Development of wireless prototype vehicle speed monitoring system.

Globally road accident is considered to be an important issue, which can be reduced by proper vehicle speed monitoring system. More recently, the advancement in wireless sensor technology shows a great promise in designing Intelligent Transportation System (ITS) due to its flexibility and cost-effectiveness for deployment. The aim of this research is to develop a prototype vehicle speed monitoring system using accelerometer-based wireless sensor. The basic concept of the system is based on the following methodology: developing an experimental system to generate random speed data, which can represent vehicle speed on the road and developing a software to monitor and manage the speed data wirelessly. A wireless sensor attached with a mechanical wheel measures the acceleration vibration of the system, which is equivalent to wheel speed and transmits the data wirelessly to a computer. A software (SpeedManage) has been developed using Java Socket programming codes which converts the vibration data to equivalent speed data and presents these in a Graphical User Interface (GUI). If the detected speed is greater than a set speed limit, the data will be automatically saved in a central database in the form of an electronic report for taking any further action. The functionality of the system has been simulated in a laboratory environment by setting different speed limits for monitoring single or multiple vehicle speed scenarios through appropriate algorithm and code development. The graphical user interface (GUI) of the software continuously presents the vehicle speeds with time and the overspeeding conditions are indicated. The speed details are also continuously updated on the left hand side of the GUI. The system is also capable of generating an automatic electronic report for a simulated speeding vehicle with vehicle number, speed details, time etc. Therefore, based on the performance of prototype system, it can be concluded that sensor-based vehicle speed monitoring system has great potential for monitoring vehicle speed wirelessly. SpeedManage software should help to effectively, automatically and intelligently monitor vehicle speed

Mobile Cell Data Structure Quality Improvement For User Positioning Purposes

In wireless telephony networks, each cell is a geographical coverage area which can distribute frequency among cellular networks for different specific mobile network regions. Good cell and bad cell are used in the cellular network to identify the proper user position in a certain geographical area. The good cell is identified by assuming a maximum distance between latitude and longitude of two cell points with reasonable shape in a particular geographical area. The bad cell is identified while the cell shapes are become as irregular shape. However, mobile location accuracy is important for good cells data. Some cell data are not precise in shape to become good cells. Moreover, locations of handset are dependent for the accuracy of cell data shape. Most of the cases mobile operators are facing problem for the positioning purposes due to inaccuracy of the shape of cell data. The proper position accuracy of user is not visualized due to inaccuracy of cell data shape. The proposed system identifies the bad cell and repairs as good cell using visualize tool. An XML data file contains cell data information with longitude and latitude. A data base has been created to store the longitude and latitude of cell data in a standard format using PHP code. The visualize tool identify bad cell and good cell from the database. Furthermore, the tool converts the bad cell into good cell. Moreover, the tool can able to repair the cells which are not converted as good cell shape. The system can able to help to improve quality of user position accuracy for GSM and CDMA mobile operator

Frequency Adaptive Parameter Estimation of Unbalanced and Distorted Power Grid

Grid synchronization plays an important role in the grid integration of renewable energy sources. To achieve grid synchronization, accurate information of the grid voltage signal parameters are needed. Motivated by this important practical application, this paper proposes a state observer-based approach for the parameter estimation of unbalanced three-phase grid voltage signal. The proposed technique can extract the frequency of the distorted grid voltage signal and is able to quantify the grid unbalances. First, a dynamical model of the grid voltage signal is developed considering the disturbances. In the model, frequency of the grid is considered as a constant and/or slowly-varying but unknown quantity. Based on the developed dynamical model, a state observer is proposed. Then using Lyapunov function-based approach, a frequency adaptation law is proposed. The chosen frequency adaptation law guarantees the global convergence of the estimation error dynamics and as a consequence, ensures the global asymptotic convergence of the estimated parameters in the fundamental frequency case. Gain tuning of the proposed state observer is very simple and can be done using Matlab commands. Some guidelines are also provided in this regard. Matlab/Simulink based numerical simulation results and dSPACE 1104 board-based experimental results are provided. Test results demonstrate the superiority and effectiveness of the proposed approach over another state-of-the art technique

