scholarly journals Evaluation of Signal Features used for Impact Localization on a Center Console with Piezoceramic Sensors

2019 ◽  
Vol 6 (1) ◽  
pp. 3-8
Author(s):  
André Böhle
Keyword(s):  
Control Element ◽  
Support Vector ◽  
Center Console ◽  
Signal Features ◽  
Wide Range ◽  
The Impact ◽  
Impact Localization ◽  
A Current

Smart components are increasingly of interest inresearch and industry due to their wide range of applications. Anexample of this is a current project of the Federal ExcellenceCluster MERGE, which is concerned with the development of acenter console that serves as a control element in an automobileand is executing actions by touching it. In order to facilitate thisfunctionality, it is necessary to evaluate the electrical signalsgenerated by piezoceramic sensors regarding to the localizationof the impact. In this respect, various signal features areinvestigated for their suitability using a support vector machine.The results show that an impact localization can be realized bythe energetic consideration of the signals but has limitations inthe practical usability.

scholarly journals Considering Performance in the Automated and Manual Coding of Sociolinguistic Variables: Lessons From Variable (ING)

2021 ◽  
Vol 4 ◽  
Author(s):  
Tyler Kendall ◽  
Charlotte Vaughn ◽  
Charlie Farrington ◽  
Kaylynn Gunter ◽  
Jaidan McLean ◽  
...  
Keyword(s):  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Language Variation And Change ◽  
The Public ◽  
Acoustic Correlates ◽  
Wide Range ◽  
Automated Coding ◽  
Automated Procedures ◽  
The Impact

Impressionistic coding of sociolinguistic variables like English (ING), the alternation between pronunciations like talkin' and talking, has been a central part of the analytic workflow in studies of language variation and change for over a half-century. Techniques for automating the measurement and coding for a wide range of sociolinguistic data have been on the rise over recent decades but procedures for coding some features, especially those without clearly defined acoustic correlates like (ING), have lagged behind others, such as vowels and sibilants. This paper explores computational methods for automatically coding variable (ING) in speech recordings, examining the use of automatic speech recognition procedures related to forced alignment (using the Montreal Forced Aligner) as well as supervised machine learning algorithms (linear and radial support vector machines, and random forests). Considering the automated coding of pronunciation variables like (ING) raises broader questions for sociolinguistic methods, such as how much different human analysts agree in their impressionistic codes for such variables and what data might act as the “gold standard” for training and testing of automated procedures. This paper explores several of these considerations in automated, and manual, coding of sociolinguistic variables and provides baseline performance data for automated and manual coding methods. We consider multiple ways of assessing algorithms' performance, including agreement with human coders, as well as the impact on the outcome of an analysis of (ING) that includes linguistic and social factors. Our results show promise for automated coding methods but also highlight that variability in results should be expected even with careful human coded data. All data for our study come from the public Corpus of Regional African American Language and code and derivative datasets (including our hand-coded data) are available with the paper.

Multifault Diagnosis of Induction Motor at Intermediate Operating Conditions Using Wavelet Packet Transform and Support Vector Machine

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Time Series Data ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Operating Conditions ◽  
Series Data ◽  
Support Vector ◽  
Wide Range ◽  
The Impact

This paper proposes advancement in the fault diagnosis of induction motors (IMs) based on the wavelet packet transform (WPT) and the support vector machine (SVM). The aim of this work is to develop and perform the fault diagnosis of IMs at intermediate operating conditions (i.e., the speed and the load) to take care of situations where the data are limited or difficult to obtain at required speeds and loads. In order to check the capability of proposed fault diagnosis, ten different IM fault (mechanical and electrical) conditions are considered simultaneously. In order to obtain the useful information from raw time series data that can characterize each of the fault classes at various operating conditions, the wavelet packet is applied to decompose the data of vibration and current signals from the experimental test rig. Fault features are then obtained using the decomposed data and further used for the diagnosis. In this work, five different wavelet functions (i.e., the Haar, Daubechies, Symlet, Coiflet, and Discrete Meyer) are considered in order to analyze the impact of different wavelets on the IM fault diagnosis. The proposed fault diagnosis has been initially attempted for the same speed and load cases and then extended innovatively to the intermediate speed and load cases. In order to check the robustness of the proposed methodology, the diagnosis is performed for a wide range of motor operating conditions. The results show the feasibility of the proposed fault diagnosis for the successful detection and isolation of various faults of IM, even with limited data or information at some motor operating conditions.

scholarly journals A Novel Tool for Supervised Segmentation Using 3D Slicer

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 627 ◽  
Author(s):  
Daniel Chalupa ◽  
Jan Mikulka
Keyword(s):  
Machine Learning ◽  
Medical Image ◽  
Tumor Staging ◽  
Feature Space ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
3D Slicer ◽  
Wide Range ◽  
The Impact

