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Anomaly detection for machinery by using Big Data Real-Time processing and clustering technique 19

Anomaly detection for machinery by using Big Data Real-Time ...

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Proceedings of the 3rd international conference on Big Data Research

作者： Zhuo Wang Yanghui Zhou Gangmin Li Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Computer Science and Software Engineering Xi'An JiaoTong-Liverpool University Suzhou China

ISBN: (纸本)9781450372015

This paper aims to apply techniques of Big Data Analytics including K-Means Clustering to diagnose potential problems for offshore rotating machinery. The innovative methods are attempted in both Batch K-Means and Streaming K-Means. Their performances are compared with the conventional signal analysis method. Both K-Means models have a better performance on detecting significant mechanical faults as anomalies for offshore rotating machinery which can be considered as appropriate method for machine operational maintenance.

关键词： Trouble Diagnose Anomaly detection Streaming K-Means Clustering Real-Time processing K-Means Clustering Big Data

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An Intelligent System for Evaluation of Descriptive Answers

An Intelligent System for Evaluation of Descriptive Answers

引用

international conference on Intelligent Sustainable Systems (ICISS)

作者： Vinal Bagaria Mohit Badve Manasi Beldar Sunil Ghane Computer Engineering Sardar Patel Institute of Technology Andheri Mumbai

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Examination is an efficient and conventional way to test the knowledge gained by the students. The evaluation of examinations is a tiresome and arduous task. Automatic answer-script evaluation makes this task convenient for teachers as it reduces the efforts and time taken. The existing systems provide teachers the facility to conduct automatic objective and short answers exams but evaluating descriptive answers automatically is a challenge. For assessment of descriptive answers, the machine needs to consider the important factors which a human assessor takes into account for manual evaluation. To solve these problems, this research work proposed an intelligent assessment platform which considers the question type, necessary keywords, structural, conceptual and language aspects to evaluate an answer. Proposed model used concept graphs, fuzzy string matching, grammar checking and other similarity metrics for the assessment. The system also generates personalized feedback and analysis reports for students and teachers to promote focused learning.

关键词： Measurement Semantics Grammar conferences Task analysis Pattern matching Natural language processing

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Energy and Width Features-Based SVM for Vehicles Classification Using Low Power Consumption Radar

Energy and Width Features-Based SVM for Vehicles Classificat...

引用

IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Haotai Liang Zhaocheng Yang Fengyuan Shi Ruimin Yang College of Electronics and Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

In this paper, we propose a novel features-based support vector machines (SVM) vehicles classification approach using low power consumption radar. This new approach exploits the energy and width features, which show different characteristics in two types of vehicles, big car and small car. The proposed approach firstly detects and separates the target mainly using image feature extraction algorithm including grey processing, median filtering, binarization, region growing, filtering small-area blocks and morphological processing. Then coherent accumulation is used to improve the signal-noise ratio and the range unit of target is determined by the endpoint detection algorithm based on dual-threshold. Finally, the energy and width features of each type of car are extracted and the support vector machines (SVM) classifier is applied. The experimental results show that the accuracy of the proposed approach can achieve 95% above.

关键词： Support vector machines Power demand Filtering Support vector machine classification Radar Feature extraction Automobiles

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XNOR-YOLO: The High Precision of The Ball and Goal Detecting on The Barelang-FC Robot Soccer

XNOR-YOLO: The High Precision of The Ball and Goal Detecting...

引用

international conference on Applied Engineering (ICAE)

作者： Susanto Susanto Febri Alwan Putra Riska Analia Electrical Engineering Politeknik Negeri Batam Batam Indonesia

ISBN: (数字)9781728199177

ISBN: (纸本)9781728199184

The essential part in developing the humanoid robot which is able to play soccer is the vision system. The vision system needs to be fast and accurate in detecting the surrounding objects on the field such as the ball, goal, teammates, or even the opponent. One of the powerful methods which was able to generate the object detection quickly with high accuracy was the deep learning. However, this method proceeded a huge computation. Even if it was generated on the GPU, it would still generat a low speed of detecting. Therefore, the high precision and fast detecting of the object method need to be considered in this area. In order to overcome this problem, we proposed the combination of the XNOR-Network (XNOR-Net) towards YOLOv3 running on the GPU with the same layer configuration as the tinyYOLO. To testify the performance of this method, some experiments has been carried out in real-time application by implementing it in the NVDIA Jetson TX1 GPU. From the experiment results, this method is able to detect the object faster than other object detection in detecting the ball and goal colored by white and generated 30 FPS in detecting each object.

关键词： Deep learning machine vision Graphics processing units Humanoid robots Object detection Real-time systems Sports

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Cloud Computing Virtual machine Migration Energy Measuring Research 2019

Cloud Computing Virtual Machine Migration Energy Measuring R...

