Weighted Combination Method Research Articles

A hybrid model based on novel SVM-SVR and weighted combination strategy for build-up rate prediction

Abstract The build-up rate prediction is of great importance for trajectory control in the field of drilling. However, it is very difficult to achieve accurate prediction due to the complexity, nonlinearity, and multiple uncertainties of the drilling system. As a consequence, a novel hybrid prediction model is proposed, which uses multiple feature selection methods, the model combination strategy based on machine learning, and three prediction models to improve the prediction accuracy of the build-up rate. More precisely, correlation analysis, importance analysis, and statistical analysis are employed to ensure the effectiveness of feature selection. Then, a novel classification prediction model called support vector machine-support vector regression (SVM-SVR) is proposed to improve the accuracy of samples with the higher build-up rate. Subsequently, the SVR optimized by grey wolf optimizer (GWO-SVR) and back propagation (BP) neural network are constructed. Finally, the three models are integrated by a weighted combination method based on SVR to realize the accurate prediction of the build-up rate. To verify the performance of the hybrid model, the data of the Z48 well in Sichuan province is used, and the results show that the hybrid model can reduce by 22.7% in mean absolute error and 32% in mean square error when compared with the existing models.

Enhancing artificial permafrost table predictions using integrated climate and ground temperature data: A case study from the Qinghai-Xizang highway

The complexities of permafrost changes, driven by climate warming and engineering activities, coupled with challenges in data acquisition, make it crucial and challenging to accurately predict the artificial permafrost table, particularly for subgrades in high-temperature unstable permafrost regions. To address this, this study developed a hybrid machine learning model (RF-LSTM-XGBoost) for permafrost table prediction. By analyzing climate change and ground temperature data from various positions and depths along the subgrade in the Tuotuo River section of the Qinghai-Xizang Highway, the Spearman correlation coefficient method was initially used to determine the important influencing factors. Random Forest (RF), Long Short-Term Memory Neural Network (LSTM), and Extreme Gradient Boosting (XGBoost) models were used to predict the artificial permafrost table, and grid search and cross-validation methods were employed to optimize the hyperparameters of each model. A linear weighted combination method based on the minimum cumulative absolute error was utilized to merge the models, and its performance was compared with the individual RF, LSTM, and XGBoost models. Subsequently, the feature importance of the variables in the machine learning model was analyzed. The results indicated a strong correlation between artificial permafrost table changes and factors such as daily average atmospheric temperature, subgrade surface ground temperature, and subgrade surface ground heat flux during the freezing-thawing cycle. The combined model highlighted daily atmospheric temperature as the most influential predictor, followed by ground heat flux, with the surface ground temperature being less impactful. The combined model demonstrated improved predictive accuracy, with MSE, MAPE, RMSE, MAE, and R2 values of 0.003, 0.052, 0.0085, 0.029, and 0.989, respectively, surpassing those of individual models. This model offers a rapid, accurate, and reliable approach for permafrost table prediction, advancing subgrade stability research in challenging permafrost environments.

Optimal weight support vector regression ensemble with cluster-based subsampling for electricity price forecasting

Accurate prediction of short-term electricity price is the key to obtain economic benefit and also an important index of power system planning and management. Support vector regression (SVR) based ensemble works have gained remarkable achievements in terms of high accuracy and steady performance, but they are highly dependent on data representativeness and have a high computational complexity O (k * N 3) of data samples and parameter selection. To further improve the data representativeness and reduce its computational complexity, this paper develops a new approach to forecast electricity price via optimal weighted ensemble. In the model, the cluster-based subsampling algorithm is proposed to categorize the inputs being seasonally decomposed into several groups, and representative data are drawn from each group in a certain proportion to ensure that each subset trained with SVR has the same representativeness and features. Moreover, the optimal weighted combination method is presented to assign weights to the sub-SVRs to obtain the optimal support vector regression ensemble model (OWSSVRE). The experimental results show that the improved support vector regression ensemble model with the same features and representativeness of the subset has better performance in electricity price forecasting. As a result, it is suitable to support decision making in the energy and other sectors.

