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IEEE Transactions on Automatic Control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model

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arXiv 2025年

作者： Liu, Chenyang Chen, Keyan Zhao, Rui Zou, Zhengxia Shi, Zhenwei The Department of Aerospace Intelligent Science and Technology School of Astronautics The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shen Yuan Honors College of Beihang University Beijing100191 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Recently, generative foundation models have significantly advanced large-scale text-driven natural image generation and have become a prominent research trend across various vertical domains. However, in the remote sensing field, there is still a lack of research on large-scale text-to-image (text2image) generation technology. Existing remote sensing image-text datasets are small in scale and confined to specific geographic areas and scene types. Besides, existing text2image methods have struggled to achieve global-scale, multi-resolution controllable, and unbounded image generation. To address these challenges, this paper presents two key contributions: the Git-10M dataset and the Text2Earth foundation model. Git-10M is a global-scale image-text dataset comprising 10.5 million image-text pairs, 5 times larger than the previous largest one. The dataset contains essential resolution information and covers a wide range of geographic scenes and contains essential geospatial metadata, significantly surpassing existing datasets in both size and diversity. Building on Git-10M, we propose Text2Earth, a 1.3 billion parameter generative foundation model based on the diffusion framework to model global-scale remote sensing scenes. Text2Earth integrates a resolution guidance mechanism, enabling users to specify image resolutions. A dynamic condition adaptation strategy is proposed for training and inference to improve image generation quality. Text2Earth not only excels in zero-shot text2image generation but also demonstrates robust generalization and flexibility across multiple tasks, including unbounded scene construction, image editing, and cross-modal image generation. This robust capability surpasses previous models restricted to the basic fixed size and limited scene types. On the previous text2image benchmark dataset, Text2Earth outperfoms previous models with a significant improvement of +26.23 FID and +20.95% Zero-shot Cls-OA metric. Our project page is https://chen-y

关键词： Optical remote sensing

Mixture of Experts-Enabled parallel Scheduling and Processing for Vehicular Generative AI Services

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Xie, Gaochang Xiong, Zehui Xie, Renchao Deng, Xiumei Guo, Song Guizani, Mohsen Han, Zhu Beijing University of Posts and Telecommunication State Key Laboratory of Networking and Switching Technology Beijing 100876 China Purple Mountain Laboratories Nanjing 211111 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Mohammad Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates University of Houston Department of Electrical and Computer Engineering Houston 77004 TX United States Kyung Hee University Department of Computer Science and Engineering Seoul 446-701 South Korea

Foundation models (FMs) have revolutionized generative AI (GAI) lifecycle with their pre-trained intelligence capabilities. While the recent success of web-based models like GPT-4 has spurred interest in extending FMs to edge scenarios like the Internet of Vehicles (IoV), challenges such as data privacy, network congestion, and limited edge resource awareness hinder the direct application of cloud-based FMs. To address these challenges, this paper reconfigures the mixture of experts (MoE) architecture to deploy Transformer-based FM experts across vehicles for distributed GAI inference. Specifically, we propose an MoE-empowered vehicular FM system with two key innovations: a physical gating network that dynamically adapts to wireless environments, and a vehicle-to-vehicle (V2V) communication-based expert parallelism mechanism to enhance efficiency and resource utilization. Then, we formulate the communication, computation, and memory models for analyzing inference latency. Furthermore, to optimize the computational resource allocation for enhancing inference performance, we establish a Stackelberg game and propose the Gradient Ascent and Evolutionary Optimization-based Competitive Pricing and Allocation (GECPA) algorithm, which balances resource allocation and usage costs by combining the rapid convergence of gradient ascent with the broader exploration capability of evolutionary optimization. Simulation results demonstrate the superior parallel processing efficiency and reduced latency of the proposed MoE-based FM inference scheme. Compared with the best-performing benchmark algorithm, GECPA improves the average utility of infrastructure vehicles by up to 2.01 times, reduces inference latency by 16.58%, and increases the successful execution rate of GAI tasks by 10.66%, thereby achieving a better balance between efficiency and incentive compatibility in dynamic IoV environments. © 2015 IEEE.

