Due to the demand of data processing for polar ice radar in our laboratory, a Curvelet Thresholding Neural Network (TNN) noise reduction method is proposed, and a new threshold function with infinite-order continuous ...Due to the demand of data processing for polar ice radar in our laboratory, a Curvelet Thresholding Neural Network (TNN) noise reduction method is proposed, and a new threshold function with infinite-order continuous derivative is constructed. The method is based on TNN model. In the learning process of TNN, the gradient descent method is adopted to solve the adaptive optimal thresholds of different scales and directions in Curvelet domain, and to achieve an optimal mean square error performance. In this paper, the specific implementation steps are presented, and the superiority of this method is verified by simulation. Finally, the proposed method is used to process the ice radar data obtained during the 28th Chinese National Antarctic Research Expedition in the region of Zhongshan Station, Antarctica. Experimental results show that the proposed method can reduce the noise effectively, while preserving the edge of the ice layers.展开更多
In the course of network supported collaborative design,the data processing plays a very vital role.Much effort has been spent in this area,and many kinds of approaches have been proposed.Based on the correlative mate...In the course of network supported collaborative design,the data processing plays a very vital role.Much effort has been spent in this area,and many kinds of approaches have been proposed.Based on the correlative materials,this paper presents extensible markup language(XML)based strategy for several important problems of data processing in network supported collaborative design,such as the representation of standard for the exchange of product model data(STEP)with XML in the product information expression and the management of XML documents using relational database.The paper gives a detailed exposition on how to clarify the mapping between XML structure and the relationship database structure and how XML-QL queries can be translated into structured query language(SQL)queries.Finally,the structure of data processing system based on XML is presented.展开更多
Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communi...Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communication has evolved into an increasingly prominent area of research in recent years.Here,we demonstrate DSP-free coherent optical transmission by analog signal processing in frequency synchronous optical network(FSON)architecture,which supports polarization multiplexing and higher-order modulation formats.The FSON architecture that allows the numerous laser sources of optical transceivers within a data center can be quasi-synchronized by means of a tree-distributed homology architecture.In conjunction with our proposed pilot-tone assisted Costas loop for an analog coherent receiver,we achieve a record dual-polarization 224-Gb/s 16-QAM 5-km mismatch transmission with reset-free carrier phase recovery in the optical domain.Our proposed DSP-free analog coherent detection system based on the FSON makes it a promising solution for next-generation,low-power,and high-capacity coherent data center interconnects.展开更多
Sensor networks provide means to link people with real world by processing data in real time collected from real-world and routing the query results to the right people. Application examples include continuous monitor...Sensor networks provide means to link people with real world by processing data in real time collected from real-world and routing the query results to the right people. Application examples include continuous monitoring of environment, building infrastructures and human health. Many researchers view the sensor networks as databases, and the monitoring tasks are performed as subscriptions, queries, and alert. However, this point is not precise. First, databases can only deal with well-formed data types, with well-defined schema for their interpretation, while the raw data collected by the sensor networks, in most cases, do not fit to this requirement. Second, sensor networks have to deal with very dynamic targets, environment and resources, while databases are more static. In order to fill this gap between sensor networks and databases, we propose a novel approach, referred to as 'spatiotemporal data stream segmentation', or 'stream segmentation' for short, to address the dynamic nature and deal with 'raw' data of sensor networks. Stream segmentation is defined using Bayesian Networks in the context of sensor networks, and two application examples are given to demonstrate the usefulness of the approach.展开更多
Nowadays, it becomes very urgent to find remain oil under the oil shortage worldwide.However, most of simple reservoirs have been discovered and those undiscovered are mostly complex structural, stratigraphic and lith...Nowadays, it becomes very urgent to find remain oil under the oil shortage worldwide.However, most of simple reservoirs have been discovered and those undiscovered are mostly complex structural, stratigraphic and lithologic ones. Summarized in this paper is the integrated seismic processing/interpretation technique established on the basis of pre-stack AVO processing and interpretation.Information feedbacks occurred between the pre-stack and post-stack processes so as to improve the accuracy in utilization of data and avoid pitfalls in seismic attributes. Through the integration of seismic data with geologic data, parameters that were most essential to describing hydrocarbon characteristics were determined and comprehensively appraised, and regularities of reservoir generation and distribution were described so as to accurately appraise reservoirs, delineate favorite traps and pinpoint wells.展开更多
