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Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70 μ g/m 3 ) and increases the correlation (e.g., R 2 : 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network.

The coordination relationship between land urbanization and population urbanization is crucial for achieving sustainable development under economic transition. Moreover, the balance between land urbanization and population urbanization is essential to guarantee the urbanization process of an entire city. This paper empirically analyzes the interaction between land urbanization and population urbanization in Nanchang from 2002 to 2017 based on the coupling coordination model (CCM). The impacts of the coordination degree on coordinated development are quantified by multivariate linear regression (MLR). The results show the following: (1) The indices of land urbanization and population urbanization in Nanchang showed an upward trend, and therefore the coordination degree in Nanchang increased significantly from 2002 to 2017. (2) The coordinated development of urbanization underwent two stages: disordered and moderately disordered. (3) The urban population proportion and the supporting capability of agricultural production had a positive impact on coordinated development. Meanwhile, the results also show that per capita education expenditures and the per capita public green area had negative impacts on the coordination degree, while economic development and the urban industrial structure were positive contributors to the coordination degree. Finally, this paper proposes that policies should be formulated to achieve coordinated development of urbanization. It can be concluded that the results regarding coordinated development of urbanization can help decision makers formulate effective measures to achieve coordinated development in the future.

Accurate estimation of shear wave velocity (Vs) is crucial for modeling hydrocarbon reservoirs. The Vs values can be directly measured using the Dipole Shear Sonic Imager data; however, it is very expensive and requires specific technical considerations. To address this issue, researchers have developed different methods for Vs prediction in underground rocks and soils. In this study, the well logging data of a wellbore in the Iranian Aboozar limestone oilfield were used for Vs estimation. The Vs values were estimated using five available empirical correlations, linear regression technique, and two machine learning algorithms including multivariate linear regression and gene expression programming. Those values were compared with the real Vs data. Furthermore, three statistical indices including correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the effectiveness of the applied techniques. The mathematical correlation obtained by the GEP algorithm delivered the most accurate Vs values with R2 = 0.972, RMSE = 0.000290, and MAE = 0.000208. Compared to the available empirical correlations, the obtained correlation from the GEP approach uses multiple parameters to estimate the Vs, thereby leading to more precise predictions. The new correlation can be used to estimate the Vs values in the Aboozar oilfield and other geologically similar reservoirs.

This study aims to design an early warning system based on machine learning for short-term prediction of nocturnal frosts in Kurdistan Province in the west of Iran. Four models of artificial neural network (ANN), support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression were used to achieve this main goal. Hourly data of six variables of dry-bulb temperature, wet-bulb temperature, cloud cover, relative humidity, and wind speed and direction were selected as the inputs of these four models at 18:30 local time, and according to them, nocturnal temperatures were predicted for 21:30, 00:30, 03:30, and 06:30 local time. Comparing the outputs of these four models with observational data, all models predicted nocturnal temperatures both in the early hours (21:30 and 00:30 local time) and in the late hours of the night (03:30 and 06:30 local time) the same or less than the observational temperatures. Considering the different performance criteria of the models, such as mean absolute errors (MAE), mean squares errors (MSE), and root-mean-squares errors (RMSE), ANN with Posline transfer function, and Trainlm training function, has less error and better performance in predicting nocturnal temperatures compared to other models. When the main goal is predicting the temperature extremes, especially frost, it is concluded that ANN did not perform very well compared to other models. In addition, ANFIS and SVM models have a better performance in this area than other models. Finally, an early warning system for nocturnal frost was designed for Kurdistan Province in the west of Iran using these four models, and its ability was tested to make short-term nocturnal frost predictions. The results show that this system is suitable for the short-term prediction of nocturnal frosts.

