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检索条件"任意字段=5th International Conference on Pattern Recognition in Bioinformatics"

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Time Series Prediction and pattern recognition of Fault Decomposition Gas for High Voltage Composite Electrical Appliances Based on Grey System OBGM (1,N) Model 5

Time Series Prediction and Pattern Recognition of Fault Deco...

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5th IEEE international conference on Computer and Communications, ICCC 2019

作者： Shiling, Zhang Jianchao, Wang State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute Chongqing401123 China

ISBN: (纸本)9781728147437

the GIL/GIS is widely used in the power system. the application of grey system to forecast and analyze the time series of gas decomposition and fault pattern recognition has good engineering value for its operation condition monitoring. Based on the programming of MATLAB software environment, time series prediction model based on grey system OBGM (1,N) is realized. Based on model, grey system time series analysis algorithm is applied to predict the time-varying sequence of the volume fraction of SF6 gas decomposition products and the characteristics of the partial discharge Atlas of the high-voltage combined electrical equipment GIL/GIS in fault state. Parametric time series, grey system is further applied to data mining and analysis of association rules of time series state change, and the time series set is applied to cluster analysis of typical fault types of high-voltage GIL/GIS power equipment. the grey time series prediction and grey relational clustering analysis of small sample test data can be carried out by using the grey system to analyze large data of high voltage GIL/GIS power equipment condition assessment. the multi-dimensional and the grey system information fusion technology proposed in this paper is especially suitable for the application of the small sample and the poor data in the high-voltage GIL/GIS power equipment operation condition detection technology. the time series prediction and the pattern recognition technology of the fault decomposition gas based on the grey system OBGM (1, N) model proposed can provide some technical support for the operation and maintenance of high voltage combinations. the characteristic parameters of partial discharge pattern and grey relational clustering analysis have the good engineering and the theoretical value. © 2019 IEEE.

关键词： Cluster analysis

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on Image Analysis and Processing, ICIAP 2019

2nd International Workshop on Recent Advances in Digital Sec...

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on Image Analysis and Processing, ICIAP 2019

ISBN: (纸本)9783030307530

the proceedings contain 39 papers. the special focus in this conference is on Recent Advances in Digital Security. the topics include: Modern vs Diplomatic Transcripts for Historical Handwritten Text recognition;improving Ancient Cham Glyph recognition from Cham Inscription Images Using Data Augmentation and Transfer Learning;Oracle Bone Inscription Detector Based on SSD;shot Boundary Detection for Automatic Video Analysis of Historical Films;the Epistle to Cangrande through the Lens of Computational Authorship Verification;a Cockpit of Measures for Image Quality Assessment in Digital Film Restoration;augmented Reality for the Valorization and Communication of Ruined Architecture;classification of Arabic Poems: from the 5th to the 15th Century;a Page-Based Reject Option for Writer Identification in Medieval Books;on the Cross-Finger Similarity of Vein patterns;minimizing Training Data for Reliable Writer Identification in Medieval Manuscripts;fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning;slide Screening of Metastases in Lymph Nodes via Conditional, Fully Convolutional Segmentation;A Learning Approach for Informative-Frame Selection in US Rheumatology Images;a Serious Game to Support Decision Making in Medical Education;nerve Contour Tracking for Ultrasound-Guided Regional Anesthesia;Skin Lesions Classification: A Radiomics Approach with Deep CNN;semantic 3D Object Maps for Everyday Robotic Retail Inspection;collecting Retail Data Using a Deep Learning Identification Experience;a Large Scale Trajectory Dataset for Shopper Behaviour Understanding;Improving Multi-scale Face recognition Using VGGFace2;An IOT Edge-Fog-Cloud Architecture for Vision Based Pallet Integrity;the Vending Shopper Science Lab: Deep Learning for Consumer Research.

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Emotion Detection of Autistic Children Using Image Processing 5

Emotion Detection of Autistic Children Using Image Processin...

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5th international conference on Image Information Processing (ICIIP)

作者： Rani, Pooja Cent Univ Punjab Dept Comp Sci & Technol Bathinda Punjab India

ISBN: (纸本)9781728108995

Facial Emotion Detection is an approach towards detecting human emotions through facial expressions. Autism Spectrum Disorder is an advance neurobehavioral disorder. Autistic people have repetitive, rude behavior. they are not ready to do social communication. People with this syndrome have problems with emotion recognition. this paper works on detecting the emotions of autistic children from the expression of their faces. this paper works on four emotions. these emotions are sad, happy, neutral, and angry. To detect the emotion of autistic children is performed with image processing and machine learning algorithms. the features are extracted from the faces of autistic children with local binary pattern. Machine learning algorithms are used for classification of emotions. Machine learning classifiers used in classification process are support vector machine and neural network.

关键词： Machine Learning Support Vector Machine Neural Network Emotion Detection Image Processing Local Binary pattern

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ICCAI 2019 - 2019 5th international conference on Computing and Artificial Intelligence

ICCAI 2019 - 2019 5th International Conference on Computing ...

