Zeitschriftenartikel: „Multioutput regression“

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Tian, Qing, Meng Cao, Songcan Chen und Hujun Yin. „Structure-Exploiting Discriminative Ordinal Multioutput Regression“. IEEE Transactions on Neural Networks and Learning Systems 32, Nr. 1 (Januar 2021): 266–80. http://dx.doi.org/10.1109/tnnls.2020.2978508.

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Li, Shunlong, Huiming Yin, Zhonglong Li, Wencheng Xu, Yao Jin und Shaoyang He. „Optimal sensor placement for cable force monitoring based on multioutput support vector regression model“. Advances in Structural Engineering 21, Nr. 15 (07.05.2018): 2259–69. http://dx.doi.org/10.1177/1369433218772342.

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Cable force monitoring is an essential and critical part of structural health monitoring for cable-supported bridges. The quality of obtained information depends considerably on the number and location of limited sensors. The purpose of this article is to provide a method for optimal sensor placement for cable force monitoring in cable-supported bridges. Based on the spatial correlation between neighbouring or symmetrical cable forces, the structural information of non-monitored cables can be predicted by multioutput support vector regression models, established between monitored (input) and the non-monitored (output) cable forces. The number and placement of cable force sensors have significant influence on prediction performance of established multioutput support vector regression models. The proposed optimal sensor configuration is to select multioutput support vector regression models with minimum prediction error from all possible sensor locations. In this study, information entropy is employed to measure the prediction performance of different sensor configurations and formulate the objective function, optimised by three computationally effective algorithms: forward sequential sensor placement algorithm, backward sequential sensor placement algorithm and genetic algorithm. The application of proposed method to Nanjing No. 3 Yangtze River Bridge confirmed the efficiency, accuracy and effectiveness of the proposed method.

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Tuia, D., J. Verrelst, L. Alonso, F. Perez-Cruz und G. Camps-Valls. „Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation“. IEEE Geoscience and Remote Sensing Letters 8, Nr. 4 (Juli 2011): 804–8. http://dx.doi.org/10.1109/lgrs.2011.2109934.

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KONDO, Tadashi. „Multiinput-Multioutput Type GMDH Algorithm Using Regression-Principal Component Analysis“. Transactions of the Institute of Systems, Control and Information Engineers 6, Nr. 11 (1993): 520–29. http://dx.doi.org/10.5687/iscie.6.520.

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Yun, Seokheon. „Performance Analysis of Construction Cost Prediction Using Neural Network for Multioutput Regression“. Applied Sciences 12, Nr. 19 (24.09.2022): 9592. http://dx.doi.org/10.3390/app12199592.

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In a construction project, construction cost estimation is very important, but construction costs are affected by various factors, so they are difficult to predict accurately. However, with the recent development of ANN technology, it has become possible to predict construction costs with consideration of various influencing factors. Unlike previous research cases, this study aimed to predict the total construction cost by predicting seven sub-construction costs using a multioutput regression model, not by predicting a single total construction cost. In addition, analysis of the change in construction cost prediction performance was conducted by scaling and regularization. We estimated the error rate of predicting construction costs through sub-construction cost prediction to be 16.80%, a level similar to that of the total construction cost prediction error rate of 17.67%. This study shows that the construction cost can be calculated by predicting detailed cost factors at once, and it is expected that various types of construction costs or partial construction costs can be predicted using the predicted detailed cost elements. As a result of predicting several sub-construction costs using multioutput-based ANN, it was found that the prediction error rate varies depending on the type of construction. To improve accuracy, it is necessary to supplement influencing factors suitable for the construction features.

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Wang, Yu, und Guohua Liu. „MLA-TCN: Multioutput Prediction of Dam Displacement Based on Temporal Convolutional Network with Attention Mechanism“. Structural Control and Health Monitoring 2023 (25.08.2023): 1–19. http://dx.doi.org/10.1155/2023/2189912.

