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Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model

JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2015年第6期21卷 748-760页

作者： Son, Hyojoo Kim, Changmin Kim, Changwan Kang, Youngcheol Chung Ang Univ Dept Architectural Engn Seoul 156756 South Korea Univ Seoul Dept Global Construct Management Seoul 130743 South Korea

Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies.

关键词： sustainable development support vector machine model energy consumption prediction government-owned building RReliefF variable selection

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A missing data processing method for dam deformation monitoring data using spatiotemporal clustering and support vector machine model

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Water Science and Engineering 2024年第4期17卷 417-424页

作者： Yan-tao Zhu Chong-shi Gu Mihai A.Diaconeasa The National Key Laboratory of Water Disaster Prevention Hohai UniversityNanjing 210098China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China School of Engineering North Carolina State UniversityRaleighNC 27695USA

Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a ***,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument faults,and human operational errors,thereby often hindering the accurate assessment of actual deformation *** study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring *** introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation *** proposed method was validated in a concrete dam project,with the model error maintaining within 5%,demonstrating its effectiveness in processing missing deformation *** approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.

关键词： Missing data recovery Concrete dam Deformation monitoring Spatiotemporal clustering support vector machine model

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Optimization on Adhesive Stamp Mass-Transfer of Micro-LEDs With support vector machine model

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IEEE JOURNAL OF THE ELECTRON DEVICES SOCIETY 2020年第1期8卷 554-558页

作者： Lu, Hao Guo, Weijie Su, Changwen Li, Xilong Lu, Yijun Chen, Zhong Zhu, Lihong Xiamen Univ Fujian Engn Res Ctr Solid State Lighting Collaborat Innovat Ctr Optoelect Semicond & Effic Dept Elect Sci Xiamen 361005 Peoples R China Xiamen Longstar Lighting Co Ltd Res & Dev Dept Xiamen 361101 Peoples R China

In this work, the process of adhesive stamp mass-transfer of micro light-emitting diode (micro-LED) is optimized by a support vector machine (SVM) model. The pick-up experiments have been performed repeatedly for hundreds of times from which the separation speed and the force between the stamp and the donor substrate are extracted as signal features. The SVM model with a Gaussian kernel function is designed to classify pick-up results into success and failure. In addition, the optimal cost parameter C as well as the Gaussian kernel function parameter gamma (gamma) has been optimized, leading to the improvement of the classification by Particle Swarm Optimization (PSO) algorithm. Finally, an 85% classification accuracy is achieved based on the SVM model, implying that more sophisticated definition of signal features is demanded in future work.

关键词： Adhesive stamp mass-transfer micro-LEDs support vector machine model particle swarm optimization

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A particle swarm optimization-based support vector machine model to detect charging pile arc faults by using the pre-treatment generalized S transform

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JOURNAL OF ENGINEERING-JOE 2022年第9期2022卷 928-935页

作者： Deng, Hanjun Zeng, Wenwei Huang, Rui Wu, Zhiyong He, Xing Liu, Xuan Xiao, Jianhong Chen, Hao Liu, Mouhai State Grid Hunan Elect Power Co Ltd Measurement Ctr Power Supply Serv Ctr Changsha 410001 Peoples R China Hunan Key Lab Intelligent Elect Measurement & App Changsha Peoples R China China Elect Power Res Inst Co Ltd Beijing Peoples R China

An arc fault is the most common cause of charging pile fire. The series arc fault current is usually lower than the short-circuit fault current and is challenging to detect, resulting in the inaction of the circuit breaker. Therefore, a method for detecting series arc faults of charging piles based on generalized Stockwell transform (GST) is proposed in this paper. First, an real time digital simulation system (RTDS)-based charging pile arc experimental platform is constructed, and the point contact and the carbonization path arc fault current are measured. Then, the fault information is extracted using GST, and the 250-Hz component is selected as the arc fault identification feature. On this basis, an improved sampling point selection method is proposed. Finally, multiple feature quantities are constructed as feature vectors, and the trained particle swarm optimization _support vector machine (PSO_SVM) is used for arc fault detection. The experimental results show that the accuracy rate reaches 98.75%;thus, the scheme proposed by this paper could detect the series arc fault current accurately and avoid the incorrect action of the circuit breaker.

