scholarly journals Wheel Obstraction Detection with Machine Learning

In this paper, to blessing an ongoing programmed innovative and insightful based absolutely rail assessment framework, which plays examinations at sixteen km/h with a casing rate of 20 fps. The framework identifies significant rail segments including ties, tie plates, and grapples, with high exactness and productivity. To accomplish this objective, to initially widen an immovable of picture and video investigation and after that prompt a particular worldwide streamlining structure to join proof from two or three cameras, Global Positioning System, and separation size apparatus to moreover improve the recognition execution. Additionally, as the grapple is a significant kind of rail clasp, to've as needs be propelled the push to hit upon stay special cases, which consolidates evaluating the grapple circumstances on the tie stage and recognizing grapple design exemptions on the consistence level. Quantitative examination performed on a huge video certainties set caught with unmistakable tune and lighting installations conditions, notwithstanding on a continuous order check, has affirmed empotoring execution on each rail perspective recognition and stay special case location. In particular, a middle of 94.67% accuracy and ninety three% remember expense has been finished for recognizing each of the 3 rail segments, and a 100% recognition charge is practiced for consistence level stay special case with three phony positives predictable with hour. To our excellent comprehension, our framework is the essential to address and clear up both perspective and special case location issues in this rail assessment region

Author(s):  
S.G. Prasad Mutchakayala ◽  
V.L. Manasa Mandalapu ◽  
J.R.K. Kumar Dabbakuti ◽  
Sai Sruti Vedula

2021 ◽  
Author(s):  
Wenhua Liang ◽  
Ishmael Rico ◽  
Yu Sun

Technological advancement has brought many the convenience that the society used to lack, but unnoticed by many, a population neglected through the age of technology has been the visually impaired population. The visually impaired population has grown through ages with as much desire as everyone else to adventure but lack the confidence and support to do so. Time has transported society to a new phase condensed in big data, but to the visually impaired population, this quick-pace living lifestyle, along with the unpredictable natural disaster and COVID-19 pandemic, has dropped them deeper into a feeling of disconnection from the society. Our application uses the global positioning system to supportthe visually impaired in independent navigation, alerts them in face of natural disasters, and remindsthem to sanitize their devices during the COVID-19 pandemic.


Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 518 ◽  
Author(s):  
Amandine Schmutz ◽  
Laurence Chèze ◽  
Julien Jacques ◽  
Pauline Martin

With the emergence of numerical sensors in sports, there is an increasing need for tools and methods to compute objective motion parameters with great accuracy. In particular, inertial measurement units are increasingly used in the clinical domain or the sports one to estimate spatiotemporal parameters. The purpose of the present study was to develop a model that can be included in a smart device in order to estimate the horse speed per stride from accelerometric and gyroscopic data without the use of a global positioning system, enabling the use of such a tool in both indoor and outdoor conditions. The accuracy of two speed calculation methods was compared: one signal based and one machine learning model. Those two methods allowed the calculation of speed from accelerometric and gyroscopic data without any other external input. For this purpose, data were collected under various speeds on straight lines and curved paths. Two reference systems were used to measure the speed in order to have a reference speed value to compare each tested model and estimate their accuracy. Those models were compared according to three different criteria: the percentage of error above 0.6 m/s, the RMSE, and the Bland and Altman limit of agreement. The machine learning method outperformed its competitor by giving the lowest value for all three criteria. The main contribution of this work is that it is the first method that gives an accurate speed per stride for horses without being coupled with a global positioning system or a magnetometer. No similar study performed on horses exists to compare our work with, so the presented model is compared to existing models for human walking. Moreover, this tool can be extended to other equestrian sports, as well as bipedal locomotion as long as consistent data are provided to train the machine learning model. The machine learning model’s accurate results can be explained by the large database built to train the model and the innovative way of slicing stride data before using them as an input for the model.


2021 ◽  
Vol 16 ◽  
pp. 294-301
Author(s):  
Reshma Verma ◽  
Lakshmi Shrinivasan ◽  
K Shreedarshan

Nowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).


INTI TALAFA ◽  
2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Yaman Khaeruzzaman

Seiring dengan pesatnya kemajuan teknologi saat ini, kebutuhan manusia menjadi lebih beragam, termasuk kebutuhan akan informasi. Tidak hanya media informasinya yang semakin beragam, jenis informasi yang dibutuhkan juga semakin beragam, salah satunya adalah kebutuhan informasi akan posisi kita terhadap lingkungan sekitar. Untuk memenuhi kebutuhan itu sebuah sistem pemosisi diciptakan. Sistem pemosisi yang banyak digunakan saat ini cenderung berfokus pada lingkup ruang yang besar (global) padahal, dalam lingkup ruang yang lebih kecil (lokal) sebuah sistem pemosisi juga diperlukan, seperti di ruang-ruang terbuka umum (taman atau kebun), ataupun dalam sebuah bangunan. Sistem pemosisi lokal yang ada saat ini sering kali membutuhkan infrastruktur yang mahal dalam pembangunannya. Aplikasi Pemosisi Lokal Berbasis Android dengan Menggunakan GPS ini adalah sebuah aplikasi yang dibangun untuk memenuhi kebutuhan pengguna akan informasi lokasi dan posisi mereka terhadap lingkungan di sekitarnya dalam lingkup ruang yang lebih kecil (lokal) dengan memanfaatkan perangkat GPS (Global Positioning System) yang telah tertanam dalam perangkat smartphone Android agar infrastruktur yang dibutuhkan lebih efisien. Dalam implementasinya, Aplikasi Pemosisi Lokal ini bertindak sebagai klien dengan dukungan sebuah Database Server yang berfungsi sebagai media penyimpanan data serta sumber referensi informasi yang dapat diakses melalui jaringan internet sehingga tercipta sebuah sistem yang terintegrasi secara global. Kata kunci: aplikasi, informasi, pemosisi, GPS.


Author(s):  
Violet Bassey Eneyo

This paper examines the distribution of hospitality services in Uyo Urban, Nigeria. GIS method was the primary tool used for data collection. A global positioning system (GPS) Garmin 60 model was used in tracking the location of 102 hospitality services in the study area. One hypothesis was stated and tested using the nearest neighbour analysis. The finding shows evidence of clustering of the various hospitality services. The tested hypothesis further indicated that hospitality services clustered in areas that guarantee a sustainable level of patronage to maximize profit. Thus, the hospitality services clustered in selected streets in the metropolis while limited numbers were found outside the city’s central area.


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