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The displacement of concrete dams effectively reflects their structural integrity and operational status. Therefore, establishing a model for predicting the displacement of concrete dams and studying the evolution mechanism of dam displacement is essential for monitoring the structural safety of dams. Current data-driven models utilize artificial data that cannot reflect the actual status of dams for network training. They also have difficulty extracting the temporal patterns from long-term dependencies and obtaining the interactions between the targets and variables. To address such problems, we propose a novel model for predicting the displacement of dams based on the temporal convolutional network (TCN) with the attention mechanism and multioutput regression branches, named MLA-TCN (where MLA is multioutput model with attention mechanism). The attention mechanism implements information screening and weight distribution based on the importance of the input variables. The TCN extracts long-term temporal information using the dilated causal convolutional network and residual connection, and the multioutput regression branch achieves simultaneous multitarget prediction by establishing multiple regression tasks. Finally, the applicability of the proposed model is demonstrated using data on a concrete gravity dam within 14 years, and its accuracy is validated by comparing it with seven state-of-the-art benchmarks. The results show that the MLA-TCN model, with a mean absolute error (MAE) of 0.05 mm, a root-mean-square error (RMSE) of 0.07 mm, and a coefficient of determination (R2) of 0.99, has a comparably high predictive capability and outperforms the benchmarks, providing an accurate and effective method to estimate the displacement of dams.

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Wu, Shengbiao, Huaning Li und Xianpeng Chen. „Parametric Model for Coaxial Cavity Filter with Combined KCCA and MLSSVR“. International Journal of Antennas and Propagation 2023 (07.06.2023): 1–10. http://dx.doi.org/10.1155/2023/2024720.

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Aiming at the problems of poor data effectiveness, low modeling accuracy, and weak generalization in the tuning process of microwave cavity filters, a parametric model for coaxial cavity filter using kernel canonical correlation analysis (KCCA) and multioutput least squares support vector regression (MLSSVR) is proposed in this study. First, the low-dimensional tuning data is mapped to the high-dimensional feature space by kernel canonical correlation analysis, and the nonlinear feature vectors are fused by the kernel function; second, the multioutput least squares support vector regression algorithm is used for parametric modeling to solve the problems of low accuracy and poor prediction performance; third, the support vector of the parameter model is optimized by the differential evolution whale algorithm (DWA) to improve the convergence and generalization ability of the model in actual tuning. Finally, the tuning experiments of two cavity filters with different topologies are carried out. The experimental results show that the proposed method has an obvious improvement in generalization performance and prediction accuracy compared with the traditional methods.

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Huang, Kai, Ming-Yi You, Yun-Xia Ye, Bin Jiang und An-Nan Lu. „Direction of Arrival Based on the Multioutput Least Squares Support Vector Regression Model“. Mathematical Problems in Engineering 2020 (30.09.2020): 1–8. http://dx.doi.org/10.1155/2020/8601376.

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The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application of this method includes the following: the construction of MLSSVR model training data, training and construction of the MLSSVR model, and the estimation of direction of arrival. Finally, the method is verified through numerical simulation. When there are comprehensive deviations in the system, the direction-finding accuracy can be effectively improved.

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Rosentreter, Johannes, Ron Hagensieker, Akpona Okujeni, Ribana Roscher, Paul D. Wagner und Bjorn Waske. „Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support Vector Regression“. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, Nr. 5 (Mai 2017): 1938–48. http://dx.doi.org/10.1109/jstars.2017.2652726.

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Zhen, Xiantong, Heye Zhang, Ali Islam, Mousumi Bhaduri, Ian Chan und Shuo Li. „Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression“. Medical Image Analysis 36 (Februar 2017): 184–96. http://dx.doi.org/10.1016/j.media.2016.11.008.

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Hornbaker, Robert H., Bruce L. Dixon und Steven T. Sonka. „Estimating Production Activity Costs for Multioutput Firms with a Random Coefficient Regression Model“. American Journal of Agricultural Economics 71, Nr. 1 (Februar 1989): 167–77. http://dx.doi.org/10.2307/1241785.

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Hariguna, Taqwa, und Athapol Ruangkanjanases. „Adaptive sentiment analysis using multioutput classification: a performance comparison“. PeerJ Computer Science 9 (09.05.2023): e1378. http://dx.doi.org/10.7717/peerj-cs.1378.

