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Erratum to“False Data Injection Attacks on Data-Driven Algorithms in Smart Grids Utilizing Distributed Power Supplies”[Engineering 51(2025)62-74] 认领 引用
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作者 Zengji Liu Mengge Liu +1 位作者 Qi Wang Yi Tang 《Engineering》 SCIE EI CSCD 2026年第5期362-362,共1页
The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualizati... The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualization,Validation,Supervision,Software,Resources,Project administration,Methodology,Investigation,Funding acquisition,Formal analysis,Data curation,Conceptualization.Mengge Liu:Writing-review&editing,Writing-original draft,Investigation.Qi Wang:Writing-review&editing,Writing-original draft.Yi Tang:Writing-review&editing,Writing-original draft. 展开更多
关键词 data driven algorithms credit authorship contribution statement false data injection attacks smart grids distributed power supplies
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Collaborative Scheduling Strategy for Computation and Power among Multiple Data Centers Based on New Energy State Recognition 认领 引用
2
作者 Qian Yang Shenglei Du +3 位作者 Boyang Chen Yalu Sun Ding Li Zhiheng Zhang 《Energy Engineering》 EI 2026年第8期346-373,共28页
In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon ... In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon neutrality”,data centers have gradually introduced new energy power such as wind and photovoltaic power.However,the randomness and volatility of their output pose challenges to efficient absorption.Based on the spatiotemporal complementary characteristics of new energy output in multiple data centers and the spatiotemporal migration capability of computing tasks,this paper proposes a new energy-aware adaptive collaborative scheduling strategy for computation and power.The strategy first constructs a regionally differentiated load model to accurately depict the characteristic differences among the Jiangsu-Zhejiang-Shanghai mixed computing power hub,the Gansu highefficiency computing power base,and the coastal green computing power nodes.Then,a dual-mode scheduling algorithm based on Lyapunov optimization is designed,integrating a prediction-reaction mechanism to achieve dynamic balance between system stability and new energy absorption rate.Furthermore,a V-parameter adaptive adjustment mechanism and a hierarchical fault-tolerant guarantee system are proposed to cope with new energy fluctuations and improve system robustness.Simulation results show that the proposed strategy achieves an average new energy absorption rate of 62.3%and 52.8%in normal weather and severe weather scenarios,respectively.The carbon emission per unit computing power is reduced by 20.9%,and the computing power-electricity efficiency is improved by 9.1%,which is significantly better than the static scheduling strategy.This verifies its effectiveness and practicability in improving new energy utilization,ensuring service quality,and reducing carbon emissions. 展开更多
关键词 Collaborative scheduling for computation and power data centers Lyapunov optimization V-parameter adjustment mechanism wind-solar volatility
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A method for cleaning wind power anomaly data by combining image processing with community detection algorithms 认领 引用
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作者 Qiaoling Yang Kai Chen +2 位作者 Jianzhang Man Jiaheng Duan Zuoqi Jin 《Global Energy Interconnection》 EI CSCD 2024年第3期293-312,共20页
Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of ... Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting. 展开更多
关键词 Wind turbine power curve Abnormal data cleaning Community detection Louvain algorithm Mathematical morphology operation
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A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing 认领 引用
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作者 Muhammad Usman Sana Zhanli Li +3 位作者 Tayybah Kiren Hannan Bin Liaqat Shahid Naseem Atif Saeed 《Computers, Materials & Continua》 SCIE EI 2023年第5期2741-2757,共17页
Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encrypt... Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed.In cloud computation,data processing,storage,and transmission can be done through laptops andmobile devices.Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients.The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data.They have to be dependent on cloud service providers for assurance of the platform’s security.Data security and privacy issues reduce the progression of cloud computing and add complexity.Nowadays;most of the data that is stored on cloud servers is in the form of images and photographs,which is a very confidential form of data that requires secured transmission.In this research work,a public key cryptosystem is being implemented to store,retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman(RSA)algorithm for the encryption and decryption of data.The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment.To enhance the user data security level,a neural network is used for user authentication and recognition.Moreover;the proposed technique develops the performance of detection as a loss function of the bounding box.The Faster Region-Based Convolutional Neural Network(Faster R-CNN)gets trained on images to identify authorized users with an accuracy of 99.9%on training. 展开更多
关键词 Cloud computing data security RSA algorithm Faster R-CNN
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Multi-Source Heterogeneous Data Fusion Analysis Platform for Thermal Power Plants 认领 引用
