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Detecting Encrypted Botnet Traffic Using Spatial-Temporal Correlation 被引量:3
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作者 Chen Wei Yu Le Yang Geng 《China Communications》 SCIE CSCD 2012年第10期49-59,共11页
In this paper, we to detect encrypted botnet propose a novel method traffic. During the traffic preprocessing stage, the proposed payload extraction method can identify a large amount of encrypted applications traffic... In this paper, we to detect encrypted botnet propose a novel method traffic. During the traffic preprocessing stage, the proposed payload extraction method can identify a large amount of encrypted applications traffic. It can filter out a large amount of non-malicious traffic, greatly in, roving the detection efficiency. A Sequential Probability Ratio Test (SPRT)-based method can find spatialtemporal correlations in suspicious botnet traffic and make an accurate judgment. Experimental resuks show that the false positive and false nega- tive rates can be controlled within a certain range. 展开更多
关键词 BOTNET encrypted traffic spatial-tenmporal correlation
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Spatial-Temporal Correlation 3D Vehicle Detection and Tracking System with Multiple Surveillance Cameras
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作者 薛炜彭 吴明虎 王琳 《Journal of Shanghai Jiaotong university(Science)》 EI 2023年第1期52-60,共9页
Compared to 3D object detection using a single camera,multiple cameras can overcome some limitations on field-of-view,occlusion,and low detection confidence.This study employs multiple surveillance cameras and develop... Compared to 3D object detection using a single camera,multiple cameras can overcome some limitations on field-of-view,occlusion,and low detection confidence.This study employs multiple surveillance cameras and develops a cooperative 3D object detection and tracking framework by incorporating temporal and spatial information.The framework consists of a 3D vehicle detection model,cooperatively spatial-temporal relation scheme,and heuristic camera constellation method.Specifically,the proposed cross-camera association scheme combines the geometric relationship between multiple cameras and objects in corresponding detections.The spatial-temporal method is designed to associate vehicles between different points of view at a single timestamp and fulfill vehicle tracking in the time aspect.The proposed framework is evaluated based on a synthetic cooperative dataset and shows high reliability,where the cooperative perception can recall more than 66%of the trajectory instead of 11%for single-point sensing.This could contribute to full-range surveillance for intelligent transportation systems. 展开更多
关键词 multi-object tracking 3D detection multiple sensors cooperative perception spatial-temporal correlation intelligent transportation system
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Adaptive spatial-temporal graph attention network for traffic speed prediction
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作者 ZHANG Xijun ZHANG Baoqi +2 位作者 ZHANG Hong NIE Shengyuan ZHANG Xianli 《High Technology Letters》 EI CAS 2024年第3期221-230,共10页
Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic... Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction. 展开更多
关键词 traffic speed prediction spatial-temporal correlation self-adaptive adjacency ma-trix graph attention network(GAT) bidirectional gated recurrent unit(BiGRU)
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Forecasting Scenario Generation for Multiple Wind Farms Considering Time-series Characteristics and Spatial-temporal Correlation 被引量:4
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作者 Qingyu Tu Shihong Miao +5 位作者 Fuxing Yao Yaowang Li Haoran Yin Ji Han Di Zhang Weichen Yang 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2021年第4期837-848,共12页
Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series c... Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences. 展开更多
关键词 Scenario generation wind farm regular vine Copula spatial-temporal correlation time-series characteristics
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Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System 被引量:1
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作者 Ke Wang Baorui Feng +5 位作者 Jingui Zhao Wenliang Lin Zhongliang Deng Dongdong Wang Yi Cen Genan Wu 《China Communications》 SCIE CSCD 2024年第1期200-214,共15页
Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear ... Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works. 展开更多
关键词 channel correlation inter-beam interference multibeam satellite Tomlinson-Harashima precoding user grouping
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Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient 被引量:1
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作者 Xiaoyan Su Shuwen Shang +2 位作者 Zhihui Xu Hong Qian Xiaolei Pan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1813-1826,共14页
With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in th... With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method. 展开更多
关键词 Reliability evaluation human reliability analysis SPAR-H performance shaping factors DEPENDENCE pearson correlation analysis