The rather impressive extension library of medical image-processing platform 3D Slicer lacks a wide range of machine-learning toolboxes. The authors have developed such a toolbox that incorporates commonly used machine-learning libraries. The extension uses a simple graphical user interface that allows the user to preprocess data, train a classifier, and use that classifier in common medical image-classification tasks, such as tumor staging or various anatomical segmentations without a deeper knowledge of the inner workings of the classifiers. A series of experiments were carried out to showcase the capabilities of the extension and quantify the symmetry between the physical characteristics of pathological tissues and the parameters of a classifying model. These experiments also include an analysis of the impact of training vector size and feature selection on the sensitivity and specificity of all included classifiers. The results indicate that training vector size can be minimized for all classifiers. Using the data from the Brain Tumor Segmentation Challenge, Random Forest appears to have the widest range of parameters that produce sufficiently accurate segmentations, while optimal Support Vector Machines’ training parameters are concentrated in a narrow feature space.

scholarly journals Impact Localization Method for Composite Plate Based on Low Sampling Rate Embedded Fiber Bragg Grating Sensors

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Zhuo Pang ◽  
Mei Yuan ◽  
Hao Song ◽  
Zongxia Jiao
Keyword(s):  
Fiber Bragg Grating ◽  
Sampling Rate ◽  
Classification Model ◽  
Support Vector ◽  
Bragg Grating ◽  
Localization Algorithm ◽  
Experimental Conditions ◽  
Localization Accuracy ◽  
The Impact ◽  
Impact Localization

Fiber Bragg Grating (FBG) sensors have been increasingly used in the field of Structural Health Monitoring (SHM) in recent years. In this paper, we proposed an impact localization algorithm based on the Empirical Mode Decomposition (EMD) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) to achieve better localization accuracy for the FBG-embedded plate. In our method, EMD is used to extract the features of FBG signals, and PSO-SVM is then applied to automatically train a classification model for the impact localization. Meanwhile, an impact monitoring system for the FBG-embedded composites has been established to actually validate our algorithm. Moreover, the relationship between the localization accuracy and the distance from impact to the nearest sensor has also been studied. Results suggest that the localization accuracy keeps increasing and is satisfactory, ranging from 93.89% to 97.14%, on our experimental conditions with the decrease of the distance. This article reports an effective and easy-implementing method for FBG signal processing on SHM systems of the composites.

scholarly journals A Novel Framework for Predictive Modeling and Optimization of Powder Bed Fusion Process

2021 ◽  
Author(s):  
Ehsan Malekipour ◽  
Mallikharjun Marrey ◽  
Hazim El-Mounayri ◽  
Eric Faierson ◽  
Mangilal Agarwal
Keyword(s):  
Mechanical Properties ◽  
Process Parameters ◽  
Support Vector ◽  
Large Set ◽  
Powder Bed Fusion ◽  
Two Phase ◽  
Powder Bed ◽  
Steel Material ◽  
Wide Range ◽  
The Impact

Powder bed fusion (PBF) process is a metal additive manufacturing process which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for optimizing a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for optimizing the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters -- i.e., laser specifications -- and mechanical properties and how to achieve parts with high density (> 98%) as well as better ultimate mechanical properties. In this paper, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage of 10.236% that are used to optimize process parameters in accordance with user or manufacturer requirements. These models use support vector regression, random forest regression, and neural network techniques. It is shown that the intelligent selection of process parameters using these models can achieve an optimized density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.

scholarly journals Optimization of spring barley cultivation technology in the Chernozem region

2021 ◽  
Vol 4 (46) ◽  
pp. 14-14
Author(s):  
Alexander Saakian ◽  
◽  
Keyword(s):  
Comparative Analysis ◽  
Weed Control ◽  
Spring Barley ◽  
Control Element ◽  
Technology Application ◽  
Central Chernozem Region ◽  
Wide Range ◽  
Cultivation Technology ◽  
The Impact ◽  
Central Chernozem