The estimation of the type and parameter of flow field is important for robotic fish.Recent estimation methods cannot meet the requirements of the robotic fish due to the lack of prior knowledge or the under-fitting o...The estimation of the type and parameter of flow field is important for robotic fish.Recent estimation methods cannot meet the requirements of the robotic fish due to the lack of prior knowledge or the under-fitting of the model.A processing method including data preprocessing,feature extraction,feature selection,flow type classification and flow field parameters estimation,is proposed based on the data of the pressure sensors in an artificial lateral line.Probabilistic Neural Network(PNN)is used to classify the flow field type and the Generalized Regressive Neural Network(GRNN)is the best choice for estimating the flow field parameters.Also,a few filtering methods for data preprocessing,three methods for feature selection and nine parameters estimation methods are analysis for choosing better method.The proposed method is verified by the experiments with both simulation and real data.展开更多
The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial...The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.展开更多
The use of blended acquisition technology in marine seismic exploration has the advantages of high acquisition efficiency and low exploration costs.However,during acquisition,the primary source may be disturbed by adj...The use of blended acquisition technology in marine seismic exploration has the advantages of high acquisition efficiency and low exploration costs.However,during acquisition,the primary source may be disturbed by adjacent sources,resulting in blended noise that can adversely affect data processing and interpretation.Therefore,the de-blending method is needed to suppress blended noise and improve the quality of subsequent processing.Conventional de-blending methods,such as denoising and inversion methods,encounter challenges in parameter selection and entail high computational costs.In contrast,deep learning-based de-blending methods demonstrate reduced reliance on manual intervention and provide rapid calculation speeds post-training.In this study,we propose a Uformer network using a nonoverlapping window multihead attention mechanism designed for de-blending blended data in the common shot domain.We add the depthwise convolution to the feedforward network to improve Uformer’s ability to capture local context information.The loss function comprises SSIM and L1 loss.Our test results indicate that the Uformer outperforms convolutional neural networks and traditional denoising methods across various evaluation metrics,thus highlighting the effectiveness and advantages of Uformer in de-blending blended data.展开更多
HashQuery,a Hash-area-based data dissemination protocol,was designed in wireless sensor networks. Using a Hash function which uses time as the key,both mobile sinks and sensors can determine the same Hash area. The se...HashQuery,a Hash-area-based data dissemination protocol,was designed in wireless sensor networks. Using a Hash function which uses time as the key,both mobile sinks and sensors can determine the same Hash area. The sensors can send the information about the events that they monitor to the Hash area and the mobile sinks need only to query that area instead of flooding among the whole network,and thus much energy can be saved. In addition,the location of the Hash area changes over time so as to balance the energy consumption in the whole network. Theoretical analysis shows that the proposed protocol can be energy-efficient and simulation studies further show that when there are 5 sources and 5 sinks in the network,it can save at least 50% energy compared with the existing two-tier data dissemination(TTDD) protocol,especially in large-scale wireless sensor networks.展开更多
In hybrid wireless sensor networks, sensor mobility causes the query areas to change dynamically. Aiming at the problem of inefficiency in processing the data aggregation queries in dynamic query areas, this paper pro...In hybrid wireless sensor networks, sensor mobility causes the query areas to change dynamically. Aiming at the problem of inefficiency in processing the data aggregation queries in dynamic query areas, this paper proposes a processing approach for event-based location aware queries （ELAQ）, which includes query dissemination algorithm, maximum distance projection proxy selection algorithm, in-network query propagation, and aggregation algorithm. ELAQs are triggered by the events and the query results are dependent on mobile sensors＇ location, which are the characteristics of ELAQ model. The results show that compared with the TinyDB query processing approach, ELAQ processing approach increases the accuracy of the query result and decreases the query response time.展开更多
The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the ...The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of sophistication.To resolve this problem,efficient and flexible malware detection tools are needed.This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations.Moreover,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:a.Trojan,b.Adware,c.Ransomware,d.Spyware,e.Worm.These network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained model.In addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional techniques.The success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.展开更多