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Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things
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作者 Mengmeng Zhao Haipeng Peng +1 位作者 Lixiang Li Yeqing Ren 《Computers, Materials & Continua》 SCIE EI 2024年第8期2815-2837,共23页
In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.A... In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods. 展开更多
关键词 Multivariate time series anomaly detection spatial-temporal network TRANSFORMER
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Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle
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作者 Xin Li Shuyi Zhang +1 位作者 Rongxi Ren Yafei Wang 《Chinese Journal of Population,Resources and Environment》 2024年第1期58-67,共10页
To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to... To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle. 展开更多
关键词 Chengdu-chongqing Economic Circle Urban resilience spatial-temporal evolution Driving factor
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AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network
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作者 Yuteng Xiao Kaijian Xia +5 位作者 Hongsheng Yin Yu-Dong Zhang Zhenjiang Qian Zhaoyang Liu Yuehan Liang Xiaodan Li 《Digital Communications and Networks》 SCIE CSCD 2024年第2期292-303,共12页
The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries an... The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models. 展开更多
关键词 Adaptive adjacency matrix Digital twin Graph convolutional network Multivariate time series prediction spatial-temporal graph
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Structural identification and antioxidative activity evaluation of flaxseed lignan macromolecules: structure-activity correlation
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作者 Chen Cheng Lei Wang +7 位作者 Xiao Yu Fenghong Huang Jing Yang Fang Geng Xiaoyang Xia Xia Xiang Shufang Xu Qianchun Deng 《Food Science and Human Wellness》 SCIE CAS CSCD 2024年第6期3224-3235,共12页
Flaxseed lignan macromolecules(FLM)are a class of important secondary metabolites in fl axseed,which have been widely concerned due to their biological and pharmacological properties,especially for their antioxidative... Flaxseed lignan macromolecules(FLM)are a class of important secondary metabolites in fl axseed,which have been widely concerned due to their biological and pharmacological properties,especially for their antioxidative activity.For the composition and structure of FLM,our results confirmed that ferulic acid glycoside(FerAG)was directly ester-linked with herbacetin diglucoside(HDG)or pinoresinol diglucoside(PDG),which might determine the beginning of FLM biosynthesis.Additionally,p-coumaric acid glycoside(CouAG)might determine the end of chain extension during FLM synthesis in fl axseed.FLM exhibited higher antioxidative activity in polar systems,as shown by its superior 1,1-diphenyl-2-picrylhydrazyl(DPPH)free radical scavenging capacity compared to the 2,2’-azinobis(3-ehtylbenzothiazolin-6-sulfnic acid)(ABTS)cation free radical scavenging capacity in non-polar systems.Moreover,the antioxidative activity of FLM was found to be highly dependent on its composition and structure.In particular,it was positively correlated with the number of phenolic hydroxyl groups(longer FLM chains)and inversely related to the steric hindrance at the ends(lower levels of FerAG and CouAG).These fi ndings verifi ed the potential application of FLM in nonpolar systems,particularly in functional food emulsions。 展开更多
关键词 Flaxseed lignan macromolecules Composition structure Antioxidative activity STRUCTURE-ACTIVITY correlation Flaxseed lignan macromolecules Composition structure Antioxidative activity STRUCTURE-ACTIVITY correlation
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A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation
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作者 Wei Wu Yuan Zhang +2 位作者 Yunpeng Li Chuanyang Li YanHao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期537-555,共19页
Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and ... Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases. 展开更多
关键词 BIOMETRICS MULTI-MODAL correlation deep learning feature-level fusion
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Study of baryon number transport dynamics and strangeness conservation effects using Ω-hadron correlations
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作者 Wei-Jie Dong Xiao-Zhou Yu +4 位作者 Si-Yuan Ping Xia-Tong Wu Gang Wang Huan-Zhong Huang Zi-Wei Lin 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第7期147-156,共10页
