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Toward Intrusion Detection of Industrial Cyber-Physical System: A Hybrid Approach Based on System State and Network Traffic Abnormality Monitoring 认领 引用 被引量：2
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作者 Junbin He Wuxia Zhang +2 位作者 Xianyi Liu Jinping Liu Guangyi Yang 《Computers, Materials & Continua》 SCIE EI 2025年第7期1227-1252,共26页
The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e... The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS. 展开更多
关键词 Industrial cyber-physical systems network intrusion detection adaptive Kalman filter abnormal state monitoring network traffic abnormality monitoring
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Progressive Colour Equalisation and Detail Refinement for Underwater Image Enhancement 认领 引用
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作者 Songbai Liu Jiacheng Huang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期709-725,共17页
Underwater image enhancement remains a critical challenge in computational vision due to complex distortions caused by wavelength-dependent light absorption and scattering.This paper introduces CEDFNet,a novel two-sta... Underwater image enhancement remains a critical challenge in computational vision due to complex distortions caused by wavelength-dependent light absorption and scattering.This paper introduces CEDFNet,a novel two-stage framework that leverages advanced computational intelligence techniques for robust and high-fidelity underwater image restoration.The first stage integrates a Colour Equalisation Transformer(CET)to perform global colour correction by modelling long-range dependencies and mitigating dominant hue distortions.The second stage combines a Residual Texture Modulation Adaptor(RTMA)with an Enhanced Bilateral Enhancement Decoder(EBED)to refine structural details and enhance local contrast through context-aware and adaptive feature learning.Extensive evaluations on benchmark datasets including UIEBD,LSUI,and Colour-Checker7 validate the superiority of CEDFNet over existing state-of-the-art approaches.Quantitatively,CEDFNet achieves significant improvements across multiple perceptual and fidelity metrics such as PSNR,SSIM,FID,and LPIPS.Comprehensive ablation studies further confirm the complementary roles of CET,RTMA,and EBED,whereas parameter sensitivity analyses highlight the framework's robust and stable behaviour.By integrating transformer-based global correction with task-adaptive local enhancement,CEDFNet advances the frontier of underwater image restoration in the domain of computational intelligence.It generalises well across diverse imaging conditions and offers a lightweight and end-to-end solution suitable for real-world deployment in marine robotics,inspection,and visual perception systems. 展开更多
关键词 detail refinement transformer‐based color correction underwater image enhancement
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The Confluence of Evolutionary Computation and Multi-Agent Systems:A Survey 认领 引用 被引量：2
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作者 Tai-You Chen Wei-Neng Chen +5 位作者 Feng-Feng Wei Xiao-Qi Guo Wen-Xiang Song Rui Zhu Qiuzhen Lin Jun Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第11期2175-2193,共19页
Both evolutionary computation(EC)and multiagent systems(MAS)study the emergence of intelligence through the interaction and cooperation of a group of individuals.EC focuses on solving various complex optimization prob... Both evolutionary computation(EC)and multiagent systems(MAS)study the emergence of intelligence through the interaction and cooperation of a group of individuals.EC focuses on solving various complex optimization problems,while MAS provides a flexible model for distributed artificial intelligence.Since their group interaction mechanisms can be borrowed from each other,many studies have attempted to combine EC and MAS.With the rapid development of the Internet of Things,the confluence of EC and MAS has become more and more important,and related articles have shown a continuously growing trend during the last decades.In this survey,we first elaborate on the mutual assistance of EC and MAS from two aspects,agent-based EC and EC-assisted MAS.Agent-based EC aims to introduce characteristics of MAS into EC to improve the performance and parallelism of EC,while EC-assisted MAS aims to use EC to better solve optimization problems in MAS.Furthermore,we review studies that combine the cooperation mechanisms of EC and MAS,which greatly leverage the strengths of both sides.A description framework is built to elaborate existing studies.Promising future research directions are also discussed in conjunction with emerging technologies and real-world applications. 展开更多
关键词 Distributed artificial intelligence distributed optimization evolutionary computation(EC) multi-agent systems(MAS)
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Robust Hyper-Polarization Protocol of Nuclear Spins via Magic Sequence 认领 引用
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作者 Haiyang Li Yongju Li +1 位作者 Hao Liao Ping Wang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第1期105-128,共24页
