scholarly journals State Evaluation Method of Robot Lubricating Oil Based on Support Vector Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Dongdong Guo ◽  
Xiangqun Chen ◽  
Haitao Ma ◽  
Zimei Sun ◽  
Zongrui Jiang
Keyword(s):  
Support Vector Regression ◽  
Evaluation Method ◽  
Prediction Models ◽  
Sample Volume ◽  
Spare Parts ◽  
Small Sample ◽  
Industrial Robots ◽  
Lubricating Oil ◽  
Support Vector ◽  
The Cost

Recently, the development of the Industrial Internet of Things (IIoT) has led enterprises to re-examine the research of the equipment-state-prediction models and intelligent manufacturing applications. Take industrial robots as typical example. Under the effect of scale, robot maintenance decision seriously affects the cost of spare parts and labor deployment. In this paper, an evaluation method is proposed to predict the state of robot lubricating oil based on support vector regression (SVR). It would be the proper model to avoid the structural risks and minimize the effect of small sample volume. IIoT technology is used to collect and store the valuable robot running data. The key features of the running state of the robot are extracted, and the machine learning model is applied according to the measured element contents of the lubricating oil. As a result, the cost of spare parts consumption can be saved for more than two million CNY per year.

scholarly journals Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fu-Qing Cui ◽  
Wei Zhang ◽  
Zhi-Yun Liu ◽  
Wei Wang ◽  
Jian-bing Chen ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Support Vector Regression ◽  
Nonlinear Regression ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Frozen Soil ◽  
Support Vector ◽  
Soil Thermal Conductivity ◽  
Fine Grained ◽  
Fine Grained Soil

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

scholarly journals A New Method for Predicting the Permeability of Sandstone in Deep Reservoirs

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Feisheng Feng ◽  
Pan Wang ◽  
Zhen Wei ◽  
Guanghui Jiang ◽  
Dongjing Xu ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Capillary Pressure ◽  
Prediction Models ◽  
Reservoir Rock ◽  
Support Vector ◽  
Pressure Curve ◽  
Pore Throat ◽  
Capillary Pressure Curve ◽  
Mercury Injection ◽  
Mercury Injection Capillary Pressure

Capillary pressure curve data measured through the mercury injection method can accurately reflect the pore throat characteristics of reservoir rock; in this study, a new methodology is proposed to solve the aforementioned problem by virtue of the support vector regression tool and two improved models according to Swanson and capillary parachor parameters. Based on previous research data on the mercury injection capillary pressure (MICP) for two groups of core plugs excised, several permeability prediction models, including Swanson, improved Swanson, capillary parachor, improved capillary parachor, and support vector regression (SVR) models, are established to estimate the permeability. The results show that the SVR models are applicable in both high and relatively low porosity-permeability sandstone reservoirs; it can provide a higher degree of precision, and it is recognized as a helpful tool aimed at estimating the permeability in sandstone formations, particularly in situations where it is crucial to obtain a precise estimation value.

scholarly journals Spatiotemporal Traffic Flow Prediction with KNN and LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xianglong Luo ◽  
Danyang Li ◽  
Yu Yang ◽  
Shengrui Zhang
Keyword(s):  
Support Vector Regression ◽  
Traffic Flow ◽  
Prediction Models ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

Green technology automotive shape design based on neural networks and support vector regression

2014 ◽  
Vol 31 (8) ◽  
pp. 1732-1745 ◽  
Author(s):  
Kuo-Kuang Fan ◽  
Chun-Hui Chiu ◽  
Chih-Chieh Yang
Keyword(s):  
Support Vector Regression ◽  
Evaluation Method ◽  
Affective Response ◽  
Green Technology ◽  
Affective Responses ◽  
Support Vector ◽  
Shape Design ◽  
Shape Features ◽  
Systematic Analysis ◽  
Content Type