Adaptive personalized privacy-preserving data collection scheme with local differential privacy

Local differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. In most recent works, it is assumed that the privacy parameter is determined solely by collectors and then dispatched to all participants. However, it is inelegant and unpromising for all participants to accept the same level of privacy preservation due to their personalized preferences. Here, an adaptive data collection scheme is proposed to realize personalized privacy preservation while achieving higher data utility, in which two different LDP perturbation methods are adaptively chosen by data participants according to their personalized privacy preferences. The adaptive boundary based on the minimum mean square error (MSE) is theoretically and accurately derived to allow participants to adaptively choose the best perturbation method. Then, a weighted combination method is demonstrated to do effective data aggregation from multiple privacy groups. Moreover, an expanded data strategy (EDS) with multiple privacy perturbations is presented to equivalently increase the sample size without harming others privacy, thereby further improving the accuracy of statistics. Finally, the experiments show that the proposed scheme performs better than the previous proposal in terms of MSE and average error rate (AER), especially using the EDS method.

Application of Weighted Combinations of Activation Functions to Defect Prediction in Software Development

Software defect prediction (SDP) method aims to identify potential bugs in programs or defective modules in software projects. SDP method can greatly help developers allocate needed testing- and debugging-efforts. Presently, SDP is typically divided into two procedures: extracting features from source code and building a classification model using machine learning methods, such as support vector machine, decision tree, and neural networks, to build a classifier for defect prediction. However, there are still some limitations for SDP. For example, machine learning models require a fixed input size, but the size of each program is mostly inconsistent. Activation functions play an essential role in the training of artificial neural networks, but every kind of activation function has its own particular strengths and inherent constraints. The main purpose of this article is to propose a general framework of combining different activation functions with given weights to improve the effectiveness of SDP. We construct 41 kinds of defect prediction models by deep belief networks (DBNs) built with double-weighted or triple-weighted combination of six most commonly used activation functions to improve the predictions. In the experiment of this study, some real data from open-source projects are selected to evaluate the performance of our proposed weighted combination methods. It is found experimentally that the weighted combinations methods can enhance the accuracy of SDP. It is also noticed that our proposed weighted combination scheme is not restricted to the DBN or a particular kind of activation functions.

Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research

The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single data source model method and feature combination method. 271 patients with invasive breast cancer were included in the retrospective study, of which 174 patients in our center were randomized into the training and validation sets, and 97 patients in the external center were as the test set. Radiomics features extracted from the ABVS-based tumor, peritumoral 3 mm region, and peritumoral 5 mm region and clinical features were used to construct the four types of the optimal single data source models, Tumor, R3mm, R5mm, and Clinical model, respectively. Then, the model weighted combination and feature combination methods were performed to optimize the combination models. The proposed weighted combination models in predicting HER2 status achieved better performance both in validation set and test set. For the validation set, the single data source model, the feature combination model, and the weighted combination model achieved the highest area under the curve (AUC) of 0.803 (95% confidence interval [CI] 0.660–947), 0.739 (CI 0.556,0.921), and 0.826 (95% CI 0.689,0.962), respectively; with the sensitivity and specificity were 100%, 62.5%; 81.8%, 66.7%; 90.9%,75.0%; respectively. For the test set, the single data source model, the feature combination model, and the weighted combination model attained the best AUC of 0.695 (95% CI 0.583, 0.807), 0.668 (95% CI 0.555,0.782), and 0.700 (95% CI 0.590,0.811), respectively; with the sensitivity and specificity were 86.1%, 41.9%; 61.1%, 71.0%; 86.1%, 41.9%; respectively. The model weighted combination was a better method to construct a combination model. The optimized weighted combination models composed of ABVS-based intratumoral and peritumoral radiomics features and clinical features may be potential biomarkers for the noninvasive and preoperative prediction of HER2 status in breast cancer.

