Real-Time Architecture for an Electro-Mech-Optical System for Detection of Vehicles on Highway

2004 ◽  
Author(s):  
Zhaoqing Wang ◽  
Harry H. Cheng ◽  
Stephen S. Nestinger ◽  
Benjamin D. Shaw ◽  
Joe Palen
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Intelligent Transportation System ◽  
Detection System ◽  
Field Tests ◽  
Laser Source ◽  
Time Data ◽  
Time Signals ◽  
Message Exchange ◽  
User Space

A real-time architecture for a highway vehicle detection system is presented in the article. The Laser Based Detection System (LBDS), focused on helping the Intelligent Transportation System (ITS), measures a key quantitative parameter of vehicles moving across a link of highway, namely, travel time. Travel time is based upon the identifying and reidentifying vehicles at various points on the highway. This article provides a method to collect real-time signals from an active laser source in the LBDS and calculate vehicle parameters using a standard computer. A method of message exchange between a real-time kernel process, for real-time data acquisition, and a user space process, for computing and displaying, is given under the RTLinux environment. Experimental results from field tests have shown that the application of the real-time architecture to the LBDS provides speeds deterministically.

Pipeline Diagnostics With Ultrasonic Meters

2016 ◽  
Author(s):  
Nicole Gailey ◽  
Noman Rasool
Keyword(s):  
Real Time ◽  
Measurement Errors ◽  
Flow Measurement ◽  
Detection System ◽  
Leak Detection ◽  
The United States ◽  
Time Data ◽  
Critical System ◽  
Detection Systems ◽  
Transient Models

Canada and the United States have vast energy resources, supported by thousands of kilometers (miles) of pipeline infrastructure built and maintained each year. Whether the pipeline runs through remote territory or passing through local city centers, keeping commodities flowing safely is a critical part of day-to-day operation for any pipeline. Real-time leak detection systems have become a critical system that companies require in order to provide safe operations, protection of the environment and compliance with regulations. The function of a leak detection system is the ability to identify and confirm a leak event in a timely and precise manner. Flow measurement devices are a critical input into many leak detection systems and in order to ensure flow measurement accuracy, custody transfer grade liquid ultrasonic meters (as defined in API MPMS chapter 5.8) can be utilized to provide superior accuracy, performance and diagnostics. This paper presents a sample of real-time data collected from a field install base of over 245 custody transfer grade liquid ultrasonic meters currently being utilized in pipeline leak detection applications. The data helps to identify upstream instrumentation anomalies and illustrate the abilities of the utilization of diagnostics within the liquid ultrasonic meters to further improve current leak detection real time transient models (RTTM) and pipeline operational procedures. The paper discusses considerations addressed while evaluating data and understanding the importance of accuracy within the metering equipment utilized. It also elaborates on significant benefits associated with the utilization of the ultrasonic meter’s capabilities and the importance of diagnosing other pipeline issues and uncertainties outside of measurement errors.

Advanced Techniques for Travel Time Data Collection

1996 ◽  
Vol 1551 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Shawn M. Turner
Keyword(s):  
Data Collection ◽  
Travel Time ◽  
Real Time ◽  
Cellular Phone ◽  
License Plate ◽  
Time Data ◽  
Video Imaging ◽  
Advantages And Disadvantages ◽  
Travel Time Data ◽  
Time Information

Travel time information is becoming more important for applications ranging from congestion measurement to real-time travel information. Several advanced techniques for travel time data collection are discussed, including electronic distance-measuring instruments (DMIs), computerized and video license plate matching, cellular phone tracking, automatic vehicle identification (AVI), automatic vehicle location (AVL), and video imaging. The various advanced techniques are described, the necessary equipment and procedures are outlined, the applications of each technique are discussed, and the advantages and disadvantages are summarized. Electronic DMIs are low in cost but typically limited to congestion monitoring applications. Computerized and video license plate matching are more expensive and would be most applicable for congestion measurement and monitoring. Cellular phone tracking, AVI, and AVL systems may require a significant investment in communications infrastructure, but they can provide real-time information. Video imaging is still in testing stages, with some uncertainty about costs and accuracy.

Research on Boom-Type Roadheader Vibration Testing Based on the LabVIEW

2011 ◽  
Vol 418-420 ◽  
pp. 1988-1991
Author(s):  
Li Juan Zhao ◽  
Xiu Mei Lv ◽  
Wei Tong
Keyword(s):  
Real Time ◽  
Detection System ◽  
Structural Characteristics ◽  
Test System ◽  
Vibration Characteristics ◽  
Acquisition Time ◽  
Vibration Source ◽  
Time Data ◽  
Vibration Signals ◽  
Time Frequency

This develops the roadheader vibration characteristics test system according to the structural characteristics and working principle of the cantilevered roadheader. Using the piezoelectric acceleration sensor detects vibration signal, and passing by signal processing and A/D conversion， vibration signals are sent to the PC with wireless transmission mode, vibration signals detected by the LabVIEW is realized real-time data acquisition, time-frequency analysis and digital processing. Based on this system testing results can effectively master roadheader operation state, identify the vibration characteristics, look for vibration source and put forward reasonable damping vibration measure, which provide the basis for roadheader in the best running condition. The development of roadheader vibration detection system uses the method that combines theory and simulation experiment , which realizes the real-time detection of roadheader vibration behavior, rational signal analysis of roadheader vibration and accurate processing of data results, it provides an important method to ensure the reliability of roadheader.

