MX2017004705A - Deteccion de lluvia basada en vision con aprendizaje profundo. - Google Patents

Deteccion de lluvia basada en vision con aprendizaje profundo.

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Publication number
MX2017004705A
MX2017004705A MX2017004705A MX2017004705A MX2017004705A MX 2017004705 A MX2017004705 A MX 2017004705A MX 2017004705 A MX2017004705 A MX 2017004705A MX 2017004705 A MX2017004705 A MX 2017004705A MX 2017004705 A MX2017004705 A MX 2017004705A
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MX
Mexico
Prior art keywords
rain
vehicle
vision
deep learning
neural network
Prior art date
Application number
MX2017004705A
Other languages
English (en)
Inventor
Elizabeth Micks Ashley
Banvait Harpreetsingh
J Jain Jinesh
Nariyambut Murali Vidya
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Ford Global Tech Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ford Global Tech Llc filed Critical Ford Global Tech Llc
Publication of MX2017004705A publication Critical patent/MX2017004705A/es

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60S—SERVICING, CLEANING, REPAIRING, SUPPORTING, LIFTING, OR MANOEUVRING OF VEHICLES, NOT OTHERWISE PROVIDED FOR
    • B60S1/00—Cleaning of vehicles
    • B60S1/02—Cleaning windscreens, windows or optical devices
    • B60S1/04—Wipers or the like, e.g. scrapers
    • B60S1/06—Wipers or the like, e.g. scrapers characterised by the drive
    • B60S1/08—Wipers or the like, e.g. scrapers characterised by the drive electrically driven
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60S—SERVICING, CLEANING, REPAIRING, SUPPORTING, LIFTING, OR MANOEUVRING OF VEHICLES, NOT OTHERWISE PROVIDED FOR
    • B60S1/00—Cleaning of vehicles
    • B60S1/02—Cleaning windscreens, windows or optical devices
    • B60S1/04—Wipers or the like, e.g. scrapers
    • B60S1/06—Wipers or the like, e.g. scrapers characterised by the drive
    • B60S1/08—Wipers or the like, e.g. scrapers characterised by the drive electrically driven
    • B60S1/0803—Intermittent control circuits
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60S—SERVICING, CLEANING, REPAIRING, SUPPORTING, LIFTING, OR MANOEUVRING OF VEHICLES, NOT OTHERWISE PROVIDED FOR
    • B60S1/00—Cleaning of vehicles
    • B60S1/02—Cleaning windscreens, windows or optical devices
    • B60S1/04—Wipers or the like, e.g. scrapers
    • B60S1/06—Wipers or the like, e.g. scrapers characterised by the drive
    • B60S1/08—Wipers or the like, e.g. scrapers characterised by the drive electrically driven
    • B60S1/0818—Wipers or the like, e.g. scrapers characterised by the drive electrically driven including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like
    • B60S1/0822—Wipers or the like, e.g. scrapers characterised by the drive electrically driven including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like characterized by the arrangement or type of detection means
    • B60S1/0833—Optical rain sensor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60S—SERVICING, CLEANING, REPAIRING, SUPPORTING, LIFTING, OR MANOEUVRING OF VEHICLES, NOT OTHERWISE PROVIDED FOR
    • B60S1/00—Cleaning of vehicles
    • B60S1/02—Cleaning windscreens, windows or optical devices
    • B60S1/04—Wipers or the like, e.g. scrapers
    • B60S1/06—Wipers or the like, e.g. scrapers characterised by the drive
    • B60S1/08—Wipers or the like, e.g. scrapers characterised by the drive electrically driven
    • B60S1/0818—Wipers or the like, e.g. scrapers characterised by the drive electrically driven including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like
    • B60S1/0822—Wipers or the like, e.g. scrapers characterised by the drive electrically driven including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like characterized by the arrangement or type of detection means
    • B60S1/0833—Optical rain sensor
    • B60S1/0844—Optical rain sensor including a camera
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24143—Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/19—Recognition using electronic means
    • G06V30/191—Design or setup of recognition systems or techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06V30/19173—Classification techniques

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Multimedia (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Mechanical Engineering (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Computing Systems (AREA)
  • Automation & Control Theory (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Databases & Information Systems (AREA)
  • Mathematical Physics (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
  • Traffic Control Systems (AREA)

