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In view of the high precision requirement of the marine diesel engine body and the difficulty of quality control, a quality prediction method of the body, based on a process error transfer network, is proposed. First, according to the processing information of the body, the network nodes and edges are abstracted to establish the process error transfer network of the body. Then, the key quality control points and key quality features of the diesel engine body are determined by the PageRank and node degree. The key quality features obtained from the network analysis are taken as the output, and the corresponding process errors and process parameters are taken as the input. Finally, the quality prediction model of the body is established based on SVR algorithm, and the C, g parameters of SVR algorithm are optimized by the K-fold cross-validation method and grid search method to improve the prediction accuracy of the body processing quality.

The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman's Rank correlation coefficient, simple regression, and support vector machine regression (SVMR) for estimating cloud security issues based on cloud control factors for improving the mitigation of cloud computing security issues based on control factors using intelligent models in a government organization. Identify the proper cloud control factors for every cloud security issue from estimation errors using a standard for performance measurement like mean square error (MSE) and root mean square error (RMSE), performance measurement to evaluate and validate proposed models. SVMR is an approach to enhance practices for cloud security platforms to mitigate risks and infrastructure for cloud adoption in developing countries in this paper.

Determination of internal qualities such as total soluble solids (TSS) and pH is a paramount concern in strawberry cultivation. Therefore, the main objective of the current study was to develop a non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries. Six hundred samples (100 samples in each ripening stage) in six ripening stages were collected randomly for measuring the biometrical characteristics, i.e., length, diameters, weight and TSS and pH values. An image of each strawberry fruit was captured for colour feature extraction using an image processing technique. Channels of each colour space (RGB, HSV and HSL) were used as input variables for developing multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The result of the study indicated that SVM-R model with HSV colour space performed slightly better than MLR model for TSS and pH prediction. The HSV based SVM-R model could explain a maximum of 84.1% and 79.2% for TSS and 78.8% and 72.6% for pH of the variations in measured and predicted data in training and testing stages, respectively. Further experiments need to be conducted with different strawberry cultivars for the prediction of more internal qualities along with the improvement of model performance.

In the current scenario of anthropogenic climate change, carbon credit security is becoming increasingly important worldwide. Topsoil is the terrestrial ecosystem component with the largest carbon sequestration capacity. Since soil organic matter (SOM), which is mostly composed of organic carbon, and can be affected by rainfall, cultivation, and pollutant inflow, predicting SOM content through regular monitoring is necessary to secure a stable carbon sink. In addition, topsoil in the Republic of Korea is vulnerable to erosion due to climate, topography, and natural and anthropogenic causes, which is also a serious issue worldwide. To mitigate topsoil erosion, establish an efficient topsoil management system, and maximize topsoil utilization, it is necessary to construct a database or gather data for the construction of a database of topsoil environmental factors and topsoil composition. Spectroscopic techniques have been used in recent studies to rapidly measure topsoil composition. In this study, we investigated the spectral characteristics of the topsoil from four major rivers in the Republic of Korea and developed a machine learning-based SOM content prediction model using spectroscopic techniques. A total of 138 topsoil samples were collected from the waterfront area and drinking water protection zone of each river. The reflection spectrum was measured under the condition of an exposure time of 136 ms using a spectroradiometer (Fieldspec4, ASD Inc., Alpharetta, GA, USA). The reflection spectrum was measured three times in wavelengths ranging from 350 to 2500 nm. To predict the SOM content, partial least squares regression and support vector regression were used. The performance of each model was evaluated through the coefficient of determination (R2) and root mean square error. The result of the SOM content prediction model for the total topsoil was R2 = 0.706. Our findings identified the important wavelength of SOM in topsoil using spectroscopic technology and confirmed the predictability of the SOM content. These results could be used for the construction of a national topsoil database.

Indoor localization using device free localization (DFL) in wireless sensor networks is gaining momentum nowadays due to the potential benefits of DFL. The techniques used in DFL can be broadly classified as statistical methods, compressive sensing, machine learning, radio tomographic method etc. Whenever loss factor and noise involved in the setup is unpredictable, techniques based on machine learning for target detection improves the result to a greater extend. The adaptability nature of support vector machines eased our choice of machine learning algorithm and support vector machine regression (SVR) is the proposed machine learning approach to address prediction of target position. Proposed link distance-support vector machine (LD-SVR) model uses link distance based DFL of single and multiple targets in indoor environment. Performance of the proposed model using SVR is analysed using parameters mean error and probability distribution function of mean error for various number of nodes and targets by imparting measurement error. The simulation results are found to be very much promising in a 3D room environment. The maximum value of mean error due to measurement error effect on link distance is less than 1 m.