Hyperpolarization of nuclear spins is crucial for advancing nuclear magnetic resonance and quantum information technologies,as nuclear spins typically exhibit extremely low polarization at room temperature due to thei... Hyperpolarization of nuclear spins is crucial for advancing nuclear magnetic resonance and quantum information technologies,as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyromagnetic ratios.A promising approach to achieving high nuclear spin polarization is transferring the polarization of electrons to nuclear spins.The nitrogen-vacancy(NV)center in diamond has emerged as a highly effective medium for this purpose,and various hyperpolarization protocols have been developed.Among these,the pulsed polarization(PulsePol)method has been extensively studied due to its robustness against static energy shifts of the electron spin.In this work,we present a novel polarization protocol and uncover a family of magic sequences for hyperpolarizing nuclear spins,with PulsePol emerging as a special case of our general approach.Notably,we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite halfpulse duration of the protocol,Rabi and detuning errors.This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields.Additionally,the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center. 展开更多
关键词 transferring polarization electrons hyperpolarization protocols quantum information technologiesas nuclear spins pulse polarization nuclear spin polarization magic sequences nuclear magnetic resonance
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Multi-Agent Swarm Optimization Method With Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association 认领 引用
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作者 Taiyou Chen Xiaomin Hu +1 位作者 Qiuzhen Lin Weineng Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1673-1688,共16页
With the development of communication and computation capabilities on terminal hardware,it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration abi... With the development of communication and computation capabilities on terminal hardware,it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration ability of sensors.In this work,we study distributed multi-target localization problem with measurement-to-measurement association(DM2M),where each sensor only accesses its own measurement data without the association of measurements from other sensors.We first reformulate DM2M into a distributed bilevel optimization problem to reduce the search space of negotiated variables caused by the data association among sensors.Then,we propose a multiagent swarm optimization method with contribution-based cooperation(MASTER).In MASTER,each sensor maintains a particle swarm to represent candidate solutions of target positions.Sensors evolve their particle swarms through two phases of local optimization and neighbor cooperation to locate the target cooperatively.To address the bilevel local objective function,we combine the Kuhn-Munkres algorithm and the competitive swarm optimization for local optimization.To promote sensors to optimize the global objective,we design a contribution-based cooperation method to guide sensors to learn from their neighbors.Through localization experiments for different target numbers and localization dimensions,the proposed algorithm achieves smaller localization errors and more stable consensus than existing algorithms. 展开更多
关键词 Distributed optimization evolutionary computation measurement-to-measurement association particle swarm optimization(PSO) wireless sensor networks(WSNs)
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A Paradigm of Temporal-Weather-Aware Transition Pattern for POI Recommendation 认领 引用
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作者 Junyang Chen Jingcai Guo +4 位作者 Huan Wang Zhihui Lai Qin Zhang Kaishun Wu Liang-Jie Zhang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第6期1675-1687,共13页
Point of interest(POI)recommendation analyses user preferences through historical check-in data.However,existing POI recommendation methods often overlook the influence of weather information and face the challenge of... Point of interest(POI)recommendation analyses user preferences through historical check-in data.However,existing POI recommendation methods often overlook the influence of weather information and face the challenge of sparse historical data for individual users.To address these issues,this paper proposes a new paradigm,namely temporal-weather-aware transition pattern for POI recommendation(TWTransNet).This paradigm is designed to capture user transition patterns under different times and weather conditions.Additionally,we introduce the construction of a user-POI interaction graph to alleviate the problem of sparse historical data for individual users.Furthermore,when predicting user interests by aggregating graph information,some POIs may not be suitable for visitation under current weather conditions.To account for this,we propose an attention mechanism to filter POI neighbours when aggregating information from the graph,considering the impact of weather and time.Empirical results on two real-world datasets demonstrate the superior performance of our proposed method,showing a substantial improvement of 6.91%-23.31% in terms of prediction accuracy. 展开更多