Abstract

Se describe un método para usar una cámara a bordo de un vehículo para determinar si la precipitación está cayendo cerca del vehículo. El método puede incluir obtener múltiples imágenes. Cada una de las múltiples imágenes puede ser conocida por representar de manera fotográfica una condición de "lluvia" o "no lluvia". Una red neuronal artificial se puede entrenar con las múltiples imágenes. Luego, la red neuronal artificial puede analizar una o más imágenes capturadas por una primera cámara asegurada en un primer vehículo. En función de ese análisis, la red neuronal artificial puede clasificar el primer vehículo como que está en un clima de "lluvia" o "no lluvia".
MX2017004705A 2016-04-11 2017-04-10 Deteccion de lluvia basada en vision con aprendizaje profundo. MX2017004705A (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US15/095,876 US10049284B2 (en) 2016-04-11 2016-04-11 Vision-based rain detection using deep learning

Publications (1)

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MX2017004705A true MX2017004705A (es) 2018-08-16

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US (1) US10049284B2 (es)
CN (1) CN107292386B (es)
DE (1) DE102017107264A1 (es)
GB (1) GB2551001A (es)
MX (1) MX2017004705A (es)
RU (1) RU2017109658A (es)

Families Citing this family (39)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10521677B2 (en) * 2016-07-14 2019-12-31 Ford Global Technologies, Llc Virtual sensor-data-generation system and method supporting development of vision-based rain-detection algorithms
US10528850B2 (en) * 2016-11-02 2020-01-07 Ford Global Technologies, Llc Object classification adjustment based on vehicle communication
EP3616387A1 (en) * 2017-04-24 2020-03-04 Carnegie Mellon University Virtual sensor system
US10282827B2 (en) * 2017-08-10 2019-05-07 Wipro Limited Method and system for removal of rain streak distortion from a video
DE102017217072B4 (de) * 2017-09-26 2023-08-31 Volkswagen Aktiengesellschaft Verfahren zum Erkennen eines Witterungsverhältnisses in einer Umgebung eines Kraftfahrzeugs sowie Steuervorrichtung und Kraftfahrzeug
CN107909070A (zh) * 2017-11-24 2018-04-13 天津英田视讯科技有限公司 一种道路积水检测的方法
CN107933507B (zh) * 2017-11-26 2019-10-15 江苏绿能汽配科技有限公司 一种智能雨刷启动方法
CN107933508B (zh) * 2017-11-26 2020-09-25 瑞安市久智电子商务有限公司 一种雨刷智能启动系统
CN111542834A (zh) * 2017-12-27 2020-08-14 大众汽车（中国）投资有限公司 一种处理方法、处理装置、控制设备和云服务器
US11328210B2 (en) 2017-12-29 2022-05-10 Micron Technology, Inc. Self-learning in distributed architecture for enhancing artificial neural network
US10889267B2 (en) 2018-03-05 2021-01-12 Tesla, Inc. Electromagnetic windshield wiper system
CN108375808A (zh) * 2018-03-12 2018-08-07 南京恩瑞特实业有限公司 Nriet基于机器学习的大雾预报方法
