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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Rehman, Sadaqat ur Tu, Shanshan Shah, Zubair Ahmad, Jawad Waqas, Muhammad Rehman, Obaid ur Kouba, Anis Abbasi, Qammer H. Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Namal Institute Mianwali42250 Pakistan Department of Computer Science and Engineering Hammad Bin Khalifa University Doha Qatar School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Electrical Engineering Sarhad University of Science and IT Peshawar25000 Pakistan Department of Computer Science Prince Sultan University Riyadh Saudi Arabia James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9783030786083

The rapid developments of artificial intelligent (AI) is being transformed for its extensive use-cases, people-centered intelligent systems focusing on care delivery, research encounter complex problems related to improve the overall infrastructure and management of intelligent delivery service;for instance, bringing transformation in healthcare sector for monitoring patients with chronic disease. Most of these systems are driven by state-of-the-art learning algorithms i.e., Convolution Neural Network. The CNN algorithm is considered to be one of the most prominent architectures of DL. Recently, due to enormous growth in the amount of annotated data and the development of CNN hardware accelerator, further, boost the research on CNN and accomplished benchmark enactment on different applications. This paper presents cutting-edge applications of CNN for an intelligent healthcare system. We provide useful findings of different CNN features such as optimization, fast computation, design, activation function, and loss function. To our knowledge, this is the first comprehensive work to address the recent trends in the architecture of CNN, which offers insight to the underlying problems and provides the potential solutions for any given intelligent healthcare applications. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

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Science China(Information Sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Global Finite-Time Stabilization for a Chain of Integrators in the Presence of Actuator Saturation by Homogeneous Feedback

Global Finite-Time Stabilization for a Chain of Integrators ...

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IEEE Conference on Decision and control

作者： Tan Hou Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

This paper considers the problem of globally stabilizing a chain of integrators with actuator saturation in finite time. The proposed method is adapted from an existing scheduled low gain approach by relaxing the range of the value of the low gain parameter. Due to the special structure of the considered system, the proposed feedback law not only results in finite-time stability but also is homogeneous in the whole state space. The homogeneity of the proposed feedback law allows its implementation through the discretization of a compact set of the state space and thus obviation from numerical difficulties in the existing scheduled low gain design. A numerical example is presented to validate our theoretical results and demonstrate that our approach achieves faster convergence than the existing methods.

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Adaptive Estimation for Environmental Monitoring Using an autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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Data-driven Polytopic Output Synchronization of Heterogeneous Multi-agent Systems from Noisy Data

arXiv

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arXiv 2023年

作者： Li, Yifei Liu, Wenjie Sun, Jian Wang, Gang Xie, Lihua Chen, Jie The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore The Department of Control Science and Engineering Tongji University Shanghai201804 China

This paper proposes a novel approach to addressing the output synchronization problem in unknown heterogeneous multi-agent systems (MASs) using noisy data. Unlike existing studies that focus on noiseless data, we introduce a distributed data-driven controller that enables all heterogeneous followers to synchronize with a leader’s trajectory. To handle the noise in the state-input-output data, we develop a data-based polytopic representation for the MAS. We tackle the issue of infeasibility in the set of output regulator equations caused by the noise by seeking approximate solutions via constrained fitting error minimization. This method utilizes measured data and a noise-matrix polytope to ensure near-optimal output synchronization. Stability conditions in the form of data-dependent semidefinite programs are derived, providing stabilizing controller gains for each follower. The proposed distributed data-driven control protocol achieves near-optimal output synchronization by ensuring the convergence of the tracking error to a bounded polytope, with the polytope size positively correlated with the noise bound. Numerical tests validate the practical merits of the proposed data-driven design and theory. © 2023, CC BY.

关键词： Multi agent systems

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Thermodynamic Analysis of a Combined Heating and Power Plant in Hybrid with Compressed Air Energy Storage and Molten Salt Heat Storage

SSRN

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SSRN 2024年

作者： Fu, Hailun Nižetić, Sandro Park, Jung-Wook Lee, Kwang Y. Sun, Li National Engineering Research Center of Power Generation Control and Safety School of Energy and Environment Southeast University Nanjing210096 China University of Split FESB Rudjera Boskovica 32 Split Croatia School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of Department of Electrical and Computer Engineering Baylor University WacoTX76798 United States Yonsei Frontier Lab Yonsei University Seoul03722 Korea Republic of

