Kernel Function Method Research Articles

COMPARISION OF RICE PRICE PREDICTION RESULTS IN EAST JAVA USING FOURIER SERIES ESTIMATOR AND GAUSSIAN KERNEL ESTIMATOR SIMULTANEOUSLY

Extreme weather changes and the El Nino phenomenon in 2023 will cause drought, resulting in a decrease in rice production and an increase in rice prices. It has significantly impacted East Java Province as it is the most extensive rice supplier in Indonesia. This study aims to predict the price of rice with six different qualities using the Fourier series estimator and Gaussian kernel function simultaneously. The results show that the Gaussian kernel method, with a bandwidth value of 1, produces a better model with a MAPE value of 0.228259% than the sine function Fourier series method in predicting rice prices based on six different qualities. The prediction results using the Gaussian kernel function method are categorized as highly accurate because they are less than 10%. This research accelerates the realization of SDG 2 related to "Zero Hunger" through government policies to control the high price of rice in Indonesia. Recommendations that can be given through the research results include cooperation with the government, which can help access information and resources needed to manage price risks.

Regularized kernel function methods for the backward heat conduction problem

In this letter, we are interested in the regularized numerical techniques for backward heat conduction problems, which aim at the detection of the previous status of physical field from its present information. It is well known that backward heat conduction problems are ill-posed. The small perturbation in the final status may result in large change in the previous status of physical field. It is difficult to get the stable numerical solutions of backward heat conduction problems. Based on the reproducing kernel theory, the letter aims at finding a new regularized kernel function methods for backward heat conduction problems.

Positioning by floors based on WiFi fingerprint

WiFi-based indoor positioning technology has gradually become a hot research topic in the field of indoor positioning, but the development of this technology has been facing the challenge of susceptibility to environmental interference. Therefore, in this paper, the kernel function method (KFM) with stronger interference resistance is used for positioning, and the adaptive algorithm is proposed for the time-consuming and laborious problem of manual parameter tuning, which incorporates the ideas of cross-validation and iteration. In addition, too many wireless access points (APs) mean higher computational cost and longer positioning time, so it is necessary to choose reasonable APs for positioning. In this paper, we use the random forest (RF) algorithm to assess the importance of APs and filter out a small number of APs with high importance. Considering the obvious differences in the WiFi signals received on different floors, a system framework for positioning by floors based on WiFi fingerprints is proposed. In the offline phase, the fingerprint library is first divided according to floors, and then perform separately AP selection and parameter tuning for each sub-fingerprint library. In the online phase, support vector machine is used to discriminate the floors first, and then KFM is used for planar positioning. Experiments are conducted on the public dataset, and the results show that the proposed algorithm has higher positioning accuracy, more robustness, and less time-consuming compared to several common algorithms.

Verification of solitary wave numerical simulation and case study on interaction between solitary wave and semi-submerged structures based on SPH model

Due to significant influence on the safety of marine structures, the interaction between extreme waves and structures is a crucial area of study in marine science. This paper focus on the verification of a solitary wave meshless SPH model and the application of the model on the interaction between solitary waves and semi-submersible structures. A solitary wave propagation model is established based on the SPH method combined with Rayleigh solitary wave theory, quintic kernel function, artificial viscosity, and Symplectic Method. The accuracy of the model is validated by comparing the calculated wave height with the theoretical value. The calculated results with relative particle spacing H0/d0 ≥ 20 are in good agreement with the analytical solution. The simulated solitary wave is also quite stable with a maximum L2 error 0.016. Therefore, the proposed SPH model can accurately simulate the propagation of the solitary waves. A case study on the interaction between solitary waves and semi-submersible platforms is conducted. The results show that the interaction between solitary waves and semi-submersible causes two double peaks with wave heights of 0.398 m and 0.410 m, respectively, induced by overtopping at the center of the platform. The wave transmission coefficient Kt is 0.880 due to that the solitary wave height reduces from 0.498 m to 0.438 m after the solitary wave propagates through the semi-submersible structure. In addition, the solitary wave induces significant vertical wave loads of the structure with a load amplitude of 0.688, while horizontal wave loads are relatively small with a load amplitude of 0.089. The solitary wave arrived the structure induces the upstream and downstream overtopping and forms a hydraulic jump leading to the complex flow field. The maximum velocity at the top and bottom of the structure is 2.2 m/s and 0.8 m/s respectively. Positive or negative vortex are formed at the bottom of the leading edge, top and downstream of the structure with the maximum intensity 28 s-1 and -40 s-1. In a word, the meshless SPH model can conveniently and accurately simulate the propagation of the solitary waves, and be applied to the investigation of the wave height, velocity, vorticity, wave load, and wave breaking of the interaction between solitary waves and structures in ocean engineering.