关键词 data mining decision making multimedia
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Next-Generation Lightweight Explainable AI for Cybersecurity: A Review on Transparency and Real-Time Threat Mitigation 认领 引用
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作者 Khulud Salem Alshudukhi Sijjad Ali +1 位作者 Mamoona Humayun Omar Alruwaili 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第12期3029-3085,共57页
Problem:The integration of Artificial Intelligence(AI)into cybersecurity,while enhancing threat detection,is hampered by the“black box”nature of complex models,eroding trust,accountability,and regulatory compliance.... Problem:The integration of Artificial Intelligence(AI)into cybersecurity,while enhancing threat detection,is hampered by the“black box”nature of complex models,eroding trust,accountability,and regulatory compliance.Explainable AI(XAI)aims to resolve this opacity but introduces a critical newvulnerability:the adversarial exploitation of model explanations themselves.Gap:Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector.There is a pressing need to systematically analyze the trade-offs between interpretability and security,evaluate defense mechanisms,and outline a path for developing robust,next-generation XAI frameworks.Solution:This review provides a systematic examination of XAI techniques(e.g.,SHAP,LIME,Grad-CAM)and their applications in intrusion detection,malware analysis,and fraud prevention.It critically evaluates the security risks posed by XAI,including model inversion and explanation-guided evasion attacks,and assesses corresponding defense strategies such as adversarially robust training,differential privacy,and secure-XAI deployment patterns.Contribution:Theprimary contributions of this work are:(1)a comparative analysis of XAI methods tailored for cybersecurity contexts;(2)an identification of the critical trade-off betweenmodel interpretability and security robustness;(3)a synthesis of defense mechanisms to mitigate XAI-specific vulnerabilities;and(4)a forward-looking perspective proposing future research directions,including quantum-safe XAI,hybrid neuro-symbolic models,and the integration of XAI into Zero Trust Architectures.This review serves as a foundational resource for developing transparent,trustworthy,and resilient AI-driven cybersecurity systems. 展开更多
关键词 Explainable AI(XAI) cybersecurity adversarial robustness privacy-preserving techniques regulatory compliance zero trust architecture
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AT-AER:Adversarial Training With Adaptive Example Reuse 认领 引用
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作者 Meng Hu Yanting Guo +3 位作者 Ran Wang Xizhao Wang Rihao Li Qin Wang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期769-783,共15页
Adversarial training(AT)is widely regarded as a crucial defense method for deep neural networks against adversarial attacks.Most of the existing AT methods suffer from the problems of insufficient coverage of perturba... Adversarial training(AT)is widely regarded as a crucial defense method for deep neural networks against adversarial attacks.Most of the existing AT methods suffer from the problems of insufficient coverage of perturbation space and robust overfitting.In view of this,we propose an AT framework with adaptive example reuse(AT-AER)to help improve the adversarial robustness of deep models.In AT-AER,a new concept named 2nd-order adversarial example(AE)is proposed by adaptively filtering AEs generated during the historical training phase,which achieves sufficient coverage of diverse attack directions.Meanwhile,by analysing the fundamental causes of robust overfitting,we propose the strategies of wave descending learning rate(WDLR),cosine increasing weight decay(CIWD)and cosine increasing attack strength(CIAS)in collaboration with AT-AER to optimise models.In addition,the Stochastic Weight Averaging(SWA)technique is introduced to further improve the stability of training.Finally,experiments on three benchmark datasets show that AT-AER exhibits significant advantages in the face of strong adversarial attacks.Its adaptive mechanism effectively alleviates the phenomenon of robust overfitting where the performance difference between the best model and the last model is less than 1%.The study further reveals that using traditional weak attacks(e.g.,FGSM)to evaluate the robustness of models may lead to a false sense of reliability,indicating the necessity of using strong attacks for robustness evaluation.This study provides a solution for AT that balances efficiency and performance. 展开更多
关键词 adversarial training example reuse 2nd‐order adversarial example adversarial robustness
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Adaptive Prescribed-Time Exact Tracking Control for Uncertain Strict-Feedback Systems With Global Prescribed-Performance 认领 引用
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作者 Yizhaotun Yan Bing Mao +2 位作者 Ling Lei Hui Liu Xiaoqun Wu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第5期1166-1175,共10页
