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Multi-Dimensional QoS Evaluation and Optimization of Mobile Edge Computing for IoT:A Survey

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Chinese Journal of Electronics 2024年第4期33卷 859-874页

作者： Jiwei HUANG Fangzheng LIU Jianbing ZHANG Beijing key laboratory of petroleum data mining China University of Petroleum Department of Computer Science and Technology China University of Petroleum

With the evolvement of the Internet of things(IoT), mobile edge computing(MEC) has emerged as a promising computing paradigm to support IoT data analysis and processing. In MEC for IoT, the differentiated requirements on quality of service(QoS) have been growing rapidly, making QoS a multi-dimensional concept including several attributes, such as performance, dependability, energy efficiency, and economic factors. To guarantee the QoS of IoT applications, theories and techniques of multi-dimensional QoS evaluation and optimization have become important theoretical foundations and supporting technologies for the research and application of MEC for IoT,which have attracted significant attention from both academia and industry. This paper aims to survey the existing studies on multi-dimensional QoS evaluation and optimization of MEC for IoT, and provide insights and guidance for future research in this field. This paper summarizes the multi-dimensional and multi-attribute QoS metrics in Io T scenarios, and then several QoS evaluation methods are presented. For QoS optimization, the main research problems in this field are summarized, and optimization models as well as their corresponding solutions are elaborated. We take notice of the booming of edge intelligence in artificial intelligence-empowered Io T scenarios, and illustrate the new research topics and the state-of-the-art approaches related to QoS evaluation and optimization. We discuss the challenges and future research directions.

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An improved deep dilated convolutional neural network for seismic facies interpretation

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petroleum Science 2024年第3期21卷 1569-1583页

作者： Na-Xia Yang Guo-Fa Li Ting-Hui Li Dong-Feng Zhao Wei-Wei Gu State key laboratory of petroleum Resources and Engineering CNPC Key Lab of Geophysical Exploration China University of Petroleum (Beijing) Dagang Branch Research InstituteBGP

With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks. These intelligent and automated methods significantly reduce manual labor, particularly in the laborious task of manually labeling seismic facies. However, the extensive demand for training data imposes limitations on their wider application. To overcome this challenge, we adopt the UNet architecture as the foundational network structure for seismic facies classification, which has demonstrated effective segmentation results even with small-sample training data. Additionally, we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range. The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries. Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results, as evidenced by various evaluation metrics for image segmentation. Obviously, the classification accuracy reaches an impressive 96%. Furthermore, the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method, which accurately defines the range of different seismic facies. This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.

关键词： Seismic facies interpretation Dilated convolution Spatial pyramid pooling Internal feature maps Compound loss function

A seismic elastic moduli module for the measurements of low-frequency wave dispersion and attenuation of fluid-saturated rocks under different pressures

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petroleum Science 2024年第1期21卷 162-181页

作者： Yan-Xiao He Shang-Xu Wang Gen-Yang Tang Chao Sun Hong-Bing Li San-Yi Yuan Xian Wei Li-Deng Gan Xiao-Feng Dai Qiang Ge Peng-Peng Wei Hui-Qing Zhang National key laboratory of petroleum Resources and Engineering CNPC Key Lab of Geophysical ExplorationCollege of GeophysicsChina University of Petroleum(Beijing)Beijing102249China School of Resources and Geosciences China University of Mining and TechnologyXuzhou221116JiangsuChina Research Institute of petroleum Exploration and Development CNPCBeijing100083China Beijing Vocational College of labour and Social Security Beijing100029China PetroChina Dagang Oilfield Exploration and Development Research Institute CNPCTianjin300280China

