Support Vector Regression Algorithm Research Articles

A Model for Fat Content Detection in Walnuts Based on Near-Infrared Spectroscopy

Near-infrared spectroscopy (NIR) is an efficient and accurate method for fat content detection in walnuts. ‘Wen 185’ walnut is grown in large quantities in southern Xinjiang, and its fat content is an important indicator for evaluating the intrinsic quality. The excessive pursuit of yield efficiency, combined with the neglect of quality, agricultural product safety and other factors, has led to the production of poor-quality walnuts. Moreover, research on predicting walnut kernel fat content based on near-infrared spectroscopy technology is rarely reported. Therefore, a technical framework for walnut kernel detection based on near-infrared spectroscopy and the technical standards for ‘Wen 185’ are urgently needed. After first optimizing the initial spectrum data using five preprocessing methods, we established separate prediction models for walnut kernel fat content based on either a back propagation neural network or a support vector regression (SVR) algorithm. The results show that the correction set and validation set coefficients of determination of the walnut kernel fat content prediction model using the back propagation neural network algorithm were 0.86 and 0.89, respectively, with root mean square errors of 1.56 and 1.58, and an RPD value of 2.57; the coefficients of determination for the calibration and validation sets of SVR were 0.90 and 0.83, respectively, with root mean square errors of 1.76 and 1.70, respectively, and an RPD value of 1.70. Thus, near-infrared spectroscopy can be used to establish a foundation for realizing the rapid detection of walnut kernel fat content.

A flexible and efficient algorithm for high dimensional support vector regression

In high dimensional statistical learning, variable selection and handling highly correlated phenomena are two crucial topics. Elastic-net regularization can automatically perform variable selection and tends to either simultaneously select or remove highly correlated variables. Consequently, it has been widely applied in machine learning. In this paper, we incorporate elastic-net regularization into the support vector regression model, introducing the Elastic-net Support Vector Regression (En-SVR) model. Due to the inclusion of elastic-net regularization, the En-SVR model possesses the capability of variable selection, addressing high dimensional and highly correlated statistical learning problems. However, the optimization problem for the En-SVR model is rather complex, and common methods for solving the En-SVR model are challenging. Nevertheless, we observe that the optimization problem for the En-SVR model can be reformulated as a convex optimization problem where the objective function is separable into multiple blocks and connected by an inequality constraint. Therefore, we employ a novel and efficient Alternating Direction Method of Multipliers (ADMM) algorithm to solve the En-SVR model, and provide a complexity analysis as well as convergence analysis for the algorithm. Furthermore, extensive numerical experiments validate the outstanding performance of the En-SVR model in high dimensional statistical learning and the efficiency of this novel ADMM algorithm.

IoT and AI: a panacea for climate change-resilient smart agriculture

The application of Internet of Things (IoT) and Artificial Intelligence (AI) for disaster preparedness and sustainable agriculture has been a topic of great interest lately. In the last few years, extreme weather swings due to climate change caused by global warming have caught the farming community off guard, especially in the developing world. One of the key objectives of smart agriculture is optimal use of freshwater, which has become an increasingly scarce resource around the world. Reference Evapotranspiration (ETo), an estimation of total flux of water evaporating from a reference surface is an important parameter for irrigation management. IoT & AI-based location-specific estimation of ETo for crop water requirements augments the decision-making process. In this work, we utilize the Hargeaves and Samani (H–S) model and six regression algorithms for the estimation of ETo. We create a location-specific dataset with locally sensed IoT data from a flood warning system and remotely sensed meteorological data, spanning over 5 years. We train and test Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Regression (SVR), Bagging and Random Forest (RF) algorithms on the locally curated dataset with 20 basic, extracted, and derived attributes. We gradually reduce number of attributes in the dataset from 20 to 3 and compare performance of the six algorithms using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Coefficient of Determination R2, Kendall Tau and Spearman Rho metrics. SVR shows superior performance with an MAE of 0.03 and an RMSE of 0.05, followed closely by MLP with an MAE of 0.04 and RMSE of 0.06 with a dataset of 12 attributes. The performance of Bagging and RF algorithms remains relatively unchanged with feature reduction whereas RBF shows slight improvement in performance when number of attributes is reduced to 3. Finally, we develop a novel ensemble hybrid model using the Stacked Generalization technique, which outperforms all individual models in prediction accuracy when using reduced-feature datasets. This work clearly delineates the performances of a diverse set of ML algorithms for feature-rich and feature-scarce scenarios and demonstrates the efficacy of our hybrid ensemble ML algorithm for estimating ETo under limited availability of data in resource-constrained environments.

