检索结果-南通市图书馆

Developing a robust correlation for prediction of sweet and sour gas hydrate formation temperature

Petroleum 2022年第2期8卷 204-209页

作者： Mohammad Mesbah Samaneh Habibnia Shahin Ahmadi Amir Hossein Saeedi Dehaghani Sareh Bayat Young Researchers and Elites Club Science and Research BranchIslamic Azad UniversityTehranIran Department of Chemical Engineering Sahand University of TechnologyTabrizIran Department of Environmental Health Engineering Faculty of Environment Health EngineeringZabol University of Medical SciencesZabolIran Department of Petroleum Engineering Faculty of Chemical EngineeringTarbiat Modares UniversityTehranIran Department of Environmental Health Engineering School of Public HealthHamedan University of Medical SciencesHamedanIran

There are numerous correlations and thermodynamic models for predicting the natural gas hydrate formation condition but still the lack of a simple and unifying general model that addresses a broad ranges of gas mixture.This study was aimed to develop a user-friendly universal correlation based on hybrid group method of data handling(GMDH)for prediction of hydrate formation temperature of a wide range of natural gas mixtures including sweet and sour gas.To establish the hybrid GMDH,the total experimental data of 343 were obtained from open articles.The selection of input variables was based on the hydrate structure formed by each gas species.The modeling resulted in a strong algorithm since the squared correlation coefficient(R2)and root mean square error(RMSE)were 0.9721 and 1.2152,respectively.In comparison to some conventional correlation,this model represented not only the outstanding statistical parameters but also its absolute superiority over others.In particular,the result was encouraging for sour gases concentrated at H2S to the extent that the model outstrips all available thermodynamic models and correlations.Leverage statistical approach was applied on datasets to the discovery of the defected and doubtful experimental data and suitability of the model.According to this algorithm,approximately all the data points were in the proper range of the model and the proposed hybrid GMDH model was statistically reliable.

关键词： Hydrate formation temperature HFT Wide range of natural gas mixtures Unified correlation Group method of data handling GMDH outlier detection

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Probabilistic modeling of multifunction radars with autoregressive kernel mixture network

引用

Defence Technology（防务技术） 2024年第5期35卷 275-288页

作者： Hancong Feng Kaili.Jiang Zhixing Zhou Yuxin Zhao Kailun Tian Haixin Yan Bin Tang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.

关键词： Probabilistic forecasting Multifunction radar Unsupervised learning Change point detection outlier detection

来源：

维普期刊数据库

同方期刊数据库博看期刊评论

在线全文

学校读者我要写书评

暂无评论

Copy Move Forgery detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

引用

Computer Systems Science & Engineering 2023年第5期45卷 1577-1593页

作者： R.Dhanya R.Kalaiselvi Department of Computer Applications Noorul Islam Centre for Higher EducationNagercoil629180India Department of Computer Science Engineering Noorul Islam Centre for Higher EducationNagercoil629180India

A severe problem in modern information systems is Digital media tampering along with fake information.Even though there is an enhancement in image development,image forgery,either by the photographer or via image manipulations,is also done in parallel.Numerous researches have been concentrated on how to identify such manipulated media or information manually along with automatically;thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced instruments.However,high complexity affects the developed methods.Presently,it is complicated to resolve the issue of the speed-accuracy trade-off.For tackling these challenges,this article put forward a quick and effective Copy-Move Forgery detection(CMFD)system utilizing a novel Quad-sort Moth Flame(QMF)Light Gradient Boosting Machine(QMF-Light GBM).Utilizing Borel Transform(BT)-based Wiener Filter(BWF)and resizing,the input images are initially pre-processed by eliminating the noise in the proposed system.After that,by utilizing the Orientation Preserving Simple Linear Iterative Clustering(OPSLIC),the pre-processed images,partitioned into a number of grids,are segmented.Next,as of the segmented images,the significant features are extracted along with the feature’s distance is calculated and matched with the input images.Next,utilizing the Union Topological Measure of Pattern Diversity(UTMOPD)method,the false positive matches that took place throughout the matching process are eliminated.After that,utilizing the QMF-Light GBM visualization,the visualization of forged in conjunction with non-forged images is performed.The extensive experiments revealed that concerning detection accuracy,the proposed system could be extremely precise when contrasted to some top-notch approaches.