Multi-type dynamic load identification algorithm in continuous system: A numerical and experimental study based on SSM-Newmark-β

Dynamic load identification based on structural responses is an important problem in the field of engineering and plays an important role in the condition assessment of mechanical structures. Current popular load identification methods such as the state-space method (SSM) and the Green's kernel function method (GKFM) are implemented on discrete systems with outstanding performance, but when dealing with complex continuous systems, there are some limitations such as low efficiency and inaccuracy. In this paper, a continuous system load identification algorithm based on SSM and Newmark-β is proposed for the first time. Using modal coordinate transformation and modal truncation methods, the number of infinite vibration differential equations of a continuous system in physical space are converted to a finite number of vibration differential equations in modal space, where the modal truncation order and the optimal layout of the sensors are combined by modal strain energy. The Newmark-β method in modal space is derived and thus combined with SSM to obtain a load identification model Y=HF called SSM-Newmark-β method, which reduces the size of the transfer matrix H. The solution time of the proposed algorithm is 0.38 s for continuous systems, which is rather shorter than that for discrete systems. Furthermore, the simulations show that it has better accuracy and noise immunity than SSM and GKFM in the identification of sinusoidal load, impact load, and random load. The effect of time step is also discussed which reveals that the larger time step has less effect on the proposed algorithm. An experimental study is carried out on a cantilever beam system. The result verifies that the SSM-Newmark-β algorithm has better accuracy in load identification for the continuous system. This research provides a new sight for the real-time identification of dynamic loads for complex structures.

Research Based on High-Dimensional Fused Lasso Partially Linear Model

In this paper, a partially linear model based on the fused lasso method is proposed to solve the problem of high correlation between adjacent variables, and then the idea of the two-stage estimation method is used to study the solution of this model. Firstly, the non-parametric part of the partially linear model is estimated using the kernel function method and transforming the semiparametric model into a parametric model. Secondly, the fused lasso regularization term is introduced into the model to construct the least squares parameter estimation based on the fused lasso penalty. Then, due to the non-smooth terms of the model, the subproblems may not have closed-form solutions, so the linearized alternating direction multiplier method (LADMM) is used to solve the model, and the convergence of the algorithm and the asymptotic properties of the model are analyzed. Finally, the applicability of this model was demonstrated through two types of simulation data and practical problems in predicting worker wages.

Optimal Noise-Boosted Estimator Design Via Adaptive Stochastic Resonance

In signal estimation, an optimal estimator is frequently unachievable because its closed form may not be analytically tractable or is too complex to implement. Alternatively, one can turn to suboptimal yet easily implementable estimators for practical signal estimation tasks. In this paper, an optimal noise-boosted estimator is designed and the adaptive stochastic resonance method is implemented to simultaneously exploit the beneficial role of the injected noise as well as the learning ability of the estimator parameter. Aiming to effectively improve the estimation performance, we use the kernel function method to find an approximate solution for the probability density function (PDF) of the optimal injected noise. During this process, the noise PDF and the estimator parameter establish a finite-dimensional non-convex optimization space for maximizing the estimation performance, which is adaptively searched by the sequential quadratic programming (SQP) algorithm at each iteration. Two representative estimation problems are explored. The obtained results demonstrate that this adaptive stochastic resonance method can improve the performance of the suboptimal estimators and bring it very close to that of the optimal estimator.

