Carbon dioxide level prediction for indoor air using neural networks

Abstract. Indoor air quality is important for our health and well-being. It has been proven that the air quality affects the performance of the workers. One way to achieve better air quality, would be to adjust the air conditioning and heating. By predicting the room conditions one can react faster to changes and ensure that the room conditions stay favourable. The existing machine learning (ML) models that are used for CO2 prediction are rather basic. This study aims to improve upon the performance of the models compared to earlier studies. The focus of this thesis is to study what type of model would give the best results, what type of training data should be used, and how long history should be fed into the model. One of the goals of this thesis is to examine whether a deep neural network is better than a wider one. The used data consists of indoor air measurements from nine variables: CO2, temperature, pressure, illuminance, volatile organic compound (VOC), movement detection, humidity, door state. The data was gathered in VTT’s Oulu office (in Finland). Different combinations of input variables are experimented on, to find out, which inputs should be fed into the network. The performance is compared to other models commonly used in prior studies. These models include previous value forward (PPV), line fit, and a Multilayer perceptron with one hidden layer (MLP1). Several hyperparameters are tested to find out which combination of parameters has the lowest error. Compared to earlier studies, the developed deep Multilayer Perceptron (MLP) model improved root mean squared error (RMSE) by 0,997 ppm. This indicates that deep models perform better for CO2 prediction tasks. The total root mean squared error was 6.07 ppm. This improvement makes it possible to give more accurate readings for the air conditioning control system, which in turn makes it easier to keep CO2 levels low. A history length of seven minutes is used as the input, and the model predicts ten minutes ahead

Some data-driven methods in process analysis and control

Data-driven methods such as artificial neural networks have already been used in the past to solve many different problems such as medical diagnoses or self-driving cars and thus the material shown here can be of use in many different fields of science. a Few studies that are related to data-driven methods in the field of process engineering will be explored in this thesis. The most important finding related to neural network predictive controller was its better performance in the control of a heat exchanger when compared to several other controller types. The benefits of this approach were both energy savings and faster control. Another finding related to Evolutionary Neural Networks (EvoNNs) was the fact that it can be used to filter out the noise that is contained in the measurement data

Cheat prevention & detection in online games

The purpose of this thesis is to describe ways in which bots (also known as robots) work in video games and other ways in which these bots can be differentiated from normal human players. Research question: Is it possible to deal with bot users in such a way that the game developers didn’t have to stray away from their core skill sets? The most important finding of this thesis was the fact that by analysing user inputs with a neural network one could differentiate between a bot and a human with a good degree of certainty

Sentence to sentence similarity:a review

Abstract This paper suggests a novel sentence-to-sentence similarity measure. The proposal makes use of both word embedding and named-entity based semantic similarity. This is motivated by the increasing short text phrases that contain named-entity tags and the importance to detect various levels of hidden semantic similarity even in case of high noise ratio. The proposal is evaluated using a set of publicly available datasets as well as an in-house built dataset, while comparison with some state of art algorithms is performed