Measurement and Monitoring System With Real Time Data Logging Based on Microcontroller

2018 ◽  
Vol 7 (2) ◽  
pp. 20-40
Author(s):  
Abdelkrim Mohrem ◽  
Boukhemis Chetate ◽  
Houssem Eddine Guia
Keyword(s):  
Real Time ◽  
Time Interval ◽  
Data Logging ◽  
Time Data ◽  
Industrial Facilities ◽  
System A ◽  
Electronic Board ◽  
Real Time Data ◽  
Data Files ◽  
Log File

This article presents a microcontroller-based system for measurement and monitoring voltage of a three-phase electrical system with real-time data logging abilities. The proposed system uses voltage signals and time data as input. The output is an LCD and data files. The system accurately records abnormal voltage variations which have occurred on the system. A PC software is developed to receive and save data in two spreadsheet files through a serial port. Ihe log file contains the measured voltage, which is recorded periodically with a predefined time interval, and the second file contains the type of the fault. The proposed system is first simulated by ISIS-Proteus and then realized and implemented on an electronic board. It is beneficial to make detailed, scientific judgments and analysis for the voltage system to be supplied to a load. Because of the very simple circuit, it finds applications in industrial facilities. It is also useful in applying final circuits for both investigation and monitoring purposes.

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

scholarly journals An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction

2020 ◽  
Vol 10 (11) ◽  
pp. 3788 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Short Term Memory ◽  
Historical Data ◽  
Time Interval ◽  
Information Coding ◽  
Time Data ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Real Time Data

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

Nature of Sub-23-nm Particles Downstream of the European Particle Measurement Programme (PMP)-Compliant System: A Real-Time Data Perspective

2012 ◽  
Vol 46 (8) ◽  
pp. 886-896 ◽  
Author(s):  
Zhongqing Zheng ◽  
Thomas D. Durbin ◽  
Georgios Karavalakis ◽  
Kent C. Johnson ◽  
Ajay Chaudhary ◽  
...  
Keyword(s):  
Real Time ◽  
Time Data ◽  
System A ◽  
Real Time Data ◽  
Particle Measurement

The Bus Dispatching Optimal Control System Based on Real-Time Data Acquisition

2012 ◽  
Vol 195-196 ◽  
pp. 1008-1016 ◽  
Author(s):  
Jian Li
Keyword(s):  
Optimal Control ◽  
Control System ◽  
Data Acquisition ◽  
Real Time ◽  
Time Interval ◽  
Time Data ◽  
Software Module ◽  
Optimal Control System ◽  
Departure Time ◽  
Real Time Data

In this paper, main software module in the subject The Bus Dispatching Optimal Control System Based on Real-time Data Acquisition has been designed. By gradually changing the departure time interval, the system uses SCM to simulate the system running status in various time periods so as to determine the optimal departure time interval, and has established the integrated and optimal scheduling model through the research and development of four functions i.e. data acquisition, remote network monitoring, C/S/S architecture, network access. This article will focus on the design of the main module of software part in the system.

scholarly journals Real Time Data Logging of Different Parameters of Airjet Looms

2019 ◽  
Vol 8 (6) ◽  
pp. 2304-2306
Keyword(s):  
Wireless Communication ◽  
Real Time ◽  
Manufacturing Industry ◽  
Data Logging ◽  
Time Data ◽  
Time System ◽  
Real Time System ◽  
Air Jet ◽  
Real Time Data ◽  
Recorded Data

Real time data logging of different parameters of Air jet looms should be implemented to reduce the time-consuming method in the textile manufacturing industry. Implementation area of this system is a reduction of efforts and errors done by workers in the textile looms. Existing system is not able to give real time data required by the user at the required time. This system actually keeps record of different stoppages that leads to break the continuity of the machine and hence reduces the machine efficiency. This is a real time system in which wireless communication is used to transfer the recorded data to user’s computer. This recorded detail in turn is transmitted to the PC of the user to do further computation of wages of the worker and manage their work efficiency. This is a real time system in which wireless communication is used to transfer the recorded data to user’s computer as well as on mobile phone. This will provide an additional facility of monitoring the working condition of machine whether it is proper or not and thus user can also keep watch on the workers.

Real-Time Data Logging and Online Curve Fitting Using Raspberry Pi in Physics Laboratories

2020 ◽  
Vol 18 (3) ◽  
pp. 57-77
Author(s):  
Wing-Kwong Wong ◽  
Kai-Ping Chen ◽  
Jia-Wei Lin
Keyword(s):  
Experimental Data ◽  
Real Time ◽  
Curve Fitting ◽  
Empirical Studies ◽  
Error Rates ◽  
Raspberry Pi ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Physics Laboratories

The results of PISA 2015 indicate that Taiwanese students have excellent mathematical and scientific knowledge but are weak in applying such knowledge and in conducting practical experiments in the laboratory. To support students conducting practical experiments in physics laboratories, a real-time data logging system and an online tool for fitting experimental data were developed. During data logging in an experiment, the data was immediately plotted, which enabled students to observe the characteristics of the plot. The online curve fitting system, which employed Internet of Things technologies, allowed students to fit experimental data to various mathematical functions and plot a function curve superimposed on the data. Two empirical studies were conducted involving first-year university students and secondary school teachers. The results indicated that these developed tools improved students' understanding of an experiment's mathematical characteristics. The average curve fitting error rates of students and teachers were 4.62% and 1.4%, respectively.

Advancements in the National Vital Statistics System to Meet the Real-Time Data Needs of a Pandemic

2021 ◽  
Vol 111 (12) ◽  
pp. 2133-2140
Author(s):  
Farida B. Ahmad ◽  
Robert N. Anderson ◽  
Karen Knight ◽  
Lauren M. Rossen ◽  
Paul D. Sutton
Keyword(s):  
Real Time ◽  
Death Certificate ◽  
Vital Statistics ◽  
Mortality Data ◽  
Annual Data ◽  
Health Statistics ◽  
Time Data ◽  
Real Time Data ◽  
Data Files ◽  
The Impact

The National Center for Health Statistics’ (NCHS’s) National Vital Statistics System (NVSS) collects, processes, codes, and reviews death certificate data and disseminates the data in annual data files and reports. With the global rise of COVID-19 in early 2020, the NCHS mobilized to rapidly respond to the growing need for reliable, accurate, and complete real-time data on COVID-19 deaths. Within weeks of the first reported US cases, NCHS developed certification guidance, adjusted internal data processing systems, and stood up a surveillance system to release daily updates of COVID-19 deaths to track the impact of the COVID-19 pandemic on US mortality. This report describes the processes that NCHS took to produce timely mortality data in response to the COVID-19 pandemic. (Am J Public Health. 2021;111(12):2133–2140. https://doi.org/10.2105/AJPH.2021.306519 )

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