Data driven prognostics for predicting remaining useful life of IGBT

Power electronic devices such IGBT (Integrated Gate Bipolar Transistor) are used in wide range of applications such as automotive, aerospace and telecommunications. The ability to predict degradation of power electronic components can minimise the risk of their failure while in operation. Research in this area aims to develop prognostics strategies for predicting degradation behaviour, failure modes and mechanisms, and remaining useful life of these electronic components. In this paper, data driven prognostics approaches based on Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models are developed and used to predict the degradation of an IGBT device. IGBT life data under thermal overstress load condition with square signal gate voltage bias, available from NASA prognostics data repository, is used to demonstrate the proposed data-driven prognostics strategy. The monitored collector-emitter voltage is used to identify the pattern and duration of different phases in the applied voltage load. The NN and ANFIS models are trained with a subset of the test data to predict remaining useful life (RUL) of the IGBT device under varying load test profiles. The predictive capability and performance of the models is observed and analysed

Machine learning for forecasting a photovoltaic (PV) generation system

To mitigate the carbon print of buildings, they should have on-site renewable energy generation systems to supply energy for the buildings without relying on the national grid. Renewable generation sources rely on weather conditions and are therefore difficult to rely on as the only source of energy. Photovoltaic (PV) is forecasted through machine learning algorithms (MLA), but different methods have varied accuracy and have different training requirements such as more inputs or more data in general. No previous research has concluded an optimal MLA but to better apply them to PV systems, this must be established. To conclude an optimal MLA for a particular application, the dataset and required outputs must be determined, and how they affect the performance of the algorithm must be evaluated. The aim of this work is to compare benchmark MLA's through accuracy and usability for an operational University campus located in central Manchester, in the north of England. The MLA's including random forest (RF), neural networks (NN), support vector machines (SVM), and linear regression (LR) have been employed to forecast the PV system. If the power output of the renewables is accurately forecasted, a building management system (BMS) can be equipped to optimise on-site renewable energy generation. To accomplish this, sixty-four MLA models are created in total for forecasting at multiple horizons and dataset sizes which are validated against real-time data. Results in this work revealed that the RF algorithms have the lowest average error of the multiple tests at 32 root mean squared error (RMSE), whereas SVM, LR, and NN showed at 32.3 RMSE, 36.5 RMSE, and 38.9 RMSE respectively. Errors between forecasted and actual results are recorded in RMSE whereas changes in error are shown in mean actual percentage error (MAPE) to show the changes with respect to the original value. No MLA outperforms all others for accuracy and for requiring less data. No previous research is conducted to evaluate the performance of various MLA PV forecasting models through various sized data sets with critical analysis on the results. The comparison of benchmark algorithms when forecasting the PV generation of a local system allows the critical analysis of the models' accuracy and surrounding characteristics

Data driven prognostics for failure of power semiconductor packages

Power chips such as Metal Oxide Field Effect Transistors (MOSFETs) are widely used and can be found in many electronics and electrical products. The ability to predict the degradation of such power electronic devices can minimise the risk of their failure during operation and support maintenance planning operations. In this study, a data driven prognostics approach using system identification and machine learning modelling technique is developed and used to predict the time-to-failure of MOSFET TO-220 packages associated with delamination failure mode of the die attachment. Run-to-failure data under thermal overstress loading conditions for power chip devices, available from the NASA Prognostics Centre data repository, is used to develop a data-driven prognostic model that can be used to predict the time-to-failure (TtF) of power MOSFETs under accelerated test loads. An increment in ON-state resistance of the MOSFET is used as precursor for device failure through die-attach degradation. Results from this research show that when monitored data from a damage indicator for a particular failure mode of an electronic package changes dynamically, data-driven modelling using engineering control techniques such as State-Space representation is capable of producing reliable, multi-step ahead predictions for the time-to-failure of the device