Dynamic load identification based on deep convolution neural network

The deep learning methods have been extensively studied in the field of dynamic load identification, due to their strong direct modeling ability between vibration response and external excitation. Dynamic load identification is a complicated inverse problem, which extremely relies on the solution of the structural model parameter. Nevertheless, the accurate computation of model parameters is always a challenge, small errors in model parameters will lead to inaccuracy of dynamic load identification. This brings various hardships to engineering applications. To achieve this problem, we propose a novel method based on a deep dilated convolution neural network (DCNN) for dynamic load identification, directly constructing the inverse model between vibration response and excitation, avoiding solving the model parameter. A dynamic load identification model, which contains two 1-D dilated convolution layers, one pooling layer, and two fully connected layers, is constructed to estimate the sinusoidal, impact, and random dynamic loads of a simply supported beam. We also appraise the anti-noise ability of the proposed method for load identification. Moreover, a vibration test is carried out to further evaluate this algorithm in the experimental aspect. Additionally, we analyze the comparison of the proposed method along with the Green kernel function method, and the dynamic system with uncertain model parameters is also analyzed. Besides, the operation of the convolution layer for response input is studied, and the applicability to different distributions of measurement points and the adaptability for frequency domain data are investigated. Ultimately, we find this method has a strong anti-noise ability due to its convolution layer, which can be regarded as a filter in dynamic load identification. Furthermore, the proposed method is of great practical for engineering applications owing to its satisfying applicability for systems with uncertain parameters, distributions of measurement points, and frequency data. All the results reveal the advantages of the identification method based on DCNN, consisting of good accuracy, reliability, and robustness. These results can be favorable for many applications.

Random Fourier Approximation of the Kernel Function in Programmable Networks

Abstract Random Fourier features represent one of the most influential and wide-spread techniques in machine learning to scale up kernel algorithms. As the methods based on random Fourier approximation of the kernel function can overcome the shortcomings of machine learning methods that require a large number of labeled sample, it is effective to be applied to the practical areas where samples are difficult to obtain. Network traffic forwarding policy making is one such practical application, and it is widely concerned in the programmable networks. With the advantages of kernel techniques and random Fourier features, this paper proposes an application of network traffic forwarding policy making method based on random Fourier approximation of kernel function in programmable networks to realize traffic forwarding policy making to improve the security of networks. The core of the method is to map traffic forwarding features to Hilbert high-dimensional space through random Fourier transform, and then uses the principle of maximum interval to detect adversarial samples. Compared with the traditional kernel function method, it improves the algorithm efficiency from square efficiency to linear efficiency. The AUC on the data set from real-world network reached 0.9984, showing that the method proposed can realize traffic forwarding policy making effectively to improve the security of programmable networks.

Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation.

Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners' pronunciation errors but also assessing their overall pronunciation level. Method. This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are proposed, respectively. External features are mainly obtained by calculating the hierarchical centrality, node density, and node coverage of nodes in the ontology tree; internal features are mainly obtained by measuring the richness of internal information. According to the similarity of ontology tree and the external features and internal features of nodes, the calculation formula of structural comprehensive similarity is improved, and the features of ontology itself can be fully considered in the calculation. According to the difference of the structure, the weights of the corresponding features during the calculation are adjusted autonomously, so that the calculation results are closer to reality. In spectral image preprocessing, endpoint detection utilizes the harmonic characteristics presented by narrowband spectrograms with high frequency resolution and eliminates useless nonspeech segments by detecting the presence of voiced segments. When building the neural network model, four convolutional layers, two fully connected layers, and one softmax output layer were conceived, and dropout was used to randomly suspend the work of some neurons to avoid overfitting. Results/Discussion. Through the data analysis of mean and variance and verified by one-way analysis of variance, it proves that the sentiment evaluation method in this paper is effective. The traditional multiple linear regression method is not suitable for the corpus and application scenarios of this paper. This paper proposes a decision tree structure, which is similar to the overall scoring process of raters, and uses the Interactive Dicremiser version 3 (ID3) algorithm to build a comprehensive evaluation decision tree for pitch, rhythm, intonation, speech rate, and emotion indicators. It is proved by experiments that the accurate consistency rate of the human-machine evaluation in this paper is 93%, the adjacent consistency rate is 96%, and the Pearson correlation coefficient value of the human-machine evaluation results is 0.89. The data results prove that the evaluation method in this paper is credible.

