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intelligent Classification of Carbonate Reservoir Quality Using Multisource Geophysical Logging and Seismic Data

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Bai, Yang Tan, Maojin Cao, Huilan Tang, Jinliang Liang, Zhiqiang China Univ Geosci Beijing Sch Geophys & Informat Technol Beijing 100083 Peoples R China Sinopec Geophys Res Inst Seism Reservoir Predict Nanjing 211103 Peoples R China Sinopec Geophys Res Inst Nanjing 211103 Peoples R China

Reservoir quality evaluation is an integral part of oil-gas exploration and development in fracture-vug carbonate formations. Varieties of geophysical data with different advantages are used in reservoir evaluation. These multisource data include conventional logs (CLs), borehole imaging logs (BILs), borehole acoustic reflection imaging logs (BARILs), and even seismic data. Obviously, combining these data can get richer formation information, which is more reliable than single-source data. In this work, the fusion dataset was constructed by calculating the principal components of CLs, BILs, and BARILs. The fusion dataset describes the fracture-vug information in different depths of investigation. Then, the agglomerative hierarchical clustering was used to analyze the fusion data and evaluate the reservoir quality in the borehole. Finally, taking the borehole-side seismic data and borehole evaluation results as training data, an intelligent model was trained based on the random forest algorithm. Also, the intelligent model can be used for reservoir quality classification in a 3-D space. In the case study, the evaluation results of reservoir quality are consistent with the validation data, and the accuracy of the final intelligent model is 85.15%.

关键词： Reservoirs Principal component analysis Imaging Acoustic reflection Meters Feature extraction Conductivity Carbonate reservoir intelligent algorithm multisource data fusion reservoir quality evaluation seismic-logs combination

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Novel intelligent Methods for Channel Path Classification and Model Determination Based on Blind Source Signals

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ATMOSPHERE 2024年第3期15卷 280页

作者： Cao, Li-Feng Liu, Cheng-Guo Cheng, Run-Sheng Tang, Guang-Pu Xiao, Tong Huang, Li-Feng Wang, Hong-Guang Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Hubei Engn Res Ctr RF Microwave Technol & Applicat Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China China Res Inst Radiowave Propagat Qingdao 266107 Peoples R China

In this paper, the urban signal propagation characteristics based on the location of blind sources are investigated. To address the issue of blind electromagnetic radiation sources in complex urban environments, intelligent methods for propagation channel path classification, and model determination are brought forth based on field test data. The intelligent classification method distinguishes between the Line-of-Sight (LoS) path channel and a direct path, the LoS multipath channel with a direct path and other multiple paths, and the Non-Line-of-Sight (NLoS) multipath channel without a direct path from the source to the test point. The modeling aspect determines the model type to which the received signal belongs based on the statistical model derived from the tested data of a specific source. A validation measurement system was constructed for the FM broadcasting band, and validation campaigns were conducted in the city of Wuhan. The process and analysis of the data using this method demonstrate the accurate distinction of the different propagation path channels and models and involve the construction of a statistical model for the FM band in Wuhan's urban area.

关键词： radio wave propagation blind source positioning intelligent algorithm channel modeling

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Visual analysis framework for network abnormal data based on multi-agent model

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SOFT COMPUTING 2021年第3期25卷 1833-1845页

作者： Li, Zhuo Chengdu Normal Univ Sch Comp Sci Chengdu 611130 Peoples R China

With the development of Internet, the amount of data increases exponentially. With the development of network technology, human beings have entered the era of big data. Therefore, it is very important to find valuable information from massive network data. At present, most of the network security products in the market record the system test results in the form of logs. Users can view and analyze the log information one by one to find out the suspicious behavior and carry out the next diagnosis. Finally, they can defend the identified attacks. Intrusion detection system usually stores detection results in the form of alert files. There may be a large number of redundant or false alarm information in the alarm file, so the heavy cognitive burden of users has become one of the disadvantages of traditional intrusion detection system. In consideration of the above situation, visualization technology has been introduced into the field of network security by researchers. The network visualization analysis displays the massive network data and log information in the way of graph and image, and uses the developed human vision to process the massive graphical data. Data visualization is a method of transforming abstract symbols into concrete geometric figures, which presents the results of simulation and calculation in the form of figures and images. In this paper, multi-agent model is used to build a network data visualization analysis model. The experimental results show that the method in this paper can effectively visualize a large number of network data, and the experimental results are easy to understand.