In this paper, the impact of dust deposition on solar photovoltaic (PV) panels was examined, using experimental and machine learning (ML) approaches for different sizes of dust pollutants. The experimental investigation was performed using five different sizes of dust pollutants with a deposition density of 33.48 g/m2 on the panel surface. It has been noted that the zero-resistance current of the PV panel is reduced by up to 49.01% due to the presence of small-size particles and 15.68% for large-size (ranging from 600 µ to 850 µ). In addition, a significant reduction of nearly 40% in sunlight penetration into the PV panel surface was observed due to the deposition of a smaller size of dust pollutants compared to the larger size. Subsequently, different ML regression models, namely support vector machine (SVMR), multiple linear (MLR) and Gaussian (GR), were considered and compared to predict the output power of solar PV panels under the varied size of dust deposition. The outcomes of the ML approach showed that the SVMR algorithms provide optimal performance with MAE, MSE and R2 values of 0.1589, 0.0328 and 0.9919, respectively; while GR had the worst performance. The predicted output power values are in good agreement with the experimental values, showing that the proposed ML approaches are suitable for predicting the output power in any harsh and dusty environment.

In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.

With the development of industrialization, fine particulate matter (PM2.5) severely chills the health of people. Studies have shown that the variation of PM2.5 concentration is related to the Global Navigation Satellite System (GNSS) tropospheric delay. Therefore, it is possible to use the widely distributed continuous operation reference station (CORS) to monitor and predict PM2.5 concentrations with high time resolution. In this paper, the zenith wet delay (ZWD) of five CORS located in Baoding, Hebei Province, China, from Sep 2014 to Feb 2015, is calculated firstly. Then, the correlation between PM2.5 and ZWD is investigated. Finally, the experimental data including PM2.5, ZWD, temperature, air pressure, and relative humidity data sampled in one hour are used to establish PM2.5 concentrations online prediction model based on support vector machine regression (SVMR) model with metabolic method. The experimental results show that in autumn and winter, the correlation coefficient between daily-mean PM2.5 and ZWD is mainly larger than 0.4, and the correlation coefficient between hourly mean PM2.5 and ZWD is mainly larger than 0.3. Meanwhile, in daily cycle analysis, air temperature, air pressure and relative humidity are related to PM2.5 concentration. Finally, Using SVMR model with metabolic method, by combining GNSS and meteorological factors, ideal short-term prediction accuracy is achieved, which shows that the use of GNSS and meteorological factors is potential in predicting PM2.5 concentration.

International audience; Blind/no-reference image quality assessment (NR-IQA) aims to assess the quality of an image without any reference image. In this paper, we propose an effective and efficient general-purpose NR-IQA model using natural scene statistics (NSS) of the luminance relative order, based on the observation that the variation of the marginal distribution of the relative order coefficients effectively reflect the degree of warping caused by different types of image distortions. In the literature, gradient-relevant methods have had a big success in full-reference (FR) IQA and reduced-reference (RR) IQA. Inspired by these, we extend it to NR-IQA in this paper. Notice that the NSS-based models usually extract their features derived from the spatial, wavelet, DCT and spectral domain etc. Unlike these metrics, the proposed method firstly extracts 32 natural scene statistics features of the luminance relative order, obtained from the log histograms of log horizontal, vertical, main-diagonal and secondary-diagonal derivatives, along with kurtosis, variance, differential entropy and entropy at two scales. Then a mapping is learned to predict the quality score using a support vector regression. The experimental results on several benchmark databases showed that the proposed method is comparable with the state-of-the-art methods and has a relatively low complexity. I Image quality assessment (IQA) is involved in numerous fields and applications since it is essential for the comparison and the optimization of different image processing methods. In many image processing tasks (e.g., image acquisition, compression, restoration, transmission, etc.), it is necessary to assess the quality of the output image. The end-user of images is human; thus the subjective assessment is always the ultimate and the most reliable test. However, the subjective assessment is time-consuming, expensive and cannot be real-time. That is why objective methods mimicking human perception have been developed to assess the perceived quality automatically. Objective methods can be divided into three categories depending on the amount of accessible information: full-reference (FR), reduced-reference (RR) and no-reference (NR). The FR IQA metric needs an ideal "reference" image, e.g. SSIM [1], ESSIM [2] and [24]. However, the reference image is not always available. Instead of utilizing the full information from the reference image, the RR metric [23] compares the distorted image and the reference one based on a short description (e.g. extracted features) of the reference image, which is transmitted along with the distorted image. The deployment of the RR metric is difficult since most operators refuse to pay this additional transmission cost for the non-visible information. The NR IQA metric blindly evaluates the distorted image quality, without any reference image. This could be very useful for the applications without reference image or with limited bandwidth. The NR-IQA metrics can further be classified into two types: distortion-specific (DS) and general-purpose. The DS metrics aim at some specific distortion(s) and must have some a priori information about the distortion(s) [3-5]. The general-purpose metrics aim to tackle different types of distortion [6-21]. This paper focuses on the general-purpose NR-IQA metrics, which may have a single-stage or two-stage framework. For a two-stage framework metric, the number of distortion types should be known a

