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Predictive Maintenance in Twin Screw Air Compressors through Unsupervised Learning 25

Predictive Maintenance in Twin Screw Air Compressors through...

引用

25th International Carpathian Control Conference (ICCC)

作者： Zanoli, Silvia Maria Hancha, Mahmoud Sfar Farooq, Ali Mir Pepe, Crescenzo Univ Politecn Marche DII Ancona AN Italy MGA Automat Falconara Mma AN Italy

ISBN: (纸本)9798350350708;9798350350715

A predictive maintenance module for oil-injected twin screw compressors is proposed in the present paper. Oil-injected twin screw compressors are widely used in the pneumatic drive industry and their maintenance represents an important issue. In fact, periodic maintenance policies are exploited in this field;this practice can conduct to a decrease of the efficiency of the plant units where these devices are installed. In order to solve this problem, an unsupervised learning approach is proposed in order to design a predictive maintenance module. Reliable and significant data are acquired through an ad hoc data acquisition and storage system, based on Industry 4.0 principles. Data analysis represented a first crucial step for the exploitation of unsupervised learning methods. Three different operating conditions were taken into account, associated to the compressor's oil degradation. Satisfactory results in terms of accuracy were obtained in the tests of the developed module on real data.

关键词： twin screw air compressors predictive maintenance Maintenance 4.0 Industry 4.0 unsupervised learning k-means clustering algorithm

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Machine Learning Techniques with Probability Vector for Cooperative Spectrum Sensing in Cognitive Radio Networks

Machine Learning Techniques with Probability Vector for Coop...

引用

IEEE Wireless Communications and Networking Conference (WCNC)

作者： Lu, Yingqi Zhu, Pai Wang, Donglin Fattouche, Michel Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada New York Inst Technol Dept Elect & Comp Engn Old Westbury NY 10023 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781467398145

We study cooperative spectrum sensing in cognitive radio networks (CRN) using machine learning techniques in this paper. A low-dimensional probability vector is proposed as the feature vector for machine learning based classification, instead of the N-dimensional energy vector in a CRN with a single primary user (PU) and N secondary users (SUs). This proposed method down-converts a high-dimensional feature vector to a constant two-dimensional feature vector for machine learning techniques while keeping the same spectrum sensing performance if not better. Due to its lower dimension, the probability vector based classification is capable of having a smaller training duration and a shorter classification time for testing vectors.

关键词： Cognitive radio network (CRN) Cooperative spectrum sensing Probability vector Energy vector k-means clustering algorithm Support vector machine (SVM)

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Collective Assessment of Arguments in an Online Intelligent Argumentation System for Collaborative Decision Support

Collective Assessment of Arguments in an Online Intelligent ...

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International Conference on Collaboration Technologies and Systems (CTS)

作者： Arvapally, Ravi Santosh Liu, Xiaoqing (Frank) Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

ISBN: (纸本)9781467364041;9781467364034

We developed an intelligent argumentation system for collaborative decision support which allows stakeholders to exchange arguments, captures their rationale, and assesses aggregate thoughts on solution alternatives [1, 21]. In this article we propose and evaluate a method for computing aggregate thoughts of arguments on individual arguments, which represents individual thoughts, in an argumentation tree and further classifying arguments based on relationships between its aggregate thoughts and individual thoughts. A novel approach is developed to derive collective determination of an argument based on total support and attack that an argument receives in the argumentation tree. The collective determination value and the strength of an argument are used to analyze the relationship between aggregate thought and individual thought of arguments and cluster the arguments. The decision maker will be able to review clusters of arguments with opposing collective thoughts and supporting collective thoughts versus their individual thoughts. We have carried out an experiment to evaluate the proposed method, and the experimental results show that the proposed method is effective.

关键词： Collaborative decision support argumentation fuzzy logic aggregate thoughts individual thoughts k-means clustering algorithm intelligent argumenation system

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Offering Strategy for a Photovoltaic Power Plant in Electricity Market

Offering Strategy for a Photovoltaic Power Plant in Electric...

