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machine learning-based methods for analyzing grade crossing safety

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2017年第2期20卷 1625-1635页

作者： Yang, Chunsheng Trudel, Eric Liu, Yan Natl Res Council Canada Ottawa ON Canada Natl Res Council Canada Rail Grp Ottawa ON Canada

A grade crossing is defined as an intersection between a roadway and a railway at the same elevation or grade. Multiple new prevention measures have been implemented to reduce the number of train-vehicle collisions;however, crossing safety remains a major issue as accidents still frequently occur. The push for machine learning-based model to analyze risks at grade crossings has also increased to keep up with new technologies. There are many different protection types (gates with bells, cross-buck, stop-sign, mirrors and etc.) that serve to warn or stop oncoming traffic. Many attributes have an inherent impact on accident frequency, including the protection type, train speed, traffic volume and etc. To find out which factors are most important, we propose a machine learning-based method to effectively analyze the impact of multiple factors that affect crossing safety and subsequently provide scientific insight for key factors for enhancing crossing safety. In this work, the Canadian crossing accident database from 2004 to 2013 was used with additional generated features to enhance the performance of models. These include features that were computed using geographical information systems (GIS) and sightline measurements. Based on the performance, the machine learning algorithm, RandomForest is used to rank and analyze 21 attributes for each protection type. From the analysis results it is possible to identify which key factors have the highest influence on improving safety and collision prediction at grade crossings.

关键词： Grade crossing Data-driven modeling Feature generation machine learning algorithm RandomForest regression Crossing safety GIS

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Strengthening Practical English Teaching and Improving College Students' Comprehensive English Ability

Strengthening Practical English Teaching and Improving Colle...

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2019 3rd International Conference on Education Technology and Economic Management(ICETEM 2019)

作者： Xiaojun Cao Xi'an Traffic Engineering Institute

Due to the development of economic globalization and the rapid improvement of China's economic strength,the importance of English is *** classroom teaching is becoming more and more popular in the teaching of colleges and universities in China because of its strong *** paper mainly studies how to enhance practical English teaching through the flipping classroom based on machine learning algorithm,so as to improve the comprehensive ability of college students.

关键词： machine learning algorithm Practical Teaching English Teaching

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Embedding Na?ve Bayes algorithm Data Model in Predicting Student Graduation

Embedding Na?ve Bayes Algorithm Data Model in Predicting Stu...

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作者： Ace C.Lagman Joseph Q.Calleja Ma.Corazon G.Fernando Joseph G.Gonzales John Benedict Legaspi John Heland Jasper C.Ortega Ronel F.Ramos Maria Vicky S.Solomo Regina C.Santos FEU Institute of Technology

In the Philippines,according to Philippine Authority of Statistics,there is an imbalance between the student enrollment and student *** half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on *** study aims to utilize how Na?ve Bayes algorithm-a data classification algorithm that is based on probabilistic analysis-can be used in educational data mining specifically in student *** study is focused on the application of the Na?ve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time,so proper remediation and retention policies can be formulated and implemented by institutions.

关键词： Na?ve Bayes machine learning algorithm data mining student graduation prediction Big Data Analytics classification algorithm

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Retargeting extreme learning machines for classification and their applications to fault diagnosis of aircraft engine

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AEROSPACE SCIENCE AND TECHNOLOGY 2017年 71卷 603-618页

作者： Zhao, Yong-Ping Song, Fang-Quan Pan, Ying-Ting Li, Bing Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Jiangsu Peoples R China

Since the original extreme learning machine (ELM) generates the hidden nodes randomly, it usually needs more hidden nodes to reach the good classification performance. However, more hidden nodes will jeopardize the real time, which limits its applications to the testing time sensitive scenarios. To this end, the commonly-used methods tend to compact its structure via optimizing the number of hidden nodes. Different from this viewpoint of network structure, in this paper two algorithms are proposed to improve the real time performance of ELM from a viewpoint of data structure. Specially, they improve the ELM classification performance by retargeting its label vectors. As thus, they need fewer hidden nodes to reach the same classification performance, which means the better real time. Finally, experimental results on the benchmark data sets validate the effectiveness and feasibility of the presented two algorithms. To be more important, they are applied to the fault diagnosis of aircraft engine and can be developed as its candidate techniques. (C) 2017 Elsevier Masson SAS. All rights reserved.

