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Mozambique Flood (2019) Caused by Tropical Cyclone Idai Monitored From Sentinel-1 and Sentinel-2 Images

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 8761-8772页

作者： Guo, Jinyun Luan, Yujie Li, Zhen Liu, Xin Li, Chengming Chang, Xiaotao Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Shandong Peoples R China Chinese Acad Surveying & Mapping Beijing 100036 Peoples R China Land Satellite Remote Sensing Applicat Ctr MNR Beijing 100048 Peoples R China

The tropical cyclone Idai caused severe floods in Mozambique in March 2019. Sentinel-1 and Sentinel-2 images are processed to monitor the flood disaster in Pungue and Buzi rivers by combining the object-based image analysis approach and the decision tree algorithm. Water is preliminarily extracted from Sentinel-1 image, and shadows of mountain and buildings in the study area are extracted from Sentinel-2 image. Mountain shadow is extracted based on the decision tree classification rules constructed by the digital elevation model and the index model, while building shadow is extracted by constructing the decision tree classification rules using the features of objects. Water extraction results are combined with shadows to eliminate confusing shadows in the preliminary extraction to obtain the accurate flood extent. Sentinel-2 images are used to classify land use types in multiple periods before and after the flood and analyze the disaster by combining with the change information of the water. Land use type changes are analyzed and predicted with the CA-Markov model. The results show that the maximum submerged area was 3.5 times of the normal water area, and that most areas were submerged for more than 10 d. The area between Pungue and Buzi rivers were severely affected, where a large number of crops and villages were submerged. Grassland and cropland were the most seriously submerged. The overall accuracy of extracted water results ranges from 86% to 92%. The results indicated that the combining method can effectively suppress the influence of shadows on water extraction results.

关键词： Floods Monitoring Satellites Data mining Rivers Feature extraction Synthetic aperture radar decision tree algorithm flood Mozambique object-based image analysis (OBIA) Sentinel-1 Sentinel-2 tropical cyclone Idai

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Resource provisioning using workload clustering in cloud computing environment: a hybrid approach

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第1期24卷 319-342页

作者： Shahidinejad, Ali Ghobaei-Arani, Mostafa Masdari, Mohammad Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

In recent years, cloud computing paradigm has emerged as an internet-based technology to realize the utility model of computing for serving compute-intensive applications. In the cloud computing paradigm, the IT and business resources, such as servers, storage, network, and applications, can be dynamically provisioned to cloud workloads submitted by end-users. Since the cloud workloads submitted to cloud providers are heterogeneous in terms of quality attributes, management and analysis of cloud workloads to satisfy Quality of Service (QoS) requirements can play an important role in cloud resource management. Therefore, it is necessary for the provisioning of proper resources to cloud workloads using clustering of them according to QoS metrics. In this paper, we present a hybrid solution to handle the resource provisioning issue using workload analysis in a cloud environment. Our solution utilized the Imperialist Competition algorithm (ICA) and K-means for clustering the workload submitted by end-users. Also, we use a decision tree algorithm to determine scaling decisions for efficient resource provisioning. The effectiveness of the proposed approach under two real workloads traces is evaluated. The simulation results demonstrate that the proposed solution reduces the total cost by up to 6.2%, and the response time by up to 6.4%, and increases the CPU utilization by up to 13.7%, and the elasticity by up to 30.8% compared with the other approaches.

关键词： Cloud computing Workload clustering Resource provisioning Imperialist competition algorithm decision tree algorithm

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Course planning of extension education to meet market demand by using data mining techniques - an example of Chinkuo technology university in Taiwan

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EXPERT SYSTEMS WITH APPLICATIONS 2008年第1期34卷 596-602页

作者： Hsia, Tai-Chang Shie, An-Jin Chen, Li-Chen Chien Kuo Technol Univ Dept Ind Engn & Management Changhua Taiwan Yuan Ze Univ Dept Ind Engn & Management Tao Yuan Taiwan Chien Kuo Technol Univ Extens Educ Ctr Changhua Taiwan

This study used data mining techniques to analyze the course preferences and course completion rates of enrollees in extension education courses at a university in Taiwan. First, extension courses were classified into five broad groups. Records of enrollees in extension courses from 2000-5 were then analyzed by three data mining algorithms: decision tree, Link Analysis, and decision Forest. decision tree was used to find enrollee course preferences, Link Analysis found the correlation between course category and enrollee profession, and decision Forest found the probability of enrollees completing preferred courses. Results will be used as a reference for curriculum development in the extension program. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： data mining extension education decision tree algorithm link analysis algorithms decision forest algorithm

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Intensified Analysis and Comparison of 5 Flacicirus with the use of decision tree and Support Vector Machine (SVM)

Intensified Analysis and Comparison of 5 Flacicirus with the...