关键词： Network data Intrusion detection Visual analysis Multi-agent model intelligent algorithm Collaboration

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Smart load scheduling strategy utilising optimal charging of electric vehicles in power grids based on an optimisation algorithm

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IET SMART GRID 2020年第6期3卷 914-923页

作者： Lu, Maxim Abedinia, Oveis Bagheri, Mehdi Ghadimi, Noradin Shafie-khah, Miadreza Catalao, Joao P. S. Nazarbayev Univ Sch Engn & Digital Sci Dept Elect & Comp Engn Nur Sultan 010000 Kazakhstan Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal

One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users' anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles' owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs' batteries during the off-peak hours and drawing it from the EVs' batteries during peak-demand hours of the day. In order to minimise the costs associated with EVs' charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.

关键词： battery powered vehicles optimisation power grids electric vehicle charging power generation scheduling smart load scheduling strategy utilising optimal charging electric vehicles power grid optimisation algorithm charging stations smart charging model grid stability peak-demand hours EV charging intelligent algorithm standard models optimisation methods charge EV battery

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Optimized trajectory planning for Cybernetic Transportation Systems

Optimized trajectory planning for Cybernetic Transportation ...

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9th IFAC Symposium on intelligent Autonomous Vehicles (IAV)

作者： Garrido, Fernando Gonzalez, David Milanes, Vicente Perez, Joshua Nashashibi, Fawzi Inria Robot & Intelligent Transportat Syst RITS Team 2 Rue Simonne Iff F-75012 Paris France Vedecom Inst 77 Rue Chantiers F-78000 Versailles France

This paper describes the development of an optimized path planning algorithm for automated vehicles in urban environments. This path planning is developed on the basis of urban environments, where Cybernetic Transportation Systems (CTS) will operate. Our approach is mainly affected by vehicles kinematics and physical road constraints. Based on this assumptions, computational time for path planning can be significantly reduced by creating an off-line database that already optimized all the potential trajectories in each curve the CTS can carry out. Therefore, this algorithm generates a database of smooth and continuous curves considering a big set of different intersection scenarios, taking into account the constraints of the infrastructure and the physical limitations of the vehicle. According to the real scenario, the local planner selects from the database the appropriate curves from searching for the ones that fit with the intersections defined on it. The path planning algorithm has been tested in simulation using the previous control architecture. The results obtained show path generation improvements in terms of smoothness and continuity. Finally, the proposed algorithm was compared with previous path planning algorithms for its assessment.(C) 2016, IFAC (International federation of Antomat Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Path planning database intelligent algorithm cost function oprtimization

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Global optimization by small-world optimization algorithm based on social relationship network

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Journal of Central South University 2012年第8期19卷 2247-2265页

作者：李晋航邵新宇龙渊铭朱海平 B.R.Schlessman School of Mechanical Science and Engineering Huazhong University of Science and Technology Air Force Research Laboratory Wright Patterson Air Force Base

A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.

关键词： global optimization intelligent algorithm small-world optimization decentralized search

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Parameter Identification Method for Wireless Power Transmission System Based on Radial Basis Function Neural Network

Parameter Identification Method for Wireless Power Transmiss...

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Chinese Automation Congress (CAC)

作者： Jia, Shiyuan Chen, Zhenyu Xi, Bingjie Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781728113128

This paper introduces a method based on the intelligent algorithm for wireless power transmission system parameter identification. The radial basis function (RBF) neural network has been used as the algorithm model, the amplitude, and frequency of the input terminal voltage of the wireless transmission system, the amplitude of the input current and as the sample eigenvalue, the phase angle predicts the resistance value and capacitance value of the receiving end of the system, thus avoiding the complicated work measured at the receiving end. The sample data used by the algorithm is generated by calculating the exact value through the system matrix equation and then adding a certain amplitude of noise, which is close to the actual operating parameters of the wireless transmission system. The RBF neural network training process is divided into two parts: sample center point position calculation and weight value optimization. The sample center point position is calculated by the k-means clustering algorithm, and the weight value is optimized by the driven adaptive learning rate gradient. algorithm. The final test results show that the RBF neural network can predict the parameters of the wireless transmission system with high precision, and has certain anti-interference ability, good generalization performance, and can be applied in the actual wireless transmission system. At the end of the paper, the application scope of the algorithm is summarized, and the improvement and possible improvement methods of the algorithm are proposed.

