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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Hu, Man Zhang, Yong Su, Mengmeng Ban, Mingfei North China Branch of State Grid Corporation of China Beijing China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350359558

In this paper, the detection of false data injection attacks (FDIA) in the smart grid based on interval observer is studied under the FlexRay communication protocol. The FDIA is highly stealthy and can modify the sensor data to affect system performance without being detected by common detection mechanisms. A cyber-attack detection mechanism against FDIA is proposed in this paper. Firstly, the smart grid is divided into several sub-areas for analysis and modeling, and a novel FlexRay communication protocol is designed to schedule the transmission of information in each sub-area. Next, an interval observer considering internal state boundaries and disturbances is designed based on the information after transmission to estimate the upper and lower bounds of the system states. An algorithm for attack detection against FDIA using interval residuals from interval observers is proposed. In addition, using the augmentation technique and the Lyapunov stability analysis method, sufficient conditions to ensure the stability of the estimation error system are given in the form of linear matrix inequality (LMI), and the interval observer gains are obtained by solving the LMI. Finally, the correctness and effectiveness of the proposed method is verified by a simulation example. © 2024 IEEE.

关键词： Smart power grids

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Identification of Important FPGA Modules Based on Complex Network

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computers, Materials & Continua 2024年第1期78卷 1027-1047页

作者： Senjie Zhang Jinbo Wang Shan Zhou Jingpei Wang Zhenyong Zhang Ruixue Wang School of Aeronautics and Astronautics University of Chinese Academy of SciencesBeijing101408China Technology and Engineering Center for Space Utilization Chinese Academy of SciencesBeijing100094China College of Control Science and Engineering Zhejiang UniversityHangzhou310063China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The globalization of hardware designs and supply chains,as well as the integration of third-party intellectual property(IP)cores,has led to an increased focus from malicious attackers on computing ***,existing defense or detection approaches often require additional circuitry to perform security verification,and are thus constrained by time and resource *** the scale of actual engineering tasks and tight project schedules,it is usually difficult to implement designs for all modules in field programmable gate array(FPGA)*** studies have pointed out that the failure of key modules tends to cause greater damage to the ***,under limited conditions,priority protection designs need to be made on key modules to improve protection *** have conducted research on FPGA designs including single FPGA systems and multi-FPGA systems,to identify key modules in FPGA *** the single FPGA designs,considering the topological structure,network characteristics,and directionality of FPGA designs,we propose a node importance evaluationmethod based on the technique for order preference by similarity to an ideal solution(TOPSIS)***,for the multi-FPGA designs,considering the influence of nodes in intra-layer and inter-layers,they are constructed into the interdependent network,and we propose a method based on connection strength to identify the important ***,we conduct empirical research using actual FPGA designs as *** results indicate that compared to other traditional indexes,node importance indexes proposed for different designs can better characterize the importance of nodes.

关键词： Hardware security FPGA circuits node importance interdependent network

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Myocardial segmentation of 2D echocardiography based on the improved self-attention mechanism 2

Myocardial segmentation of 2D echocardiography based on the ...

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2nd International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2024

作者： Li, Delong Sun, Yixin Han, Xue Dong, Suyu College of Computer and Control Engineering Northeast Forestry University Harbin China The Department of Ultrasound Harbin Medical University Cancer Hospital Harbin China

ISBN: (纸本)9798331543037

The measurement of different clinical indicators, such as the myocardial wall's thickness and the ventricle's volume, depends on accurately segmenting the left ventricle in 2D echocardiography. This segmentation is essential for making various clinical decisions, such as whether surgery is necessary. However, segmenting the myocardium requires clinical expertise and makes accurate segmenting time-consuming due to the poor quality of echocardiograms. It is essential to develop an automatic myocardial segmentation method. Some deep learning methods, especially the U-net structure, were proposed to support the automatic myocardium segmentation in echocardiography. The model must simultaneously capture the local and global dependency relationships in the ultrasound images for myocardial segmentation in echocardiography. Given the inherent attribute of local operation of convolution operations, this poses significant limitations for U-Net based on the traditional CNN as the basic architecture. For example, when dealing with complex myocardial textures and structures, we need to accurately identify the myocardium's global morphological features and overall structural patterns. Moreover, this locality may lead to deviations and errors when segmenting the boundary regions or the myocardial parts closely related to the surrounding tissues. Our primary focus will be redefining the skip connection using the attention mechanism. More integrated attention mechanisms can further enhance the segmentation effect of myocardial in echocardiography. Therefore, we integrated the cross-attention mechanism when concatenating features in the skip connection. Through conducting rigorous ablation studies on multiple echocardiography segmentation datasets, the performance of the proposed mechanism was proven. ©2024 IEEE.