关键词： carbonization path arc fault transforms series arc faults arc fault identification feature circuit breaker fault information electric vehicle charging series arc fault current GST fault currents support vector machines RTDS particle swarm optimisation point contact SVM circuit breakers feature extraction charging pile fire fault diagnosis generalized Stockwell transform power engineering computing arcs (electric) real time digital simulation system pretreatment generalized S transform PSO charging pile arc faults electrical safety particle swarm optimization fire safety support vector machine model

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Cross-border trade risk prediction model based on support vector machine

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Wei, Qing Suzhou Vocat Univ Sch Business Int Educ Pk 106 Zhineng Ave Suzhou 215000 Peoples R China

Exchange rate fluctuations in cross-border trade often increase the difficulty of risk management for enterprises, and traditional forecasting methods are difficult to accurately assess risk levels, thereby affecting the optimization of financial decisions. Therefore, this article designed a cross-border trade risk prediction model based on support vector machines (SVM), mainly to solve the problem of financial uncertainty caused by exchange rate fluctuations. The study used SVM model to extract and analyze features from exchange rate data, economic indicators, and other related data, mapped the data to high-dimensional space using kernel function, and optimized hyperparameters using Bayesian optimization to achieve accurate prediction of cross-border trade risks. The experimental results showed that the accuracy of the designed model in high, medium, and low-risk classification was 92%, 81%, and 89%, respectively, with an average absolute error between 0.09 and 0.13, demonstrating high prediction accuracy and model stability. This model can effectively distinguish different risk categories and performs very well in handling high-risk and low-risk samples. The model also performs well in predicting stability and adaptability to exchange rate fluctuations, providing a reasonable reference range for risk management of cross-border trade enterprises.

关键词： cross-border trade risk support vector machine model exchange rate fluctuation risk prediction accuracy Bayesian optimization

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DEVELOPING THE MUSIC THERAPY REGULATION SYSTEM BASED ON FUZZY THEORY AND support vector machine

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JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY 2025年

作者： Han, Jing Zaozhuang Univ Elementary Educ Coll Zaozhuang 277100 Shandong Peoples R China

This study aims to help users achieve deep relaxation, alleviate negative emotions such as anxiety, depression, and tension, and promote overall mental well-being through music. To achieve this, the study adopts a theoretical framework based on a comprehensive review of recent research on music therapy systems. Additionally, it proposes a supply chain information assessment process for the music therapy regulation system using fuzzy theory and the development of a music therapy regulation system based on support vector machine (SVM) and fuzzy theory from a supply chain management perspective. The effectiveness of the music therapy regulation system is evaluated by collecting Electroencephalogram (EEG) data from 25 participants using portable EEG collection equipment. The study introduces a music therapy regulation system based on fuzzy theory and SVM, enhanced by supercomputing and high-performance computing (HPC) techniques. These technologies significantly improve the scientific and accurate interpretation of users' mental and physical states in response to music, providing a solid foundation for comparing changes in their states before and after relaxation and for alleviating negative emotions. The study findings indicate that the incorporation of supercomputing and HPC enhances the evaluation model's effectiveness in managing the music therapy regulation system. The research findings show that the SVM classification model achieves a higher classification accuracy of 90.43% in the automatic classification of music genres task compared to random forests and the Back Propagation Neural Network (BPNN). Additionally, the application of HPC technology significantly enhances the scientific and accurate interpretation of users' physiological and psychological responses to music. In summary, this study focuses on classifying the music therapy regulation system using the SVM model and establishes a management effectiveness evaluation model based on fuzzy theory.

关键词： Supply chain management fuzzy theory support vector machine model music therapy regulation electroencephalogram signal

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Prediction of cavitation damage using SVM model based on air-water two-phase flow over dam spillway

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APPLIED WATER SCIENCE 2025年第4期15卷 1-15页

作者： Bagherzadeh, Saghi Ghaeini-Hessaroeyeh, Mahnaz Fadaei-Kermani, Ehsan Shahid Bahonar Univ Kerman Fac Engn Dept Civil Engn Kerman Iran

