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An investigation into the effect of geomechanical properties of reservoir rock on drilling parameters-a case study

ARABIAN JOURNAL OF GEOSCIENCES 2021年第17期14卷 1-25页

作者： Delavar, Mohammad Reza Ramezanzadeh, Ahmad Tokhmechi, Behzad Shahrood Univ Technol Dept Min Petr & Geophys Engn Shahrood Iran

Drilling operation is among the most complex engineering activities as it is affected by a wide variety of factors. Many researchers have considered the effects of geomechanical parameters (GPs) of the reservoir rock on the drilling parameters (DPs), so that one can contribute to enhancing the drilling efficiency by building on the knowledge about the interaction between the GPs and the DPs. In this paper, artificial intelligence (AI) methods were used to investigate the association between wide range of the DPs and the GPs and provided new approach to improve drilling operations in carbonate reservoirs. Indeed, as aspect of the types of parameters and also machine learning methods, the study is novel method and seeks coverage in the lake of considering GPs in terms of drilling and its effectiveness. Dataset was collected from two oil wells located in the southwest of Iran. Firstly, 1-D geomechanical models were built to represent two reservoir formations, namely Asmari and Sarvak. Acknowledging the differences in resolution between the mud-logging and petrophysical data, the data was preprocessed (up-scaled) to have it prepared for further analysis. Subsequently, two methods were applied on the preprocessed data: (1) k2 algorithm as an instance of the so-called Bayesian network, showing both direct and indirect associations between the considered variables, and (2) multilayer perceptron (MLP) as an artificial neural network. The results of the k2 algorithm confirmed the direct impact of the unconfined compressive strength (UCS), a GP, and on the rate of penetration (ROP), a DP. It was further found that the internal friction angle and vertical effective stress (GPs) tend to affect the weight on bit (WOB) and torque (DPs). Focusing on the Asmari and Sarvak Formations, the results of the MLP implied that, as far as the effects of GPs were concerned, the rotational speed (RPM) was most greatly affected by the internal friction angle followed by the UCS. Comparative ch

关键词： Geomechanical properties Drilling parameters 1-D geomechanical model k2 algorithm Multilayer perceptron (MLP) Geomechanical unit (GMU)

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An efficient Bayesian network for differential diagnosis using experts’knowledge

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International Journal of Intelligent Computing and Cybernetics 2020年第1期13卷 103-126页

作者： Mohammad Mahdi Ershadi Abbas Seifi Department of Industrial Engineering and Management Systems Amirkabir University of TechnologyTehranIran

Purpose-This study aims to differential diagnosis of some diseases using classification methods to support effective medical *** this purpose,different classification methods based on data,experts’knowledge and both are considered in some ***,feature reduction and some clustering methods are used to improve their ***/methodology/approach-First,the performances of classification methods are evaluated for differential diagnosis of different ***,experts’knowledge is utilized to modify the Bayesian networks’*** of the results show that using experts’knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification.A total of ten different diseases are used for testing,taken from the Machine Learning Repository datasets of the University of California at Irvine(UCI).Findings-The proposed method improves both the computation time and accuracy of the classification methods used in this *** networks based on experts’knowledge achieve a maximum average accuracy of 87 percent,with a minimum standard deviation average of 0.04 over the sample datasets among all classification *** implications-The proposed methodology can be applied to perform disease differential diagnosis ***/value-This study presents the usefulness of experts’knowledge in the diagnosis while proposing an adopted improvement method for ***,the Bayesian network based on experts’knowledge is useful for different diseases neglected by previous papers.

关键词： Bayesian network k2 algorithm Experts’knowledge Classification methods Disease and cancer diagnosis

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Learning qualitative probabilistic networks with reduced ambiguous signs from complete data

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2023年第4期18卷 392-404页

作者： Wang, Qian Xu, Wei Lv, Yali Wang, Lifang Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China Shanxi Univ Finance & Econ Sch Informat Taiyuan 030006 Peoples R China

Qualitative probabilistic networks (QPNs) are the qualitative abstractions of probabilistic networks or Bayesian networks, summarising probabilistic influences by qualitative signs. But the ambiguous sign is undesirable as it leads to uninformative results upon QPN inference. In this paper, we focus on learning QPN with reduced ambiguous signs from complete data. We first extend the definition of qualitative influences based on the situational signs, and prove the theorem on the range of qualitative signs. Second, we design an algorithm for learning QPN with reduced ambiguous (RAQPN algorithm). The QPN structure can be optimised by the k2 search algorithm, and the corresponding qualitative signs can be acquired by ordering the conditional probabilities that are represented by the frequency formats. Furthermore, when the ambiguous signs appear, they can be reduced by being given the situational state of the network. Finally, experiment results verify the validity and feasibility of the proposed methods.