In face of the increasing penetration of renewable energy, compressed air energy storage (CAES) is promising in improving the flexibility of the conventional coal-fired combined heating and power plant (CHPP). In order to further broaden the operational range and improve the overall efficiency, this paper introduces the molten salt heat storage (MSHS) into the CHPP-CAES system. Based on the thermodynamic model, the effect of the MSHS is discussed in terms of the energy efficiency, exergy efficiency, and the power regulation based on the topology with and without MSHS. The inclusion of the MSHS can improve the exergy efficiency and reduce the average coal consumption for power supply, while also effectively widening the power regulation range. Parameter analysis is carried out in terms of the compression and expansion stages of CAES, the steam extraction position of MSHS, throttle valve pressure drop, and the capacities of CAES and MSHS. It is revealed that, extracting steam from the boiler reheater and reducing the CAES capacity can lead to better efficiency but smaller operational range. Furthermore, reducing the throttle valve pressure drop and improving the MSHS capacity are simultaneously favorable for the overall performance and operating range. These conclusions can provide theoretical guidance for the structure and parameter design of the CHPP in hybrid with CAES and MSHS. © 2024, The Authors. All rights reserved.

关键词： Heat storage

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Efficientfacev2s: A Lightweight Model and a Benchmarking Approach for Drone-Captured Face Recognition

SSRN

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SSRN 2024年

作者： Alansari, Mohamad Alnuaimi, Khaled Ganapathi, Iyyakutti Iyappan Alansari, Sara Javed, Sajid Shoufan, Abdulhadi Zweiri, Yahya Werghi, Naoufel Department of Electrical Engineering College Of Computing And Mathematical Sciences Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates Center for Cyber-Physical Systems Khalifa University Abu Dhabi United Arab Emirates Department of Aerospace Engineering Khalifa University Abu Dhabi United Arab Emirates Khalifa University Abu Dhabi United Arab Emirates

Face Recognition (FR) in aerial imagery encounters distinctive challenges, notably stemming from Low Resolution (LR) and varying pitch angles. The influence of image quality, particularly resolution, on the performance of contemporary FR systems is well-established. However, limited exploration exists regarding the performance of FR models on drone-captured images, primarily due to the scarcity of suitable *** address this gap, our study investigates the efficacy of FR models when applied to drone-captured facial images. We utilize state-of-the-art (SOTA) lightweight and large FR models, evaluating them across three drone-captured FR benchmarks and one dataset focused on LR FR. To ensure a comprehensive evaluation, additional seven widely recognized benchmarks are adopted and artificially downsampled and rotated to simulate the impact of distance and altitude on the view from a drone to a *** highlight a substantial decrease in accuracy across all FR models in these scenarios. In response to this challenge, we introduce a novel FR model, EfficientFaceV2S, demonstrating consistent SOTA performance across all benchmarks while imposing minimal computational demands. This characteristic makes it particularly suitable for real-time and resource-constrained applications. © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Hamiltonian Quantum Generative Adversarial Networks

arXiv

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arXiv 2022年

作者： Kim, Leeseok Lloyd, Seth Marvian, Milad Department of Electrical & Computer Engineering Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

We propose Hamiltonian Quantum Generative Adversarial Networks (HQuGANs), to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the success of classical generative adversarial networks in learning high-dimensional distributions. The quantum optimal control approach not only makes the algorithm naturally adaptable to the experimental constraints of near-term hardware, but also offers a more natural characterization of overparameterization compared to the circuit model. We numerically demonstrate the capabilities of the proposed framework to learn various highly entangled many-body quantum states, using simple two-body Hamiltonians and under experimentally relevant constraints such as low-bandwidth controls. We analyze the computational cost of implementing HQuGANs on quantum computers and show how the framework can be extended to learn quantum dynamics. Furthermore, we introduce a new cost function that circumvents the problem of mode collapse that prevents convergence of HQuGANs and demonstrate how to accelerate the convergence of them when generating a pure state. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum computers

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Optimizing temperature distributions for training neural quantum states using parallel tempering

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Physical Review E 2025年第5期111卷 055306-055306页

作者： Conor Smith Quinn T. Campbell Tameem Albash Center for Quantum Information and Control University of New Mexico University of New Mexico Albuquerque New Mexico 87131 USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Center for Computational Quantum Physics Flatiron Institute New York New York 10010 USA Center for Computing Research Sandia National Laboratories Albuquerque New Mexico 87185 USA

Parametrized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and stymied by the presence of local minima in the parameter landscape. One approach to mitigate this issue is to use parallel tempering methods, and in this work, we focus on the role played by the temperature distribution of the parallel tempering replicas. Using an adaptive method that adjusts the temperatures in order to equate the exchange probability between neighboring replicas, we show that this temperature optimization can significantly increase the success rate of the variational algorithm with negligible computational cost by eliminating bottlenecks in the replicas' random walk. We demonstrate this using two different neural networks, a restricted Boltzmann machine and a feedforward network, which we use to study a toy problem based on a permutation invariant Hamiltonian with a pernicious local minimum and the J1−J2 model on a rectangular lattice.

关键词： Hamiltonians

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