Developing and Delivering a Remote Experiment based on the Experiential Learning framework during COVID-19 Pandemic

The students following Engineering disciplines should acquire a conceptual understanding of the concepts and the processors and attitudes. There are two recognizable learning environments for students: classroom and laboratory environments. With the COVID-19 Pandemic, both environments merged to online environments, impacting students' processes and characteristic attitudes development. This paper introduces a theoretical framework based on experiential learning to plan and deliver processes online. A case study based on the power-factor correction experiment was presented. The traditional experiment that runs for 2 hours was broken into smaller tasks such as pre-lab activity, simulation exercise, PowerPoint presentation, remote laboratory activity, and final report based on the experiential learning approach. The delivery of the lab under online mode delivery was presented. Then students' performance was compared before and after the online mode of delivery. It was found that students' performance on average has a distinct improvement. In order to obtain students' reflections about the online experiential learning approach, a questionnaire that carries close and open-ended questions was administered. The majority of the students liked the approach followed and praised for allowing them to experiment in a novel way during the COVID-19

Comparative Evaluation of Exact Extreme Value Stochastic Flood Model for Mixed Populations.

Two alternative formulations of the exact extreme value stochastic flood model are presented in explaining the behaviour of observed flood series resulting from mixed climatological processes. As a marginal distribution for flood exceedances, a more flexible Weibull distribution is introduced in the place of the traditional exponential distribution. The asymptotic predictive performances of both the exponential and Weibull model formulations are evaluated in terms of relative bias (BIAS) and relative root mean square error (RMSE) of quantile estimation. The model with Weibull marginal performs well over the exponential for population conditions shown to exist in most of the observed flood series of Louisiana. In identifying a robust estimator for the Weibull distribution, the predictive performances of the principle of maximum entropy (POME) and probability weighted moments (PWM) are evaluated. On the basis of BIAS and RMSE of quantile estimation, the POME emerges as the most robust Weibull estimator for a wide range of population conditions. The compound model formulations for both exponential and Weibull marginals are discussed as against the respective simple model formulations in analyzing mixed flood populations. For a wide variation of the means of mixed populations, the predictive performances of both the simple and compound formulations of the exponential model are evaluated in terms of BIAS and RMSE of quantile estimation. Similarly, for a wide variation of the coefficient of variance of mixed populations, the performances of both formulations of the Weibull model are evaluated. The compound formulations of both the exponential and Weibull models demonstrate superior performances over the respective simple formulations if statistically distinct sub-populations are present in the mixed populations. The descriptive properties of those selected formulations of the exact extreme value stochastic flood model are tested for on the observed flood series in Louisiana. The flood series are hydroclimatically separated and tested for the assumption of identical distribution. The flood series are also tested for the Poisson assumption in ensuring the mutual independency. The validity of the exponential and Weibull distributions as marginals for flood exceedances are examined

Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method which incorporates appliance usage patterns (AUPs) to improve performance of active load identi- fication and forecasting. In the first stage, the AUPs of a given residence were learnt using a spectral decomposition based standard NILM algorithm. Then, learnt AUPs were utilized to bias the priori probabilities of the appliances through a specifically constructed fuzzy system. The AUPs contain likelihood measures for each appliance to be active at the present instant based on the recent activity/inactivity of appliances and the time of day. Hence, the priori probabilities determined through the AUPs increase the active load identification accuracy of the NILM algorithm. The proposed method was successfully tested for two standard databases containing real household measurements in USA and Germany. The proposed method demonstrates an improvement in active load estimation when applied to the aforementioned databases as the proposed method augments the smart meter readings with the behavioral trends obtained from AUPs. Furthermore, a residential power consumption forecasting mechanism, which can predict the total active power demand of an aggregated set of houses, five minutes ahead of real time, was successfully formulated and implemented utilizing the proposed AUP based technique

Non-intrusive load monitoring under residential solar power influx

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed

Non-intrusive load monitoring based on low frequency active power measurements

A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on ac- tive power signal is presented. This method works e ectively with a single active power measurement taken at a low sampling rate (1 s). The proposed method utilizes the Karhunen Lo ́ eve (KL) expan- sion to decompose windows of active power signals into subspace components in order to construct a unique set of features, referred to as signatures, from individual and aggregated active power signals. Similar signal windows were clustered in to one group prior to feature extraction. The clustering was performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal windows and power levels of subspace components were utilized to reduce the number of possible ap- pliance combinations and their energy level combinations. Then, the turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Pos- teriori (MAP) estimation. Finally, the proposed method was modified to adaptively accommodate the usage patterns of appliances at each residence. The proposed NILM method was validated using data from two public databases: tracebase and reference energy disaggregation data set (REDD). The pre- sented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations in real households. Furthermore, the results emphasise the importance of clustering and the integration of the usage behaviour pattern in the proposed NILM method for real household

Entropy analysis of kinetic and kinematic gait parameters as a potential tool to predict osteoarthritis onset

Osteoarthritis (OA) is a debilitating joint degenerative disease that is expected to drastically increase in prevalence by 2050. Therefore, it is necessary to find innovative ways to predict OA onset with the hopes of implementing preventative measures early on.OA incidence is positively correlated with increasing age and the female gender. Furthermore, gait analysis has been used to elucidate the biomechanical differences that arise from OA onset. Modern gait analysis research has moved towards non-linear analysis due to the understanding of the deterministic properties of human movement. Approximate Entropy (ApEn) and Fuzzy Entropy (FuzzyEn) are both tools that assess signal regularity under the theory of Optimal Movement Variability, and ApEn has been used in a limited capacity to investigate gait imbalance. The goal of this study was to investigate the efficacy of ApEn and FuzzyEn in assessing differences in gait imbalance parameters between males and females of a fixed age, and females of younger and older age groups, under the context of OA onset factors.Healthy young males (n=20) and females (n=20) between the ages of 18-25 were analyzed in the first study, while younger females of age 18-25 (n=8) and older females of age 50-60 (n=8) were analyzed in the second study. Subjects walked barefoot on a force sensitive treadmill surrounded by six motion capture cameras at a self-selected speed. ApEn and FuzzyEn calculations were then performed at varying k values on the signals of the peak ground reaction force during heel strike and toe off, medial-lateral center of pressure displacement and range of motion of the hip, knee and ankle.Our results confirmed that gait was a chaotic and deterministic process, since all subjects showed high entropy values at an input parameter k value of 0.2. FuzzyEn and ApEn values were within 22% for all parameters at k=0.2, showing agreement. ApEn found more significant differences between groups, however FuzzyEn showed more consistency. Therefore FuzzyEn remains a promising tool in the investigation of OA and as a future OA prediction tool

Performance comparison of optimum power flow based on the sequential second-order cone programming in unbalanced low voltage distribution networks with distributed generators

A solution technique using sequential second-order cone programming to solve the optimum power flow problem in low voltage (LV) distribution networks with distributed generation is developed. A novel bound tightening method is suggested to get exact solutions with few iterations. A novel approximation method is suggested to increase exactness by approximating phase angle dependent components. The performance of the suggested solution method is compared with linear programming, genetic algorithm, particle swarm, sequential quadratic programming with multiple start points, and global search-based optimization methods. The exactness of the generated solutions is validated after comparison with a load flow. The proposed algorithm provides better performance in optimality, execution time, and exactness compared to other methods