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The primary objective of this research is to create a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees. In doing so, we aim to identify the optimal algorithm performance and role within the model. The data utilized in this study is derived from customer reviews of cryptocurrencies in Indonesia. Our results indicate that LinearSVC and Stacking exhibit a high accuracy (90%) compared to the other eight algorithms. The resulting multi-output model demonstrates an average accuracy of 88%, which can be considered satisfactory. This research endeavors to innovate in adaptive sentiment analysis classification by developing a multi-output model that utilizes a combination of 10 classification algorithms.

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JAHANSEIR, Mercedeh, Seyed Kamaledin SETAREHDAN und Sirous MOMENZADEH. „Estimation of the depth of anesthesia by using a multioutput least-square support vector regression“. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 26, Nr. 6 (29.11.2018): 2793–802. http://dx.doi.org/10.3906/elk-1802-189.

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Cui, ZhenKai, Cheng Wang, Jianwei Chen und Ting He. „Multipoint Vibration Response Prediction under Uncorrelated Multiple Sources Load Based on Elastic-Net Regularization in Frequency Domain“. Shock and Vibration 2021 (02.03.2021): 1–10. http://dx.doi.org/10.1155/2021/6614020.

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In order to solve the problems of large number of conditions at inherent frequencies and low prediction accuracy when using multiple multivariate linear regression methods for vibration response prediction alone, an elastic-net regularization method is proposed. Firstly, a multi-input and multioutput linear regression model of the multipoint frequency domain vibration response is trained using historical data at each frequency point. Secondly, the trained model under each frequency point is improved by the elastic regularization. Finally, the model is used in a working situation. The predicted vibration response on the experimental dataset of cylindrical shell acoustic vibration showed that the improvement of the multivariate regression vibration response prediction model by elastic regularization can better improve the accuracy and reduce the large number of conditions at some frequencies.

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Aydın, Yaren, Gebrail Bekdaş, Sinan Melih Nigdeli, Ümit Isıkdağ, Sanghun Kim und Zong Woo Geem. „Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns“. Applied Sciences 13, Nr. 7 (23.03.2023): 4117. http://dx.doi.org/10.3390/app13074117.

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CO2 emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to reduce CO2 emissions. This research proposes an optimization-machine learning pipeline and a set of models trained for the prediction of the design variables of an ecofriendly concrete column. In this research, the harmony search algorithm was used as the optimization algorithm, and different regression models were used as predictive models. Multioutput regression is applied to predict the design variables such as section width, height, and reinforcement area. The results indicated that the random forest algorithm performed better than all other machine learning algorithms that have also achieved high accuracy.

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Moumouh, Jihane, Saad Benjelloun, Abderrazak Latifi und Lhachmi Khamar. „Data-driven modeling and optimization of an industrial phosphoric acid production unit“. MATEC Web of Conferences 379 (2023): 07008. http://dx.doi.org/10.1051/matecconf/202337907008.

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In this work, a supervised machine learning (ML) multi-output regression approach is investigated to build predictive models for an industrial unit of phosphoric acid production. More specifically, multioutput data-driven regression is applied to simultaneously estimate nine outputs (Reactor temperature, chemical yield (RC), P2O5 concentration in the phosphoric acid, and chemical losses in gypsum) under different operating conditions. The presented methods are linear regression and decision tree regression models. The use of decision tree regression provides high accuracy compared to linear regression. The decision tree model leads to a high value of the coefficient of determination (R2 = 0.994, on the testing set not used for the modeling), and to low values of the mean squared error (MSE) and mean absolute error (MAE). The best parameters of the decision tree provide higher fitness values than other depth levels. The optimal values in the training stage are 0.002, 0.007, and 0.994 for MSE, MAE, and R2, respectively. Applying decision tree regression can correctly model the data of the phosphoric acid manufacturing unit with satisfying fitness criterion and important conclusions on the process coherent with phenomenological models, as well as supplementary and novel insights.

