An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values

In recent years, Air Quality Index (AQI) have been widely used to describe the severity of haze and other air pollutions yet suffers from inefficiency and compatibility on real-time perception and prediction. In this paper, an Auto-Regressive (AR) prediction model based on sensed AQI values is proposed, where an adaptive Kalman Filtering (KF) approach is fitted to achieve efficient prediction of the AQI values. The AQI values were collected monthly from January 2018 to March 2019 using a WSN-based network, whereas daily AQI values started to be collected from October 1, 2018 to March 31, 2019. These data have been used for creation and evaluation purposes on the prediction model. According to the results, predicted values have shown high accuracy compared with the actual sensed values. In addition, when monthly AQI values were used, it has depicted higher accuracy compared to the daily ones depending on the experimental results. Therefore, the hybrid AR-KF model is accurate and effective in predicting haze weather, which has practical significance and potential value

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education

Kalman Filter Bibliography: Agriculture, Biology, and Medicine

27 pages, 1 article*Kalman Filter Bibliography: Agriculture, Biology, and Medicine* (Federer, Walter T.; Murty, B. R.) 27 page

Optimized Forecasting Air Pollution Model Based On Multi-Objective Staked Feature Selection Approach Using Deep Featured Neural Classifier

In recent days air pollution has been an essential issue affecting the environment nature leads to various natural causes. Especially the Covid-19 pandemic period has a variation environment changes due to vehicle controls and industrial facts at regular intervals. So air pollution has different scaling factors before and after the pandemic, period produces non-scaled data features. Many methodologies provide the differential solution to analyze the air quality measurements under various conditions to make warnings to avoid air pollution. By the impact of exiting forecasting, ML approaches do not provide the accuracy in precision levels because feature dependencies are non-relevant in high dimension nature. To create the best Air quality index, we need to improve the feature analysis and classification objectives to produce higher prediction performance. This paper proposes a new forecasting model based on the Multi-objective Staked Feature Selection Approach (MoSFS) using the Deep Featured Neural Classifier (DFNC) model to predict air pollution. Initially, the Successive Feature Defect Scaling Rate (SFDSR) was carried out Auto Regressive Integrated Moving Average (ARIMA) rate for finding variation dependencies. The multi-objective relational successive feature index was scaled using the Spider Herding Algorithm (SHA) to select the features based on these variations in feature limits. Then the chosen features get activated to logical activation function with Long Short Term Memory (LSTM) and trained with a Fuzzified Convolution Neural Network (F-CNN) to predict the class by variance. This resultant factor proves the performance of RMSE values attaining the best level to forecast the features and in precision rate produce higher performance in classification accuracy compared to the other system