US10522038B2 (en) 2018-04-19 2019-12-31 Micron Technology, Inc. Systems and methods for automatically warning nearby vehicles of potential hazards
EP3629240B1 (en) * 2018-09-07 2023-08-23 Panasonic Intellectual Property Corporation of America Generative adversarial networks for local noise removal from an image
CN112970029B (zh) * 2018-09-13 2024-06-07 辉达公司 用于自主机器应用中传感器视盲检测的深度神经网络处理
US11508049B2 (en) * 2018-09-13 2022-11-22 Nvidia Corporation Deep neural network processing for sensor blindness detection in autonomous machine applications
CN109519077B (zh) * 2018-09-28 2020-06-16 天津大学 一种基于图像处理对雨水进行检测的车窗控制系统
DE102018126825A1 (de) * 2018-10-26 2020-04-30 Bayerische Motoren Werke Aktiengesellschaft Steuerung eines Kraftfahrzeugs
FI20186112A1 (en) * 2018-12-19 2020-06-20 Actim Oy System and method for analysing a point-of-care test result
CN109858369A (zh) * 2018-12-29 2019-06-07 百度在线网络技术（北京）有限公司 自动驾驶方法和装置
US11373466B2 (en) 2019-01-31 2022-06-28 Micron Technology, Inc. Data recorders of autonomous vehicles
US11410475B2 (en) 2019-01-31 2022-08-09 Micron Technology, Inc. Autonomous vehicle data recorders
CN109849851B (zh) * 2019-03-21 2021-02-02 中国联合网络通信集团有限公司 雨刮器控制方法及系统
CN109927675B (zh) * 2019-04-09 2022-02-08 深圳创维汽车智能有限公司 一种雨刷控制方法、装置、设备及存储介质
CN110084218A (zh) * 2019-05-06 2019-08-02 广州小鹏汽车科技有限公司 车辆的雨水分布数据处理方法和装置
CN110163184A (zh) * 2019-05-31 2019-08-23 智宇科技股份有限公司 智能化视频监控系统及方法
WO2021017445A1 (zh) * 2019-07-31 2021-02-04 浙江大学 一种针对雨天图片的卷积神经网络降雨强度分类方法及量化方法
US11392796B2 (en) 2019-08-20 2022-07-19 Micron Technology, Inc. Feature dictionary for bandwidth enhancement
US11755884B2 (en) 2019-08-20 2023-09-12 Micron Technology, Inc. Distributed machine learning with privacy protection
US11636334B2 (en) 2019-08-20 2023-04-25 Micron Technology, Inc. Machine learning with feature obfuscation
CN110562201A (zh) * 2019-09-19 2019-12-13 广州小鹏汽车科技有限公司 雨刮器的控制方法、控制装置和车辆
CN110562202B (zh) * 2019-09-19 2021-07-13 广州小鹏汽车科技有限公司 雨刮器的控制方法、控制装置和车辆
DE102019130922A1 (de) * 2019-11-15 2021-05-20 Bayerische Motoren Werke Aktiengesellschaft System und Verfahren für ein Fahrzeug
US11961335B1 (en) 2020-06-26 2024-04-16 Harris County Toll Road Authority Dual mode electronic toll road system
CN112380930B (zh) * 2020-10-30 2022-04-29 浙江预策科技有限公司 一种雨天识别方法和系统
DE102020215859A1 (de) * 2020-12-15 2022-06-15 Conti Temic Microelectronic Gmbh Korrektur von Bildern einer Kamera bei Regen, Lichteinfall und Verschmutzung
CN113673361B (zh) * 2021-07-28 2024-07-05 东风汽车集团股份有限公司 一种雨雾识别方法、清扫系统和计算机可读存储介质
EP4198573A1 (en) * 2021-12-14 2023-06-21 Tusimple, Inc. System and method for detecting rainfall for an autonomous vehicle
JP2024139833A (ja) * 2023-03-28 2024-10-10 株式会社デンソー 画像処理装置