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Proceedings of the 3rd international conference on Vision, Image and signal processing

作者： Liu Jun Zhang Jie Pu DingHong School of Education Science Guizhou Normal University/ Wisdom Education research center Guiyang Guizhou China

ISBN: (纸本)9781450376259

This paper research virtual machine migration energy measuring on IPv4/IPv6 network based on cloud computing infrastructure platform, it conducts a research on the energy measuring in IPv4/IPv6 cloud computing platform, and presents a dynamic energy measuring mathematical model based on analyzing CPU energy consumption changes brought by random assignment works. The research determines mathematical model parameter values and it completes the IPv4/IPv6 cloud computing platform virtual machine migration experimentally. This paper achieves the energy consumption of IPv4/IPv6 transition prior-period, mid-period and last-period cloud computing platform virtual machine migration, the conclusions in line with the cloud energy measurement needs, it builds the theoretical foundation for cloud computing platform energy consumption optimize.

关键词： Energy Measuring Cloud Computing Virtual machine Migration Migration Energy Consumption

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Prediction of PM10 Concentration in South Korea Using Gradient Tree Boosting Models 2019

Prediction of PM10 Concentration in South Korea Using Gradie...

引用

Proceedings of the 3rd international conference on Vision, Image and signal processing

作者： Khaula Qadeer Moongu Jeon School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology (GIST) Gwangju South Korea

ISBN: (纸本)9781450376259

Particulate matter (PM) is a term generally used for very small particles and liquid droplets in the atmosphere. PM10 is the particle pollution with diameter less than or equal to 10 micrometers. Exposure to particle pollution is a public health hazard which leads to serious diseases such as asthma, bronchitis and even cancer; especially in elderly, children and sensitive people. It is crucial to predict the concentration of PM before-hand so that people can take precautionary measures and avoid the hazardous impact of pollution. These days the gradient boosting is one of popular methods in regression and classification tasks. In this study, we predict the PM10 concentration using Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting machine (LightGBM) algorithms after combining meteorological, emission rate data and output features of Community Multi-Scale Air Quality (CMAQ) model. All the missing values are removed because handling them is quite challenging and requires feature engineering. The results show that XGBoost performs better than LightGBM in terms of prediction estimation with the RMSE of 12.846; but takes longer to train and tune the model's parameters. RMSE of LightGBM is 12.9066, which is slightly higher; but on the contrary, it is 29 times faster than XGBoost.

关键词： machine learning (ML) Light Gradient Boosting machine (LightGBM) eXtreme Gradient Boosting (XGBoost)

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Resilient machine learning (rML) Ensemble Against Adversarial machine learning Attacks 3rd

Resilient Machine Learning (rML) Ensemble Against Adversaria...

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3rd international conference on Dynamic Data Driven Application Systems, DDDAS 2020

作者： Yao, Likai Tunc, Cihan Satam, Pratik Hariri, Salim NSF Center for Cloud and Autonomic Computing University of Arizona Tucson United States Department of Computer Science and Engineering The University of North Texas DentonTX United States

ISBN: (纸本)9783030617240

machine learning (ML) algorithms have been widely used in many critical applications, including Dynamic Data Driven Applications Systems (DDDAS) applications, automated financial trading applications, autonomous vehicles, and intrusion detection systems for the decision-making process of users or automated systems. However, malicious adversaries have strong interests in manipulation the operations of machine learning algorithms to achieve their objectives in gaining financially, injecting injury or disasters. Adversaries against ML can be classified based on their capabilities and goals into two types: Adversary who has full knowledge of the ML models and parameters (white-box scenario) and one that does not have any knowledge and use guessing techniques to figure out the ML model and its parameters (black-box scenario). In both scenarios, the adversaries will attempt to maliciously manipulate model either during training or testing. Defending against these attacks can be successful by following three methods: 1) making the ML model robust to adversary, 2) validating and verifying input, or 3) changing ML architecture. In this paper, we present a resilient machine learning (rML) ensemble against adversarial attacks by dynamically changing the ML architecture and the ML models to be used such that the adversaries have no knowledge about the current ML model being used and consequently stop their attempt to manipulate the ML operations at testing phase. We evaluate the effectiveness of our rML ensemble using the benchmarking, zero-query dataset "DAmageNet" that contains both clean and adversarial image samples. We use three main neural networks in our ensemble that includes VGG16, ResNet-50, and ResNet-101. The experimental results show that our rML can tolerate the adversarial samples and achieve high classification accuracy with small execution time degradation. © 2020, Springer Nature Switzerland AG.

关键词： machine learning

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Thyroid Nodules Stratification Based on Orientation Characteristics Using machine learning Approach

Thyroid Nodules Stratification Based on Orientation Characte...

