113,407 research outputs found
A machine learning practice of predicting CO2 levels with the measurement data from university departmental offices
No full textIntroduction: In office spaces, users spend around 1/3 of their daily time, thus maintaining good air quality is an important aspect of keeping a healthy and efficient working environment. As office occupants usually have a regular daily and weekly schedule, machine learning can be a useful method to find the air quality pattern. Its result can benefit the indoor activities to improve the indoor activities and even further development on the smart buildings when the building infrastructure is ready.
In a department area of Politecnico di Milano, multiple low-cost air quality sensors have been installed since September 2023 which measure parameters mainly including temperature, humidity, radon, CO2, VOCs, air pressure and light. In this research, the CO2 data measured in the selected offices are used in this analysis practice.
This research aims to explore how machine learning can benefit long-term monitoring in improving air quality in the offices, taking CO2 as a practice, and how the existing monitoring system in the building can be improved to response to the changing indoor air quality (IAQ) level according to the model training process and the prediction performance.
Methodology: This research tried to apply the regression learner from MATLAB to train the models based on the data collected from the selected offices respectively in the past 1 year (from 2023 Sep 05 to 2024 Sep 05). The training used the basic information as a predictor including the date of the year, time of day, room code, and day of week. Meanwhile, the level of CO2 is selected as the response in training. After the comparison of the results in RMSE (Root-mean-square Error) with different models, the training eventually selected the bagged tree model in terms of its performance and the total training time.
The trained model then was optimized by the Experiment Manager tools from MATLAB with 50 trials by tuning the 4 hyperparameters of the bagged tree model, including method, number of learning cycles, learn rate and min leaf size. The one with the best performance in RMSE was selected in the validation session.
The validation was the comparison between the measured data from Sep 06 to Oct 30 2024 and the prediction from the trained and optimized model. The performance of validation and the model were interpreted in terms of its RMSE, residuals and Coefficient of determination (R2).
Result: The model of rooms has RMSE results between 14.95 to 16.09 after the training and optimization varying from different rooms. Then, in comparisons between the original and model predicted CO2 values, the predicted CO2 levels basically follow the schedule of the rooms, with similar variation rates at the beginning and end of the working hours. This means that the model is able to catch the features of CO2 level variations in the selected offices based on the historical data.
However, the predictions show differences from the measurements with the dates as predictors in 2024, with RMSE from 100.52 to 107.74. These differences lay in the daily variations, especially the peaks of several days are much higher than the prediction results. These are due to several realistic reasons such as the number of occupants in 2024 being more than in 2023 and the schedule of occupants changing each week, etc. which are not included in monitoring and training.
Conclusion: In general, the performance of this model currently is limited by the predictor parameters from the historical data monitored in the past 1 year, but it can already be useful in reflecting the CO2 variations in these offices. The training process also shows the 3 types of information that could be added to the monitoring system to help benefit and respond to the IAQ level changes more smoothly and accurately, including the daily number of people, the occupancy schedule, and the ventilations by window operations.
During this training, it can be found that, in the model training for CO2 level in these offices, the number of occupants and their ventilation behaviours are the 2 influential factors that are important but not measured in the existing monitoring system, especially the number of occupants which dynamic during the year and highly related to the CO2 increasing rate and the peak level. The number of occupants and the schedule can be added as one monitoring parameter in the future to make the prediction more accurate.
On the other hand, other factors such as the dimensions of the room are less influential and can be simulated based on the calculation with the CO2 historical records and the number of occupants.
In addition, this method can be used in spaces with more occupants, such as classrooms, open offices or shopping centres with large numbers of occupants by minimizing the influence of the changes on the average number of occupants on the CO2 prediction
Interaction effects of soybean oil, coconut oil, palm oil and simmering treatment on low fat emulsified meatballs
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Correction to: Long-term changes in rainfed olive production, rainfall and farmer’s income in Bailén (Jaén, Spain) (Euro-Mediterranean Journal for Environmental Integration, (2021), 6, 2, (58), 10.1007/s41207-021-00268-1)
No full textThe article Long‐term changes in rainfed olive production, rainfall and farmer’s income in Bailén (Jaén, Spain), written by Jesús Rodrigo‐Comino, José María Senciales‐Gonzalez, Yang Yu, Luca Salvati, Antonio Gimenez‐Morera and Artemi Cerdà, was originally published electronically on the publisher’s internet portal on 18 June 2021 without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 3 July 2021 to © The Author(s) 2021 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4.0. The original article has been corrected
Historia de la muerte y glorioso martyrio del sancto Innocente, que llaman de la Guardia, natural de la ciudad de Toledo ... : con otros tractados de mucha doctrina y preouecho, que son los de la plana siguiente