Wireless Sensor Networks (WSNs) typically use in-network processing to reduce the communication overhead. Due to the fusion of data items sourced at different nodes into a single one during in-network processing, the ...Wireless Sensor Networks (WSNs) typically use in-network processing to reduce the communication overhead. Due to the fusion of data items sourced at different nodes into a single one during in-network processing, the sanctity of the aggregated data needs to be ensured. Especially, the data integrity of the aggregated result is critical as any malicious update to it can jeopardize not one, but many sensor readings. In this paper, we analyse three different approaches to providing integrity support for SDA in WSNs. The first one is traditional MAC, in which each leaf node and intermediate node share a key with parent (symmetric key). The second is aggregate MAC (AMAC), in which a base station shares a unique key with all the other sensor nodes. The third is homomorphic MAC (Homo MAC) that is purely symmetric key-based approach. These approaches exhibit diverse trade-off in resource consumption and security assumptions. Adding together to that, we also propose a probabilistic and improved variant of homomorphic MAC that improves the security strength for secure data aggregation in WSNs. We carry out simulations in TinyOS environment to experimentally evaluate the impact of each of these on the resource consumption in WSNs.展开更多
With the continuous development and expansion of automotive industry, car safety problems are widely concerned by most of us. In order to make it easier to adjust car′s real-time body posture, it is necessary to desi...With the continuous development and expansion of automotive industry, car safety problems are widely concerned by most of us. In order to make it easier to adjust car′s real-time body posture, it is necessary to design a real-time data acquisition system, thus reducing the number of casualties in normal driving. A brief account of the development status of data acquisition system at home and abroad, and the concept, principle,function of virtual instrument platform are given. The advantages and disadvantages of the data acquisitionsystem, based on LabVIEW in different methods of data acquisition, are analyzed and compared, and put forward some improvements and optimization, so the real-time data acquisition system can meet the requirement of adjusting the state of the vehicle, improving real-time of data acquisition and processing. Finally, it indicates the development direction and prospect of data acquisition system based on LabVIEW.展开更多
As one of the commonly used queries in modern databases, skyline query has received extensive attention from database research community. The uncertainty of the data in wireless sensor networks makes the corresponding...As one of the commonly used queries in modern databases, skyline query has received extensive attention from database research community. The uncertainty of the data in wireless sensor networks makes the corresponding skyline uncertain and not unique. This paper investigates the Pr-Skyline problem, i.e., how to compute the skyline with the highest existence probability in a computational and energy-efficient way. We formulate the problem and prove that it is NP-Complete and cannot be approximated in a given expression. However, the proposed algorithm SKY-SEARCH with pruning techniques can guarantee the computational efficiency given relatively large input size, while the filter-based distributed optimization strategy significantly reduces the transmission cost and the required storage space of the sensor nodes. Extensive experiments verify the efficiency and scalability of SKY-SEARCH and the distributed optimizing strategy.展开更多
In wireless sensor networks, secure data aggregation protocols target the two major objectives, namely, security and en route aggregation. Although en route aggregation of reverse multi-cast traffic improves energy ef...In wireless sensor networks, secure data aggregation protocols target the two major objectives, namely, security and en route aggregation. Although en route aggregation of reverse multi-cast traffic improves energy efficiency, it becomes a hindrance to end-to-end security. Concealed data aggregation protocols aim to preserve the end-to-end privacy of sensor readings while performing en route aggregation. However, the use of inherently malleable privacy homomorphism makes these protocols vulnerable to active attackers. In this paper, we propose an integrity and privacy preserving end-to-end secure data aggregation protocol. We use symmetric key-based homomorphic primitives to provide end-to-end privacy and end-to-end integrity of reverse multicast traffic. As sensor network has a non-replenishable energy supply, the use of symmetric key based homomorphic primitives improves the energy efficiency and increase the sensor network’s lifetime. We comparatively evaluate the performance of the proposed protocol to show its efficacy and efficiency in resource-constrained environments.展开更多
The big data cloud computing is a new computing mode,which integrates the distributed processing,the parallel processing,the network computing,the virtualization technology,the load balancing and other network technol...The big data cloud computing is a new computing mode,which integrates the distributed processing,the parallel processing,the network computing,the virtualization technology,the load balancing and other network technologies.Under the operation of the big data cloud computing system,the computing resources can be distributed in a resource pool composed of a large number of the computers,allowing users to connect with the remote computer systems according to their own data information needs.展开更多
In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN i...In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading(MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process(SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio.Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.展开更多
