With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing r...With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
Antarctic clouds and their vertical structures play a significant role in influencing the regional radiation budget and ice mass balance;however,substantial uncertainties persist.Continuous monitoring and research are...Antarctic clouds and their vertical structures play a significant role in influencing the regional radiation budget and ice mass balance;however,substantial uncertainties persist.Continuous monitoring and research are essential for enhancing our understanding of these clouds.This study presents an analysis of cloud occurrence frequency and cloud-base heights(CBHs)at Zhongshan Station in East Antarctica for the first time,utilizing data from a C12 ceilometer covering the period from January 2022 to December 2023.The findings indicate that low clouds dominate at Zhongshan Station,with an average cloud occurrence frequency of 75%.Both the cloud occurrence frequency and CBH distribution exhibit distinct seasonal variations.Specifically,the cloud occurrence frequency during winter is higher than that observed in summer,while winter clouds can develop to greater heights.Over the Southern Ocean,the cloud occurrence frequency during summer surpasses that at Zhongshan Station,with clouds featuring lower CBHs and larger extinction coefficients.Furthermore,it is noteworthy that CBHs derived from the ceilometer are basically consistent with those obtained from radiosondes.Importantly,ERA5 demonstrates commendable performance in retrieving CBHs at Zhongshan Station when compared with ceilometer measurements.展开更多
Precisely forecasting the performance of Deep Learning(DL)models,particularly in critical areas such as Uniform Resource Locator(URL)-based threat detection,aids in improving systems developed for difficult tasks.In c...Precisely forecasting the performance of Deep Learning(DL)models,particularly in critical areas such as Uniform Resource Locator(URL)-based threat detection,aids in improving systems developed for difficult tasks.In cybersecurity,recognizing harmful URLs is vital to lowering risks associated with phishing,malware,and other online-based attacks.Since it directly affects the model’s capacity to differentiate between benign and harmful URLs,finding the optimum mix of hyperparameters in DL models is a significant difficulty.Two commonly used architectures for sequential and spatial data processing,Long Short-Term Memory(LSTM)/Gated Recurrent Unit(GRU)and Convolutional Neural Network(CNN)/Long Short-Term Memory(LSTM)models are targeted in this study to have higher predictive capacity by modifying crucial hyperparameters such as learning rate,batch size,and dropout rate using cloud capability.Research finds the best settings for the models by testing 50 dropout rates(between 0.1 and 0.5)with different learning rates and batch sizes.Performances were measured in the form of accuracy,precision,recall,F1-score,and errors such as Mean Absolute Error(MAE),Mean Squared Error(MSE),Root Mean Squared Error(RMSE)and Mean Absolute Percent Error(MAPE).In our results,CNN/LSTM performed better often than LSTM/GRU,with up to 10%better F1-score and much lower MAPE when the learning rate was 0.001 and the dropout rate was 0.2.These results show the value of fine-tuning hyperparameters to increase model performance and reduce errors.Higher on many of the parameters,CNN/LSTM architecture became obvious as the more trustworthy one.It also discussed the importance of DL in enhancing URL attack detection mechanisms to provide increased accuracy and precision for real-world cybersecurity.展开更多
The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive...The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive connectivity,5G is poised to revolutionize real-time communication and coordination in manufacturing environments.This paper explores the prospects and challenges of applying 5G technology in industrial robots,focusing on cloud-based control systems that enable scalable,flexible,and efficient operations.Key advantages of 5G,including improved communication speed,enhanced real-time control,scalability,and predictive maintenance capabilities,are discussed.However,the transition to 5G also presents challenges,such as network reliability,security concerns,integration with legacy systems,and high implementation costs.The paper also examines case studies in the automotive,electronics,and aerospace industries,providing real-world examples of 5G adoption in industrial automation.The conclusion highlights key insights and outlines potential research directions for overcoming existing barriers and fully realizing the potential of 5G technology in industrial robot control.展开更多
The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved...The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective search.Attribute-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud storage.However,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations.In a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE schemes.In the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network.Thus,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS.With the assistance of blockchain technology,the proposed scheme offers two main benefits.First,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of failure.Second,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network.Specifically,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus nodes.This eliminates the need of the trusted authority and reduces the burden of data users,respectively.Further,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users.It has been verified both theoretically and practically in the performance analysis section.展开更多