Real-Time Detection System of Soil Moisture Based on GPRS

2011 ◽  
Vol 464 ◽  
pp. 175-178
Author(s):  
Rong Biao Zhang ◽  
Jing Jing Guo ◽  
Qi Wang ◽  
Lei Zhang ◽  
Xian Lin Wang
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Detection System ◽  
Field Tests ◽  
Support Vector ◽  
Calibration Model ◽  
Vector Machines ◽  
Moisture Detection ◽  
Software And Hardware ◽  
Seamless Connection

Real-time monitoring of soil moisture is essential for agricultural production. In this paper, an improved system is designed based on GPRS technology for real-time detecting soil moisture, a salinity calibration model is established based on Least Squares Support Vector Machines on MatLAB (LS-SVMlab) for improving detection precision. The transmission of soil moisture information is the key technology of the system, by software and hardware design we have solved the problems of data congestion, off-line, and moving the monitoring terminal at any time, which still restrict the application of GPRS in soil moisture detection. Field tests show that the system can realize seamless connection between the collection nodes and remote host, and acquire soil moisture accurately. Simultaneously, the time of re-networking has been shortened greatly.

scholarly journals Iot Enabled Pipeline Leakage Detection and Real Time Alert System in Oil and Gas Industry

2020 ◽  
Vol 8 (5) ◽  
pp. 2582-2586
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Detection System ◽  
Oil And Gas Industry ◽  
Petroleum Products ◽  
Time Data ◽  
Gas Industry ◽  
Processing Plants ◽  
Automation And Control ◽  
And Control

Automation and control systems are necessary throughout oil & gas industries, to production and processing plants, and distribution and retailing of petroleum products. Pipelines are the efficient mode of transportations of fuels for processing plants over long distances. At present Automation is achieved by using PLC’s that are communicated through SCADA. But it is complex and remote operation is not possible. With the introduction of IoT, the pipeline leak detection system is improved through real-time monitoring of the pipelines. Our Proposed system is designed to detect even small leakage that occurs within the pipeline. The implementation of IoT in oil and gas industries prevents accidents and to make quick decisions based on real-time data

scholarly journals A PRELIMINARY STUDY ON UPDATING HIGH DEFINITION MAPS: DETECTING AND POSITIONING A TRAFFIC CONE BY USING A STEREO CAMERA

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 271-274
Author(s):  
M. L. R. Lagahit ◽  
Y. H. Tseng
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Autonomous Vehicles ◽  
Detection System ◽  
Time Data ◽  
High Definition ◽  
Stereo Camera ◽  
The Road ◽  
On The Road ◽  
Self Driving Cars

Abstract. The concept of Autonomous Vehicles (AV) or self-driving cars has been increasingly popular these past few years. As such, research and development of AVs have also escalated around the world. One of those researches is about High-Definition (HD) maps. HD Maps are basically very detailed maps that provide all the geometric and semantic information on the road, which helps the AV in positioning itself on the lanes as well as mapping objects and markings on the road. This research will focus on the early stages of updating said HD maps. The methodology mainly consists of (1) running YOLOv3, a real-time object detection system, on a photo taken from a stereo camera to detect the object of interest, in this case a traffic cone, (2) applying the theories of stereo-photogrammetry to determine the 3D coordinates of the traffic cone, and (3) executing all of it at the same time on a Python-based platform. Results have shown centimeter-level accuracy in terms of obtained distance and height of the detected traffic cone from the camera setup. In future works, observed coordinates can be uploaded to a database and then connected to an application for real-time data storage/management and interactive visualization.