The extent of the peril associated with cancer can be perceivedfrom the lack of treatment, ineffective early diagnosis techniques, and mostimportantly its fatality rate. Globally, cancer is the second leading cause of...The extent of the peril associated with cancer can be perceivedfrom the lack of treatment, ineffective early diagnosis techniques, and mostimportantly its fatality rate. Globally, cancer is the second leading cause ofdeath and among over a hundred types of cancer;lung cancer is the secondmost common type of cancer as well as the leading cause of cancer-relateddeaths. Anyhow, an accurate lung cancer diagnosis in a timely manner canelevate the likelihood of survival by a noticeable margin and medical imagingis a prevalent manner of cancer diagnosis since it is easily accessible to peoplearound the globe. Nonetheless, this is not eminently efficacious consideringhuman inspection of medical images can yield a high false positive rate. Ineffectiveand inefficient diagnosis is a crucial reason for such a high mortalityrate for this malady. However, the conspicuous advancements in deep learningand artificial intelligence have stimulated the development of exceedinglyprecise diagnosis systems. The development and performance of these systemsrely prominently on the data that is used to train these systems. A standardproblem witnessed in publicly available medical image datasets is the severeimbalance of data between different classes. This grave imbalance of data canmake a deep learning model biased towards the dominant class and unableto generalize. This study aims to present an end-to-end convolutional neuralnetwork that can accurately differentiate lung nodules from non-nodules andreduce the false positive rate to a bare minimum. To tackle the problem ofdata imbalance, we oversampled the data by transforming available images inthe minority class. The average false positive rate in the proposed method isa mere 1.5 percent. However, the average false negative rate is 31.76 percent.The proposed neural network has 68.66 percent sensitivity and 98.42 percentspecificity.展开更多
Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility ...Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility in accordance with its users＇ demand. In this paper, the authors proposed a method to analyze trait of users in market areas near stations by analyzing location based social network. After the datum collection from geotagged tweets, these GPS （global positioning system） datum were plotted to map attained from yahoo open location platform. Then the morphological analysis and terminology extraction system extracted the keywords and their scores. After calculating the distance from stations and users＇ GPS coordination, the authors extracted the array of keywords and corresponding scores in some station market area. Lastly, ratios of all users＇ scores and city＇s scores were calculated to examine the locality. Full combination of data collection, natural language processing and visualization enabled the authors to envisage distribution of collective background in city.展开更多
Our understanding of intricate biological systems has been completely transformed by the development of multi-omics approaches,which entail the simultaneous study of several different molecular data types.However,ther...Our understanding of intricate biological systems has been completely transformed by the development of multi-omics approaches,which entail the simultaneous study of several different molecular data types.However,there are many obstacles to overcome when analyzing multi-omics data,including the requirement for sophisticated data processing and analysis tools.The integration of multi-omics research with artificial intelligence(AI)has the potential to fundamentally alter our understanding of biological systems.AI has emerged as an effective tool for evaluating complicated data sets.The application of AI and data processing techniques in multiomics analysis is explored in this study.The present study articulates the diverse categories of information generated by multi-omics methodologies and the intricacies involved in managing and merging these datasets.Additionally,it looks at the various AI techniquesDsuch as machine learning,deep learning,and neural networksDthat have been created for multi-omics analysis.The assessment comes to the conclusion that multiomics analysis has a lot of potential to change with the integration of AI and data processing techniques.AI can speed up the discovery of new biomarkers and therapeutic targets as well as the advancement of personalized medicine strategies by enabling the integration and analysis of massive and complicated data sets.The necessity for high-quality data sets and the creation of useful algorithms and models are some of the difficulties that come with using AI in multi-omics study.In order to fully exploit the promise of AI in multi-omics analysis,more study in this area is required.展开更多
基金Supported by the National High Technology Research and Development Program of China (No. 2011AA040202)the National Natural Science Foundation of China (No. 40976114)
文摘Due to the demand of data processing for polar ice radar in our laboratory, a Curvelet Thresholding Neural Network (TNN) noise reduction method is proposed, and a new threshold function with infinite-order continuous derivative is constructed. The method is based on TNN model. In the learning process of TNN, the gradient descent method is adopted to solve the adaptive optimal thresholds of different scales and directions in Curvelet domain, and to achieve an optimal mean square error performance. In this paper, the specific implementation steps are presented, and the superiority of this method is verified by simulation. Finally, the proposed method is used to process the ice radar data obtained during the 28th Chinese National Antarctic Research Expedition in the region of Zhongshan Station, Antarctica. Experimental results show that the proposed method can reduce the noise effectively, while preserving the edge of the ice layers.