Optimization design of load-sharing and light weight of the twin rotors concentric face gear power-split transmission system based on gear tooth matching condition

To improve the dynamic load-sharing performance and achieve lightweight, the twin rotors concentric face gear power-split transmission system (TRFGPSTS) was optimized. The gear tooth matching conditions of the system based on the design characteristics of power flow closed-loop and virtual constraint configuration of the system are established, and the basic parameters of each gear meet the requirements of the system load-sharing layout and synchronous meshing of each branch movement are calculated. Based on the lumped mass parameter method, combined with the calculation results of gear tooth matching, the 45-DOF bending torsional coupling dynamic model of the system was established considering time-varying meshing stiffness, support stiffness, manufacturing and installation errors, and other factors, and the influence of each factor on the dynamic load-sharing performance of the system was analyzed. Combined with the influence law of various factors, a multi-objective dynamic load-sharing optimization design model was established with the best dynamic load-sharing performance and the minimum system quality as the objective function. Through the weighted combination method, the global optimal solution of the system optimization design was determined, and the influence of each parameter on the optimization objective function was analyzed. The correctness of the results was verified by using the genetic algorithm based on Pareto frontier distribution. The results show that the dynamic load-sharing characteristics of the system become worse with the increase of error and torsional stiffness, and are more sensitive to the influence of various error parameters. The optimization process focuses on the geometric parameters such as the Module, the teeth Number, the Pressure angle, and the Tooth width of each gear, which can reflect the degree of influence on the dynamic load-sharing and lightweight of the system. After optimization, the dynamic load sharing coefficient (DLSC) was reduced to 1.017, and the overall geometric volume was reduced to 1.473 × 108 mm3. It provides a theoretical basis for the design and research of a new type of helicopter power transmission system with a compact structure, good maneuverability, and high reliability.

Model of Interprofessional Talent Cultivation for Theatre, Film and Television Performance in Universities in the Context of Big Data Analysis

Abstract This paper first analyzes the current situation facing the development of professional education of drama, film and television performance and the innovation opportunities of interprofessional talent training mode for drama, film and television performance. Secondly, the evaluation system of interprofessional talent cultivation quality is constructed, and then the evaluation model is constructed using the algorithms of subjective and objective weight combination method, hierarchical analysis method and entropy value method, and the evaluation data are pre-processed, and the indicators are assigned weights. Finally, the quantitative data analysis was carried out by testing the index weights and evaluating the index. The results showed that from the index weights, the six index weights of psychological quality, ideological and moral quality, knowledge and cultural cultivation, learning ability, communication ability, and practical ability were 0.0971, 0.2269, 0.1911, 0.2416, 0.1184, and 0.1249, respectively.

Research on Risk Evaluation of Hydropower Engineering EPC Project Based on Improved Fuzzy Evidence Reasoning Model

As clean renewable energy with strong advantages, hydropower plays an extremely important role in promoting green development and energy allocation patterns. Hydropower project construction is characterized by long duration, large scale, high cost, many participants, and complex construction conditions, and is closely related to the economy, society, and ecological environment, and its construction management mode and construction risk management have become the focus of extensive attention from all walks of life. In this paper, the risk evaluation index system of hydropower engineering EPC project is constructed, and the linear weighted combination method is introduced to determine the comprehensive weights based on the calculation of weights by sequential relationship method and entropy weight method, and the improved fuzzy normal distribution is introduced as the subordinate function distribution of fuzzy evaluation level based on DS evidence theory and fuzzy theory. The risk evaluation model of a hydropower engineering EPC project is also established. Meanwhile, the model was analyzed with hydropower project examples to verify the accuracy and practicality of the model, which can guide hydropower project stakeholders to manage hydropower project risks comprehensively, collaboratively, and efficiently, and provide decision support for hydropower project construction risk management.

Integrated Influence Zone Calculation Method for the Existing Metro Tunnel Under the Influence of Adjacent Construction

Adjacent construction around metro tunnels is a common situation in urban cities, which usually leads to the disturbance of surrounding soils and existing tunnels. Based on the influence zone theory, construction risks can be described in a graded manner. However, the calculation of the influence zone in existing research is mostly based on a single indicator, which is difficult to adapt to the increasingly stringent requirements of real projects. To address this limitation, a normalized method for the influence function and a weighted combination method for multi-indicators were proposed. These methods can combine any number of indicators to obtain an integrated result of the influence zone, facilitating direct guidance of engineering. Based on a case study of the Metro construction in Qingdao, a series of numerical simulations and calculations of the integrated influence zone was carried out. The results show that the shape of the integrated influence zone in this case is a necked bottle, i.e., the new tunnel causes a greater influence when it is below the existing tunnel. The change of soil layer and the weight of indicators both play important roles on the shape of the influence zone. The relevant conclusions and methods provide a basis for the safety assessment of similar projects.