Purpose – The green technology cars have received much attention due to the air pollution and energy crisis. The purpose of this paper is to increase automotive designers’ understanding of the affective response of consumers about automotive shape design. Consumers’ preference is mainly based on a vehicle's shape features that are traditionally manipulated by designers’ intuitive experience rather than by an effective and systematic analysis. Therefore, when encountering increasing competition in today's automotive market, enhancing car designers’ understanding of consumers’ preferences on the shape features of green technology vehicles to fulfil customers’ demands, has become a common objective for automotive makers. Design/methodology/approach – In this paper, questionnaires were first used to gather consumer evaluations of certain adjectives describing automobile shape. Then, automotive styling features were systematically examined by numerical definition-based shape representations. Finally, models were individually constructed using support vector regression (SAR), which predicted consumer's affective responses, based on the adjectives selected, and which also incorporated the relationship between consumer's affective responses and automotive styling features. Findings – In order to predict and suggest the best automotive shape design, the results of this experiment of SVR can provide a basis for the future development of automobiles, particularly for green vehicle design, and support automotive makers in ensuring that automotive shape design to satisfy consumer needs. Originality/value – SVR is a valuable choice as an evaluation method to be applied in the design field of green vehicles.

scholarly journals Research on water pollution prediction of township enterprises based on support vector regression machine

2021 ◽  
Vol 228 ◽  
pp. 02014
Author(s):  
Yue Wang ◽  
Song Xue ◽  
Junming Ding
Keyword(s):  
Water Pollution ◽  
Support Vector Regression ◽  
Rural Areas ◽  
Rural Economy ◽  
Negative Impact ◽  
Water Environment ◽  
Small Sample ◽  
Support Vector ◽  
Rural Water

The construction and development of township enterprises plays a key role in promoting the development of rural economy. With the implementation of the rural revitalization strategy, township enterprises develop rapidly, but there are problems in the development process that have a negative impact on the quality of local rural water environment. Rural water environment is related to the health of farmers, the healthy development of agriculture and the sustainable development of rural areas, so it is necessary to predict the water pollution of township enterprises. The application of support vector regression forecasting model to the prediction of water pollution of township enterprises can better predict the water pollution of township enterprises with the characteristics of complexity, nonlinear and small sample. This intelligent forecasting method will help to scientifically prevent the development of township enterprises from having negative impact on the quality of local water environment.

scholarly journals Improving intermittent demand forecasting based on data structure

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
S. Fatemeh Faghidian ◽  
◽  
Mehdi Khashei ◽  
Mohammad Khalilzadeh ◽  
◽  
...  
Keyword(s):  
Hybrid Model ◽  
Prediction Models ◽  
Moving Average ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Percentage Error ◽  
Complex Nature ◽  
Intermittent Demand ◽  
Classical Models ◽  
The Cost

Forecasting spare parts requirements is a challenging problem, because the normally intermittent demand has a complex nature in patterns and associated uncertainties, and classical forecasting approaches are incapable of modeling these complexities. The present study introduces a hybrid model that can impressively overcome the limitations of classical models while simultaneously using their unique advantages in dealing with the complexities in intermittent demand. The strategy of the proposed hybrid model is to use the three individual autoregressive moving average (ARMA), single exponential smoothing (SES), and multilayer perceptron (MLP) models simultaneously. Each of them has the potential of modeling a different structure and patterns of behavior among the data. The accuracy in forecasting ability is also increased by the suitable examination of these in the intermittent data. Croston’s method is the backbone of the suggested model. The proposed hybrid model is based on CV2 and ADI criteria, which improve its efficacy in examining inappropriate structures by reducing the cost of inappropriate modeling while increasing the prediction model accuracy. Using these results prevents the hybrid model from being confused or weakened in the modeling of all groups and reduces the risk of choosing the disproportionate model. The accuracy of prediction models was evaluated and compared using mean absolute percentage error (MAPE) by implementing an example, and promising results were achieved.

scholarly journals Dielectric technique combined with artificial neural network and support vector regression in moisture content prediction of olive

