Department of Electrical Engineering
Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/18
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Item Real-Time Air Pollution Monitoring Using Machine(COMSATS University Islamabad Lahore Campus, 2020) Rana Ahtisham Ali,; SP18-REE-015; Dr. Abbas Javed, Assistant Profesor [Supervisor]; LHR TP 7466According to World Health Organization (WHO), the air in Lahore, Pakistan, has an annual average of 68 µg/m3 of PM 2.5 particles which are 6.8 times more than safe levels recommended by WHO. In December 2016, the amount of PM2.5 went above 100 µg/m3 which was more than 10 times safe levels set by WHO. Currently, there is no network for real time air pollution prediction available in Lahore, nor Pakistan. In this research, an Internet of Things (IoT) based low cost/low power outdoor air quality monitoring system has been developed. We developed the wireless sensor nodes for the monitoring and prediction of the PM2.5 particles in the air. The nodes comprised of different sensors for measuring PM2.5, SO2, O3, CO, NH3, NO2, humidity, and temperature values and Long Range (LoRa) transceiver for uploading the data on the cloud. The coverage range of LoRa gateway has been tested in Lahore, Pakistan and it is found that coverage range of the sensor node is around 2.7 KM in densely populated areas. Five different artificial neural network models are used for the prediction ahead of 1hr, 2hr, 3hr, 4hr, 5hr and 6hr. We obtained the accuracy of 99% for tested model of LSTM. The accuracy of LSTM model for 1hr ahead is 13% more than FFNN, 2% than Elman NN, 0.41% than NARX and 0.45% than layer recurrent. The accuracy of LSTM model to predict 6hr ahead is above 95%. For 6hr ahead prediction, accuracy of LSTM is 15% better than FFN, 12% than Elam NN, 7% than layer recurrent and 9% than NARX NN. Results show that the LSTM model outclass all other models. The computational time of LSTM model to predict 6hr ahead is 150.22565msec. While FFN takes 30 msec, Elman NN takes 60msec, NARX takes 80msec and layer recurrent take 90 msec. The 1hr ahead predicted model of feed-forward NN embedded in all nodes and placed at different places for real-time testing. The dataset collected at the server side analyzed and it is found that our air pollution monitoring system is capable of accurately predicting the future (1 hour ahead) concentration of PM 2.5 in outdoor air. Its accuracy for 1hr ahead prediction is up-to 90% and its algorithm takes 0.642 micro seconds computational time for the prediction level of PM2.5.Item Mitigation Of Harmonics Using Soft Computing(COMSATS University Islambad Lahore Campus, 2020) Muhammad Waleed Rafique,; SP18-REE-008; Dr. Mujtaba Hussain Jaffery, Assistant Profesor [Supervisor]; LHR TP 7467Excessive use of non-linear devices in industries results in current harmonics that degrade the power quality and has an unfavorable impact on the entire performance of power system. In this research, a Hybrid Shunt Active Power Filter (HSAPF) is implemented for compensation of reactive power and harmonic current component for balanced load by improving the power factor, total harmonic distortion (THD), and performance of the system. For extracting three-phase reference current for HSAPF, a novel control technique for harmonics mitigation based on Particle Swarm Optimization (PSO) and Fuzzy logic Controller (FLC) is proposed in this research. SIMULINK is used to implement Pq0 and Id-Iq control approaches for harmonics mitigation, with a PI controller for voltage regulation and a hysteresis controller for the reference current generation. Following a comparative analysis of both techniques i-e Pq0 and Id-Iq control techniques, implement the Soft computing technique (PSO) on PI controller technique on any of these techniques that perform better for harmonics mitigation. As the performance of the PI controller is dependent on its gains, the best gains produce better results and improve system response. The given results show that PSO technique is an efficient technique that helped to give better performance as compared to the conventional PI controller by improving steady-state response by giving the best gains to PI controller. Because of the system's robustness and non linearity, a single PI controller does not provide better performance So, by using gains obtained from PSO adaptively tuning of PI controller has been carried out by using FLC that can help to improve the dynamic performance of HSAPF. Therefore, from the comparative analysis, it can be inferred that PSO based Adaptive Fuzzy PI system has more efficient results with a minimum THD, improved stability time, and a power factor nearer to unity as compared to other techniques.Item A Hybrid Framework For Attitude Estimation Of(Publisher COMSATS University Islambad Lahore Campus, 2020) Talal Ali Khan; SP18-REE-005; Dr. Khuram ALi, Assistant Profesor [Supervisor]; LHR TP 7471Inertial Measurement Unit (IMU) sensors are being actively used for motion tracking or attitude estimation due to their low weighted structure and cheap cost. IMU is a fusion of gyroscope, accelerometer, and magnetometer. There are many applications of IMU sensors. Some of which are gesture recognition, estimation of vehicular motion and orientation of objects, etc. Mostly gyroscope and accelerometer are used to estimate the attitude of any object where magnetometer provides the heading and course information. The gyroscope provides short-term stability because of inherited bias. Due to this bias an error starts to occur as the difference between actual measurement and expected measurement of the attitude tends to increase with time. This error can be large as 70 degrees/hour. This problem can be