Irvan Bastian Arief, PhD

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - Human Occupancy Counter Double Plus (DA-HOC++), a robust way to estimate the number of people within in one room by using data from a carbon dioxide sensor. In our previous work, the proposed Seasonal Decomposition for Human Occupancy Counting (SD-HOC) model can accurately predict the number of… Lihat selengkapnya

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - Human Occupancy Counter Double Plus (DA-HOC++), a robust way to estimate the number of people within in one room by using data from a carbon dioxide sensor. In our previous work, the proposed Seasonal Decomposition for Human Occupancy Counting (SD-HOC) model can accurately predict the number of individuals when the training and labelled data are adequately available. DA-HOC++ is able to predict the number of occupancy with minimal training data, as little as one-day data. DA-HOC++ accurately predicts indoor human occupancy for five different rooms across different countries using a model trained from a small room and adapted to the other rooms. We evaluate DA-HOC++ with two baseline methods - support vector regression technique and SD-HOC model. The results demonstrate that DA-HOC++'s performance on average is better by 10.87% in comparison to SVR and 8.65% in comparison to SD-HOC. Lihat lebih sedikit

Mining time series data is a difficult process due to the lag factor and different time of data arrival. In this paper, we present Seasonal Decomposition for Human Occupancy Counting (SD-HOC), a customised feature transformation decomposition, novel way to estimate the number of people within a closed space using only a single carbon dioxide sensor. SD-HOC integrates time lag and line of best fit model in the preprocessing algorithms. SD-HOC utilises seasonal-trend decomposition with moving… Lihat selengkapnya

Mining time series data is a difficult process due to the lag factor and different time of data arrival. In this paper, we present Seasonal Decomposition for Human Occupancy Counting (SD-HOC), a customised feature transformation decomposition, novel way to estimate the number of people within a closed space using only a single carbon dioxide sensor. SD-HOC integrates time lag and line of best fit model in the preprocessing algorithms. SD-HOC utilises seasonal-trend decomposition with moving average to transform the preprocessed data and for each trend, seasonal and irregular component, different regression algorithms are modelled to predict each respective human occupancy component value. Utilising M5 method linear regression for trend and irregular component and dynamic time warping for seasonal component, a set of the prediction value for each component was obtained. Zero pattern adjustment model is infused to increase the accuracy and finally, additive decomposition is used to reconstruct the prediction value. The accuracy results are compared with other data mining algorithms such as decision tree, multi-layer perceptron, Gaussian processes - radial basis function, support vector machine, random forest, naïve Bayes and support vector regression in two different locations that have different contexts. Lihat lebih sedikit

Human occupancy information is crucial for any modern Building Management System (BMS). Implementing pervasive sensing and leveraging Carbon Dioxide data from BMS sensor, we present large Room Utilisation Prediction with carbon dioxide sensor (RUP), a novel way to estimate the number of people within a closed space from a single carbon dioxide sensor. RUP de-noises and pre-processes the carbon dioxide and indoor human occupancy data. We utilise both seasonal-trend decomposition based on Loess… Lihat selengkapnya

Human occupancy information is crucial for any modern Building Management System (BMS). Implementing pervasive sensing and leveraging Carbon Dioxide data from BMS sensor, we present large Room Utilisation Prediction with carbon dioxide sensor (RUP), a novel way to estimate the number of people within a closed space from a single carbon dioxide sensor. RUP de-noises and pre-processes the carbon dioxide and indoor human occupancy data. We utilise both seasonal-trend decomposition based on Loess and seasonal-trend decomposition with moving average to factorise both datasets. For each trend, seasonal and irregular component, we model different regression algorithms to predict each respective human occupancy component value. We propose a zero pattern adjustment model to increase the accuracy and finally, we use additive decomposition to reconstruct the prediction value. We run our model in two different locations that have different contexts. The first location is an academic staff room and the second is a cinema theatre with up to 300 people. Our results show an average of 4.33% increment in accuracy for the small room with 94.68% indoor human occupancy counting and 8.46% increase for the cinema theatre in comparison to the accuracy of the baseline method, support vector regression. Lihat lebih sedikit

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - Human Occupancy Counter (DA-HOC), a robust way to estimate the number of people within in one room by using data from a carbon dioxide sensor. In our previous work, the proposed Seasonal Decomposition for Human Occupancy Counting (SD-HOC) model can accurately predict the number of individuals when the… Lihat selengkapnya

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - Human Occupancy Counter (DA-HOC), a robust way to estimate the number of people within in one room by using data from a carbon dioxide sensor. In our previous work, the proposed Seasonal Decomposition for Human Occupancy Counting (SD-HOC) model can accurately predict the number of individuals when the training and labelled data are adequately available. DA-HOC is able to predict the number of occupancy with minimal training data, as little as one-day data. DA-HOC accurately predicts indoor human occupancy for a large room using a model trained from a small room and adapted to the larger room. We evaluate DA-HOC with two baseline methods - support vector regression technique and SD-HOC model. The results demonstrate that DA-HOC's performance is better by 12.29% in comparison to SVR and 10.14% in comparison to SD-HOC. Lihat lebih sedikit

With advancement in sensors and Internet of Things, gathering spatiotemporal information from one's surroundings has become easier, to an extent that we can start to use sensor data to infer indoor occupancy patterns. This paper aims to identify which ambient sensor is the most dominant in recognising human presence. Four different types of off-the-shelf sensors from two manufacturers were deployed to ensure that we could collect the following data reliably: illumination, temperature, humidity,… Lihat selengkapnya

With advancement in sensors and Internet of Things, gathering spatiotemporal information from one's surroundings has become easier, to an extent that we can start to use sensor data to infer indoor occupancy patterns. This paper aims to identify which ambient sensor is the most dominant in recognising human presence. Four different types of off-the-shelf sensors from two manufacturers were deployed to ensure that we could collect the following data reliably: illumination, temperature, humidity, levels of carbon dioxide, pressure and sound from within one staff office. Motion, power consumption, door opening and closing data and annotations from a self-developed mobile app were also collected as ground truth. We present our methods to preprocess the data and compute the number of people in the room with different classifiers, and identify sensors with strong and weak correlations. We explain our methodology for integrating large amounts of sensor data, discuss our experiments and findings in relation to the binary occupancy of a single person office, providing a baseline for recognising human occupancy. Lihat lebih sedikit