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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhou, Jingfeng Song, Ziqiu Fang, Fang School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798331527471

The oscillatory movement, aerodynamic damping effect, gyroscopic effect, and other phenomena in floating wind turbines result in distinct response characteristics of output and load under varying wind-wave coupling scenarios. Designing controllers within traditional control intervals using wind speed or wind-wave metrics is insufficient for fundamentally improving the wind turbine’s performance. Firstly, according to the response distribution of the floating wind turbine, the response characteristics in different scenarios are extracted based on the autoencoder. And the response characteristics are clustered to form a control interval with response as the measurement index, which provides a basis for the partitioning in the construction of digital twin models for floating wind turbines. Secondly, within these reconstructed control intervals, control parameters are optimized based on the specific efficiency improvement and load reduction requirements of each scenario. This process leads to the development of a variable parameter controller. Finally, simulation experiments using FAST in various wind-wave coupling scenarios demonstrate that the proposed variable parameter controller effectively stabilizes power output and reduces loads. © 2024 IEEE.

关键词： Wind turbines

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Resilient control Strategies for Multi-Area Interconnected Power Systems: Combating External FDI Attacks with UIO and Adaptive control 2

Resilient Control Strategies for Multi-Area Interconnected P...

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2nd International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Zheng, Zhou School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373318

This work proposes a robust control technique to mitigate false data injection (FDI) attacks on multi-region interconnected power systems. The strategy is based on unknown input observer (UIO) and adaptive control. In order to preserve system performance and stability, adaptive control technology dynamically modifies controller parameters based on estimates from UIO. The efficacy and resilience of the suggested approach are confirmed by numerical simulation and real-world examples, which provide crucial technical support for the power system stability and safety. © 2024 IEEE.

关键词： control system stability

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Graph Dual Adversarial Network for Hyperspectral Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 922-932页

作者： Cheng, Yuhu Chen, Yang Kong, Yi Philip Chen, C.L. Wang, Xuesong China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of EducationSchool of Information and Control Engineering Xuzhou221116 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China

An end-to-end unsupervised domain adaptation method for hyperspectral image (HSI) classification based on a graph dual adversarial network is proposed in this article. First, in order to extract the domain-invariant features of the source and target domains, the rich spectral information and spatial position of HSI are used to construct a spectral-spatial nearest neighbor graph, which is input into the graph convolutional network. Then, a prototype adversarial strategy is proposed, which uses the labeled data of the source domain to reliably calculate the feature prototypes of different classes. Through the prototype adversarial strategy, the distances between different prototypes are appropriately extended, so that the clusters of different classes are far away from their respective decision boundaries, and the discriminability of features are also enhanced. The dual adversarial strategy is composed of the prototype adversarial strategy and the domain adversarial strategy. It is worth noting that the dual adversarial strategy does not require feature extractor and discriminator to work in turn, which can be implemented through a gradient reversal layer. Finally, on the basis of adapting the overall features of both the domains via the domain adversarial strategy, the source- and target-domain features are further adapted by minimizing the correlation alignment loss of each class of samples. Experimental results on two real HSI datasets of Botswana and Kennedy Space Center show the effectiveness of our proposed method. © 2020 IEEE.

关键词： Spectroscopy

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MedSyn: Medical Image Synthesis of Brain Magnetic Resonance Images Based on Hybrid Information Aggregation 8

MedSyn: Medical Image Synthesis of Brain Magnetic Resonance ...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Jiang, Minbo Song, Zhiwei Zhu, Chuanzhen Wang, Yi School of Computer and Control Engineering Yantai University Yantai China

