Least Squares Support Vector Regression Research Articles

Quantification method of tubing defects based on machine learning algorithm and magnetic flux leakage signals.

Tubing is the pipeline that transports crude oil and natural gas from the oil and gas layer to the surface of the earth. Due to the harsh operating environment, the tubing will suffer from etch pits, scratches, cracks, perforations, and even direct fractures of different degrees of defective conditions. If tubing defects are not detected and quantified in a timely manner, the continued use of tubing will result in tubing leakage and failure. Magnetic flux leakage (MFL) testing as a nondestructive testing method enables the identification and quantitative analysis of defects in metal tubing. To improve the quantification accuracy of defects in the wellhead MFL testing of tubing defects during workover operations, this paper proposes a multi-output least-squares support vector regression machine (MLSSVR) model optimized based on the simulated annealing algorithm. The size of tubing defects can be quantified by establishing the mapping between the characteristic quantity of MFL signals and the defect size. The experimental results of MFL testing of tubing defects show that the root mean square error (RMSE) of the diameter of tubing defects of the simulated annealing algorithm optimized multi-output least-squares support vector regression (SA-MLSSVR) machine model proposed in this paper is 0.4562mm, and the RMSE of the depth of tubing defects is 0.1504mm. Compared with the non-optimized MLSSVR model, the overall RMSE of tubing defects is reduced by 36.48%. The SA-MLSSVR model only needs one-ninth of the time to achieve the same quantification accuracy as the particle swarm optimized multi-output least-squares support vector regression machine model.

FCDT-IWBOA-LSSVR: An innovative hybrid machine learning approach for efficient prediction of short-to-mid-term photovoltaic generation

The short-term photovoltaic (PV) power prediction is mainly used to prevent photovoltaic power generation fluctuations, grid overvoltage, reverse current, and islanding effects. At the same time, the mid-term PV power forecast is of great significance to improving the availability of PV power generation and the benefit of PV grid connection. Because of the significant differences in time scales between mid-term and short-term power prediction, the short-term power prediction model or method cannot be directly applied to mid-term power prediction. Therefore, few scholars have developed effective short-to-mid-term power forecasting strategies for photovoltaics. As a result, this research proposes a prediction model based on a fast cull outlier algorithm (FC), a decision tree (DT), least squares support vector regression (LSSVR), and an improved whale bat optimization algorithm (IWBOA), which is used for the prediction of the short-to-mid-term photovoltaic power on different time scales, one day and one month in advance. The FC algorithm is used to analyze the difference between the overall data and the abnormal data in the local density and distance to remove the outliers in the original data set. The revised DT model differentiates the data according to climate characteristics to obtain different input data subsets with the best correlation and constructs an independent LSSVR-based model corresponding to the data subsets. The WOA algorithm is improved by independently designing the Chebyshev chaotic initialization strategy, chaotic adaptive inertia weight and BOA algorithm, and the kernel function width and penalty factor of each short-to-mid-term independent sub-PV power prediction model are optimized by IWBOA, thereby improving the computational speed and prediction accuracy of the LSSVR model. Finally, the effectiveness of the proposed strategy is evaluated using actual data from photovoltaic power plants in China. The regression coefficient (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) of short-term forecasting ability of the FCDT-IWBOA-LSSVR model established in this paper are 0.99657, 0.22009, 0.32515, and 0.20959, respectively, while the R2, MSE, RMSE, and MAE of medium-term forecasting ability are 0.98374, 1.91371, 1.38336, and 0.6256. The calculation results show that the prediction accuracy of the FCDT-IWBOA-LSSVR model is higher than that of the WOA-LSSVR, PSO-LSSVR, PSO-BP, I-ACO-SVR, ANN, EMS-DSM-RT, 5 ML, and ELM models.

Machine learning prediction of dual and dose-response effects of flavone carbon and oxygen glycosides on acrylamide formation.