The diameter at breast height (DBH) is an important factor used to estimate important forestry indices like forest growing stock, basal area, biomass, and carbon stock. The traditional DBH ground surveys are time-consuming, labor-intensive, and expensive. To reduce the traditional ground surveys, this study focused on the prediction of unknown DBH in forest stands using existing measured data. As a comparison, the tree age was first used as the only independent variable in establishing 13 kinds of empirical models to fit the relationship between the age and DBH of the forest subcompartments and predict DBH growth. Second, the initial independent variables were extended to 19 parameters, including 8 ecological and biological factors and 11 remote sensing factors. By introducing the Spearman correlation analysis, the independent variable parameters were dimension-reduced to satisfy very significant conditions (p ≤ 0.01) and a relatively large correlation coefficient (r ≥ 0.1). Finally, the remaining independent variables were involved in the modeling and prediction of DBH using a multivariate linear regression (MLR) model and generalized regression neural network (GRNN) model. The (root-mean-squared errors) RMSEs of MLR and GRNN were 1.9976 cm and 1.9655 cm, respectively, and the R2 were 0.6459 and 0.6574 respectively, which were much better than the values for the 13 traditional empirical age–DBH models. The use of comprehensive factors is beneficial to improving the prediction accuracy of both the MLR and GRNN models. Regardless of whether remote sensing image factors were included, the experimental results produced by GRNN were better than MLR. By synthetically introducing ecological, biological, and remote sensing factors, GRNN produced the best results with 1.4688 cm in mean absolute error (MAE), 13.78% in MAPE, 1.9655 cm for the RMSE, 0.6574 for the R2, and 0.0810 for the Theil’s inequality coefficient (TIC), respectively. For modeling and prediction based on more complex tree species and a wider range of samples, GRNN is a desirable model with strong generalizability.

The steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has received increasing attention in recent years. The present study proposes a new method for recognition based on system identification. The method relies on modeling the electroencephalogram (EEG) signals using the Box-Jenkins model. In this approach, the recorded EEG signal is considered as a combination of an SSVEP signal evoked by periodic visual stimulation and a background EEG signal whose components are modeled by a moving average (MA) process and an auto-regressive moving average (ARMA) process, respectively. Then, the target frequency is determined by comparing the modeled SSVEP signals for all stimulation frequencies. The experimental results of the proposed method for recorded EEG signals from five subjects (each subject with four stimulation frequencies) demonstrated a significant improvement in the accuracy of the SSVEP recognition in contrast to canonical correlation analysis, least absolute shrinkage and selection operator, and multivariate linear regression methods. The proposed method exhibits enhanced accuracy especially for short data length and a small number of channels. This superiority suggests that the proposed method is an appropriate choice for the implementation of real-time SSVEP based BCI systems.

Purpose>The purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC) algorithm, delay-and-sum (DAS) beamforming, support vector regression (SVR), multivariate linear regression (MLR) and multivariate curvilinear regression (MCR).Design/methodology/approach>The relative delay between the microphone signals is the key attribute for the implementation of any of these techniques. The machine-learning models SVR, MLR and MCR have been trained using correlation coefficient as the feature set. However, MUSIC uses noise subspace of the covariance-matrix of the signals recorded with the microphone, whereas DAS uses the constructive and destructive interference of the microphone signals.Findings>Variations in root mean square angular error (RMSAE) values are plotted using different DOA estimation techniques at different signal-to-noise-ratio (SNR) values as 10, 14, 18, 22 and 26dB. The RMSAE curve for DAS seems to be smooth as compared to PR1, PR2 and RR but it shows a relatively higher RMSAE at higher SNR. As compared to (DAS, PR1, PR2 and RR), SVR has the lowest RMSAE such that the graph is more suppressed towards the bottom.Originality/value>DAS has a smooth curve but has higher RMSAE at higher SNR values. All the techniques show a higher RMSAE at the end-fire, i.e. angles near 90°, but comparatively, MUSIC has the lowest RMSAE near the end-fire, supporting the claim that MUSIC outperforms all other algorithms considered.

