Development of Real-Time Mine Road Maintenance Management System Using Haul Truck and Road Vibration Signature Analysis

2003 ◽  
Vol 1819 (1) ◽  
pp. 305-312 ◽  
Author(s):  
Roger Thompson ◽  
Alex Visser ◽  
Rusty Miller ◽  
Ted Lowe
Keyword(s):  
Real Time ◽  
Economic Feasibility ◽  
Maintenance Management ◽  
Road User ◽  
Signature Analysis ◽  
Road Maintenance ◽  
Real Time System ◽  
Traffic Volumes ◽  
Vibration Signature ◽  
Haul Road

The unpaved road network of a surface mine is extensive, comprising numerous roads of varying construction and material qualities with highly variable traffic volumes. Existing haul road maintenance management systems (MMSs) work well for predictable traffic volumes, but for complex mine road networks, the MMS becomes onerous and results in suboptimal road maintenance strategies, with the attendant increase in total road-user costs and reduction in service. A real-time MMS was thus sought to overcome the deficiencies of existing systems for mine roads. Because most large mines operate trucks with onboard diagnostic data collation, linked through a centralized communication and Global Positioning System backbone, it was proposed that road condition could be monitored on a real-time basis through onboard vibration signature analysis. A real-time mine haul road MMS was developed. Mine road maintenance practices were reviewed. The real-time system architecture was devised, and a field trial was conducted of onboard vibration signature assessment. Trial results were evaluated in the light of road defect signature recognition, analysis, signature repeatability, and system limitations. This approach is applicable to other situations, such as a network of district roads, subject to an analysis of economic feasibility. The conclusion is reached that modern technology has the potential to apply maintenance as and where needed, with possible reductions in authority cost and an improvement in service provided to road users.

Real-Time System for Sending Data from Three Clients to One Server with MATLAB

2015 ◽  
Vol 2 (1) ◽  
pp. 35-41
Author(s):  
Rivan Risdaryanto ◽  
Houtman P. Siregar ◽  
Dedy Loebis
Keyword(s):  
Information System ◽  
Real Time ◽  
Time System ◽  
Real Time System ◽  
The Real ◽  
Performance Time ◽  
Data Process ◽  
Effectiveness And Efficiency

The real-time system is now used on many fields, such as telecommunication, military, information system, evenmedical to get information quickly, on time and accurate. Needless to say, a real-time system will always considerthe performance time. In our application, we define the time target/deadline, so that the system should execute thewhole tasks under predefined deadline. However, if the system failed to finish the tasks, it will lead to fatal failure.In other words, if the system cannot be executed on time, it will affect the subsequent tasks. In this paper, wepropose a real-time system for sending data to find effectiveness and efficiency. Sending data process will beconstructed in MATLAB and sending data process has a time target as when data will send.

The Goal-Setting Model in a Multiagent Hard Real-Time System

Vestnik MEI ◽  
2018 ◽  
Vol 5 (5) ◽  
pp. 73-78
Author(s):  
Igor В. Fominykh ◽  
◽  
Sergey V. Romanchuk ◽  
Nikolay Р. Alekseev ◽  
◽  
...  
Keyword(s):  
Real Time ◽  
Goal Setting ◽  
Time System ◽  
Real Time System ◽  
Hard Real Time

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

Design of Preliminary Experiments with the Sun Java Real-Time System

2006 ◽  
Author(s):  
T. S. Cook ◽  
D. Drusinsky ◽  
J. B. Michael ◽  
T. W. Otani ◽  
M. Shing
Keyword(s):  
Real Time ◽  
The Sun ◽  
Time System ◽  
Real Time System

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

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