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A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.Fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting the *** framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse *** the second main stage,the residual attention u-net was used for *** framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of *** framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of *** experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

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Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm

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computers, Materials & Continua 2023年第12期77卷 3095-3117页

作者： Wenlong Zhu Yu Miao Shuangshuang Yang Zuozheng Lian Lianhe Cui College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of ***,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social *** bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation ***,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes ***,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their *** the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability *** process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based ***,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node ***,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

关键词： Temporal social networks influence maximization voting strategy interactive properties self-similarity

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Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition (Extended Abstract) 39

Tensor Canonical Correlation Analysis Networks for Multi-vie...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Yang, Xinghao Liu, Weifeng Liu, Wei College of Control Science and Engineering Qingdao China University of Technology Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350322279

Remote sensing (RS) images are frequently observed from multiviews. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle the multiview RS recognition problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Furthermore, we also develop a Multiple Scale version of TCCANet, i.e., MS-TCCANet, to extract enriched representation of the RS data by incorporating all previous convolutional layers. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition. © 2023 IEEE.

关键词： Convolution

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CORMAND2--针对工业机器人的欺骗攻击

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engineering 2024年第1期32卷 186-201页

作者： Hongyi Pu Liang He Peng Cheng Jiming Chen Youxian Sun College of Control Science and Engineering Zhejiang UniversityHangzhou 310000China Department of Computer Science and Engineering University of Colorado Denver DenverCO 999039USA

Industrial robots are becoming increasingly vulnerable to cyber incidents and attacks,particularly with the dawn of the Industrial Internet-of-Things(IIoT).To gain a comprehensive understanding of these cyber risks,vulnerabilities of industrial robots were analyzed empirically,using more than three million communication packets collected with testbeds of two ABB IRB120 robots and five other robots from various original equipment manufacturers(OEMs).This analysis,guided by the confidentiality-integrity-availability(CIA)triad,uncovers robot vulnerabilities in three dimensions:confidentiality,integrity,and *** vulnerabilities were used to design Covering Robot Manipulation via Data Deception(CORMAND2),an automated cyber-physical attack against industrial ***2 manipulates robot operation while deceiving the Supervisory control and Data Acquisition(SCADA)system that the robot is operating normally by modifying the robot’s movement data and data ***2 and its capability of degrading the manufacturing was validated experimentally using the aforementioned seven robots from six different ***2 unveils the limitations of existing anomaly detection systems,more specifically the assumption of the authenticity of SCADA-received movement data,to which we propose mitigations for.

关键词： Industrial robots Vulnerability analysis Deception attacks Defenses

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ScDCMAC:adaptive feedback clustering of scRNA-seq data with deep gene correlation mining

ScDCMAC:adaptive feedback clustering of scRNA-seq data with ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Wang, Ruiqian Wu, Wenhao Li, Hengxiao Wu, Jiarui 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

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder's dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis. © 2024 IEEE.

关键词： Dimensionality reduction

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ASLiquid: Non-Intrusive Liquid Counterfeit Identification with Your Earphones 24

ASLiquid: Non-Intrusive Liquid Counterfeit Identification wi...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Li, Zhaohui Luo, Wei Zhang, Yongmin Chen, Jianxi Shu, Yuanchao Zhang, Yaoxue School of Computer Science and Engineering Central South University China College of Control Science and Engineering Zhejiang University China Department of Computer Science and Technology BNRist Tsinghua University China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798400706974

As society progresses, liquid identification plays an increasingly important role in human life. But for now, minority of existing liquid identification solutions on the market can meet daily requirements of being ubiquitous, cost-effective and non-intrusive enough. In this work, we propose ASLiquid, the first liquid counterfeit identification system with commercial off-the-shelf earphones. Our core insight is that earphones can effectively induce acoustic resonance in container, and this phenomenon is observed highly associated with the changes in liquid density and solute compositions. Deploying ASLiquid introduces three main challenges: hardware heterogeneity among different earphones, diversity of user operations, and data complexity due to variations in liquid volume and device placement. To address these issues, we first propose to eliminate the existence of hardware noise and frequency response diversity for an earphone-irrelevant solution. Afterwards, we design a user operation adaptation algorithm to extract valuable feature data during each measurement period. To alleviate problems in data complexity, we propose a spectrum projection algorithm that can effectively generate CFR data of unknown liquid volumes and a VAE based anomaly detection model for counterfeit identification. We evaluate our system with six different earphones and under various conditions. Experimental results reveal that ASLiquid can achieve F1 scores of 95%-99.25% for seven frequently occurring liquid counterfeit tasks, even in specialized attacks on liquids with 1% difference in mass fraction and different types of solutions but with the same density. © 2024 Copyright is held by the owner/author(s).