In nuclear collisions at RHIC energies, an excess of Ω hyperons over ■ is observed, indicating that Ω has a net baryon number despite s and s quarks being produced in pairs. The baryon number in Ω may have been tr... In nuclear collisions at RHIC energies, an excess of Ω hyperons over ■ is observed, indicating that Ω has a net baryon number despite s and s quarks being produced in pairs. The baryon number in Ω may have been transported from the incident nuclei and/or produced in the baryon-pair production of Ω with other types of anti-hyperons such as Ξ. To investigate these two scenarios, we propose to measure the correlations between Ω and K and between Ω and anti-hyperons. We use two versions, the default and string-melting, of a multiphase transport(AMPT) model to illustrate the method for measuring the correlation and to demonstrate the general shape of the correlation. We present the Ω-hadron correlations from simulated Au+Au collisions at ■ =7.7 and 14.6 Ge V and discuss the dependence on the collision energy and on the hadronization scheme in these two AMPT versions. These correlations can be used to explore the mechanism of baryon number transport and the effects of baryon number and strangeness conservation on nuclear collisions. 展开更多
关键词 Baryon number transport Strangeness conversation correlation Gluon junction
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Track correlation algorithm based on CNN-LSTM for swarm targets
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作者 CHEN Jinyang WANG Xuhua CHEN Xian 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第2期417-429,共13页
The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms... The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets. 展开更多
关键词 track correlation correlation accuracy rate swarm target convolutional neural network(CNN) long short-term memory(LSTM)neural network
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A News Media Bias and Factuality Profiling Framework Assisted by Modeling Correlation
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作者 Qi Wang Chenxin Li +3 位作者 Chichen Lin Weijian Fan Shuang Feng Yuanzhong Wang 《Computers, Materials & Continua》 SCIE EI 2024年第11期3351-3369,共19页
News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension indep... News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results. 展开更多
关键词 News media profiling FACTUALITY BIAS correlated features
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Anomalous Hall effect and electronic correlation in a spin-reoriented kagome antiferromagnet LuFe_(6)Sn_(6)
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作者 Meng Lyu Yang Liu +8 位作者 Shen Zhang Junyan Liu Jinying Yang Yibo Wang Yiting Feng Xuebin Dong Binbin Wang Hongxiang Wei Enke Liu 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第10期46-51,共6页
The kagome lattice system has been identified as a fertile ground for the emergence of a number of new quantumstates,including superconductivity,quantum spin liquids,and topological electronic states.This has attracte... The kagome lattice system has been identified as a fertile ground for the emergence of a number of new quantumstates,including superconductivity,quantum spin liquids,and topological electronic states.This has attracted significantinterest within the field of condensed matter physics.Here,we present the observation of an anomalous Hall effect in aniron-based kagome antiferromagnet LuFe_(6)Sn_(6),which implies a non-zero Berry curvature in this compound.By means ofextensive magnetic measurements,a high Neel temperature,T_(N)=552 K,and a spin reorientation behavior were identifiedand a simple temperature-field phase diagram was constructed.Furthermore,this compound was found to exhibit a largeSommerfeld coefficient ofγ=87 mJ·mol^(-1)·K^(-2),suggesting the presence of a strong electronic correlation effect.Ourresearch indicates that LuFe_(6)Sn_(6)is an intriguing compound that may exhibit magnetism,strong correlation,and topologicalstates. 展开更多
关键词 kagome lattice anomalous Hall effect MAGNETISM electronic correlation
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Dynamics of Non-Markovianity, Quantum Correlations and Information Scrambling of Three Qubits Systems Interacting via Rashba Interaction
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作者 Nasser Metwally Fawzeya Ebrahim 《Journal of Quantum Information Science》 CAS 2024年第2期52-67,共16页
The behavior of the quantum correlations, information scrambling and the non-Markovianity of three entangling qubits systems via Rashba is discussed. The results showed that, the three physical quantities oscillate be... The behavior of the quantum correlations, information scrambling and the non-Markovianity of three entangling qubits systems via Rashba is discussed. The results showed that, the three physical quantities oscillate between their upper and lower bounds, where the number of oscillations increases as the Rashba interaction strength increases. The exchanging rate of these three quantities depends on the Rashba strength, and whether the entangled state is generated via direct/indirect interaction. Moreover, the coherence parameter can be used as a control parameter to maximize or minimize the three physical quantities. 展开更多
关键词 Markovianity correlations Rashba Interaction Scrambling Information
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Evaluation of frictional pressure drop correlations for air-water and air-oil two-phase flow in pipeline-riser system
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作者 Nai-Liang Li Bin Chen 《Petroleum Science》 SCIE EI CAS CSCD 2024年第2期1305-1319,共15页
