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Wind Power Icing Fault Diagnosis Based on Slow Feature Analysis and Support Vector Machines

Wind Power Icing Fault Diagnosis Based on Slow Feature Analy...

引用

IEEE international conference on Power and Energy Systems (ICPES)

作者： Peng Jia Guangyu Chen School of Electric Power Engineering Nanjing Institute of Technology Nanjing China

ISBN: (数字)9781665404945

ISBN: (纸本)9781665404952

Blade icing is a worldwide problem in the field of wind power. A lot of prior knowledge is needed to detect blade icing by using mechanism modeling method, which cannot be satisfied in most cases. With the development of artificial intelligence (AI) technology, data-driven approaches have attracted widespread attention. In this paper, a combined strategy of slow feature analysis (SFA) and support vector machine (SVM) is proposed to detect the icing fault of wind blades. It is significant to improve the reliability and economy of wind turbines. First, SFA is used to extract the features that change slowly in the process. After that, the extracted slow features are input into SVM for fault detection. The lowest error rate of the proposed method is 0.234 when the number of show features is reduced to 5 while it is 0.317 by directly putting into SVM, which is verified by wind power experiments. So it is crucial to select the number of slow features.

关键词： Support vector machines Blades Computational modeling Wind power generation Feature extraction data models Classification algorithms

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Monitoring of Exascale data processing

Monitoring of Exascale data processing

引用

2019 IEEE international conference on Advanced Scientific Computing, ICASC 2019

作者： Iuhasz, Gabriel Petcu, Dana West University of Timisoara Institut e-Austria Timisoara Timisoara Romania

ISBN: (纸本)9781728144542

Exascale systems are a hot topic of research in computer science. These systems in contrast to current Cloud, Big data and HPC systems will routinely contain hundreds of thousand of nodes generating millions of events. At this scale of hardware fault and anomalous behaviour is not only more likely but to be *** this paper we describe the architecture of and Exascale monitoring solution coupled with an event detection component. The latter component is extremely important in order to handle the multitude of potential events. We describe the major lacking research that needs to be done, which will make event detection freezable in real world Exascale systems. © 2019 IEEE.

关键词： Anomaly detection

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Cleavage self : a new concept in reproduction stage of genetic algorithm for rainfall prediction

Cleavage self : a new concept in reproduction stage of genet...

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international conference on Science in Information Technology (ICSITech)

作者： Arief Bramanto Wicaksono Putra Anggri Sartika Wiguna Achmad Fanany Onnilita Gaffar Rheo Malani Politeknik Negeri Samarinda East Kalimantan Indonesia Kanjuruhan Univerisy-Malang East Java Indonesia

ISBN: (数字)9781728173498

ISBN: (纸本)9781728173504

Rainfall prediction which considering climate variables such as air temperature, air humidity, air pressure, and wind speed are categorized to a non-stationary stochastic processes. Modeling of time series data of precipitation is carried out using MISO ARX model. Genetic Algorithm (GA) is used to optimize the entire model coefficients so that the results obtained is quite accurate. A new concept in the reproductive stage called Cleavage Self (CS) is designed to improve performance of GA in terms of speed and accuracy of the prediction process. In order to prove its performance, prediction of time series precipitation is also conducted by applying AG without CS. The result is the speed of the process AG with CS is approximately 9.6 times faster than that without CS. The absolute differences of the MSE ideal for AG with CS is 0.0004 compare to 0.0151 for AG without CS.

关键词： Temperature distribution Atmospheric modeling Wind speed Time series analysis Humidity data models Genetic algorithms

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Sketch-BERT: Learning Sketch Bidirectional Encoder Representation From Transformers by Self-Supervised Learning of Sketch Gestalt

Sketch-BERT: Learning Sketch Bidirectional Encoder Represent...

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hangyu Lin Yanwei Fu Xiangyang Xue Yu-Gang Jiang School of Data Science Fudan University School of Data Science MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Fudan University School of Computer Science Fudan University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Previous researches of sketches often considered sketches in pixel format and leveraged CNN based models in the sketch understanding. Fundamentally, a sketch is stored as a sequence of data points, a vector format representation, rather than the photo-realistic image of pixels. SketchRNN studied a generative neural representation for sketches of vector format by Long Short Term Memory networks (LSTM). Unfortunately, the representation learned by SketchRNN is primarily for the generation tasks, rather than the other tasks of recognition and retrieval of sketches. To this end and inspired by the recent BERT model, we present a model of learning Sketch Bidirectional Encoder Representation from Transformer (Sketch-BERT). We generalize BERT to sketch domain, with the novel proposed components and pre-training algorithms, including the newly designed sketch embedding networks, and the self-supervised learning of sketch gestalt. Particularly, towards the pre-training task, we present a novel Sketch Gestalt Model (SGM) to help train the Sketch-BERT. Experimentally, we show that the learned representation of Sketch-BERT can help and improve the performance of the downstream tasks of sketch recognition, sketch retrieval, and sketch gestalt.

