scholarly journals A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1151 ◽  
Author(s):  
Aili Wang ◽  
Minhui Wang ◽  
Haibin Wu ◽  
Kaiyuan Jiang ◽  
Yuji Iwahori
Keyword(s):  
Spatial Relationship ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Residual Network ◽  
Propagation Process ◽  
Ground Target ◽  
Feature Information ◽  
Relationship Of ◽  
Detailed Feature

LiDAR data contain feature information such as the height and shape of the ground target and play an important role for land classification. The effect of convolutional neural network (CNN) for feature extraction on LiDAR data is very significant, however CNN cannot resolve the spatial relationship of features adequately. The capsule network (CapsNet) can identify the spatial variations of features and is widely used in supervised learning. In this article, the CapsNet is combined with the residual network (ResNet) to design a deep network-ResCapNet for improving the accuracy of LiDAR classification. The capsule network represents the features by vectors, which can account for the direction of the features and the relative position between the features. Therefore, more detailed feature information can be extracted. ResNet protects the integrity of information by passing input information to the output directly, which can solve the problem of network degradation caused by information loss in the traditional CNN propagation process to a certain extent. Two different LiDAR data sets and several classic machine learning algorithms are used for comparative experiments. The experimental results show that ResCapNet proposed in this article `improve the performance of LiDAR classification.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Sensors connection for yield determination on round balers with variable chamber

2011 ◽  
Vol 57 (No. 2) ◽  
pp. 51-55 ◽  
Author(s):  
M. Kroulík ◽  
J. Mašek ◽  
Z. Kvíz ◽  
B. Jošt ◽  
V. Prošek
Keyword(s):  
Strong Dependence ◽  
Spatial Relationship ◽  
Forage Yield ◽  
Measuring System ◽  
Technical Solution ◽  
Data Sets ◽  
Trial Field ◽  
Instantaneous Position ◽  
Yield Determination ◽  
Relationship Of

The main aim of this article is to present a technical solution for straw and forage yield mapping when using round balers with variable chamber for harvest. The yield measurement is based on monitoring of instantaneous position of a tension roller mechanism for press chamber circular belt. Wheat straw was harvested &ndash; baled, during our trial measurements. The acreage of the trial field was 12 ha. Calibration of the measuring system showed a strong dependence of the tension roller position on the amount of pressed straw (R<sup>2</sup> = 0.99). Geostatistical evaluation confirms a spatial relationship of measured data sets with a moderate spatial dependence. Finally, yield map of straw was created

Regional Mapping of Vineyards Using Machine Learning and LiDAR Data

2020 ◽  
Vol 11 (4) ◽  
pp. 1-22
Author(s):  
Adriaan Jacobus Prins ◽  
Adriaan van Niekerk
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Window Size ◽  
Machine Learning Algorithms ◽  
Surface Model ◽  
Lidar Data ◽  
Data Sets ◽  
Random Forest Algorithm ◽  
Spectral Mixing

This study evaluates the use of LiDAR data and machine learning algorithms for mapping vineyards. Vineyards are planted in rows spaced at various distances, which can cause spectral mixing within individual pixels and complicate image classification. Four resolution where used for generating normalized digital surface model and intensity derivatives from the LiDAR data. In addition, texture measures with window sizes of 3x3 and 5x5 were generated from the LiDAR derivatives. The different combinations of the resolutions and window sizes resulted in eight data sets that were used as input to 11 machine learning algorithms. A larger window size was found to improve the overall accuracy for all the classifier–resolution combinations. The results showed that random forest with texture measures generated at a 5x5 window size outperformed the other experiments, regardless of the resolution used. The authors conclude that the random forest algorithm used on LiDAR derivatives with a resolution of 1.5m and a window size of 5x5 is the recommend configuration for vineyard mapping using LiDAR data.

Ultrastructural evidence for mast cell-macrophage interrelationships in the dura mater of the rat: Infrequent mast cell-nerve associations

1990 ◽  
Vol 48 (3) ◽  
pp. 352-353
Author(s):  
Ruth V.W. Dimlich
Keyword(s):  
Mast Cell ◽  
Mast Cells ◽  
Dura Mater ◽  
Spatial Relationship ◽  
Potassium Phosphate ◽  
Plasma Extravasation ◽  
Close Spatial Relationship ◽  
Male Wistar Rats ◽  
Headache Pain ◽  
Relationship Of

Mast cells in the dura mater of the rat may play a role in cerebral pathologies including neurogenic inflammation (vasodilation; plasma extravasation) and headache pain . As has been suggested for other tissues, dural mast cells may exhibit a close spatial relationship to nerves. There has been no detailed ultrastructural description of mast cells in this tissue; therefore, the goals of this study were to provide this analysis and to determine the spatial relationship of mast cells to nerves and other components of the dura mater in the rat.Four adult anesthetized male Wistar rats (290-400 g) were fixed by perfusion through the heart with 2% glutaraldehyde and 2.8% paraformaldehyde in a potassium phosphate buffer (pH 7.4) for 30 min. The head of each rat was removed and stored in fixative for a minimum of 24 h at which time the dural coverings were removed and dissected into samples that included the middle meningeal vasculature. Samples were routinely processed and flat embedded in LX 112. Thick (1 um) sections from a minimum of 3 blocks per rat were stained with toluidine blue (0.5% aqueous).