Environmental time series are often affected by missing data, namely data unavailability at certain time points. In this paper, it is presented an Iterated Prediction and Imputation algorithm, that makes possible time series prediction in presence of missing data. The algorithm uses Dynamics Reconstruction and Machine Learning methods for estimating the model order and the skeleton of time series, respectively. Experimental validation of the algorithm on an environmental time series with missing data, expressing the concentration of Ozone in a European site, shows an average percentage prediction error of \(0.45\%\) on the test set.

Highlights • High-resolution beach surface moisture estimation from the LiDAR intensity data. • No need to calibrate the intensity data in advance based on the Machine Learning. • Detailed investigation of the impacts of SVR training samples’ size and density., Beach Surface Moisture (BSM) is a key attribute in the coastal investigations of land-atmospheric water and energy fluxes, groundwater resource budgets and coastal beach/dune development. In this study, an attempt has been made for the first time to estimate BSM from terrestrial LiDAR intensity data based on the Support Vector Regression (SVR). A long-range static terrestrial LiDAR (Riegl VZ-2000) was adopted to collect point cloud data of high spatiotemporal resolution on the Ostend-Mariakerke beach, Belgium. Based on the field moisture samples, SVR models were developed to retrieve BSM, using the backscattered intensity, scanning ranges and incidence angles as input features. The impacts of the training samples’ size and density on the predictive accuracy and generalization capability of the SVR models were fully investigated based on simulated BSM-intensity samples. Additionally, we compared the performance of the SVR models for BSM estimation with the traditional Stepwise Regression (SR) method and the Artificial Neural Network (ANN). Results show that SVR could accurately retrieve the BSM from the backscattered intensity with high reproducibility (average test RMSE of 0.71% ± 0.02% and R2 of 0.98% ± 0.002%). The Radial Basis Function (RBF) was the most suitable kernel for SVR model development in this study. The impacts of scanning geometry on the intensity could also be accurately corrected in the process of estimating BSM by the SVR models. However, compared to the SR method, the predictive accuracy and generalization performance of SVR models were significantly dependent on the training samples’ coverage, size and distribution, suggesting the need for the training samples of uniform distribution and representativeness. The minimum size of training samples required for SVR model development was 54. Under this condition, SVR performed similarly to ANN with a test RMSE of 1.06%, but SVR still performed acceptably (with an RMSE of 1.83%) even using extremely few training samples (only 16 field samples of uniform distribution), far better than the ANN (with an RMSE of 4.02%).

Improving conventional prediction systems is widely used to optimize the learning process, achieve higher performance, and avoid overfitting. This paper’s purpose is to propose a new predictor for solar tracking systems applications based on oring operator and ranking equation with a conventional predictor including Multi-Layer Perceptron (MLP) and Support Vector Machine Regression (SVR). The point of using oring and ranking equation is to create a new variable that stores the information of combined attributes. This process aims to increase the accuracy of predictors and increase the efficiency of intelligent solar tracking systems. The experiments used 6 different datasets for solar tracking systems. The results revealed that the proposed predictors performed better than conventional predictors. Using the proposed predictors has improved both Root Mean Square Error (RMSE) and Coefficient of Determination (R2 ). The developed MLP models showed lower RMSE and higher R2 compared to conventional MLP models. The improvement ranges for using MLP are from 1.0013 to 1.4614 degrees for RMSE, and from 1.0019 to 1.4984 times for R2 , while the improvement ranges using SVM are from 1.001 to 1.988 degrees for RMSE and from 1.000 to 2.385 times for R2.

