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Journal of Systems Science & Complexity 2024年第3期37卷 1023-1036页

作者： ZHANG Ziwei SHEN Hao SU Lei School of Electrical and information Engineering Anhui University of TechnologyMa'anshan 243002China Anhui Province Key Laboratory of Special Heavy Load Robot and School of Electrical and information Engineering Anhui University of TechnologyMa'anshan243002China

In this work,an H_(∞)/passive-based secure synchronization control problem is investigated for continuous-time semi-Markov neural networks subject to hybrid attacks,in which hybrid attacks are the combinations of denial-of-service attacks and deception attacks,and they are described by two groups of independent Bernoulli *** this foundation,via the Lyapunov stability theory and linear matrix inequality technology,the H_(∞)/passive-based performance criteria for semi-Markov jump neural networks are ***,an activation function division approach for neural networks is adopted to further reduce the conservatism of the ***,a simulation example is provided to verify the validity and feasibility of the proposed method.

关键词： Activation function division approach deception attacks denial-of-service attacks H_(∞)/passive synchronization semi-Markov jump neural networks

A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

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Computers, Materials & Continua 2024年第8期80卷 2099-2119页

作者： Tajmal Hussain Jungpyo Hong Jongwon Seok Department of information and Communication Engineering Changwon National UniversityChangwon51140Republic of Korea

Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things(IoT)and artificial intelligence(AI).Quality control is an important part of today’s smart manufacturing process,effectively reducing costs and enhancing operational *** technology in the industry becomes more advanced,identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing *** this study,we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques,incorporating a global average pooling(GAP)layer and a machine learning-based SVM classifier,with the aim of enhancing ***,features are extracted by the VGG19 convolutional ***,after processing through the GAP layer,the extracted features are fed to the SVM classifier for *** this purpose,we collected images from publicly available datasets,including the Xsteel surface defect dataset(XSDD)and the NEU surface defect(NEU-CLS)datasets,and we employed offline data augmentation techniques to balance and increase the size of the *** outcome of experiments shows that the proposed methodology achieves the highest metrics score,with 99.79%accuracy,99.80%precision,99.79%recall,and a 99.79%F1-score for the NEU-CLS ***,it achieves 99.64%accuracy,99.65%precision,99.63%recall,and a 99.64%F1-score for the XSDD dataset.A comparison of the proposed methodology to the most recent study showed that it achieved superior results as compared to the other studies.

关键词： Smart manufacturing steel defect detection deep learning CNN

Two Monte Carlo-based simulators for imaging-system modeling and projection simulation of flat-panel X-ray source

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Nuclear Science and Techniques 2024年第7期35卷 31-46页

作者： Meng-Ke Qi Ting He Yi-Wen Zhou Jing Kang Zeng-Xiang Pan Song Kang Wang-Jiang Wu Jun Chen Ling-Hong Zhou Yuan Xu School of Biomedical Engineering Southern Medical UniversityGuangzhou510515China State Key Laboratory of Optoelectronic Materials and technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou510275China Department of Radiotherapy Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhou510515China

The advantages of a flat-panel X-ray source(FPXS)make it a promising candidate for imaging *** imaging-system modeling and projection simulation are critical for analyzing imaging performance and resolving overlapping projection issues in *** conventional analytical ray-tracing approach is limited by the number of patterns and is not applicable to FPXS-projection ***,the computation time of Monte Carlo(MC)simulation is independent of the size of the patterned arrays in *** study proposes two high-efficiency MC projection simulators for FPXS:a graphics processing unit(GPU)-based phase-space sampling MC(gPSMC)simulator and GPU-based fluence sampling MC(gFSMC)*** two simulators comprise three components:imaging-system modeling,photon initialization,and physical-interaction simulations in the ***-system modeling was performed by modeling the FPXS,imaging geometry,and *** gPSMC simulator samples the initial photons from the phase space,whereas the gFSMC simulator performs photon initialization from the calculated energy spectrum and fluence *** entire process of photon interaction with the geometry and arrival at the detector was simulated in parallel using multiple GPU kernels,and projections based on the two simulators were *** accuracies of the two simulators were evaluated by comparing them with the conventional analytical ray-tracing approach and acquired projections,and the efficiencies were evaluated by comparing the computation *** results of simulated and realistic experiments illustrate the accuracy and efficiency of the proposed gPSMC and gFSMC simulators in the projection calculation of various phantoms.

关键词： Flat-panel X-ray source(FPXS) Imaging-system modeling Projection simulation Monte Carlo-based simulators

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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Computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

Recent Advances on Deep Learning for Sign Language Recognition

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2399-2450页

作者： Yanqiong Zhang Xianwei Jiang School of Mathematics and information Science Nanjing Normal University of Special EducationNanjing210038China

Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting *** language recognition(SLR),the process of automatically recognizing and interpreting sign language gestures,has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing *** emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing *** paper presents a comprehensive and up-to-date analysis of the advancements,challenges,and opportunities in deep learning-based sign language recognition,focusing on the past five years of *** explore various aspects of SLR,including sign data acquisition technologies,sign language datasets,evaluation methods,and different types of neural *** Neural Networks(CNN)and Recurrent Neural Networks(RNN)have shown promising results in fingerspelling and isolated sign ***,the continuous nature of sign language poses challenges,leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition(CSLR).Despite significant advancements,several challenges remain in the field of *** challenges include expanding sign language datasets,achieving user independence in recognition systems,exploring different input modalities,effectively fusing features,modeling co-articulation,and improving semantic and syntactic ***,developing lightweight network architectures for mobile applications is crucial for practical *** addressing these challenges,we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community.

