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Solvent effects on the measured bubble-point pressures and pseudo bubble-point pressures of different heavy crude oil-solvent systems

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Petroleum 2022年第4期8卷 577-586页

作者： jiangyuan yao Wei Zou Yongan Gu Petroleum Technology Research Centre(PTRC) Petroleum Systems EngineeringFaculty of Engineering and Applied ScienceUniversity of ReginaReginaS4S 0A2Canada

In this paper,the bubble-point pressures and pseudo bubble-point pressures of various heavy crude oil-solvent systems were measured and studied by conducting the constant-composition-expansion(CCE)tests,during which the test pressure was depleted in a stepwise manner.A total of fourteen CCE tests were performed for five heavy crude oil-CO_(2)systems,four heavy crude oil-CH_(4)systems,and five heavy crude oil-C_(3)H_(8)systems,*** the CCE tests were conducted by using a pressure-volume-temperature(PVT)*** was found that for most heavy crude oil-solvent systems with relatively low solvent concentrations,the measured PVT cell pressure *** volume(Pcell-nmix)data in the CCE tests had three distinct regions,which were one-liquid phase region(Regions I),foamy-oil region(Region II)and two-phase region(Region III).Accordingly,the PVT cell pressure at the intersection point of Regions I and II was referred to as the measured bubble-point pressure,whereas the PVT cell pressure at the intersection point of Regions II and III was termed as the measured pseudo bubble-point *** some heavy crude oil-solvent systems with high solvent concentrations,however,the measured Pcell-nmix data may have two regions *** this special case,the PVT cell pressure at the intersection point of the two regions was considered as the measured bubble-point pressure and no pseudo bubble-point pressure could be *** was also found that the heavy crude oil-CH_(4)system had not only the highest bubble-point pressure and pseudo bubble-point pressure but also the largest difference between the bubble-point pressure and pseudo bubble-point pressure,in comparison with the heavy crude oil-CO_(2)system and the heavy crude oil-C_(3)H_(8)system at the same solvent *** facts indicate that CH_(4)not only is the most volatile solvent but also can remain as dispersed gas bubbles to induce the most stable foamy oil in the largest pressure range after it is nucl

关键词： Heavy crude oil-solvent systems Phase behaviours PVT studies Constant-composition-expansion(CCE)tests Bubble-point pressure Pseudo bubble-point pressure

Event-Based Anomaly Detection for Non-Public Industrial Communication Protocols in SDN-Based Control Systems

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Computers, Materials & Continua 2018年第6期55卷 447-463页

作者： Ming Wan jiangyuan yao Yuan Jing Xi Jin School of Information Liaoning UniversityShenyang 110036China College of Information Science&Technology University of HainanHaikou 570228China Department of Computer Science and Engineering Washington UniversitySt Louis MO 63130USA Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110016China

As the main communication mediums in industrial control networks,industrial communication protocols are always vulnerable to extreme exploitations,and it is very difficult to take protective measures due to their serious *** on the SDN(Software Defined Network)technology,this paper proposes a novel event-based anomaly detection approach to identify misbehaviors using non-public industrial communication protocols,and this approach can be installed in SDN switches as a security software appliance in SDN-based control ***,aiming at the unknown protocol specification and message format,this approach first restructures the industrial communication sessions and merges the payloads from industrial communication *** that,the feature selection and event sequence extraction can be carried out by using the N-gram model and K-means *** on the obtained event sequences,this approach finally trains an event-based HMM(Hidden Markov Model)to identify aberrant industrial communication *** results clearly show that the proposed approach has obvious advantages of classification accuracy and detection efficiency.

关键词： Event sequence anomaly detection non-public industrial communication protocols SDN

Applying Feature-Weighted Gradient Decent K-Nearest Neighbor to Select Promising Projects for Scientific Funding

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Computers, Materials & Continua 2020年第9期64卷 1741-1753页

作者： Chuqing Zhang jiangyuan yao Guangwu Hu Thomas Schott School of Economics and Management North China Electric Power UniversityBeijing102206China School of Computer Science&Cyberspace Security Hainan UniversityHaikou570228China School of Computer Science Shenzhen Institute of Information TechnologyShenzhen518172China The Faculty of Business and Social Science University of Southern DenmarkKoldingDK-6000Denmark

