Research on Air Content Estimation of Tributyl Phosphate Hydraulic Fluids: A Novel Approach Based on the Vacuum Method

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Xiaoping Ouyang ◽  
Boqian Fan ◽  
Huayong Yang ◽  
Rong Qing
Keyword(s):  
Tributyl Phosphate ◽  
Good Condition ◽  
Fluid Flows ◽  
Least Square ◽  
Air Content ◽  
Novel Approach ◽  
Pressure Time ◽  
Vacuum Method ◽  
Least Square Problem ◽  
Problem Comparison

The air content in hydraulic transmission fluids significantly reduces bulk modulus of the fluid and causes a drop in the stiffness and response of the hydraulic system. It is consequently very important to monitor the air content in hydraulic fluid for ensuring the hydraulic works in good condition. In this paper, a novel method is presented in which the sampled fluid flows slowly into a vacuum chamber and the pressure of separated air is measured. A model of pressure-time characteristics is established, with moisture content taken into account as well, since moisture is volatile in vacuum and its content in tributyl phosphate (TBP) based fluid is usually too high to be neglected. The model can be simplified, which turned out to be a nonlinear least square problem. Comparison between the measured and calculated value shows that the model matches well with the experimental data.

scholarly journals Numerical method to a class of boundary value problems

2018 ◽  
Vol 22 (4) ◽  
pp. 1877-1883 ◽  
Author(s):  
Yu-Yang Qiu
Keyword(s):  
Boundary Value Problems ◽  
Numerical Method ◽  
Conjugate Gradient ◽  
Iteration Method ◽  
Boundary Value ◽  
Solution Process ◽  
Least Square ◽  
Least Square Problem ◽  
Matrix Iteration

A class of boundary value problems can be transformed uniformly to a least square problem with Toeplitz constraint. Conjugate gradient least square, a matrix iteration method, is adopted to solve this problem, and the solution process is elucidated step by step so that the example can be used as a paradigm for other applications.

scholarly journals Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Kun Zhang ◽  
Minrui Fei ◽  
Xin Li ◽  
Huiyu Zhou
Keyword(s):  
Support Vector Machine ◽  
Mean Shift ◽  
Least Square ◽  
Support Vector ◽  
Svm Classifier ◽  
Least Square Estimation ◽  
Unstructured Environments ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

scholarly journals Automatic Detection of Atrial Fibrillation and Other Arrhythmias in ECG Recordings Acquired by a Smartphone Device

Electronics ◽  
2018 ◽  
Vol 7 (9) ◽  
pp. 199 ◽  
Author(s):  
Lucia Billeci ◽  
Magda Costi ◽  
David Lombardi ◽  
Franco Chiarugi ◽  
Maurizio Varanini
Keyword(s):  
Atrial Fibrillation ◽  
Support Vector Machine Classifier ◽  
Least Square ◽  
Support Vector ◽  
Personal Health ◽  
Cardiac Complications ◽  
Linear Discriminant ◽  
Novel Approach ◽  
Health Monitoring Systems

Atrial fibrillation (AF) is the most common cardiac disease and is associated with other cardiac complications. Few attempts have been made for discriminating AF from other arrhythmias and noise. The aim of this study is to present a novel approach for such a classification in short ECG recordings acquired using a smartphone device. The implemented algorithm was tested on the Physionet Computing in Cardiology Challenge 2017 Database and, for the purpose of comparison, on the MIT-BH AF database. After feature extraction, the stepwise linear discriminant analysis for feature selection was used. The Least Square Support Vector Machine classifier was trained and cross-validated on the available dataset of the Challenge 2017. The best performance was obtained with a total of 30 features. The algorithm produced the following performance: F1 Normal rhythm = 0.92; F1 AF rhythm: 0.82; F1 Other rhythm = 0.75; Global F1 = 0.83, obtaining the third best result in the follow-up phase of the Physionet Challenge. On the MIT-BH ADF database the algorithm gave the following performance: F1 Normal rhythm = 0.98; F1 AF rhythm: 0.99; Global F1 = 0.98. Since the algorithm reliably detect AF and other rhythms in smartphone ECG recordings, it could be applied for personal health monitoring systems.

scholarly journals Grey power models based on optimization of initial condition and model parameters

2014 ◽  
Vol 4 (2) ◽  
pp. 370-382 ◽  
Author(s):  
Yunchol Jong ◽  
Sifeng Liu
Keyword(s):  
Least Square Method ◽  
Least Square ◽  
Model Parameters ◽  
Initial Condition ◽  
Content Type ◽  
Novel Approach ◽  
New Information ◽  
Power Models ◽  
Grey Verhulst Model ◽  
Verhulst Model

Purpose – The purpose of this paper is to propose a novel approach to improve prediction accuracy of grey power models including GM(1, 1) and grey Verhulst model. Design/methodology/approach – The modified new models are proposed by optimizing the initial condition and model parameters. The new initial condition consists of the first item and the last item of a sequence generated by applying the first-order accumulative generation operator on the sequence of raw data. Findings – It is shown that the newly modified grey power model is an extension of the previous optimized GM(1, 1) and grey Verhulst model. And the optimized initial condition reflected the principle of new information priority. Practical implications – The result of a numerical example indicates that the modified grey model presented in this paper with better prediction performance. Originality/value – The new initial condition are derived by weighted combination of the first item and the last item. The coefficients of weight obtained by the least square method.

scholarly journals Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

2020 ◽  
Vol 12 (3) ◽  
pp. 442 ◽  
Author(s):  
Jesús Balado ◽  
Elena González ◽  
Pedro Arias ◽  
David Castro
Keyword(s):  
False Positive Rate ◽  
Good Condition ◽  
Point Clouds ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Novel Approach ◽  
Mapping System ◽  
Positive Rate ◽  
Mobile Mapping System

Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection–classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.

Sign in / Sign up

Export Citation Format

Share Document