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作者 Jianqiu Wang Jianting Wen +1 位作者 Hui Gao Chenchen Kang 《Journal of Architectural Research and Development》 2025年第6期24-28,共5页
With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter... With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%. 展开更多
关键词 Thermal power plant Multi-source heterogeneous data Data fusion analysis platform Edge computing
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How can the new paradigm of LNG cold energy+wind and solar power generation+green fuel reshape the energy gene of data centers? 认领 引用
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作者 Dashu Li Xinghui Wang +4 位作者 Mengdi Liu Hongyin Chen Zhenlan Dou Shiman Zhang Duo Zhang 《The Innovation》 EI 2026年第1期13-14,共2页
INTRODUCTION Large-scale computing power has become pivotal in enhancing national core competitiveness as the new quality productive force in the digital economy era.With the rapid advancement of artificial general in... INTRODUCTION Large-scale computing power has become pivotal in enhancing national core competitiveness as the new quality productive force in the digital economy era.With the rapid advancement of artificial general intelligence,the computing power industry is experiencing exponential growth.Projections suggest that China’s total computing capacity will exceed 300 exaflops per second(EFLOPS)by 2025,with associated electricity consumption accounting for approximately 5%of national power demand. 展开更多
关键词 wind power solar power lng enhancing national core competitiveness data centers cold energy computing power green fuel
Research on a Fog Computing Architecture and BP Algorithm Application for Medical Big Data 认领 引用
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作者 Baoling Qin 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期255-267,共13页
Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficie... Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level. 展开更多
关键词 Medical big data IoT fog computing distributed computing BP algorithm model
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Design and Development of a Novel Symmetric Algorithm for Enhancing Data Security in Cloud Computing 认领 引用 被引量：1
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作者 Mohammad Anwar Hossain Ahsan Ullah +1 位作者 Newaz Ibrahim Khan Md Feroz Alam 《Journal of Information Security》 2019年第4期199-236,共38页
Cloud computing is a kind of computing that depends on shared figuring assets instead of having nearby servers or individual gadgets to deal with applications. Technology is moving to the cloud more and more. It’s no... Cloud computing is a kind of computing that depends on shared figuring assets instead of having nearby servers or individual gadgets to deal with applications. Technology is moving to the cloud more and more. It’s not just a trend, the shift away from ancient package models to package as service has steadily gained momentum over the last ten years. Looking forward, the following decade of cloud computing guarantees significantly more approaches to work from anyplace, utilizing cell phones. Cloud computing focused on better performances, better scalability and resource consumption but it also has some security issue with the data stored in it. The proposed algorithm intents to come with some solutions that will reduce the security threats and ensure far better security to the data stored in cloud. 展开更多
关键词 Data Security Cloud Computing Encryption Decryption Secret Key Symmetric Algorithm 192 Bits Hashing Permutation SHA-512
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AI-Based Power Distribution Optimization in Hyperscale Data Centers 认领 引用
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作者 Chirag Devendrakumar Parikh 《Journal on Artificial Intelligence》 2025年第1期571-584,共14页
With the increasing complexity and scale of hyperscale data centers,the requirement for intelligent,real-time power delivery has never been more critical to ensure uptime,energy efficiency,and sustainability.Those tec... With the increasing complexity and scale of hyperscale data centers,the requirement for intelligent,real-time power delivery has never been more critical to ensure uptime,energy efficiency,and sustainability.Those techniques are typically static,reactive(since CPU and workload scaling is applied to performance events that occur after a request has been submitted,and is thus can be classified as a reactive response.),and require manual operation,and cannot cope with the dynamic nature of the workloads,the distributed architectures as well as the non-uniform energy sources in today’s data centers.In this paper,we elaborate on how artificial intelligence(AI)is revolutionizing power distribution in hyperscale data centers,making predictive load forecasting,real-time fault detection,and autonomous power optimization possible.We explain how ML(machine learning)and RL(reinforcement learning)-based models have been introduced in PDN(power delivery networks)for load balancing in three-phase systems,overprovisioning reduction,and energy flow optimization from the grid to the rack.The paper considers the architectural pieces of the AI-led systems,such as data ingestion pipelines,anomaly detection frameworks,and control algorithms to manage the power switching,cooling synchronization,and grid/microgrid interaction.Practical use cases show the value of these systems on PUE,infrastructure reliability,and environmental footprint.Key implementation challenges,including data quality,legacy systemintegration,and AI decision-making governance,are also discussed.Last,the paper speculates on the future of autonomous DC power infrastructure where AI becomes not only an assistive resource to the operator but really takes control over infrastructure behavior end-to-end,from procuring energy,to phase balancing,to predicting maintenance.Integrating technology innovation with operational sustainability,AI-powered power distribution is emerging as a core competence for the Smart Digital Power Facility of the Future. 展开更多