关键词： expert parallelism foundation models (FMs) Generative AI (GAI) Internet of Vehicles (IoV) mixture of experts (MoE) resource allocation

Acoustic amplifying diode using non-reciprocal Willis coupling

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arXiv 2022年

作者： Wen, Xinhua Yip, Heung Kit Cho, Choonlae Li, Jensen Park, Namkyoo Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

We propose a concept called acoustic amplifying diode in combining both signal isolation and amplification in a single device. The signal is exponentially amplified in one direction with no reflection and is completely absorbed in another. In this case, the reflection is eliminated from the device in both directions due to impedance matching, preventing backscattering to the signal source. Here, we experimentally demonstrate the amplifying diode using an active metamaterial with non-reciprocal Willis coupling. We also discuss the situation with the presence of both reciprocal and non-reciprocal Willis couplings for more flexibility in implementation. The concept of acoustic amplifying diode will enable applications in sound isolation, sensing and communication, in which non-reciprocity can play an important role. Copyright © 2022, The Authors. All rights reserved.

关键词： Diodes

NiO Junction Termination Extension for Ga2O3 Devices: High Blocking Field, Low Capacitance, and Fast Switching Speed

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NiO Junction Termination Extension for Ga2O3 Devices: High B...

International Symposium on Power Semiconductor Devices and Ics (ISPSD)

作者： Ming Xiao Boyan Wang Ruizhe Zhang Qihao Song Joseph Spencer Zhonghao Du Yuan Qin Kohei Sasaki Han Wang Marko Tadjer Yuhao Zhang Center for Power Electronics Systems Virginia Tech Blacksburg USA U.S. Naval Research Laboratory Washington DC USA Department of Electrical and Computer Engineering University of Southern California Los Angeles USA Novel Crystal Technology Inc. Saitama Japan

This work investigates the blocking electric field, capacitance, and switching speed of the p-type NiO based junction termination extension (JTE) for vertical Ga 2 O 3 devices. The JTE comprises multiple NiO layers sputtered on the surface of Ga 2 O 3 drift region, the acceptor concentration and length of which are carefully optimized. This NiO JTE enabled a breakdown voltage over 3 kV in vertical Ga 2 O 3 diodes with a parallel-plate junction field of 4.2 MV/cm. Large-area Ga 2 O 3 p-n diodes with a current over 1 A were fabricated to evaluate the JTE's capacitance and switching characteristics. The JTE accounts for only, ~11 % of the junction capacitance of this 1 A diode, and the percentage is expected to be even smaller for higher-current diodes. The turn-ON/OFF speed and reverse recovery time of the diode are comparable to commercial SiC Schottky barrier diodes. These results show the good promise of NiO JTE as an effective edge termination for Ga 2 O 3 power devices.

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Study on an Adaptive Clamping Device for Passive Safety Delivery of Vascular Intervention Robot

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Study on an Adaptive Clamping Device for Passive Safety Deli...

IEEE International Conference on Mechatronics and Automation

作者： Sheng Cao Shuxiang Guo Jian Guo Jian Wang Chunying Li Huiyin Xu Bin Wang Mingchao Ding Aerospace Center Hospital & the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology School of Life Science & Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China Shenzhen Institute of Advanced Biomedical Robot Company Limited Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China National Engineering Laboratory of Computer Technology for Big Data Systems Shenzhen University Shenzhen China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

In practical clinical applications, vascular intervention surgical robots have developed sophisticated human-machine interaction systems, enabling assistance to physicians in performing remote surgeries and providing intelligent visual feedback. However, regarding surgical safety, current research predominantly focuses on force feedback and robot control logic, with clamping mechanisms targeting the locking of catheters and guide wires. Excessive clamping force may lead to surface damage to intervention instruments, while insufficient force delivery may result from slippery surfaces. Therefore, addressing these issues, this study proposed a passive, compliant safety strategy for an adaptive clamping device based on a vascular intervention surgical robot platform. This device can accommodate different diameter catheters and maintain a constant delivery force during the intervention process. Finally, through experimentation, the effectiveness, safety, and stability of the device were demonstrated.