基金supported by National High Technology Research and Development Program of China(863 Program)(No.AA420060)
文摘In the course of network supported collaborative design,the data processing plays a very vital role.Much effort has been spent in this area,and many kinds of approaches have been proposed.Based on the correlative materials,this paper presents extensible markup language(XML)based strategy for several important problems of data processing in network supported collaborative design,such as the representation of standard for the exchange of product model data(STEP)with XML in the product information expression and the management of XML documents using relational database.The paper gives a detailed exposition on how to clarify the mapping between XML structure and the relationship database structure and how XML-QL queries can be translated into structured query language(SQL)queries.Finally,the structure of data processing system based on XML is presented.
基金supported by the National Natural Science Foundation of China(Grant Nos.62405250 and 62471404)the China Postdoctoral Science Foundation(Grant No.2024M762955)+1 种基金the Key Project of Westlake Institute for Optoelectronics(Grant No.2023GD003)the Optical Com-munication and Sensing Laboratory,School of Engineering,Westlake University.
文摘Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communication has evolved into an increasingly prominent area of research in recent years.Here,we demonstrate DSP-free coherent optical transmission by analog signal processing in frequency synchronous optical network(FSON)architecture,which supports polarization multiplexing and higher-order modulation formats.The FSON architecture that allows the numerous laser sources of optical transceivers within a data center can be quasi-synchronized by means of a tree-distributed homology architecture.In conjunction with our proposed pilot-tone assisted Costas loop for an analog coherent receiver,we achieve a record dual-polarization 224-Gb/s 16-QAM 5-km mismatch transmission with reset-free carrier phase recovery in the optical domain.Our proposed DSP-free analog coherent detection system based on the FSON makes it a promising solution for next-generation,low-power,and high-capacity coherent data center interconnects.
文摘Sensor networks provide means to link people with real world by processing data in real time collected from real-world and routing the query results to the right people. Application examples include continuous monitoring of environment, building infrastructures and human health. Many researchers view the sensor networks as databases, and the monitoring tasks are performed as subscriptions, queries, and alert. However, this point is not precise. First, databases can only deal with well-formed data types, with well-defined schema for their interpretation, while the raw data collected by the sensor networks, in most cases, do not fit to this requirement. Second, sensor networks have to deal with very dynamic targets, environment and resources, while databases are more static. In order to fill this gap between sensor networks and databases, we propose a novel approach, referred to as 'spatiotemporal data stream segmentation', or 'stream segmentation' for short, to address the dynamic nature and deal with 'raw' data of sensor networks. Stream segmentation is defined using Bayesian Networks in the context of sensor networks, and two application examples are given to demonstrate the usefulness of the approach.
文摘Nowadays, it becomes very urgent to find remain oil under the oil shortage worldwide.However, most of simple reservoirs have been discovered and those undiscovered are mostly complex structural, stratigraphic and lithologic ones. Summarized in this paper is the integrated seismic processing/interpretation technique established on the basis of pre-stack AVO processing and interpretation.Information feedbacks occurred between the pre-stack and post-stack processes so as to improve the accuracy in utilization of data and avoid pitfalls in seismic attributes. Through the integration of seismic data with geologic data, parameters that were most essential to describing hydrocarbon characteristics were determined and comprehensively appraised, and regularities of reservoir generation and distribution were described so as to accurately appraise reservoirs, delineate favorite traps and pinpoint wells.