Knowledge about the seismic elastic modulus dispersion,and associated attenuation,in fluid-saturated rocks is essential for better interpretation of seismic observations taken as part of hydrocarbon identification and time-lapse seismic surveillance of both conventional and unconventional reservoir and overburden performances.A Seismic Elastic Moduli Module has been developed,based on the forced-oscillations method,to experimentally investigate the frequency dependence of Young's modulus and Poisson's ratio,as well as the inferred attenuation,of cylindrical samples under different confining pressure conditions.Calibration with three standard samples showed that the measured elastic moduli were consistent with the published data,indicating that the new apparatus can operate reliably over a wide frequency range of f∈[1-2000,10^(6)]Hz.The Young's modulus and Poisson's ratio of the shale and the tight sandstone samples were measured under axial stress oscillations to assess the frequency-and pressure-dependent effects.Under dry condition,both samples appear to be nearly frequency independent,with weak pressure dependence for the shale and significant pressure dependence for the sandstone.In particular,it was found that the tight sandstone with complex pore microstructure exhibited apparent dispersion and attenuation under brine or glycerin saturation conditions,the levels of which were strongly influenced by the increased effective pressure.In addition,the measured Young's moduli results were compared with the theoretical predictions from a scaled poroelastic model with a reasonably good agreement,revealing that the combined fluid flow mechanisms at both mesoscopic and microscopic scales possibly responsible for the measured dispersion.

关键词： Low-frequency measurements Dispersion and attenuation Rock physics Fluid flow

Effect of mesopore spatial distribution of HZSM-5 catalyst on zinc state and product distribution in 1-hexene aromatization

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Chinese Journal of Chemical Engineering 2024年第3期67卷 16-26页

作者： Chenhao Wei Di Gao Guohao Zhang Liang Zhao Jinsen Gao Chunming Xu The State key lab of Heavy Oil Processing China University of Petroleum(Beijing)Beijing 102249China SINOPEC Research Institute of petroleum Processing Co. Ltd.Beijing 100083China

1-hexene aromatization is a promising technology to convert excess olefin in fluid catalytic cracking(FCC)gasoline to high-value benzene(B),toluene(T),and xylene.Besides,the increasing market demand of xylene has put forward higher requirements for new generation of catalyst.For increasing xylene yield in 1-hexene aromatization,the effect of mesopore structure and spatial distribution on product distribution and Zn loading was studied.Catalysts with different mesopore spatial distribution were prepared by post-treatment of parent HZSM-5 zeolite,including NaOH treatment,tetra-propylammonium hydroxide(TPAOH)treatment,and recrystallization.It was found the evenly distributed mesopore mainly prolongs the catalyst lifetime by enhancing diffusion properties but reduces the aromatics selectivity,as a result of damage of micropores close to the catalyst surface.While the selectivity of high-value xylene can be highly promoted when the mesopore is mainly distributed interior the catalyst.Besides,the state of loaded Zn was also affected by mesopores spatial distribution.On the optimized catalyst,the xylene selectivity was enhanced by 12.4%compared with conventional Zn-loaded parent HZSM-5 catalyst at conversion over 99%.It was attributed to the synergy effect of mesopores spatial distribution and optimized acid properties.This work reveals the role of mesopores in different spatial positions of 1-hexene aromatization catalysts in the reaction process and the influence on metal distribution,as well as their synergistic effect two on the improvement of xylene selectivity,which can improve our understanding of catalyst pore structure and be helpful for the rational design of high-efficient catalyst.

关键词： 1-Hexene aromatization Alkali treatment Xylene selectivity Mesopores Zinc state

Acid-rock reaction kinetics in a two-scale model based on reaction order correction

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petroleum Science 2024年第3期21卷 2089-2101页

作者： Xue-Song Li Ning Qi Ze-Hui Zhang Lian Liu Xia-Qing Li Xu-Hang Su School of petroleum Engineering China University of Petroleum(East China) key lab of Unconventional Oil & Gas Development Ministry of Education China University of Petroleum(East China) SINOPEC Northwest Oilfield Branch Company petroleum Engineering Technology Research Institute SINOPEC Shengli Oilfield Company