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International Conference on Advanced Communication Technology

作者： Eujin Yang Bokyung Gu Taeseon Yoon Natural Science Hankuk Academy of Foreign Studies Young-in South Korea Faculty Hankuk Academy of Foreign Studies Young-in South Korea

ISBN: (纸本)9781509048922

Flavivirus is spreaded with the help of intermediary, especially mosquitoes. In preceding research, we found out that Leucine has high frequency. Wanting to know specific relationship between 5 flaviviruses;Yellow fever, West Nile virus, Dengue virus, Tick borne encephalitis, decision tree and support vector machine algorithm were used. Analyzing results of the algorithms, difference or similarity about the viruses and a group as flavivirus were found.

关键词： Zika virus Yellow fever West Nile virus Dengue virus Tick borne encephalitis Flavivirus decision tree algorithm Support Vector Machine(SVM)

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Power System Islanding Detection & Identification using Topology Approach and decision tree

Power System Islanding Detection & Identification using Topo...

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IEEE Power and Energy Society General Meeting

作者： Rui Sun Zhongyu Wu Virgilio A. Centeno Southeast University Nanjing Jiangsu CN Electrical engineering from Virginia Polytechnic Institute and State University (Virginia Tech) Blacksburg

ISBN: (纸本)9781457710001

Power system islanding contingency has always been one of the most severe wide area system failures. The recent Hurricane Gustav landed in west coast in 2008 was one exact case to illuminate the importance and severity of this kind of contingencies. Longtime the researches of islanding situations focus on studying the coherence groups of the generations under an islanding contingency. In this paper, a new method in detecting power system islanding contingencies using both the system's topological structure and real-time system dynamic state variables is presented and tested. An islanding severity index concept is given for ranking the severity of the islanding cases.

关键词： Phasor measurements PMU placement strategy Islanding contingency decision tree algorithm Islanding severity index decision trees power systems phasor measurement contingency US WEST COAST HURRICANE GUSTAV

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A decision tree-based missing value imputation technique for data pre-processing 11

A decision tree-based missing value imputation technique for...

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Proceedings of the Ninth Australasian Data Mining Conference - Volume 121

作者： Geaur Rahman Zahidul Islam Charles Sturt University Wagga Wagga Australia

ISBN: (纸本)9781921770029

Data pre-processing plays a vital role in data mining for ensuring good quality of data. In general data pre-processing tasks include imputation of missing values, identification of outliers, smoothening out of noisy data and correction of inconsistent data. In this paper, we present an efficient missing value imputation technique called DMI, which makes use of a decision tree and expectation maximization (EM) algorithm. We argue that the correlations among attributes within a horizontal partition of a data set can be higher than the correlations over the whole data set. For some existing algorithms such as EM based imputation (EMI) accuracy of imputation is expected to be better for a data set having higher correlations than a data set having lower correlations. Therefore, our technique (DMI) applies EMI on various horizontal segments (of a data set) where correlations among attributes are high. We evaluate DMI on two publicly available natural data sets by comparing its performance with the performance of EMI. We use various patterns of missing values each having different missing ratios up to 10%. Several evaluation criteria such as coefficient of determination (R2), Index of agreement (d2) and root mean squared error (RMSE) are used. Our initial experimental results indicate that DMI performs significantly better than EMI.

关键词： decision tree algorithm data cleansing data pre-processing missing value imputation EM algorithm

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Semantic weldability prediction with RSW quality dataset and knowledge construction

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ADVANCED ENGINEERING INFORMATICS 2018年 38卷 41-53页

作者： Kim, Kyoung-Yun Ahmed, Fahim Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA

This paper presents a semantic Resistance Spot Welding (RSW) weldability prediction framework. The framework constructs a shareable weldability knowledge database based on the regression rules from inconsistent RSW quality datasets. This research aims to effectively predict the weldability of RSW process for existing or new weldment design. A real welding test dataset collected from an automotive OEM is used to extract decision rules using a decision tree algorithm, Classification and Regression trees (CART). The extracted decision rules are converted systematically into SWRL rules for capturing the semantics and to increase the shareability of the constructed knowledge. The experiments show that the RSW ontology, along with SWRL rules that contains weldability rules constructed from the datasets, successfully predicts the weldability (nugget width) values for RSW cases. The predicted nugget width values are found to be in-dose proximity of the actual values. This paper shows that semantic prediction framework construes an intelligent way for constructing accurate and transparent predictive models for RSW weldability verification.