Prognostics of automotive electronics with data driven approach: A review

Prognostics and Health Management (PHM) is a comprehensive framework that can deal with solutions for predicting and maintaining electronic system's health. The emerging concept of PHM is increasingly considered for adoption in many engineering fields such as automotive, mechanical, electrical, industrial, aerospace and railway. PHM of electronic components and systems can offer competitive advantages by improving performance, reliability, safety, maintainability and availability. In this paper, a brief description of PHM concept, current PHM approaches, key prognostics components and corresponding monitored/sensed parameters in automotive PHM applications are presented. Software tools developed for PHM applications are also reviewed. Particular focus is given on data driven approaches for prognostics of performance and reliability of automotive electronic systems. Based on the undertaken review of state-of-art in this area, key requirements and attributes of prognostic frameworks for automotive electronics are formulated and future prognostics challenges for the sector are discussed

Visualization and Cell Data Analysis Tool based on XML Log Files

Mobility management is an essential feature of cellular networks. High accuracy of mobile user positioning is needed to handle mobility efficiently enough and bad cell data can harm this feature significantly. Inaccuracy of cell shapes, lack of cell data measurements, and inaccurate coordination in a geographical area are major shortcomings when it comes to positioning of mobile users in cellular networks. This paper describes a tool that visualizes and analyzes cell data based on XML log files. The tool evaluates a mathematical expression to identify bad cells from the log file and successfully fixes most of the bad cells identified. The tool repairs bad cell shapes in order to achieve better positioning of mobile users

Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the global reaction to it severely affected the world economy, causing a significant increase in global inflation rates, unemployment, and the cost of energy commodities. To stop the spread of the virus and dampen its global effect, it is imperative to detect infected patients early on. Convolutional neural networks (CNNs) can effectively diagnose a patient’s chest X-ray (CXR) to assess whether they have been infected. Previous medical image classification studies have shown exceptional accuracies, and the trained algorithms can be shared and deployed using a computer or a mobile device. CNN-based COVID-19 detection can be employed as a supplement to reverse transcription-polymerase chain reaction (RT-PCR). In this research work, 11 ensemble networks consisting of 6 CNN architectures and a classifier layer are evaluated on their ability to differentiate the CXRs of patients with COVID-19 from those of patients that have not been infected. The performance of ensemble models is then compared to the performance of individual CNN architectures. The best ensemble model COVID-19 detection accuracy was achieved using the logistic regression ensemble model, with an accuracy of 96.29%, which is 1.13% higher than the top-performing individual model. The highest F1-score was achieved by the standard vector classifier ensemble model, with a value of 88.6%, which was 2.06% better than the score achieved by the best-performing individual model. This work demonstrates that combining a set of top-performing COVID-19 detection models could lead to better results if the models are integrated together into an ensemble. The model can be deployed in overworked or remote health centers as an accurate and rapid supplement or back-up method for detecting COVID-19

Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal

Intelligent fault diagnosis methods have replaced time consuming and unreliable human analysis, increasing anomaly detection efficiency. Deep learning models are clear cut techniques for this purpose. This paper’s fundamental purpose is to automatically detect leakage in tanks during production with more reliability than a manual inspection, a common practice in industries. This research proposes an inspection system to predict tank leakage using hydrophone sensor data and deep learning algorithms after production. In this paper, leak detection was investigated using an experimental setup consisting of a plastic tank immersed underwater. Three different techniques for this purpose were implemented and compared with each other, including fast Fourier transform (FFT), wavelet transforms, and time-domain features, all of which are followed with 1D convolution neural network (1D-CNN). Applying FFT and converting the signal to a 1D image followed by 1D-CNN showed better results than other methods. Experimental results demonstrate the effectiveness and the superiority of the proposed methodology for detecting real-time leakage inaccuracy