Cloud-based satellite and terrestrial spectrum shared networks(CB-STSSN)combines the triple advantages of efficient and flexible net-work management of heterogeneous cloud access(H-CRAN),vast coverage of satellite net...Cloud-based satellite and terrestrial spectrum shared networks(CB-STSSN)combines the triple advantages of efficient and flexible net-work management of heterogeneous cloud access(H-CRAN),vast coverage of satellite networks,and good communication quality of terrestrial networks.Thanks to the complementary coverage characteristics,any-time and anywhere high-speed communications can be achieved to meet the various needs of users.The scarcity of spectrum resources is a common prob-lem in both satellite and terrestrial networks.In or-der to improve resource utilization,the spectrum is shared not only within each component but also be-tween satellite beams and terrestrial cells,which intro-duces inter-component interferences.To this end,this paper first proposes an analytical framework which considers the inter-component interferences induced by spectrum sharing(SS).An intelligent SS scheme based on radio map(RM)consisting of LSTM-based beam prediction(BP),transfer learning-based spec-trum prediction(SP)and joint non-preemptive prior-ity and preemptive priority(J-NPAP)-based propor-tional fair spectrum allocation is than proposed.The simulation result shows that the spectrum utilization rate of CB-STSSN is improved and user blocking rate and waiting probability are decreased by the proposed scheme.展开更多
With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issu...With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection(PS-ACS). In the PS-ACS scheme, we divide users into private domain(PRD) and public domain(PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption(KAE) and the Improved Attribute-based Signature(IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption(CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.展开更多
This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including th...This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including the design of experiments,surrogate models,model validation and selection,prediction,optimization,and sensitivity analysis.Moreover,it also includes an exclusive ensemble surrogate modeling technique,the extended hybrid adaptive function,which can make use of the advantages of each surrogate and eliminate the effort of selecting the appropriate individual surrogate.To improve ease of use,DADOS provides a user-friendly graphical user interface and employed flow-based programming so that users can conduct design optimization just by dragging,dropping,and connecting algorithm blocks into a workflow instead of writing massive code.In addition,DADOS allows users to visualize the results to gain more insights into the design problems,allows multi-person collaborating on a project at the same time,and supports multi-disciplinary optimization.This paper also details the architecture and the user interface of DADOS.Two examples were employed to demonstrate how to use DADOS to conduct data-driven design optimization.Since DADOS is a cloud-based system,anyone can access DADOS at www.dados.com.cn using their web browser without the need for installation or powerful hardware.展开更多
The cloud fraction （CF） and cloud-base heights （CBHs）, and cirrus properties, over a site in southeastern China from June 2008 to May 2009, are examined by a ground-based lidar. Results show that clouds occupied t...The cloud fraction （CF） and cloud-base heights （CBHs）, and cirrus properties, over a site in southeastern China from June 2008 to May 2009, are examined by a ground-based lidar. Results show that clouds occupied the sky 41% of the time. Significant seasonal variations in CF were found with a maximum/minimum during winter/summer and similar magnitudes of CF in spring and autumn. A distinct diurnal cycle in the overall mean CF was seen. Total, daytime, and nighttime annual mean CBHs were 3.05 ± 2.73 km, 2.46 ± 2.08 kin, and 3.51 ± 3.07 km, respectively. The lowest/highest CBH occurred around noon/midnight. Cirrus clouds were present ～36.2% of the time at night with the percentage increased in summer and decreased in spring. Annual mean values for cirrus geometrical properties were 8.89 ± 1.65 km, 9.80 ± 1.70 kin, 10.73 ± 1.86 km and 1.83± 0.91 km for the base, mid-cloud, top height, and the thickness, respectively. Seasonal variations in cirrus geometrical properties show a maximum/minimum in summer/winter for all cirrus geometrical parameters. The mean cirrus lidar ratio for all cirrus cases in our study was ～ 25 ± 17 sr, with a smooth seasonal trend. The cirrus optical depth ranged from 0.001 to 2.475, with a mean of 0.34 ± 0.33. Sub-visual, thin, and dense cirrus were observed in ～12%, 43%, and 45% of the cases, respectively. More frequent, thicker cirrus clouds occurred in summer than in any other season. The properties of cirrus cloud over the site are compared with other lidar-based retrievals of midlatitude cirrus cloud properties.展开更多
An e-tag used on the freeway is a kind of passive sensors composed of sensors and radio- frequency identification （RFID） tags. The principle of the electronic toll collection system is that the sensor emits radio wa...An e-tag used on the freeway is a kind of passive sensors composed of sensors and radio- frequency identification （RFID） tags. The principle of the electronic toll collection system is that the sensor emits radio waves touching the e-tag within a certain range, the e-tag will respond to the radio waves by induction, and the sensor will read and write information of the vehicles. Although the RFID technology is popularly used in campus management systems, there is no e-tag technology application used in a campus parking system. In this paper, we use the e-tag technology on a campus parking management system based on the cloud-based construction. By this, it helps to achieve automated and standardized management of the campus parking system, enhance management efficiency, reduce the residence time of the vehicles at the entrances and exits, and improve the efficiency of vehicles parked at the same time.展开更多