Sustainable agriculture in arid regions necessitates that the quality of groundwater be carefully monitored, otherwise, low-quality irrigation water may cause soil degradation and negatively impact crop productivity. This study aimed to evaluate the quality of groundwater samples collected from the wells in the quaternary aquifer, which are located in the Western Desert (WD) and the Central Nile Delta (CND), by integrating a multivariate analysis, proximal remote sensing data, and data-driven modeling (adaptive neuro-fuzzy inference system (ANFIS) and support vector machine regression (SVMR)). Data on the physiochemical parameters were subjected to multivariate analysis to ease the interpretation of groundwater quality. Then, six irrigation water quality indices (IWQIs) were calculated, and the original spectral reflectance (OSR) of groundwater samples were collected in the 302&ndash, 1148 nm range, with the optimal spectral wavelength intervals corresponding to each of the six IWQIs determined through correlation coefficients (r). Finally, the performance of both the ANFIS and SVMR models for evaluating the IWQIs was investigated based on effective spectral reflectance bands. From the multivariate analysis, it was concluded that the combination of factor analysis and principal component analysis was found to be advantageous to examining and interpreting the behavior of groundwater quality in both regions, as well as predicting the variables that may impact groundwater quality by illuminating the relationship between physiochemical parameters and the factors or components of both analyses. The analysis of the six IWQIs revealed that the majority of groundwater samples from the CND were highly suitable for irrigation purposes, whereas most of the groundwater from the WD can be used with some limitations to avoid salinity and alkalinity issues in the long term. The high r values between the six IWQIs and OSR were located at wavelength intervals of 302&ndash, 318, 358&ndash, 900, and 1074&ndash, 1148 nm, and the peak value of r for these was relatively flat. Finally, the ANFIS and SVMR both obtained satisfactory degrees of model accuracy for evaluating the IWQIs, but the ANFIS model (R2 = 0.74&ndash, 1.0) was superior to the SVMR (R2 = 0.01&ndash, 0.88) in both the training and testing series. Finally, the multivariate analysis was able to easily interpret groundwater quality and ground-based remote sensing on the basis of spectral reflectance bands via the ANFIS model, which could be used as a fast and low-cost onsite tool to estimate the IWQIs of groundwater.

Chronic kidney disease (CKD) is becoming a major public health problem worldwide, and excessive potassium intake is a health threat to patients with CKD. In this study, visible–short-wave near-infrared (Vis–SWNIR) spectroscopy and chemometric algorithms were investigated as nondestructive methods for assessing the potassium concentration in fresh lettuce to benefit the CKD patients’ health. Interactance and transmittance measurements were performed and the competencies were compared based on the multivariate methods of partial least-square regression (PLS) and support vector machine regression (SVR). Meanwhile, several preprocessing methods [first- and second-order derivatives in combination with standard normal variate (SNV)] and wavelength selection method of competitive adaptive reweighted sampling (CARS) were applied to eliminate noise and highlight the spectral characteristics. The PLS models yielded better prediction than the SVR models with higher correlation coefficients ([Formula: see text]) and residual predictive deviation (RPD), and lower root-mean-square error of prediction (RMSEP). Excellent prediction of green leaves was obtained by the interactance measurement with [Formula: see text], [Formula: see text][Formula: see text]mg/100[Formula: see text]g, and [Formula: see text]; while the transmittance spectra of petioles provided optimal prediction with [Formula: see text], [Formula: see text][Formula: see text]mg/100[Formula: see text]g, and RPD[Formula: see text]=[Formula: see text]3.34, respectively. Therefore, the results indicated that Vis–SWNIR spectroscopy is capable of intelligently detecting potassium concentration in fresh lettuce to benefit CKD patients around the world in maintaining and enhancing their health.

In this work a Support Vector Machine Regression (SVMR) algorithm is used to calculate local magnitude (Ml) using only five seconds of signal after the P wave onset of one three component seismic station. This algorithm was trained with 863 records of historical earthquakes, where the input regression parameters were an exponential function of the waveform envelope estimated by least squares and the maximum value of the observed waveform for each component in a single station. Ten-fold cross validation was applied for a normalized polynomial kernel obtaining the mean absolute error for different exponents and complexity parameters. The local magnitude (Ml) could be estimated with 0.19 units of mean absolute error. The proposed algorithm is easy to implement in hardware and may be used directly after the field seismological sensor to generate fast decisions at seismological control centers, increasing the possibility of having an effective reaction.