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Bu, Aiguo, und Jie Li. „A Learning-Based Framework for Circuit Path Level NBTI Degradation Prediction“. Electronics 9, Nr. 11 (22.11.2020): 1976. http://dx.doi.org/10.3390/electronics9111976.

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Negative bias temperature instability (NBTI) has become one of the major causes for temporal reliability degradation of nanoscale circuits. Due to its complex dependence on operating conditions, it is a tremendous challenge to the existing timing analysis flow. In order to get the accurate aged delay of the circuit, previous research mainly focused on the gate level or lower. This paper proposes a low-runtime and high-accuracy machining learning framework on the circuit path level firstly, which can be formulated as a multi-input–multioutput problem and solved using a linear regression model. A large number of worst-case path candidates from ISCAS’85, ISCAS’89, and ITC’99 benchmarks were used for training and inference in the experiment. The results show that our proposed approach achieves significant runtime speed-up with minimal loss of accuracy.

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Zhang, Renhui, und Xutao Zhao. „Inverse Method of Centrifugal Pump Blade Based on Gaussian Process Regression“. Mathematical Problems in Engineering 2020 (28.02.2020): 1–10. http://dx.doi.org/10.1155/2020/4605625.

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The inverse problem is always one of the important issues in the field of fluid machinery for the complex relationship among the blade shape, the hydraulic performance, and the inner flow structure. Based on Bayesian theory of posterior probability obtained from known prior probability, the inverse methods for the centrifugal pump blade based on the single-output Gaussian process regression (SOGPR) and the multioutput Gaussian process regression (MOGPR) were proposed, respectively. The training sample set consists of the blade shape parameters and the distribution of flow parameters. The hyperparameters in the inverse problem models were trained by using the maximum likelihood estimation and the gradient descent algorithm. The blade shape corresponding to the objective blade load can be achieved by the trained inverse problem models. The MH48-12.5 low specific speed centrifugal pump was selected to verify the proposed inverse methods. The reliability and accuracy of both inverse problem models were confirmed and compared by implementing leave-one-out (LOO) cross-validation and extrapolation characteristic analysis. The results show that the blade shapes within the sample space can be reconstructed exactly by both models. The root mean square errors of the MOGPR inverse problem model for the pump blade are generally lower than those of the SOGPR inverse problem model in the LOO cross-validation. The extrapolation characteristic of the MOGPR inverse problem model is better than that of the SOGPR inverse problem model for the correlation between the blade shape parameters can be fully considered by the correlation matrix of the MOGPR model. The proposed inverse methods can efficiently solve the inverse problem of centrifugal pump blade with sufficient accuracy.

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Kumari, Sumit, Vikas Siwach, Yudhvir Singh, Dheerdhwaj Barak und Rituraj Jain. „A Machine Learning Centered Approach for Uncovering Excavators’ Last Known Location Using Bluetooth and Underground WSN“. Wireless Communications and Mobile Computing 2022 (03.03.2022): 1–11. http://dx.doi.org/10.1155/2022/9160031.

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Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc. Additionally, mining and hospitals are two of the most critical industries where applications when deployed security, accuracy, and cost effectiveness are the major concerns, due to the huge involvement of man and machines. In this paper, the problem of finding out the location of man and machines has been focused on in case of an accident during the mining process. The primary scope of the research is to guarantee that the projected position is near to the real place so that the trained model’s performance can be tested. The solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators’ Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area. Second, machine learning techniques are proposed to develop and train multioutput regression models using linear regression, K-nearest neighbor regression, decision tree regression, and random forest regression. These techniques can predict the live locations of the required persons and machines with a high level of precision from the last beacon strengths received.

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Aydın, Yaren, Farnaz Ahadian, Gebrail Bekdaş und Sinan Melih Nigdeli. „Prediction of optimum design of welded beam design via machine learning“. Challenge Journal of Structural Mechanics 10, Nr. 3 (17.09.2024): 86. http://dx.doi.org/10.20528/cjsmec.2024.03.001.