Mixed redundancy strategies are generally used in cloud-based systems,with different node switch mechanisms from traditional fault-tolerant strategies.Existing studies often concentrate on optimizing a single strategy...Mixed redundancy strategies are generally used in cloud-based systems,with different node switch mechanisms from traditional fault-tolerant strategies.Existing studies often concentrate on optimizing a single strategy in cloud computing environment and ignore the impact of mixed redundancy strategies.Therefore,a model is proposed to evaluate and optimize the reliability and performance of cloud-based degraded systems subject to a mixed active and cold standby redundancy strategy.In this strategy,node switching is triggered by a continual monitoring and detection mechanism when active nodes fail.To evaluate the transient availability and the expected job completion rate of systems with such kind of strategy,a continuous-time Markov chain model is built on the state transition process and a numerical method is used to solve the model.To choose the optimal redundancy for the mixed strategy under system constraints,a greedy search algorithm is proposed after sensitivity analysis.Illustrative examples were presented to explain the process of calculating the transient probability of each system state and in turn,the availability and performance of the whole system.It was shown that the near-optimal redundancy solution could be obtained using the optimizationmethod.The comparison with optimization of the traditional mixed redundancy strategy proved that the system behavior was different using different kinds of mixed strategies and less redundancy was assigned for the new type of mixed strategy under the same system constraint.展开更多
Clouds are critical to the global radiation budget and hydrological cycle, but knowledge is still poor concerning the observed climatology of cloud-base height （CBH） in China. Based on fine-resolution sounding obser...Clouds are critical to the global radiation budget and hydrological cycle, but knowledge is still poor concerning the observed climatology of cloud-base height （CBH） in China. Based on fine-resolution sounding observations from the China Radiosonde Network （CRN）, the method used to estimate CBH was modified, and uncertainty analyses indicated that the CBH is good enough. The accuracy of CBH estimation is verified by the comparison between the sounding-derived CBHs and those estimated from the micro-pulse lidar and millimeter-wave cloud radar. As such, the CBH climatology was compiled for the period 2006-16. Overall, the CBH exhibits large geographic variability across China, at both 0800 Local Standard Time （LST） and 2000 LST, irrespective of season. In addition, the summertime cloud base tends to be elevated to higher altitudes in dry regions [i.e., Inner Mongolia and the North China Plain （NCP）]. By comparison, the Tibetan Plateau （TP）, Pearl River Delta （PRD） and Sichuan Basin （SCB） have relatively low CBHs （〈 2.4 km above ground level）. In terms of seasonality, the CBH reaches its maximum in summer and minimum in winter. A low cloud base tends to occur frequently （〉 70%） over the TP, PRD and SCB. In contrast, at most sites over the Yangtze River Delta （YRD） and the NCP, about half the cloud belongs to the high-cloud category. The CBH does not exhibit marked diurnal variation in summer, throughout all CRN sites, probably due to the persistent cloud coverage caused by the East Asia Summer Monsson. To the best of our knowledge, this is the first CBH climatology produced from sounding measurements in China, and provides a useful reference for obtaining observational cloud base information.展开更多
Under cold conditions,the driving range of electric vehicles decreases significantly,and inaccuracies in the displayed remaining driving range(RDR)exacerbate range anxiety.This study proposes a knowledge-enhanced hier...Under cold conditions,the driving range of electric vehicles decreases significantly,and inaccuracies in the displayed remaining driving range(RDR)exacerbate range anxiety.This study proposes a knowledge-enhanced hierarchical framework that breaks down the RDR estimation problem into the prediction of energy consumption rate and effective energy coefficient.Both modules employ deep learning as their core models,using data sourced from a cloud-based big data platform with a focus on cold regions in Northeast China.To address real-world driving scenarios,the energy consumption rate module uses a switching mechanism:a base model,using region-specific collaborative features as inputs,is applied in the early stages of trips,while a sequential neural network is used in the later stages.The effective energy coefficient module incorporates battery degradation and environmental factors,correcting discrepancies in nominal battery energy under low-temperature and aging conditions.The model’s performance is validated using real-world data from 8 electric vehicles under cold conditions,demonstrating a 15–20%improvement in prediction accuracy over traditional methods,thereby enhancing RDR accuracy and reliability.展开更多
While cloud-based BPM（Business Process Management） shows potentials of inherent scalability and expenditure reduction,such issues as user autonomy,privacy protection and efficiency have popped up as major concerns.U...While cloud-based BPM（Business Process Management） shows potentials of inherent scalability and expenditure reduction,such issues as user autonomy,privacy protection and efficiency have popped up as major concerns.Users may have their own rudimentary or even full-edged BPM systems,which may be embodied by local EAI systems,at their end,but still intend to make use of cloud-side infrastructure services and BPM capabilities,which may appear as PaaS（Platform-as-a-Service） services,at the same time.A whole business process may contain a number of non-compute-intensive activities,for which cloud computing is over-provision.Moreover,some users fear data leakage and loss of privacy if their sensitive data is processed in the cloud.This paper proposes and analyzes a novel architecture of cloud-based BPM,which supports user-end distribution of non-compute-intensive activities and sensitive data.An approach to optimal distribution of activities and data for synthetically utilizing both user-end and cloud-side resources is discussed.Experimental results show that with the help of suitable distribution schemes,data privacy can be satisfactorily protected,and resources on both sides can be utilized at lower cost.展开更多