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5th international conference on Computing and Artificial Intelligence, ICCAI 2019

ISBN: (纸本)9781450361064

the proceedings contain 46 papers. the topics discussed include: popularity prediction for artists based on user songs dataset;assessing CSU students’ academic performance on iLearn portal using data analytics;characterization of disaster related tweets according to its urgency: a pattern recognition;using big data analysis to retain customers for telecom industry;methods of neural network analysis of oilfield data;a novel NLP application to automatically generate text extraction concepts from textual descriptions;algorithm of recurring concept drift base on main feature extraction;virtual public cloud model in honeypot for data security: a new technique;a hybrid similarity measure based on binary and decimal data for data mining;proposing automatic dataset generation system to support android sensitive data leakage detection;and an investigation of the situation of the using handheld devices on learning mathematics of high school teachers in mainland China.

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Characterization of Disaster Related Tweets According to Its Urgency: A pattern recognition 19

Characterization of Disaster Related Tweets According to Its...

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5th international conference on Computing and Artificial Intelligence (ICCAI)

作者： Acosta, Michael E. Palaoag, thelma D. Pangasinan State Univ Informat Technol Dept Urdaneta City Philippines Univ Cordilleras Dept Comp Sci Baguio Philippines

ISBN: (纸本)9781450361064

With the pervasive utilization of social media platforms such as microblogging site eg. Twitter to express and share information, it has also become a very useful and helpful tool in times of evolving crisis situations. Extracting interesting and meaningful patterns in the context of disaster is very helpful to determine relationships among them. Association rule mining aims to discover frequent patterns, relationships among set of items in the database. this paper describes text mining technique for extracting association rules from disaster related tweets. It sought to characterize disaster related tweets in terms of urgency, whether they are carrying information that requires immediate attention or not. they were harvested from Twitter using Rapid Miner and from existing collection of tweets. We employed association rules using the FP-Growth algorithm for discovering significant and interesting patterns on disaster related tweets for the emergency responders to determine behavior of users in times of mass emergencies. We used support and confidence as statistical measure to observe the usefulness of the association rules. Based on the result, we discover meaningful patterns of urgent tweets, however, for the not urgent Twitter posts, we only discovered 4 interesting patterns.

关键词： Association rule mining text mining association rules FP-Growth algorithm

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A Deep Convolutional Neural Network for classifying waste containers as full or not full 5

A Deep Convolutional Neural Network for classifying waste co...

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5th IEEE Annual international Smart Cities conference (ISC2)

作者： Fonseca, Bania J. Ali, Felermino D. M. A. Saide, Saide M. Lurio Univ Dept Informat Engn Pemba Mozambique

ISBN: (纸本)9781728108469

there is a common understanding that cleanliness is somehow proportional to the economic development of a country. thus, in order to become clean, a country needs to have an efficient garbage monitoring system. One important component of such a system is garbage collection time because if we delay emptying the bins, the trash ends up to putting public health at risk. this paper is about creating a Deep Convolutional Neural Networks (DCNNs) based model for classifying a waste container as full or not, so that can be later on used by real-time garbage monitoring systems to process images acquired by cameras installed nearby the trash bins or smartphones. To achieve this, we trained and tested different well-known DCNNs architectures, namely, ResNet34, ResNet50, Inception_v4 and DarkNet53. the models were trained and tested using Repeated K-Fold Cross-Validation, running 5-Fold Cross-Validation 6 times. the results have showed that Inception_v4 outperformed the other models, with near- perfect results: PR-AUC = 0.994, F1 = 0.988, Precision = 0.989, Recall = 0.987 and ACC = 0.987. With these results can be said: a high Precision DCNNs based model was built.

关键词： Deep Learning Image Classification pattern recognition Artificial Neural Networks

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Facial expression recognition by considering nonuniform local binary patterns 5th

Facial expression recognition by considering nonuniform loca...

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5th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2018

作者： Srinivasa Reddy, K. Sunil Reddy, E. Baswanth, N. Information Technology Institute of Aeronautical Engineering HyderabadTelangna India

ISBN: (纸本)9789811360008

Recognizing a face with an expression has paying attention due to its well-known applications in a broad range of fields like data-driven animation, human–machine interaction, robotics, and driver fatigue detection. People can vary significantly their facial expression;hence, facial expression recognition is not an easy problem. this paper presents a significant contribution for facial expression recognition by deriving a new set of stable transitions of local binary pattern by selecting the significant nonuniform local binary patterns. the proposed patterns are stable, because of the transitions from two or more consecutive ones to two or more consecutive zeros. For better recognition rate, the new set of patterns are combined with uniform patterns of local binary pattern. A distance function is used on proposed texture features for effective facial expression recognition. Preprocessing method is also used to get rid of the effects of illumination changes in facial expression by preserving the significant appearance details that are needed for facial expression recognition. the investigational analysis was done on the popular JAFFE facial expression database and has shown good performance. © Springer Nature Singapore Pte Ltd 2019.