Identification modeling of ship manoeuvring motion based on ISSA-SVR

This paper introduces the sparrow search algorithm–support vector regression (SSA-SVR) for the first time to improve the precision of mathematical modelling for ship motion through parameter identification. To address the nonsmoothness in ship motion, this study proposes a multistrategy enhanced modelling method named the improved SSA-SVR (ISSA-SVR) for predicting ship motion. Using the KRISO container ship (KCS) test data, this study establishes first-order linear response, second-order nonlinear response, and manoeuvring model group mathematical models through ISSA-SVR identification. The generalisability of the models derived from support vector regression, backpropagation neural networks, particle swarm optimisation-SVR, SSA-SVR, and ISSA-SVR is validated using distinct data from different samples. The results show that the algorithmic model proposed herein excels in prediction compared with other algorithms, thus highlighting its superior generalisation. Furthermore, the proposed method offers solutions with increased accuracy and improved stability for modelling ship motion.

Rice Leaf Nitrogen Content Estimation Through A Methodological Framework Using Single-Sensor Multispectral Images

Using non-destructive evaluation tools based on imaging techniques, including single-sensor multispectral cameras, provides a cost-effective solution for optimizing rice nitrogen fertilization through site-specific nutrient management. However, their accuracy and precision have been identified as areas for improvement. This study aims to develop a methodology to improve the accuracy of estimations through field experiments. It utilizes multispectral images captured by MAPIR Survey3W Orange Cyan Near-Infrared and MAPIR Survey3W Red Edge cameras. The Normalized Difference Vegetation Index and Red Edge values derived from these images are correlated with Soil Plant Analysis Development values to assess rice nitrogen levels. A prediction model is then built using the Support Vector Regression algorithm. Findings from the experiments underscore the importance of addressing shadow effects, integrating the dataset on light intensity and image capture time, conducting radiometric calibration, filtering outlier data, employing image segmentation, and utilizing nonlinear Canova tests to enhance estimation accuracy. By configuring the Support Vector Regression model with RBF kernel, gamma set to 1.24, and epsilon set to 0.1, the R2 of the train data and validation data reaches 0.851, and 0.840 respectively. Meanwhile, the R2 of the test data achieves 0.793 with a mean absolute percentage error of 3.49 % and a root mean square error of 1.70. These findings underscore the potential of the proposed methodology to improve the estimation of rice nitrogen status based on single-sensor multispectral images, paving the way for more effective nutrient management strategies in rice cultivation.

Machine Learning-Based Univariate Time Series Imputation Method for Estimating Missing Values in Non-Stationary Data

Handling missing values in time series data is crucial because they can disrupt data analysis and interpretation. Sequentially missing values in time series often pose a more complex challenge compared to randomly missing values. One of the promising recent methods is Machine Learning-Based Univariate Time Series Imputation (MLBUI), although it is still not widely used and its accessibility is limited. MLBUI employs Random Forest Regression (RFR) and Support Vector Regression (SVR) algorithms. This study evaluates the performance of MLBUI in addressing missing data scenarios in non-stationary univariate time series data. The data used in this research is the average temperature data from Bogor Regency. The missing data scenarios considered include rates of 6%, 10%, and 14%. Besides MLBUI, five other comparison methods are used: Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The results show that MLBUI performs poorly for non-stationary data, although the obtained Mean Absolute Percentage Error (MAPE) is below 10%.