Management of poultry farms in China mostly relies on manual labor.Since such a large amount of valuable data for the production process either are saved incomplete or saved only as paper documents,making it very diff...Management of poultry farms in China mostly relies on manual labor.Since such a large amount of valuable data for the production process either are saved incomplete or saved only as paper documents,making it very difficult for data retrieve,processing and analysis.An integrated cloud-based data management system(CDMS)was proposed in this study,in which the asynchronous data transmission,distributed file system,and wireless network technology were used for information collection,management and sharing in large-scale egg production.The cloud-based platform can provide information technology infrastructures for different farms.The CDMS can also allocate the computing resources and storage space based on demand.A real-time data acquisition software was developed,which allowed farm management staff to submit reports through website or smartphone,enabled digitization of production data.The use of asynchronous transfer in the system can avoid potential data loss during the transmission between farms and the remote cloud data center.All the valid historical data of poultry farms can be stored to the remote cloud data center,and then eliminates the need for large server clusters on the farms.Users with proper identification can access the online data portal of the system through a browser or an APP from anywhere worldwide.展开更多
Recent research has shown an increase in the number of extreme tornado outbreaks per year.The characterization of the spatio-temporal pattern of tornado events is therefore a critical task in the analysis of meteorolo...Recent research has shown an increase in the number of extreme tornado outbreaks per year.The characterization of the spatio-temporal pattern of tornado events is therefore a critical task in the analysis of meteorological data.Currently,there are a large number of available meteorological datasets that can be used for such analysis.However,much of these data are distributed across multiple websites and are not accessible in a central location.This poses a significant challenge for a scientist who is interested in exploring meteorological patterns associated with tornado events.This paper presents a novel system which uses cloud-based technology for integrating,storing,exploring,analyzing,and visualizing meteorological data associated with tornado outbreaks.The system employs a novel NoSQL database schema and web services architecture for data integration and provides a user friendly interface that allows scientists to explore the spatio-temporal pattern of tornado events.Furthermore,scientists can use this interface to analyze the relationship between different meteorological variables and properties of tornado outbreaks using a number of spatio-temporal statistical and data mining methods.The efficacy of the system is demonstrated on a use case centered on the analysis of climatic indicators of large spatio-temporally clustered tornado outbreaks.展开更多
At present,hundreds of cloud vendors in the global market provide various services based on a customer’s requirements.All cloud vendors are not the same in terms of the number of services,infrastructure availability,...At present,hundreds of cloud vendors in the global market provide various services based on a customer’s requirements.All cloud vendors are not the same in terms of the number of services,infrastructure availability,security strategies,cost per customer,and reputation in the market.Thus,software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental activities.Thus,there is a need to evaluate various cloud service providers(CSPs)and platforms before choosing a suitable vendor.Already existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service attributes.However,they require more time to collect data,simulate and evaluate the vendor.The proposed work compares various CSPs in terms of major metrics,such as establishment,services,infrastructure,tools,pricing models,market share,etc.,based on the comparison,parameter ranking,and weightage allocated.Furthermore,the parameters are categorized depending on the priority level.The weighted average is calculated for each CSP,after which the values are sorted in descending order.The experimental results show the unbiased selection of CSPs based on the chosen parameters.The proposed parameter-ranking priority level weightage(PRPLW)algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.展开更多
文摘With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFC2802501)the National Natural Science Foundation of China(Grant Nos.42175154 and 42305084)+1 种基金the Hunan Provincial Natural Science Foundation of China(Grant No.2024JJ2058)Research Project of the National University of Defense Technology(Grant No.202401-YJRC-XX-030)。
文摘Antarctic clouds and their vertical structures play a significant role in influencing the regional radiation budget and ice mass balance;however,substantial uncertainties persist.Continuous monitoring and research are essential for enhancing our understanding of these clouds.This study presents an analysis of cloud occurrence frequency and cloud-base heights(CBHs)at Zhongshan Station in East Antarctica for the first time,utilizing data from a C12 ceilometer covering the period from January 2022 to December 2023.The findings indicate that low clouds dominate at Zhongshan Station,with an average cloud occurrence frequency of 75%.Both the cloud occurrence frequency and CBH distribution exhibit distinct seasonal variations.Specifically,the cloud occurrence frequency during winter is higher than that observed in summer,while winter clouds can develop to greater heights.Over the Southern Ocean,the cloud occurrence frequency during summer surpasses that at Zhongshan Station,with clouds featuring lower CBHs and larger extinction coefficients.Furthermore,it is noteworthy that CBHs derived from the ceilometer are basically consistent with those obtained from radiosondes.Importantly,ERA5 demonstrates commendable performance in retrieving CBHs at Zhongshan Station when compared with ceilometer measurements.