The extensive occurrence of acrylamide in heat processing foods has continuously raised a potential health risk for the public in the recent 20 years. Machine learning emerging as a robust computational tool has been highlighted for predicting the generation and control of processing contaminants. We used the least squares support vector regression (LS-SVR) as a machine learning approach to investigate the effects of flavone carbon and oxygen glycosides on acrylamide formation under a low moisture condition. Acrylamide was prepared through oven heating via a potato-based model with equimolar doses of asparagine and reducing sugars. Both inhibition and promotion effects were observed when the addition levels of flavonoids ranged 1-10,000 μmol/L. The formation of acrylamide could be effectively mitigated (37.6%-55.7%) when each kind of flavone carbon or oxygen glycoside (100 μmol/L) was added. The correlations between acrylamide content and trolox-equivalent antioxidant capacity (TEAC) within inhibitory range (R 2 = 0.85) had an advantage over that within promotion range (R 2 = 0.87) through multiple linear regression. Taking ΔTEAC as a variable, a LS-SVR model was optimized as a predictive tool to estimate acrylamide content (R 2 inhibition = 0.87 and R 2 promotion = 0.91), which is pertinent for predicting the formation and elimination of acrylamide in the presence of exogenous antioxidants including flavonoids.

Using machine learning to determine acceptable levels of groundwater consumption in Iran

Groundwater footprint index (GFI) is an essential indicator to assess the sustainability of groundwater aquifers. Prediction of future GFI can significantly help managers and decision-makers of groundwater supply to better plan for future resilient consumption of surface and groundwater. In this context, artificial intelligence and machine learning models can aid to predict GFI in view of lacking or uncertain data. We used this technique to predict GFI for 178 Iranian aquifers. To our knowledge, this is the first time that GFI was predicted using machine learning models. Four models, i.e., adaptive neuro-fuzzy inference system, least-squares support vector regression, random forest, and gene expression programming, were used to predict GFI. Systematic combinations of eight variables, including precipitation, recharge, return water, infiltration from the river to the aquifer, groundwater exploitation, aquifer area, evaporation, and river drainage from the aquifer were used in the form of nine input scenarios for GFI prediction. The results showed that inclusion of all input variables gave the best results for predicting the GFI. Predicted GFIs were generally between 0.5 and 8 with an average of 1.9. A value above 1 indicates that groundwater consumption is not resilient that can adversely affect available groundwater resources in the future. Over-use of groundwater can lead to land subsidence. Especially, aquifers located in Qom, Qazvin, Varamin, and Hamedan provinces of Iran may be affected due to large over-use. Among the four models, least-squares support vector regression resulted in the highest prediction performance. Due to the poor performance of adaptive neuro-fuzzy inference system, the novel Harris hawks optimization algorithm was used to improve the performance of adaptive neuro-fuzzy inference system. The Harris hawks optimization - adaptive neuro-fuzzy inference system hybrid model improved the GFI prediction performance. Machine learning methods improve prediction of GFI for aquifers and thus, can be used to better manage groundwater in areas with less reliable data.

Support vector regression for the temperature-stimulated drug release

This paper presents a new coupled nonlinear convection–diffusion–reaction model for the temperature-stimulated drug release and a numerical algorithm based on a machine learning technique. By incorporating the fractional derivatives into the model, we take advantage of anomalous diffusion in biological tissues. In the proposed technique a least-squares support vector regression is developed for the numerical simulation of the problem. The Bernstein polynomials are used as the kernel function and some training points are considered for the residual system of the coupled problem in an inverse process. Some numerical experiments are carried out to support the method and its spectral convergence.

Prediction of evaporation from dam reservoirs under climate change using soft computing techniques.