For uncertain strict-feedback systems under the prescribed performance control(PPC)problem,an innovative adaptive prescribed-time tracking control method is proposed.This method combines a novel error transformation f... For uncertain strict-feedback systems under the prescribed performance control(PPC)problem,an innovative adaptive prescribed-time tracking control method is proposed.This method combines a novel error transformation function with the prescribed-time stability theory,thereby achieving exact tracking of desired trajectories within a prescribed time while ensuring that the tracking error stays within predefined boundaries globally.By integrating a newly-designed Lyapunov-like energy function with dynamic surface control,it resolves the error surface issues that result in the semi-global boundedness of tracking error in traditional approaches.Furthermore,through a generalized Filippov solution definition,this approach overcomes the issue of non-existence of the system solution,which arises during the prescribed-time stability analysis due to the discontinuous control input.Simulation results validate the effectiveness of the proposed method. 展开更多
关键词 Dynamic surface control global prescribed performance mismatched uncertainty prescribed-time exact tracking strict-feedback nonlinear systems
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A review of optimization methods for computation offloading in edge computing networks 认领 引用 被引量：17
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作者 Kuanishbay Sadatdiynov Laizhong Cui +3 位作者 Lei Zhang Joshua Zhexue Huang Salman Salloum Mohammad Sultan Mahmud 《Digital Communications and Networks》 SCIE CSCD 2023年第2期450-461,共12页
Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational problem.Edge Computing is an emerging computation paradigm that is employed to conquer this problem.It can brin... Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational problem.Edge Computing is an emerging computation paradigm that is employed to conquer this problem.It can bring computation power closer to the end devices to reduce their computation latency and energy consumption.Therefore,this paradigm increases the computational ability of SMDs by collaboration with edge servers.This is achieved by computation offloading from the mobile devices to the edge nodes or servers.However,not all applications benefit from computation offloading,which is only suitable for certain types of tasks.Task properties,SMD capability,wireless channel state,and other factors must be counted when making computation offloading decisions.Hence,optimization methods are important tools in scheduling computation offloading tasks in Edge Computing networks.In this paper,we review six types of optimization methods-they are Lyapunov optimization,convex optimization,heuristic techniques,game theory,machine learning,and others.For each type,we focus on the objective functions,application areas,types of offloading methods,evaluation methods,as well as the time complexity of the proposed algorithms.We discuss a few research problems that are still open.Our purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks. 展开更多
关键词 Edge computing Computation offloading Latency and energy consumption minimization
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Asynchronism of the spreading dynamics underlying the bursty pattern 认领 引用 被引量：1
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作者 Tong Wang Ming-Yang Zhou Zhong-Qian Fu 《Chinese Physics B》 SCIE EI CAS CSCD 2020年第5期586-592,共7页
The potential mechanisms of the spreading phenomena uncover the organizations and functions of various systems.However,due to the lack of valid data,most of early works are limited to the simulated process on model ne... The potential mechanisms of the spreading phenomena uncover the organizations and functions of various systems.However,due to the lack of valid data,most of early works are limited to the simulated process on model networks.In this paper,we track and analyze the propagation paths of real spreading events on two social networks:Twitter and Brightkite.The empirical analysis reveals that the spreading probability and the spreading velocity present the explosive growth within a short period,where the spreading probability measures the transferring likelihood between two neighboring nodes,and the spreading velocity is the growth rate of the information in the whole network.Besides,we observe the asynchronism between the spreading probability and the spreading velocity.To explain the interesting and abnormal issue,we introduce the time-varying spreading probability into the susceptible-infected(SI)and linear threshold(LT)models.Both the analytic and experimental results reproduce the spreading phenomenon in real networks,which deepens our understandings of spreading problems. 展开更多
关键词 social network information diffusion spreading probability asynchronism
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Empirical topological investigation of practical supply chains based on complex networks 认领 引用 被引量：1