Behavioral modeling and digital predistortion of RF power amplifiers based on time-delay kernel ridge regression

This paper, investigates and presents the behavioral modeling and digital pre-distortion (DPD) of radio frequency power amplifiers (RFPAs) using a time-delay kernel ridge regression (KRR) algorithm. The KRR is an advanced machine learning algorithm that can be effectively used for modeling the baseband characteristics of the RFPA considering both the effects of memory and transistor non-linearity. Compared to the traditional artificial neural network (ANN) based approach which is computationally intensive, the proposed approach using KRR with radial basis kernel function and min-max normalization method, extracts the PA behavioral and DPD model in a short time and yield consistently better results. The performance of the proposed approach in extracting PA and DPD model is demonstrated experimentally on a GaN based class AB power amplifier. The experimental results illustrates that, when compared to ANN based model, the proposed approach yields more accurate PA and DPD models, with an improvement in modeling performance by 2 dB in terms of normalized mean square error (NMSE). In addition, compared to the ANN based approach, the DPD model developed using the proposed approach exhibit improved linearization performance in suppressing spectral regrowth due to PA non-linearity.

Thickness-Related Fault Diagnosis of Steel Strip Based on W-KPLS Method Considering Mechanism Weight Optimization

Due to the lack of a reasonable mechanism explanation for the data model used in the process of quality-related fault diagnosis, the diagnosis model has insufficient ability to identify faults, resulting in the phenomenon of failure detection or false positive. Therefore, this paper adopted the method of mechanism and data model fusion to solve the problem of insufficient interpretation of the influence of existing diagnosis methods on rolling process variables. Firstly, the KPLS achieves strip quality-related fault detection for nonlinear processes. In order to find out the abnormal variables, a nonlinear contribution plot was introduced to calculate the contribution value of each variable to the monitoring index. Secondly, based on the bounce equation of the rolling process, the static comprehensive analysis of the steady rolling process was carried out to reveal the influence of various variables on strip thickness. Thirdly, based on the above analysis of the steady rolling process mechanism, the influence weight method and kernel function method were used to reconstruct and map the original input matrix. A kernel partial least squares method based on influence weight W optimization (W-KPLS) was proposed for quality-related fault monitoring and diagnosis. Finally, the model was applied in the cold rolling process of an aluminum alloy sheet, and the validity of the model was further verified by practical industrial data. The results show that the new method improves the fault detection rate by more than 20% compared with the traditional monitoring method, and the proportion of data points reaching the early warning limit was increased to more than 95%.

Equivalent Identification of Distributed Random Dynamic Load by Using K–L Decomposition and Sparse Representation

By aiming at the common distributed random dynamic loads in engineering practice, an equivalent identification method that is based on K–L decomposition and sparse representation is proposed. Considering that the establishment of a probability model of the distributed random dynamic load is usually unfeasible because of the requirement of a large number of samples, this method describes it by using an interval process model. Through K–L series expansion, the interval process model of the distributed random dynamic load is recast as the sum of the load median function and the load uncertainty. Then, the original load identification problem is transformed into two deterministic ones: the identification of the load median function and the reconstruction of the load covariance matrix, which reveals the load uncertainty characteristics. By integrating the structural modal parameters, and by adopting the Green’s kernel function method and sparse representation, the continuously distributed load median function is equivalently identified as several concentrated dynamic loads that act on the appropriate positions. On the basis of the realization of the first inverse problem, the forward model of the load covariance matrix reconstruction is derived by using K–L series expansion and spectral decomposition. The resolutions to both inverse problems are assisted by the regularization operation so as to overcome the inherent ill-posedness. At the end, a numerical example is presented to show the effectiveness of the proposed method.