This study aimed to predict evaporation from dam reservoirs using artificial intelligence considering climate change. Mahabad Dam, near Lake Urmia, in northwestern Iran, is used to investigate the proposed approach. There are three parts to the study presented herein. In the first part, two machine learning models, namely group method of data handling (GMDH) and least squares support vector regression (LS-SVR), were used to model the inflow to the dam reservoir. Temperature, precipitation, and inflow during the previous month from 1990 to 2017 were used as input data. In the second part, the evaporation from the dam reservoir was modeled using the adaptive neuro-fuzzy inference system (ANFIS) and optimized ANFIS using Harris hawks optimization (HHO) and the arithmetic optimization algorithm (AOA) optimization algorithms. The input parameters in this part were temperature, precipitation, inflow to the dam reservoir, along with evaporation from the dam reservoir in the previous month. In the third part, precipitation and temperature were predicted using the fifth report of IPCC based on RCP2.6, RCP4.5, and RCP8.5 scenarios for the period 2020-2040. Out of 28 models presented in the fifth report, EC-ERATH and FIO-ESM had the greatest similarity with observational data of temperature and precipitation, respectively. The results of scatter plots and Taylor's diagram showed the higher performance of LS-SVR (root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) of 8.65, 4.69, and 0.96) compared to GMDH (RMSE, MAPE, and NSE of 11.65, 7.81, and 0.93) in modeling the inflow. Moreover, both hybrid modes (AOA-ANFIS and HHO-ANFIS) improved the performance of ANFIS in modeling the evaporation from the dam reservoir. The RMSE, MAPE, and NSE values for ANFIS were 0.56, 0.52, and 0.89, respectively, while these values for the AOA-ANFIS (RMSE, MAPE, and NSE of 0.31, 0.24, and 0.93) and HHO-ANFIS (RMSE, MAPE, and NSE of 0.20, 0.30, and 0.96) were improved remarkably. The impact of climate change reduced the inflow to the dam reservoir by about 0.45, 0.80, and 1.7 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Also, the effect of climate change caused the evaporation from the dam reservoir to increase by about 0.2, 0.9, and 1 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. The findings of this study show that the correct management of dam reservoirs needs to consider the potential effects of climate change in the future. Moreover, the hybrid machine learning models used in this study can be used to predict the amount of evaporation in other reservoirs.

Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media

This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer-drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer-drug systems' stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future.

A Novel Approach for Therapeutic Drug Monitoring of Valproic Acid Using FT-IR Spectroscopy and Nonlinear Support Vector Regression.

Recent technological progress has bolstered efforts to bring personalized medicine from theory into clinical practice. However, progress in areas such as therapeutic drug monitoring (TDM) has remained somewhat stagnant. In drugs with well-known dose-response relationships, TDM can enhance patient outcomes and reduce health care costs. Traditional monitoring methods such as chromatography-based or immunoassay techniques are limited by their higher costs and slow turnaround times, making them unsuitable for real-time or onsite analysis. In this work, we propose the use of a fast, direct, and simple approach using Fourier transform infrared spectroscopy (FT-IR) combined with chemometric techniques for the therapeutic monitoring of valproic acid (VPA). In this context, a database of FT-IR spectra was constructed from human plasma samples containing various concentrations of VPA; these samples were characterized by the reference method (immunoassay technique) to determine the VPA contents. The FT-IR spectra were processed by two chemometric regression methods: partial least-squares regression (PLS) and support vector regression (SVR). The results provide good evidence for the effectiveness of the combination of FT-IR spectroscopy and SVR modeling for estimating VPA in human plasma. SVR models showed better predictive abilities than PLS models in terms of root-mean-square error of calibration and prediction RMSEC, RMSEP, R2Cal, R2Pred, and residual predictive deviation (RPD). This analytical tool offers potential for real-time TDM in the clinical setting. FTIR spectroscopy was evaluated for the first time to predict VPA in human plasma for TDM. Two regressions were evaluated to predict VPA in human plasma, and the best-performing model was obtained using nonlinear SVR.