关键词： Borel transform based wiener filter(BWF) orientation preserving simple linear iterative clustering(OPSLIC) keypoint features block features outlier detection

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

引用

Computer Systems Science & Engineering 2022年第3期40卷 1085-1098页

作者： Farzaneh Khorasani Morteza Mohammadi Zanjireh Mahdi Bahaghighat Qin Xin Computer Engineering Department Imam Khomeini International UniversityQazvinIran Faculty of Science and Technology University of the Faroe IslandsTorshavnFaroe Islands

With a sharp increase in the information volume,analyzing and retrieving this vast data volume is much more essential than ever.One of the main techniques that would be beneficial in this regard is called the Clustering method.Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible.One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm.The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters.Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time.Besides,the selection of appropriate initial seeds can reduce the cluster’s inconsistency.In this paper,we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm.For this purpose,a new method is proposed considering the average distance between objects to determine the initial seeds.Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm.The experimental results showed that our proposed approach outperforms the Chithra with 1.7%and 2.1%in terms of clustering accuracy for Wine and Abalone detection data,respectively.Furthermore,achieved results indicate that comparing with the Reverse Nearest Neighbor(RNN)search approach,the proposed method has a higher convergence speed.

关键词： Data clustering k-means algorithm information retrieval outlier detection clustering accuracy unsupervised learning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Finding and detection of outlier Regions in Satellite Image

Finding and Detection of Outlier Regions in Satellite Image

引用

2011 1st International Conference on Network and Electronics Engineering(ICNEE 2011）

作者： Kitti Koonsanit Chuleerat Jaruskulchai Department of Computer Science Faculty of ScienceKasetsart University

outlier cluster detection has always been a hot research field of data mining.outlier clusterdetection is important in many fields.Automatic determination of the outlier cluster is often needed toeliminate that outlier cluster.In this paper,a method has been developed to determine the outlier regions insatellite image using a data mining algorithm based on the co-occurrence matrix technique in order todeterminate that outlier.Our method consists of four stages,the first stage estimate a number of cluster byco-occurrence matrix,the second stage cluster dataset by automatic clustering algorithm,the third stagedetect outlier regions by automatic threshold value and the final stage defines outlier regions,which arelower than threshold value,to be outlier regions.The results from the tests confirm the effectiveness of theproposed method in finding the outlier regions.

关键词： outlier regions Anomaly detection determination outliers cluster co-occurrence statistics outlier detection

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Fish forewarning of comprehensive toxicity in water environment based on Bayesian sequential method

引用

Journal of Environmental Sciences 2021年第12期33卷 150-159页

作者： Kaifeng Rao Li Tang Xin Zhang Heyu Xiang Liang Tang Yong Liu Wei Wang Jie Jiang Mei Ma Yiping Xu Zijian Wang State Key Joint Laboratory of Environment Simulation and Pollution Control Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Key Laboratory of Drinking Water Science and Technology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Shenzhen Monitoring Centre of Ecology and Environment Shenzhen 518049China Department of Basic Courses Beijing Union UniversityBeijing 100101China Faculty of Mathematics and Statistics Hubei UniversityWuhan 430062China CASA(Wuxi)Environmental Technology Co.Ltd. Wuxi 214024China College of Resources and Environment University of Chinese Academy of SciencesBeijing 101407China

Environmental impact of pollutants can be analyzed effectively by acquiring fish behavioral signals in water with biological behavior sensors. However, a variety of factors, such as the complexity of biological organisms themselves, the device error and the environmental noise, may compromise the accuracy and timeliness of model predictions. The current methods lack prior knowledge about the fish behavioral signals corresponding to characteristic pollutants, and in the event of a pollutant invasion, the fish behavioral signals are poorly discriminated. Therefore, we propose a novel method based on Bayesian sequential,which utilizes multi-channel prior knowledge to calculate the outlier sequence based on wavelet feature followed by calculating the anomaly probability of observed values. Furthermore, the relationship between the anomaly probability and toxicity is analyzed in order to achieve forewarning effectively. At last, our algorithm for fish toxicity detection is verified by integrating the data on laboratory acceptance of characteristic pollutants. The results show that only one false positive occurred in the six experiments, the present algorithm is effective in suppressing false positives and negatives, which increases the reliability of toxicity detections, and thereby has certain applicability and universality in engineering applications.

