scholarly journals A model for estimating the value of the applied pressure based on the analysis of tactile sensor signals using machine learning methods

2021 ◽  
pp. 119-130
Author(s):  
П.С. Козырь ◽  
Р.Н. Яковлев
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Applied Pressure ◽  
Tactile Sensor ◽  
Least Squares Regression ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Machine Learning Model ◽  
Sensor Signals ◽  
Fully Connected

В рамках настоящего исследования был проведен анализ существующих работ, посвященных интерпретации показаний тактильных сенсорных устройств, по результатам которого была предложена модель машинного обучения, позволяющая осуществлять оценку величины приложенного давления к поверхности тактильного сенсора давления емкостного типа. В качестве опорных моделей обработки и интерпретации сигналов данного устройства в работе рассматривались несколько методов машинного обучения: линейная регрессия, полиномиальная регрессия, регрессия дерева решений, частичная регрессия наименьших квадратов и полносвязная нейронная сеть прямого распространения. Обучение опорных моделей и апробация конечного решения проводилась на авторском наборе данных, включающем в себя более 3000 экземпляров данных. Согласно полученным результатам, наилучшее качество определения величины приложенного давления продемонстрирован решением на основе полносвязной нейронной сети прямого распространения. Коэффициент детерминации и средний модуль отклонения для данного решения на тестовой выборке составили 0,93 и 13,14 кПа соответственно. Currently, in the field of developing sensing systems for robotic means, one of the urgent tasks is the problem of interpreting the data of tactile pressure and proximity sensors. As a rule, the solution to this problem is complicated both by the dependence of the indicators of tactile sensors on the type of object’s material and by the design features of each individual device. In this study, an analysis of existing works devoted to the interpretation of the readings of tactile sensor devices was carried out. According to the analysis results a machine learning model was proposed that allows estimating the amount of pressure applied to the surface of a tactile pressure sensor of a capacitive type. The architecture of the proposed model includes two key blocks of data analysis, the first one is aimed at recognizing the type of interaction object’s material and the second is devoted to the direct assessment of the magnitude of the pressure applied to the sensor. Several machine learning methods were considered as supporting models for processing and interpreting the signals of this device: linear regression, polynomial regression, decision tree regression, partial least squares regression and a fully connected feedforward neural network.

Application of machine learning methods to automate the detection of obstacles to the movement of a train through a level crossing

2020 ◽  
pp. 46-51
Author(s):  
Yu.M. Iskanderov ◽  
B.E. Katarushkin ◽  
A.A. Ershov
Keyword(s):  
Machine Learning ◽  
Traffic Control ◽  
Intelligent System ◽  
Learning Methods ◽  
Similar System ◽  
Intelligent Information Systems ◽  
Machine Learning Methods ◽  
Machine Learning Model ◽  
Intelligent Information ◽  
Railway Crossing

Aim. Currently, when creating intelligent information systems in various fields of practical activity, machine learning methods are used. The article shows the possibilities of using these methods in automating the detection of obstacles in the interest of improving safety and reducing the number of emergencies at level crossings. Materials and methods. The article discusses advanced computer vision technologies used as the basis of an intelligent system for detecting obstacles to the movement of a train through a railway crossing. Results. Based on the analysis of the conditions and features of the functioning of the technologies considered, the relevance of introducing a similar system is shown, options for constructing its structure and operating principle are proposed, approaches are formulated when developing a machine learning model for classifying of the used images. Conclusions. The approach underlying the formation of an intelligent system for detecting obstacles to the movement of a train through a railway crossing allows it to be used as an additional independent security tool that implements an alarm for a duty officer on a specific section of the railway and / or to a traffic control dispatch center to prevent emergencies.

scholarly journals FORECASTING PRICES IN THE RENTAL HOUSING MARKET WITH MACHINE LEARNING METHODS

2020 ◽  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Regression Models ◽  
Data Science ◽  
Polynomial Regression ◽  
Short Term ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pricing Factors

The study of pricing factors in the market of the short-term rental has been done. Airbnb was chosen as the object of the study; it is a platform for accommodation, search, and rental around the world. At the beginning of 2021, the company offers 7 million homes from more than 220 countries. The Data Science methods play a significant role in the company's success. One of the key algorithms of the company is the pricing algorithm. Using the "Price Recommendations" feature, the homeowner can analyze which dates are most likely to be booked at the current price and which are not, it helps form a favorable offer. The system calculates the recommended cost of housing based on hundreds of parameters, some of which are easy to recognize, but there are less obvious factors that can also affect demand. The paper proposes an algorithm for identifying implicit pricing factors in the short-term rental market using machine learning methods, which includes: 1) data mining and data preparation; 2) building and analysis of linear regression models; 3) building and analysis of nonlinear regression models. The study was based on ads from the Airbnb site in Washington and New York using scripts developed in Python. The following models are built and analyzed: simple linear regression, multiple linear regression, polynomial regression, decision trees, random forest, and boosting. The results of the study showed that the most important factors are accommodates, cleaning_fee, room_type, bedrooms. But based on the model evaluation criteria, they cannot be used for implementation: linear models are of low quality, while the random forest, boosting, and trees are overfitted. Still the results can be used in conducting business analysis.