关键词： Extreme learning machine machine learning algorithm Fault diagnosis Aircraft engine

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machine learning gesture analysis of yoga for exergame development

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IET CYBER-PHYSICAL SYSTEMS: THEORY & APPLICATIONS 2018年第2期3卷 106-110页

作者： Pullen, Paula Seffens, William Univ North Georgia Dept Kinesiol Gainesville GA USA Seftec Inc Atlanta GA 30318 USA

Many successful and innovative information technology applications use gestures as input. These programs span a wide variety of genres, platforms and input technologies, from the touch screen of a smart phone to the full-motion, the natural input of devices like the Kinect Sensor. Visual Gesture Builder, a data-driven machine-learning solution for gesture detection, was used to capture useful yoga gestures with high accuracy. This gesture analysis technology is being explored for incorporation into exergames for personalised medical interventions. The research goal is to test whether a machine learning algorithm in a basic computer video exergame can assess yoga skill acquisition in targeted select populations as a means to promote healthy physical activity.

关键词： computer games learning (artificial intelligence) gesture recognition biomechanics patient treatment exergame development input technologies touch screen smart phone Kinect Sensor Visual Gesture Builder gesture detection gesture analysis technology personalised medical interventions machine learning algorithm basic computer video exergame yoga skill acquisition information technology applications gesture analysis healthy physical activity

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ANALYSIS OF REAL TIME DISASTER DATA ON SOCIAL MEDIA (TWITTER): SRI LANKAN FLOODS AND CYCLONE OCKHI

ANALYSIS OF REAL TIME DISASTER DATA ON SOCIAL MEDIA (TWITTER...

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作者： Hareesh N A Tata Institute of Social Sciences

学位级别：硕士

Social media has been used widely during crisis and disasters. The data generated by the social media is huge and has a potential for deep analysis. This study aims to find out the uses of social media before, during and after disasters and take it forward to further harness the usage through the output of this study. The study will help in understanding the categories for which the social media in particular Twitter is used and also to find out the areas in which attention is needed for further effective communication. machine learning algorithms are used to predict the output of the social media data. The study also tries to find out the best possible algorithm for predicting the output from social media data. The twitter data is collected during Sri Lankan Floods and Cyclone Ockhi which occurred during the year 2017. The collected data is then categorised using Keyword analysis and then the data is analysed for trends of usage. Also, this data is given as input to an open source visualization tool called as weka. machine learning algorithms such as Decision Tree, Naive Bayes and IBK are implemented on the categorised data and the classes are predicted by the algorithms. It is found that the best suitable algorithm among the three is Decision tree. The study also proposes a framework for social media usage before, during and after a disaster and lists out the critical impacts of social media on the user community.

关键词： Social media Twitter analysis machine learning algorithm Decision Tree Naive Bayes IBK

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An Approach to Improve Flexible Manufacturing Systems with machine learning algorithms 42

An Approach to Improve Flexible Manufacturing Systems with M...

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42nd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Li, Hang Tech Univ Munich Inst Automat & Informat Syst Garching Germany

ISBN: (纸本)9781509034741

The electricity consumption in the industry occupies considerable ratio in the gross electricity consumption compared with the consumption in other sectors e.g. residential, agriculture etc. One crucial solution to this problem is to optimize the production structure. The grand plan "Industry 4.0" provides a more adaptable and flexible perspective for the smart factory. The complexity of a manufacturing system, on the other hand, has been enhanced. machine learning algorithms are a cluster of excellent approaches to control a complex system and to optimize a stochastic process. In order to improve the performance of a production system, it must be formulated to an executive model at first, then the optional control policies can be selected to cope with it. In this paper, the classification algorithm and the Q-learning algorithm have been implemented to reduce the electricity consumption in an automation system. The simulation results prove that they are capable for manipulating the multi routes transporting system and the system can performance better with the implementation of the machine learning algorithms.

关键词： machine learning algorithm flexible manufacturing system Q-learning electricity consumption reduction

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Implementation of machine learning for Breath Collection 10

Implementation of Machine Learning for Breath Collection

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10th International Joint Conference on Biomedical Engineering Systems and Technologies

作者： Santos, Paulo Vassilenko, Valentina Vasconcelos, Fabio Gil, Flavio Univ Nova Lisboa Fac Ciencia & Tecnol Lab Instrumentat Biomed Engn & Radiat Phys LibPhy Campus FCT UNL P-2896516 Caparica Portugal NMT SA Edificio Madan ParqueRua Inventores P-2825282 Caparica Portugal