关键词： Bayesian sequential method Fish electrical signal outlier detection Anomaly probability Time series forecasting

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Valuable Data Extraction for Resistivity Imaging Logging Interpretation

引用

Tsinghua Science and Technology 2020年第2期25卷 281-293页

作者： Yili Ren Renbin Gong Zhou Feng Meichao Li Institute of Computer Application Technology PetroChina Research Institute of Petroleum Exploration and Development(RIPED)Beijing 100083China Department of Well Logging&Remote Sensing Technology of RIPED Beijing 100083China

Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata.The manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential factors.As a result,manual interpretation accuracy is low.Therefore,it is highly useful to develop effective automatic imaging logging interpretation by machine learning.Resistivity imaging logging is the most widely used technology for imaging logging.In this paper,we propose an automatic extraction procedure for the geological features in resistivity imaging logging images.This procedure is based on machine learning and achieves good results in practical applications.Acknowledging that the existence of valueless data significantly affects the recognition effect,we propose three strategies for the identification of valueless data based on binary classification.We compare the effect of the three strategies both on an experimental dataset and in a production environment,and find that the merging method is the best performing of the three strategies.It effectively identifies the valueless data in the well logging images,thus significantly improving the automatic recognition effect of geological features in resistivity logging images.

关键词： machine learning binary classification multiclass classification outlier detection imaging logging

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Data-Driven Approaches for Spatio-Temporal Analysis:A Survey of the State-of-the-Arts

引用

Journal of Computer Science & Technology 2020年第3期35卷 665-696页

作者： Monidipa Das Soumya K.Ghosh School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Computer Science and Engineering Indian Institute of Technology KharagpurKharagpur 721302India

With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal references.This huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST datasets.In this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis.The focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various domains.We start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary area.Compared with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives.We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.

关键词： data-driven modeling spatio-temporal data prediction change pattern detection outlier detection hotspot detection partitioning/summarization (tele-)coupling visual analytics

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An Intelligent Gestational Diabetes Diagnosis Model Using Deep Stacked Autoencoder

引用

Computers, Materials & Continua 2021年第12期69卷 3109-3126页

作者： A.Sumathi S.Meganathan BVijila Ravisankar SRC SASTRA Deemed to be UniversityKumbakonamTamil Nadu612001India Consultant Obstetrician and Gynecologist Anbu Hospital and Nalam ClinicKumbakonamTamil Nadu612001India

Gestational Diabetes Mellitus(GDM)is one of the commonly occurring diseases among women during pregnancy.Oral Glucose Tolerance Test(OGTT)is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective.So,there is a need to design an effective and automated GDM diagnosis and classification model.The recent developments in the field of Deep Learning(DL)are useful in diagnosing different diseases.In this view,the current research article presents a new outlier detection with deep-stacked Autoencoder(OD-DSAE)model for GDM diagnosis and classification.The goal of the proposed OD-DSAE model is to find out those mothers with high risks and make them undergo earlier diagnosis,monitoring,and treatment compared to low-risk women.The presented ODDSAE model involves three major processes namely,preprocessing,outlier detection,and classification.In the first step i.e.,data preprocessing,there exists three stages namely,format conversion,class labelling,and missing value replacement using k-nearest neighbors(KNN)model.outliers are superior values which considerably varies from other data observations.So,it might represent the variability in measurement,experimental errors or novelty too.So,Hierarchical Clustering(HC)-based outlier detection technique is incorporated in OD-DSAE model,and thereby classification performance can be improved.The proposed model was simulated using Python 3.6.5 on a dataset collected by the researcher themselves.A series of experiments was conducted and the results were investigated under different aspects.The experimental outcomes inferred that the OD-DSAE model has outperformed the compared methods and achieved high precision of 96.17%,recall of 98.69%,specificity of 89.50%,accuracy of 96.18%,and F-score of 97.41%.

关键词： Gestational diabetes mellitus data classification outlier detection deep learning hierarchical clustering

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Name disambiguation in AMiner

引用

Science China(Information Sciences) 2021年第4期64卷 214-216页

作者： Jing ZHANG Jie TANG Information School Renmin University of China Department of Computer Science and Technology Tsinghua University

Name disambiguation, aiming at disambiguating who is who, is one of the fundamental problems of the online academic network platforms such as Google scholar, microsoft academic and AMiner. This study takes AMiner;), a free online academic search and mining system [1], as the example to explain how we deal with the name ambiguity problem under three different scenarios.

关键词： name disambiguation academic network graph embedding outlier detection clustering algorithm

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：