Categorising Vaccine Confidence with Transformer-Based Machine Learning Model: The Nuances of Vaccine Sentiment on Twitter (Preprint)

2021 ◽  
Author(s):  
Per Kummervold ◽  
Sam Martin ◽  
Sara Dada ◽  
Eliz Kilich ◽  
Chermain Denny ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Resource Constraints ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Maternal Vaccination ◽  
Machine Learning Model ◽  
Vaccine Confidence ◽  
The Individual ◽  
Automated Processes

BACKGROUND With growing conversations online and less than desired maternal vaccination uptake rates, these conversations could provide useful insight to inform future interventions. Automated processes for this type of analysis, such as natural language processing (NLP), have faced challenges extracting complex stances, like attitudes toward vaccines, from large text. OBJECTIVE In this study, we aimed to build upon recent advances in Transformer-based machine learning methods, and test if this could be used as a tool to assess the stance of social media posts towards vaccination during pregnancy. METHODS A total of 16,604 Tweets posted between 1 November 2018 and 30 April 2019 were selected by boolean searches related to maternal vaccination. Tweets were coded by three individual researchers into the categories “Promotional”, “Discouraging”, “Ambiguous” and “Neutral” After creating a final dataset of 2,722 unique tweets, multiple machine learning methods were trained on the dataset and then tested and compared to the human annotators. RESULTS We received an accuracy of 81.8% (F-score= 0.78) compared to the agreed score between the three annotators. For comparison, the accuracies of the individual annotators compared to the final score were 83.3%, 77.9% and 77.5%. CONCLUSIONS This study demonstrates the ability to achieve close to the same accuracy in categorising tweets using our machine learning models as could be expected by a single human annotator. The potential to use this reliable and accurate automated process could free up valuable time and resource constraints of conducting this analysis, in addition to inform potentially effective and necessary interventions. CLINICALTRIAL N/A

scholarly journals An Analysis of Sindhi Annotated Corpus using Supervised Machine Learning Methods

2019 ◽  
Vol 38 (1) ◽  
pp. 185-196 ◽  
Author(s):  
Mazhar Ali ◽  
Asim Imdad Wagan
Keyword(s):  
Machine Learning ◽  
Computational Linguistics ◽  
Semantic Analysis ◽  
Research Work ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Part Of Speech ◽  
Machine Learning Model ◽  
Work Done

The linguistic corpus of Sindhi language is significant for computational linguistics process, machine learning process, language features identification and analysis, semantic and sentiment analysis, information retrieval and so on. There is little computational linguistics work done on Sindhi text whereas, English, Arabic, Urdu and some other languages are fully resourced computationally. The grammar and morphemes of these languages are analyzed properly using dissimilar machine learning methods. The development and research work regarding computational linguistics are in progress on Sindhi language at this time. This study is planned to develop the Sindhi annotated corpus using universal POS (Part of Speech) tag set and Sindhi POS tag set for the purpose of language features and variation analysis. The features are extracted using TF-IDF (Term Frequency and Inverse Document Frequency) technique. The supervised machine learning model is developed to assess the annotated corpus to know the grammatical annotation of Sindhi language. The model is trained with 80% of annotated corpus and tested with 20% of test set. The cross-validation technique with 10-folds is utilized to evaluate and validate the model. The results of model show the better performance of model as well as confirm the proper annotation to Sindhi corpus. This study described a number of research gaps to work more on topic modeling, language variation, sentiment and semantic analysis of Sindhi language.

scholarly journals Modeling water level using downstream river water level observations and machine learning methods

2020 ◽  
Vol 163 ◽  
pp. 01009
Author(s):  
Mikhail Sarafanov ◽  
Eduard Kazakov ◽  
Yulia Borisova
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Water Level ◽  
Statistical Models ◽  
Polynomial Regression ◽  
Water Levels ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Temperature And Precipitation ◽  
Gauging Station

The article presents the results of the development of a model for calculating levels at one gauging station using the levels at another. To link the levels at two gauging stations, the data on levels, temperature and precipitation were used. The use of machine learning methods to solve the problem of predicting water levels made it possible to achieve an accuracy of about 6 cm. At the same time, traditional statistical models (linear regression, polynomial regression) have 14-16 cm error.

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