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Design optimization is an important engineering design topic. One of the important issues in structural design is to minimize the cost. This study based on an engineering problem of Welded Beam Design aims to minimize the cost of the beam with machine learning (ML) models depending on the constraints on applied load, shear stress, bending stress and end deflection. The data set to be used in this context was created using a metaheuristic optimization algorithm. This hybrid algorithm is based on the classical Jaya algorithm by adding the student phase of Teaching Learning Based Optimization. The dataset obtained as a result of the optimization is a dataset with 1189 rows. Six different algorithms were used for prediction analyses. These are Linear Regression, Decision Tree, Elastic Net, K-Nearest Neighbour, Random Forest, and XGBoost algorithm. In the data set, load, length, and displacement are input; the design variables such as b, h, l, t and minimum cost are output. Since there is more than one output in the dataset, Multioutput Regression is applied. The performance of regression models was assessed using the Coefficient of Determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). According to the results obtained, the Decision Tree Model showed the best performance among the other models (R2=1, MAE=6.13e-11, RMSE=9.47e-10).

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Zhang, Lu, Junbiao Zhang, Tao Xiong und Chiao Su. „Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables“. Discrete Dynamics in Nature and Society 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/5730295.

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This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR) and particle swarm optimization (PSO), in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have strong influence on carbon futures prices, in modeling process is useful for achieving better prediction performance. Aiming at testing its effectiveness, we benchmark the forecasting performance of our approach against four competitors. The daily interval prices of carbon futures contracts traded in the Intercontinental Futures Exchange from August 12, 2010, to November 13, 2014, are used as the experiment dataset. The statistical significance of the interval forecasts is examined. The proposed hybrid approach is found to demonstrate the higher forecasting performance relative to all other competitors. Our application offers practitioners a promising set of results with interval forecasting in carbon futures market.

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Mashuri, M., H. Khusna, Wibawati und F. D. Putri. „Mixed Multivariate EWMA-CUSUM (MEC) Chart based on MLS-SVR Model for Monitoring Drinking Water Quality“. Journal of Physics: Conference Series 2123, Nr. 1 (01.11.2021): 012019. http://dx.doi.org/10.1088/1742-6596/2123/1/012019.

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Abstract Monitoring the quality of drinking water needs to be conducted considering the important role of water in human life. Mixed Multivariate EWMA-CUSUM (MEC) chart is a multivariate control chart developed for observing the mean process. Based on the previous studies, this chart has better performance in detecting a shift in the process mean. In this research, the MEC is applied to observe the grade of drinking water. However, there is autocorrelation in drinking water data which lead to more false alarm occurred. Therefore, the Multioutput Least Square Support Vector Regression (MLS-SVR) model is employed to reduce or even remove the autocorrelation in the data. Using the optimal hyperparameter, the MLS-SVR algorithm produces the residuals of phase I with no autocorrelation. Those residuals are then used to form the MEC control charts. When the MEC is used to monitor the residual in phase I, there is no signal of out-of-control found. Further, in phase II, out-of-control observations are detected. The MEC chart can detect more signals out of control compared to the conventional Hotelling’s T 2 and Multivariate Exponentially Moving Average (MEWMA) charts.

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Ahsan, M., und T. R. Aulia. „Comparing the Performance of Several Multivariate Control Charts Based on Residual of Multioutput Least Square SVR (MLS-SVR) Model in Monitoring Water Production Process“. Journal of Physics: Conference Series 2123, Nr. 1 (01.11.2021): 012018. http://dx.doi.org/10.1088/1742-6596/2123/1/012018.