PSXI-23 Development of sub-models to estimate protein requirements and supply of lactating dairy cows using machine learning algorithms

Abstract The objectives of this study were to develop sub-models for predicting protein requirements and supply, encompassing 1) net protein for maintenance (NPm), 2) lactation (NPl), 3) rumen undegradable protein (RUP), and 4) duodenal microbial nitrogen (MicN) from the feed protein. The dataset used in this study was constructed by integrating in vivo experimental data collected from open databases (the National Animal Nutrition Program) and articles (Journal of Dairy Science), which includes a total of 1,779 observations from 436 publications. In the development of the model, animal information and feed chemical components were used as candidate variables, and two types of machine learning algorithms, Random Forest Regression (RFR) and Support Vector Regression (SVR) were employed. After testing, the following predictors were selected for predicting: 1) NPm: body weight (BW) and dry matter intake (DMI), 2) NPl: BW, DMI, days in milk (DIM), and dietary organic matter (OM) and crude protein (CP) contents, 3) RUP: DIM, DMI, dietary DM content, and CP fraction intake (B and C), and 4) MicN: DIM, DMI, DM, dietary neutral detergent fiber (NDF) content, CP fraction intake (A, B, and C). The selected models were assessed using a cross-validation method with the following statistical metrics including the coefficient of determination (R2), root-mean-square error of prediction (RMSEP), residual analyses, and concordance correlation coefficient (CCC). For the RUP and MicN models, they were compared with the NASEM (2021) model. In predicting NPm, both SVR and RFR algorithms demonstrated increased precision (R2 = 0.965 vs. 0.969) and accuracy (RMSEP = 9.7 vs. 9.2 g/d); however, during residual analysis, the RFR model showed a statistically significant slope bias (P < 0.05). In the NPl prediction, the RFR algorithm showed slightly greater performance compared with SVR (R2 = 0.864 vs. 0.814 and RMSEP = 86.7 vs. 98.5 g/d). However, similar to the NPm prediction, the RFR model displayed a statistically significant slope bias (P < 0.05). In the supply model, the RFR model exhibited the greatest precision and accuracy in predicting RUP (R2 = 0.60, RMSEP = 0.326 kg/d, and CCC = 0.71) without any biases. This model achieved a 2.37-fold increase in R2 and a decrease of 0.111 kg/d in RMSEP compared with the NASEM model. As for MicN prediction, the SVR model performed the best (R2 = 0.76, RMSEP = 42.4 g/d, and CCC = 0.86) without biases. This model attained a 19-times improvement in R2 and a reduction of 38.7 g/d in RMSEP when compared with the NASEM model. In conclusion, the models developed using machine learning algorithms can be helpful for accurately and precisely predicting protein requirements and supply based on animal information and the chemical composition of feed.

Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques

Accurate root zone soil moisture (RZSM) estimation using remote sensing (RS) in areas with dense vegetation is essential for real-time field monitoring and precise irrigation scheduling. Traditional methods often face challenges due to the dense crop cover and the complexity of soil and climate interactions. These challenges include the coarse spatial resolution of available soil moisture products, the influence of vegetation and surface roughness, and the difficulty of estimating RZSM from surface data. Aiming to overcome these limitations, two RZSM estimation methods were developed by combining synthetic aperture radar (SAR) data from Sentinel-1 (VV and VH polarizations) and optical and thermal RS data from Landsat-8. These data sources were used in conjunction with various machine learning (ML) models such as M5-pruned (M5P), support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest regression (RFR) to improve the accuracy of soil moisture estimation. In addition to RS data, soil physical and hydraulic properties, meteorological variables, and topographical parameters were selected as inputs to the ML models for estimating the RZSM of sugarcane crops in Khuzestan, Iran. This study identified the temperature vegetation dryness index (TVDI) as a critical parameter for estimating RZSM in combination with the Sentinel-1 SAR data under high vegetation conditions. In both methods, the RFR algorithm outperformed, with similar performance, the XGBoost, SVR, and M5P algorithms in estimating soil surface moisture (R2 = 0.89, RMSE = 0.04 cm3cm−3). However, the accuracy of the RFR algorithm decreased with increasing depth for both the optical-thermal and combined SAR and optical-thermal RS data. This decrease was more pronounced in the combined approach, particularly for the root zone, where the RMSE reached approximately 0.073 cm3cm−3. Accordingly, the key findings demonstrated that the optical-thermal RS data outperformed the SAR RS data for retrieving RZSM in high-vegetated areas. However, combining TVDI with SAR data is a substantial improvement that opens a new path in radar-based RZSM estimation methods under high vegetation conditions.

Predicting demand for new products in fashion retailing using censored data

In today’s highly competitive fashion retail market, it is crucial to have accurate demand forecasting systems, namely for new products. Many experts have used machine learning techniques to forecast product sales. However, sales that do not happen due to lack of product availability are often ignored, resulting in censored demand and service levels that are lower than expected. Motivated by the relevance of this issue, we developed a two-stage approach to forecast the demand for new products in the fashion retail industry. In the first stage, we compared four methods of transforming historical sales into historical demand for products already commercialized. Three methods used sales-weighted averages to estimate demand on the days with stock-outs, while the fourth method employed an Expectation-Maximization (EM) algorithm to account for potential substitute products affected by stock-outs of preferred products. We then evaluated the performance of these methods and selected the most accurate one for calculating the primary demand for these historical products. In the second stage, we predicted the demand for the products of the following collection using Random Forest, Deep Neural Networks, and Support Vector Regression algorithms. In addition, we applied a model that consisted of weighting the demands previously calculated for the products of past collections that were most similar to the new products. We validated the proposed methodology using a European fashion retailer case study. The results revealed that the method using the Expectation-Maximization algorithm had the highest potential, followed by the Random Forest algorithm. We believe that this approach will lead to more assertive and better-aligned decisions in production management.

Predictive Modeling of UHPC Compressive Strength: Integration of Support Vector Regression and Arithmetic Optimization Algorithm

Based on an in-depth analysis of the factors influencing the compressive strength of ultra-high-performance concrete (UHPC), this study examined the impact of both single factorsand combined factors on UHPC performance using experimental data. The correlation analysis indicates that cement content, water content, steel fiber, and fly ash significantly affect the strength of UHPC, whereas silica fume, superplasticizers, and slag powder have a relatively smaller influence. This analysis provides a scientific basis for model development. Furthermore, the support vector regression (SVR) model was optimized using the arithmetic optimization algorithm (AOA). The superior performance and computational efficiency of the AOA–SVR model in predicting UHPC compressive strength were validated. Compared to SVR, support vector machine (SVM), and other single models, the AOA–SVR model achieves the highest R2 value and the lowest error rates. The results demonstrate that the optimized AOA–SVR model possesses excellent generalization ability and can more accurately predict the compressive strength of UHPC.

Research on the framework and meteorological parameter optimization method of dynamic heating load prediction model for heat-exchange stations