关键词： qualitative probabilistic networks ambiguous signs Bayesian networks k2 algorithm

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The Multimode Estimation Modeling for Flight Delay of a Busy Hub-Airport in Flight Chain

The Multimode Estimation Modeling for Flight Delay of a Busy...

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IITA International Conference on Services Science, Management and Engineering

作者： Liu, Yujie Ma, Song Tianjin Univ Coll Comp Sci & Technol Tianjin 300072 Peoples R China Civil Aviat Univ China Coll Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9780769537290

Flight delay is the root of Delay Propagation, while delay propagated within a Flight Chain. Busy hub-airport plays an important role in a Flight Chain, since the Initial Delay often happens there. To research more possible impacting factors in the Flight Chain, a busy hub-airport is chosen as the object in this research. After analyzing the complex and uncertain relationship between flight delays and all possible impacting factors, Bayesian network (BN) adapt to model and estimate about flight delay in a busy hub-airport. We proposed two learning methods with three different models. Model I is an estimation model for Arrival Delay based on parameter learning of BN with the Expectation-Maximization (EM) algorithm. Model II is estimation model for Arrival Delay based on structure learning of BN with k2 algorithm. The model learned by k2 is proved more suitable for modeling the estimation of flight delay, with a higher estimating correct rate than Model I. Then ModelIII is estimation model for Departure Delay with the same algorithm of Model II.

关键词： Multimode Modeling Flight Chain Hub-airport Flight Delay Delay Estimation k2 algorithm Baysian Network

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Learning Bayesian Networks Structure Based Part Mutual Information for Reconstructing Gene Regulatory Networks 1

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13th International Conference on Intelligent Computing (ICIC)

作者： Meng, Qingfei Chen, Yuehui Wang, Dong Meng, Qingfang Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China

ISBN: (数字)9783319633121

ISBN: (纸本)9783319633121;9783319633114

As a kind of high-precision correlation measurement method, Part Mutual Information (PMI) was firstly introduced into Bayesian Networks (BNs) structure learning algorithm in the paper. Compared to the general search scoring algorithm which set the initial network as an empty network without edge, our training algorithm initialized the network structure as an undirected network. That meant that our initial network identified the genes related to each other. And then the following algorithm only needed to determine the direction of the edges in the network. In the paper, we quoted the classic k2 algorithm based on Bayesian Dirichlet Equivalence (BDE) scoring function to search the direction of the edges. To test the proposed method, We carried out our experiment on two networks: the simulated gene regulatory network and the SOS DNA Repair network of Ecoli bacterium. And via comparison of different methods for SOS DNA Repair network, our proposed method was proved to be effective.

关键词： Gene regulatory networks Bayesian Networks Part Mutual Information k2 algorithm BDE scoring function

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Probabilistic Estimation of Travel Behaviors Using Zone Characteristics

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13th International Conference on knowledge-Based Intelligent Information and Engineering Systems

作者： Takamiya, Masatoshi Yamamoto, kosuke Watanabe, Toyohide Nagoya Univ Grad Sch Informat Sci Dept Syst & Social Informat Chikusa Ku Nagoya Aichi 4648603 Japan

ISBN: (纸本)9783642045912

There are many prior works of modeling travel behaviors. Most of them are investigated under the assumption that many kinds of data such as that of Person Trip (PT), which surveys travel behaviors, are available. Therefore, they do not, consider an application to cities where the survey is not examined. In this paper, we propose a method for estimating travel behaviors rising zone characteristics which is obtained from structural data, of city. Focusing on dependent relationships between travel behaviors and city structure, we estimate the travel behaviors by means of the relationships. We first define trip and zone characteristics, and then introduce our method. With our method, we make use of Bayesian network constructed with PT data and the structural data. In addition, we show the effectiveness of our method through evaluation experiments.

关键词： travel behaviors Bayesian network k2 algorithm

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Bayesian Network Structure Learning algorithms of Optimizing Fault Sample Set

Bayesian Network Structure Learning Algorithms of Optimizing...