关键词： Wireless transmission system parameter identification RBF Neural network Introduction intelligent algorithm

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Unmanned Trolley Control Based on Kinect

Unmanned Trolley Control Based on Kinect

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Chinese Automation Congress (CAC)

作者： Luo, Ying Wang, Tian Zhu, Aichun Wang, Ziyu Shan, Guangcun Snoussi, Hichem Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Nanjing Tech Univ Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China Nanjing Dingtiger Informat Technol Co ltd Nanjing Jiangsu Peoples R China Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing Peoples R China Univ Technol Troyes CNRS UMR 6279 Inst Charles DelaunaySTMRLM2S Troyes France

ISBN: (纸本)9781728113128

The highly intelligent and fast-paced life in modern society has made the requirements of higher efficiency and higher precision in the field of control. Based on the characteristics of pattern recognition, control theory and intelligent manufacturing, we propose a more efficient control system design with Kinect, apply it to the vehicle control project, and get better results. In this paper, the Kinect equipment is applied to design a remote multi-person control program. By recognizing the skeleton information of Kinect in the software Processing, we combine Kinect with the skeleton extraction algorithm Alphapose to optimize the recognition process, and finally controls the vehicle designed with Arduino UNO board. Meanwhile, we make the prospect of applying the scheme to the actual project and put forward a new idea for the design of control systems.

关键词： Skeleton recognition intelligent algorithm Control scheme design

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High-Dimensional Real Parameter Clonal Selection Memory algorithm 4

High-Dimensional Real Parameter Clonal Selection Memory Algo...

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4th IEEE International Conference on Progress in Informatics and Computing (IEEE PIC)

作者： Song, Dan Fan, Xiaoping Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Hunan Inst Engn Dept Comp & Commun Xiangtan 411104 Peoples R China Hunan Univ Finance & Econ Dept Informat Management Changsha 410205 Peoples R China

ISBN: (纸本)9781509034840

For numerical high-dimensional real parameter optimization problem, a new clonal selection memory algorithm is proposed in the paper by introducing a short-term memory mechanism into the optimization algorithm. Through non-genetic information storage and guiding the subsequent evolution the algorithm can effectively increase the local convergence rate. On the other hand, the search depth threshold setting, supplement operator and crossover operator makes the algorithm can effectively escape local optimum traps. Combined with other intelligent algorithms, the simulation experiment of the 20 and 30 dimensional standard test functions show that the new algorithm has obvious advantages in convergence speed, convergence precision and global convergence. Furthermore, the simulation experiment of the 100 and 200 dimensional standard test functions shows that, the new algorithm shows better global convergence speed, convergence precision and stability.

关键词： numerical optimization high-dimensional real parameter intelligent algorithm

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The Research of IOT of Agriculture based on Three Layers Architecture 2

The Research of IOT of Agriculture based on Three Layers Arc...

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2nd International Conference on Cloud Computing and Internet of Things (CCIOT)

作者： Bing, Fu Yangtze Univ Coll Arts & Sci Jingzhou Hubei Peoples R China Yangtze Univ Coll Comp Sci Jingzhou Hubei Peoples R China

ISBN: (纸本)9781467398237

Internet of things (IOT) of agriculture is urgent to be developed to support precision agriculture. The IOT system in agriculture mainly consists of three layers, e.g., perception, transportation and application. In this paper, we researched and developed a set of agricultural Internet system with expert guidance. Specifically, we investigated the key sensors in the perception layer, the application mode of Bluetooth and 4G in the transportation layer, and an intelligent algorithm and an application framework for the application layer. The small scale of farmland experiment is carried out to help farmers enjoy benefits of monitoring the plant production process, early warning of main diseases and pests, and rapid diagnosis.

关键词： IOT agriculture sensor intelligent algorithm

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