关键词 Artificial intelligence(AI) machine learning optimization power distribution management hyperscale data centers energy efficiency in computing infrastructure load forecasting and balancing sustainable computing
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Research on Geological Structure Inversion Method with Acoustic Computed Tomography and Borehole Data 认领 引用 被引量：1
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作者 GUO Qiang LIANG Zhi-wei LIAO Zheng-biao 《Journal of Shanghai Jiaotong university(Science)》 EI 2013年第3期284-288,共5页
This article firstly proposes two problems related to geological structure inversion with acoustic computed tomography (CT): ① the results surveyed are different from true stratum layers; ② the existing acoustic CT ... This article firstly proposes two problems related to geological structure inversion with acoustic computed tomography (CT): ① the results surveyed are different from true stratum layers; ② the existing acoustic CT inversion methods are based on wave's travel route and velocity analysis, which is short of comprehensive analysis of the revealed geological data. Then, it puts forward the method of applying the borehole data to revise acoustic CT investigation result through controlling the boundary velocity. This method comprehensively uses acoustic data and borehole data to invert the rock masses' shear wave speed. Comparing to calculating the rock mass' wave speed with acoustic data alone, it makes full use of the information, and the results obtained are closer to real stratum. Finally, it applies the method to engineering project and the results gotten with the method are more accurate, which shows the reliability and accuracy of the method. 展开更多
关键词 borehole acoustic computed tomography （CT） borehole data velocity correction minimum path algorithm
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Research on Data Routing Model Based on Ant Colony Algorithms 认领 引用 被引量：1
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作者 龚跃 吴航 +2 位作者 鲍杰 王君军 张艳秋 《Defence Technology（防务技术）》 CAS 2010年第4期269-272,共4页
Improved traditional ant colony algorithms,a data routing model used to the data remote exchange on WAN was presented.In the model,random heuristic factors were introduced to realize multi-path search.The updating mod... Improved traditional ant colony algorithms,a data routing model used to the data remote exchange on WAN was presented.In the model,random heuristic factors were introduced to realize multi-path search.The updating model of pheromone could adjust the pheromone concentration on the optimal path according to path load dynamically to make the system keep load balance.The simulation results show that the improved model has a higher performance on convergence and load balance. 展开更多
关键词 computer software data transmission ant colony algorithm routing model
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Dynamic Multi-Objective Gannet Optimization(DMGO):An Adaptive Algorithm for Efficient Data Replication in Cloud Systems 认领 引用
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作者 P.William Ved Prakash Mishra +3 位作者 Osamah Ibrahim Khalaf Arvind Mukundan Yogeesh N Riya Karmakar 《Computers, Materials & Continua》 SCIE EI 2025年第9期5133-5156,共24页
Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat... Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance. 展开更多
关键词 Cloud computing data replication dynamic optimization multi-objective optimization gannet optimization algorithm adaptive algorithms resource efficiency scalability latency reduction energy-efficient computing
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Simulation and modeling in cloud computing-based smart grid power big data analysis technology 认领 引用 被引量：1
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作者 K.Padmanaban Y.Baby Kalpana +3 位作者 M.Geetha K.Balan V.Mani S.S.Sivaraju 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2025年第6期44-70,共27页
Cloud computing’s simulation and modeling capabilities are crucial for big data analysis in smart grid power;they are the key to finding practical insights,making the grid resilient,and improving energy management.Du... Cloud computing’s simulation and modeling capabilities are crucial for big data analysis in smart grid power;they are the key to finding practical insights,making the grid resilient,and improving energy management.Due to issues with data scalability and real-time analytics,advanced methods are required to extract useful information from the massive,ever-changing datasets produced by smart grids.This research proposed a Dynamic Resource Cloud-based Processing Analytics(DRC-PA),which integrates cloudbased processing and analytics with dynamic resource allocation algorithms.Computational resources must be able to adjust the changing grid circumstances,and DRC-PA ensures that big data analysis can scale as well.The DRC-PA method has several potential uses,including power grid optimization,anomaly detection,demand response,and predictive maintenance.Hence the proposed technique enables smart grids to proactively adjust to changing conditions,boosting resilience and sustainability in the energy ecosystem.A thorough simulation analysis is carried out using realistic circumstances within smart grids to confirm the usefulness of the DRC-PA approach.The methodology is described in the intangible,showing how DRC-PA is more efficient than traditional methods because it is more accurate,scalable,and responsive in real-time.In addition to resolving existing issues,the suggested method changes the face of contemporary energy systems by paving the way for innovations in grid optimization,decision assistance,and energy management. 展开更多