Purpose The purpose of this study is to investigate and analysis of energy consumption for this industry. The core part of any energy management system (EnMS) in industry is to perfectly monitor the energy consumption of significant users and to continuously improve the energy performance. In petrochemical plants, production deals with energy-intensive processes, and measuring energy performance for recognition and assessment of potentials for saving is critical. Design/methodology/approach The required data are exploited for the period of March 2011-August 2016 (data set: 2,012 days). Multivariate linear regression (MLR) and multi-layer perceptron artificial neural network (ANN) methods are separately used to anticipate the energy consumption. The baseline will be assumed as a reference to be compared with the actual data to estimate the real saving values. Finally, cumulative summations (CUSUM) are proposed and applied as an effective indicator for measurement of energy performance in an LDPE. Findings In this study, two statistical methods of MLR and ANN were used to design and develop a comprehensive energy baseline representing the predicted amounts of energy consumption based on the recognized drivers. Although both models imply robust outcomes, when the relative errors are taken into account, performance of ANN models appears fairly superior compared to the MLR model. Originality/value It is highly suggested to the ISO technical committee dealing with energy management standards, to consider the proposed model for baseline development in the future version of the standard ISO 50006 as the supplementary extension for the ISO 50001 for measuring energy performance using EnB and EnPI. As for future studies, the research can be extended to investigate the uncertainty and the model could also become completed applying more advanced ANNs such as recurrent neural networks.

A genetic algorithm was developed and assessed in order to select pairs of proper structural descriptors able to estimate and predict octanol-water partition coefficients of polychlorinated biphenyls (PCBs). The molecular descriptors family was calculated for a sample of 206 PCBs. The problem of searching for the proper descriptors in order to identify structure-activity relationships was translated in genetic terms. The following parameters were imposed in the genetic algorithm (GA) search: sample size − 12, number of variables in multivariate linear regression − 4, imposed adaptation requirements − 3 criteria, maximum number of generations − 50,000, selection strategy − tournament, probability of parent/child mutation − 0.05, number of genes implied in the mutation − 2, optimization parameter - determination coefficient, optimization score - minimum in the sample, and optimization objective - maximum. The highest determination coefficient was obtained in the generation 17,277. Twenty-one evolutions were studied until the optimum solution was obtained. The model identified by the implemented genetic algorithm proved not to be statistically different from the model identified through complete search (Z Steiger  = 1.37, p = 0.0861). According to this GA model, the relationship between the structure of PCBs and octanol-water partition coefficients was of geometric and topological nature as previously revealed by the complete search. The genetic algorithm proved its ability to identify two pairs of molecular descriptors able to characterize the relationship between the structure of PCBs and the octanol-water partition coefficient.

Water quality information of Beijiang River, a tributary of Pearl River in Guangdong, China, was analyzed to provide an overview of the hydrochemical functioning of a major agricultural/rural area and an industrial/urban area. Eighteen water quality parameters were surveyed at 13 sites from 2005 to 2006 on a monthly basis. A bivariate correlation analysis was carried out to evaluate the regional correlations of the water quality parameters, while the principal component analysis (PCA) technique was used to extract the most influential variables for regional variations of river water quality. Six principal components were extracted in PCA which explained more than 78% and 84% of the total variance for agricultural/rural and industrial/urban areas, respectively. Physicochemical factor, organic pollution, sewage pollution, geogenic factor, agricultural nonpoint source pollution, and accumulated pesticide usage were identified as potential pollution sources for agricultural/rural area, whereas industrial wastewaters pollution, mineral pollution, geogenic factor, urban sewage pollution, chemical industrial pollution, and water traffic pollution were the latent pollution sources for industrial/urban area. A multivariate linear regression of absolute principal component scores (MLR-APCS) technique was used to estimate contributions of all identified pollution sources to each water quality parameter. High coefficients of determination of the regression equations suggested that the MLR-APCS model was applicable for estimation of sources of most water quality parameters in the Beijiang River Basin.