Life-Cycle Production Optimization of the CO2-Water-Alternating-Gas Injection Process Using Least-Squares Support-Vector Regression Proxy

SummaryIn this study, we present a framework for efficient estimation of the optimal carbon dioxide (CO2)-water-alternating-gas (WAG) parameters for robust production-optimization problems by replacing a high-fidelity model with a least-squares support-vector regression (LS-SVR) model. We provide insight and information on the proper selection of feature space and training samples of the LS-SVR proxy model for the CO2-WAG life cycle production optimization problem. Given a set of training points generated from high-fidelity model-based simulation results, an LS-SVR-based proxy model is built to approximate a reservoir-simulation model. The estimated optimal design parameters are then found by maximizing net present value (NPV) using the LS-SVR proxy as the forward model within an iterative-sampling-refinement (ISR) optimization algorithm that is designed specifically to promote the accuracy of the proxy model for robust production optimization. As an optimization tool, the sequential quadratic programming (SQP) method is used. CO2-WAG design variables are CO2 injection and water injection rates for each injection well at each cycle, production bottomhole pressure (BHP) for each production well at each WAG half-cycle, and inflow control valve (ICV) for each well at each WAG half-cycle and at each valve. We study different scenarios where we fix some of the design variables to investigate the importance of design variables on the life cycle production optimization of the CO2-WAG problem. We compare the performance of the proposed method using the LS-SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir simulation runs for a synthetic example considering a three-layer, channelized reservoir with four injectors and nine producers. Results show that with the properly selected feature space and training points, the proposed LS-SVR-based ISR optimization framework is at least 1.5–8 times computationally more efficient, depending on the cases considered, than the StoSAG using a high-fidelity numerical simulator. However, we observe that the size and sampling of the training data, as well as the selection of well controls and their bound constraints for the well controls, seem to be influential on the performance of the LS-SVR-based optimization method. This is the first LS-SVR application to the CO2-WAG optimal well-control problem. The proposed LS-SVR-based ISR optimization framework has the potential to be used as an efficient tool for the CO2-WAG optimization problem.

Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning

In a small sample welding test space, and to achieve online prediction and self-optimisation of process parameters for the resistance welding joint quality of power lithium battery packs, this paper proposes a welding quality prediction model. The model combines a chaos game optimisation algorithm (CGO) with the multi-output least-squares support vector regression machine (MLSSVR), and a multi-objective process parameter optimisation method based on a particle swarm algorithm. First, the MLSSVR model was constructed, and a hyperparameter optimisation strategy based on CGO was designed. Next, the welding quality was predicted using the CGO–MLSSVR prediction model. Finally, the particle swarm algorithm (PSO) was used to obtain the optimal welding process parameters. The experimental results show that the CGO–MLSSVR prediction model can effectively predict the positive and negative electrode nugget diameters, and tensile shear loads, with root mean square errors of 0.024, 0.039, and 5.379, respectively, which is better than similar methods. The average relative error in weld quality for the optimal welding process parameters is within 4%, and the proposed method has a good application value in the resistance spot welding of power lithium battery packs.

Predicting seasonal patterns of energy production: A grey seasonal trend least squares support vector machine

This paper proposed a hybrid ensemble forecasting technique that incorporates the merits of the accumulation generation operation (AGO), least-square support vector regression (LSSVR), dummy variable, and time trend item to forecast the seasonal time series characterized by nonlinearity and uncertainty. This proposed framework, a grey seasonal trend least squares support vector machine (GSTLSSVR), can make it easy to add arbitrary seasonality to any feature, which improves model realism significantly. Furthermore, the grid search method and the last block validation are developed to identify the optimal hyperparameter, making it possible for the developed method to capture seasonal and nonlinear patterns of the original data. For illustration and verification, the quarterly petroleum coke production and monthly crude oil production in China are employed to construct diverse competing models, including the seasonal fluctuation grey model (SGM(1,1)), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters (HW), backpropagation neural network (BPNN), a grey seasonal least squares support vector machine (GSLSSVR), long short term memory (LSTM) and LSSVR. The forecasting values of quarterly petroleum coke production are 668.74, 682.68, 679.99, 704.35 for 2019 and 717.59, 734.99, 727.16, 741.53 for 2020, with the lowest MAPEP value (3.7566%). Moreover, the monthly predicted crude oil production for 2019 are 3113.9, 1645, 1562.2, 1625.4, 1599.3, 1619.1, 1627, 1576.9, 1629.6, 1583.6, and 1615.6, while those counterparts for 2020 are 3093.2, 1625, 1547.5, 1621.5, 1610.6, 1644.9, 1662.9, 1615.4, 1667.4, 1615, 1667.4, 1615, and 1640.2, with the lowest MAPEP value (1.0034%). Generally, the empirical results show that the proposed model obtains superior overall performance to other benchmarks in both case studies. Therefore, this proposed model exhibits promising prospects for identifying seasonal patterns in energy production sequences.