关键词 Simulation modeling cloud computing smart grid power big data dynamic resource
DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems 认领 引用
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作者 A.Arokiaraj Jovith S.Rama Sree +4 位作者 Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim 《Computers, Materials & Continua》 SCIE EI 2023年第3期6429-6444,共16页
The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can b... The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods. 展开更多
关键词 DNA computing data storage image compression vector quantization ws algorithm space saving
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A Data-Placement Strategy Based on Genetic Algorithm in Cloud Computing 认领 引用
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作者 Qiang Xu Zhengquan Xu Tao Wang 《International Journal of Intelligence Science》 2015年第3期145-157,共13页
With the development of Computerized Business Application, the amount of data is increasing exponentially. Cloud computing provides high performance computing resources and mass storage resources for massive data proc... With the development of Computerized Business Application, the amount of data is increasing exponentially. Cloud computing provides high performance computing resources and mass storage resources for massive data processing. In distributed cloud computing systems, data intensive computing can lead to data scheduling between data centers. Reasonable data placement can reduce data scheduling between the data centers effectively, and improve the data acquisition efficiency of users. In this paper, the mathematical model of data scheduling between data centers is built. By means of the global optimization ability of the genetic algorithm, generational evolution produces better approximate solution, and gets the best approximation of the data placement at last. The experimental results show that genetic algorithm can effectively work out the approximate optimal data placement, and minimize data scheduling between data centers. 展开更多
关键词 Cloud Computing Data Placement Genetic Algorithm Data Scheduling
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Protecting Data Mobility in Cloud Networks Using Metadata Security 认领 引用 被引量：1
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作者 R.Punithavathi M.Kowsigan +3 位作者 R.Shanthakumari Miodrag Zivkovic Nebojsa Bacanin Marko Sarac 《Computer Systems Science & Engineering》 SCIE EI 2022年第7期105-120,共16页
At present,health care applications,government services,and banking applications use big data with cloud storage to process and implement data.Data mobility in cloud environments uses protection protocols and algorith... At present,health care applications,government services,and banking applications use big data with cloud storage to process and implement data.Data mobility in cloud environments uses protection protocols and algorithms to secure sensitive user data.Sometimes,data may have highly sensitive information,lead-ing users to consider using big data and cloud processing regardless of whether they are secured are not.Threats to sensitive data in cloud systems produce high risks,and existing security methods do not provide enough security to sensitive user data in cloud and big data environments.At present,several security solu-tions support cloud systems.Some of them include Hadoop Distributed File Sys-tem(HDFS)baseline Kerberos security,socket layer-based HDFS security,and hybrid security systems,which have time complexity in providing security inter-actions.Thus,mobile data security algorithms are necessary in cloud environ-ments to avoid time risks in providing security.In our study,we propose a data mobility and security(DMoS)algorithm to provide security of data mobility in cloud environments.By analyzing metadata,data are classified as secured and open data based on their importance.Secured data are sensitive user data,whereas open data are open to the public.On the basis of data classification,secured data are applied to the DMoS algorithm to achieve high security in HDFS.The pro-posed approach is compared with the time complexity of three existing algo-rithms,and results are evaluated. 展开更多
关键词 Data mobility data security cloud computing big data DMoS algorithm
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Distributed Quasi-Newton Algorithm for Non-Randomly Stored Data 认领 引用
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作者 LIU Xirui WU Mixia LIU Bangshu 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2026年第1期456-480,共25页
Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant pe... Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant performance degradation in classical distributed algorithms.In this paper,the authors propose a novel Distributed Quasi-Newton Pilot(DQNP)method for distributed learning with non-randomly distributed data.The proposed approach accommodates both randomly and non-randomly distributed data settings and imposes no constraints on the uniformity of local sample sizes.Additionally,it avoids the need to transfer the Hessian matrix or compute its inversion,thereby greatly reducing computational and communication complexity.The authors theoretically demonstrate that the resulting estimator achieves statistical efficiency under mild conditions.Extensive numerical experiments on synthetic and real-world data validate the theoretical findings and illustrate the effectiveness of the proposed method. 展开更多