Heat-exchange station is the key mid-link between heat source and heat users in district heating system, playing a role in distributing heat and regulating supply and demand. Accurate load prediction at the level of heat-exchange stations and then regulating the heat supply is the important step to optimize district heating system. Based on real and limited data, the framework of heat-exchange station load prediction method is established in this study, which includes 5 procedures ‘data preprocessing method, input variable selection, solar radiation substitution, prediction model establishment and model transplantation application’. The sliding box plot method was proposed for data preprocessing, and the effects of data preprocessing under the widths of 24 h, 48 h, 72 h, 96 h and 120 h were compared, with 48 h as the optimal sliding window width. Support Vector Regression (SVR) algorithm was used to establish a heating load prediction model for heat-exchange station in residential buildings. And parameter substitution was made for solar radiation data that cannot be obtained in weather forecasts. High accuracy was obtained with a MAPE of 5.80 % and RMSE of 0.18 GJ. The above model was transplanted and applied in three other heat-exchange stations of different building types. The MAPEs were 5.51 %, 7.79 % and 6.54 %, with corresponding RMSEs of 0.017 GJ, 0.04 GJ and 0.04 GJ, respectively, which all achieved good prediction results. The research results provide certain technical support for the operation and regulation of district heating systems.

The credibility assessment of Twitter/X users based organization objectives by heterogeneous resources in big data life cycle

Social network data, such as Twitter/X, is of Big Social Data type. Big social data describes people's social behaviors and interactions. They have high business value for decision-making in organizations. However, because of the anonymous nature of social network users, their credibility is ambiguous. Credibility expresses the accuracy and value of big social data. Despite extensive research on the credibility of big social data, most methods have not paid sufficient attention to the important dimensions of their assessment, including user expertise based on topic, selecting social network features, and labeling them. Furthermore, these methods cannot manage the time, high volume, and speed of big social data. To address these issues, this paper presents a novel model for assessing the credibility of Twitter/X users by integrating Twitter/X with Google Scholar. The model automatically defines users' credibility labels using Google Scholar. Machine learning feature selection methods also select features that affect the credibility of Twitter/X users based on the topic. This study uses Google Scholar and the BerTopic algorithm for effective topic modeling on Twitter/X. The model considers unrelated data management, dynamic user credibility, and organizing activities based on the Big Data lifecycle. Finally, using Linear Regression, Support Vector Regression, K-Nearest Neighbor, Random Forest, Classification and Regression Trees algorithms, the model predicts the credibility of Twitter/X users and proves that it performed better than similar models through Classification and Regression Trees. In addition, the model is generalizable for all organizational purposes due to the integration of heterogeneous resources and feature selection methods.

Multi-objective QoS optimization in swarm robotics

The “Internet of Robotic Things” (IoRT) is a concept that connects sensors and robotic objects. One of the practical applications of IoRT is swarm robotics, where multiple robots collaborate in a shared workspace to accomplish assigned tasks that may be challenging or impossible for a single robot to conquer. Swarm robots are particularly useful in critical situations, such as post-earthquake scenarios, where they can locate survivors and provide assistance in areas inaccessible to humans. In these life-saving situations, reliable and prompt communication among swarm robots is of utmost importance. To address the need for highly dependable and low-latency communication in swarm robotics, this research introduces a novel hybrid approach called Multi-objective QoS optimization based on Support vector regression and Genetic algorithm (MQSG). The MQSG method consists of two main phases: Parameter Relationship Identification and Parameter Optimization. In the Parameter Relationship Identification phase, the relationship between network inputs (Packet inter-arrival time, Packet size, Transmission power, Distance between sender and receiver) and outputs (quality of service (QoS) parameters) is established using support vector regression. In the parameter optimization phase, a multi-objective function is created based on the obtained relationships from the Parameter Relationship Identification phase. By solving this multi-objective function, optimal values for each QoS parameter are determined, leading to enhanced network performance. Simulation results demonstrate that the MQSG method outperforms other similar algorithms in terms of transmission latency, packet delivery rate, and the number of retransmitted packets.