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作者 廖好 沈婧 +2 位作者 吴兴桐 陈博奎 周明洋 《Chinese Physics B》 SCIE EI CAS CSCD 2017年第11期144-150,共7页
The industrial supply chain networks basically capture the circulation of social resource, dominating the stability and efficiency of the industrial system. In this paper, we provide an empirical study of the topology... The industrial supply chain networks basically capture the circulation of social resource, dominating the stability and efficiency of the industrial system. In this paper, we provide an empirical study of the topology of smartphone supply chain network. The supply chain network is constructed using open online data. Our experimental results show that the smartphone supply chain network has small-world feature with scale-free degree distribution, in which a few high degree nodes play a key role in the function and can effectively reduce the communication cost. We also detect the community structure to find the basic functional unit. It shows that information communication between nodes is crucial to improve the resource utilization. We should pay attention to the global resource configuration for such electronic production management. 展开更多
关键词 China supply chain networks complex networks data science network science
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Unsupervised feature selection based on Markov blanket and particle swarm optimization 认领 引用 被引量：2
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作者 Yintong Wang Jiandong Wang +1 位作者 Hao Liao Haiyan Chen 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2017年第1期151-161,共11页
Feature selection plays an important role in data mining and recognition, especially in the large scale text, image and biological data. Specifically, the class label information is unavailable to guide the selection ... Feature selection plays an important role in data mining and recognition, especially in the large scale text, image and biological data. Specifically, the class label information is unavailable to guide the selection of minimal feature subset in unsupervised feature selection, which is challenging and interesting. An unsupervised feature selection based on Markov blanket and particle swarm optimization is proposed named as UFSMB-PSO. The proposed method seeks to find the high-quality feature subset through multi-particles' cooperation of particle swarm optimization without using any learning algorithms. Moreover, the features' relevance will be computed based on an information metric of relevance gain, which provides an information theoretical foundation for finding the minimization of the redundancy between features. Our results on several benchmark datasets demonstrate that UFSMB-PSO can achieve significant improvement over state of the art unsupervised methods. © 1990-2011 Beijing Institute of Aerospace Information. 展开更多
关键词 Character recognition Data mining Feature extraction Information theory
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Optimisation of sparse deep autoencoders for dynamic network embedding 认领 引用
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作者 Huimei Tang Yutao Zhang +4 位作者 Lijia Ma Qiuzhen Lin Liping Huang Jianqiang Li Maoguo Gong 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第6期1361-1376,共16页
Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature space.However,the existing deep learningbased NE methods are time-consuming as they need to tra... Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature space.However,the existing deep learningbased NE methods are time-consuming as they need to train a dense architecture for deep neural networks with extensive unknown weight parameters.A sparse deep autoencoder(called SPDNE)for dynamic NE is proposed,aiming to learn the network structures while preserving the node evolution with a low computational complexity.SPDNE tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic NE.Then,an adaptive simulated algorithm to find the optimal sparse architecture for the deep autoencoder is proposed.The performance of SPDNE over three dynamical NE models(i.e.sparse architecture-based deep autoencoder method,DynGEM,and ElvDNE)is evaluated on three well-known benchmark networks and five real-world networks.The experimental results demonstrate that SPDNE can reduce about 70%of weight parameters of the architecture for the deep autoencoder during the training process while preserving the performance of these dynamical NE models.The results also show that SPDNE achieves the highest accuracy on 72 out of 96 edge prediction and network reconstruction tasks compared with the state-of-the-art dynamical NE algorithms. 展开更多
关键词 deep autoencoder dynamic networks low-dimensional feature space network embedding sparse structure
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A Novel Flexible Kernel Density Estimator for Multimodal Probability Density Functions 认领 引用
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作者 Jia-Qi Chen Yu-Lin He +3 位作者 Ying-Chao Cheng Philippe Fournier-Viger Ponnuthurai Nagaratnam Suganthan Joshua Zhexue Huang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第6期1759-1782,共24页
