Enhancing Feature Selection in Single Shot Robot Learning by Using Multi-Modal Inputs

2021 ◽  
Author(s):  
Christian Groth
Keyword(s):  
Feature Selection ◽  
Robot Learning ◽  
Single Shot ◽  
Learning By Using

scholarly journals Hybrid Feature Selection for Myoelectric Signal Classification Using MICA

2010 ◽  
Vol 61 (2) ◽  
pp. 93-99 ◽  
Author(s):  
Ganesh Naik ◽  
Dinesh Kumar
Keyword(s):  
Feature Selection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Signal Classification ◽  
Computer Assisted ◽  
Single Shot ◽  
Myoelectric Signal ◽  
Novel Method ◽  
Selection For

Hybrid Feature Selection for Myoelectric Signal Classification Using MICA This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100% accuracy.

scholarly journals Color Distribution Pattern Metric for Person Reidentification

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Yingsheng Ye ◽  
Xingming Zhang ◽  
Wing W. Y. Ng
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Distribution Pattern ◽  
State Of The Art ◽  
Feature Representation ◽  
Selection Strategy ◽  
Single Shot ◽  
Experimental Setting ◽  
Color Distribution ◽  
Video Searching

Accompanying the growth of surveillance infrastructures, surveillance IP cameras mount up rapidly, crowding Internet of Things (IoT) with countless surveillance frames and increasing the need of person reidentification (Re-ID) in video searching for surveillance and forensic fields. In real scenarios, performance of current proposed Re-ID methods suffers from pose and viewpoint variations due to feature extraction containing background pixels and fixed feature selection strategy for pose and viewpoint variations. To deal with pose and viewpoint variations, we propose the color distribution pattern metric (CDPM) method, employing color distribution pattern (CDP) for feature representation and SVM for classification. Different from other methods, CDP does not extract features over a certain number of dense blocks and is free from varied pedestrian image resolutions and resizing distortion. Moreover, it provides more precise features with less background influences under different body types, severe pose variations, and viewpoint variations. Experimental results show that our CDPM method achieves state-of-the-art performance on both 3DPeS dataset and ImageLab Pedestrian Recognition dataset with 68.8% and 79.8% rank 1 accuracy, respectively, under the single-shot experimental setting.

Feature Selection Strategies and Perceptual Expertise in Configuration Search Tasks

2011 ◽  
Author(s):  
Lindsey M. Kitchell ◽  
Francisco J. Parada ◽  
Brandi L. Emerick ◽  
Tom A. Busey
Keyword(s):  
Feature Selection ◽  
Perceptual Expertise ◽  
Search Tasks ◽  
Selection Strategies

UPAYA MENINGKATKAN KOMPETENSI PEDAGOGIK GURU SDN TURUSGEDE DI SEMESTER I TAHUN PELAJARAN 2018/2019 DALAM MEMBUKA DAN MENUTUP PEMBELAJARAN MELALUI SUPERVISI AKADEMIK TEKNIK PENILAIAN DIRI SENDIRI

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Siti Ulfah
Keyword(s):  
Elementary School ◽  
Action Observation ◽  
The Self ◽  
Self Assessment ◽  
Test Technique ◽  
Assessment Technique ◽  
First Semester ◽  
Opening And Closing ◽  
Academic Year ◽  
Learning By Using

The purposes of this reasearch are 1) describing the efforts of increasing the elementary school of Turusgede teachers pedagogic competence at the first semester of 2018/2019 academic year in opening and closing the learning by using the self assessment technique and 2) analysing the increase of the elementary school of Turusgede teachers pedagogic competence at the first semester of 2018/2019 academic year in opening and closing the learning by using the self assessment technique. This research is School Action Research (SAR). This research is taken palce in elementary school of Turusgede, Subdistrict of Rembang, Regency of Rembang. The time of this research is the early-middle first semester of 2018/2019 academic year. The subjects of this research are teachers in the elementary school of Turusgede, Subdistrict of Rembang, Regency of Rembang, consist of twelve teachers. The data of this research is teachers pedagogic competence in opening and closing the learning. The techniques of collecting data are using nontest technique and test technique. The tools of collecting data are using the sheets of observation, camera application on hand phone and the form of self assessment. The technique of analizing data in this research is decriptive comparation. The procedure of this research is using Cycle Model, consist of four steps: planning, action, observation and reflection. Each cycle is going on one week. The results of this research are 1) the academic supervision with self assessment technique is previously sharing the form of self assessment to the subjects of this research, 2) self assessment technique is self assessment according to the next theme and matter, 3) self assessment technique is self assessment after the learning finish and 4) teachers pedagogic competence with self assessment technique is increasing and including good category (B) that according with the result of observation and including very good category (A) that according with the result of self assessment. Key words: Pedagogic, Supervision, Self Assessment Technique.

Improving Q-learning by Using the Agent's Action History

2016 ◽  
Vol 136 (8) ◽  
pp. 1209-1217 ◽  
Author(s):  
Masanori Saito ◽  
Teruji Sekozawa
Keyword(s):  
Learning By Using
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