Human activities have increased the concentration of CO2 into the atmosphere, thus it has caused global warming. Construction equipment are semi-stationary machines and spend at least 30% of its life time during idling. The majority of the construction equipment is diesel powered and emits toxic emission into the environment. In this work, the idling will be investigated through adopting several statistical regressions models to quantify the fuel consumption of construction equipment during idling. The regression models which are studied in this work: Multivariate Linear Regression (ML-R), Support Vector Machine Regression (SVM-R), Gaussian Process regression (GP-R), Artificial Neural Network (ANN), Partial Least Square Regression (PLS-R) and Principal Components Regression (PC-R). Findings show that pre-processing has a significant impact on the goodness of the prediction of the explanatory data analysis in this field. Moreover, through mean centering and application of the max-min scaling feature, the accuracy of models increased remarkably. ANN and GP-R had the highest accuracy (99%), PLS-R was the third accurate model (98% accuracy), ML-R was the fourth-best model (97% accuracy), SVM-R was the fifth-best (73% accuracy) and the lowest accuracy was recorded for PC-R (83% accuracy). The second part of this project estimated the CO2 emission based on the fuel used and by adopting the NONROAD2008 model. Keywords

Sustainable forest land management requires the development of rapid, accurate, and cost effective methods to determine forest soil organic carbon (SOC). Reflectance spectroscopy in the visible, near infrared (Vis-NIR) region could be an alternative to standard laboratory methods. The work was aimed to evaluate the performance of different multivariate calibrations techniques analysing soil reflectance spectra to estimate soil organic carbon (SOC). The study was developed within two study areas located in southern Italy: (1) the Bonis catchment (139 ha), mainly covered by Calabrian pine, and (2) the "Marchesale" Biogenetic Nature Reserve (33.2 ha) covered by forest beech. The two study area show relatively homogeneous features in terms of parent material and soil type, whereas they differ in soil sampling density: 135 samples collected in the Bonis catchment and 231 in the "Marchesale" Biogenetic Nature Reserve. In both areas, soil samples were collected up to a depth of 0.20 m, oven dried at 40° and sieved at 2 mm, and then used for spectroscopic measurements and analyses of SOC content. SOC content was determined using a TOC-analyzer (Shimadzu Corporation, Kyoto, Japan), whereas Vis-NIR reflectance was measured in laboratory, under artificial light, using an ASD FieldSpec IV 350-2500 nm spectroradiometer (Analytical Spectral Devices Inc., Boulder, Colorado, USA).To reduce the amount of data and computation time, the spectra were averaged every 10 nm. Spectral reflectance (R) was transformed to apparent absorbance (A) by A = Log (1/R). Each data set was randomly split into a calibration and a validation set (70% and 30%, respectively). Three techniques including principal components regression (PCR), partial least squares regression (PLSR) and support vector machine regression (SVMR) were used to estimate the calibration models, which were validated through the independent datasets. The models were compared through the coefficient of determination (R2), root mean square error of prediction (RMSEP) and interquartile distance (RPIQ). For both study areas, the results showed that PLSR outperformed with higher R2 and RPIQ values and lower RMSEP. PCR showed similar results than PLSR but with lower R2 and RPIQ values and higher RMSEP, whereas the worst results were obtained for SWMR. These preliminary results bring out the need to analyse the effects of different factors (soil type, sample size, etc.) on the relative performance of the prediction techniques.

International audience; Recently, the emergence of low-cost sensors have allowed electronic noses to be considered for densifying the actual air pollution monitoring networks in urban areas. Electronic noses are affected by changes in environmental conditions and sensor drifts over time. Therefore, they need to be calibrated periodically and also individually because the characteristics of identical sensors are slightly different. For these reasons, the calibration process has become very expensive and time consuming. To cope with these drawbacks, calibration transfer between systems constitutes a satisfactory alternative. Among them, direct standardization shows good efficiency for calibration transfer. In this paper, we propose to improve this method by using kernel SPXY (sample set partitioning based on joint x-y distances) for data selection and support vector machine regression to match between electronic noses. The calibration transfer approach introduced in this paper was tested using two identical electronic noses dedicated to monitoring nitrogen dioxide. Experimental results show that our method gave the highest efficiency compared to classical direct standardization.