关键词： Echocardiography

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IEARACDA: Predicting circRNA-disease associations based on information enhancement and arctangent rank approximation

IEARACDA: Predicting circRNA-disease associations based on i...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Song, Zheqi Liu, Tiyao Wu, Wenhao Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Circular RNAs (circRNAs) are non-coding RNA molecules that play a significant role in cell regulation and disease occurrence. In recent years, the use of computational methods to predict circRNAs associated with diseases has become a research focus. However, these methods are often constrained by the noise interference and an insufficient exploration of the underlying information in known associations. Therefore, this paper proposes an information enhancement and arctangent rank approximation based method to predict potential circRNA-disease associations (IEARACDA). Firstly, information enhancement methods are used to make circRNA and disease similarity information more complete and to reduce the sparsity of known association. Subsequently, the arctangent rank approximation method is employed to mitigate the potential bias issues that may arise from the nuclear norm, and optimization is carried out using the inexact augmented Lagrange multiplier (IALM) method. Finally, potential associations are predicted through heterogeneous graph inference method. The experimental results demonstrate that IEARACDA exhibits superior performance compared to existing methods. Furthermore, case studies for hepatocellular carcinoma and lung cancer provide additional confirmation of the accuracy and practical significance of the model predictions. © 2024 IEEE.

关键词： Lung cancer

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STC: Student-Teacher Collaborative Model for Multi-Target Domain Adaptation 21

STC: Student-Teacher Collaborative Model for Multi-Target Do...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Jiayi, Xu Jiaqi, Yu Yixin, Wang Shijia, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9798331519254

Cross-domain object detection poses significant challenges due to the susceptibility of object detection models to data variance, particularly the domain shifts that can occur between different domains. To address the limitation, we draw inspiration from knowledge distillation, proposing a collaborative learning framework. Our method employs CycleGAN to generate target-style images, and during pretraining, an unsupervised domain adaptation teacher model is trained for each source-target pair. In the distillation process, our proposed algorithm implements an out-of-distribution estimation strategy to select samples that best align with the current model, thereby enhancing the cross-domain distillation process. Furthermore, each expert model is encouraged to collaborate by designating the student model as a bridge between different target domains, facilitated by the Exponential Moving Average (EMA) algorithm. Experiments show that the proposed method leverages structured information, not only does it perform well across various target domains, but it also yields favorable results compared to state-of-the-art unsupervised methods that are specifically trained on single source-target pair. © 2024 IEEE.

关键词： Contrastive Learning

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Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

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computer Modeling in engineering & Sciences 2023年第2期134卷 767-782页

作者： Tingshun Li Jiaohui Xu Zesan Liu Dadi Wang Wen Tan School of Control and Computer Engineering North China Electric Power UniversityBeijing102206China R&D State Grid Information&Telecommunication Group Co.Beijing102211China

The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter,where this affects their capacity for power generation as well as their *** identifying the icing of the blades of wind turbines in remote areas is thus important,and a general model is needed to this *** paper proposes a universal model based on a Deep Neural Network(DNN)that uses data from the Supervisory control and Data Acquisition(SCADA)*** datasets from SCADA are first preprocessed through undersampling,that is,they are labeled,normalized,and *** features of icing of the blades of a turbine identified in previous studies are then used to extract training data from the training dataset.A middle feature is proposed to show how a given feature is correlated with icing on the *** indicators for the model,including a reward function,are also designed to assess its predictive ***,the most suitable model is used to predict the testing data,and values of the reward function and the predictive accuracy of the model are *** proposed method can be used to relate continuously transferred features with a binary status of icing of the blades of the turbine by using variables of the middle *** results here show that an integrated indicator systemis superior to a single indicator of accuracy when evaluating the prediction model.

关键词： DNN predicting blade icing SCADA data wind power reward function

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Interval Observer Design for Multi-Rate Systems with Multiplicative Noises

Interval Observer Design for Multi-Rate Systems with Multipl...