ISBN: (纸本)9798350305838

Magnetic resonance imaging (MRI) is widely used in clinical scenarios. However, acquisition of certain image modalities may be in limitation due to multiple considerations such as time duration and labor costs. Hence, medical image synthesis can resolve those limitations by estimating the desired target image modality without inducing an actual scan. In this paper, we proposed a novel method which aggregated the hybrid information of MRI to improve the performance of medical image synthesis and addressed it as MedSyn. Specifically, the MedSyn adopted the GAN framework, and made some great improvement. In generator, we proposed a simple yet efficient Tri-Focus Visual Center (TVC) to activate contextual features by establishing inter-dimensional dependencies with the assistance of rotation operations and gaussian functions. Furthermore, we proposed a Hybrid Global-Local Feature Extractor (HGL) module to capture long-range dependencies and exploit potential relationships between spatial and channel dimensions to extract global-local information. Benefitting from the modules of TVC and HGL jointly, the generator could capture obvious image features and underlying semantic information. In discriminator, we proposed a Dynamic Multi-Scale (DMS) Discriminator which could enhance discriminative ability by combining features of different scales dynamically. To evaluate the performance of the proposed method, we conducted experiments on three brain datasets (the Meningioma dataset, the IXI dataset and BRATS dataset), and compared the results with some state-of-the-art methods. The experimental results showed that our MedSyn outperformed other state-of-the-art methods in terms of both quantitative evaluation and qualitative visual assessments. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Towards Autonomous computer Networks in Support of Critical Systems 36

Towards Autonomous Computer Networks in Support of Critical ...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Sacco, Alessio Marchetto, Guido Politecnico di Torino Department of Control and Computer Engineering Italy

ISBN: (纸本)9781665477161

A recent trend dictating evolution of management and orchestration of computer networks is constituted by the softwarization and virtualization of them, which have drastically simplified the deployment and real-time reconfiguration of network functions, allowing them to continuously adapt and to deal with dynamic demands in an automated way. Alongside, recent management and orchestration approaches for softwarized networks employ Artificial Intelligence (AI) and Machine Learning (ML) to further reduce reaction time and improve the accuracy of decisions, where the network operations can be automated to the point of realizing autonomous driving networks. However, while automating operations can improve the overall system (it is acknowledged that 70% of network faults are caused by manual errors), AI/ML methods are not the panaceas, and we are still far from having a fully operating and efficient automated architecture. In this dissertation, we present a novel class of software network solutions that share the goal of enabling intelligent and autonomous computer networks, exploring how to exploit the power of AI/ML to handle the growing complexity of critical systems. We start with a new network management scheme for adaptive routing and autonomous scaling of virtual network resources. Then, acting on the hosts, we propose to adjust the TCP congestion control with a ML-based solution, whose goal is to select the proper congestion window learning from end-to-end features and (when available) network signals. We believe that the proposed solutions, and their combination, can lay the foundation for automated systems that better suit modern edge environments and cellular networks by providing unprecedented flexibility and adaptation to even unseen and unknown network conditions. © 2023 IEEE.

关键词： Artificial intelligence

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EFFNet: Semantic Segmentation Network for Enhanced Feature Fusion in Traffic Scenes 3

EFFNet: Semantic Segmentation Network for Enhanced Feature F...

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3rd International Conference on computer Graphics, Image, and Virtualization, ICCGIV 2023

作者： Zhang, Shiwei Liu, Qicheng College of Computer and control engineering Yantai University Yantai China

ISBN: (纸本)9781510671720

Semantic segmentation of high-resolution traffic scene images is a challenging task due to complex backgrounds, diverse object shapes, similar appearances of multiple objects, and multi-scale characteristics of the same object. Many existing semantic segmentation networks only perform simple feature fusion when dealing with different object shapes and multi-scale properties of the same object, often failing to provide satisfactory results. To address these issues, we propose an end-to-end multi-scale feature fusion network called EFFNet for traffic scenes semantic segmentation. EFFNet adopts an encoder-decoder structure, where ResNet-34 is used as the backbone network for feature extraction. At each stage of feature extraction, we introduce the Feature Fusion Module for multi-scale information. The Feature Fusion Module fuses the feature maps extracted by the backbone network at different stages, enriching the spatial and semantic information of the feature maps. Through end-to-end training, the accuracy of the segmentation results is significantly improved. We evaluated EFFNet on the traffic datasets Camvid and Cityscapes using a device equipped with a GTX 1650Ti graphics card. The results show that EFFNet achieves Miou scores of 55.3% and 50% on these two datasets, respectively. This indicates that EFFNet outperforms existing methods and demonstrates excellent segmentation accuracy in traffic scenes semantic segmentation tasks. © 2023 SPIE.

关键词： Semantic Segmentation

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Design of temperature and humidity control device for substation terminal cabinet 6

Design of temperature and humidity control device for substa...

