To overcome the difficulty of realizing large-scale quantum Fourier transform(QFT)within existing technology,this paper implements a resource-saving method(named t-bit semiclassical QFT over Z_(2n)),which could realiz...To overcome the difficulty of realizing large-scale quantum Fourier transform(QFT)within existing technology,this paper implements a resource-saving method(named t-bit semiclassical QFT over Z_(2n)),which could realize large-scale QFT using an arbitrary-scale quantum register.By developing a feasible method to realize the control quantum gate Rk,we experimentally realize the 2-bit semiclassical QFT over Z_(2-3)on IBM's quantum cloud computer,which shows the feasibility of the method.Then,we compare the actual performance of 2-bit semiclassical QFT with standard QFT in the experiments.The squared statistical overlap experimental data shows that the fidelity of 2-bit semiclassical QFT is higher than that of standard QFT,which is mainly due to fewer two-qubit gates in the semiclassical QFT.Furthermore,based on the proposed method,N=15 is successfully factorized by implementing Shor's algorithm.展开更多
Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphys...Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.展开更多
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
The Pantone Color of the Year 2026,PANTONE 11-4201 Cloud Dancer,has been introduced as a soft,lofty white symbolizing calm and clarity in an increasingly noisy world.This gentle shade invites a sense of peace and spac...The Pantone Color of the Year 2026,PANTONE 11-4201 Cloud Dancer,has been introduced as a soft,lofty white symbolizing calm and clarity in an increasingly noisy world.This gentle shade invites a sense of peace and spaciousness,encouraging focus and creating room for creativity and reflection.Cloud Dancer embodies a desire for simplicity and renewal-a blank canvas that allows our minds to wander and new ideas to take shape.Its expansive presence fosters environments where tranquility meets inspiration,offering visual calm that supports wellbeing and mental lightness.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementat...The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementation,and validation of a real-time monitoring framework based on the Internet ofThings(IoT)and cloud computing to enhance the thermal performance of evacuated tube solar water heaters(ETSWHs).A commercial system and a custom-built prototype were instrumented with Industry 4.0 technologies,including platinum resistance temperature detectors(PT100),solar irradiance and wind speed sensors,a programmable logic controller(PLC),a SCADAinterface,and a cloud-connected IoT gateway.Data were processed locally and transmitted to cloud storage for continuous analysis and visualization via amobile application.Experimental results demonstrated the prototype’s superior thermal energy storage capacity−47.4 vs.36.2 MJ for the commercial system,representing a 31%—achieved through the novel integration of Industry 4.0 architecture with an optimized collector design.This improvement is attributed to optimized geometric design parameters,including a reduced tilt angle,increased inter-tube spacing,and the incorporation of an aluminum reflective surface.These modifications collectively enhanced solar heat absorption and reduced optical losses.The framework effectively identified thermal stratification,monitored environmental effects on heat transfer,and enabled real-time system diagnostics.By integrating automation,IoT,and cloud computing,the proposed architecture establishes a scalable and replicable model for the intelligent management of solar thermal systems,facilitating predictive maintenance and future integration with artificial intelligence for performance forecasting.This work provides a practical,data-driven approach to digitizing and optimizing heat transfer systems,promoting more efficient and sustainable solar thermal energy applications.展开更多
In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to...In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.展开更多
3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with m...3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.展开更多
Word cloud visualization is a compelling graphical representation that visually depicts the frequency of words within a given text or dataset[1].Research on word clouds focuses on two main aspects.The first emphasizes...Word cloud visualization is a compelling graphical representation that visually depicts the frequency of words within a given text or dataset[1].Research on word clouds focuses on two main aspects.The first emphasizes processing words,such as using the latent Dirichlet allocation(LDA)algorithm to uncover topics in the documents[2],while the second involves visual impact through striking word arrangements[3,4].In the realm of extensive biomedical data,effectiveknowledge delivery to biologists is crucial.展开更多
Our primary research hypothesis stands on a simple idea:The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related topics.And this even when there are no clea...Our primary research hypothesis stands on a simple idea:The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related topics.And this even when there are no clear relations or some concepts appear to cease to exist and leave place for newer ones starting many years ago.We implemented our model based on Computer Science Ontology(CSO)and analyzed 44 years of publications.Then we derived the most important concepts related to Cloud Computing(CC)from the scientific collection offered by Clarivate Analytics.Our methodology includes data extraction using advanced web crawling techniques,data preparation,statistical data analysis,and graphical representations.We obtained related concepts after aggregating the scores using the Jaccard coefficient and CSO Ontology.Our article reveals the contribution of Cloud Computing topics in research papers in leading scientific journals and the relationships between the field of Cloud Computing and the interdependent subdivisions identified in the broader framework of Computer Science.展开更多