ISBN: (纸本)9789897582165

Economic and technologic progresses states the analysis of human's exhaled air as a promising tool for medical diagnosis and therapy monitoring. Challenges of most pulmonary breath acquisition devices are related to the substances' concentrations that are source (oral cavity, esophageal and alveolar) dependent and their low values (in ppbv - pptv range). We introduce a prototype that is capable of collecting samples of exhaled air according to the respiratory source and independent of the metabolic production of carbon dioxide. It also allows to access the breathing cycle in real-time, detects the optimized sampling instants and selects the collection pathway through the implementation of an algorithm containing a machine learning process. A graphical interface allows the interaction between the operator/user and the process of acquisition making it easy, quick and reliable. The imposition of breath rhythm led to improvements in accuracy of obtaining samples from specific parts of the respiratory tract and it should be adapted according to their age and physiological/health condition. The technology implemented in the proposed system should be taken into consideration for further studies, since the prototype is suitable for selectively sampling exhaled air from persons according to its age, genre and physiological condition.

关键词： Exhaled Air Selective Air Acquisition Air Sampling/Monitoring Breath Rhythm Imposition Modelled Breath algorithm Average Time of Expiration machine learning algorithm

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machine learning Based VP9-to-HEVC Video Transcoding 12

Machine Learning Based VP9-to-HEVC Video Transcoding

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12th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Li, Xiangyu Xie, Rong Song, Li Zhang, Liang Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Cooperat Medianet Innovat Ctr Shanghai Peoples R China

ISBN: (纸本)9781509049370

As more and more coding standards are developed for videos with higher resolution and higher frame rates, the conversion between them is in demand. This paper focuses on the development of fast VP9-to-HEVC transcoding algorithm. Since the decoding-and-full-re-encoding process is time consuming, we explore Naive-Bayes based machine learning algorithm to accelerate VP9-to-HEVC transcoding. The machine learning process can bridge VP9 decoding information and HEVC splitting decision throughout training results, so the HEVC CU searching process can be simplified for faster transcoding. Two new features closely related to VP9, sub-block counting and depth map, are added for VP9-to-HEVC video transcoder. Using coding features and Naive-Bayes algorithm, several models are built for different QPs and block sizes to reduce transcoding time. Experiments show that a maximum time reduction of 53% and an average reduction of 44% can be achieved and the loss of decoded image quality in BD-rate is almost ignorable.

关键词： VP9 HEVC video transcoding machine learning algorithm

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Retrieval of Daily PM_2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing-Tianjin-Hebei Region, China

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REMOTE SENSING 2018年第12期10卷 2006-2006页

作者： Li, Lijuan Chen, Baozhang Zhang, Yanhu Zhao, Youzheng Xian, Yue Xu, Guang Zhang, Huifang Guo, Lifeng Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Land Surface Pattern & Simulat 11A Datun Rd Beijing 100101 Peoples R China Univ Chinese Acad Sci 19A Yuquan Rd Beijing 100049 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst 11A Datun Rd Beijing 100101 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China Hebei Xingtai Environm Monitoring Ctr 998 Pk East St Qiaoxi Dist 054000 Xingtai Peoples R China Yancheng Environm Monitoring Ctr Stn 7 Wengang North Rd Tinghu Dist 224000 Yancheng Peoples R China

Exposure to fine particulate matter (PM2.5) is associated with adverse health impacts on the population. Satellite observations and machine learning algorithms have been applied to improve the accuracy of the prediction of PM2.5 concentrations. In this study, we developed a PM2.5 retrieval approach using machine-learning methods, based on aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System (EOS) Terra and Aqua polar-orbiting satellites, near-ground meteorological variables from the NASA Goddard Earth Observing System (GEOS), and ground-based PM2.5 observation data. Four models, which are orthogonal regression (OR), regression tree (Rpart), random forests (RF), and support vector machine (SVM), were tested and compared in the Beijing-Tianjin-Hebei (BTH) region of China in 2015. Aerosol products derived from the Terra and Aqua satellite sensors were also compared. The 10-repeat 5-fold cross-validation (10 x 5 CV) method was subsequently used to evaluate the performance of the different aerosol products and the four models. The results show that the performance of the Aqua dataset was better than that of the Terra dataset, and that the RF algorithm has the best predictive performance (Terra: R = 0.77, RMSE = 43.51 g/m(3);Aqua: R = 0.85, RMSE = 33.90 g/m(3)). This study shows promise for predicting the spatiotemporal distribution of PM2.5 using the RF model and Aqua aerosol product with the assistance of PM2.5 site data.

关键词： daily PM2.5 concentrations remote sensing MODIS AOD machine learning algorithm spatial and temporal distribution

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