Full text linkEl pie de imp. consta en colofón, en v. de h. 78 y de h. 96 de la primera secuencia de fol.Colofón en v. de h. 43 y de h. 88 de la segunda secuencia de fol.Sign.: ¶\p10\s, A-M\p8\s, ¶\p4\s ; A-L\p8\s, O\p3\s.Ports. con grab xil.Ilustraciones xil.Contiene: Tractado y platica de la ciudad de Toledo a sus vezinos afligidos .../author el maestro Alexio Venegas de Busto ; corregido por ... Rodrigo de Yepes ..., de h. 79 a 96 de la primera secuencia de fol. ; Tractado y descripcion breue y c¯opendiosa de la tierra sancta de Palestina ..., h. 1-43 de de la segunda secuencia de fol., con port. propia ; Tractado de la peregrinacion que Iesu Christo ... hizo en este mundo, h. 44-88 de la segunda secuencia de fol
Measurement of the prompt J/ψ and ψ(2S) polarizations in pp collisions at s=7 TeV
Full text linkThis is the pre-print version of the final published paper that is available from the link below.The polarizations of prompt J/ψ and ψ(2S) mesons are measured in proton-proton collisions at s=7 TeV, using a dimuon data sample collected by the CMS experiment at the LHC, corresponding to an integrated luminosity of 4.9 fb. The prompt J/ψ and ψ(2S) polarization parameters λ, λ, and λ, as well as the frame-invariant quantity λ~, are measured from the dimuon decay angular distributions in three different polarization frames. The J/ψ results are obtained in the transverse momentum range 14<p<70 GeV, in the rapidity intervals |y|<0.6 and 0.6<|y|<1.2. The corresponding ψ(2S) results cover 14<p<50 GeV and include a third rapidity bin, 1.2<|y|<1.5. No evidence of large polarizations is seen in these kinematic regions, which extend much beyond those previously explored
Spatial effect of biomass energy consumption on carbon emissions reduction: the role of globalization
No full textAs globalization proceeds, increasing biomass energy consumption is an important pathway to replace fossil fuels for tackling climate change by reducing emissions. This study explores the spatial spillover effect in biomass energy carbon reduction, which is frequently ignored when investigating environmental factors. It uncovers whether globalization and its dimensions can strengthen the spatial effect of biomass energy carbon reduction. Besides, we reveal whether biomass energy consumption can promote CO2 emissions reduction while ensuring economic progress. Results show that (1) owing to the spillover effect, biomass energy consumption plays a significant role in direct and indirect enhancing carbon emissions reduction, with their feedback effects of − 0.003 or 3.3% of the direct effect. (2) Increasing overall, social and political globalization enhances biomass energy consumption’s carbon reduction effect. (3) In countries with higher economic development, overall, economic and political globalization has a better promotion in the spatial effect of biomass energy carbon reduction. (4) Developing biomass energy can support the environment quality while enhancing economic growth
An efficient transient electromagnetic uncertainty 1-D inversion method based on mixture density network
No full textThe transient electromagnetic method (TEM) is one of the geophysical methods that can quickly detect underground space information. It obtains the resistivity structure of underground medium by inversion technology. The traditional inversion methods can only give a unique solution conforming to TEM data but cannot solve the multiplicity problem of solutions. The TEM Bayesian inversion technology can provide uncertainty information to solve this problem. However, its long calculation time makes it difficult to apply to engineering detection with high real-time performance. To solve these problems, a TEM one-dimensional (1-D) inversion mixture density network (TEMIMDNet) is proposed in this article. This method combines Bayesian theory with deep learning (DL) methods. By inputting TEM data into the trained network, the statistical parameters of the posterior probability density (PPD) of the corresponding geological model can be quickly obtained. Thus, the uncertainty information of the geological model can be obtained. This method overcomes the problem of low efficiency of traditional Bayesian inversion. The simulated experiment shows that the TEMIMDNet method can not only obtain the probability density function (pdf) graph of the geological model but also the average relative error (RE) between the maximum a posteriori (MAP) model and the corresponding TEM response, which is less than 0.02. The field experiment shows that the TEMIMDNet method can output the statistical parameters of 52 survey points in only 4 ms, and the imaging results are consistent with the spatially constrained inversion method and OCCAM
Multi-source domain adversarial graph convolutional networks for rolling mill health states diagnosis under variable working conditions
No full textAs the rolling mill often encounters variable and complicated working conditions and shock loads, unsupervised domain adaptive (UDA) methods are imperative in its health monitoring. However, efforts of applying UDA methods on the rolling mill are negligible, and many existing approaches have constraints in domain adaptation, domain label, and data construction that prevent meaningful features from being extracted. Hence, a multi-source domain adversarial graph convolutional networks framework (MSDAGCNs) is presented to overcome these challenges and combine three essential elements to achieve cross-domain health states diagnosis under variable working conditions. First, a shared feature extract module is introduced to extract common features. Then, the features are input to a multi-source feature extract module to extract the data construction from the graphs generated by a graph construction module. Meanwhile, a multi-source domain adversarial classifier module is modeled to extract multi-source invariant features and classify them. After that, the local maximum mean discrepancy is employed to align the domain categories. Next, a task classifier module integrates the results of the multi-source classifier for reliable health state diagnosis. Results on the two cases can verify that the proposed MSDAGCNs can not only outperform other state-of-the-art methods, but also extract domain-invariant knowledge. Compared with the best-performing method, the proposed method can boost accuracy by 0.53% and 0.83% in the simplest task of the two case studies, respectively. Furthermore, the arrangement of sensors on the rolling mill is discussed to select the optimal location for collecting vibrations
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