Combined Economic-Emission Dispatch of External Optimization Integrating Renewable Energy Resources Stochastic Wind and Solar

Economic dispatch in the standard power system network frequently prioritizes instant economic gains while overlooking the detrimental environmental effects of gas discharges from thermal power stations. Economic emission dispatch (EED) has received a lot of consideration in current years as a way to alleviate this shortage. The unpredictability and intermittence of renewable energy sources like wind and solar power are raising the difficulty of electric grid planning as renewable production penetration rises. This research proposes a modified MMOCE to improve dispatch efficiency with considerable renewable energy consumption. A congestion calculation method and a new external storage process are added to the traditional kernel function method to address multi-objective optimization issues. The energy and transportation sectors are two of the most significant producers of GHG emissions. Improved vehicle productivity is a partial answer, but to limit the rate of GHG emissions, it is important to use RESs in the network. It is, however, critical to evaluate the efficacy of each approach. In this research, the environmental and economic aspects of employing RESs will be examined using a mixture of generating cost and GHG emissions from the two businesses stated. To overcome the ELD problem, an enhanced PSO is employed. However, solutions for reducing time consumption for the high-dimensional ELD issue are not considered in this study.

Variations in the spatial distribution of crime events in an urban environment during the COVID-19 lockdown

ABSTRACT The article presents the impact of the COVID-19 pandemic on selected types of crime in Krakow and their spatial distribution during the lockdown period during the spring of 2020. We compared crimes committed over a three-month period from March 15 up to 15 June 2020 with crimes committed during the corresponding periods of previous years, i.e. in 2015–2019. To characterize their spatial distribution, we used the Nearest Neighbor Analysis method (NNA). In the next stage of the study, an intensity distribution map was prepared for five different crime categories in 2015–2019 using the kernel function method. The point data layer from 2020 was then superimposed on the intensity layer thus formed. The final step was to examine how generators/attractors affect the spatial clustering of crime before and during the pandemic. These two analyses allowed us to identify variation in those areas with the highest concentration of crime events (“hotspots”) that occurred as well as the impact of generators/attractors on the spatial distribution of crimes. The changes in crime patterns as a result of the pandemic restrictions were compared with existing criminological theories.

Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model

Attribute reduction is the core research content in rough set theory. At present, the attribute reduction of numerical information system mostly adopts the neighborhood rough set method. In order to further improve the similarity measurement effect between data objects, kernel function method is used to construct a new rough set model in numerical information system, and an uncertainty measurement method and attribute reduction method are proposed. Firstly, the similarity between objects of numerical information system is calculated by kernel function, and a granular structure model and rough set model based on kernel similarity relation are proposed. Then, from the perspective of kernel similarity rough approximation, an information system uncertainty measurement method called kernel approximation precision and kernel approximation roughness is proposed. Because these two measurement methods do not meet the strict monotonicity of information granulation, the concept of kernel knowledge granularity based on kernel similarity granular structure is further proposed in this paper. By combining kernel approximation precision and kernel approximation roughness with kernel knowledge granularity, an uncertainty measurement method of kernel similarity combination measurement is proposed. Finally, using the strict monotonicity of kernel similarity combination measurement, an attribute reduction algorithm for numerical information system is designed. Experimental analysis shows the effectiveness and superiority of the proposed method.

Impacts of green certification programs on energy consumption and GHG emissions in buildings: A spatial regression approach

For better energy assessment and management in the buildings industry, a spatial regression method is presented to quantitively measure the impacts of green certification programs on energy consumption and Greenhouse Gas (GHG) emissions in buildings. Spatial autocorrelation analysis is developed to determine the spatial pattern of energy consumption and GHG emissions. A Gaussian kernel function method is employed to calculate the spatial weighting matrix, and an Akaike Information Criterion method is implemented to choose the function bandwidth in the spatial weighting matrix. Subsequently, a geographically weighted regression (GWR) model based on the spatial weighting matrix is constructed to estimate energy consumption and GHG emissions in buildings. Several error criteria are employed to verify the development of the GWR model compared with the ordinary least square (OLS) model. Regression coefficient, Pearson correlation coefficient, and Spearman correlation coefficient analyses are implemented to investigate the effects of green certification programs on building energy consumption and GHG emissions. A case study in Seattle city in 2015 is used to implement the GWR model and validate the improved goodness-of-fit of this regression model. The results imply that: (1) Energy stars programs as the driving force would save energy consumption by 13.2% on average and reduce GHG emissions by 3.24% on average; (2) The fitted GWR models can significantly improve the model’s goodness-of-fit and accuracy with an adjusted R2 value of 0.572 in modeling energy consumption and that of 0.626 in modeling GHG emissions compared with the OLS models (with an R2 of 0.538 and 0.594, respectively); (3) Older buildings, taller buildings, and larger buildings with energy star labels have been demonstrated to have a big potential to significantly save more energy and reduce more GHG emissions. This spatial regression approach can be used as an assessment tool for professionals and authorities to detect the influence factors on energy consumption and GHG emissions in buildings and make environmentally sustainable plans for buildings in cities.

