-based UAV. Our constructed virtual warehouse environment and RFID mechanism models are open-source for academic community use. © CLAWAR Association Ltd.

关键词： Robots

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