scholarly journals Design of Automatic Detection System for Railway Facility Maintenance

2018 ◽  
Vol 2 (2) ◽  
pp. 120-126
Author(s):  
Natriya Faisal Rachman ◽  
Arief Darmawan
Keyword(s):  
Detection System ◽  
Data Retrieval ◽  
Active Role ◽  
Check List ◽  
Motorcycle Safety ◽  
Network Connection ◽  
Facility Maintenance ◽  
Automatic Detection System ◽  
Money Order ◽  
History Database

The use of android now not only acts as a means of communication for the community, but also very active role in the dissemination of information quickly and easily. With the current situation which is very practical, in this study, the design of a motorcycle safety history database was designed using android-based QRcode method. Because previously the executor still used the check list sheet, with the design of the Android-based barcode method, the executor did not need to see the data in the archive or add data through the check list sheet. The design of the andoid-based money order motor maintenance database was made in three stages, namely the creation of a database using Firebase. Making android as an interface using Android Studio and making code scanning with QRcode. Data displayed in real time, i.e. data can update data by other devices automatically so that data can be recorded and stored. Data can be opened on more than two devices that have an application installed, provided that the device must scan the QRcode. From the results of testing of the database design that has been made, it can be concluded that the android-based database system can speed up the process of data retrieval compared to taking attendance manually which is the archive of maintenance results. This system can only be used if the device used is on an internet network connection.

Survey on Safety Devices Using IoT

2020 ◽  
pp. 450-453
Keyword(s):  
Traffic Congestion ◽  
Detection System ◽  
Text Message ◽  
Emergency Situation ◽  
Heart Beat ◽  
Advanced Technology ◽  
Night Time ◽  
Remote Areas ◽  
Alert Message ◽  
Automatic Detection System

Traffic congestion is becoming a huge problem, which is arising due to vehicle failure or accidents. Transportation and use of advanced technology has great importance in society and that has made many of our lives much easier. By automatic accident detection and alerting GSM & GPS based technology can be used to overcome these problems. Where as in case of Child and Women there are very few efficient security and safety measures adopted. Now in India the safety for women has become a major issue while travelling. Nowadays women think twice before taking any steps out of their homes, especially in the night time. Hence, this is unfortunately, the sad reality of our country and also due to various crimes like child abuse, rape, dowry deaths, trafficking and many more. At the time of women facing unsecured situations, there is a need to ensure safety while travelling. Hence automatic detection system needs to be established where one can send alert message to the police station or the relatives which detects the current location of the required ones by use of such technologies the women and children can get protection. Mainly in remote areas children use bicycles as means of transport from several years and nowadays, despite due to the large vailability of new and faster means, the bicycle users is not decreased. Despites the cyclists find difficult to travel within them and other vehicles find difficult to find them during night time. In case of any emergency situation faced at unknown remote areas the cyclist can send their location to required ones to help them. In this paper, report the survey on the existing mechanism for detecting locations, and sending signals and to collect parameters such as temperature of the human body, heart beat etc. using sensors. With the help of GPS and GSM we can track the location of the child, women or vehicle. Hence, by these we can save the life of person’s being injured in various locations by sending a text message using IOT technologies

scholarly journals Research on automatic detection system of encoder accuracy based on PID algorithm

2021 ◽  
Vol 1754 (1) ◽  
pp. 012233
Author(s):  
Han Hou ◽  
Guohua Cao ◽  
Hongchang Ding ◽  
Changfu Zhao ◽  
Aijia Wang
Keyword(s):  
Detection System ◽  
Automatic Detection ◽  
Pid Algorithm ◽  
Automatic Detection System

An ECG Automatic Detection System with Baseline Drift Removal Based on SG Filter

2021 ◽  
Author(s):  
Jiangbo Wei ◽  
Chenghao Zhang ◽  
Jiaji Ma ◽  
Zhihang Li ◽  
Maliang Liu
Keyword(s):  
Detection System ◽  
Automatic Detection ◽  
Baseline Drift ◽  
Automatic Detection System

scholarly journals Automated Detection of Laryngeal Carcinoma in Laryngoscopic Images from a Multicenter Database using a Convolutional Neural Network

2021 ◽  
Author(s):  
Peikai Yan ◽  
Shaohua Li ◽  
Zhou Zhou ◽  
Qian Liu ◽  
Jiahui Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Laryngeal Carcinoma ◽  
Detection System ◽  
Tertiary Care ◽  
Diagnostic System ◽  
Intelligence System ◽  
Testing Dataset ◽  
Malignant Group ◽  
Automatic Detection System ◽  
Tertiary Care Centers

OBJECTIVE Little is known about the efficacy of using artificial intelligence to identify laryngeal carcinoma from images of vocal lesions taken in different hospitals with multiple laryngoscope systems. This multicenter study was aimed to establish an artificial intelligence system and provide a reliable auxiliary tool to screen for laryngeal carcinoma. Study Design: Multicentre case-control study Setting: Six tertiary care centers Participants: The laryngoscopy images were collected from 2179 patients with vocal lesions. Outcome Measures: An automatic detection system of laryngeal carcinoma was established based on Faster R-CNN, which was used to distinguish vocal malignant and benign lesions in 2179 laryngoscopy images acquired from 6 hospitals with 5 types of laryngoscopy systems. Pathology was the gold standard to identify malignant and benign vocal lesions. Results: Among 89 cases of the malignant group, the classifier was able to evaluate the laryngeal carcinoma in 66 patients (74.16%, sensitivity), while the classifier was able to assess the benign laryngeal lesion in 503 cases among 640 cases of the benign group (78.59%, specificity). Furthermore, the CNN-based classifier achieved an overall accuracy of 78.05% with a 95.63% negative prediction for the testing dataset. Conclusion: This automatic diagnostic system has the potential to assist clinical laryngeal carcinoma diagnosis, which may improve and standardize the diagnostic capacity of endoscopists using different laryngoscopes.

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