solved by using accelerometer in fusion with gyroscope. Accelerometer that measures accelerations acting on a body due to gravitation and other exterior forces. It is relatively easy to estimate attitude when there are no outliers but in presence of external interference its measurement is contaminated which creates jitters and lag in measured values. As the external forces are always present that’s why it never accurate. This is why we use these sensors in conjunction so; they can complement each other to give accurate results. To solve this problem, we have introduced an effective technique in which complementary filter is used to give accurate attitude estimate of the body in the presence of prolonged dynamic conditions and gyro bias. Two filters are used in cascade form where one filter deals with precise attitude estimation of the motion of a rigid body and external acceleration through synchronicity of accelerometer and gyroscope and the other filter deals with gyro bias estimation, this enables the proposed architecture to be used in a different application with minimum calibration. A novel threshold-based method is used for external acceleration detection so that external accelerations can be dealt efficiently. This will help in changing the confidence level on the measurements of accelerometer in case of prolonged accelerations and also compliments attitude estimation. Second filter is designed in a way that it estimates bias in the by using attitude estimation and accelerations profile and then the bias is removed from the system. The architecture is also compared with different estimators existing in the literature under different dynamical conditions on different datasets.Item Human Activity Recognition System For Long Term(Publisher COMSATS University Islambad Lahore Campus, 2020) Shan E Ali,; SP18-REE-026; Dr. Ali Nawaz Khan, Assistant Profesor [Supervisor]; LHR TP 6444The study of Human Activity Recognition (HAR) for Long Term Health Monitoring (LTHM) has gained significant importance for its wide range of applications. These applications range from sports and rehabilitation sciences to assisted living for older people. In addition to that LTHM is an efficient solution for the prevention of lifestyle diseases like stroke, heart failure, and various health problems that occur due to prolonged inactivity. With the increased availability of accelerometer sensors embedded in mobile phones, we can efficiently explore the Activities of Daily Living (ADLs) of an individual. This research aims to develop a LTHM system for evaluating ADLs of a person using a mobile phone-based accelerometer sensor and the ‘MyNeuroHealth’ application. Data collected in an unconstrained environment by various individuals throughout the day to create templates of ADLs. Collected data is prepared and preprocessed by assigning hourly labels to the ADLs, encoding categorical values and random sampling of data. This data is used for training the machine learning model and for classifying activities according to their energy expenditure or user exhaustion levels. Collected dataset further extended to daily, weekly, and monthly basis to provide long-term health profiling (LTHP). 23 types of basic, complex and transitional activities were evaluated for each day. The results show that an Artificial Neural Network (ANN) can efficiently identify and detect ADLs with more than 90% accuracy. Person independent ADLs templates for weeks 1, 2, 3 and 4 achieved an accuracy of 89, 96, 93 and 89 percent correspondingly. On the other hand, person dependent ADLs templates from various walks of life achieved on average 94% accuracy. Person independent ADLs templates for weeks 1, 2, 3 and 4 achieved an accuracy of 89%, 96%, 93% and 89% percent correspondingly.Item Robust Kalman Filter For 2-D Nonlinear Systems(Publisher COMSATS University Islambad Lahore Campus, 2020) Talha Zeb,; SP18-REE-030; Dr, Muhammad Yaqoob Javeed, Assistant Profesor [Supervisor]; LHR TP 6449Kalman filter (KF), being an optimal estimator for linear systems and known statistics of Gaussian noise, is an optimal filter thus widely used method for state estimation. However, the central obstruction in its implementation is that the dynamics of the model must be exactly known. Many advances in robust control and estimation to reduce the effects of model uncertainties have attracted various studies on robust Kalman filtering. There are three renowned approaches for robust linear state estimation i.e. H∞ filtering, set valued filtering and guaranteed cost (GC) filtering; all of them are used with Kalman filtering framework due to its wider applicability. Since, the exact minimum of estimation error variance is not mathematically computed, the usual choice, therefore, is to enforce a constant upper bound on error variances for all acceptable uncertainties. This upper bound is then minimized instead of obtaining (global) minimum variance. Thus, the robust KFs (RKFs) are sub-optimal but adaptable to practical scenarios. Since, all of the above methods perform de-regularization, they either suffer from inherent conservativeness or existence conditions. Practically, these conditions cannot be ensured to be satisfied at all arbitrary iterations. If the conditions are not met at any iteration, the filter may perform poorly. However, a RKF based on least square (LS) approach employed unique Regularized Least Square (RLS) criterion for uncertain models. The distinctive features of this method are it performs regularization thus avoids existence conditions and vector optimization problem is reduced to scalar minimization problem. Hence, this approach, according to the best of our knowledge, is most suitable for online implementation.