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4th International Conference on Frontiers Technology of Information and computer, ICFTIC 2022

作者： Zheng, Wenjuan Luo, Hanxu Ji, YuXin Li, Haipeng Cong, Longjian National Key Laboratory of Aerospace Intelligent Control Technology Master of Computer Vision Beijing100854 China Beijing Aerospace Automatic Control Institute Beijing100854 China Wuhan University of Technology Department of Information Engineering Hubei Province Wuhan430000 China

ISBN: (纸本)9798350321951

The classic adversarial samples of black-box attacks are all aimed at the models of Convolutional neural networks (CNNs), but they do not perform well on the new recognition networks based on Transformer. In this paper, we propose an adversarial sample generation algorithm based on Vision transformers (VITs)' self-attention mechanism and patch partition. We noticed that different blocks in the VIT have uneven attention distribution, so we first generated a patch-based attention map and performed threshold segmentation, which is used as a mask to perform data enhancement operations based on patches with high weight and patches with low weight, and then exchanged information between patches to generate adversarial samples. The experiment of simulating black-box attack shows that the adversarial samples generated by the algorithm in this paper have a high success rate in all kinds of models based on Transformer of attacks, and also perform well on CNNs. © 2022 IEEE.

关键词： Convolutional neural networks

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Event-triggered leader-following formation control for multi-agent systems under communication faults: application to a fleet of unmanned aerial vehicles

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Journal of Systems engineering and Electronics 2021年第5期32卷 1014-1022页

作者： VAZQUEZ TREJO Juan Antonio GUENARD Adrien ADAM-MEDINA Manuel PONSART Jean-Christophe CIARLETTA Laurent ROTONDO Damiano THEILLIOL Didier Research Center for Automatic Control of Nancy University of LorraineNancy F-54000France Electronic Engineering Department National Institute of Technology of MexicoCuernavaca Morelos 62490Mexico Lorraine Research Laboratory in Computer Science and Its Applications University of LorraineNancy F-54000France University of Stavanger Department of Electrical and Computer EngineeringStavanger 4021Norway

The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems(MASs)under communication *** the agents must follow the trajectories of a virtual leader despite communication faults considered as smooth time-varying delays dependent on the distance between the *** matrix inequalities(LMIs)-based conditions are obtained to synthesize a controller gain that guarantees stability of the synchronization *** on the closed-loop system,an event-triggered mechanism is designed to reduce the control law update and information exchange in order to reduce energy *** proposed approach is implemented in a real platform of a fleet of unmanned aerial vehicles(UAVs)under communication faults.A comparison between a state-of-the-art technique and the proposed technique has been provided,demonstrating the performance improvement brought by the proposed approach.

关键词： event-triggered leader-following consensus communication fault formation control unmanned aerial vehicle(UAV) experimental result

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Fusion Learning of Multimodal Neuroimaging with Weighted Graph AutoEncoder

Fusion Learning of Multimodal Neuroimaging with Weighted Gra...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Gen Zhu, Yifan Zhang, Fuquan Liu, Wenjin Yao, Yuxiang Li, Xuesong Beijing Institute of Technology School of Computer Science and Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China Minjiang University College of Computer and Control Engineering Fuzhou China Yangzhou University Northern Jiangsu People's Hospital Department of Nephrology Yangzhou China

ISBN: (纸本)9781665468190

Neuroimaging plays an significant role in diagnosing and pathological study of brain diseases. Considering that both functional and structural abnormalities may lead to brain dis-eases and disorders, single modal neuroimaging approach may not fully characterize brain activities and working modes. Fusion of multimodal neuroimaging data is expected to provide more comprehensive characterization of brain diseases, given that the different modalities contain more complementary information. Recently, Graph Convolutional Networks (GCNs) is shown to have powerful capacity in representation learning for graph-structure data, which is considered to integrate both graph se-mantic structure and node information. Therefore, in this paper, we propose the Weighted Graph AutoEncoder (WGAE), a GCN- driven multimodal fusion model, to learn the combinational latent node representation of fMRI and DTI neuroimaging data, which are used as node features and graph structure respectively in the graph in unsupervised manner. Experimental results on two real-world datasets show the superiority of the proposed model over other existing single-modal or multi-modal methods in learning representations for disease prediction as the downstream task. Furthermore, ablation experiments also show the collaborative contribution of multimodal neuroimaging fusion in the proposed model, and also show the feasibility of assessing the respective importance of the two modalities during the disease prediction. © 2022 IEEE.

关键词： Neuroimaging

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94LVI Algorithms of OIpU(One-Iteration-per-Update) Type Handling Time-Dependent QP(Quadratic Programming) with Two Constraints 41

94LVI Algorithms of OIpU(One-Iteration-per-Update) Type Hand...