基金National Natural Science Foundation of China(NSFC)under Grant 62073017.
文摘The estimation of the type and parameter of flow field is important for robotic fish.Recent estimation methods cannot meet the requirements of the robotic fish due to the lack of prior knowledge or the under-fitting of the model.A processing method including data preprocessing,feature extraction,feature selection,flow type classification and flow field parameters estimation,is proposed based on the data of the pressure sensors in an artificial lateral line.Probabilistic Neural Network(PNN)is used to classify the flow field type and the Generalized Regressive Neural Network(GRNN)is the best choice for estimating the flow field parameters.Also,a few filtering methods for data preprocessing,three methods for feature selection and nine parameters estimation methods are analysis for choosing better method.The proposed method is verified by the experiments with both simulation and real data.
基金supported by China Southern Power Grid Technology Project under Grant 03600KK52220019(GDKJXM20220253).
文摘The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.
基金supported by the National Natural Science Foundation of China(Research on Dynamic Location of Receiving Points and Wave Field Separation Technology Based on Deep Learning in OBN Seismic Exploration,No.42074140)the Sinopec Geophysical Corporation,Project of OBC/OBN Seismic Data Wave Field Characteristics Analysis and Ghost Wave Suppression(No.SGC-202206)。
文摘The use of blended acquisition technology in marine seismic exploration has the advantages of high acquisition efficiency and low exploration costs.However,during acquisition,the primary source may be disturbed by adjacent sources,resulting in blended noise that can adversely affect data processing and interpretation.Therefore,the de-blending method is needed to suppress blended noise and improve the quality of subsequent processing.Conventional de-blending methods,such as denoising and inversion methods,encounter challenges in parameter selection and entail high computational costs.In contrast,deep learning-based de-blending methods demonstrate reduced reliance on manual intervention and provide rapid calculation speeds post-training.In this study,we propose a Uformer network using a nonoverlapping window multihead attention mechanism designed for de-blending blended data in the common shot domain.We add the depthwise convolution to the feedforward network to improve Uformer’s ability to capture local context information.The loss function comprises SSIM and L1 loss.Our test results indicate that the Uformer outperforms convolutional neural networks and traditional denoising methods across various evaluation metrics,thus highlighting the effectiveness and advantages of Uformer in de-blending blended data.
基金Project(07JJ1010) supported by Hunan Provincial Natural Science Foundation of ChinaProjects(2006AA01Z202, 2006AA01Z199) supported by the National High-Tech Research and Development Program of China+2 种基金Project(7002102) supported by the City University of Hong Kong, Strategic Research Grant (SRG)Project(IRT-0661) supported by the Program for Changjiang Scholars and Innovative Research Team in UniversityProject(NCET-06-0686) supported by the Program for New Century Excellent Talents in University
文摘HashQuery,a Hash-area-based data dissemination protocol,was designed in wireless sensor networks. Using a Hash function which uses time as the key,both mobile sinks and sensors can determine the same Hash area. The sensors can send the information about the events that they monitor to the Hash area and the mobile sinks need only to query that area instead of flooding among the whole network,and thus much energy can be saved. In addition,the location of the Hash area changes over time so as to balance the energy consumption in the whole network. Theoretical analysis shows that the proposed protocol can be energy-efficient and simulation studies further show that when there are 5 sources and 5 sinks in the network,it can save at least 50% energy compared with the existing two-tier data dissemination(TTDD) protocol,especially in large-scale wireless sensor networks.
基金Supported by the National Pre-research Foundation Project of China (513150402)
文摘In hybrid wireless sensor networks, sensor mobility causes the query areas to change dynamically. Aiming at the problem of inefficiency in processing the data aggregation queries in dynamic query areas, this paper proposes a processing approach for event-based location aware queries （ELAQ）, which includes query dissemination algorithm, maximum distance projection proxy selection algorithm, in-network query propagation, and aggregation algorithm. ELAQs are triggered by the events and the query results are dependent on mobile sensors＇ location, which are the characteristics of ELAQ model. The results show that compared with the TinyDB query processing approach, ELAQ processing approach increases the accuracy of the query result and decreases the query response time.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Funding Program,Grant No.(FRP-1443-15).