A Support Vector Machine Regression (SVMR) algorithm was applied to calculate the epicenter distance using a ten seconds signal, after primary waves arrive at a seismological station near to Bogota - Colombia. This algorithm was tested with 863 records of earthquakes, where the input parameters were an exponential function of waveform envelope estimated by least squares and maximum value of recorded waveforms for each component of the seismic station. Cross validation was applied to normalized polynomial kernel functions, obtaining mean absolute error for different exponents and complexity parameters. The epicenter distance was estimated with 10.3 kilometers of absolute error, improving the results previously obtained for this hypocentral parameter. The proposed algorithm is easy to implement in hardware and can be employed directly in the field, generating fast decisions at seismological control centers increasing the possibilities of effective reactions. Resumen Se aplicó un algoritmo de máquinas de vector de soporte para calcular la distancia epicentral utilizando una señal de diez segundos, después del arribo de ondas primarias a una estación sismológica cercana a Bogotá - Colombia. Este algoritmo fue probado con 863 registros de terremotos donde los parámetros de entrada fueron una función exponencial de la envolvente estimada para los mínimos cuadrados y el valor máximo de las formas de ondas registradas en cada componente de la estación sísmica. Validación cruzada fue aplicada a funciones kernel polinomiales normalizadas, obteniendo la media del error absoluto para diferentes exponentes y parámetros de complejidad. La distancia epicentral se estimó con 10.3 kilómetros de error absoluto, mejorando los resultados previamente obtenidos para este parámetro hipocentral. El algoritmo propuesto es fácil de implementar y puede ser empleado directamente en campo, generando decisiones rápidas en centros de control sismológico incrementado posibilidades de tener reacciones efectivas.

A Support Vector Machine Regression (SVMR) algorithm was applied to calculate the epicenter distance using a ten seconds signal, after primary waves arrive at a seismological station near to Bogota - Colombia. This algorithm was tested with 863 records of earthquakes, where the input parameters were an exponential function of waveform envelope estimated by least squares and maximum value of recorded waveforms for each component of the seismic station. Cross validation was applied to normalized polynomial kernel functions, obtaining mean absolute error for different exponents and complexity parameters. The epicenter distance was estimated with 10.3 kilometers of absolute error, improving the results previously obtained for this hypocentral parameter. The proposed algorithm is easy to implement in hardware and can be employed directly in the field, generating fast decisions at seismological control centers increasing the possibilities of effective reactions. Se aplicó un algoritmo de máquinas de vector de soporte para calcular la distancia epicentral utilizando una señal de diez segundos, después del arribo de ondas primarias a una estación sismológica cercana a Bogotá - Colombia. Este algoritmo fue probado con 863 registros de terremotos donde los parámetros de entrada fueron una función exponencial de la envolvente estimada para los mínimos cuadrados y el valor máximo de las formas de ondas registradas en cada componente de la estación sísmica. Validación cruzada fue aplicada a funciones kernel polinomiales normalizadas, obteniendo la media del error absoluto para diferentes exponentes y parámetros de complejidad. La distancia epicentral se estimó con 10.3 kilómetros de error absoluto, mejorando los resultados previamente obtenidos para este parámetro hipocentral. El algoritmo propuesto es fácil de implementar y puede ser empleado directamente en campo, generando decisiones rápidas en centros de control sismológico incrementado posibilidades de tener reacciones efectivas.

Driving Decision-making Mechanism (DDM) is identified as the key technology to ensure the driving safety of autonomous vehicle, which is mainly influenced by vehicle states and road conditions. However, previous studies have seldom considered road conditions and their coupled effects on driving decisions. Therefore, road conditions are introduced into DDM in this paper, and are based on a Support Vector Machine Regression (SVR) model, which is optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm, this study designs a DDM for autonomous vehicle. Then, the SVR model with RBF (Radial Basis Function) kernel function and BP (Back Propagation) neural network model are tested to validate the accuracy of the optimized SVR model. The results show that the optimized SVR model has the best performance than other two models. Finally, the effects of road conditions on driving decisions are analyzed quantitatively by comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis of DDM with added road conditions. The results demonstrate the significant improvement in the performance of DDM with added road conditions. It also shows that road conditions have the greatest influence on driving decisions at low traffic density, among those, the most influential is road visibility, then followed by adhesion coefficient, road curvature and road slope, while at high traffic density, they have almost no influence on driving decisions.

Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

This paper presents GM (1, N) models with linear cross effect and nonlinear cross effect, and discusses the difference of driving factors between these two types of models to solve the cross effects of GM (1, N) model. The model with a linear cross effect in this paper preserves the solution of whitenization in the GM (1, 1) model. While the model with nonlinear cross effect integrates the sequences of systemic features, driving factors, and the cross effect of these driving factors. While applying support vector machine (SVM) regression, it transfers the nonlinear relationship among these sequences to a linear relationship. To test the GM (1, N) model that is based on support vector machine (SVM) with nonlinear effect, the study applies it to forecast the total output of the pharmaceutical industry. The range of the data is selected from 2005&ndash, 2017, which the data from 2005&ndash, 2013 are used to fit into the model. The GM (1, N) model based on SVM with nonlinear cross effect achieves 0.6566 and 0.2956 in its fitted total of relative error and the forecast total of relative error, respectively. The new model presents a more accurate analysis on fitting and forecast precision than the classic GM (1, N) model and GM (1, N) with the linear cross effect model.