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第41届中国控制会议

作者： Yunong Zhang Zhiwen Yuan School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Research Institute of Sun Yat-sen University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper presents three relatively effective numerical algorithms,i.e.,94 LVI algorithms of OIpU(one-iteration-perupdate) type,to handle time-dependent QP(quadratic programming) problems subject to two constraints respectively(i.e.,equality and inequality constraints,equality and bound constraints,or inequality and bound constraints).Specifically,two "bridges" play essential parts of the methods(i.e.,the first "bridge" makes a QP problem convert to an LVI [linear variational inequality],and the second "bridge" makes the LVI convert to a PLPE [piecewise-linear projection equation]).On the basis of the standard94 LVI algorithm,the methods only update once through the solution of the previous sampling time ***,we conduct a number of experiments to substantiate the three algorithms respectively,which use exponential functions,log functions,and trigonometric functions in corresponding coefficients of the experimental examples.

关键词： Quadratic programming Time-dependent Numerical algorithms Linear variational inequalities(LVI)

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A Dynamic Time Slot Algorithm Based on Throughput Optimization in IEEE 802.11 Wireless Networks 12

A Dynamic Time Slot Algorithm Based on Throughput Optimizati...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Jin, Guonian Lu, Shejie Hu, Siping Xu, Haihong Gao, Jinfeng Liu, Fang Wu, Haitao Hubei University of Science and Technology School of Computer Science and Technology Institute of Engineering and Technology Xianning437100 China Huanghuai University International Joint Laboratory of Behavior Optimization Control for Smart Robots College of Computer and Artificial Intelligence Zhumadian463000 China

ISBN: (纸本)9798350310153

In IEEE 802.11 wireless LAN, as the number of nodes increases, collisions will increase correspondingly, channel utilization will decline, and the total throughput will decline instead of increasing, leading to system performance degradation. Through in-depth analysis of the root cause of this problem, this paper designs a simple and effective algorithm, which uses ARMA filtering algorithm to measure the conditional collision probability and then calculate the number of nodes in the network. The AP tells all nodes in the network in the form of broadcast when the number of nodes in the network changes, after all nodes receive the broadcast sent by the AP, they dynamically adjust the network parameters accordingly. Finally, NS2 simulation software is used to conduct simulation experiments in a variety of network scenarios, and the experimental results verify that the algorithm in this paper is simple and effective, with less changes to IEEE 802.11, and is suitable for use and promotion in wireless networks. It can optimize the network system performance based on the number of nodes, improve the system throughput, and significantly improve the network performance. According to the number and density of nodes, the algorithm reduces the collision probability by dynamically adjusting the timeslot, accordingly increases the total network throughput, and achieves the purpose of effectively improving the network performance. © 2022 IEEE.

关键词： IEEE Standards

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${\sf Img2Acoustic}$Img2Acoustic: A Cross-Modal Gesture Recognition Method Based on Few-Shot Learning

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IEEE Transactions on Mobile Computing 2024年第3期24卷 1496-1512页

作者： Yongpan Zou Jianhao Weng Wenting Kuang Yang Jiao Victor C. M. Leung Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Information Hub Hong Kong University of Science and Technology Guangzhou China

Acoustic-based human gesture recognition (HGR) offers diverse applications due to the ubiquity of sensors and touch-free interaction. However, existing machine learning approaches require substantial training data, making the process time-consuming, costly, and labor-intensive. Recent studies have explored cross-modal methods to reduce the need for large training datasets in behavior recognition, but they typically rely on open-source datasets that closely align with the target domain, limiting flexibility and complicating data collection. In this paper, we propose ${\sf Img2Acoustic}$ , a novel cross-modal acoustic-based HGR approach that leverages models trained on open-source image datasets (i.e., EMNIST, Omniglot) to effectively recognize custom gestures detected via acoustic signals. Our model incorporates a task-aware attention layer (TAAL) and a task-aware local matching layer (TALML), enabling seamless transfer of knowledge from image datasets to acoustic gesture recognition. We implement ${\sf Img2Acoustic}$ on commercial devices and conduct comprehensive evaluations, demonstrating that our method not only delivers superior accuracy and robustness compared to existing approaches but also eliminates the need for extensive training data collection.

关键词： Training Gesture recognition Acoustics Data models Sensors Feature extraction Target recognition Mobile computing Image recognition Few shot learning

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Prediction of Heat Transfer in Smart Buildings using Internet of Things based Artificial Intelligence 3

Prediction of Heat Transfer in Smart Buildings using Interne...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Rehman, TasneemBano Madiajagan, M. Ramya, D. Saratha, M. Mohan Krishna, P.B. Kumar, Prashant Sage University Department of Computer Science and Engineering Madhya Pradesh Bhopal India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Sathyabama Institute of Science and Technology Department of Eee Tamil Nadu Chennai India M.Kumarasamy College of Engineering Department of Mca Tamil Nadu Karur India National Institute of Technology Department of Process Control Telangana Warangal India Lovely Professional University Department of Chemical Engineering Punjab India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

