scholarly journals Human-Touch-Inspired Material Recognition for Robotic Tactile Sensing

2019 ◽  
Vol 9 (12) ◽  
pp. 2537
Author(s):  
Yu Xie ◽  
Chuhao Chen ◽  
Dezhi Wu ◽  
Wenming Xi ◽  
Houde Liu
Keyword(s):  
Machine Learning ◽  
Short Duration ◽  
Learning Algorithm ◽  
Tactile Sensing ◽  
Machine Learning Algorithm ◽  
Mechanical Stimuli ◽  
Recognition Method ◽  
Material Recognition ◽  
Novel Material ◽  
Human Touch

This paper proposes a novel material recognition method for robotic tactile sensing. The method is composed of two steps. Firstly, a human-touch-inspired short-duration (1 s) slide action is conducted by the robot to obtain the tactile data. Then, the tactile data is processed with a machine learning algorithm, where 11 bioinspired features were designed to imitate the mechanical stimuli towards the four main types of tactile receptors in the skin. In this paper, a material database consisting of 144,000 tactile images is used to train seven classifiers, and the most accurate classifier is selected to recognize 12 household objects according to their properties and materials. In the property recognition, the materials are classified into 4 categories according to their compliance and texture, and the best accuracy reaches 96% in 36 ms. In the material recognition, the specific materials are recognized, and the best accuracy reaches 90% in 37 ms. The results verify the effectiveness of the proposed method.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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