Evolutionary optimization of thermo-physical properties of MWCNT-Fe[formula omitted]O[formula omitted]/water hybrid nanofluid using least-squares support vector regression-based models

Decisions on optimizing design and operating parameters are challenging when using hybrid nanofluids (HNFs). A procedure is proposed and implemented for predicting and optimizing the thermal conductivity and dynamic viscosity of MWCNT-Fe3O4/water HNF. The procedure involves using precise least-squares support vector regression (LSSVR) models, multi-objective genetic optimization of thermal properties, and automated selection of optimal design conditions. Tuned parameters are the volume fractions of nanoparticles and the operating temperature. The cross-validated and carefully optimized LSSVR models for thermal conductivity and dynamic viscosity showed excellent performances, with testing mean percentage errors of −0.246 and −0.103%, and relative root mean square errors of 1.325 and 2.165%, respectively. By assigning equal importance to the two response parameters, an HNF with volume fractions of 0.302 (Fe3O4) and 0.183% (MWCNT), operating at 55 °C, is highlighted as the optimal design within the considered range of tuned parameters. This corresponds to a particle mixing proportion (PMP) of 0.605, and the corresponding values of thermal conductivity and dynamic viscosity are 0.803 W/m K and 0.625 mPa s, respectively.

Forecasting Hourly Intermittent Rainfall by Deep Belief Networks with Simple Exponential Smoothing

Accurate rainfall forecasting is essential in planning and managing water resource systems efficiently. However, intermittent rainfall patterns increase the difficulty of accurately forecasting rainfall values. Deep learning techniques have recently been popular and powerful in forecasting. Thus, this study employed deep belief networks with a simple exponential smoothing procedure (DBNSES) to forecast hourly intermittent rainfall values in Taiwan. Weather factors were used as independent variables to forecast rainfall volume. The simple exponential smoothing data preprocessing procedure was used to deal with the intermittent data patterns. The other three forecasting models, namely the least squares support vector regression (LSSVR), the generalized regression neural network (GRNN), and the backpropagation neural network (BPNN), were employed to forecast rainfall using the same data sets. In addition, genetic algorithms were utilized to determine the parameters of four forecasting models. The empirical results indicate that the developed DBNSES models are superior to the other forecasting models in terms of forecasting accuracy. In addition, the DBNSES can obtain smaller values of RMSE than those in the previous studies. Therefore, the DBNSES model is a suitable and effective way of forecasting rainfall with intermittent data patterns.

Predictions of multiple food quality parameters using near-infrared spectroscopy with a novel multi-task genetic programming approach

In order to meet the increasing demand for food safety and quality, new methods for simultaneous and rapid determination of multiple food quality parameters (FQPs) are urgently needed in the food industry. Incorporating near-infrared (NIR) spectroscopy and spectral prediction model for rapid, repeatable, non-destructive, and low running costs quantitative analysis of FQPs is enjoying increasing popularity in the food industry. However, most existing spectrum-based prediction models are trained under a single-task learning framework, that is, a prediction model for each quality parameter and spectrum is constructed separately. This paradigm ignores possible connections among prediction tasks of different FPQs, which may result in the performance degradation of a single FPQ prediction model. This study proposes a novel multi-task genetic programming-based approach named EM4GPO for building multiple FQPs predictions simultaneously. In EM4GPO, the multi-dimensional trees are used to encode the raw NIR spectrum to shared features of multiple FQPs; for each FQP, a least square support vector regression (LS-SVR) modeling is performed on the shared features to obtain private features and prediction model; during the optimization process, a new algorithm is developed to optimize the previously obtained shared and private features, and LS-SVR prediction models through population evolution by combining the multidimensional multiclass genetic programming with multidimensional populations optimization method with nondominated sorting method. The proposed EM4GPO model is evaluated and compared with nine popular NIR prediction models using 10 NIR spectral datasets. The experimental results showed that EM4GPO outperformed other commonly used methods in all datasets which indicates that EM4GPO is competitive and effective in solving the problem of multiple FQPs predictions using the NIR spectrum.