With rapid growth and a higher standard of living, the demand for usable energy has increased tremendously over the last few decades, with the construction industry being one of the most notable examples. The energy efficiency of a building is influenced by a variety of factors, including the weather in the area, the structure characteristics, and how people use electricity. These algorithms can be used to generate a prediction model for smart building indoor temperature forecasting using the Internet of Things and several commonly used artificial intelligence algorithms, which can then be used to generate a prediction model for smart building outdoor temperature forecasting. It was necessary to employ freely accessible software tools in order to make geometric and sensory data available in a virtual reality application and to build the PMV and PPD indices. On a low-cost edge computing device, semi-automatic analysis of streaming thermal images from a non-intrusive sensor was performed in order to offer near real-time Mean Radiant Temperature (MRT)estimations. An experimental space that had been mechanically preconditioned was filled with a dynamic temperature environment, and a prototype of the system was employed in a series of trials. Results show that the data backs up the fact that the system output and the heat sensations felt under different settings are consistent. © 2022 IEEE.

关键词： Application programs

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Merging Parameter Estimation and Classification Using LASSO 63

Merging Parameter Estimation and Classification Using LASSO

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wang, Le Wang, Ying Qiu, Yu Li, Mian Hjalmarsson, Hakan School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Division of Decision and Control Systems Stockholm10044 Sweden UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China Saic Motor Rd Innovation Headquarters Shanghai201807 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China Centre for Advanced Bio Production Stockholm10044 Sweden

ISBN: (纸本)9798350316339

Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not a priori be evident which sensors provide useful information about the target signal, and various operating conditions often necessitate different models. In this paper, we provide a systematic method to construct a soft sensor that can deal with these issues. We propose a single estimation criterion, where the objectives are encoded in terms of model fit, model sparsity (reducing the number of different models), and model parameter coefficient sparsity (to exclude irrelevant sensors). The proposed method is tested on real-world scenarios involving prototype vehicles, demonstrating its effectiveness. © 2024 IEEE.

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Investigation of Mode Transformation in Motor control System of Unmanned Aerial Vehicle Exposed to Intentional Electromagnetic Interference

Investigation of Mode Transformation in Motor Control System...

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Asia-Pacific Symposium on Electromagnetic Compatibility (APEMC)

作者： Huamin Jie Zhenyu Zhao Yongqi Chang Fei Fan Changdong Wang Kye Yak See School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9784885523472

ISBN: (纸本)9798350349498

Susceptibility acts as a critical metric for assessing an electrical apparatus’s performance and reliability amidst the external electromagnetic environment, especially the intentional electromagnetic interference (IEMI). Given that the IEMI often invades electrical systems in common-mode (CM) and converts to the differential-mode (DM) signal to disrupt the normal operation of devices, investigating the mode transformation in the system under attack is pivotal for its susceptibility analysis. This paper first investigates the CM to DM conversion in the motor control system of the unmanned aerial vehicle (UAV) illuminated by an external IEMI. Experimental studies explore the influence of the distance between UAV and copper ground on mode conversion, revealing that adjusting the UAV’s landing gear height have the potential to enhance its susceptibility.

关键词： Performance evaluation Motor drives Electric potential Gears Electromagnetic interference RF signals Immunity testing

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Worst-Scenario Prediction for Field-to-Cable Coupling Under Continuous Intentional Electromagnetic Interference Attack

Worst-Scenario Prediction for Field-to-Cable Coupling Under ...

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Asia-Pacific Symposium on Electromagnetic Compatibility (APEMC)

作者： Huamin Jie Eng Kee Chua Zhenyu Zhao Yongqi Chang Changdong Wang Kye Yak See School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9784885523472

ISBN: (纸本)9798350349498

Studying intentional electromagnetic interference (IEMI) is crucial for safeguarding hardware safety, as it helps to address the deliberate disruption of electronic devices. The field-to-cable coupling serves as a prevalent attack mechanism of IEMI, precipitating unexpected failures of the device under test. Many efforts focus on the analysis of field-to-cable coupling under high-power electromagnetic pulse, but less target on the phenomenon illuminated with continuous waves. This paper proposes a novel method to predict the worst-case for field-tocable coupling under continuous IEMI attack. By employing the transmission line theory to characterize the cables and obtaining the impedance information of the devices at both ends of the cable, an analytical model is built for determining the terminal voltage frequency responses induced by IEMI. Subsequently, the sensitive frequency region of this cable-connected electrical system can be determined, facilitating purposive immunity improvements. Using an unmanned aerial vehicle (UAV) as an example, the analytical calculations are compared with the radiated susceptibility test. The results exhibit a good agreement with experiments and validate the effectiveness of the proposed method.

关键词： Couplings Analytical models Systematics Electromagnetic interference Voltage Immunity testing Autonomous aerial vehicles

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