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Load pattern recognition and prediction based on DTW K-mediods clustering and markov model 3

Load pattern recognition and prediction based on DTW K-medio...

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3rd IEEE international conference on Energy Internet, ICEI 2019

作者： Shen, Shen Kaili Liu, Wei Zhang, Tao College of System Engineering National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781728114934

With the development of smart grid, the electricity consumption data of consumers are recorded through smart meters. Analyzing and modeling the power user data, exploring the behavior habits and characteristics of electricity use, is of great significance of demand side management, dynamic pricing, and improving energy efficiency. This study proposed DTW K-mediods clustering and Markov model for Load pattern recognition and load prediction. Firstly, K-mediods clustering and DTW-based similarity measure are used to get shape-based typical load pattern. Then, the load data of time series are coded as discrete state series based on clustering. Finally, Markov model is used to explore the rule of load mode transform, and make prediction of lode mode, which achieves good results in the test of open data sets. © 2019 IEEE.

关键词： pattern recognition

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Automatic CAD-RADS Scoring Using Deep Learning 23rd

Automatic CAD-RADS Scoring Using Deep Learning

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Denzinger, Felix Wels, Michael Breininger, Katharina Gülsün, Mehmet A. Schöbinger, Max André, Florian Buß, Sebastian Görich, Johannes Sühling, Michael Maier, Andreas Pattern Recognition Lab Universität Erlangen-Nürnberg Erlangen Germany Computed Tomography Siemens Healthcare GmbH Forchheim Germany Das Radiologische Zentrum - Radiology Center Sinsheim-Eberbach-Erbach-Walldorf-Heidelberg Heidelberg Germany

ISBN: (纸本)9783030597245

Coronary CT angiography (CCTA) has established its role as a non-invasive modality for the diagnosis of coronary artery disease (CAD). The CAD-Reporting and Data System (CAD-RADS) has been developed to standardize communication and aid in decision making based on CCTA findings. The CAD-RADS score is determined by manual assessment of all coronary vessels and the grading of lesions within the coronary artery tree. We propose a bottom-up approach for fully-automated prediction of this score using deep-learning operating on a segment-wise representation of the coronary arteries. The method relies solely on a prior fully-automated centerline extraction and segment labeling and predicts the segment-wise stenosis degree and the overall calcification grade as auxiliary tasks in a multi-task learning setup. We evaluate our approach on a data collection consisting of 2,867 patients. On the task of identifying patients with a CAD-RADS score indicating the need for further invasive investigation our approach reaches an area under curve (AUC) of 0.923 and an AUC of 0.914 for determining whether the patient suffers from CAD. This level of performance enables our approach to be used in a fully-automated screening setup or to assist diagnostic CCTA reading, especially due to its neural architecture design – which allows comprehensive predictions. © 2020, Springer Nature Switzerland AG.

关键词： Grading

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A robust matching algorithm for shape recognition based on geometric consistency

A robust matching algorithm for shape recognition based on g...

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IEEE international conference on Information Communication and Signal Processing (ICICSP)

作者： Wei Wang Jianhua Shi Bing Lei Jin Liu Jia He College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha China The troops of 66133 China

ISBN: (数字)9781728188232

ISBN: (纸本)9781728188249

Though there are many algorithms which devote to extracting the descriptors for the shapes, the correspondences between shapes established only by descriptor distance are not reliably. To address this issue, a new shape matching algorithm is proposed on the basis of the geometric consistency between shapes. Each shape pair is represented as a node in a graph, and the weight of each edge is computed while the descriptor R-histogram which represents the topological relationship between shapes is adopted. Then, the problem of descriptor matching can be formed as finding the principal cluster of the graph, which is solve by the Hungarian algorithm in this paper. Our proposed approach has been implemented and gives encouraging results under rotation, scaling, shearing and noise.

关键词： Shape Clustering algorithms Signal processing algorithms Shearing Image recognition pattern recognition Distortion

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Vocabulary trees with OSS detectors 1

Vocabulary trees with OSS detectors

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1st international conference on Pervasive Computing Advances and Applications, PerCAA 2019

作者： Vinay, A. Acharya, Harshith R. Singh, Harshita Satyanarayana, Vibha Murthy, K.N.B. Natarajan, S. Deptartment of Computer Science PES University Banashankari 3rd Stage Bangalore Karnataka560085 India Center for Pattern Recognition and Machine Intelligence PES University Banashankari 3rd Stage Bangalore Karnataka560085 India