© 2019 World Scientific Publishing Company. Return distributions in the class of pure jump limit laws are observed to reflect numerous asymmetries between the upward and downward motions of asset prices. The return distributions are modeled by self-decomposable parametric laws with all parameters continuously responding to each other. Fixed points of the response functions define equilibrium distributions. The equilibrium distributions that can arise in practice are constrained by the level of return acceptability they may attain. As a consequence, expected returns are equated to risk measured by the cost of purchasing the negative of the centered return. The asymmetries studied include differences in scale, speed, power variation, excitation and cross-excitation. ispartof: INTERNATIONAL JOURNAL OF THEORETICAL AND APPLIED FINANCE vol:22 issue:2 status: published

In order to accurately predict costs of the thousands of interim products that are assembled in shipyards, it is necessary to use skilled engineers to develop detailed Gantt charts for each interim product separately which takes many hours. It is helpful to develop a prediction tool to estimate the cost of interim products accurately and quickly without the need for skilled engineers. This will drive down shipyard costs and improve competitiveness. Data mining is used extensively for developing prediction models in other industries. Since ships consist of thousands of interim products, it is logical to develop a data mining methodology for a shipyard or any other manufacturing industry where interim products are produced. The methodology involves analysis of existing interim products and data collection. Pre-processing and principal component analysis is done to make the data “user-friendly” for later prediction processing and the development of both accurate and robust models. The support vector machine is demonstrated as the better model when there are a lower number of tuples. However as the number of tuples is increased to over 10000, then the artificial neural network model is recommended.

This thesis proposes a novel forecasting method that elaborates on the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process is developed to take a whole range of financial time series and analyze their temporal information through multivariate dynamic kernels within a statistical machine learning algorithm, namely support vector machines. A number of dynamic kernels are designed to make the computational process more tractable without sacrifice on accuracy. Unlike most publications in the field, a complete analytical framework directly from the training data is provided for tuning hyperparameters. Experiments, based on predicting the S&P500 market, show promising results. Other potential applications of dynamic kernels are envisioned in such diverse areas as risk measurement, bioinformatics and industrial processes.

In this study, an air heated solar collector (AHSC) dryer was designed to determine the drying characteristics of the pear. Flat pear slices of 10 mm thickness were used in the experiments. The pears were dried both in the AHSC dryer and under the sun. Panel glass temperature, panel floor temperature, panel inlet temperature, panel outlet temperature, drying cabinet inlet temperature, drying cabinet outlet temperature, drying cabinet temperature, drying cabinet moisture, solar radiation, pear internal temperature, air velocity and mass loss of pear were measured at 30 min intervals. Experiments were carried out during the periods of June 2017 in Elazig, Turkey. The experiments started at 8:00 a.m. and continued till 18:00. The experiments were continued until the weight changes in the pear slices stopped. Wet basis moisture content (MCw), dry basis moisture content (MCd), adjustable moisture ratio (MR), drying rate (DR), and convective heat transfer coefficient (hc) were calculated with both in the AHSC dryer and the open sun drying experiment data. It was found that the values of hc in both drying systems with a range 12.4 and 20.8 W/m2 °C. Three different kernel models were used in the support vector machine (SVM) regression to construct the predictive model of the calculated hc values for both systems. The mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative absolute error (RRAE) analysis were performed to indicate the predictive model’s accuracy. As a result, the rate of drying of the pear was examined for both systems and it was observed that the pear had dried earlier in the AHSC drying system. A predictive model was obtained using the SVM regression for the calculated hc values for the pear in the AHSC drying system. The normalized polynomial kernel was determined as the best kernel model in SVM for estimating the hc values.

International audience; In this paper, seismic vulnerability assessment is addressed under the umbrella of remote sensing. A study for estimating and evaluating information for assessing seismic vulnerability based on a building basis is presented. The proposed methodology utilizes the capabilities of remote sensing and combines in-situ data tested in the area of Grenoble (France). A map is estimated in agreement with in-situ data, as support information system for seismic risk in the context of building vulnerability assessment. In the methodology proposed, building attributes such as roof identification, building height and characteristic scale are extracted from very high resolution panchromatic data, and an accurate digital elevation model. Support vector machine regression is used to estimate building vulnerability and in-situ data are available for evaluation.