Family Cites Families (38)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7660437B2 (en) * 1992-05-05 2010-02-09 Automotive Technologies International, Inc. Neural network systems for vehicles
FR2693557B1 (fr) * 1992-07-09 1994-09-30 Rhea Procédé et dispositif pour l'évaluation des précipitations sur une zone de terrain.
US5453676A (en) * 1994-09-30 1995-09-26 Itt Automotive Electrical Systems, Inc. Trainable drive system for a windshield wiper
JPH09240433A (ja) * 1996-03-08 1997-09-16 Yazaki Corp ワイパー制御装置
JPH10239452A (ja) * 1997-02-27 1998-09-11 Nippon Telegr & Teleph Corp <Ntt> 降雨降雪予測装置
US5923027A (en) * 1997-09-16 1999-07-13 Gentex Corporation Moisture sensor and windshield fog detector using an image sensor
AU2001243285A1 (en) * 2000-03-02 2001-09-12 Donnelly Corporation Video mirror systems incorporating an accessory module
US7565006B2 (en) * 2002-08-21 2009-07-21 Gentex Corporation Image acquisition and processing methods for automatic vehicular exterior lighting control
JP4353127B2 (ja) 2005-04-11 2009-10-28 株式会社デンソー レインセンサ
WO2008117392A1 (ja) * 2007-03-26 2008-10-02 Vpec, Inc. 電力システム
CN101470900B (zh) * 2007-12-25 2011-05-25 东风汽车有限公司 车用智能控制方法及装置
DE102008062977A1 (de) * 2008-12-23 2010-06-24 Adc Automotive Distance Control Systems Gmbh Optisches Modul mit multifokaler Optik zur Erfassung von Fern- und Nahbereich in einem Bild
JP5216010B2 (ja) 2009-01-20 2013-06-19 本田技研工業株式会社 ウインドシールド上の雨滴を同定するための方法及び装置
CN101751782A (zh) * 2009-12-30 2010-06-23 北京大学深圳研究生院 一种基于多源信息融合的十字路口交通事件自动检测系统
KR20120064474A (ko) 2010-12-09 2012-06-19 엘지이노텍 주식회사 카메라 모듈을 이용한 우적 감지 장치 및 방법
GB201104168D0 (en) * 2011-03-11 2011-04-27 Life On Show Ltd Information capture system
FR2976866B1 (fr) * 2011-06-27 2013-07-05 Peugeot Citroen Automobiles Sa Procede d'aide a la conduite d'un vehicule
JP5917697B2 (ja) 2011-09-07 2016-05-18 ヴァレオ・シャルター・ウント・ゼンゾーレン・ゲーエムベーハー 車両のフロントガラス上の雨滴を検出するための方法およびカメラアセンブリ
WO2013034167A1 (en) 2011-09-07 2013-03-14 Valeo Schalter Und Sensoren Gmbh Method and camera assembly for detecting raindrops on a windscreen of a vehicle
TWI478834B (zh) * 2012-04-13 2015-04-01 Pixart Imaging Inc 雨刷控制裝置、光學雨滴偵測裝置及其偵測方法
US8824742B2 (en) * 2012-06-19 2014-09-02 Xerox Corporation Occupancy detection for managed lane enforcement based on localization and classification of windshield images
CN102722989B (zh) * 2012-06-29 2014-05-07 山东交通学院 基于模糊神经网络的高速公路微气象交通预警方法
DE102012215287A1 (de) 2012-08-29 2014-05-28 Bayerische Motoren Werke Aktiengesellschaft Verfahren zum Betreiben eines Fahrzeugs
CN103150903B (zh) * 2013-02-07 2014-10-29 中国科学院自动化研究所 一种自适应学习的视频车辆检测方法
JP2014160031A (ja) * 2013-02-20 2014-09-04 Aisin Aw Co Ltd 走行案内システム、走行案内方法及びコンピュータプログラム
US9045112B2 (en) 2013-03-15 2015-06-02 Honda Motor Co., Ltd. Adjustable rain sensor setting based on proximity vehicle detection
CN103543638B (zh) * 2013-10-10 2015-10-21 山东神戎电子股份有限公司 一种自动雨刷控制方法
KR101528518B1 (ko) * 2013-11-08 2015-06-12 현대자동차주식회사 차량 및 그 제어방법
US9335178B2 (en) * 2014-01-28 2016-05-10 GM Global Technology Operations LLC Method for using street level images to enhance automated driving mode for vehicle
DE102014207994A1 (de) 2014-04-29 2015-10-29 Conti Temic Microelectronic Gmbh Vorrichtung zum Erkennen von Niederschlag für ein Kraftfahrzeug
US9443142B2 (en) * 2014-07-24 2016-09-13 Exelis, Inc. Vision-based system for dynamic weather detection
CN104268638A (zh) * 2014-09-11 2015-01-07 广州市香港科大霍英东研究院 一种基于elman神经网络的光伏发电系统功率预测方法
CN104463196B (zh) * 2014-11-11 2017-07-25 中国人民解放军理工大学 一种基于视频的天气现象识别方法
CN104477132A (zh) * 2015-01-02 2015-04-01 江苏新瑞峰信息科技有限公司 一种汽车自动雨刮控制系统
US9465987B1 (en) * 2015-03-17 2016-10-11 Exelis, Inc. Monitoring and detecting weather conditions based on images acquired from image sensor aboard mobile platforms
CN204821319U (zh) * 2015-08-04 2015-12-02 内蒙古麦酷智能车技术有限公司 一种无人驾驶汽车雨雪天自适应系统
CN105196910B (zh) * 2015-09-15 2018-06-26 浙江吉利汽车研究院有限公司 一种雨雾天气下的安全驾驶辅助系统及其控制方法
JP6565619B2 (ja) * 2015-11-11 2019-08-28 株式会社デンソー 雨滴検出装置、車両ワイパ装置及び雨滴検出方法

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CN107292386A (zh) 2017-10-24
GB201704559D0 (en) 2017-05-03
GB2551001A (en) 2017-12-06
CN107292386B (zh) 2022-11-15
RU2017109658A (ru) 2018-09-24
US20170293808A1 (en) 2017-10-12
DE102017107264A1 (de) 2017-10-12
US10049284B2 (en) 2018-08-14

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