关键词： Semantic weldability prediction Resistance spot welding Welding quality Weldability knowledge construction decision tree algorithm Semantic rules

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Detection and Classification of Lamination Faults in a 15 kVA Three-Phase Transformer Core Using SVM, KNN and DT algorithms

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IEEE ACCESS 2022年 10卷 50925-50932页

作者： Altayef, Ehsan Anayi, Fateh Packianather, M. Benmahamed, Youcef Kherif, Omar Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff CF24 3AA Wales Ecole Natl Polytech ENP Lab Rech Electrotech LRE Algiers 16200 Algeria Cardiff Univ Adv High Voltage Engn Res Ctr Sch Engn Cardiff CF24 3AA Wales

This paper deals with the detection and classification of two types of lamination faults (i.e., edge burr and lamination insulation faults) in a three-phase transformer core. Previous experimental results are exploited, which are obtained by employing a 15 kVA transformer under healthy and faulty conditions. Different test conditions were considered such as the flux density, number of the affected laminations, and fault location. Indeed, the current signals were used where four features (Average, Fundamental, Total Harmonic Distortion (THD), and Standard Deviation (STD)) were extracted. Elaborating A total of 328 samples, these features are utilized as input vectors to train and test classification models based on SVM, KNN, and DT algorithms. Based on the selected features, the results confirmed that the transformer current can be used for the detection of lamination faults. An accuracy rate of more than 84% was obtained using three different classifiers. Such findings provided a promising step toward fault detection and classification in electrical transformers, helping to prevent the system and avoid other related issues such as the increase in power loss and temperature.

关键词： Power transformers Power transformer insulation Lamination Feature extraction Transformer cores Insulation Circuit faults Edge burrs classification algorithm decision tree algorithm fault detection lamination insulation KNN classifier SVM classifier transformer core

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Rule-based fuzzy inference system for estimating the influent COD/N ratio and ammonia load to a sequencing batch reactor

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WATER SCIENCE AND TECHNOLOGY 2006年第1期53卷 199-207页

作者： Kim, YJ Bae, H Ko, JH Poo, KM Kim, S Kim, CW Woo, HJ Pusan Natl Univ Dept Environm Engn Pusan 609735 South Korea Pusan Natl Univ Sch Elect & Comp Engn Pusan 609735 South Korea

A fuzzy inference system using sensor measurements was developed to estimate the influent COD/N ratio and ammonia load. The sensors measured ORP, DO and pH. The sensor profiles had a close relationship with the influent COD/N ratio and ammonia load. To confirm this operational knowledge for constructing a rule set, a correlation analysis was conducted. The results showed that a rule generation method based only on operational knowledge did not generate a sufficiently accurate relationship between sensor measurements and target variables. To compensate for this defect, a decision tree algorithm was used as a standardized method for rule generation. Given a set of inputs, this algorithm was used to determine the output variables. However, the generated rules could not estimate the continuous influent COD/N ratio and ammonia load. Fuzzified rules and the fuzzy inference system were developed to overcome this problem. The fuzzy inference system estimated the influent COD/N ratio and ammonia load quite well. When these results were compared to the results from a predictive polynomial neural network model, the fuzzy inference system was more stable.

关键词： decision tree algorithm fuzzy inference system polynomial neural network rule-base SBR

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Machine learning for optical chemical multi-analyte imaging Why we should dare and why it's not without risks

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2023年第14期415卷 2749-2761页

作者： Zieger, Silvia E. Koren, Klaus Aarhus Univ Ctr Water Technol WATEC Dept Biol Sect Microbiol Ny Munkegade 114 DK-8000 Aarhus C Denmark

Simultaneous sensing of metabolic analytes such as pH and O-2 is critical in complex and heterogeneous biological environments where analytes often are interrelated. However, measuring all target analytes at the same time and position is often challenging. A major challenge preventing further progress occurs when sensor signals cannot be directly correlated to analyte concentrations due to additional effects, overshadowing and complicating the actual correlations. In fields related to optical sensing, machine learning has already shown its potential to overcome these challenges by solving nested and multidimensional correlations. Hence, we want to apply machine learning models to fluorescence-based optical chemical sensors to facilitate simultaneous imaging of multiple analytes in 2D. We present a proof-of-concept approach for simultaneous imaging of pH and dissolved O-2 using an optical chemical sensor, a hyperspectral camera for image acquisition, and a multi-layered machine learning model based on a decision tree algorithm (XGBoost) for data analysis. Our model predicts dissolved O-2 and pH with a mean absolute error of < 4.50 center dot 10(-2) and < 1.96 center dot 10(-1), respectively, and a root mean square error of < 2.12 center dot 10(-1) and < 4.42 center dot 10(-1), respectively. Besides the model-building process, we discuss the potentials of machine learning for optical chemical sensing, especially regarding multi-analyte imaging, and highlight risks of bias that can arise in machine learning-based data analysis.

关键词： Supervised pattern recognition XGBoost decision tree algorithm Intensity-based sensing pH Dissolved oxygen

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