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international conference of Computer and Informatics Engineering (IC2IE)

作者： Hanung Adi Nugroho Eka legya Frannita Augustine Herini Tita Hutami Faculty of Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9781728182476

ISBN: (纸本)9781728182483

Patients with thyroid nodules should undergo further assessment to define malignancy stratification. One of the assessments is conducted using ultrasound. There are ten characteristics of thyroid nodules in the ultrasound examination. One of the characteristics is orientation. The orientation characteristic has its perks as it can tell the malignancy state from the growth direction to the orientation category. This study develops an approach to determine the stratification of the thyroid nodules based on orientation characteristics. The proposed approach consists of pre-processing, segmentation, feature extraction, and classification by using a support vector machine. Nine geometrical and moment features are used to describe orientation characteristics. Furthermore, the features are trained to differentiate two classes, namely are parallel and non-parallel. The testing results achieve more than 0.98 for accuracy, sensitivity, and NPV, respectively, while specificity and PPV achieve a perfect score. Looking by the results, it can be concluded that the proposed method has a good performance in differentiating orientation characteristics into a parallel and non-parallel category.

关键词： Thyroid Support vector machines Testing Feature extraction Training Image segmentation Ultrasonography

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A machine learning Approach for Detection Plant Disease: Taking Orchid as Example 2019

A Machine Learning Approach for Detection Plant Disease: Tak...

引用

Proceedings of the 3rd international conference on Vision, Image and signal processing

作者： Li-Hua Li Yu-Sheng Chu Jui-Yuan Chu Shian-Hau Guo Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9781450376259

The export value of flower production is very high in many countries. Among these export flowers, orchids are considered one of the most valuable flowers. In order to increase orchid production, it is important to take closely care of orchid in the cultivation period. Due to the plant diseases may cause the economic losses in agricultural industry, the daily inspection and early recognition of plant diseases are *** and prevention of plant diseases are a worldwide agricultural problem. Many researchers have proposed biocontrol or IT technology to handle plant disease problems. Some researchers applied image recognition to find out leaf problems such as banana leaves, alfalfa leaves, and citrus leaves. However, these researches all focus on trees or fruits in the lab. They did not provide the long-distance leaf identification for orchid flowers. To help flower farmers and to enhance the quality of production, this research has proposed a machine learning method to capture the image of orchid leaf and to identify the leaf *** research analyzes the leaf image with feature space, i.e., HSI, RGB, and grayscale. We use histogram to analysis the leaf color and we analyze the green color threshold so that the image can be classified into various color zones. Based on the generated threshold, this research is able to segment the leaf image into healthy area and unhealthy area. Finally, we use the Artificial Neural Network (ANN) and Deep learning ANN to learn the image patterns of orchid leaf. Our proposed method is then applied to identify the orchid leaves and to determine whether the orchid is healthy or sick. With our proposed model, the accuracy of recognizing the leaf disease can achieve 100% for training data and 90% for testing data. This research enables flower farmers to recognize the orchid disease and can prevent the disease in early stage. As a result, the farmer can take better care to the orchid plants and enhance the cultivation of orc

关键词： machine learning Orchid Image processing Neural Network Plant Disease Deep learning

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machine and Deep learning Based Comparative Analysis Using Hybrid Approaches for Intrusion Detection System

Machine and Deep Learning Based Comparative Analysis Using H...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Azam Rashid Muhammad Jawaid Siddique Shahid Munir Ahmed Board of Intermediate and Secondary Education Lahore Lahore Pakistan Faculty of Information Technology Pakistan Institute of Management Lahore Pakistan Network Administrator Institute of Business Administration University of Punjab Lahore Pakistan

ISBN: (数字)9781728142357

ISBN: (纸本)9781728142364

Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for vulnerable source code, viruses, worms and unauthorized intruders for many intranet/internet applications. Despite many open source APIs and tools for intrusion detection, there are still many network security problems exist. These problems are handled through the proper pre-processing, normalization, feature selection and ranking on benchmark dataset attributes prior to the enforcement of self-learning-based classification algorithms. In this paper, we have performed a comprehensive comparative analysis of the benchmark datasets NSL-KDD and CIDDS-001. For getting optimal results, we have used the hybrid feature selection and ranking methods before applying self-learning (machine / Deep learning) classification algorithmic approaches such as SVM, Naïve Bayes, k-NN, Neural Networks, DNN and DAE. We have analyzed the performance of IDS through some prominent performance indicator metrics such as Accuracy, Precision, Recall and F1-Score. The experimental results show that k-NN, SVM, NN and DNN classifiers perform approx. 100% accuracy regarding performance evaluation metrics on the NSL-KDD dataset whereas k-NN and Naïve Bayes classifiers perform approx. 99% accuracy on the CIDDS-001 dataset.

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