Estimating probability density functions(PDFs)is critical in data analysis,particularly for complex multimodal distributions.traditional kernel density estimator(KDE)methods often face challenges in accurately capturi... Estimating probability density functions(PDFs)is critical in data analysis,particularly for complex multimodal distributions.traditional kernel density estimator(KDE)methods often face challenges in accurately capturing multimodal structures due to their uniform weighting scheme,leading to mode loss and degraded estimation accuracy.This paper presents the flexible kernel density estimator(F-KDE),a novel nonparametric approach designed to address these limitations.F-KDE introduces the concept of kernel unit inequivalence,assigning adaptive weights to each kernel unit,which better models local density variations in multimodal data.The method optimises an objective function that integrates estimation error and log-likelihood,using a particle swarm optimisation(PSO)algorithm that automatically determines optimal weights and bandwidths.Through extensive experiments on synthetic and real-world datasets,we demonstrated that(1)the weights and bandwidths in F-KDE stabilise as the optimisation algorithm iterates,(2)F-KDE effectively captures the multimodal characteristics and(3)F-KDE outperforms state-of-the-art density estimation methods regarding accuracy and robustness.The results confirm that F-KDE provides a valuable solution for accurately estimating multimodal PDFs. 展开更多
关键词 data analysis learning(artificial intelligence) machine learning optimisation probability
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Guest Editorial Special Issue on the Next-Generation Deep Learning Approaches to Emerging Real-World Applications 认领 引用
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作者 Yu Zhou Eneko Osaba Xiao Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第7期237-242,共6页
Introduction Deep learning(DL),as one of the most transformative technologies in artificial intelligence(AI),is undergoing a pivotal transition from laboratory research to industrial deployment.Advancing at an unprece... Introduction Deep learning(DL),as one of the most transformative technologies in artificial intelligence(AI),is undergoing a pivotal transition from laboratory research to industrial deployment.Advancing at an unprecedented pace,DL is transcending theoretical and application boundaries to penetrate emerging realworld scenarios such as industrial automation,urban management,and health monitoring,thereby driving a new wave of intelligent transformation.In August 2023,Goldman Sachs estimated that global AI investment will reach US$200 billion by 2025[1].However,the increasing complexity and dynamic nature of application scenarios expose critical challenges in traditional deep learning,including data heterogeneity,insufficient model generalization,computational resource constraints,and privacy-security trade-offs.The next generation of deep learning methodologies needs to achieve breakthroughs in multimodal fusion,lightweight design,interpretability enhancement,and cross-disciplinary collaborative optimization,in order to develop more efficient,robust,and practically valuable intelligent systems. 展开更多
关键词 health monitoringthereby deep learning industrial deployment intelligent transformationin deep learning dl artificial intelligence ai penetrate emerging realworld scenarios transformative technologies
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An FCM-based microseismic phase arrival picking method and application 认领 引用
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作者 Zhiqiang Lan Yaqi Zhang +4 位作者 Yaojun Wang Keyu Chen Haoxiang Yang Yinzhu Chen Yangyang Yu 《Artificial Intelligence in Geosciences》 EI CSCD 2026年第1期93-105,共13页
Artificial intelligence-based methods for picking microseismic phase arrivals have been widely adopted.How-ever,these methods are frequently challenged by complex and dynamic monitoring scenarios,where various types o... Artificial intelligence-based methods for picking microseismic phase arrivals have been widely adopted.How-ever,these methods are frequently challenged by complex and dynamic monitoring scenarios,where various types of environmental noise mask low-energy microseismic signals.Moreover,the paucity of labelled data often impairs the reliability and accuracy of their results.To address these issues,this study proposes a novel super-vised learning framework named FC-Net,which integrates automatic labelling via Fuzzy C-means clustering(FCM)with the U-Net architecture.Specifically,the FCM algorithm is employed to derive the probabilistic distributions of microseismic phase arrival times,which are then used as training labels for model training.The proposed FC-Net is equipped with soft attention gates(AGs)and recurrent-residual convolution units(RRCUs),which effectively enhance the network's ability to focus on key seismic features.The arrival time is determined as the moment when the predicted probability exceeds a predefined threshold for the first arrival pick.Evaluated on a field dataset collected from Southwest China,FC-Net is demonstrated to outperform the conventional U-Net method.The experimental results demonstrate that FC-Net achieves adaptive labeling,enhances the detection rate of microseismic events,and improves the precision of phase arrival picking.Furthermore,it exhibits strong generalization performance across microseismic events with varying signal-to-noise ratios(SNRs). 展开更多