文摘Precisely forecasting the performance of Deep Learning(DL)models,particularly in critical areas such as Uniform Resource Locator(URL)-based threat detection,aids in improving systems developed for difficult tasks.In cybersecurity,recognizing harmful URLs is vital to lowering risks associated with phishing,malware,and other online-based attacks.Since it directly affects the model’s capacity to differentiate between benign and harmful URLs,finding the optimum mix of hyperparameters in DL models is a significant difficulty.Two commonly used architectures for sequential and spatial data processing,Long Short-Term Memory(LSTM)/Gated Recurrent Unit(GRU)and Convolutional Neural Network(CNN)/Long Short-Term Memory(LSTM)models are targeted in this study to have higher predictive capacity by modifying crucial hyperparameters such as learning rate,batch size,and dropout rate using cloud capability.Research finds the best settings for the models by testing 50 dropout rates(between 0.1 and 0.5)with different learning rates and batch sizes.Performances were measured in the form of accuracy,precision,recall,F1-score,and errors such as Mean Absolute Error(MAE),Mean Squared Error(MSE),Root Mean Squared Error(RMSE)and Mean Absolute Percent Error(MAPE).In our results,CNN/LSTM performed better often than LSTM/GRU,with up to 10%better F1-score and much lower MAPE when the learning rate was 0.001 and the dropout rate was 0.2.These results show the value of fine-tuning hyperparameters to increase model performance and reduce errors.Higher on many of the parameters,CNN/LSTM architecture became obvious as the more trustworthy one.It also discussed the importance of DL in enhancing URL attack detection mechanisms to provide increased accuracy and precision for real-world cybersecurity.
文摘The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive connectivity,5G is poised to revolutionize real-time communication and coordination in manufacturing environments.This paper explores the prospects and challenges of applying 5G technology in industrial robots,focusing on cloud-based control systems that enable scalable,flexible,and efficient operations.Key advantages of 5G,including improved communication speed,enhanced real-time control,scalability,and predictive maintenance capabilities,are discussed.However,the transition to 5G also presents challenges,such as network reliability,security concerns,integration with legacy systems,and high implementation costs.The paper also examines case studies in the automotive,electronics,and aerospace industries,providing real-world examples of 5G adoption in industrial automation.The conclusion highlights key insights and outlines potential research directions for overcoming existing barriers and fully realizing the potential of 5G technology in industrial robot control.
文摘The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective search.Attribute-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud storage.However,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations.In a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE schemes.In the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network.Thus,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS.With the assistance of blockchain technology,the proposed scheme offers two main benefits.First,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of failure.Second,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network.Specifically,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus nodes.This eliminates the need of the trusted authority and reduces the burden of data users,respectively.Further,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users.It has been verified both theoretically and practically in the performance analysis section.