scholarly journals A novel framework for designing a multi-DoF prosthetic wrist control using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chinmay P. Swami ◽  
Nicholas Lenhard ◽  
Jiyeon Kang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Upper Limb ◽  
Daily Living ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Random Forest Regression ◽  
Prosthetic Devices ◽  
Upper Limb Function ◽  
The Neural Network

AbstractProsthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.

scholarly journals Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities

2021 ◽  
Vol 10 (7) ◽  
pp. 432
Author(s):  
Nicolai Moos ◽  
Carsten Juergens ◽  
Andreas P. Redecker
Keyword(s):  
Population Density ◽  
Large Scale ◽  
Methodological Approach ◽  
Spatial Relationship ◽  
Postal Code ◽  
Annual Income ◽  
Choropleth Maps ◽  
Clustering Approach ◽  
Relationship Of ◽  
Insight Into

This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons.

Intrasellar Intercarotid Communicating Artery Associated with Agenesis of the Right Internal Carotid Artery: Case Report

Neurosurgery ◽  
1988 ◽  
Vol 23 (6) ◽  
pp. 770-773 ◽  
Author(s):  
Masahiko Udzura ◽  
Hiroo Kobayashi ◽  
Yoshio Taguchi ◽  
Hiroaki Sekino
Keyword(s):  
Magnetic Resonance Imaging ◽  
Carotid Artery ◽  
Internal Carotid Artery ◽  
Internal Carotid ◽  
Spatial Relationship ◽  
Resonance Imaging ◽  
Anomalous Artery ◽  
Magnetic Resonance Imaging Mri ◽  
The Right ◽  
Relationship Of

Abstract A 54-year-old man with a right hemiparesis was found to have an intrasellar intercarotid communicating artery associated with agenesis of the right internal carotid artery. Magnetic resonance imaging (MRI) studies demonstrated the spatial relationship of the anomalous artery to the surrounding structures, thus suggesting an embryonic enlargement of the capsular artery as a source of this anomalous artery.

scholarly journals Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

2018 ◽  
Vol 7 (2.28) ◽  
pp. 312
Author(s):  
Manu Kohli
Keyword(s):  
Data Extraction ◽  
Indirect Costs ◽  
True Positive Rate ◽  
Spare Parts ◽  
Machine Learning Algorithms ◽  
Machine Language ◽  
Support Vector ◽  
Data Sets ◽  
Equipment Failure ◽  
Using Data

Asset intensive Organizations have searched long for a framework model that would timely predict equipment failure. Timely prediction of equipment failure substantially reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author proposes a model that can predict equipment failure by using data from SAP Plant Maintenance module. To achieve that author has applied data extraction algorithm and numerous data manipulations to prepare a classification data model consisting of maintenance records parameters such as spare parts usage, time elapsed since last completed maintenance and the period to the next scheduled maintained and so on. By using unsupervised learning technique of clustering, the author observed a class to cluster evaluation of 80% accuracy. After that classifier model was trained using various machine language (ML) algorithms and subsequently tested on mutually exclusive data sets with an objective to predict equipment breakdown. The classifier model using ML algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) returned an accuracy and true positive rate (TPR) of greater than 95% to predict equipment failure. The proposed model acts as an Advanced Intelligent Control system contributing to the Cyber-Physical Systems for asset intensive organizations. 

An Improved Residual Network for Electricity Power Meter Error Estimation

2019 ◽  
Vol 33 (08) ◽  
pp. 1959024
Author(s):  
Zichao Kou ◽  
Yanjun Fang
Keyword(s):  
Deep Learning ◽  
Error Estimation ◽  
Back Propagation ◽  
Electrical Energy ◽  
Residual Network ◽  
Propagation Process ◽  
Power Meter ◽  
Energy Metering ◽  
Electricity Power ◽  
Comparable Model

The lack of research on the metering characteristics of electricity power meters under complex conditions is a major obstacle to the on-site verification of electrical energy metering equipment. Establishing a predictive model for electricity power meter errors offers an effective way of dealing with this issue. Deep learning has been proven to have the capacity to reduce end-to-end dimensionality and improve recognition. Through the analysis of the back propagation process in residual networks, an improved residual network is set out in this paper. While preserving the advantages of residual network gradient propagation, it adds an adjustable shortcut and designs a convex [Formula: see text]-parameter strategy that can be improved according to different processing objects. Experimental results show that the predicted errors produced by the proposed technique are significantly lower than in a comparable model. At the same time, the improved residual network does not increase the network’s complexity.

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