The reaction order plays a crucial role in evaluating the response rate of acid-rock. However, the conventional two-scale model typically assumes that the reaction order is constant as one, which can lead to significant deviations from reality. To address this issue, this study proposes a novel multi-order dynamic model for acid-rock reaction by combining rotating disk experimental data with theoretical derivation.Through numerical simulations, this model allows for the investigation of the impact of acidification conditions on different orders of reaction, thereby providing valuable insights for on-site construction.The analysis reveals that higher response orders require higher optimal acid liquid flow rates, and lower optimal H+diffusion coefficients, and demonstrate no significant correlation with acid concentration.Consequently, it is recommended to increase the displacement and use high-viscosity acid for reservoirs with high calcite content, while reducing the displacement and using low-viscosity acid for reservoirs with high dolomite content.

关键词： Reaction order Two-scale model Wormhole propagation Carbonate rocks Numerical simulation

An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2201-2237页

作者： Thi-Kien Dao Trong-The Nguyen Fujian Provincial key laboratory of Big data mining and Applications Fujian University of TechnologyFuzhou350118China Multimedia Communication lab University of Information TechnologyHo Chi Minh CityVietnam

Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.

关键词： Node localization whale optimization algorithm wireless sensor networks siege whale optimization algorithm optimization

A reweighted damped singular spectrum analysis method for robust seismic noise suppression

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petroleum Science 2024年第3期21卷 1671-1682页

作者： Wei-Lin Huang Yan-Xin Zhou Yang Zhou Wei-Jie Liu Ji-Dong Li State key laboratory of petroleum Resources and Engineering China University of Petroleum (Beijing) SINOPEC petroleum Exploration and Production Research Institute SINOPEC Geophysical Research Institute

(Multichannel) Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression. It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem. However, in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers. The presence of this kind of noise, called erratic noise, makes singular spectrum analysis(SSA) reconstruction unstable and has undesirable effects on the final results. We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA) method. It incorporates the damped SSA, an improved version of SSA, into a reweighted framework. The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise. The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations. The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance. The feasibility of RD-SSA is validated via both synthetic and field data examples.

关键词： Singular spectrum analysis Damping operator Seismic erratic noise Seismic signal processing Robust low-rank reconstruction

A regression approach for seismic first-break picking

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petroleum Science 2024年第3期21卷 1584-1596页

作者： Huan Yuan San-Yi Yuan Jie Wu Wen-Jing Sang Yu-He Zhao CNPC key laboratory of Geophysical Exploration China University of Petroleum (Beijing) Research Institute of petroleum Exploration & Development—Northwest (NWGI) Key Laboratory of reservoir characterization of CNPCPetroChina

The picking efficiency of seismic first breaks(FBs) has been greatly accelerated by deep learning(DL)technology. However, the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR) situations. To address this issue, we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM) neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region. We employ a regressive model that represents the relationships among the elevation of shots, offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs, from common-shot gathers with sparsely distributed traces. Different from image segmentation methods which automatically extract image features and classify FBs from seismic data, the proposed method can learn the inner relationship between field geometry and FBs. In addition, the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution. The picking results of synthetic data shows that the proposed method has low dependence on label data, and can obtain reliable and similar predicted results using two types of label data with large differences. The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data. The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method. The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.

关键词： First-break picking Low signal-to-noise ratio Regression BiLSTM Traveltime Geometry Noisy seismic data

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

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petroleum Science 2024年第1期21卷 621-640页

作者： Xue-Qiang Zhang Qing-Lin Cheng Wei Sun Yi Zhao Zhi-Min Li key lab of Ministry of Education for Enhancing the Oil and Gas Recovery Ratio Northeast Petroleum UniversityDaqing163318HeilongjiangChina China petroleum Pipeline Material and Equipment Co. LtdLangfang065000HebeiChina Research Institute of petroleum Engineering and Technology Sinopec Northwest Oilfield CompanyUrumqi830011XinjiangChina

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

关键词： Crude oil gathering and transportation system GA-BP neural network Energy efficiency evaluation TOPSIS evaluation method Energy saving and consumption reduction