In today’s diversified world, with every person having their own idiosyncratic identity, the field of face detection and recognition has received significant scope. Since birth, humans develop and harvest similar facial features, making their recognition a challenging problem. Thus, the development of faster, more accurate and robust algorithms is of prime importance. The most critical requirements in developing a reliable face recognition algorithm is a large database of facial images and a well-developed systematic procedure to evaluate the system. In this paper, we bring out an approach to implement quick facial recognition using the concept of vocabulary trees in accordance with ORB, SIFT and SURF detectors and compare their performance. The model is trained using the Grimace, Face95 and ORL databases. The experiments are conducted by employing various state-of-the-art clustering algorithms and the results are compared for accuracy. The results and comparisons obtained on the databases show that the ORB detector clustered using Spectral clustering algorithm to construct the Vocabulary Tree outperforms the other techniques. © 2019 The Authors. Published by Elsevier Ltd.

关键词： Clustering algorithms

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Research on fine-grained pattern recognition based on attention pattern-generated model 3

Research on fine-grained pattern recognition based on attent...

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3rd international Symposium on Resource Exploration and Environmental Science, REES 2019

作者： Zhou, Jiayin Feng, Kaiping Luo, Lihong School of Computer Science Guangdong University of Technology Guangzhou Guangdong510006 China School of Art and Design Guangdong University of Technology Guangzhou Guangdong510090 China

The major difficulty in classifying the type of fine-grained patterns is that the visual differences between subordinate types are probably very subtle, which, however, may be extremely big in the same kind. A fine-grained pattern recognition algorithm is put forward based on the attention pattern-generated model in this article. First, extract high dimensional characteristic pattern from the image via CNN. Then apply several separators to outputting category predictive activation volumes on the characteristic mapping. After that, polymerize these activation volumes to obtain attention mapping. As for the fine-grained recognition, we use the generated attention pattern to tailor and zoom the intentional area so as to carry out multi-scale prediction. This article has been assessed on CUB-200-2011, FGVC-Aircraft, Stanford Cars, and the model mentioned in this article is more superior than baseline multi-scale resnet50 by more than 2%. Attention pattern generated from the model is helpful for other models to increase their recognition accuracy as well. © Published under licence by IOP Publishing Ltd.

关键词： Mapping

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Proceedings of the 2019 3rd international conference on Virtual and Augmented Reality Simulations, ICVARS 2019

Proceedings of the 2019 3rd International Conference on Virt...

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3rd international conference on Virtual and Augmented Reality Simulations, ICVARS 2019

ISBN: (纸本)9781450365925

The proceedings contain 17 papers. The topics discussed include: influence of interactive questions on the sense of presence and anxiety in a virtual-reality job-interview;development of chemical incident response training program by applying virtual reality technology;therapeutic virtual reality for nyctophobic disorder;virtual reality for anatomical vocabulary learning;development of dual cognitive task virtual reality game addressing stroke rehabilitation;game cinematography - towards understanding relationship between spatial distortion and game-play;a new method to render virtual walls for haptic systems: ’tracking wall’. application to needle insertion simulation;collaborative hands-on training on haptic simulators;PD pattern recognition in transformers based on greyscale images and affinity propagation algorithm;development of automated platform for image capturing and counting algorithm for viral plaque;and design of an autonomous sentry gun system for the detection of people in restricted zones.

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Indoor activity recognition by using recurrent neural networks 3rd

Indoor activity recognition by using recurrent neural networ...

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3rd international conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the international conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Zhao, Yu Li, Qingjuan Farha, Fadi Zhu, Tao Chen, Liming Ning, Huansheng University of Science and Technology Beijing Beijing100083 China Nanhua University Beijing421001 China Ulster University 601 BelfastBT48 7JL United Kingdom

ISBN: (纸本)9789811519246

Because of the development of the ageing population, most countries are facing an increasingly serious pension resources problem. With the development of Internet of Things, the integration of smart home and smart retirement provides a new solution for the new smart home for the elderly, to achieve the elderly to intelligently support the elderly. This paper is based on the development of this background, mainly to solve the problem of indoor activity recognition of the elderly, so as to prepare for the construction of smart medical care. The specific research process is to process the sensor data collected from the smart environment, identify different activities using RNN, LSTM and GRU models with strong ability to process time series data, realize the target of activity recognition. © Springer Nature Singapore Pte Ltd. 2019.

关键词： pattern recognition

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ICDTE 2019 - 2019 the 3rd international conference on Digital Technology in Education

ICDTE 2019 - 2019 the 3rd International Conference on Digita...