Application of machine learning in monitoring fouling in heat exchangers in chemical engineering: A systematic review

AbstractThe present work consists of a systematic literature review that examines studies on using machine learning to monitor fouling in heat exchangers in the chemical engineering area. The research was conducted in four renowned databases: SCOPUS, Science Direct, IEEE, and Web of Science. The main objective of the investigation was to identify the most prevalent machine learning methods, evaluate their performance, and analyze the challenges associated with their implementation and prospects. Using the StArt software, seven relevant scientific papers from the established review protocol. The most frequently identified methods were support vector machine (SVM) and k‐nearest neighbours (k‐NN), followed by decision tree. However, long‐term and short‐term predictors and long short‐term memory (LSTM) and non‐linear autoregressive with exogenous inputs (NARX) algorithms were the most successful, followed by Gaussian process regression (GPR), SVM, k‐NN, and improved grey wolf optimization–support vector regression (IGWO‐SVR) algorithms. Although these methods inspire confidence, it is important to highlight that they are still in the software testing phase. Key gaps identified include the need for further studies on real industrial applications and the integration of advanced sensors and measurement systems. Future directions point to developing more robust and generalized algorithms capable of dealing with the complexity of real systems.

Root phenotype detection of rice seedling under nitrogen conditions based on terahertz imaging technique

Root phenotype detection is the basis for screening of dominant root and improving of seed breeding. The efficiency of root phenotype detection is restricted by the concealment and complexity of crop roots. In this paper, a new method based on terahertz imaging technology was proposed to detect the phenotypic characteristics of rice root and quantitatively predict root nitrogen content. First, the terahertz imaging spectral data of rice root were preprocessed with de-overlapping, reconstruction, enhancement, and segmentation. Then, the phenotypic characteristics of root length, diameter and surface area of rice roots were extracted according to the refinement algorithm. In addition, three linear regression models were fitted between root phenotype and root nitrogen content. Finally, Convolutional Neural Network (CNN), Genetic Algorithm-Back Propagation Neural Network (GA-BPNN), and Sparrow Search Algorithm-Support Vector Regression (SSA-SVR) were used to predict nitrogen content in rice roots by training and testing terahertz time domain data. Compared with two kinds of root phenotypic analysis software, the average errors of root length, diameter and root surface area calculated in this paper were 10.68 %, 7.29 % and 3.49 %, respectively. The fitting determination coefficients between root length, root surface area and the root nitrogen content were 0.88 and 0.87 after removing three groups of high-nitrogen data and one group of contrast data. The prediction accuracy of SSA-SVR model was 0.99 and the root mean square error was 0.05. Studies show that terahertz imaging technique can provide an effective analytical way for qualitative analysis of root phenotype.

Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R2 of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R2 of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels.

Estimation of confined compressive strength of LRS‐FRP concrete specimens with computational intelligence

AbstractReinforced concrete structures deteriorate due to changes in temperature, corrosion, and attacks of sulfate and chloride contents. Retrofitting techniques like fiber‐reinforced polymer (FRP) jacketing, known for their strength and corrosion resistance, are increasingly used to strengthen and retrofit deteriorated structural elements. Large rupture strain (LRS)‐FRP composite, composed of polyethylene terephthalate and polyethylene naphthalate, both of which have high tensile strength and high strain at rupture have been used in the studies of many researchers. This research aims to develop a reliable and accurate machine learning (ML) model to estimate the compressive strength of LRS‐FRP confined specimens. A total of 303 LRS‐FRP confined specimens were gathered after a thorough literature review to develop ML models, utilizing the linear regression, support vector regression, regression tree, and artificial neural network (ANN) algorithms. Additionally, 44 analytical models (AMs) were used to compare the performance of the developed ML models. The results revealed that the performance of the developed ANN model was higher among all the ML and AMs. The R‐value and the mean absolute percentage error (MAPE) value of the developed ANN model were 0.9822 and 6.17%, respectively. The sensitivity analysis results show that the height of the specimens had the highest impact followed by the diameter of the specimen, the number of FRP layers and thickness, and then the tensile strength of LRS‐FRP. The ANN‐based mathematical expression is simple and easy to use to predict the compressive strength of the LRS‐FRP strengthened specimens.