关键词： Task analysis Bit error rate Shape Natural language processing Supervised learning data science Two dimensional displays

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Foreground Detection Algorithm via Fusing Confidence by Weight and Visual Attention

Foreground Detection Algorithm via Fusing Confidence by Weig...

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Internet of Things and Intelligent Applications (ITIA), international conference on

作者： Cheng Keyang Wu Jinxia Liu Qing Sun Shuang School of Computer Science and Telecommunications Engineering Jiangsu University Zhenjiang China National Engineering Laboratory for Public Safety Risk Perception Control by Big Data Zhenjiang China

ISBN: (数字)9781728193014

ISBN: (纸本)9781728193021

In the foreground detection of video, the result of pixel-level background subtraction is clear and flexible. However, the pixel-level classification method based on sample consistency can't make full use of the pixel information effectively and get full foreground mask when meeting the complex situation of color camouflage and static object. In order to solve this problem effectively, we proposed an algorithm based on fusing confidences with weight and visual attention. Firstly, the foreground is determined by the sum of fusing with color confidence and texture confidence based on weight, then the confidence and weight of the samples are updated adaptively; the static foreground is determined by constructing the visual attention mechanism in the subsequence, and finally the background model is dynamically updated with the strategy of updating the minimum confidence samples. The method proposed in this paper is tested on the open dataset. It is found that the proposed algorithm is superior to the contrast algorithms, and get the accuracy and recall rate of foreground detection in the complex situation of color camouflage and still objects, and get better detection effects.

关键词： Image color analysis Adaptation models Visualization Color Detection algorithms Telecommunications Computer science

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Generating Chinese Poetry from Images via Concrete and Abstract Information

Generating Chinese Poetry from Images via Concrete and Abstr...

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international Joint conference on Neural Networks (IJCNN)

作者： Yusen Liu Dayiheng Liu Jiancheng Lv Yongsheng Sang Sichuan University Chengdu China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In recent years, the automatic generation of classical Chinese poetry has made great progress. Besides focusing on improving the quality of the generated poetry, there is a new topic about generating poetry from an image. However, the existing methods for this topic still have the problem of topic drift and semantic inconsistency, and the image-poem pairs dataset is hard to be built when training these models. In this paper, we extract and integrate the Concrete and Abstract information from images to address those issues. We proposed an infilling-based Chinese poetry generation model which can infill the Concrete keywords into each line of poems in an explicit way, and an abstract information embedding to integrate the Abstract information into generated poems. In addition, we use non-parallel data during training and construct separate image datasets and poem datasets to train the different components in our framework. Both automatic and human evaluation results show that our approach can generate poems which have better consistency with images without losing the quality.

关键词： data mining Decoding Semantics Task analysis Training Concrete Genetic algorithms

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Video-Based Gender Profiling on Challenging Camera Viewpoint for Restaurant data Analytics

Video-Based Gender Profiling on Challenging Camera Viewpoint...

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international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Jefferson James Keh Meygen Cruz Ramiel Deticio Carl Vincent Tan John Anthony Jose Elmer Dadios Alexis Fillone De La Salle University-Manila Manila Metro Manila De La Salle University-Manila Manila Philippines

ISBN: (纸本)9781665429979

The customer gender composition is an important consideration for certain business decisions. This study highlights the feasibility of implementing a vision-based gender classification feature on top of a pre-existing people counting system that makes use of a CCTV camera in a retail establishment. The main problem lies in doing so using the fixed angled viewpoint of the restaurant's camera, which represents a practical use case as CCTV systems are often geared for security purposes instead of data analytics. Thus, these existing camera configurations are not ideal for existing computer vision models. A two-step neural networks-based approach, namely head detection via YOLOv2 and gender classification via Inception V3, is used to integrate this feature into the pre-existing system. The researchers partnered with a restaurant in a business district to obtain footage and test the system. The proponents were able to attain the target accuracy of 80%, albeit the feature's speed and accuracy can be further improved through the exploration of other object classification algorithms and through the use of downsizing and down sampling.