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Abstract Water that is used as the basic human need, requires a processing process to get it. Water quality control in Tirtanadi Water Treatment Plant is still univariate, while theoretically the quality characteristics of water quality are correlated and there is also an autocorrelation due to the continuous process. In this study, quality control is performed on three main variables of water quality characteristics, namely acidity (pH), chlorine residual (ppm), and turbidity (NTU) using several multivariate control charts based on Multioutput Least Square Support Vector Regression (MLS-SVR) residuals. MLS-SVR modelling is used to overcome and get rid of autocorrelation. The input results of the MLS-SVR model are specified from the significant lag of the Partial Autocorrelation Function (PACF), which in this study, is the first lag. The results of the MLS-SVR input model and the optimal combination of hyper-parameters produce residual values that have no autocorrelation anymore. The residuals are used to develop the Hotelling’s T 2, Multivariate Exponentially Weighted Moving Average (MEWMA), and Multivariate Cumulative Sum (MCUSUM) control charts. In phase I, we found that the processes are statically controlled. Meanwhile, in phase II, the monitoring results show that there are several out-of-control observations.

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Fagin, Joshua, Ji Won Park, Henry Best, James H. H. Chan, K. E. Saavik Ford, Matthew J. Graham, V. Ashley Villar, Shirley Ho und Matthew O’Dowd. „Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties“. Astrophysical Journal 965, Nr. 2 (01.04.2024): 104. http://dx.doi.org/10.3847/1538-4357/ad2988.

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Abstract Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling.

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Chen, Zhiming. „Technology Focus: Formation Evaluation (August 2024)“. Journal of Petroleum Technology 76, Nr. 08 (01.08.2024): 52–53. http://dx.doi.org/10.2118/0824-0052-jpt.

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Formation evaluation stands as a cornerstone in determining the potential of a wellbore in the exploration, development, and production of oil and gas resources. Using a suite of tools such as well logging, core analysis, formation testing, and well-testing analysis, formation evaluation provides critical insights into reservoir properties. For instance, well-test analysis enables reservoir-parameter inversions, well-productivity evaluation, and reservoir-boundaries identification for both conventional and unconventional reservoirs. The inherent subjectivity of human interpretation, however, can introduce challenges, often resulting in nonunique solutions during certain formation evaluation processes. This subjectivity can lead to inefficiencies and inaccuracies in predictions or estimations. Fortunately, recent advancements in computing power and artificial-intelligence (AI) algorithms have catalyzed a paradigm shift within the field. Machine-learning techniques, renowned for their robust nonlinear regression capabilities, are being increasingly explored for formation evaluation, offering the potential for enhanced accuracy and efficiency. Consequently, the integration of AI into formation evaluation workflows has garnered significant attention. The papers featured here highlight the practical applications of AI in formation evaluation. In paper IPTC 23411, a case study combines rock typing and machine-learning neural-network techniques to predict permeability in heterogeneous carbonate formations in an Abu Dhabi offshore field. Shifting focus to southern Iraq, paper SPE 218890 evaluates various imputation techniques for predicting missing core permeability and porosity in a carbonate reservoir, demonstrating the adaptability of AI across diverse geological settings. Furthermore, a case study in paper IPTC 23381 from Northern Colombia demonstrates the efficacy of AI in determining electrical properties by integrating log data, rock types, facies, and digital core analyses. The integration of AI within the oil and gas industry is rapidly gaining momentum. With ongoing advancements, the future of formation evaluation promises transformative changes, leading to more-efficient and accurate reservoir characterization methodologies. Recommended additional reading at OnePetro: www.onepetro.org. SPE 218881 Advancing Reservoir Understanding: Integrating Deep Learning Into Automated Formation Microimaging Log Interpretation for Enhanced Reservoir Characterization and Management by Amr Gharieb, Khalda Petroleum, et al. SPE 218859 Formation Evaluation and Behind-Casing Opportunity Analysis Using Multioutput Regression and Machine-Learning Techniques by H. Hassani, RiseHill Energy Solution, et al. IPTC 23372 Decoding the Stratigraphic Heterogeneity of Bengal Basin, India, Using Supervised Machine Learning—A Case Study by Arijit Sahu, Oil and Natural Gas Corporation, et al.

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Jiang, Mingfeng, Feng Liu, Yaming Wang, Guofa Shou, Wenqing Huang und Huaxiong Zhang. „A Hybrid Model of Maximum Margin Clustering Method and Support Vector Regression for Noninvasive Electrocardiographic Imaging“. Computational and Mathematical Methods in Medicine 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/436281.

