Use of support vector regression in structural optimization: Application to vehicle crashworthiness design

New approach to predicting proconvulsant activity with the use of Support Vector Regression

Software cost estimation is the process of predicting the effort required to develop a software system. The basic input for the software cost estimation is coding size and set of cost drivers, the output is Effort in terms of Person-Months (PM’s). Here, the use of support vector regression (SVR) has been proposed for the estimation of software project effort. We have used the COCOMO dataset and our results are compared to Intermediate COCOMO as well as to MOPSO model results for this dataset. It has been observed from the simulation that SVR outperforms other estimating techniques. This paper provides a comparative study on support vector regression (SVR), Intermediate COCOMO and Multiple Objective Particle Swarm Optimization (MOPSO) model for estimation of software project effort. We have analyzed in terms of accuracy and Error rate. Here, data mining tool Weka is used for simulation

On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression

This study aimed to extract apigenin and luteolin from celery seeds using 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) based ultrasonic-assisted technique. Optimal extraction conditions were determined through the integration of support vector regression combined with various optimization algorithms, including Genetic Algorithm, Particle Swarm Optimization, Gray Wolf Optimizer, Bat Algorithm, Firefly Algorithm, and Cuckoo Search Algorithm. The identified optimal conditions included an extraction time of 52 min, an HP-β-CD concentration of 24 mg/mL, and a solvent-to-solid ratio of 24 mL/g. Under these conditions, the predicted apigenin and luteolin content in the extract were 0.156 and 0.814 mg/g, respectively, closely aligning with the actual values of 0.158 ± 0.005 mg/g and 0.807 ± 0.008 mg/g. Notably, the HP-β-CD/water extract exhibited significantly higher concentrations of active ingredients and antioxidant activity compared to the water extract, with the content of apigenin and luteolin in the water extract being only 25 and 45%, respectively, compared to the HP-β-CD/water extract. Regarding the antioxidant effect, the water extract displayed a 50% scavenging capacity (SC50) of 331.29 ± 20.5 g/mL, whereas the SC50 for the HP-β-CD/water extract was 89.04 ± 4.70 g/mL. Molecular modeling and IR spectra analysis also suggested the formation of inclusion complexes between HP-β-CD and apigenin/luteolin. These findings suggested that HP-β-CD could be a preferable alternative to conventional solvents due to its advantages, such as easier control, safety, and cost-effectiveness. In addition, the use of support vector regression and nature-inspired optimization algorithms could be promising alternatives to traditional response surface methodology.

In this paper machine-learning (ML) dependent digital-predistortion (DPD) methodology is presented that overcomes the impairments of signal and non-linearities in Optical Front-hauls with the use of Radio over Fiber (RoF) systems. Volterra methods have been realized in the past for this purpose, however, they are not straight forward while classical artificial networks necessitate the optimum arrangement. The proffered support vector regression (SVR) method using grid search lessens the issues in Volterra based DPD and traditional artificial neural networks techniques In this work, the experimental evaluation for Long Term Evolution 20 MHz, 256 quadrature amplitude modulated signal using 850 nm Multi Mode Vertical cavity surface emitting lasers and Multi-Mode Fiber is linearized using SVR based DPD. The experimental analysis finds that SVR-DPD gathers the RoF non-linearities hence proving to be extraordinarily robust.

Phosphorus (P) is widely known as a limiting nutrient of water eutrophication for inland freshwater ecosystems. Owing to the complexity of P chemistry, remote sensing detection of total phosphorus (TP) concentrations currently remains limited especially for optically complex turbid inland waters. To address this need, a new TP remote sensing algorithm is developed based on prior water optical classification and the use of support vector regression (SVR) machine. The in situ observed datasets, used in this study, were collected at specific times during 2009 ~ 2011, covering a total of 232 stations from eight cruises in Lakes Taihu, Chaohu, Dianchi, and Three Gorges reservoir of China. Three types of waters were first classified by using a recently developed NTD675 (Normalized Trough Depth of spectral reflectance at 675 nm) water classification method. Then, spectral regions sensitive specifically to each water type were explored and expressed via several band ratios and used for retrieval algorithm development. The established type-specific SVR algorithms yield relatively high predictive accuracies. Specifically, the mean absolute percentage errors (MAPE) produced with the independent validation samples were achieved at 32.7, 23.2, and 14.1 % for type 1, type 2, and type 3 waters, respectively. Such water type-specific SVR algorithms are more accurate for the classified waters than an aggregated SVR algorithm for the nonclassified water and also superior to commonly used statistical algorithms. Moreover, application of the developed algorithms with HJ1A/HSI image data demonstrates that the algorithms have a large potential for remote sensing estimation of TP concentrations in optically complex turbid inland waters.

This paper discusses the application of Support Vector Regression (SVR) for modeling the non-linear and hysteretic behavior exhibited by mechanical snubbing systems. Though the discussion in this paper is limited to the application of SVR to snubbing in elastomeric isolators, the approach is generic and can be applied to other dynamic systems or to systems exhibiting hysteretic behavior. A theoretical model that represents the coupled dynamics of an isolation system with the corresponding snubbing system for a single degree-of-freedom system is proposed. The theoretical model is experimentally validated and is subsequently used to build a metamodel using SVR. The results of the metamodel are compared to the theoretical model for a simulation example and are found to be comparable, thereby reducing the computational time for the design and analysis of the snubbing system by orders of magnitude. The SVR based metamodel can, therefore, be used to substitute the computationally intense theoretical model for performing design iterations and design optimization of the snubbing system, significantly reducing model complexity as well as computational time during the design cycle.

Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system

Approximation models (also known as metamodels) have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is directly related to the sampling strategies used. Our goal in this paper is to investigate the general applicability of sequential sampling for creating global metamodels. Various sequential sampling approaches are reviewed and new approaches are proposed. The performances of these approaches are investigated against that of the one-stage approach using a set of test problems with a variety of features. The potential usages of sequential sampling strategies are also discussed.

Supercomputers represent the pinnacle of computational power, offering unparalleled capabilities to tackle complex mathematical problems across various domains. This paper provides a comprehensive overview of the ways supercomputers are utilized in solving intricate mathematical issues, ranging from understanding the nature of these problems to exploring the challenges and future directions in the field. Supercomputers play a critical role in the practical applications of queueing theory across various industries by enabling the analysis and optimization of complex systems that involve waiting lines and service processes. In telecommunications networks, for instance, supercomputers are used to model and optimize traffic flow, ensuring efficient data transmission and minimal latency. By simulating network conditions under different traffic loads and service protocols, supercomputers help identify optimal resource allocations and configurations, significantly improving network reliability and performance. This capability is crucial for managing the ever-increasing data demands of modern communication systems.

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is strongly affected by the sampling methods. In this paper, a new sequential optimization sampling method is proposed. Based on the new sampling method, metamodels can be constructed repeatedly through the addition of sampling points, namely, extrema points of metamodels and minimum points of density function. Afterwards, the more accurate metamodels would be constructed by the procedure above. The validity and effectiveness of proposed sampling method are examined by studying typical numerical examples.

Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter

The need for an efficient assessment of the occupational risk of the employees involved in the construction of buildings and structures was established. A study was conducted for substantiating the system for ensuring the safety of personnel in the construction complex using quantitative risk criteria developed based on of mathematical modeling. Technological processes at the construction sites and in the organizations are considered. The methods of complex (integral) system analysis, process analysis, aggregation and optimization of risk parameters were also applied. Based on the systematic studies of the technological processes at facilities under construction, a methodology for assessing occupational risk was developed. The ways and means of achieving the strategic goal of the occupational safety, which is to ensure the safe professional activity of personnel, are considered. The possibility of forming a set of criteria for the quantitative assessment of occupational risk based on the mathematical modeling is shown. The key to obtaining efficient evaluative expressions is the use of real statistical data in the numerical form. The importance is noted considering the interaction of various harmful and hazardous production factors that affect personnel and are formed according to the principle of systems emergence. The developed criteria for the quantitative assessment of occupational risk are an objective and reliable basis for the development of an efficient system for ensuring the safety of construction employees. The described technique is based on the fundamental provisions of the physical and mathematical theory, the scientific discipline «Life Safety», the theory of prognostic and experimental research. It can be used to develop measures for creating a safe working environment in all the industries.

Structures exhibit highly nonlinear characters under severe loads such as strong seismic excitations. Therefore, it is crucial to make nonlinear structural identification in civil engineering. However, nonlinear hysteretic structural identification is still a challenging topic due to structural model complexity and the strong noises existing in input and output (I/O) data. An efficient approach based on the incremental support vector regression (SVR) is proposed here to identify nonlinear hysteretic structural parameters on-line. Instead of the Gaussian loss function utilized in the least squares method, a novel einsensitive loss function is employed in SVR, and therefore the suggested SVR-based approach produces robust and accurate identification results. Furthermore, as an incremental algorithm employed to train SVR in a sequential way, the presented SVR-based approach not only works rapidly, but also identifys nonlinear structural constitutive parameters on-line. The performance of the proposed approach is verified by a five degree of freedom nonlinear hysteretic structural identification problem, in which two cases (power parameter is known/unknown) are both investigated. The identified results show evidently that the proposed technique has potential performance in robustness and accuracy for nonlinear structural identification, even when the measurement data in the presence of noises.

A support vector regression based on the mean impact value (MIV) model was constructed to identify the bioactive compounds inhibiting proliferation of HeLa cells in a combination of turmeric (Curcuma longa L.) and liquorice (Glycyrrhiza) extracts. The quantitative chemical fingerprint from 50 batches of turmeric and liquorice extracts was established using high performance liquid chromatography hyphenated to an ultraviolet visible detector. Qualitative results were obtained using ultra performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry. A total of 46 peaks (peaks 1–15 from turmeric and 16–46 from liquorice) were selected as “common peaks” for analysis. The inhibitory effect of the combined extracts on HeLa cells was measured by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. It was found that 15 compounds (peaks: 8, 12, 30, 24, 46, 11, 14, 9, 3, 1, 44, 18, 7, 45 and 43) possessing high absolute MIV exhibited a significant correlation with the cytotoxicity against HeLa cells; most of these have already been confirmed with potential cytotoxicity in previous research. The important potential application of the present model can be extended to help discover active compounds from complex herbal medicine prior to traditional bioassay-guided separation. It is considered that this could be a useful tool for re-developing herbal medicine based on the use of these active compounds.