关键词： Training data analysis Biometrics (access control) Detectors Cameras Security Face detection

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Evaluation of transfer learning techniques for classifying small surgical dataset

Evaluation of transfer learning techniques for classifying s...

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international conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Shweta Bali S S Tyagi Dept. of Computer Science Engineering (CSE) FET Manav Rachna International Institute of Research and Studies (MRIIRS) Faridabad India

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

Deep learning is the key technology used in a large variety of applications such as self-driving cars, image recognition, automatic machine translation, automatic handwriting generation. The success was fueled due to accessibility of huge datasets, GPUs, max pooling. Earlier machine learning techniques employed two phases: features extraction and classification. The performance of such algorithms was highly dependent on how well the features are extracted and that was the major bottleneck of these techniques. Deep learning techniques employ Convolutional Neural Networks (CNNs) with numerous layers of non-linear processing for extracting the features automatically and classification that solves the previous problem. In the real time applications most of the time, either the dataset is unavailable or has less amount of data which makes it difficult to achieve accurate results for classifying the images. CNNs are hard to be trained using the small datasets. Transfer learning has emerged as a very powerful technique where in the knowledge gained from the larger dataset is transferred to the new dataset. data augmentation and dropout are also powerful techniques that are useful for dealing with small datasets. In this paper, different techniques using the VGG16 pretrained model are compared on the small dataset.

关键词： Feature extraction Machine learning Convolutional neural networks conferences Cloud computing data science Image classification

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Intelligent Evaluation Model of Precision Mobile Network Software Evaluation Accuracy

Intelligent Evaluation Model of Precision Mobile Network Sof...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Biao Wan Chunmei Xu Wuhan Polytechnic Wuhan China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

The existing intelligent evaluation models of the evaluation accuracy of mobile network software all have some disadvantages, such as poor evaluation accuracy and large evaluation error. Therefore, the intelligent evaluation model of fine mobile network software evaluation accuracy is put forward to study on the intelligent evaluation model of the evaluation accuracy of mobile network software. The data acquisition card is used to collect the evaluation data of fine mobile network software, and it is pre-processed by data checking and data cleaning. Based on the pre-processed data, Bootstrap sampling method is used to obtain the evaluation data. Based on the accurate evaluation data of fine mobile network software, this paper uses integrated learning algorithm to intelligently evaluate the accuracy of fine mobile network software evaluation. The intelligent evaluation of the accuracy of fine mobile network software evaluation is realized. The experimental results show that compared with the existing intelligent evaluation model for the accuracy of fine mobile network software, the intelligent evaluation model for the accuracy of fine mobile network software improves the evaluation accuracy and reduces the evaluation error. It fully shows that the intelligent evaluation model of fine mobile network software evaluation accuracy has better evaluation performance

关键词： conferences Software algorithms data acquisition Interference Sampling methods Software Information systems

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Using BLE beacons and Machine Learning for Personalized Customer Experience in Smart Cafés

Using BLE beacons and Machine Learning for Personalized Cust...

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international conference on Electronics, Information and Communications (ICEIC)

作者： Imran A. Zualkernan Michel Pasquier Sakib Shahriar Mohammed Towheed Shilpa Sujith American University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9781728162898

ISBN: (纸本)9781728162904

Despite recent advances in technology, providing personalized experiences to customers in physical environments like retail shops, restaurants and cafes remains challenging. This paper proposes a pervasive environment that utilizes Bluetooth Low Energy (BLE) beacons in conjunction with unsupervised machine learning to personalize a customer's visit to a coffee shop. Some key aspects of the proposed solution include personalized content delivery, providing customers with an automatic table reservation based on their preferences, using a barista interface to provide personalized interaction with customers, and allowing customers to monitor real-time coffee shop conditions. An architecture utilizing MQTT and an NSQL database was implemented. Historical traces of customer's physical behaviour acquired using beacons in the coffee shops were used to create clusters of similar customers. The best performing clustering algorithms were K-Medoids and Hierarchical clustering using the Optimal Matching (OM) distance resulting in a Purity of 0.910 and 0.941 respectively.

关键词： Bluetooth catering industry customer satisfaction customer services pattern clustering retail data processing ubiquitous computing unsupervised learning user experience user interfaces Cafes Restaurants business data processing customer services Bluetooth Unsupervised learning User Interface Device Component Ubiquitous computing Consumer Satisfaction Cluster Analysis Beacons Purchasing customers Personalizing Wheat

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