关键词： Visualization Medical robotics Force Wires Robot control Surgery Safety

Preserving Privacy in GANs Against Membership Inference Attack

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arXiv 2023年

作者： Shateri, Mohammadhadi Messina, Francisco Labeau, Fabrice Piantanida, Pablo The Department of Systems Engineering École de tech-nologie supérieure QC Canada The School of Engineering Universidad de Buenos Aires Buenos Aires Argentina The Department of Electrical and Computer Engineering McGill University QC Canada ILLS - International Laboratory on Learning Systems and MILA Quebec AI Institute CNRS CentraleSupélec MontrealQCH3C 1K3 Canada

Generative Adversarial Networks (GANs) have been widely used for generating synthetic data for cases where there is a limited size real-world data set or when data holders are unwilling to share their data samples. Recent works showed that GANs, due to overfitting and memorization, might leak information regarding their training data samples. This makes GANs vulnerable to Membership Inference Attacks (MIAs). Several defense strategies have been proposed in the literature to mitigate this privacy issue. Unfortunately, defense strategies based on differential privacy are proven to reduce extensively the quality of the synthetic data points. On the other hand, more recent frameworks such as PrivGAN and PAR-GAN are not suitable for small-size training data sets. In the present work, the overfitting in GANs is studied in terms of the discriminator, and a more general measure of overfitting based on the Bhattacharyya coefficient is defined. Then, inspired by Fano’s inequality, our first defense mechanism against MIAs is proposed. This framework, which requires only a simple modification in the loss function of GANs, is referred to as the maximum entropy GAN or MEGAN and significantly improves the robustness of GANs to MIAs. As a second defense strategy, a more heuristic model based on minimizing the information leaked from the generated samples about the training data points is presented. This approach is referred to as mutual information minimization GAN (MIMGAN) and uses a variational representation of the mutual information to minimize the information that a synthetic sample might leak about the whole training data set. Applying the proposed frameworks to some commonly used data sets against state-of-the-art MIAs reveals that the proposed methods can reduce the accuracy of the adversaries to the level of random guessing accuracy with a small reduction in the quality of the synthetic data samples. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

Convergence and Stability of Coupled Belief-Strategy Learning Dynamics in Continuous Games

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arXiv 2022年

作者： Wu, Manxi Amin, Saurabh Ozdaglar, Asuman The School of Operations Research and Information Engineering Cornell University United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We propose a learning dynamics to model how strategic agents repeatedly play a continuous game while relying on an information platform to learn an unknown payoff-relevant parameter. In each time step, the platform updates a belief estimate of the parameter based on players' strategies and realized payoffs using Bayes's rule. Then, players adopt a generic learning rule to adjust their strategies based on the updated belief. We present results on the convergence of beliefs and strategies and the properties of convergent fixed points of the dynamics. We obtain sufficient and necessary conditions for the existence of globally stable fixed points. We also provide sufficient conditions for the local stability of fixed points. These results provide an approach to analyzing the long-term outcomes that arise from the interplay between Bayesian belief learning and strategy learning in games, and enable us to characterize conditions under which learning leads to a complete information equilibrium. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamics

A Comprehensive Review of Active EV Battery Cell Voltage Balancing systems: Current Issues and Prospective Solutions

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A Comprehensive Review of Active EV Battery Cell Voltage Bal...

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Chandan Chetri Alvin Huynh Sheldon S. Williamson Department of Electrical Computer and Software Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered.

关键词： Industrial electronics Electric potential Costs Equalizers Battery management systems Transformers Power electronics

Design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises

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Design and Application of a One-Stop Mobile Application Shar...

Chinese Control Conference (CCC)

作者： Xinzhe Chen Luefeng Chen Jie Hu Qingfeng Yang Min Wu Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance.

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