A dynamic clustering method based on multispectral satellite imagery to identify the different features is described. The channel combinations selected are for the different purposes in classification. Several cases a...A dynamic clustering method based on multispectral satellite imagery to identify the different features is described. The channel combinations selected are for the different purposes in classification. Several cases are presented using the polar-orbiting satellite imageries.展开更多
A precipitation enhancement operation using an aircraft was conducted from 1415 to 1549 LST 14 March 2000 in Shaanxi Province. The NOAA-14 satellite data received at 1535 LST soon after the cloud seeding shows that a ...A precipitation enhancement operation using an aircraft was conducted from 1415 to 1549 LST 14 March 2000 in Shaanxi Province. The NOAA-14 satellite data received at 1535 LST soon after the cloud seeding shows that a vivid cloud track appears on the satellite image. The length, average width and maximum width of the cloud track are 301 km, 8.3 and 11 km, respectively. Using a three-dimensional numerical model of transport and diffusion of seeding material within stratiform clouds, the spatial concentration distribution characteristics of seeding material at different times, especially at the satellite receiving time, are simulated. The model results at the satellite receiving time are compared with the features of the cloud track. The transported position of the cloud seeding material coincides with the position of the track. The width, shape and extent of diffusion of the cloud seeding material are similar to that of the cloud track. The spatial variation of width is consistent with that of the track. The simulated length of each segment of the seeding line accords with the length of every segment of the track. Each segment of the cloud track corresponds to the transport and diffusion of each segment of the seeding line. These results suggest that the cloud track is the direct physical reflection of cloud seeding at the cloud top. The comparison demonstrates that the numerical model of transport and diffusion can simulate the main characteristics of transport and diffusion of seeding material, and the simulated results are sound and trustworthy. The area, volume, vidth, depth, and lateral diffusive rate corresponding to concentrations 1, 4, and 10 L-1are simulated in order to understand the variations of influencing range.展开更多
We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and c...We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.展开更多
With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate...With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.展开更多
In recent years,the use of mobile devices such as smart phones,tablet PCs,etc.is rapidly increasing.In case of these mobile devices,the storage space is limited due to their characteristics.To make up for the limited ...In recent years,the use of mobile devices such as smart phones,tablet PCs,etc.is rapidly increasing.In case of these mobile devices,the storage space is limited due to their characteristics.To make up for the limited space of storage in mobile devices,several methods are being researched.Of these,cloud storage service(CSS),one of cloud computing services,is an efficient solution to compensate such limited storage space.CSS is a service of storing files to the storage and thus getting access to stored files through networks(Internet)at anytime,anywhere.As for the existing CSS,users store their personally important files in the cloud storage,not in their own computers.It may cause security problems such as the leaking of information from private files or the damaging to the information.Thus,we propose a cloud storage system which can solve the security problem of CSS for mobile devices using the personal computer.Our system is deigned to store and manage files through the direct communication between mobile devices and personal computer storages by using the software as a service(SaaS),one of computing services,instead of directly storing files into cloud storages.展开更多
基金Project supported by the National Basic Research Program of China(Grant No.2013CB338002)the National Natural Science Foundation of China(Grant No.61502526)
文摘To overcome the difficulty of realizing large-scale quantum Fourier transform(QFT)within existing technology,this paper implements a resource-saving method(named t-bit semiclassical QFT over Z_(2n)),which could realize large-scale QFT using an arbitrary-scale quantum register.By developing a feasible method to realize the control quantum gate Rk,we experimentally realize the 2-bit semiclassical QFT over Z_(2-3)on IBM's quantum cloud computer,which shows the feasibility of the method.Then,we compare the actual performance of 2-bit semiclassical QFT with standard QFT in the experiments.The squared statistical overlap experimental data shows that the fidelity of 2-bit semiclassical QFT is higher than that of standard QFT,which is mainly due to fewer two-qubit gates in the semiclassical QFT.Furthermore,based on the proposed method,N=15 is successfully factorized by implementing Shor's algorithm.
