The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to elimin...
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The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linearregression is constructed by an example. The results show that the model has good effects for simulation and prediction.
This paper discusses the establishment of multiple linear regression (MLR)-based spring durability models for predicting the fatigue life of automotive coil springs based on the vertical vibrations of the vehicle and ...
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This paper discusses the establishment of multiple linear regression (MLR)-based spring durability models for predicting the fatigue life of automotive coil springs based on the vertical vibrations of the vehicle and natural frequencies of the vehicle suspension system. These models were developed in order to simplify the design and development process of vehicle suspension systems, which is both time-intensive and cost-intensive. The simulated force-time histories were processed to obtain the fatigue life of the automotive coil spring based on the strain-life models whereas the acceleration-time histories were weighted according to the ISO-2631-1:1997 standard to determine the vertical vibrations of the vehicle. MLR was used to establish the spring durability models and the goodness of fit, linearity, normality, and homoscedasticity of the models were assessed. The highest coefficient of determination at 0.8820 was obtained for the Morrow MLR-based spring durability model, with the mean square error of 0.5855. The models were validated by comparing the fatigue life values predicted by the models with those predicted from strain measurements. The results show a good agreement between the predicted and experimental values, indicating the suitability of these models in predicting the fatigue life of automotive coil springs. (C) 2018 Elsevier Ltd. All rights reserved.
Many biomedical classification problems are multi-label by nature, e.g., a gene involved in a variety of functions and a patient with multiple diseases. The majority of existing classification algorithms assumes each ...
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Many biomedical classification problems are multi-label by nature, e.g., a gene involved in a variety of functions and a patient with multiple diseases. The majority of existing classification algorithms assumes each sample with only one class label, and the multi-label classification problem remains to be a challenge for biomedical researchers. This study proposes a novel multi-label learning algorithm, hMuLab, by integrating both feature-based and neighbor-based similarity scores. The multiple linear regression modeling techniques make hMuLab capable of producing multiple label assignments for a query sample. The comparison results over six commonly-used multi-label performance measurements suggest that hMuLab performs accurately and stably for the biomedical datasets, and may serve as a complement to the existing literature.
Limited monitoring budgets restrict the type and number of sensors that can be installed for field-based studies. Therefore, sensor selection should be both informative and efficient. We propose a method to optimize s...
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Limited monitoring budgets restrict the type and number of sensors that can be installed for field-based studies. Therefore, sensor selection should be both informative and efficient. We propose a method to optimize sensor network design, prior to data collection, by combining multiple linear regression (MLR) and robust decision-making (RDM). multiple linear regression inherently considers the strength of the relationship between observations and predictions of interest and correlations among proposed observations. In our approach, we use universal multiple linear regression (uMLR) to quantify the explanatory power of all possible combinations of model-simulated candidate observations (of different sensor types and locations). A model-ensemble approach allows for network design in the context of user-defined uncertainties, including expected measurement error and parameter and structural uncertainty. Application of uMLR with RDM produces a comprehensive assessment of the likely value of many observation sets. These results can be used to design sensor networks to address specific experimental objectives and to balance the cost and effort of installing sensors to the expected value of the data for model testing and decision support.
The chemokine receptor CXCR2 plays an important role in recruiting granulocytes to sites of inflammation and has been proposed as an important therapeutic target. A linear quantitative structure-activity relationship ...
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The chemokine receptor CXCR2 plays an important role in recruiting granulocytes to sites of inflammation and has been proposed as an important therapeutic target. A linear quantitative structure-activity relationship model is presented for modeling and predicting biological activities of CXCR2 antagonists. The model was produced by using the multiple linear regression technique on a database that consists of 55 nonpeptide antagonists of CXCR2. Stepwise regression as a variable selection method was used to develop a regression equation based on 43 training compounds, and predictive ability was tested on 12 compounds reserved for that purpose. Appropriate models with low standard errors and high correlation coefficients were obtained. The mean effect of descriptors and standardized coefficients shows that the mean atomic van der Waals volume is the most important property affecting the biological activities of the molecules. The square regression coefficient of prediction set for the multiple linear regression method was 0.912.
Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective ...
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Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process. (C) 2010 Elsevier Ireland Ltd. All rights reserved.
This study aims to understand the changes in the water quality of Hanyuan Lake and to show these changes over time. In this study, monthly sampling was conducted at three sampling sites in Hanyuan Lake, and water samp...
