Complex Dynamic Modelling of Mobile Robot

2004 ◽  
Author(s):  
Robert Grepl ◽  
Radek Vlach ◽  
Cestmir Ondrusek
Keyword(s):  
Neural Networks ◽  
Computational Model ◽  
Thermal Model ◽  
Dynamic Modelling ◽  
Local Approximation ◽  
Complex Model ◽  
Control Algorithms ◽  
Complex Dynamic ◽  
Data Generator ◽  
Global And Local

This paper deals with the design of complex dynamic model of quadruped walking mobil robot. There is described the method of building of the numerical computational model and its simulating. Complex model consist of submodels of robotic mechanism, DC motor, gearbox model and thermal model of electrical motor. Control algorithms are also considered in model. In the paper is also discussed application of computational model directly for control of robot and also as a data generator for global and local approximation method, mainly artificial neural networks.

Decentralized Control of Complex Dynamic Systems Employing Function Emulation by Neural Networks

2016 ◽  
pp. 249-266
Author(s):  
Yuanwei Jing ◽  
Yanxin Zhang ◽  
Vesna M. Ojleska ◽  
Tatjana D. Kolemisevska-Gugulovska ◽  
Georgi M. Dimirovski
Keyword(s):  
Neural Networks ◽  
Decentralized Control ◽  
Dynamic Systems ◽  
Complex Dynamic ◽  
Complex Dynamic Systems

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

2021 ◽  
Author(s):  
Kianoosh Kazemi ◽  
Juho Laitala ◽  
Iman Azimi ◽  
Pasi Liljeberg ◽  
Amir M. Rahmani
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Detection Methods ◽  
Method Performance ◽  
Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

scholarly journals Fusion High-Resolution Network for Diagnosing ChestX-ray Images

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 190 ◽  
Author(s):  
Zhiwei Huang ◽  
Jinzhao Lin ◽  
Liming Xu ◽  
Huiqian Wang ◽  
Tong Bai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Disease Classification ◽  
Deep Convolutional Neural Networks ◽  
Learning Methods ◽  
Global And Local

The application of deep convolutional neural networks (CNN) in the field of medical image processing has attracted extensive attention and demonstrated remarkable progress. An increasing number of deep learning methods have been devoted to classifying ChestX-ray (CXR) images, and most of the existing deep learning methods are based on classic pretrained models, trained by global ChestX-ray images. In this paper, we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network (FHRNet). The FHRNet concatenates the global average pooling layers of the global and local feature extractors—it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification. Compared with the results of other available methods, our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset, according to the receiver operating characteristic curve and area-under-the-curve score. An ablation study further confirmed the effectiveness of the global and local branch networks in improving the classification accuracy of thorax diseases.

Prediction optimization of mechanical properties of ferrite stainless steels after forging treatment with use of genetic algorithms

2019 ◽  
Vol 1-2 (100) ◽  
pp. 13-20
Author(s):  
R. Honysz
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Computational Model ◽  
Stainless Steels ◽  
Mechanical Characteristics ◽  
Heat Processing ◽  
Chemical Components ◽  
Artificial Neural ◽  
Input Variables

Purpose: The paper describes the use of artificial neural networks to research and predict the effect of chemical components and thermal treatment conditions on stainless steel's mechanical characteristics optimized by genetic algorithm. Design/methodology/approach: The quantity of input variables of artificial neural networks has been optimized using genetic algorithms to enhance the prediction quality of artificial neural network and to enhance their efficiency. Then a computational model was trained and evaluated with optimized artificial neural networks. Findings: Optimization, with the exception of tensile strength, has enabled the creation of artificial neural networks, which either showed a better or similar performance from base networks, as well as a decreased amount of input variables As a consequence, noise data is decreased in the computational model built with the use of these networks. Research limitations/implications: Data analysis was required to confirm the relevance of obtaining information used for modelling to use in training procedures for artificial neural networks. Practical implications: Using artificial intelligence enables the multi-faceted growth of stainless steel engineering, even though there is only a relatively small amount of descriptors. Built and optimized computational model building using optimized artificial neural networks enables prediction of mechanical characteristics after normalization of forged ferritic stainless steels. Originality/value: In order to decrease production expenses of products, an introduced model can be obtained in manufacturing industry. It can also simplify the selection of materials if the engineer has to correctly choose chemical elements and appropriate plastics and/or heat processing of stainless steels, having the necessary mechanical characteristics.

The Cocktail Party Listener

1994 ◽  
Vol 23 (482) ◽  
Author(s):  
A. R. Kian Abolfazlian ◽  
Brian K. Karlsen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Model ◽  
Spectral Data ◽  
Spatial Data ◽  
Speaker Identification ◽  
Cocktail Party ◽  
Human Ability

A complex computational model of the human ability to listen to certain signals in preference of others, also called the cocktail party phenomenon, is built on the basis of surveys into the relevant psychological, DSP, and neural network literature. This model is basically binaural and as such it makes use of both spectral data and spatial data in determining which speaker to listen to. The model uses two neural networks for filtering and speaker identification. Results from some experimentation with type and architecture of these networks are presented along with the results of the model. These results indicate that the model has a distinctive ability to focus on a particular speaker of choice.

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