Visual behavior, and specifically gaze directed at objects and people, is a fundamental cue for understanding how children with Autism Spectrum Disorder (ASD) experience and respond to social interaction. Indeed atypical behaviors such as averting the gaze from faces, looking out of the corner of the eyes, and having difficulties disengaging from non-social stimuli, are well known symptoms of this developmental disorder. However, studying these atypicalities presents several technical challenges, both at a hardware and software level. Traditionally this type of analysis is done by viewing video recordings of the child's interaction and manually rating gaze behavior episodes. This data collection procedure is often a time consuming process, and all the results have to be checked by multiple raters to ensure reliability. When automated systems are used, issues of intrusiveness, robustness and reliability have to be taken into consideration, and their impact on the behavior of the child needs to be accounted for. Moreover, children often do not cooperate to the same extent as an adult participant in the experimental process. This thesis addresses the problem of studying the visual behavior of children during dyadic interactions, using tools from machine learning and computer vision. One focus is on the technical aspects of how to measure the direction of the gaze, and how to recognize what the child is looking at. Another is on the development of semi-automated data analysis collection methods that can be efficiently checked and corrected by the experimenter. The concrete contribution of this work is a set of algorithms and software tools that allow firstly the measurement of multiple gaze factors and the quantification of attentional episodes to objects and people, and secondly the analysis of their distributions, extrema, and spatio-temporal correlations. These tools are applied to the specific case of children affected by ASD, which allows the assessment of how their gaze strategies differ from typically developing children. To measure the direction of the gaze of children in a robust and unobtrusive way, the main recording tool employed is the WearCam: a head-mounted camera developed in our laboratory, that records the eyes of the child and an image of the broad field of view. The reflected images of the eyes are mapped to the coordinates of the gaze in the field of view by means of data-driven appearance-based methods. This is a different approach to the standard methods for gaze tracking, as it makes no use of active lighting, and allows the gathering of data to be postponed until after the recording. This is especially useful as it relieves the children from actively participating in a calibration phase. The task of identifying at which object or person the child is looking is approached through a semi-automated analysis of the recordings. A computationally intensive face and object detection system provides a preliminary estimation, while a faster on-line method can be used by the experimenter to correct that estimation by providing active cues. Furthermore, to allow faster processing of the recordings, a set of features specific to the relevant objects is extracted. The combination of these approaches greatly reduces the time needed to collect the data, while ensuring that reliable data is obtained. The development of these tools facilitates the studying of the behavior of children during natural interactions with an adult. Our approach is able to obtain information on several gaze factors, such as the frequency and length of fixations to faces, how extensively the gaze explores the broad field of view and the use of central vs. peripheral vision. The experimental observations obtained from the comparison of 29 children with autism to 29 typically developing children provide, in two separate studies, quantitative measurements in support of known deficits, such as reduced gaze to faces, downcast gaze and shorter fixations to social stimuli; deficits that were so far documented only in a qualitative manner. More importantly, the results highlight a novel hypothesis, not yet documented, suggesting that ASD children may use a strategy of continuous peripheral viewing to overcome their visual processing difficulties. These observations thus provide an experimental contribution to clinical research in autism.

The conventional sliding mode controller needs the exact knowledge of system state measurements. In this study, nonlinear second order systems with unmeasured system states and bounded external disturbances are considered. The sliding mode observer based on nonlinear observation error dynamics is considered and the observer gain is adjusted by using a support vector machine based plant model. From the output of the support vector machine model, k-step ahead predictions are obtained. Therefore, the value of k is first analyzed to search for a proper value. It is also shown with the simulations that the stability conditions are satisfied for the chosen observer gains. Computer simulations are presented to show the effect of the proposed gain adjustment mechanism on the performance of output feedback sliding mode controller. It is seen that the trajectory tracking performance is improved with respect to a conventional output feedback sliding mode control scheme having constant sliding mode observer gains.

In this study, nonlinear second order systems with unmeasured system states and bounded external disturbances are considered. The observer gain of the sliding mode observer is adjusted by using a support vector machine based plant model. Computer simulations are presented to show the effect of the proposed adjustment mechanism on the performance of the output feedback sliding mode controller. It is shown that the trajectory tracking performance is improved with respect to a conventional output feedback sliding mode control scheme having constant sliding mode observer gains.