检索结果-内蒙古大学图书馆

Design, Fabrication, and Performance Analysis of Intelligent Mesoscale Capacitive Accelerometer for Vibration Measurement

引用

JOURNAL OF TESTING AND EVALUATION 2021年第4期49卷 2407-2424页

作者： Gomathi, K. Sakthivel, R. Marshal, S. Joseph John Pratheep, V. G. Kongu Engn Coll Dept Mechatron Engn Perundurai 638060 Tamil Nadu India Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Mech Engn Coimbatore 641112 Tamil Nadu India Karunya Inst Technol & Sci Dept Mech Engn Coimbatore 641114 Tamil Nadu India

This paper portrays an accelerometer as a motion sensor used to measure acceleration, vibration, and tilt angles which plays a vital role in the constructional field, inertial measurements, automotive safety system, biomedical, and yet other safety applications. A small packaged accelerometer can be made larger in size by acquiring an external power source or battery. The designed micro-accelerometer is fabricated using clean room technology, which is not quite cost effective, and even the silicon base is replaced by steel because of steel's high yield strength and low cost to fabricate a mesoscale accelerometer. Design parameters are optimized by a decision tree algorithm where the optimized dimensions are used to model the mesoscale accelerometer in INTELLISUITE 8.6 software. A Wire-cut discharger is used for the fabrication of mechanical elements of an accelerometer with a spring steel foil to reduce fabrication cost. Finally, the fabricated accelerometer with a mesoscale was made to undergo experimentation with a hammer drill and compared with simulation results to evaluate its performance in a harsh construction environment. 0.576 (mu m/g) of mechanical sensitivity and 0.629 (pF/g) of electrical sensitivity is achieved.

关键词： mesoscale capacitive accelerometer decision tree algorithm INTELLISUITE sensitivity electro-discharge-machining

来源：评论

学校读者我要写书评

暂无评论

The Application of Information Classification in Agricultural Production Based on Internet of Things and Deep Learning

引用

IEEE ACCESS 2022年 10卷 22622-22630页

作者： Gao, Suwei Ningbo Univ Res Inst Chinas Rural Policy & Practice Ningbo 315000 Peoples R China

Under the China's increasing attention to the technological innovation of agricultural production, all kinds of agricultural information have exploded on the internet, and agricultural informatization has developed rapidly. The related information has spread all over the whole network, which makes it gradually difficult to extract useful information from the network. To solve the deficiency of information classification ability of traditional agricultural information collection methods, the classification method of agricultural information is optimized, to realize searching the required agricultural information quickly. At first, the study introduces Deep Learning (DL) technology and the Internet of Things (IoT) and their advantages. Then, based on Bayesian Networks (BN) and decision tree (DT) algorithm, the agricultural information classification model is implemented and trained. Using various agricultural economic development theories, analyzation is made on the present situation of domestic agricultural informatization development. Finally, the advantages are put forward of agricultural production informatization development and economic management development based on IoT technology. The research results show that, the agricultural information classification model based on DL and IoT technology can accurately select the required effective information from the network, and the application of IoT technology in agricultural production big data plays an important role in production and economic management. Therefore, the agricultural information classification model based on DL and IoT technology can make an effective and accurate judgment on the classification of agricultural information, and then provide a focus for agricultural production and economic development. A new idea is provided for the application of new technologies in agricultural production and management.

关键词： Neurons Production Text categorization Mathematical models Internet of Things Convolutional neural networks Economics Deep learning Internet of Things Bayesian network decision tree algorithm agricultural production economic management

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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)

来源：评论

学校读者我要写书评

暂无评论

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

Power System Islanding Detection & Identification using Topo...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Semantic weldability prediction with RSW quality dataset and knowledge construction

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：