基金National Natural Science Foundation of China(42375153,42105153,42205157)Development of Science and Technology at Chinese Academy of Meteorological Sciences(2023KJ038)。
文摘Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
文摘The Pantone Color of the Year 2026,PANTONE 11-4201 Cloud Dancer,has been introduced as a soft,lofty white symbolizing calm and clarity in an increasingly noisy world.This gentle shade invites a sense of peace and spaciousness,encouraging focus and creating room for creativity and reflection.Cloud Dancer embodies a desire for simplicity and renewal-a blank canvas that allows our minds to wander and new ideas to take shape.Its expansive presence fosters environments where tranquility meets inspiration,offering visual calm that supports wellbeing and mental lightness.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
基金funded by the National Council of Science,Technology,and Technological Innovation(CONCYTEC)the National Program of Scientific Research and Advanced Studies(PROCIENCIA)under the E041-2022-“Applied Research Projects”competition.Contract number:PE501078609-2022-PROCIENCIA.
文摘The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementation,and validation of a real-time monitoring framework based on the Internet ofThings(IoT)and cloud computing to enhance the thermal performance of evacuated tube solar water heaters(ETSWHs).A commercial system and a custom-built prototype were instrumented with Industry 4.0 technologies,including platinum resistance temperature detectors(PT100),solar irradiance and wind speed sensors,a programmable logic controller(PLC),a SCADAinterface,and a cloud-connected IoT gateway.Data were processed locally and transmitted to cloud storage for continuous analysis and visualization via amobile application.Experimental results demonstrated the prototype’s superior thermal energy storage capacity−47.4 vs.36.2 MJ for the commercial system,representing a 31%—achieved through the novel integration of Industry 4.0 architecture with an optimized collector design.This improvement is attributed to optimized geometric design parameters,including a reduced tilt angle,increased inter-tube spacing,and the incorporation of an aluminum reflective surface.These modifications collectively enhanced solar heat absorption and reduced optical losses.The framework effectively identified thermal stratification,monitored environmental effects on heat transfer,and enabled real-time system diagnostics.By integrating automation,IoT,and cloud computing,the proposed architecture establishes a scalable and replicable model for the intelligent management of solar thermal systems,facilitating predictive maintenance and future integration with artificial intelligence for performance forecasting.This work provides a practical,data-driven approach to digitizing and optimizing heat transfer systems,promoting more efficient and sustainable solar thermal energy applications.
基金supported by Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2019-0-01842,Artificial Intelligence Graduate School Program(GIST))supported by Korea Planning&Evaluation Institute of Industrial Technology(KEIT)grant funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea)(RS-2025-25448249+1 种基金Automotive Industry Technology Development(R&D)Program)supported by the Regional Innovation System&Education(RISE)programthrough the(Gwangju RISE Center),funded by the Ministry of Education(MOE)and the Gwangju Metropolitan City,Republic of Korea(2025-RISE-05-001).
文摘In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.
基金supported by the National Natural Science Foundation of China(Grant Nos.52304139,52325403)the CCTEG Coal Mining Research Institute funding(Grant No.KCYJY-2024-MS-10).
文摘3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.
基金supported by the National Key R&D Program of China(2022YFC2704304 and 2021YFF0702000)the National Natural Science Foundation of China(32341020 and 32341021)+1 种基金Hubei Innovation Group Project(2021CFA005)the Research Core Facilities for Life Science(HUST).