Design and Implementation of Logistics Intelligent Management and Control Systems for Steel Enterprises

2013 ◽  
Vol 380-384 ◽  
pp. 716-721 ◽  
Author(s):  
Guan Qi Guo ◽  
Xiang Jun Su ◽  
Xiang Liu
Keyword(s):  
Real Time ◽  
Target Location ◽  
Detection System ◽  
Three Dimensional ◽  
Automatic Generation ◽  
Time Data ◽  
Information Updating ◽  
Wireless Data Acquisition ◽  
And Control ◽  
Warehouse Operations

Based on the logistics management and control system of the cold-rolled finished products, this system has realized many features, including three-dimensional positioning of the crane, a variety of real-time data collected through the RFID systems and magnet detection system and wireless data acquisition in the coil storage or out of the library or inverted treasury job process transmitted to the server, coil warehouse operations process optimization and control, forming automatically a plan and target location to crane, Prompting crane operations, real-time monitoring of crane operating, recording crane operating results, tracking and positioning of the treasury coil information, updating treasury stock coil position in real time, inquiry treasury stock and automatic generation of warehouse operations statements by teams or date or month or year, communication with ERP or MES etc. Through seamless connectivity between the crane operating system and warehouse management systems and enterprise information systems, the system can achieve precise synchronization of logistics and information flow.

scholarly journals Development of an Automatic Image Cropping and Feature Extraction System for Real-Time Warping Monitoring in 3D Printing

2021 ◽  
Author(s):  
Jiarui Xie
Keyword(s):  
Real Time ◽  
Detection System ◽  
Region Of Interest ◽  
Cost Effective ◽  
Time Warping ◽  
Time Data ◽  
Fused Filament Fabrication ◽  
System A ◽  
Effective Manner

Fused Filament Fabrication (FFF) is an additive manufacturing technology that can produce complicated structures in a simple-to-use and cost-effective manner. Although promising, the technology is prone to defects, e.g. warping, compromising the quality of the manufactured component. To avoid the adverse effects caused by warping, this thesis utilizes deep-learning algorithms to develop a warping detection system using Convolutional Neural Networks (CNN). To create such a system, a real-time data acquisition and analysis pipeline is laid out. The system is responsible for capturing a snapshot of the print layer-bylayer and simultaneously extracting the corners of the component. The extracted region-of-interest is then passed through a CNN outputting the probability of a corner being warped. If a warp is detected, a signal is sent to pause the print, thereby creating a closed-loop monitoring system. The underlying model is tested on a real-time manufacturing environment yielding a mean accuracy of 99.21%.

scholarly journals Anomaly Detection and Classification of Physiological Signals in IoT- Based Healthcare Framework

2021 ◽  
Author(s):  
Menaa Nawaz ◽  
Jameel Ahmed
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Real Time ◽  
Signal Analysis ◽  
Detection System ◽  
Muscular Activity ◽  
Physiological Signals ◽  
Time Data ◽  
Cloud Data ◽  
Medical Sensors

Abstract Physiological signals retrieve the information from sensors implanted or attached to the human body. These signals are vital data sources that can assist in predicting the disease well before time and thus proper treatment can be made possible. With the addition of Internet of Things in healthcare, real-time data collection and pre-processing for signal analysis has reduced burden of in-person appointments and decision making on healthcare. Recently, Deep learning-based algorithms have been implemented by researchers for recognition, realization and prediction of diseases by extracting and analyzing the important features. In this research real-time 1-D timeseries data of on-body non-invasive bio-medical sensors have been acquired and pre-processed and analyzed for anomaly detection. Feature engineered parameters of large and diverse dataset have been used to train the data to make the anomaly detection system more reliable. For comprehensive real-time monitoring the implemented system uses wavelet time scattering features for classification and deep learning based autoencoder for anomaly detection of time series signals for assisting the clinical diagnosis of cardiovascular and muscular activity. In this research, an implementation of IoT based healthcare system using bio-medical sensors has been presented. This paper also aims to provide the analysis of cloud data acquired through bio-medical sensors using signal analysis techniques for anomaly detection and timeseries classification has been done for the disease prognosis in real-time. Wavelet time scattering based signals classification accuracy of 99.88% is achieved. In real time signals anomaly detection, 98% accuracy is achieved. The average Mean Absolute Error loss of 0.0072 for normal signals and 0.078 is achieved for anomaly signals.

Dynamic Travel Time Estimation Techniques for Urban Freight Transportation Networks Using Historical and Real-Time Data

2011 ◽  
pp. 252-273
Author(s):  
Vasileios Zeimpekis
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Real Life ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Travel Time Prediction ◽  
Time Data ◽  
Urban Freight ◽  
Real Time Traffic ◽  
Real Time Data

Effective travel time prediction is of great importance for efficient real-time management of freight deliveries, especially in urban networks. This is due to the need for dynamic handling of unexpected events, which is an important factor for successful completion of a delivery schedule in a predefined time period. This chapter discusses the prediction results generated by two travel time estimation methods that use historical and real-time data respectively. The first method follows the k-nn model, which relies on the non-parametric regression method, whereas the second one relies on an interpolation scheme which is employed during the transmission of real-time traffic data in fixed intervals. The study focuses on exploring the interaction of factors that affect prediction accuracy by modelling both prediction methods. The data employed are provided by real-life scenarios of a freight carrier and the experiments follow a 2-level full factorial design approach.

Sign in / Sign up

Export Citation Format

Share Document