Cavitation is one of the primary causes of breakdown and failure on chute spillways, causing surface damage and structural destruction. In this research, a three-dimensional two-phase flow over an ogee spillway was modeled using the FLOW-3D model for the Gelevard-Neka spillway and validated with the available field data. After analyzing the hydrodynamic parameters of flow, a method was presented to predict the intensity and location of cavitation damage on the spillway surface based on the support vector machine (SVM) model. The hydraulic parameters, including flow velocity, pressure, and cavitation index, were introduced to the SVM model, and the cavitation damage level, from no damage to major damage, was predicted along the spillway structure. The validation flow results agreed well with the field data, and the normalized root-mean-square error value of 0.0196 was obtained. In the prediction of cavitation damage using the SVM model, the MAE, R, and RMSE for the training stage were, respectively, 0.32, 0.882, and 0.127, and for the testing stage were 0.024, 0.857, and 0.133. The results show reasonable performance of the SVM model in the prediction of cavitation damage. According to the results, the spillway is susceptible to cavitation damage with the most significant damage anticipated to occur in the distance range of 70-190 m from the spillway origin. Based on the importance of the aerators in protecting the spillway from cavitation damage, it is recommended to investigate the various effects of aerators on mitigating cavitation damage.

关键词： Cavitation Two-phase model FLOW-3D support vector machine model

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Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

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BMC BIOINFORMATICS 2007年第1期8卷 1-29页

作者： Peek, Andrew S. Integrated DNA Technol Inc Dept Bioinformat Coralville IA 52241 USA

Background: RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel support vector machine (SVM) approach was used to quantitatively model RNA interference activities. Results: Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion: The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at

关键词： support vector machine Mean Square Error Feature Subset support vector machine model siRNA Sequence

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Study on model Evolution Method Based on the Hybrid modeling Technology With support vector machine for an SOFC-GT System

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JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE 2023年第1期20卷 011015-011015页

作者： Chen, Jinwei Sun, Shengnan Chen, Yao Zhang, Huisheng Lu, Zhenhua Shanghai Jiao Tong Univ Gas Turbine Res Inst Shanghai 200240 Peoples R China

The mechanism models of solid oxide fuel cell-gas turbine (SOFC-GT) systems are very useful to analyze the thermodynamic performance details, including the internal complex transfers of mass, heat, and electrochemical processes. However, several physical-property parameters in the mechanism model are unmeasurable and difficult to accurately quantify from the operation data when the inevitable degradation occurs. As a result, it is difficult for the mechanism model to accurately capture the SOFC electrochemical characteristic during the full operating cycle. In this paper, a model evolution method based on hybrid modeling technology is proposed to address this problem. A hybrid modeling framework of a SOFC-GT system is designed by combining a least squares-support vector machine algorithm (LS-SVM) electrochemical model with our previous mechanism model. The electrochemical characteristic of SOFC is easily identified and evolved by re-training the LS-SVM model from operating data, no longer needing a mechanism electrochemical model. The validated full-mechanism model from our previous work is taken to simulate a physical SOFC-GT system to generate the operating data. Various LS-SVM models are trained by different data sets. The comparison results demonstrate that the LS-SVM model trained by large-size data set 3 performs the highest accuracy in predicting the local current density. The maximum absolute error of prediction is only about 1.379 A/m(2), and the prediction mean square error of the normalized test data reaches 4.36 x 10(-9). Then, the LS-SVM hybrid model is applied to evaluate the thermodynamic performance of a SOFC-GT system. The comparison results between the hybrid model and our previous full-mechanism model show that the hybrid model can accurately predict the SOFC-GT system performance. The maximum error is 1.97% at the design condition and 0.60% at off-design conditions. Therefore, the LS-SVM hybrid model is significant for accurately identifying the

关键词： solid oxide fuel cell-gas turbine hybrid modeling method mechanism model support vector machine model fuel cells novel numerical and analytical simulations

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SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines

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BMC BIOINFORMATICS 2014年第1期15卷 1-8页

作者： Cao, Renzhi Wang, Zheng Wang, Yiheng Cheng, Jianlin Univ Missouri Christopher S Bond Life Sci Ctr Inst Informat Dept Comp Sci Columbia MO 65211 USA Univ So Mississippi Sch Comp Hattiesburg MS 39406 USA

Background: It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. Results: We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5 angstrom. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637 angstrom. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. Conclusion: SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://***/multicom_toolbox/.

关键词： support vector machine model Local Quality Distance Deviation Relative Solvent Accessibility model Quality Assessment

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