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This study aims to understand the changes in the water quality of Hanyuan Lake and to show these changes over time. In this study, monthly sampling was conducted at three sampling sites in Hanyuan Lake, and water samples were measured for water quality indicators in the laboratory according to the methods specified in the Environmental Quality Standards for Surface Water (GB3838-2002). Based on the monitoring data from January to December 2019, the WQI comprehensive evaluation method was used to conduct multiplelinear stepwise regression analysis, extract key indicators, and establish the WQI(min) model. The results show that according to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, and the grade is excellent. The overall water quality of Hanyuan Lake is excellent, and most of the water quality indexes reach the Class I standard in the Environmental Quality Standards for Surface Water (GB3838-2002). WQI(min1) (R-2 = 0.86, p < 0.001, PE = 4.28) as the best WQI(min) model. In this study, a model with fewer parameters was established by multiple linear regression method, which is conducive to better monitoring of water quality at monitoring stations while saving costs. Practitioner Points According to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, the rating is excellent. From January 2019 to September 2020, the monthly change trend of each section is roughly the same, showing a trend of first decreasing, then rising, then decreasing, and finally rising and flattening. The WQI(min) model was developed to completely describe the change in the water body.
This paper presents a novel procedure for short-term load forecasting in distribution management systems. The load is forecasted for feeders that can be of a primarily residential, commercial, industrial or combined t...
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This paper presents a novel procedure for short-term load forecasting in distribution management systems. The load is forecasted for feeders that can be of a primarily residential, commercial, industrial or combined type. Each feeder has various amounts of distributed energy resources installed, which accounts for multiple different load patterns. Hence, the distribution management systems cannot use a single short-term load forecasting model for all forecasts. The proposed procedure addresses the specificity of each particular feeder type, by creating customized short-term load forecasting models. It uses a genetic algorithm to select the best inputs for different multiple linear regression models. The genetic algorithm chooses variables from a dataset constructed using load and temperature measurements. The dataset is extended by adding non-linear transformations and mutual interaction effects of the measurements, as well as calendar variables. This extension enables for the modelling of non-linear influences and extracts the non-linearity to the domain of input variables. The models' performance is assessed by the mean absolute percentage error. The proposed procedure is applied to a set of measurements collected from an US electric power utility, which operates in the city of Burbank, Cal., USA. The obtained multiple linear regression model is compared with a previously proposed naive benchmark, and a special comparison model, developed by correlation analysis. The proposed method is extendable to suit distribution management systems with different types of electricity consumers.
multiple linear regression （MLR） method was applied to quantify the effects of the net heat flux （NHF）, the net freshwater flux （NFF） and the wind stress on the mixed layer depth （MLD） of the South China Sea ...
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multiple linear regression （MLR） method was applied to quantify the effects of the net heat flux （NHF）, the net freshwater flux （NFF） and the wind stress on the mixed layer depth （MLD） of the South China Sea （SCS） based on the simple ocean data assimilation （SODA） dataset. The spatio-temporal distributions of the MLD, the buoyancy flux （combining the NHF and the NFF） and the wind stress of the SCS were presented. Then using an oceanic vertical mixing model, the MLD after a certain time under the same initial conditions but various pairs of boundary conditions （the three factors） was simulated. Applying the MLR method to the results, regression equations which modeling the relationship between the simulated MLD and the three factors were calculated. The equations indicate that when the NHF was negative, it was the primary driver of the mixed layer deepening; and when the NHF was positive, the wind stress played a more important role than that of the NHF while the NFF had the least effect. When the NHF was positive, the relative quantitative effects of the wind stress, the NHF, and the NFF were about i0, 6 and 2. The above conclusions were applied to explaining the spatio-temporal distributions of the MLD in the SCS and thus proved to be valid.
The most frequent type of traffic accident is a low-speed rear-end collision, which can damage parts of the vehicle, including the bumper, and cause neck injury to the occupants. Even in minor damage accidents, such a...
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The most frequent type of traffic accident is a low-speed rear-end collision, which can damage parts of the vehicle, including the bumper, and cause neck injury to the occupants. Even in minor damage accidents, such as scratches on bumper covers, 26.3% of occupants received treatment for bodily injuries whose main symptom was neck injuries through auto insurance. This study was conducted to evaluate the potential for neck injuries in low-speed accidents. Fifty-nine low-speed rear-end impact tests were conducted, and the motion of the struck vehicle and the neck injury criterion (NIC) of the occupant according to the test conditions were predicted using multiple linear regression derived via supervised machine learning. It was confirmed that the NIC can be predicted using vehicle motion values that can be obtained through an event data recorder. The coefficients of determination of the regression equations were 0.67-0.83. Lastly, we investigated whether neck injuries can be predicted through bumper cover damage that can be checked immediately after a vehicle accident. In the case of the vehicle damage type 1/2/3 category applied to auto insurance by the Korean government, an occupant would have a very low possibility of neck injury or symptoms. No symptoms or injuries were reported in the volunteer tests conducted for this study.
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