基金the National Nat-ural Science Foundation of China under Grants 61771163the Natural Science Foundation for Out-standing Young Scholars of Heilongjiang Province un-der Grant YQ2020F001the Science and Technol-ogy on Communication Networks Laboratory under Grants SXX19641X072 and SXX18641X028.(Cor-respondence author:Min Jia)。
文摘Cloud-based satellite and terrestrial spectrum shared networks(CB-STSSN)combines the triple advantages of efficient and flexible net-work management of heterogeneous cloud access(H-CRAN),vast coverage of satellite networks,and good communication quality of terrestrial networks.Thanks to the complementary coverage characteristics,any-time and anywhere high-speed communications can be achieved to meet the various needs of users.The scarcity of spectrum resources is a common prob-lem in both satellite and terrestrial networks.In or-der to improve resource utilization,the spectrum is shared not only within each component but also be-tween satellite beams and terrestrial cells,which intro-duces inter-component interferences.To this end,this paper first proposes an analytical framework which considers the inter-component interferences induced by spectrum sharing(SS).An intelligent SS scheme based on radio map(RM)consisting of LSTM-based beam prediction(BP),transfer learning-based spec-trum prediction(SP)and joint non-preemptive prior-ity and preemptive priority(J-NPAP)-based propor-tional fair spectrum allocation is than proposed.The simulation result shows that the spectrum utilization rate of CB-STSSN is improved and user blocking rate and waiting probability are decreased by the proposed scheme.
基金financially supported by the National Natural Science Foundation of China(No.61303216,No.61272457,No.U1401251,and No.61373172)the National High Technology Research and Development Program of China(863 Program)(No.2012AA013102)National 111 Program of China B16037 and B08038
文摘With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection(PS-ACS). In the PS-ACS scheme, we divide users into private domain(PRD) and public domain(PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption(KAE) and the Improved Attribute-based Signature(IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption(CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.
基金Supported by National Key Research and Development Program of China (Grant No.2018YFB1700704)National Natural Science Foundation of China (Grant No.52075068)。
文摘This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including the design of experiments,surrogate models,model validation and selection,prediction,optimization,and sensitivity analysis.Moreover,it also includes an exclusive ensemble surrogate modeling technique,the extended hybrid adaptive function,which can make use of the advantages of each surrogate and eliminate the effort of selecting the appropriate individual surrogate.To improve ease of use,DADOS provides a user-friendly graphical user interface and employed flow-based programming so that users can conduct design optimization just by dragging,dropping,and connecting algorithm blocks into a workflow instead of writing massive code.In addition,DADOS allows users to visualize the results to gain more insights into the design problems,allows multi-person collaborating on a project at the same time,and supports multi-disciplinary optimization.This paper also details the architecture and the user interface of DADOS.Two examples were employed to demonstrate how to use DADOS to conduct data-driven design optimization.Since DADOS is a cloud-based system,anyone can access DADOS at www.dados.com.cn using their web browser without the need for installation or powerful hardware.
基金supported by the Ministry of Science and Technology of China (Grant Nos. Change: 2013CB955802 to 2012AA120901)State Laboratory of Earth Surface Process and Resource Ecology, National Science Foundation of China (41175019)the US Department of Energy (Grant Nos. DEFG0208ER64571and DE-SC0007171)
文摘The cloud fraction （CF） and cloud-base heights （CBHs）, and cirrus properties, over a site in southeastern China from June 2008 to May 2009, are examined by a ground-based lidar. Results show that clouds occupied the sky 41% of the time. Significant seasonal variations in CF were found with a maximum/minimum during winter/summer and similar magnitudes of CF in spring and autumn. A distinct diurnal cycle in the overall mean CF was seen. Total, daytime, and nighttime annual mean CBHs were 3.05 ± 2.73 km, 2.46 ± 2.08 kin, and 3.51 ± 3.07 km, respectively. The lowest/highest CBH occurred around noon/midnight. Cirrus clouds were present ～36.2% of the time at night with the percentage increased in summer and decreased in spring. Annual mean values for cirrus geometrical properties were 8.89 ± 1.65 km, 9.80 ± 1.70 kin, 10.73 ± 1.86 km and 1.83± 0.91 km for the base, mid-cloud, top height, and the thickness, respectively. Seasonal variations in cirrus geometrical properties show a maximum/minimum in summer/winter for all cirrus geometrical parameters. The mean cirrus lidar ratio for all cirrus cases in our study was ～ 25 ± 17 sr, with a smooth seasonal trend. The cirrus optical depth ranged from 0.001 to 2.475, with a mean of 0.34 ± 0.33. Sub-visual, thin, and dense cirrus were observed in ～12%, 43%, and 45% of the cases, respectively. More frequent, thicker cirrus clouds occurred in summer than in any other season. The properties of cirrus cloud over the site are compared with other lidar-based retrievals of midlatitude cirrus cloud properties.