关键词： Sign language recognition deep learning artificial intelligence computer vision gesture recognition

Simulating the Dynamics of Bimetallic Clusters Deposited onto a Surface Using Molecular Dynamics

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Journal of Applied Mathematics and Physics 2024年第8期12卷 2820-2828页

作者： Akbarali Rasulov Nodirbek Ibrokhimov Jaxongir Khodjimatov Azamatjon Tukhtasinov Fergana Branch of the Tashkent University of information technologies named after Muhammad al-Khwarizmi Fergana Uzbekistan Fergana Polytechnic Institute Fergana Uzbekistan

This paper examines the interface development between a single crystalline Ag matrix and core-shell AgnCom nanoclusters that have been deposited with energies varying between 0.25 eV and 1.5 eV per atom using computer modeling techniques. Clusters undergo deformation as a result of the slowing down;they may also become epitaxial with the substrate and maintain their core-shell structure. A detailed analysis of the effects of the cluster-surface interaction is conducted over a realistic size and energy range, and a model is created to show how clusters accumulate. It is discovered that both the silver shells and the cobalt cluster cores exhibit limited epitaxy with the substrate, and that the contact produced is only a few atomic layers thick. The effect is higher for Ag shells than for Co cores, and it is not very energy dependent.

关键词： Cluster Slowing Down Low-Energy Molecular Dynamics Metrоpоlis Mоnte Cаrlо Bi-Metallic

Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

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The Crop Journal 2024年第3期12卷 927-937页

作者： Haiyu Tao Ruiheng Zhou Yining Tang Wanyu Li Xia Yao Tao Cheng Yan Zhu Weixing Cao Yongchao Tian National Engineering and Technology Center for information Agriculture Key Laboratory of Crop System Analysis and Decision MakingMinistry of Agriculture and Rural AffairsJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityNanjing 210095JiangsuChina

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of ***,it’s essential to construct and estimate a yield-related phenotypic trait considering spike *** on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning *** results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area ***3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike *** spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,*** results provide new insights into the role of fruiting organs for the accurate spectral prediction of *** high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

关键词： Wheat spike photosynthesis Yield-related phenotypic trait Spectral indices Machine learning Estimation

UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

Polar solvent induced in-situ self-assembly and oxygen vacancies on Bi_(2)MoO_(6)for enhanced photocatalytic degradation of tetracycline

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Nano Research 2024年第6期17卷 4951-4960页

作者： Fangyan Liu Dongyue Su Weizhen Liu Baiquan Liu Chuan Liu Hong Wang Mengye Wang State Key Laboratory of Optoelectronic Materials and technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-Sen UniversityGuangzhou 510275China School of Materials Sun Yat-Sen UniversityShenzhen 518107China School of Environment and Energy Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and RecyclingSouth China University of TechnologyGuangzhou 510006China

It has been proved to be an effective route to efficiently ameliorate photocatalytic performance of catalysts via designing three-dimensional(3D)hierarchical nanostructures and constructing oxygen vacancies(VOs).However,controlling the self-assembly of organization into 3D hierarchical nanostructures while introducing VOs in photocatalysts remains a ***,we reported an ethylene glycol(EG)mediated approach to craft 3D hydrangea-structure Bi_(2)MoO_(6)with VOs for efficient photocatalytic degradation of *** manipulating the EG concentration during the fabrication process,the influence of EG concentration on the Bi_(2)MoO_(6)structure was systematically *** could promote the self-assembly of Bi_(2)MoO_(6)nanosheets to form a 3D hierarchical *** with 2D nanoplates,3D hierarchical architecture enhanced the surface area and the amount of active sites of Bi_(2)MoO_(6).In addition,the reduction effect of EG on metallic oxide enabled the generation of VOs in Bi_(2)MoO_(6).The VOs adjusted the electronic structure of Bi_(2)MoO_(6),which not only enhanced the light harvesting,but also facilitated the simultaneous utilization of photo-induced electrons and holes to form reactive oxygen species(·O2−and·OH)for the efficient tetracycline decomposition.3D Bi_(2)MoO_(6)hydrangea with VOs achieved a 79.4%removal efficiency of tetracycline after 75 *** work provides a simple yet robust EG-mediated strategy,which not only promotes the self-assembly of nano-catalysts into 3D hierarchical architectures,but also crafts tunable VOs for highly efficient photocatalysis.

关键词： three-dimensional(3D)hierarchical nanostructures tunable oxygen vacancies ethylene glycol(EG)-mediated strategy bismuth molybdate photocatalytic degradation tetracycline

Non-Deterministic Liveness-Enforcing Supervisor Tolerant to Sensor-Reading Modification Attacks

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IEEE/CAA Journal of automatica Sinica 2024年第1期11卷 240-248页

作者： Dan You Shouguang Wang IEEE the School of information and Electronic Engineering Sussex Artificial Intelligence InstituteZhejiang Gongshang UniversityHangzhou 310018China

In this paper, we study the supervisory control problem of discrete event systems assuming that cyber-attacks might occur. In particular, we focus on the problem of liveness enforcement and consider a sensor-reading modification attack(SM-attack) that may disguise the occurrence of an event as that of another event by intruding sensor communication channels. To solve the problem, we introduce non-deterministic supervisors in the paper, which associate to every observed sequence a set of possible control actions offline and choose a control action from the set randomly online to control the system. Specifically, given a bounded Petri net(PN) as the reference formalism and an SMattack, an algorithm that synthesizes a liveness-enforcing nondeterministic supervisor tolerant to the SM-attack is proposed for the first time.

关键词： Cyber-attacks cyber-physical system(CPS) liveness non-deterministic supervisors Petri net(PN)