PPT-Automatic KY guardrail detection by machine learning

KYTC KTC VIS KYTC Photo logging van The Kentucky Transportation Cabinet operates a fleet of three asset collection vehicles Automated data collection is conducted annually on the Interstate and NHS routes and on a two year cycle for all nonNHS routes  Average yearly collection is 35000 lane miles  This data collection includes automated pavement distress rutting cross slope IRI faulting curve amp grade GPS data and roadway images In addition to network testing the KYTC also performs IRI acceptance testing for new construction. . Automatic and computerized sewing machine, replace 1 worker + 1 flat sewing machine working style, 1 auto sewing machine (2 heads) + 1 worker = 2 flat sewing machine + 2 workers.. World . first Multi-Head Automatic Sewing Machine, Invention Patent Number: ZL 2013 1 . Prafulla Dawadi. Topics in Machine Learning. Outline. Part I. Examples. Rare Class, Imbalanced Class, Outliers. Part II. (Rare)Category Detection. Part III. Kernel Density Estimation . Mean Shift and Hierarchal Mean Shift. By . T . Sivels , . I . Hicklin , . R . Knox. About the invention. A . machine gun. is a . fully automatic. mounted or portable . firearm. , usually designed to fire . bullets. in quick succession from an . Lecture . 4. Multilayer . Perceptrons. G53MLE | Machine Learning | Dr Guoping Qiu. 1. Limitations of Single Layer Perceptron. Only express linear decision surfaces. G53MLE | Machine Learning | Dr Guoping Qiu. Problem motivation. Machine Learning. Anomaly detection example. Aircraft engine features:. . = heat generated. = vibration intensity. …. (vibration). (heat). Dataset:. New engine:. Density estimation. Stanford University. Learning. . to improve our lives. Input. Computers Can Learn?. Computers can learn to . predict. Computers can learn to . act. Output. Program. Parameters. Learned to get desired input/output mapping. Prafulla Dawadi. Topics in Machine Learning. Outline. Part I. Examples. Rare Class, Imbalanced Class, Outliers. Part II. (Rare)Category Detection. Part III. Kernel Density Estimation . Mean Shift and Hierarchal Mean Shift. What is an IDS?. An . I. ntrusion . D. etection System is a wall of defense to confront the attacks of computer systems on the internet. . The main assumption of the IDS is that the behavior of intruders is different from legal users.. DistributedattackdetectionschemeusingdeeplearningapproachforInternetofThingsAbebeAbeshuDiro,NaveenChilamkurtiPII:S0167-739X(17)30848-8DOI:http://dx.doi.org/10.1016/j.future.2017.08.043Reference:FUTURE Presented by Aditi . Kuchi. Supervisor: . Dr.. Md . Tamjidul. Hoque. 1. Presentation Overview. Sand boils – What, How, Why +Motivation. Dataset. Methods used & explanations, discussion. Viola-Jones’ algorithm (. Yonggang Cui. 1. , Zoe N. Gastelum. 2. , Ray Ren. 1. , Michael R. Smith. 2. , . Yuewei. Lin. 1. , Maikael A. Thomas. 2. , . Shinjae. Yoo. 1. , Warren Stern. 1. 1 . Brookhaven National Laboratory, Upton, USA. Dr. Alex Vakanski. Lecture . 10. AML in . Cybersecurity – Part I:. Malware Detection and Classification. . Lecture Outline. Machine Learning in cybersecurity. Adversarial Machine Learning in cybersecurity. Institute of High Energy Physics, CAS. Wang Lu (Lu.Wang@ihep.ac.cn). Agenda. Introduction. Challenges and requirements of anomaly detection in large scale storage systems . Definition and category of anomaly. Applications (Part I). S. Areibi. School of Engineering. University of Guelph. Introduction. 3. Machine Learning. Types of Learning:. Supervised learning. : (also called inductive learning) Training data includes desired outputs. This is spam this...