引用

IEEE Power and Energy Conference at Illinois (PECI)

作者： Saranya, A. Swarup, k. Shanti Indian Inst Technol Madras Elect Engn Dept Chennai Tamil Nadu India

ISBN: (纸本)9781538641163

With the large-scale manufacturing and production, the per-unit cost of energy from photovoltaic modules have decreased, and the incentives given by the governments are slowly withdrawn. Consequently, the photovoltaic power plants are able to, and asked to, participate in the electricity markets to trade its power competing with conventional generators. This work contributes to the offering strategies for photovoltaic power plants in the day-ahead electricity market with balancing market. The uncertain variables considered are day-ahead electricity prices and actual solar power production for which scenarios are created from historical data. The photovoltaic power plant is modeled as a price taker and the offering problem is solved as a linear programming problem with stochastic optimization.

关键词： Offering strategy price taker electricity market photovoltaic power plant k-means clustering algorithm stochastic optimization linear programming

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Image Segmentation algorithm for Disease Detection of Wheat Leaves

Image Segmentation Algorithm for Disease Detection of Wheat ...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Niu, Xiaojing Guo, Shihui Wang, Meili Zhang, Hongming Chen, Xianqiang He, Dongjian Northwest A&F Univ Coll Informat Engn Yangling Shaanxi Peoples R China Bournemouth Univ Media Sch Natl Ctr Comp Animat Bournemouth Dorset England Northwest A&F Univ Mech & Elect Engn Yangling Shaanxi Peoples R China

ISBN: (纸本)9781479963812

Wheat diseases are harmful to wheat production, but there are few segmentation algorithms that can effectively identify common diseases of wheat leaves. This paper proposes an automatic and efficient solution with k-means clustering. Firstly, the colour image is transformed to Lab colour space from RGB. clustering is then done by taking the absolute difference between each pixel and the clustering centre in Lab colour space. Unlike traditional methods, our method does not need manual setting for threshold value and is not affected by the selected channel. Our results show that the segmentation accuracy rates for three common diseases (powdery mildew, leaf rust and stripe rust) is more than 90%, which proves the efficacy of our method.

关键词： wheat disease k-means clustering algorithm segmentation algorithm

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Estimation of Types of States in Partial Observable Network Systems 7

Estimation of Types of States in Partial Observable Network ...

引用

IEEE International Congress on Big Data (IEEE BigData) Part of the IEEE World Congress on Services

作者： Guha, Sayantan Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA

ISBN: (纸本)9781538672327

Estimating of types of states in network systems is essential for protecting network infrastructures from cyberattacks, managing network traffic, and detecting changes in network systems. It is very difficult to estimate the types of states in network systems due to their high complexity. The accuracy of the estimating the states in network systems depends heavily on the completeness of the collected sensor information. But the state of a network system at a given point in time may be never fully known due to noisy sensors;making more difficult to estimate the entire true state of a network system because certain features of the input data may be missing. In order to estimate the states in a network system in partially observable environments, an approach to estimating the types of states in partially observable cyber systems is presented. This approach involves the use of a convolutional neural network (CNN), and unsupervised learning with elbow method and k-means clustering algorithm.

关键词： Traffic management cyber-attacks noisy sensor partially observable convolutional neural network unsupervised learning elbow method k-means clustering algorithm

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Evaluation index system and methodology for actively responding to ageing population

Evaluation index system and methodology for actively respond...

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IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Yang, Sijing Li, Aihua Li, Guijun Cent Univ Finance & Econ Sch Management Sci & Engn Beijing Peoples R China

ISBN: (纸本)9781450391870

Ageing population is a challenge faced by the global community. According to international standards for measuring ageing society, China has entered the ageing society since 2000. Currently, actively responding to ageing population has become one of China's national strategies. Increasingly, actively responding to ageing population is gaining greater attention, but there lacks a comprehensive evaluation index system and model. Based on China's real situations, we scientifically and comprehensively construct an evaluation index system for actively responding to ageing population, using source statistical surveys, social tracking surveys and other multi-source heterogeneous data. Best-Worst Method (BWM) and k-means clustering algorithm are used here to evaluate ageing population measuring nationwide with a scientific and comprehensive index system. This paper also analyses and evaluates the measurement taken by 31 districts in China, thus carrying both theoretical and practical implications. With the emergence of big data for government administration, the rigor of the evaluation outcome will enhance.