Study on sporty exhaust sound of economical vehicle under acceleration

Exhaust sound quality is an important part of vehicle performance. In this paper, the sporty exhaust sound quality of an economical vehicle equipped with a 4-cylinder and 4-stroke engine is evaluated, analyzed, and improved under acceleration. Firstly, a sporty feeling evaluation method with engine speed divided is proposed, and the influence of exhaust sound order components on sporty exhaust sound is analyzed. The results show that while the A-weighted sound pressure level (ASPL) of Order 2 is lower and the ASPLs of Orders 4 and 6 are higher, the exhaust sound is sportier. Then, a hybrid predicted model of vehicle sporty exhaust sound under acceleration is established based on convolutional neural network (CNN) and support vector regression (SVR) algorithm. The relative errors between the predicted results of CNN-SVR hybrid model and the subjective evaluation results are limited within 2%, which indicates that the CNN-SVR hybrid prediction model achieves a high accuracy in assessing the sporty feeling of exhaust sound. Finally, considering the frequency ranges corresponding with the above order components under the practical accelerating condition, a strategy is proposed to enhance the sporty feeling of exhaust sound by reducing the sound energy within 100 Hz and increasing the sound energy within 100–450 Hz. Based on this strategy, a muffler with different structure is selected and installed on the economical vehicle, and the sporty feeling of exhaust sound is 0.63 points higher than before.

Fast dentification of overlapping fluorescence spectra of oil species based on LDA and two-dimensional convolutional neural network

Although most petroleum oil species can be identified by their fluorescence spectra, overlapping fluorescence spectra make identification difficult. This study aims to address the issue that fluorescence spectroscopy is ineffective in identifying overlapping oil species. In this study, an equivalent model of overlapping oil species with fluorescence spectra was established. The linear discriminant analysis (LDA)-assisted machine learning (ML) algorithms K nearest neighbor (KNN), decision tree (DT), and random forest (RF) improved the identification of fluorescent spectrally overlapping oil species for diesel-lubricant oils. The identification accuracies of two-dimensional convolutional neural network (2DCNN), LDA combined with the ML algorithms effectively all 100 %. Furthermore, Partial Least Squares Regression (PLSR) algorithm, Support Vector Regression (SVR) algorithm, DT regression algorithm, and RF regression algorithm were also used to identify the lubricant concentration in diesel-lubricant oils. The coefficient of determination of the DT was 1, and the root-mean-square error was 0, which identified the concentration of lubricant oils in them accurately and without error.

Toward coupling of nonlinear support vector regression and crowd intelligence optimization algorithms in estimation of suspended sediment load

Sediment phenomenon is very important in hydraulic and water resources issues. The existence of this phenomenon causes many problems in water storage. Sediment simulation in rivers helps in controlling sediment as well as reducing damages. In this study, an attempt was made to estimate the suspended sediment load using the corresponding river flow rate in the Zohreh River, Iran using the newest intelligent simulation methods. This study seeks to couple the nonlinear support vector regression (SVR) with crowd intelligence optimization algorithms. For this purpose, support vector regression was optimized using four new crowd optimization algorithms including the ant colony optimizer (ACO), the ant lion optimizer (ALO), the dragonfly algorithm (DA), and the salp swarm algorithm (SSA). Simulation was done in the two phases of train and test. Due to the integration of the nonlinear support vector regression with the optimization algorithms, the model train phase requires more time than usual situations. Therefore, in the current study, taking into account the number of different iterations including 25, 50, 100 and 200 iterations to perform the optimization of the model and tried to find the best optimizer by considering the calculated error and the run time. It was generally found that the SVR model is accurate in estimating the suspended sediment load. Finally, according to the calculated error as well as the run time, the support vector regression model optimized with the salp swarm algorithm with 25 iterations was chosen as the best model. Also, the values of R2, NSE, and RMSE for the best model in the test phase were calculated as 1, 1, and 10.2 tons per day, respectively, and the algorithm run time was 252 s.