2020 ◽  
Vol 66 (No. 1) ◽  
pp. 1-7
Author(s):  
Mahdi Rashvand ◽  
Mahmoud Soltani Firouz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Support Vector Regression ◽  
Prediction Models ◽  
Electronic Device ◽  
Support Vector ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Olives are one of the most important agriculture crops in the world, which are harvested in different stages of growth for various uses. One of the ways to detect the adequate time to process the olives is to determine their moisture content. In this study, to determine the moisture content of olives, a dielectric technique was used in seven periods of harvesting and three different varieties of olive including Oily, Mary and Fishemi. The dielectric properties of the olive fruits were measured using an electronic device in the range of 0.1–30 MHz. Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods were applied to develop the prediction models by using the obtained data acquired by the system. The best results (R = 0.999 and MSE = 0.014) were obtained by the ANN model with a topology of 384–12–1 (384 features in the input vector, 12 neurons in the hidden layer and 1 output). The results obtained indicated the acceptable accuracy of the dielectric technique combined with the ANN model.

scholarly journals End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Chuang Gao ◽  
Minggang Shen ◽  
Xiaoping Liu ◽  
Lidong Wang ◽  
Maoxiang Chu
Keyword(s):  
Wavelet Transform ◽  
Support Vector Regression ◽  
Error Bound ◽  
Performance Test ◽  
Prediction Models ◽  
Basic Oxygen Furnace ◽  
Control Model ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Hit Rate

A static control model is proposed based on wavelet transform weighted twin support vector regression (WTWTSVR). Firstly, new weighted matrix and coefficient vector are added into the objective functions of twin support vector regression (TSVR) to improve the performance of the algorithm. The performance test confirms the effectiveness of WTWTSVR. Secondly, the static control model is established based on WTWTSVR and 220 samples in real plant, which consists of prediction models, control models, regulating units, controller, and BOF. Finally, the results of proposed prediction models show that the prediction error bound with 0.005% in carbon content and 10°C in temperature can achieve a hit rate of 92% and 96%, respectively. In addition, the double hit rate of 90% is the best result by comparing with four existing methods. The results of the proposed static control model indicate that the control error bound with 800 Nm3 in the oxygen blowing volume and 5.5 tons in the weight of auxiliary materials can achieve a hit rate of 90% and 88%, respectively. Therefore, the proposed model can provide a significant reference for real BOF applications, and also it can be extended to the prediction and control of other industry applications.

scholarly journals Model Ensembles of Artificial Neural Networks and Support Vector Regression for Improved Accuracy in the Prediction of Vegetation Conditions and Droughts in Four Northern Kenya Counties

2019 ◽  
Vol 8 (12) ◽  
pp. 562 ◽  
Author(s):  
Chrisgone Adede ◽  
Robert Oboko ◽  
Peter W. Wagacha ◽  
Clement Atzberger
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Regression ◽  
Prediction Models ◽  
Weighted Averaging ◽  
Support Vector ◽  
Heterogeneous Model ◽  
Drought Prediction ◽  
Artificial Neural ◽  
Model Ensembles

For improved drought planning and response, there is an increasing need for highly predictive and stable drought prediction models. This paper presents the performance of both homogeneous and heterogeneous model ensembles in the satellite-based prediction of drought severity using artificial neural networks (ANN) and support vector regression (SVR). For each of the homogeneous and heterogeneous model ensembles, the study investigates the performance of three model ensembling approaches: (1) non-weighted linear averaging, (2) ranked weighted averaging, and (3) model stacking using artificial neural networks. Using the approach of “over-produce then select”, the study used 17 years of satellite data on 16 selected variables for predictive drought monitoring to build 244 individual ANN and SVR models from which 111 models were automatically selected for the building of the model ensembles. Model stacking is shown to realize models that are superior in performance in the prediction of future drought conditions as compared to the linear averaging and weighted averaging approaches. The best performance from the heterogeneous stacked model ensembles recorded an R2 of 0.94 in the prediction of future (1 month ahead) vegetation conditions on unseen test data (2016–2017) as compared to an R2 of 0.83 and R2 of 0.78 for ANN and SVR, respectively, in the traditional approach of selection of the best (champion) model. We conclude that despite the computational resource intensiveness of the model ensembling approach, the returns in terms of model performance for drought prediction are worth the investment, especially in the context of the continued exponential increase in computational power and the potential benefits of improved forecasting for vulnerable populations.

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