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8th international conference on bioinformatics and Biomedical Science, ICBBS 2019

The proceedings contain 23 papers. The topics discussed include: discrimination of cardiac arrhythmia using a fusion classifier with ECG morphological features;a novel seizure diagnostic model based on generalized hurst exponent and extremely randomized trees;the long-term depression of brain activity induced by electrical stimulation of cerebral cortex;an automated animal training apparatus for operant conditioning studies in rodents;analysis of the differences about emotion recognition between male and female based on extracted chaos characteristic;determination of computationally efficient multi-pool model fitting approach for pulsed chemical exchange saturation transfer MRI;brain tumor screening using adaptive gamma correction and deep learning;and a software for the lung image database consortium and image database resource initiative.

关键词： Magnetic resonance imaging

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Deep disentangled hashing with momentum triplets for neuroimage search 23rd

Deep disentangled hashing with momentum triplets for neuroim...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Yang, Erkun Yao, Dongren Cao, Bing Guan, Hao Yap, Pew-Thian Shen, Dinggang Liu, Mingxia Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel HillNC27599 United States Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783030597092

Neuroimaging has been widely used in computer-aided clinical diagnosis and treatment, and the rapid increase of neuroimage repositories introduces great challenges for efficient neuroimage search. Existing image search methods often use triplet loss to capture high-order relationships between samples. However, we find that the traditional triplet loss is difficult to pull positive and negative sample pairs to make their Hamming distance discrepancies larger than a small fixed value. This may reduce the discriminative ability of learned hash code and degrade the performance of image search. To address this issue, in this work, we propose a deep disentangled momentum hashing (DDMH) framework for neuroimage search. Specifically, we first investigate the original triplet loss and find that this loss function can be determined by the inner product of hash code pairs. Accordingly, we disentangle hash code norms and hash code directions and analyze the role of each part. By decoupling the loss function from the hash code norm, we propose a unique disentangled triplet loss, which can effectively push positive and negative sample pairs by desired Hamming distance discrepancies for hash codes with different lengths. We further develop a momentum triplet strategy to address the problem of insufficient triplet samples caused by small batch-size for 3D neuroimages. With the proposed disentangled triplet loss and the momentum triplet strategy, we design an end-to-end trainable deep hashing framework for neuroimage search. Comprehensive empirical evidence on three neuroimage datasets shows that DDMH has better performance in neuroimage search compared to several state-of-the-art methods. © Springer Nature Switzerland AG 2020.

关键词： Momentum

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Are fast labeling methods reliable? a case study of computer-aided expert annotations on microscopy slides 23rd

Are fast labeling methods reliable? a case study of computer...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Marzahl, Christian Bertram, Christof A. Aubreville, Marc Petrick, Anne Weiler, Kristina Gläsel, Agnes C. Fragoso, Marco Merz, Sophie Bartenschlager, Florian Hoppe, Judith Langenhagen, Alina Jasensky, Anne-Katherine Voigt, Jörn Klopfleisch, Robert Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Research and Development EUROIMMUN Medizinische Labordiagnostika AG Lübeck Lübeck Germany Institute of Veterinary Pathology Freie Universität Berlin Berlin Germany Department of Veterinary Clinical Sciences Clinical Pathology and Clinical Pathophysiology Justus-Liebig-Universität Giessen Giessen Germany Laboklin GmbH und Co. KG Bad Kissingen Germany

ISBN: (纸本)9783030597092

Deep-learning-based pipelines have shown the potential to revolutionalize microscopy image diagnostics by providing visual augmentations and evaluations to a trained pathology expert. However, to match human performance, the methods rely on the availability of vast amounts of high-quality labeled data, which poses a significant challenge. To circumvent this, augmented labeling methods, also known as expert-algorithm-collaboration, have recently become popular. However, potential biases introduced by this operation mode and their effects for training deep neuronal networks are not entirely understood. This work aims to shed light on some of the effects by providing a case study for three pathologically relevant diagnostic settings. Ten trained pathology experts performed a labeling tasks first without and later with computer-generated augmentation. To investigate different biasing effects, we intentionally introduced errors to the augmentation. In total, the pathology experts annotated 26,015 cells on 1,200 images in this novel annotation study. Backed by this extensive data set, we found that the concordance of multiple experts was significantly increased in the computer-aided setting, versus the unaided annotation. However, a significant percentage of the deliberately introduced false labels was not identified by the experts. © Springer Nature Switzerland AG 2020.

关键词： Pathology

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