文摘The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of sophistication.To resolve this problem,efficient and flexible malware detection tools are needed.This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations.Moreover,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:a.Trojan,b.Adware,c.Ransomware,d.Spyware,e.Worm.These network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained model.In addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional techniques.The success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.
文摘Wireless Sensor Networks (WSNs) typically use in-network processing to reduce the communication overhead. Due to the fusion of data items sourced at different nodes into a single one during in-network processing, the sanctity of the aggregated data needs to be ensured. Especially, the data integrity of the aggregated result is critical as any malicious update to it can jeopardize not one, but many sensor readings. In this paper, we analyse three different approaches to providing integrity support for SDA in WSNs. The first one is traditional MAC, in which each leaf node and intermediate node share a key with parent (symmetric key). The second is aggregate MAC (AMAC), in which a base station shares a unique key with all the other sensor nodes. The third is homomorphic MAC (Homo MAC) that is purely symmetric key-based approach. These approaches exhibit diverse trade-off in resource consumption and security assumptions. Adding together to that, we also propose a probabilistic and improved variant of homomorphic MAC that improves the security strength for secure data aggregation in WSNs. We carry out simulations in TinyOS environment to experimentally evaluate the impact of each of these on the resource consumption in WSNs.
基金supported by Key Scientific Research Project of Henan Province(17A580003)Key Scientific and Technological Project of Henan Province(172102210022)Henan Polytechnic University Education Teaching Reform Research Projects(2015JG034)
文摘With the continuous development and expansion of automotive industry, car safety problems are widely concerned by most of us. In order to make it easier to adjust car′s real-time body posture, it is necessary to design a real-time data acquisition system, thus reducing the number of casualties in normal driving. A brief account of the development status of data acquisition system at home and abroad, and the concept, principle,function of virtual instrument platform are given. The advantages and disadvantages of the data acquisitionsystem, based on LabVIEW in different methods of data acquisition, are analyzed and compared, and put forward some improvements and optimization, so the real-time data acquisition system can meet the requirement of adjusting the state of the vehicle, improving real-time of data acquisition and processing. Finally, it indicates the development direction and prospect of data acquisition system based on LabVIEW.
文摘As one of the commonly used queries in modern databases, skyline query has received extensive attention from database research community. The uncertainty of the data in wireless sensor networks makes the corresponding skyline uncertain and not unique. This paper investigates the Pr-Skyline problem, i.e., how to compute the skyline with the highest existence probability in a computational and energy-efficient way. We formulate the problem and prove that it is NP-Complete and cannot be approximated in a given expression. However, the proposed algorithm SKY-SEARCH with pruning techniques can guarantee the computational efficiency given relatively large input size, while the filter-based distributed optimization strategy significantly reduces the transmission cost and the required storage space of the sensor nodes. Extensive experiments verify the efficiency and scalability of SKY-SEARCH and the distributed optimizing strategy.
文摘In wireless sensor networks, secure data aggregation protocols target the two major objectives, namely, security and en route aggregation. Although en route aggregation of reverse multi-cast traffic improves energy efficiency, it becomes a hindrance to end-to-end security. Concealed data aggregation protocols aim to preserve the end-to-end privacy of sensor readings while performing en route aggregation. However, the use of inherently malleable privacy homomorphism makes these protocols vulnerable to active attackers. In this paper, we propose an integrity and privacy preserving end-to-end secure data aggregation protocol. We use symmetric key-based homomorphic primitives to provide end-to-end privacy and end-to-end integrity of reverse multicast traffic. As sensor network has a non-replenishable energy supply, the use of symmetric key based homomorphic primitives improves the energy efficiency and increase the sensor network’s lifetime. We comparatively evaluate the performance of the proposed protocol to show its efficacy and efficiency in resource-constrained environments.
文摘The big data cloud computing is a new computing mode,which integrates the distributed processing,the parallel processing,the network computing,the virtualization technology,the load balancing and other network technologies.Under the operation of the big data cloud computing system,the computing resources can be distributed in a resource pool composed of a large number of the computers,allowing users to connect with the remote computer systems according to their own data information needs.