文摘An e-tag used on the freeway is a kind of passive sensors composed of sensors and radio- frequency identification （RFID） tags. The principle of the electronic toll collection system is that the sensor emits radio waves touching the e-tag within a certain range, the e-tag will respond to the radio waves by induction, and the sensor will read and write information of the vehicles. Although the RFID technology is popularly used in campus management systems, there is no e-tag technology application used in a campus parking system. In this paper, we use the e-tag technology on a campus parking management system based on the cloud-based construction. By this, it helps to achieve automated and standardized management of the campus parking system, enhance management efficiency, reduce the residence time of the vehicles at the entrances and exits, and improve the efficiency of vehicles parked at the same time.
基金supported by the National Natural Science Foundation of China(Grant No.61309005)the Basic and Frontier Research Program of Chongqing(Grant No.cstc2014jcyj A40015)
文摘Mixed redundancy strategies are generally used in cloud-based systems,with different node switch mechanisms from traditional fault-tolerant strategies.Existing studies often concentrate on optimizing a single strategy in cloud computing environment and ignore the impact of mixed redundancy strategies.Therefore,a model is proposed to evaluate and optimize the reliability and performance of cloud-based degraded systems subject to a mixed active and cold standby redundancy strategy.In this strategy,node switching is triggered by a continual monitoring and detection mechanism when active nodes fail.To evaluate the transient availability and the expected job completion rate of systems with such kind of strategy,a continuous-time Markov chain model is built on the state transition process and a numerical method is used to solve the model.To choose the optimal redundancy for the mixed strategy under system constraints,a greedy search algorithm is proposed after sensitivity analysis.Illustrative examples were presented to explain the process of calculating the transient probability of each system state and in turn,the availability and performance of the whole system.It was shown that the near-optimal redundancy solution could be obtained using the optimizationmethod.The comparison with optimization of the traditional mixed redundancy strategy proved that the system behavior was different using different kinds of mixed strategies and less redundancy was assigned for the new type of mixed strategy under the same system constraint.
基金the Ministry of Science and Technology of China (Grant Nos. 2017YFC1501701, 2017YFC1501401, 2017YFA0603501 and 2016YFA0600403)the National Natural Science Foundation of China (Grant Nos. 91544217, 41771399 and 41471301)+1 种基金the Chinese Academy of Meteorological Sciences (Grant Nos. 2017Z005 and 2017R001)the Fundamental Research Funds for the Central Universities (Grant No. 2017STUD17)
文摘Clouds are critical to the global radiation budget and hydrological cycle, but knowledge is still poor concerning the observed climatology of cloud-base height （CBH） in China. Based on fine-resolution sounding observations from the China Radiosonde Network （CRN）, the method used to estimate CBH was modified, and uncertainty analyses indicated that the CBH is good enough. The accuracy of CBH estimation is verified by the comparison between the sounding-derived CBHs and those estimated from the micro-pulse lidar and millimeter-wave cloud radar. As such, the CBH climatology was compiled for the period 2006-16. Overall, the CBH exhibits large geographic variability across China, at both 0800 Local Standard Time （LST） and 2000 LST, irrespective of season. In addition, the summertime cloud base tends to be elevated to higher altitudes in dry regions [i.e., Inner Mongolia and the North China Plain （NCP）]. By comparison, the Tibetan Plateau （TP）, Pearl River Delta （PRD） and Sichuan Basin （SCB） have relatively low CBHs （〈 2.4 km above ground level）. In terms of seasonality, the CBH reaches its maximum in summer and minimum in winter. A low cloud base tends to occur frequently （〉 70%） over the TP, PRD and SCB. In contrast, at most sites over the Yangtze River Delta （YRD） and the NCP, about half the cloud belongs to the high-cloud category. The CBH does not exhibit marked diurnal variation in summer, throughout all CRN sites, probably due to the persistent cloud coverage caused by the East Asia Summer Monsson. To the best of our knowledge, this is the first CBH climatology produced from sounding measurements in China, and provides a useful reference for obtaining observational cloud base information.
基金supported by the National Natural Science Foundation of China under the Regional Innovation and Development Joint Fund(Grant No.U21A20166)the Jilin Provincial Department of Science and Technology(Grant No.20230508095RC).
文摘Under cold conditions,the driving range of electric vehicles decreases significantly,and inaccuracies in the displayed remaining driving range(RDR)exacerbate range anxiety.This study proposes a knowledge-enhanced hierarchical framework that breaks down the RDR estimation problem into the prediction of energy consumption rate and effective energy coefficient.Both modules employ deep learning as their core models,using data sourced from a cloud-based big data platform with a focus on cold regions in Northeast China.To address real-world driving scenarios,the energy consumption rate module uses a switching mechanism:a base model,using region-specific collaborative features as inputs,is applied in the early stages of trips,while a sequential neural network is used in the later stages.The effective energy coefficient module incorporates battery degradation and environmental factors,correcting discrepancies in nominal battery energy under low-temperature and aging conditions.The model’s performance is validated using real-world data from 8 electric vehicles under cold conditions,demonstrating a 15–20%improvement in prediction accuracy over traditional methods,thereby enhancing RDR accuracy and reliability.