Chelyshkov least squares support vector regression for nonlinear stochastic differential equations by variable fractional Brownian motion

The main aim of this study is to introduce an efficient method based on the Chelyshkov polynomials and least squares support vector regression (LS-SVR) for solving a class of nonlinear stochastic differential equations (SDEs) by variable fractional Brownian motion (VFBm). The derivative operational matrix and variable-order fractional integral operator of Chelyshkov polynomials (ChPs) are obtained. These operators, the standard Brownian motion with help of the Gauss–Legendre quadrature are applied for generating VFBm. We apply the Chelyshkov polynomials kernel and the collocation LS-SVR method for training the network. Then, the formulation of the scheme gives rise to an optimization problem. Finally, the classical optimization and Newton’s iterative scheme are used to train this problem. Moreover, we discuss convergence and error analysis of mentioned scheme. In the end, to reveal the superiority and efficiency of current paper, some test problems are applied.

A Study Using Optimized LSSVR for Real-Time Fault Detection of Liquid Rocket Engine

Health monitoring and fault diagnosis of liquid rocket engine (LRE) are the most important concerning issue for the safety of rocket’s flying, especially for the man-carried aerospace engineering. Based on the sensor measurement signals of a certain type of hydrogen-oxygen rocket engine, this paper proposed a real-time fault detection approach using a genetic algorithm-based least squares support vector regression (GA-LSSVR) algorithm for the real-time fault detection of the rocket engine. In order to obtain effective training samples, the data is normalized in this paper. Then, the GA-LSSVR algorithm is derived through comprehensive considerations of the advantages of the Support Vector Regression (SVR) algorithm and Least Square Support Vector Regression (LSSVR). What is more, this paper provided the genetic algorithm to search for the optimal LSSVR parameters. In the end, the computational results of the suggested approach using the rocket practical experimental data are given out. Through the analysis of the results, the effectiveness and the detection accuracy of this presented real-time fault detection method using LSSVR GA-optimized is verified. The experiment results show that this method can effectively diagnose this hydrogen-oxygen rocket engine in real-time, and the method has engineering application value.

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

Precise streamflow estimation plays a key role in optimal water resource use, reservoirs operations, and designing and planning future hydropower projects. Machine learning models were successfully utilized to estimate streamflow in recent years In this study, a new approach, covariance matrix adaptation evolution strategy (CMAES), was utilized to improve the accuracy of seven machine learning models, namely extreme learning machine (ELM), elastic net (EN), Gaussian processes regression (GPR), support vector regression (SVR), least square SVR (LSSVR), extreme gradient boosting (XGB), and radial basis function neural network (RBFNN), in predicting streamflow. The CMAES was used for proper tuning of control parameters of these selected machine learning models. Seven input combinations were decided to estimate streamflow based on previous lagged temperature and streamflow data values. For numerical prediction accuracy comparison of these machine learning models, six statistical indexes are used, i.e., relative root mean squared error (RRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), and the Kling–Gupta efficiency agreement index (KGE). In contrast, this study uses scatter plots, radar charts, and Taylor diagrams for graphically predicted accuracy comparison. Results show that SVR provided more accurate results than the other methods, especially for the temperature input cases. In contrast, in some streamflow input cases, the LSSVR and GPR were better than the SVR. The SVR tuned by CMAES with temperature and streamflow inputs produced the least RRMSE (0.266), MAE (263.44), and MAPE (12.44) in streamflow estimation. The EN method was found to be the worst model in streamflow prediction. Uncertainty analysis also endorsed the superiority of the SVR over other machine learning methods by having low uncertainty values. Overall, the SVR model based on either temperature or streamflow as inputs, tuned by CMAES, is highly recommended for streamflow estimation.