Due to its outstanding ability in processing large quantity and high-dimensional data,machine learning models have been used in many cases,such as pattern recognition,classification,spam filtering,data mining and *** an outstanding machine learning algorithm,K-Nearest Neighbor(KNN)has been widely used in different situations,yet in selecting qualified applicants for winning a funding is almost *** major problem lies in how to accurately determine the importance of *** this paper,we propose a Feature-weighted Gradient Decent K-Nearest Neighbor(FGDKNN)method to classify funding applicants in to two types:approved ones or not approved *** FGDKNN is based on a gradient decent learning algorithm to update *** updates the weight of labels by minimizing error ratio iteratively,so that the importance of attributes can be described *** investigate the performance of FGDKNN with Beijing *** results show that FGDKNN performs about 23%,20%,18%,15%better than KNN,SVM,DT and ANN,***,the FGDKNN has fast convergence time under different training scales,and has good performance under different settings.

关键词： FGDKNN project selection scientific funding machine learning

Functional Pattern-Related Anomaly Detection Approach Collaborating Binary Segmentation with Finite State Machine

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Computers, Materials & Continua 2023年第12期77卷 3573-3592页

作者： Ming Wan Minglei Hao Jiawei Li jiangyuan yao Yan Song School of Information Liaoning UniversityShenyang110036China School of Computer Science and Technology Hainan UniversityHaikou570228China School of Physics Liaoning UniversityShenyang110036China

The process control-oriented threat,which can exploit OT(Operational Technology)vulnerabilities to forcibly insert abnormal control commands or status information,has become one of the most devastating cyber attacks in industrial automation *** effectively detect this threat,this paper proposes one functional pattern-related anomaly detection approach,which skillfully collaborates the BinSeg(Binary Segmentation)algorithm with FSM(Finite State Machine)to identify anomalies between measuring data and control *** detecting the change points of measuring data,the BinSeg algorithm is introduced to generate some initial sequence segments,which can be further classified and merged into different functional patterns due to their backward difference means and *** analyzing the pattern association according to the Bayesian network,one functional state transition model based on FSM,which accurately describes the whole control and monitoring process,is constructed as one feasible detection ***,we use the typical SWaT(Secure Water Treatment)dataset to evaluate the proposed approach,and the experimental results show that:for one thing,compared with other change-point detection approaches,the BinSeg algorithm can be more suitable for the optimal sequence segmentation of measuring data due to its highest detection accuracy and least consuming time;for another,the proposed approach exhibits relatively excellent detection ability,because the average detection precision,recall rate and F1-score to identify 10 different attacks can reach 0.872,0.982 and 0.896,respectively.

关键词： Process control-oriented threat anomaly detection binary segmentation FSM

Traffic Engineering in Dynamic Hybrid Segment Routing Networks

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Computers, Materials & Continua 2021年第7期68卷 655-670页

作者： Yingya Guo Kai Huang Cheng Hu jiangyuan yao Siyu Zhou College of Mathematics and Computer Science Fuzhou UniversityFuzhou350000China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350000China Key Laboratory of Spatial Data Mining&Information Sharing Ministry of EducationFuzhou350003China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyberspace Security Hainan UniversityHaiKou570228China Tandon School of Engineering New York UniversityNew York10012USA Department of Computing Hong Kong Polytechnic UniversityHong Hom999077Hong Kong

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet *** SR architecture,the paths that the packets choose to route on are indicated at the ingress *** with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress *** the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical *** partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable *** the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR *** algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering ***,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)***,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network *** the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in *** extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

关键词： Traffic engineering routing optimization segment routing deep reinforcement learning ultra-scalable spectral clustering

A Machine Learning Based Funding Project Evaluation Decision Prediction

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Computer Systems Science & Engineering 2023年第5期45卷 2111-2124页

作者： Chuqing Zhang jiangyuan yao Guangwu Hu Xingcan Cao Economic Research Institute Beijing Language and Culture UniversityBeijing100083China School of Computer Science and Technology Hainan UniversityHaikou570228China School of Computer Science Shenzhen Institute of Information TechnologyShenzhen518172China Faculty of Arts University of British ColumbiaVancouverCV6T 1Z1Canada

Traditional linear statistical methods cannot provide effective prediction results due to the complexity of human *** this paper,we apply machine learning to the field of funding allocation decision making,and try to explore whether personal characteristics of evaluators help predict the outcome of the evaluation decision?and how to improve the accuracy rate of machine learning methods on the imbalanced dataset of grant funding?Since funding data is characterized by imbalanced data distribution,we propose a slacked weighted entropy decision tree(SWE-DT).We assign weight to each class with the help of slacked *** experimental results show that the SWE decision tree performs well with sensitivity of 0.87,specificity of 0.85 and average accuracy of *** also provides a satisfied classification accuracy with Area Under Curve(AUC)=*** implies that the proposed method accurately classified minority class instances and suitable to imbalanced *** adding evaluator factors into the model,sensitivity is improved by over 9%,specificity improved by nearly 8%and the average accuracy also increased by 7%.It proves the feasibility of using evaluators’characteristics as *** by innovatively using machine learning method to predict evaluation decisions based on the personal characteristics of evaluators,it enriches the literature in the field of decision making and machine learning field.