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2023 China Automation Congress, CAC 2023

作者： Cheng, Kaijie Li, Xin Zhu, Liangkuan Hong, Qige College of Computer and Control Engineering Northeast Forestry University Harbin China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350303759

An interval observer is designed for multi-rate systems with multiplicative noises in this paper. First, a multi-rate sampling system is investigated. At the same time, the presence of multiplicative noise would couple with the multi-rate sampling mechanism, which brings a significant challenge for the estimate of the system states. To address this problem, some arithmetic rules are proposed to separate the noise-contaminated terms, and the multi-rate system is transformed into an equivalent system with a uniform sampling period using the lifting technique and augmentation methods. Based on this, an interval observer is developed to estimate the bounds of the system states, sufficient conditions to ensure the effectiveness of the interval observer, and the input-to-state stability (ISS) of the estimation error dynamics are derived. Finally, simulations verify the proposed method. © 2023 IEEE.

关键词： Phase noise

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A Weighted Average Consensus Approach for Decentralized Federated Learning

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Machine Intelligence Research 2022年第4期19卷 319-330页

作者： Alessandro Giuseppi Sabato Manfredi Antonio Pietrabissa Department of Computer Controland Management EngineeringUniversity of Rome La SapienzaRome 00185Italy Department of Electrical Engineering and Information Technology University of Naples Federico ⅡNaples 80125Italy

Federated learning(FedL)is a machine learning(ML)technique utilized to train deep neural networks(DeepNNs)in a distributed way without the need to share data among the federated training *** was proposed for edge computing and Internet of things(IoT)tasks in which a centralized server was responsible for coordinating and governing the training *** remove the design limitation implied by the centralized entity,this work proposes two different solutions to decentralize existing FedL algorithms,enabling the application of FedL on networks with arbitrary communication topologies,and thus extending the domain of application of FedL to more complex scenarios and new *** the two proposed algorithms,one,called FedLCon,is developed based on results from discrete-time weighted average consensus theory and is able to reconstruct the performances of the standard centralized FedL solutions,as also shown by the reported validation tests.

关键词： Federated learning(FedL) deep learning federated averaging(FedAvg) machine learning(ML) artificial intelligence discrete-time consensus distributed systems.

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Intelligent Proximate Analysis of Coal Based on Near-Infrared Spectroscopy and Multioutput Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1398-1410页

作者： Zou, Liang Qiao, Jiahui Yu, Xinhui Chen, Xun Lei, Meng School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and multiscale feature fusion modules, which can improve the representation power of the U-shape network through aggregating features of shallower layers and concatenating features of deeper layers. Second, four individual subnetworks with fully connected layers, designed for four outputs, are utilized for regressing those four characteristics. We employ the gradient normalization algorithm to alleviate the gradient magnitude masking effect caused by training imbalance among different tasks. The proposedMOA-Unet is compared with classical chemometric methods on 670 coal samples from on-site *** experimental results demonstrate that the proposedmodel achieves state-of-the-art performance with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884, corresponding to moisture, ash, volatile content, and calorific value, respectively. Impact Statement-The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coa

关键词： Feature extraction

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Improving Security and Performance of Databases Through Single Cache System 4

Improving Security and Performance of Databases Through Sing...

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4th International Conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Ul Haq, Ikram Khan, Salabat Khan, Mansoor Tamsal, Saher School of Computer Science and Technology Southwest University of Science and Technology Mianyang China School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China University of Agriculture Department of Computer Science Faisalabad Pakistan

ISBN: (纸本)9798331528324

In today's world, connectivity and performance-driven technology are rapidly advancing each year, and new platforms and mechanisms are being utilized to enhance the security and performance of databases and cloud computing, which are trending in the global market. This examination focused on customer information security in cloud computing, i.e., information stockpiling security issues and how to control unapproved access to data by proposing access control;valuable measures were characterized subsequently, and an entrance control show was prescribed. Cloud computing can be described as an innovation that depends on the web and the sharing of resources. It has an adaptable foundation that customers can use with much of a stretch. With its wide event relationship, there are numerous obstacles to continuing with cloud computing because of security issues, mostly considering customer data security. The proposed study helped hesitant clients sensibly continue forward in the cloud while understanding the dangers of cloud computing. The research objective is to examine the security techniques employed in cloud technology to improve its performance by utilizing data mining within a unified cache system. We will apply the Apriori algorithm as a proposed solution to be used in a single cache while considering the security measures in the cloud. The study expects to apply this method to all cloud providers and vendors by entering data into a single cache mechanism that can be extended into different components and implemented using SPSS modelers to improve the security of the database. © 2024 IEEE.

关键词： Cloud security

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