文摘Word cloud visualization is a compelling graphical representation that visually depicts the frequency of words within a given text or dataset[1].Research on word clouds focuses on two main aspects.The first emphasizes processing words,such as using the latent Dirichlet allocation(LDA)algorithm to uncover topics in the documents[2],while the second involves visual impact through striking word arrangements[3,4].In the realm of extensive biomedical data,effectiveknowledge delivery to biologists is crucial.
基金Pawel Lula’s participation in the research has been carried out as part of a research initiative financed by Ministry of Science and Higher Education within“Regional Initiative of Excellence”Programme for 2019-2022.Project no.:021/RID/2018/19.Total financing 11897131.40 PLN.The other authors received no specific funding for this study.
文摘Our primary research hypothesis stands on a simple idea:The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related topics.And this even when there are no clear relations or some concepts appear to cease to exist and leave place for newer ones starting many years ago.We implemented our model based on Computer Science Ontology(CSO)and analyzed 44 years of publications.Then we derived the most important concepts related to Cloud Computing(CC)from the scientific collection offered by Clarivate Analytics.Our methodology includes data extraction using advanced web crawling techniques,data preparation,statistical data analysis,and graphical representations.We obtained related concepts after aggregating the scores using the Jaccard coefficient and CSO Ontology.Our article reveals the contribution of Cloud Computing topics in research papers in leading scientific journals and the relationships between the field of Cloud Computing and the interdependent subdivisions identified in the broader framework of Computer Science.
文摘A dynamic clustering method based on multispectral satellite imagery to identify the different features is described. The channel combinations selected are for the different purposes in classification. Several cases are presented using the polar-orbiting satellite imageries.
文摘A precipitation enhancement operation using an aircraft was conducted from 1415 to 1549 LST 14 March 2000 in Shaanxi Province. The NOAA-14 satellite data received at 1535 LST soon after the cloud seeding shows that a vivid cloud track appears on the satellite image. The length, average width and maximum width of the cloud track are 301 km, 8.3 and 11 km, respectively. Using a three-dimensional numerical model of transport and diffusion of seeding material within stratiform clouds, the spatial concentration distribution characteristics of seeding material at different times, especially at the satellite receiving time, are simulated. The model results at the satellite receiving time are compared with the features of the cloud track. The transported position of the cloud seeding material coincides with the position of the track. The width, shape and extent of diffusion of the cloud seeding material are similar to that of the cloud track. The spatial variation of width is consistent with that of the track. The simulated length of each segment of the seeding line accords with the length of every segment of the track. Each segment of the cloud track corresponds to the transport and diffusion of each segment of the seeding line. These results suggest that the cloud track is the direct physical reflection of cloud seeding at the cloud top. The comparison demonstrates that the numerical model of transport and diffusion can simulate the main characteristics of transport and diffusion of seeding material, and the simulated results are sound and trustworthy. The area, volume, vidth, depth, and lateral diffusive rate corresponding to concentrations 1, 4, and 10 L-1are simulated in order to understand the variations of influencing range.
基金supported by the National Natural Science Foundation of China(Grant No.92365206)the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)+1 种基金supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.
基金supported by the Beijing Academy of Quantum Information Sciencessupported by the National Natural Science Foundation of China(Grant No.92365206)+2 种基金the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.
基金The MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Infor mation Technology Research Center)support programsupervised by the NIPA(National ITIndustry Promotion Agency)(NIPA-2012-H0301-12-2006)
文摘In recent years,the use of mobile devices such as smart phones,tablet PCs,etc.is rapidly increasing.In case of these mobile devices,the storage space is limited due to their characteristics.To make up for the limited space of storage in mobile devices,several methods are being researched.Of these,cloud storage service(CSS),one of cloud computing services,is an efficient solution to compensate such limited storage space.CSS is a service of storing files to the storage and thus getting access to stored files through networks(Internet)at anytime,anywhere.As for the existing CSS,users store their personally important files in the cloud storage,not in their own computers.It may cause security problems such as the leaking of information from private files or the damaging to the information.Thus,we propose a cloud storage system which can solve the security problem of CSS for mobile devices using the personal computer.Our system is deigned to store and manage files through the direct communication between mobile devices and personal computer storages by using the software as a service(SaaS),one of computing services,instead of directly storing files into cloud storages.