关键词： Expert evaluation prediction machine learning grant allocation

State-Based Control Feature Extraction for Effective Anomaly Detection in Process Industries

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Computers, Materials & Continua 2020年第6期63卷 1415-1431页

作者： Ming Wan Jinfang Li jiangyuan yao Rongbing Wang Hao Luo School of Information Liaoning UniversityShenyang110036China School of Computer Science&Cyberspace Security Hainan UniversityHaikou570228China School of Informatics Edinburgh UniversityEdinburghEH89ABUK

In process industries,the characteristics of industrial activities focus on the integrality and continuity of production process,which can contribute to excavating the appropriate features for industrial anomaly *** this perspective,this paper proposes a novel state-based control feature extraction approach,which regards the finite control operations as different ***,the procedure of state transition can adequately express the change of successive control operations,and the statistical information between different states can be used to calculate the feature ***,OCSVM(One Class Support Vector Machine)and BPNN(BP Neural Network),which are optimized by PSO(Particle Swarm Optimization)and GA(Genetic Algorithm)respectively,are introduced as alternative detection engines to match with our feature extraction *** experimental results clearly show that the proposed feature extraction approach can effectively coordinate with the optimized classification algorithms,and the optimized GA-BPNN classifier is suggested as a more applicable detection engine by comparing its average detection accuracies with the ones of PSO-OCSVM classifier.

关键词： State-based control feature anomaly detection PSO-OCSVM GA-BPNN

Compared Insights on Machine-Learning Anomaly Detection for Process Control Feature

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Computers, Materials & Continua 2022年第11期73卷 4033-4049页

作者： Ming Wan Quanliang Li jiangyuan yao Yan Song Yang Liu Yuxin Wan School of Information Liaoning UniversityShenyang110036China School of Computer Science and Technology Hainan UniversityHaikou570228China School of Physics Liaoning UniversityShenyang110036China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016China Department of Electrical Engineering and Information Technology Karlsruhe Institute of TechnologyKarlsruhe76131Germany

Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber ***,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature *** a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning *** on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching ***,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming *** the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively *** experimental results can clearly explain that SVM(Support Vector Machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases.

关键词： Anomaly detection machine-learning algorithm process control feature qualitative and quantitative comparisons

Memory-Occupied Routing Algorithms for Quantum Relay Networks

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Computers, Materials & Continua 2023年第6期75卷 5929-5946页

作者： jiangyuan yao Kaiwen Zou Zheng Jiang Shuhua Weng Deshun Li Yahui Li Xingcan Cao School of Computer Science and Technology Hainan UniversityHaikou570228China School of Cyberspace Security Hainan UniversityHaikou570228China School of Software Beijing Jiaotong UniversityBeijing10004China The Faculty of Art University of British ColumbiaVancouverV5K1K5Canada

Quantum transmission experiments have shown that the success-ful transmission rate of entangled quanta in optical fibers decreases *** current quantum networks deploy quantum relays to establish long-distance connections,the increase in transmission distance and entanglement switching costs still need to be considered when selecting the next ***,most of the existing quantum network models prefer to consider the parameters of the physical layer,which ignore the influence factors of the network *** this paper,we propose a meshy quantum network model based on quantum teleportation,which considers both net-work layer and physical layer *** proposed model can reflect the realistic transmission characteristics and morphological characteristics of the quantum relay ***,we study the network throughput of different routing algorithms with the same given parameters when multiple source-destination pairs are interconnected *** solve the chal-lenges of routing competition caused by the simultaneous transmission,we present greedy memory-occupied algorithm Q-GMOA and random memory-occupied algorithm *** proposed meshy quantum network model and the memory-occupied routing algorithms can improve the utilization rate of resources and the transmission performance of the quantum *** the evaluation results indicate that the proposed methods embrace a higher transmission rate than the previous methods with repeater occupation.

关键词： Quantum relay network routing algorithm quantum memory