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Chinese Intelligent Systems Conference (CISC)

作者： Guo, Hongyang Zhang, Rupei Yong, Jiaqi Jiang, Bin Nanjing Univ Aeronaut & Astronaut Automat Coll Nanjing 210016 Jiangsu Peoples R China

ISBN: (纸本)9783662483862;9783662483848

As a representative heuristic search algorithm of structure learning algorithms for Bayesian network, k2 algorithm is easily trapped in local optimum in the process of Bayesian network structure optimization. This paper proposes a k2 algorithm based on chaotic perturbation. First of all, define the order of the node based on mutual information to construct a Bayesian network structure. Then introduce distribution function skew tent map combined with chaotic search. And ergodicity is used to jump out of local optimum and achieve global optimization. Finally, taking steering shaft control structure of B777 control system as an example, the proposed algorithm presents a good effect in Bayesian network structure learning.

关键词： Bayesian network Chaotic perturbation k2 algorithm Mutual information

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Approaching Multi-dimensional Classification by Using Bayesian Network Chain Classifiers 6

Approaching Multi-dimensional Classification by Using Bayesi...

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6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Zhang, Ping Yang, Youlong Zhu, Xiaofeng Xidian Univ Sch Math & Stat Xian Peoples R China

ISBN: (纸本)9781479949557

Multi-dimensional classification assigns an unseen instance to more than one class variable simultaneously. Bayesian chain classifiers have been recently proposed to address the task since they were first proposed in 2011. However, when the simplistic structure of Bayesian network is built to represent the dependency relationships among classes, the predictive performance will degrade somewhat. In this paper, we further study Bayesian chain classifiers in more explicit dependency structures for class variables by using score-based methods, that is, we learn general structures of Bayesian network by using the k2 and Hill-Climbing algorithms to represent the dependency relationships among classes. Meanwhile, we employ a fast clustering-based feature subset selection method for constructing selective Naive Bayesian network classifiers, as base classifiers. Ultimately, we develop the alternative Bayesian chain classifiers. Experimental results show that our models can be able to achieve better or at least comparable performance compared against other state-of-the-art methods, both in term of predictive performance and time complexity.

关键词： Multi-dimensional classification Bayesian chain classifiers k2 algorithm Hill-Climbing algorithm Feature selection

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Modeling and estimation of travel behaviors using bayesian network

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2010年第4期4卷 297-305页

作者： Takamiya, Masatoshi Yamamoto, kosuke Watanabe, Toyohide Nagoya Univ Grad Sch Informat Sci Dept Syst & Social Informat Furo Cho Nagoya Aichi 4648603 Japan

Computer simulation method has been used to measure the effect of new Intelligent Transportation Systems (ITS). Prior works which make use of the method simulated simplified travel demand, thus it is necessary to represent the demand correctly. Most of existing researches for forecasting travel behaviors need survey data of travel activity in target city. The data is called Person Trip (PT) data. Therefore, they are not able to be applied to cities where the survey was not conducted. In this paper, we propose a method for modeling and estimating travel behaviors, using Bayesian network (BN). BN is constructed based on dependency zone and trip characteristics. The zones are characterized by the important facilities for travelers. Our method is able to apply to the cities since the zone characteristics are available without PT data. In addition, the dependency is represented as graph structure obtained by using k2 algorithm. Our experimental results show the effectiveness of our method for estimating the behaviors.

关键词： Travel behaviors bayesian network k2 algorithm

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MaSO:A Tool for Aiding the Management of Schedule Overrun

MaSO:A Tool for Aiding the Management of Schedule Overrun

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2nd International Advance Computing Conference

作者： Jeet, kawal Dhir, Renu Mago, Vijay kumar Minhas, Rajinder Singh DAV Coll Dept Comp Sci Jalandhar India NIT Dept Comp Sci & Engn Jalandhar India MLUDAV Coll Dept Comp Sci Phagwara India

ISBN: (纸本)9781424447909

Curbing schedule slippage is a daunting task in software industry. This problem evolves mainly because of poor analysis of risk factors and their management. This paper aims to handle this predicament with the help of Influence Diagram (ID). The three main risk factors that adversely affect the schedule are creeping user requirements, requirement instability and use of unnecessary features in the project. An integration of impacts of these with experts' opinion and with the existing databases (consisting of probability of occurrence of risk factors) helped us to create an ID based system that is capable to model the schedule slippage. This system can be used by the software manager at any stage of software development.

关键词： Influence Diagram Schedule Overrun Software Developmen Schedule Management Risk Management Bayesian Network k2 algorithm

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