Integral equation fast solver with truncated and degenerated kernel for computing flaw signals in eddy current non-destructive testing

In this article, the kernel independent H-matrix method is applied as the mathematical framework. The dense matrix generated by the selected integral equation, without low frequency breakdown problem for solving 3D arbitrary shaped eddy current nondestructive evaluation (NDE) forward problems, is compressed by the truncated and degenerated (TD) kernel function method. It results in a significant reduction in computational burden with nearly linear complexity. The kernel functions (Green's function) are degenerated by Lagrange polynomials which leads to two advantages: firstly, the double integrals are separated in two single integrals, and secondly, they are represented in a factorized form with good accuracy control. Meanwhile, due to the nature of the kernel function with exponential decay in the lossy medium, it is truncated to improve the overall performance. The TD kernel function-based boundary element method (BEM) fits well for solving eddy current NDE problems over the adaptive cross approximation based BEM, especially for detections of flaws or slots in planar objects. Several numerical predictions calculated by the proposed method are compared with those achieved by others such as the experiment, analytical and semi-analytical methods from benchmarks. The robustness and efficiency are demonstrated with excellent agreements and reduced complexity.

Development of cyclic variation prediction model of the gasoline and n-butanol rotary engines with hydrogen enrichment

To investigate the influence of operation parameters on the cyclic variation of the Wankel rotary engine with hydrogen enrichment, an intelligent regression model based on the support vector machine (SVM) was implemented to predict the cyclic variation. For modeling the prediction model, the cyclic variation of speed (CoVn) and cyclic variation of the main combustion duration (CoVCA10-90) were used as an evaluator for idle and part load conditions, respectively. The operation conditions including main fuel type (gasoline and n-butanol), hydrogen volume percentage (βH2), excess air ratio (λ), ignition timing (IT)and speed were used as independent variables. When optimizing the prediction model, the data processing method, kernel function, loss function and optimization method on the prediction performance were discussed in detail. The results indicated that an optimized model can be obtained by using genetic algorithm combined with [0, 1] data processing method, and the coefficient of determination, mean square error and mean absolute percentage error of CoVn were 0.9904, 0.0783 and 0.3845%, corresponding to CoVCA10-90 were 0.9972, 0.0197 and 1.1729%, respectively. For the CoVn, gasoline as the main fuel was lower than the n-butanol at the same operating condition. The CoVn at high speed was greater than that at low speed. When operating at part load conditions, the CoVCA10-90 decreased with the increasing βH2, and first decreased and then increased with advancing IT.

An Equivalent Identification Method for Dynamic Loads Acting on Nonlinear Structures

An efficient dynamic load identification method for nonlinear structures is proposed. Assuming that the nonlinear elements of a structure can be separated, or the structural kinetic equation can be available, the whole structural damping and stiffness matrices can be divided into linear and nonlinear sub matrices. Regarding the effects of the nonlinear elements on the linear ones as dynamic load constraints, which can be realized by moving the product of the nonlinear sub matrices and their corresponding responses to the right-hand side of the original kinetic equation, a remaining linear structure subjected to multi-source of dynamic loads can then be obtained. The instability of the obtained structure caused by the load equivalence is discussed and improved. The case that the characteristics of the nonlinear parameters are unknown is also investigated. Finally, based on the improved structure, Green’s kernel function method, LSQR regularization is employed to reconstruct the dynamic loads. The effectiveness of the proposed method is proved by two numerical examples and an engineering application.