Accurate prediction of the frictional pressure drop is important for the design and operation of subsea oil and gas transporting system considering the length of the pipeline. The applicability of the correlations to ... Accurate prediction of the frictional pressure drop is important for the design and operation of subsea oil and gas transporting system considering the length of the pipeline. The applicability of the correlations to pipeline-riser flow needs evaluation since the flow condition in pipeline-riser is quite different from the original data where they were derived from. In the present study, a comprehensive evaluation of 24prevailing correlation in predicting frictional pressure drop is carried out based on experimentally measured data of air-water and air-oil two-phase flows in pipeline-riser. Experiments are performed in a system having different configuration of pipeline-riser with the inclination of the downcomer varied from-2°to-5°to investigated the effect of the elbow on the frictional pressure drop in the riser. The inlet gas velocity ranges from 0.03 to 6.2 m/s, and liquid velocity varies from 0.02 to 1.3 m/s. A total of885 experimental data points including 782 on air-water flows and 103 on air-oil flows are obtained and used to access the prediction ability of the correlations. Comparison of the predicted results with the measured data indicate that a majority of the investigated correlations under-predict the pressure drop on severe slugging. The result of this study highlights the requirement of new method considering the effect of pipe layout on the frictional pressure drop. 展开更多
关键词 Frictional pressure drop Pipeline-riser Gas-liquid two-phase flow Severe slugging correlation
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Correlation Study on Grain Size Characteristics and Geotechnical Properties of Surface Sediments in Qingdao Offshore Area
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作者 LI Anlong WANG Panpan +5 位作者 GUO Xijun JI Xiangkun SHEN Kunming LIN Lin YAN Zhichao YUAN Lin 《Journal of Ocean University of China》 SCIE CAS CSCD 2024年第3期721-730,共10页
The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study del... The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study delineated the sedimentary environment zoning in the northern sea area of Qingdao through cluster analysis of grain size parameters derived from 123 surface sediment samples.The study analyzed the correlation between sediment geotechnical indices and grain size parameters across diverse sedimentary environments.A correlation equation was established for samples exhibiting a strong correlation.The study found four distinct sedimentary environments in the study area:coastal,transitional,shallow sea,and residual.Within the same sedimentary environment,the average grain size and sorting coefficient exhibit significant correlations with geotechnical indices such as water content,density,shear strength,plastic limit,liquid limit,and plastic index.However,notable disparities in the correlation between grain size parameters and geotechnical indices emerge across different sedimentary environments. 展开更多
关键词 grain size parameters cluster analysis sedimentary environment correlationS geotechnical indexes
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Variational data encoding and correlations in quantum-enhanced machine learning
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作者 Ming-Hao Wang Hua L¨u 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第9期298-306,共9页
Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers.This paper tac... Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers.This paper tackles two pivotal challenges in the realm of quantum computing:firstly,the development of an effective encoding protocol for translating classical data into quantum states,a critical step for any quantum computation.Different encoding strategies can significantly influence quantum computer performance.Secondly,we address the need to counteract the inevitable noise that can hinder quantum acceleration.Our primary contribution is the introduction of a novel variational data encoding method,grounded in quantum regression algorithm models.By adapting the learning concept from machine learning,we render data encoding a learnable process.This allowed us to study the role of quantum correlation in data encoding.Through numerical simulations of various regression tasks,we demonstrate the efficacy of our variational data encoding,particularly post-learning from instructional data.Moreover,we delve into the role of quantum correlation in enhancing task performance,especially in noisy environments.Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference,thus advancing the frontier of quantum computing. 展开更多
关键词 quantum machine learning variational data encoding quantum correlation
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SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm
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作者 Jiahui Liu Lang Li +1 位作者 Di Li Yu Ou 《Computers, Materials & Continua》 SCIE EI 2024年第6期4641-4657,共17页
Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key de... Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods. 展开更多
关键词 Side-channel analysis correlation power analysis genetic algorithm CROSSOVER MUTATION
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