RLC Circuit Forecast in Analog IC Packaging and Testing by Machine Learning Techniques.

For electronic products, printed circuit boards are employed to fix integrated circuits (ICs) and connect all ICs and electronic components. This allows for the smooth transmission of electronic signals among electronic components. Machine learning (ML) techniques are popular and employed in various fields. To capture the nonlinear data patterns and input–output electrical relationships of analog circuits, this study aims to employ ML techniques to improve operations from modeling to testing in the analog IC packaging and testing industry. The simulation calculation of the resistance, inductance, and capacitance of the pin count corresponding to the target electrical specification is a complex process. Tasks include converting a two-dimensional circuit into a three-dimensional one in simulation and modeling-buried structure operations. In this study, circuit datasets are employed for training the ML model to predict resistance (R), inductance (L), and capacitance (C). The least squares support vector regression (LSSVR) with Genetic Algorithms (GA) (LSSVR-GA) serves as an ML model for forecasting RLC values. Genetic algorithms are used to select parameters of LSSVR models. To demonstrate the performance of LSSVR models in forecasting RLC values, three other ML models with genetic algorithms, including backpropagation neural networks (BPNN-GA), random forest (RF-GA), and eXtreme gradient boosting (XGBoost-GA), were employed to cope with the same data. Numerical results illustrated that the LSSVR-GA outperformed the three other forecasting models by around 14.84% averagely in terms of mean absolute percentage error (MAPE), weighted absolute percent error measure (WAPE), and normalized mean absolute error (NMAE). This study collected data from an IC packaging and testing firm in Taiwan. The innovation and advantage of the proposed method is using a machine approach to forecast RLC values instead of through simulation ways, which generates accurate results. Numerical results revealed that the developed ML model is effective and efficient in RLC circuit forecasting for the analog IC packaging and testing industry.

Feature representation of audible sound signal in monitoring surface roughness of the grinding process

ABSTRACT This study proposes the feature representation method of audible sound (AS) signal in the grinding process. The extracted sound feature is provided as the input for the machine learning model to predict the machining surface roughness. Firstly, a sensitive EEMD-IMPE feature set is extracted from the AS signal basing on the combination of the ensemble empirical mode decomposition (EEMD) and improved multiscale permutation entropy (IMPE) methods. Then, an optimized PSO-LS-SVR predictor model is- established basing on the particle swarm optimization algorithm (PSO) and least square support vector regression (LS-SVR) to predict the surface roughness. The experiments demonstrated the consistent AS feature, which is specific to the grinding surface quality in a cutting parameter set. The results of the PSO-LS-SVR model show that the extracted EEMD-IMPE feature is used to predict the grinding surface roughness with the high prediction accuracy and can be controlled within 8% of testing data.

Wide area monitoring system control management of the IEEE-14 bus system using least square support vector regression

The <span>wide area monitoring system (WAMS) records and monitors every fault or disturbance that occurs in a power system network using phasor measuring units (PMUs). Extensive monitoring of the condition of the electrical power system can ensure the sustainability of reliable energy. The accuracy of the PMUs placement can be determined using the least square support vector regression (LS-SVR) technique. The primary goal of this study is to assess the level of accuracy of the PMUs placement using mean square error (MSE). First, the IEEE-14 bus system equipped with PMUs was built in Matlab software using Simulink. The MSE of the PMUs was then calculated using the LS-SVR. The results revealed that the lower the MSE, the better the PMUs placement. It was also observed that placing the PMUs on bus 2, bus 6, and bus 9 produced the lowest value of MSE</span>.