Research on upper limb biomechanical system

Iliescu, Mihaiela; Vlădăreanu, Luige; Frant, Corina; Dugaesescu, Ileana; Pandelea, Marius; Marin, Doina

doi:10.21533/pen.v7i1.375

Cited by 2 publications

(2 citation statements)

References 3 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of model optimization for robotic applications, the studies reveal different approaches such as developing advanced control systems for the upper and lower limb [19,20], applying Dezert-Smarandache Theory (DSmT) for decision-making algorithms [21], Extenics control [22], and fuzzy dynamic modelling [23].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification

2020

View full text Add to dashboard Cite

This paper presents an extensive research carried out for enhancing the performances of convolutional neural network (CNN) object detectors applied to municipal waste identification. In order to obtain an accurate and fast CNN architecture, several types of Single Shot Detectors (SSD) and Regional Proposal Networks (RPN) have been fine-tuned on the TrashNet database. The network with the best performances is executed on one autonomous robot system, which is able to collect detected waste from the ground based on the CNN feedback. For this type of application, a precise identification of municipal waste objects is very important. In order to develop a straightforward pipeline for waste detection, the paper focuses on boosting the performance of pre-trained CNN Object Detectors, in terms of precision, generalization, and detection speed, using different loss optimization methods, database augmentation, and asynchronous threading at inference time. The pipeline consists of data augmentation at the training time followed by CNN feature extraction and box predictor modules for localization and classification at different feature map sizes. The trained model is generated for inference afterwards. The experiments revealed better performances than all other Object Detectors trained on TrashNet or other garbage datasets with a precision of 97.63% accuracy for SSD and 95.76% accuracy for Faster R-CNN, respectively. In order to find the optimal higher and lower bounds of our learning rate where the network is actually learning, we trained our model for several epochs, updating the learning rate after each epoch, starting from 1 × 10−10 and decreasing it until reaching 1 × 10−1.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification

2020

View full text Add to dashboard Cite

show abstract

“…In terms of the human-robot interaction, the research is concentrated on different levels and implies, among others, the grasping configurations of robot dexterous hands using Dezert-Smarandache theory (DSmT) decision-making algorithms [36], developing advanced intelligent control systems for the upper limb [37][38][39][40][41] or other artificial intelligence techniques such as neutrosophic logic [36,41,42], extenics control [43,44] and fuzzy dynamic modeling [45][46][47], applicable for the human-robot interaction with feedback through facial expressions recognition. The research in this paper is focused on developing an AI based computer vision system to achieve the communication human-NAO robot aiming the future researches to develop an end-to-end pipeline system with advanced intelligent control using feedback from the interaction between a human and NAO robot.…”

Section: Introductionmentioning

confidence: 99%

Facial Expressions Recognition for Human–Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer

Melinte

Vlădăreanu

2020

Sensors

Self Cite

View full text Add to dashboard Cite

The interaction between humans and an NAO robot using deep convolutional neural networks (CNN) is presented in this paper based on an innovative end-to-end pipeline method that applies two optimized CNNs, one for face recognition (FR) and another one for the facial expression recognition (FER) in order to obtain real-time inference speed for the entire process. Two different models for FR are considered, one known to be very accurate, but has low inference speed (faster region-based convolutional neural network), and one that is not as accurate but has high inference speed (single shot detector convolutional neural network). For emotion recognition transfer learning and fine-tuning of three CNN models (VGG, Inception V3 and ResNet) has been used. The overall results show that single shot detector convolutional neural network (SSD CNN) and faster region-based convolutional neural network (Faster R-CNN) models for face detection share almost the same accuracy: 97.8% for Faster R-CNN on PASCAL visual object classes (PASCAL VOCs) evaluation metrics and 97.42% for SSD Inception. In terms of FER, ResNet obtained the highest training accuracy (90.14%), while the visual geometry group (VGG) network had 87% accuracy and Inception V3 reached 81%. The results show improvements over 10% when using two serialized CNN, instead of using only the FER CNN, while the recent optimization model, called rectified adaptive moment optimization (RAdam), lead to a better generalization and accuracy improvement of 3%-4% on each emotion recognition CNN.

show abstract

Research on upper limb biomechanical system

Cited by 2 publications

References 3 publications

Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification

Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification

Facial Expressions Recognition for Human–Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer

Contact Info

Product

Resources

About