关键词 Seismic detection Phase picking Fuzzy c-means clustering U-net
SAVNFV:Towards a Scalable and Accurate Inter-Domain Source Address Validation Platform with Network Functions Virtualization 认领 引用
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作者 Shu Yang Zequn Zhang Laizhong Cui 《Big Data Mining and Analytics》 EI CSCD 2026年第2期536-553,共18页
Nowadays,Source Address Validation(SAV)is increasingly important for defending Distributed Denial of Service(DDoS)attacks and other malicious activities.Existing ingress/edge filtering-based solutions,such as SAVI,fil... Nowadays,Source Address Validation(SAV)is increasingly important for defending Distributed Denial of Service(DDoS)attacks and other malicious activities.Existing ingress/edge filtering-based solutions,such as SAVI,filter spoofed source addresses using Access Control Lists(ACL)or unicast Reverse Path Forwarding(uRPF),but they only provide coarse-grained filtering.Source Address Validation in intra-domain and inter-domain NETworks(SAVNET)has recently attracted much attention in both industry and the Internet Engineering Task Force(IETF),deploying SAV inside Internet Service Provider(ISP)networks and generating SAV tables by binding source address prefixes with incoming interfaces.However,SAVNET requires upgrading almost all routers across networks,which is impractical—especially in inter-domain scenarios.Moreover,due to policy routing and load balancing,determining the exact incoming interface for each source prefix is challenging.In this paper,we propose SAVNFV,a Network Functions Virtualization(NFV)based platform that provides SAV capabilities by building a“clean”virtual overlay network.SAVNFV randomly generates paths for each flow through a centralized controller,making the SAV table easy to obtain.The paths are periodically refreshed,and packets are transmitted through multiple routes,making it nearly impossible for attackers to identify the correct incoming interface.We formulate the multi-path transmission as an optimization problem and prove it to be NP-Complete,then design approximation algorithms with theoretical guarantees.Comprehensive simulations show that SAVNFV blocks 94.6%more malicious traffic than traditional solutions while maintaining acceptable path stretch.We also implement the system using open-source routing software and build a real-world experimental platform to further validate our design. 展开更多
关键词 Source Address Validation(SAV) Network Functions Virtualization(NFV) Distributed Denial of Service(DDoS)attacks multi-path routing
Multiresolution Taxi Demand Prediction:A Big Data Statistical and Zero-Inflated Spatiotemporal GNN Approach 认领 引用
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作者 Yifei Shen Wenlong Shi +3 位作者 Jiaxing Shen Hengzhi Wang Hanqing Wu Jiannong Cao 《Big Data Mining and Analytics》 EI CSCD 2026年第1期39-56,共18页
Urban taxi demand prediction faces a critical resolution paradox:high-resolution forecasts enable operational agility but suffer from extreme sparsity-induced volatility,while low-resolution predictions sacrifice resp... Urban taxi demand prediction faces a critical resolution paradox:high-resolution forecasts enable operational agility but suffer from extreme sparsity-induced volatility,while low-resolution predictions sacrifice responsiveness for stability.We present a Scalable SpatioTemporal Zero-Inflated Poisson Graph Neural Network(SSTZIP-GNN),that resolves this paradox through three innovations:(1)Zero-Inflated Poisson(ZIP)integration that explicitly models structural zeros in sparse demand distributions,distinguishing genuine low-demand periods from data artifacts;(2)Adaptive spatiotemporal learning that dynamically adjusts kernel dilation factors and graph diffusion rates across temporal resolutions using Diffusion Graph Convolutional Networks(DGCNs)and Temporal Convolutional Networks(TCNs);(3)Multimodal feature fusion incorporating real-time crowd-sourced mobility data,socioeconomic indicators,and Global Position System(GPS)trajectories for enhanced robustness under variable urban conditions.Extensive evaluation on 130 million real-world mobility records demonstrates superior performance,achieving 34.8%Mean Absolute Error(MAE)reduction over state-of-the-art baselines.The model reduces computational costs by 46.3%compared to ensemble approaches while maintaining high accuracy across resolutions,delivering 33.4%-53.3%Root Mean Square Error(RMSE)reduction across different prediction resolution scenarios.This unified framework enables cities to implement demand-responsive fleet management,dynamic pricing,and sustainable mobility planning across diverse urban landscapes. 展开更多
关键词 statistical big data analytics urban transportation taxi demand prediction multi-resolution prediction data sparsity Zero-Inflated Poisson(ZIP)distributi
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