A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion

A single sensor is prone to decline recognition accuracy in the face of a complex environment, while the existing multi-sensor evidence theory fusion methods do not comprehensively consider the impact of evidence conflict and fuzziness. In this paper, a new evidence weight combination and probability allocation method is proposed, which calculated the degree of evidence fuzziness through the maximum entropy principle, and also considered the impact of evidence conflict on fusing results. The two impact factors were combined to calculate the trusted discount and reallocate the probability function. Finally, Dempster's combination rule was used to fuse every piece of evidence. On this basis, experiments were first conducted to prove that the existing weight combination methods produce results contrary to common sense when handling high-conflicting and high-clarity evidence, and then comparative experiments were conducted to prove the effectiveness of the proposed evidence weight combination and probability allocation method. Moreover, it was verified, on the PAMAP2 data set, that the proposed method can obtain higher fusing accuracy and more reliable fusing results in all kinds of behavior recognition. Compared with the traditional methods and the existing improved methods, the weight allocation method proposed in this paper dynamically adjusts the weight of fuzziness and conflict in the fusing process and improves the fusing accuracy by about 3.3% and 1.7% respectively which solved the limitations of the existing weight combination methods.

Prediction of tide level based on variable weight combination of LightGBM and CNN-BiGRU model

Accurate tide level prediction is crucial to human activities in coastal areas. Many practical applications show that compared with traditional harmonic analysis, long short-term memory (LSTM), gated recurrent units (GRUs) and other neural networks, along with ensemble learning models, such as light gradient boosting machine (LightGBM) and eXtreme gradient boosting (XGBoost), can achieve extremely high prediction accuracy in relatively stationary time series. Therefore, this paper proposes a variable weight combination model based on LightGBM and CNN-BiGRU with relevant research. It uses the variable weight combination method to weight and synthesize the prediction results of the two base models so that the combination model has a stronger ability to capture time series features and fits the data well. The experimental results show that in contrast to the base model LightGBM, the RMSE value and MAE value of the combination model are reduced by 43.2% and 44.7%, respectively; in contrast to the base model CNN-BiGRU, the RMSE value and MAE value of the combination model are reduced by 35.3% and 39.1%, respectively. This means that the variable weight combination model can greatly improve the accuracy of tide level prediction. In addition, we use tidal data from different geographical environments to further verify the good universality of the model. This study provides a new idea and method for tide prediction.

Evaluation Model of Location Light Pollution Level based on Analytic Hierarchy Process and Entropy Weight Method

With the development of society, the impact of light pollution has gradually intensified. It not only endangers human health and animal and plant activities but also indirectly damages the climate and environment. In the past, the research on the evaluation of urban light pollution often focused on the comparison between the light pollution level measured by the relevant photometry equipment and the natural light level. This means that many sites need to be selected for measurement in the evaluation city. To save resources and ensure the evaluation results have a certain validity, we built a Location Light Pollution Risk Index (LLPRI) model. First, we selected 16 indicators from 6 aspects and built a three-level indicator evaluation system. After that, based on combining the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to calculate the index weight, we also use the Weight Combination Method to obtain the final weight of the index. Finally, we will bring the collected site sample data into the model and then use K-means Algorithm to cluster the model results and obtain the model metric We compare the results of the assessment of location light pollution risk level by the model with the data from the Military Meteorological Satellite Program (DMSP) and Visible Infrared Imaging Radiometer (VIIRS). The experimental results show that the LLRPI model has good effectiveness.