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Noninvasive electrocardiographic imaging, such as the reconstruction of myocardial transmembrane potentials (TMPs) distribution, can provide more detailed and complicated electrophysiological information than the body surface potentials (BSPs). However, the noninvasive reconstruction of the TMPs from BSPs is a typical inverse problem. In this study, this inverse ECG problem is treated as a regression problem with multi-inputs (BSPs) and multioutputs (TMPs), which will be solved by the Maximum Margin Clustering- (MMC-) Support Vector Regression (SVR) method. First, the MMC approach is adopted to cluster the training samples (a series of time instant BSPs), and the individual SVR model for each cluster is then constructed. For each testing sample, we find its matched cluster and then use the corresponding SVR model to reconstruct the TMPs. Using testing samples, it is found that the reconstructed TMPs results with the MMC-SVR method are more accurate than those of the single SVR method. In addition to the improved accuracy in solving the inverse ECG problem, the MMC-SVR method divides the training samples into clusters of small sample sizes, which can enhance the computation efficiency of training the SVR model.

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Yu, Hang, Jie Lu und Guangquan Zhang. „MORStreaming: A Multioutput Regression System for Streaming Data“. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 1–13. http://dx.doi.org/10.1109/tsmc.2021.3102978.

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Zhen, Xiantong, Mengyang Yu, Ali Islam, Mousumi Bhaduri, Ian Chan und Shuo Li. „Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression“. IEEE Transactions on Neural Networks and Learning Systems, 2016, 1–13. http://dx.doi.org/10.1109/tnnls.2016.2573260.

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Tan, Chao, Sheng Chen, Genlin Ji und Xin Geng. „Multilabel Distribution Learning Based on Multioutput Regression and Manifold Learning“. IEEE Transactions on Cybernetics, 2020, 1–15. http://dx.doi.org/10.1109/tcyb.2020.3026576.

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Liu, Tong, Sheng Chen, Kang Li, Shaojun Gan und Chris J. Harris. „Adaptive Multioutput Gradient RBF Tracker for Nonlinear and Nonstationary Regression“. IEEE Transactions on Cybernetics, 2023, 1–14. http://dx.doi.org/10.1109/tcyb.2023.3235155.

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Zhong, Huaiyang, Margaret L. Brandeau, Golnaz Eftekhari Yazdi, Jianing Wang, Shayla Nolen, Liesl Hagan, William W. Thompson, Sabrina A. Assoumou, Benjamin P. Linas und Joshua A. Salomon. „Metamodeling for Policy Simulations with Multivariate Outcomes“. Medical Decision Making, 23.06.2022, 0272989X2211050. http://dx.doi.org/10.1177/0272989x221105079.

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Purpose Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes. Methods: We combine 2 algorithm adaptation methods—multitarget stacking and regression chain with maximum correlation—with different base learners including linear regression (LR), elastic net (EE) with second-order terms, Gaussian process regression (GPR), random forests (RFs), and neural networks. We optimize integrated models using variable selection and hyperparameter tuning. We compare the accuracy, efficiency, and interpretability of different approaches. As an example application, we develop metamodels to emulate a microsimulation model of testing and treatment strategies for hepatitis C in correctional settings. Results Output variables from the simulation model were correlated (average ρ = 0.58). Without multioutput algorithm adaptation methods, in-sample fit (measured by R2) ranged from 0.881 for LR to 0.987 for GPR. The multioutput algorithm adaptation method increased R2 by an average 0.002 across base learners. Variable selection and hyperparameter tuning increased R2 by 0.009. Simpler models such as LR, EE, and RF required minimal training and prediction time. LR and EE had advantages in model interpretability, and we considered methods for improving the interpretability of other models. Conclusions In our example application, the choice of base learner had the largest impact on R2; multioutput algorithm adaptation and variable selection and hyperparameter tuning had a modest impact. Although advantages and disadvantages of specific learning algorithms may vary across different modeling applications, our framework for metamodeling in policy analyses with multivariate outcomes has broad applicability to decision analysis in health and medicine.