基金Supported by the National Basic Research 973 Program of China under Grant No.2007CB310805the National Natural Science Foundation of China under Grant Nos. 90412010,60970131 and 60903048+1 种基金the National High-Tech Research and Development 863 Program of China under Grant No.2006AA01A106the Beijing Natural Science Foundation under Grant No.4092046
文摘While cloud-based BPM（Business Process Management） shows potentials of inherent scalability and expenditure reduction,such issues as user autonomy,privacy protection and efficiency have popped up as major concerns.Users may have their own rudimentary or even full-edged BPM systems,which may be embodied by local EAI systems,at their end,but still intend to make use of cloud-side infrastructure services and BPM capabilities,which may appear as PaaS（Platform-as-a-Service） services,at the same time.A whole business process may contain a number of non-compute-intensive activities,for which cloud computing is over-provision.Moreover,some users fear data leakage and loss of privacy if their sensitive data is processed in the cloud.This paper proposes and analyzes a novel architecture of cloud-based BPM,which supports user-end distribution of non-compute-intensive activities and sensitive data.An approach to optimal distribution of activities and data for synthetically utilizing both user-end and cloud-side resources is discussed.Experimental results show that with the help of suitable distribution schemes,data privacy can be satisfactorily protected,and resources on both sides can be utilized at lower cost.
基金the“12th Five-Year-Plan”for National Science and Technology for Rural Development in China(No.2014BAD08B05).
文摘Management of poultry farms in China mostly relies on manual labor.Since such a large amount of valuable data for the production process either are saved incomplete or saved only as paper documents,making it very difficult for data retrieve,processing and analysis.An integrated cloud-based data management system(CDMS)was proposed in this study,in which the asynchronous data transmission,distributed file system,and wireless network technology were used for information collection,management and sharing in large-scale egg production.The cloud-based platform can provide information technology infrastructures for different farms.The CDMS can also allocate the computing resources and storage space based on demand.A real-time data acquisition software was developed,which allowed farm management staff to submit reports through website or smartphone,enabled digitization of production data.The use of asynchronous transfer in the system can avoid potential data loss during the transmission between farms and the remote cloud data center.All the valid historical data of poultry farms can be stored to the remote cloud data center,and then eliminates the need for large server clusters on the farms.Users with proper identification can access the online data portal of the system through a browser or an APP from anywhere worldwide.
文摘Recent research has shown an increase in the number of extreme tornado outbreaks per year.The characterization of the spatio-temporal pattern of tornado events is therefore a critical task in the analysis of meteorological data.Currently,there are a large number of available meteorological datasets that can be used for such analysis.However,much of these data are distributed across multiple websites and are not accessible in a central location.This poses a significant challenge for a scientist who is interested in exploring meteorological patterns associated with tornado events.This paper presents a novel system which uses cloud-based technology for integrating,storing,exploring,analyzing,and visualizing meteorological data associated with tornado outbreaks.The system employs a novel NoSQL database schema and web services architecture for data integration and provides a user friendly interface that allows scientists to explore the spatio-temporal pattern of tornado events.Furthermore,scientists can use this interface to analyze the relationship between different meteorological variables and properties of tornado outbreaks using a number of spatio-temporal statistical and data mining methods.The efficacy of the system is demonstrated on a use case centered on the analysis of climatic indicators of large spatio-temporally clustered tornado outbreaks.
文摘At present,hundreds of cloud vendors in the global market provide various services based on a customer’s requirements.All cloud vendors are not the same in terms of the number of services,infrastructure availability,security strategies,cost per customer,and reputation in the market.Thus,software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental activities.Thus,there is a need to evaluate various cloud service providers(CSPs)and platforms before choosing a suitable vendor.Already existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service attributes.However,they require more time to collect data,simulate and evaluate the vendor.The proposed work compares various CSPs in terms of major metrics,such as establishment,services,infrastructure,tools,pricing models,market share,etc.,based on the comparison,parameter ranking,and weightage allocated.Furthermore,the parameters are categorized depending on the priority level.The weighted average is calculated for each CSP,after which the values are sorted in descending order.The experimental results show the unbiased selection of CSPs based on the chosen parameters.The proposed parameter-ranking priority level weightage(PRPLW)algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.