Fisher’s Combined Probability Test for High-Dimensional Covariance Matrices

Testing large covariance matrices is of fundamental importance in statistical analysis with high-dimensional data. In the past decade, three types of test statistics have been studied in the literature: quadratic form statistics, maximum form statistics, and their weighted combination. It is known that quadratic form statistics would suffer from low power against sparse alternatives and maximum form statistics would suffer from low power against dense alternatives. The weighted combination methods were introduced to enhance the power of quadratic form statistics or maximum form statistics when the weights are appropriately chosen. In this article, we provide a new perspective to exploit the full potential of quadratic form statistics and maximum form statistics for testing high-dimensional covariance matrices. We propose a scale-invariant power-enhanced test based on Fisher’s method to combine the p-values of quadratic form statistics and maximum form statistics. After carefully studying the asymptotic joint distribution of quadratic form statistics and maximum form statistics, we first prove that the proposed combination method retains the correct asymptotic size under the Gaussian assumption, and we also derive a new Lyapunov-type bound for the joint distribution and prove the correct asymptotic size of the proposed method without requiring the Gaussian assumption. Moreover, we show that the proposed method boosts the asymptotic power against more general alternatives. Finally, we demonstrate the finite-sample performance in simulation studies and a real application. Supplementary materials for this article are available online.

3D Quality Evaluation of Rock Mass in Urban Underground Space Based on Improved Fuzzy Analytic Hierarchy Process

Urban underground space (UUS) is an important resource to address the shortage of land, save resources and reduce environmental pollution, but it is irreversible. Therefore, it is necessary to evaluate its suitability before development. Rock mass is not only the carrier of UUS utilization, but also a key evaluation index, as it directly affects the evaluation results. Therefore, it is necessary to evaluate the 3D quality of rock mass from the perspective of UUS development. In this paper, a 3D quality evaluation model of rock mass in UUS is established based on an improved fuzzy analytic hierarchy process (IFAHP). Firstly, the subjective weight is calculated by analytic hierarchy process (AHP), and the objective weight is calculated by super-standard multiple method (SSMM). Then a new weight combination method (grey correlation analysis) is adopted. This method organically combines the subjective and objective weights, and the results show that the combined weight is more reasonable. Then the combination weight and fuzzy comprehensive evaluation method are combined with MapGIS to establish a 3D evaluation model. The case analysis shows that the evaluation results of the national standard BQ method are conservative, the traditional fuzzy analytic hierarchy process (TFAHP) is smaller, and the IFAHP is more robust. The case analysis also shows that the 3D evaluation has the advantages of strong intuition, good visualization and high precision, which is better than the traditional 2D evaluation. Moreover, the 3D evaluation model can not only be applied to this case, but also to other projects, and can provide decision-making for the development of UUS.

Enhancing Cross-Modal Hash Retrieval with Multiple Similarity Matrices

In order to further improve the performance of cross-modal retrieval, a cross-modal hash retrieval method integrating multi-level similarity information is proposed. First, self-attention method is used to enhance the text features, and a new fusion feature is constructed based on the original features and hash features of different modalities. Then, based on these three features, three auxiliary similarity matrices are constructed, and the fourth auxiliary similarity matrix is constructed by a weighted combination method. Finally, these four different matrices are used to calculate the loss functions between different similarity matrices and between different modalities. Since the four matrices include different feature forms and different matrix construction methods, they can better express similarity information of different modalities and improve the retrieval performance. The experiments are conducted on three benchmark datasets of Wikipedia, MIRFlickr and NUS-WIDE. The results show that the mAP values at different code bits of proposed method is better than that of many state-of-the-art methods, which verifies the effectiveness and robustness of our method.

Research on Safety Evaluation Method of Hydrogen Diaphragm Compressor Based on Multi-Model Weighted Combination

By establishing a remote monitoring system for the hydrogen diaphragm compressor, the thermodynamic real-time performance parameters of the unit are obtained. Using the multi-model weighted combination method, the safety evaluation system of hydrogen diaphragm compressors is established, and the risk assessment and safety evaluation analysis of three hydrogen diaphragm compressors are carried out. The analysis and calculation results show that the three hydrogen diaphragm compressors are all within the rating range of safe operation, which verifies that the multi-model weighted combination is a high-precision unit safety evaluation method, which can be used to evaluate the safe operation status of the hydrogen diaphragm compressor, so as to ensure the long-term safe and stable operation of the pressurized equipment of the hydrogen refueling station.

Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model

Traffic flow is used as an essential indicator to measure the performance of the road network and a pivotal basis for road classification. However, the combined prediction model of traffic flow based on seasonal characteristics has been given little attention at present. Because the seasonal autoregressive integrated moving average model (SARIMA) has superior linear fitting characteristics, it is often used to process seasonal time series. In contrast, the non-autoregressive dynamic neural network (NAR) has a vital memory function and nonlinear interpretation capabilities. They are suitable for constructing combined forecasting models. The traffic flow time series of a highway in southwest China is taken as the research object in this paper. Combining the SARIMA (0,1,2) (0,1,2)12 model and the NAR model with 15 hidden layer neurons and fourth-order delay, two combined models are constructed: the linear and nonlinear component combination method is realized by the SARIMA-NAR combination model 1, and the MSE weight combination method is used by the SARIMA-NAR combination model 2. We calculated that the prediction accuracy of SARIMA-NAR combined model 1 is as high as 0.92, and the prediction accuracy of SARIMA-NAR combined model 2 is 0.90. In addition, the traffic flow forecast under the influence of the epidemic is also discussed. Through a comprehensive comparison of multiple indicators, the results show that the SARIMA-NAR combined model 1 has better road traffic flow fitting and prediction effects and is suitable for the greater volatility of traffic flow during the epidemic. This model improves the effectiveness and reliability of traffic flow forecasting, and the forecasting process is more convenient and efficient.

Prediction of Gas Concentration Based on LSTM-LightGBM Variable Weight Combination Model

Gas accidents threaten the safety of underground coal mining, which are always accompanied by abnormal gas concentration trend. The purpose of this paper is to improve the prediction accuracy of gas concentration so as to prevent gas accidents and improve the level of coal mine safety management. Combining the LSTM model with the LightGBM model, the LSTM-LightGBM model is proposed with variable weight combination method based on residual assignment, which considers not only the time subsequence feature of data, but also the nonlinear characteristics of data. During the data preprocessing, the optimal parameters of gas concentration prediction are determined through the analysis of the Pearson correlation coefficients of different sensor data. The experimental results demonstrate that the mean absolute errors of LSTM-LighGBM, LSTM and LightGBM are 1.94%, 2.19% and 2.77%, respectively. The accuracy of LSTM-LightGBM variable weight combination model is better than that of the two above models, respectively. In this way, this study provides a novel idea and method for gas accident prevention based on gas concentration prediction.

METHODS FOR FORECASTING DEMAND IN REPAIRING AN AIRLINE’S REPAIRABLE LINE REPLACEABLE UNIT PARTS

Scattered failure frequency, variable and complex influencing factors, and a low accuracy in predicting inventory demand are characteristics of line replaceable unit (LRU) parts. Some high-priced repairable LRU (HR-LRU) parts have a considerable impact on the cost of aircraft spare parts.This study presents procedures to identify the optimal model for forecasting the demand for HR-LRU parts. First, a traditional prediction model, seven single measurement models, and four combined models were selected and used to predict failure data. Subsequently, evaluating indexes were selected for assessment to obtain the optimal model. Finally, we compared the actual and predicted values to verify the conclusions drawn during the previous evaluation step. The results indicated that, among the single models, the negative binomial regression model and the Holt-Winters model were most suitable for HR-LRU parts. The SSE (sum of squares error) and MAE (mean absolute error) of the negative binomial regression were the lowest at 118.4114 and 1.97352 respectively, and the Holt-Winters model’s MAE was the lowest at 1. 13270. The IOWA operator prediction model and the error reciprocal variable weight combination method produced predictions closest to the actual values among the combined models. In addition to constructing a set of processes to prediction, we also discuss the fit of different methods, the reasons for the change in the guaranteed rate, and the reasons for the occurrence of special years. We also compare the similarities and differences between this article and other papers.