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Li, Ximing, Yang Wang, Zhao Zhang, Richang Hong, Zhuo Li und Meng Wang. „RMoR-Aion: Robust Multioutput Regression by Simultaneously Alleviating Input and Output Noises“. IEEE Transactions on Neural Networks and Learning Systems, 2020, 1–14. http://dx.doi.org/10.1109/tnnls.2020.2984635.

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Hua, Huichun, Jian Li, Donglin Fang und Min Guo. „An Adaptive Multioutput Fuzzy Gaussian Process Regression Method for Harmonic Source Modeling“. IEEE Transactions on Power Delivery, 2024, 1–11. http://dx.doi.org/10.1109/tpwrd.2024.3421609.

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Pretto, Tatiane, Fábio Baum, Rogério A. Gouvêa, Alexandre G. Brolo und Marcos J. Leite Santos. „Optimizing the Synthesis Parameters of Double Perovskites with Machine Learning Using a Multioutput Regression Model“. Journal of Physical Chemistry C, 18.04.2024. http://dx.doi.org/10.1021/acs.jpcc.3c06801.

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Nugroho, Waego Hadi, Samingun Handoyo, Hsing-Chuan Hsieh, Yusnita Julyarni Akri, Zuraidah - und Donna DwinitaAdelia. „Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation“. International Journal of Advanced Computer Science and Applications 13, Nr. 9 (2022). http://dx.doi.org/10.14569/ijacsa.2022.0130992.

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Mollick, Tajrian, Galib Hashmi und Saifur Rahman Sabuj. „A multifaceted journey in coastal meteorological projections through multioutput regression: a two-layer stacking ensemble approach“. Theoretical and Applied Climatology, 16.03.2024. http://dx.doi.org/10.1007/s00704-024-04923-9.

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Février, Stéphane, Lionel Mathelin, Stéphane Nachar, Frédéric Giordano und Bérengère Podvin. „Data-Driven Model to Predict Aircraft Vibration Environment“. AIAA Journal, 25.07.2023, 1–13. http://dx.doi.org/10.2514/1.j062735.

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Vibration levels that onboard equipment must be able to withstand throughout their lives for correct operation are mainly determined experimentally because predicting the dynamic behavior of a complete aircraft requires computational means and methods that are currently difficult to implement. We present a data-driven methodology that leverages flight-test accelerometer data to produce a predictive model. This model, based on an ensemble of artificial neural networks, performs a multioutput multivariate regression to estimate vibration spectra from a set of aircraft general parameters without having to characterize excitation sources. The model is compared with baseline models over two protocols, which are 1) standard training and testing as well as 2) extrapolation to high dynamic pressures, in order to assess physical consistency. Although the first protocol shows that all models can produce results accurate enough for this context, the second protocol shows that only the ensemble model is able to correctly extrapolate the energy. Using the Shapley additive explanations method, also known as SHAP, we show that these results can be explained by the ability of our model to identify the dynamic pressure as the core feature used in the extrapolation protocol. The proposed model can be used in multiple applications, such as anomaly detection and vibration flight envelope opening.

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Yunika, Annisa, und Muhammad Ahsan. „Pengendalian Kualitas Proses Produksi Hasil Gula Kristal Putih di PG Djatiroto PTPN XI Menggunakan Diagram Kontrol Maximum Multivariate Cumulative Sum (Max-MCUSUM) Berbasis Residual Model Multioutput Least Square Support Vector Regression (MLS-SVR)“. Jurnal Sains dan Seni ITS 12, Nr. 1 (01.05.2023). http://dx.doi.org/10.12962/j23373520.v12i1.101891.

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Lei, Chi-Un, Wincy Chan und Yuyue Wang. „Evaluation of UN SDG-related formal learning activities in a university common core curriculum“. International Journal of Sustainability in Higher Education, 11.12.2023. http://dx.doi.org/10.1108/ijshe-02-2023-0050.

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Purpose Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs. Design/methodology/approach In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions. Findings The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data. Research limitations/implications The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs. Originality/value The proposed approach explores the possibility of using machine learning for SDG classifications in scale.

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