In the conditions of the Central Chernozem region of Russia, an optimized technology for growing spring barley of the "Vakula" P4 variety was tested. Optimization of the technology was carried out according to the weed control element in five variants, depending on the use of herbicides: Puma Super (farm technology), Ballerina super (optimization), Prima (optimization), Ballerina forte (optimization), Lancelot (optimization). The conducted studies have shown that perennial and annual dicotyledonous weeds have a significant distribution in spring barley crops in the conditions of the Central Chernozem zone. Of these, according to the abundance indicators, white mar, medicinal dymyanka, white sandman, bindweed mountaineer, field yarutka, yellow osot, frankincense pickle predominates. A comparative analysis of optimization on the impact on plant productivity revealed that the most effective technologies for a wide range of weeds were technologies using herbicides Lancelot, Ballerina Super and Ballerina Forte. The highest barley yield of 34.4, 32.3 and 33.7 c/ha, respectively, was noted on these variants of technology application. Key words: SPRING BARLEY, HERBICIDES, CULTIVATION TECHNOLOGY, OPTIMIZATION

scholarly journals Quinoa in Ecuador: Recent Advances under Global Expansion

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 298
Author(s):  
Leonardo Hinojosa ◽  
Alex Leguizamo ◽  
Carlos Carpio ◽  
Diego Muñoz ◽  
Camilo Mestanza ◽  
...  
Keyword(s):  
State University ◽  
Andean Region ◽  
Washington State University ◽  
Pests And Diseases ◽  
Wide Range ◽  
Participatory Breeding ◽  
Tropical Areas ◽  
The Impact ◽  
A Current

Quinoa is a highly diverse crop domesticated in the Andean region of South America with broad adaptation to a wide range of marginal environments. Quinoa has garnered interest worldwide due to its nutritional and health benefits. Over the last decade, quinoa production has expanded outside of the Andean region, prompting multiple studies investigating the potential for quinoa cultivation in novel environments. Currently, quinoa is grown in countries spanning five continents, including North America, Europe, Asia, Africa, and Oceania. Here, we update the advances of quinoa research in Ecuador across different topics, including (a) current quinoa production situation with a focus on breeding progress, (b) traditional seed production, and (c) the impact of the work of the nongovernment organization “European Committee for Training and Agriculture” with quinoa farmers in Chimborazo province. Additionally, we discuss genetic diversity, primary pests and diseases, actions for adapting quinoa to tropical areas, and recent innovations in quinoa processing in Ecuador. Finally, we report a case study describing a participatory breeding project between Washington State University and the Association of Andean Seed and Nutritional Food Producers Mushuk Yuyay in the province of Cañar.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

THE IMPACT OF LEADERSHIP PRACTICES TO TEAM MEMBERS’ SATISFACTION

2009 ◽  
Vol 8 (1) ◽  
Author(s):  
Chalimah .
Keyword(s):  
Conflict Resolution ◽  
Leadership Practices ◽  
Hotel Industry ◽  
Leader Behavior ◽  
Team Leader ◽  
Shop Floor ◽  
Team Members ◽  
Top Executives ◽  
Wide Range ◽  
The Impact

eamwork is becoming increasingly important to wide range of operations. It applies to all levels of the company. It is just as important for top executives as it is to middle management, supervisors and shop floor workers. Poor teamwork at any level or between levels can seriously damage organizational effectiveness. The focus of this paper was therefore to examine whether leadership practices consist of team leader behavior, conflict resolution style and openness in communication significantly influenced the team member’s satisfaction in hotel industry. Result indicates that team leader behavior and the conflict resolution style significantly influenced team member satisfaction. It was surprising that openness in communication did not affect significantly to the team members’ satisfaction.

Stress, anxiety, depression and burnout in frontline health care workers during COVID-19 pandemic: a brief systematic review and new data from Russia

2021 ◽  
Author(s):  
Ekaterina Mosolova ◽  
Dmitry Sosin ◽  
Sergey Mosolov
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Female Gender ◽  
Assessment Tools ◽  
Care Workers ◽  
Younger Age ◽  
Wide Range ◽  
Associated Risk Factors ◽  
Anxiety Depression ◽  
The Impact

During the COVID-19 pandemic, healthcare workers (HCWs) have been subject to increased workload while also exposed to many psychosocial stressors. In a systematic review we analyze the impact that the pandemic has had on HCWs mental state and associated risk factors. Most studies reported high levels of depression and anxiety among HCWs worldwide, however, due to a wide range of assessment tools, cut-off scores, and number of frontline participants in the studies, results were difficult to compare. Our study is based on two online surveys of 2195 HCWs from different regions of Russia during spring and autumn epidemic outbreaks revealed the rates of anxiety, stress, depression, emotional exhaustion and depersonalization and perceived stress as 32.3%, 31.1%, 45.5%, 74.2%, 37.7% ,67.8%, respectively. Moreover, 2.4% of HCWs reported suicidal thoughts. The most common risk factors include: female gender, nurse as an occupation, younger age, working for over 6 months, chronic diseases, smoking, high working demands, lack of personal protective equipment, low salary, lack of social support, isolation from families, the fear of relatives getting infected. These results demonstrate the need for urgent supportive programs for HCWs fighting COVID-19 that fall into higher risk factors groups.

Sign in / Sign up

Export Citation Format

Share Document