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6th IEEE International Conference on Automation, Electronics and Electrical engineering, AUTEEE 2023

作者： Yumu, Chen Jingjing, Su Zihan, Zhang Chengpu, Song Xin, Chen Jiancheng, Sheng Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9798350305623

Based on the substation outdoor box cabinet in the operation process, which is prone to condensation and overheating, this paper designs a temperature and humidity control device for outdoor box cabinet of substation based on STM32 micro-controller. The device uses STM32 as the main control unit, and uses DHT 11 temperature and humidity sensor to detect the temperature and humidity of the box cabinet, and compare the threshold value to judge the temperature and humidity in the box cabinet and control the heater and fan to effectively drive moisture and heat. © 2023 IEEE.

关键词： Intelligent control outdoor box cabinet STM32 single-chip microcomputer Temperature and humidity

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Neural Reactivity to Haptics: Virtual Tasks Versus Physical Tasks

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IEEE Sensors Journal 2024年第7期24卷 11817-11828页

作者： Liu, Yi-Hung Vaitheeshwari, R. Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan

This study investigates the effectiveness of haptic feedback in hand rehabilitation exercises, within both virtual reality (VR) and real-world settings, to enhance upper limb functionality in post-stroke recovery. We developed a VR system that incorporates haptic feedback into pinch simulations and compared its efficacy with similar real-world pinch training exercises, primarily focusing on the rehabilitation of the paretic hand. Our main objective was to ascertain the similarity of the simulated haptic feedback in the VR environment to actual tactile feedback in the real world. This was achieved by analyzing electroencephalogram (EEG) features and psychometric data collected from 35 healthy subjects. A key aspect of our approach was the use of machine learning (ML) techniques to classify the EEG signal features as originating from VR or Real tasks. This classification was based on data from the top-n electrode channels, enabling us to assess the degree of similarity in brain responses to tactile stimulation in both environments. The study's results revealed that the brain activity patterns elicited by haptic feedback were comparable in both the VR and real-world setups. Additionally, the VR feedback system was positively received by users, scoring high on experience and satisfaction metrics. This research provides a replicable methodology for assessing tactile feedback in existing rehabilitation systems and lays the groundwork for the development of novel therapeutic applications. Our findings are crucial for designing future rehabilitation interventions that effectively utilize haptic feedback, potentially leading to improved treatment outcomes for stroke patients. © 2001-2012 IEEE.

关键词： Electroencephalography

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Near-Source Attack for Isolated Networks with Covert Channel Transmission 10

Near-Source Attack for Isolated Networks with Covert Channel...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Ruan, Zhiqiang Yang, Yuchen Chen, Lejia Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9798350312461

This paper investigates a new attack method called "near-source attack". It leverages the broadcast frames of the 802.11 protocol to establish a hidden tunnel and bypass physical isolation networks or air-gapped networks. We first analyze and implement a common technology known as Ghost Tunnel, which allows the attacker to control the target host and transmit information without being detected. However, this method suffers from frame loss, repeated frame, and attack transparency. We then propose an improved solution to deliver malicious programs to the target host using a modified BadUSB hardware device. Once the attackers successfully get in the isolated networks, they can bypass security protection devices and exploit vulnerabilities of communication protocols, so that they can remote control of target devices and hidden data transmission. We further conducted experiments to verify the feasibility and effectiveness of this attack scheme. The results indicate that the attack logic is capable of inducing the target host to engage in covert communication. Finally, we give some defense measures for such attacks. © 2023 IEEE.

关键词： Remote control

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A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

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computers, Materials & Continua 2024年第1期78卷 569-588页

作者： Haizhen Wang Jinying Yan Na Jia College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqiharChina

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured *** extraction of encrypted traffic attributes and their subsequent identification presents a formidable *** existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s *** the present study,a new model,referred to as UD-VLD(Unbalanced Dataset-VAE-LSTM-DRN),was proposed to address above *** proposed model is an encrypted traffic identification model for handling unbalanced *** encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced *** enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related ***,the data is classified and *** UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced *** UD-VLD model was tested using the publicly available Tor dataset and VPN *** UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC *** results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced ***,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic *** can serve as a novel solution for encrypted traffic identification.

关键词： Data enhancement LSTM deep residual network VAE

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