关键词 Communication-efficient computation efficiency distributed inference non-randomly distributed data quasi-Newton algorithm
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Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems 认领 引用 被引量：5
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作者 Fadwa Alrowais Ahmed S.Almasoud +5 位作者 Radwa Marzouk Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Abdelwahed Motwakel Ishfaq Yaseen 《Computers, Materials & Continua》 SCIE EI 2022年第8期2783-2795,共13页
Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation tasks.Currently,the introduction o... Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation tasks.Currently,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in MEC.This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)systems.The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC system.Also,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC systems.Moreover,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC systems.For examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680. 展开更多
关键词 Data offloading mobile edge computing security machine learning artificial intelligence XGBoost salp swarm algorithm
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Replication Strategy with Comprehensive Data Center Selection Method in Cloud Environments 认领 引用
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作者 M.A.Fazlina Rohaya Latip +1 位作者 Hamidah Ibrahim Azizol Abdullah 《Computers, Materials & Continua》 SCIE EI 2023年第1期415-433,共19页
As the amount of data continues to grow rapidly,the variety of data produced by applications is becoming more affluent than ever.Cloud computing is the best technology evolving today to provide multi-services for the ... As the amount of data continues to grow rapidly,the variety of data produced by applications is becoming more affluent than ever.Cloud computing is the best technology evolving today to provide multi-services for the mass and variety of data.The cloud computing features are capable of processing,managing,and storing all sorts of data.Although data is stored in many high-end nodes,either in the same data centers or across many data centers in cloud,performance issues are still inevitable.The cloud replication strategy is one of best solutions to address risk of performance degradation in the cloud environment.The real challenge here is developing the right data replication strategy with minimal data movement that guarantees efficient network usage,low fault tolerance,and minimal replication frequency.The key problem discussed in this research is inefficient network usage discovered during selecting a suitable data center to store replica copies induced by inadequate data center selection criteria.Hence,to mitigate the issue,we proposed Replication Strategy with a comprehensive Data Center Selection Method(RS-DCSM),which can determine the appropriate data center to place replicas by considering three key factors:Popularity,space availability,and centrality.The proposed RS-DCSM was simulated using CloudSim and the results proved that data movement between data centers is significantly reduced by 14%reduction in overall replication frequency and 20%decrement in network usage,which outperformed the current replication strategy,known as Dynamic Popularity aware Replication Strategy(DPRS)algorithm. 展开更多
关键词 Cloud computing data replication replica placement data center merits replication algorithm
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Introduction to the Special Issue on Scientific Computing and Its Application to Engineering Problems 认领 引用
20
作者 Higinio Ramos M Chandru 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期22-24,共3页
Scientific computing has become a cornerstone of modern scientific discovery and engineering innovation.With the rapid advancement of computational power and numerical algorithms,problems that were once analytically i... Scientific computing has become a cornerstone of modern scientific discovery and engineering innovation.With the rapid advancement of computational power and numerical algorithms,problems that were once analytically intractable can now be studied through accurate simulations and large-scale numerical experiments.Scientific computing provides a bridge between mathematical theory,computational algorithms,and real-world engineering applications,enabling researchers to model complex phenomena such as fluid flow,structural deformation,wave propagation,stochastic processes,and nonlinear dynamical systems.The special issue entitled“Scientific Computing and Its Application to Engineering Problems”was conceived to bring together high-quality research contributions that demonstrate the role of computational mathematics in solving challenging engineering problems.The issue highlights both theoretical advances in numerical methods and their practical deployment in real-world engineering scenarios,with emphasis on robustness,computational efficiency,stability,and scalability. 展开更多
关键词 scientific computing accurate simulations fluid flows numerical algorithmsproblems computational power numerical algorithms engineering problems model complex phenomena
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