关键词： Actively responding to ageing population BWM k-means clustering algorithm

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A Study of Spore Image Classification Based on Feature Extraction

A Study of Spore Image Classification Based on Feature Extra...

引用

International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Yue, Lulu Yang, Ming Peng, Li Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China

ISBN: (纸本)9783038351153

Fungal diseases are the major diseases of agricultural production, and have brought tremendous impact to it. Identification of spore morphology plays an important role in the identification of fungi. This paper uses the microscopy images of two kinds of fungal spore and utilizes the technology of image analysis and recognition to classify them. We firstly get the underlying feature descriptors of these two kinds of microscopy images by RGB SIFT (Scale Invariant Feature Transform), then create the visual word dictionary using k-means clustering algorithm, at finally we use LDA, kNN and SVM to classify these two kinds of images. The results indicate that the classification of spore image based on feature extraction is feasible. In our future work, we will conduct the classification of related species and highly similar spore images.

关键词： RGB SIFT (Scale Invariant Feature Transform) k-means clustering algorithm Latent Dirichlet Allocation k Nearest Neighbor algorithm Support Vector Machine

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The Improved YOLOV5 algorithm and Its Application in Small Target Detection 15th

The Improved YOLOV5 Algorithm and Its Application in Small T...

引用

15th International Conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

作者： Zhao, Yu Shi, Yuanbo Wang, Zelong Liaoning Petrochem Univ Fushun 113000 Peoples R China

ISBN: (纸本)9783031138416;9783031138409

According to the characteristics of YOLOV5, a method based on YOLOV5 is proposed. First, this method is used for the identification of small objects. Secondly, this paper conducts experiments on the improved YOLOV5 algorithm and conducts experiments on it, and compares the performance of the two methods. In modern detection technology, the most commonly used is the target detection technology. In recent years, small object detection has been an important research topic in industrial inspection. The small target has a small number of pixels, and its features are blurred. Larger targets have lower detection rates and higher false detection rates. In order to improve the accuracy and accuracy of target detection, this paper proposes a method based on depthwise convolution and k-means clustering, which realizes the clustering of the dimension and aspect ratio of the target image. On the basis of CCPD, the YOLOv5 algorithm is optimized and compared with the original YOLOv5 algorithm. Experiments show that the improved YOLOv5 can better detect smaller targets, so that the recall rate and average precision of small targets are significantly improved.

关键词： Machine vision Small object detection YOLOv5 CCPD dataset k-means clustering algorithm Mosaic

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Performance Enhancement in Image Edge Detection Technique

Performance Enhancement in Image Edge Detection Technique

引用

International Conference on Signal and Information Processing (IConSIP)

作者： Patel, Anil Patel, Amrut Ganpat Univ UVPCE Elect & Commun Dept Mehsana India

ISBN: (纸本)9781509015221

Edges are instant change in intensity value of pixels in the digital image. Image edge detection converts original image in to binary image. This binary image contains information about edges of digital image. Recently Ant Colony Optimization Meta-heuristic algorithm is used for the image edge detection. Ant colony optimization algorithm is inspired from the behaviour of real ants. From this ACO based algorithm we get pheromone matrix at end, which contains the information about edge pixels of original image. Discrete wavelet transform is used to decompose original image in for sub-bands. ACO algorithm is separately applied on that four sub-bands and reconstructed image of these four components give better result than single ACO edge detected image. Dual tree complex wavelet transform is advance than discrete wavelet transform. Problems in DWT like overemphasized, distortion and less visible image are removed using DT-CWT with ACO algorithm. It has higher orientation property. k-means clustering algorithm and Fuzzy C-means algorithms are used to decide whether pixels in image are edge or not. So that proposed algorithms which are ACO + DT-CWT with k-means clustering algorithm and Fuzzy C-means algorithm, they are able to find all possible edges from image and give better result compared to other edge detection algorithms.

关键词： Edge Detection Ant Colony Optimization Direction Controlled Ant Colony Optimization Discrete Wavelet Transform Dual Tree Complex Wavelet Transform Fuzzy Cmean clustering algorithm k-means clustering algorithm kappa Hausdorff Distance

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