关键词： Local binary pattern

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5th international conference on Artificial Intelligence and Security, ICAIS 2019

5th International Conference on Artificial Intelligence and ...

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5th international conference on Artificial Intelligence and Security, ICAIS 2019

ISBN: (纸本)9783030242701

the proceedings contain 229 papers. the special focus in this conference is on Artificial Intelligence and Security. the topics include: Design of an IoT-based efficient security scheme in home wireless system;discussion on the application of industrial internet;Performance analysis of a NB-IoT based smart lamp solution with application enabled platform;mining hypernym-hyponym relations from social tags via tag embedding;Equipment fault detection based on SENCForest;Accurate and fast DV-hop localization algorithm in irregular networks;shape recognition with recurrent neural network;WSN multi-hop routing algorithm based on path quality comparison in new energy consumption distance node relation model;tobacco intelligent management system based on internet of things;research on fault self diagnosis technology of agricultural internet of things information collection system;Analysis of antenna array parameter effect for massive MIMO transmission;intelligent irrigation system of balcony flowers based on internet of things;the impact of using facebook on the academic performance of university students;Robust audio watermarking algorithm based on moving average and DCT;computer evidence analysis technology based on weighted frequent pattern growth algorithm;research on telemedical monitoring system based on homomorphic encryption;Reversible data hiding in JPEG images based on multiple histograms modification;Image hashing based on CS-LBP and DCT for copy detection;KM3SVM: A efficient min-max modular support vector machine based on clustering;An image forensic method for AI inpainting using faster R-CNN;kernel generalized canonical correlation and a new feature fusion strategy;multiple obstacle detection for assistance driver system using deep neural networks;preface.

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Fault pattern recognition in Power Distribution Integrated Network with Renewable Energy Source

Fault Pattern Recognition in Power Distribution Integrated N...

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international conference on Renewable Energies for Developing Countries (REDEC)

作者： K. Moloi Y. Hamam J. A. Jordaan Dept. of Electrical Engineering Tshwane University of Technology eMalahleni South Africa

ISBN: (数字)9781728155951

ISBN: (纸本)9781728155968

the challenge with most developing countries is to maintain a reliable and sustainable electricity supply. this has a depleting effect on the economic development of most states. In order to reduce the impact of energy shortages, there has been an extensive attempt to use renewable energy sources to generate electricity. there are however technical challenges of integrating the existing power system grid with the renewable external sources. these challenges include adequate power system protection, energy security, and reliability of external sources. In this paper, we investigate the fault pattern recognition and detection in a power distribution grid integrated with the wind energy source. A reduced Eskom 22kV and wind power energy source integrated is modeled using MATLAB/Simulink. From the integrated model, various types of power systems faults are generated. We further investigated the use of local polynomial approximation (LPA) for signal decomposition and support vector machine (SVM) for fault classification and detection. We also tested the performance of the naive Bayes classifier. In this paper, a hybrid technique based on LPA and SVM is proposed for fault pattern recognition and detection in a power distribution integrated system with the wind energy source. the proposed method was further tested using machine learning platforms WEKA and Orange. the results of the classifiers gave the accuracy of between 98 and 99 %.

关键词： Support vector machines Delays Circuit faults Feature extraction pattern recognition Power system reliability Reliability

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6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 and 4th international Workshop on Visual Performance Analysis, VPA 2017 and Workshop on Extreme-Scale Programming Tools, ESPT 2018 and 5th international Workshop on Visual Performance Analysis, VPA 2018

6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 a...

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6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 and 4th international Workshop on Visual Performance Analysis, VPA 2017 and Workshop on Extreme-Scale Programming Tools, ESPT 2018 and 5th international Workshop on Visual Performance Analysis, VPA 2018

ISBN: (纸本)9783030178710

the proceedings contain 17 papers. the special focus in this conference is on Extreme-Scale Programming Tools. the topics include: Gotcha: An Function-Wrapping Interface for HPC Tools;Projecting Performance Data over Simulation Geometry Using SOSflow and ALPINE;Visualizing, Measuring, and Tuning Adaptive MPI Parameters;visual Analytics Challenges in Analyzing Calling Context Trees;paScal Viewer: A Tool for the Visualization of Parallel Scalability Trends;using Deep Learning for Automated Communication pattern Characterization: Little Steps and Big Challenges;visualizing Multidimensional Health Status of Data Centers;generic Library Interception for Improved Performance Measurement and Insight;improved Accuracy for Automated Communication pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning;Moya—A JIT Compiler for HPC;polyhedral Optimization of TensorFlow Computation Graphs;CAASCADE: A System for Static Analysis of HPC Software Application Portfolios;visual Comparison of Trace Files in Vampir;understanding the Scalability of Molecular Simulation Using Empirical Performance Modeling;Advanced Event-Sampling Support for PAPI.

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