关键词： Earphones

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A Positive Influence Maximization Algorithm in Signed Social Networks

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computers, Materials & Continua 2023年第8期76卷 1977-1994页

作者： Wenlong Zhu Yang Huang Shuangshuang Yang Yu Miao Chongyuan Peng College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social *** positive influence maximization(PIM)problem is an extension of the IM problem,which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely *** solve the PIM problem,this paper proposes the polar and decay related independent cascade(IC-PD)model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social *** overcome the low efficiency of the greedy based algorithm,this paper defines the polar reverse reachable(PRR)set and devises a signed reverse influence sampling(SRIS)*** algorithm utilizes the ICPD model as well as the PRR set to select *** are two phases in *** is the sampling phase,which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR *** other is the node selection phase,which uses a greedy coverage algorithm to select optimal ***,Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in *** on the Slashdot dataset,SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.

关键词： Positive influencemaximization polar relation information decay reverse influence sampling

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Fault Detection for Battery Energy Storage Systems in Microgrids Under Event-Triggered Protocols: An Interval Observer Approach 7

Fault Detection for Battery Energy Storage Systems in Microg...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Su, Mengmeng Zhang, Yong Liu, Zhibin Li, Xin 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, we investigate a method to realize fault detection using interval observer for battery energy storage systems containing actuator faults in microgrids. In order to reduce the data communication burden of the battery energy storage system, an improved event triggering mechanism with time-varying threshold is proposed to control the received signal of the interval observer. In addition, the desired interval observer gain is obtained by solving a matrix inequality. The output of the interval observer is utilized to construct the upper and lower bounds of the output of the system when the system does not contain actuator faults, and the actuator fault detection is achieved by whether the output of the system exceeds the constructed upper and lower bounds. Finally, the correctness and effectiveness of the proposed method are verified by numerical simulation. © 2024 IEEE.

关键词： Fault detection

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Interval Observer-Based Attack Detection in Smart Grid Under FlexRay Communication Protocols 7

Interval Observer-Based Attack Detection in Smart Grid Under...

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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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Configuration Optimization of Hybrid Electrolyzer for Wind-Based Green Hydrogen Production System 7

Configuration Optimization of Hybrid Electrolyzer for Wind-B...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Lin, Jiahu Hao, Wenbo Ban, Mingfei Northeast Forestry University College of Computer and Control Engineering Harbin China State Grid Heilongjiang Electric Power Research Institute Harbin China

ISBN: (纸本)9798350359558

As the development of renewable energy progresses, the prospects for the application of green hydrogen are gradually expanding, with countries strengthening the construction of industries related to green hydrogen. This paper investigates the hybrid electrolytic hydrogen production system and its configuration optimization problem, considering wind power decomposition and multiple types of electrolyzers. Firstly, a model for wind power redistribution is established based on the characteristics of different types of electrolyzers to determine the power loads of each type. Subsequently, based on the solution results of the redistribution model, a hybrid electrolyzer system configuration optimization model with the objective of maximizing annual revenue is proposed. Finally, case studies show that Hybrid electrolyzer system offers advantages in terms of energy efficiency, reliability and economy. Compared to alkaline electrolyzer system, hybrid system can improve the efficiency and stability of energy utilization and better cope with the uncertainty of renewable energy sources. The hybrid system can significantly reduce the initial capital investment and number of configurations compared to proton exchange membrane electrolyzer system, thereby lowering the operating cost of the overall energy system. © 2024 IEEE.

关键词： Wind power

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