基金the National Natural Science Foundation of China under Grants 61631005 and U1801261the National Key R&D Program of China under Grant 2018YFB1801105+3 种基金the Central Universities under Grant ZYGX2019Z022the Key Areas of Research and Development Program of Guangdong Province, China, under Grant 2018B010114001the 111 Project under Grant B20064the China Postdoctoral Science Foundation under Grant No. 2018M631075
文摘In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading(MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process(SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio.Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.
基金supported this research through the National Research Foundation of Korea (NRF)funded by the Ministry of Science,ICT (2019M3F2A1073387)this work was supported by the Institute for Information&communications Technology Promotion (IITP) (NO.2022-0-00980Cooperative Intelligence Framework of Scene Perception for Autonomous IoT Device).
文摘The extent of the peril associated with cancer can be perceivedfrom the lack of treatment, ineffective early diagnosis techniques, and mostimportantly its fatality rate. Globally, cancer is the second leading cause ofdeath and among over a hundred types of cancer;lung cancer is the secondmost common type of cancer as well as the leading cause of cancer-relateddeaths. Anyhow, an accurate lung cancer diagnosis in a timely manner canelevate the likelihood of survival by a noticeable margin and medical imagingis a prevalent manner of cancer diagnosis since it is easily accessible to peoplearound the globe. Nonetheless, this is not eminently efficacious consideringhuman inspection of medical images can yield a high false positive rate. Ineffectiveand inefficient diagnosis is a crucial reason for such a high mortalityrate for this malady. However, the conspicuous advancements in deep learningand artificial intelligence have stimulated the development of exceedinglyprecise diagnosis systems. The development and performance of these systemsrely prominently on the data that is used to train these systems. A standardproblem witnessed in publicly available medical image datasets is the severeimbalance of data between different classes. This grave imbalance of data canmake a deep learning model biased towards the dominant class and unableto generalize. This study aims to present an end-to-end convolutional neuralnetwork that can accurately differentiate lung nodules from non-nodules andreduce the false positive rate to a bare minimum. To tackle the problem ofdata imbalance, we oversampled the data by transforming available images inthe minority class. The average false positive rate in the proposed method isa mere 1.5 percent. However, the average false negative rate is 31.76 percent.The proposed neural network has 68.66 percent sensitivity and 98.42 percentspecificity.
文摘Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility in accordance with its users＇ demand. In this paper, the authors proposed a method to analyze trait of users in market areas near stations by analyzing location based social network. After the datum collection from geotagged tweets, these GPS （global positioning system） datum were plotted to map attained from yahoo open location platform. Then the morphological analysis and terminology extraction system extracted the keywords and their scores. After calculating the distance from stations and users＇ GPS coordination, the authors extracted the array of keywords and corresponding scores in some station market area. Lastly, ratios of all users＇ scores and city＇s scores were calculated to examine the locality. Full combination of data collection, natural language processing and visualization enabled the authors to envisage distribution of collective background in city.
文摘Our understanding of intricate biological systems has been completely transformed by the development of multi-omics approaches,which entail the simultaneous study of several different molecular data types.However,there are many obstacles to overcome when analyzing multi-omics data,including the requirement for sophisticated data processing and analysis tools.The integration of multi-omics research with artificial intelligence(AI)has the potential to fundamentally alter our understanding of biological systems.AI has emerged as an effective tool for evaluating complicated data sets.The application of AI and data processing techniques in multiomics analysis is explored in this study.The present study articulates the diverse categories of information generated by multi-omics methodologies and the intricacies involved in managing and merging these datasets.Additionally,it looks at the various AI techniquesDsuch as machine learning,deep learning,and neural networksDthat have been created for multi-omics analysis.The assessment comes to the conclusion that multiomics analysis has a lot of potential to change with the integration of AI and data processing techniques.AI can speed up the discovery of new biomarkers and therapeutic targets as well as the advancement of personalized medicine strategies by enabling the integration and analysis of massive and complicated data sets.The necessity for high-quality data sets and the creation of useful algorithms and models are some of the difficulties that come with using AI in multi-omics study.In order to fully exploit the promise of AI in multi-omics analysis,more study in this area is required.
