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Energy consumption prediction method of energy saving building based on deep reinforcement learning

INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2022年第5-6期44卷 524-536页

作者： He, Chuan Xiong, Ying Lin, Yeda Yu, Lie Xiong, Hui-Hua Changsha Univ Sci Technol Sch Architecture Changsha 410114 Hunan Peoples R China Changsha Univ Sci Technol Sch Hydraul Engn Changsha 410114 Hunan Peoples R China Hunan City Univ Sch Architecture & Urban planning Yiyang 413000 Hunan Peoples R China

In order to overcome the problems of low-prediction accuracy and long prediction time of traditional building energy consumption prediction methods, this paper proposes a new energy-saving building energy consumption prediction method based on deep reinforcement learning. Through the deep reinforcement learning algorithm, a number of energy consumption behaviour return information of specific value network and strategy network are calculated, respectively to build the energy consumption probability model of energy-saving building energy consumption equipment. The linear rectification function with leakage is used to update the probability model and parameters, and the linear relationship prediction function of energy consumption parameters is constructed by using the learning process and results to complete the dynamic prediction of energy consumption of energy-saving buildings. The experimental results show that the proposed method has fast prediction speed and high accuracy, which can provide reference for the implementation of energy-saving building.

关键词： deep intensive learning energy saving building energy consumption forecasting time series multivariate linear regression

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A Sharp Analysis of Covariate Adjusted Precision Matrix Estimation via Alternating Projected Gradient Descent

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IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 877-881页

作者： Lv, Xiao Cui, Wei Liu, Yulong Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Sch Phys Beijing 100081 Peoples R China

In this letter, we present a sharp algorithmic analysis for alternating projected gradient descent which is used to solve the covariate adjusted precision matrix estimation problem in high-dimensional settings. By introducing a new analytical tool (the generic chaining), we remove the impractical resampling assumption used in the literature. The new analysis also demonstrates that this algorithm not only enjoys a linear convergence rate in the absence of convexity, but also attains the minimax rate with optimal order of sample complexity. Our results, meanwhile, reveal a time-data tradeoff in this problem. Numerical experiments are provided to verify our theoretical results.

关键词： Signal processing algorithms Convergence Estimation Complexity theory Estimation error linear regression Prediction algorithms multivariate linear regression covariate adjusted precision matrix estimation alternating gradient descent

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Comparing Halogen Atom Abstraction Kinetics for Mn(I), Fe(I), Co(I), and Ni(I) Complexes by Combining Electroanalytical and Statistical Modeling

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EUROPEAN JOURNAL OF ORGANIC CHEMISTRY 2022年第14期2022卷 27-33页

作者： Tang, Tianhua Friede, Nathan C. Minteer, Shelley D. Sigman, Matthew S. Univ Utah Dept Chem 315 South 1400 East Salt Lake City UT 84112 USA

First-row transition metal complexes have garnered attention due to their ability to activate aliphatic halides for catalytic cross-coupling reactions. However, mechanistic investigation of these systems is challenging as a consequence of difficulties associated with preparing the relevant metal complexes and the resultant poor stability of such intermediates for subsequent analytical interrogation. In this context, we have developed a platform to rapidly measure kinetic data using electroanalytical methods, which facilitates a wide range of physical organic studies. As a result, we have investigated and compared the reaction of benzyllic halides with electrogenerated Mn-I(PyBox)Cl, Fe-I(Pyrox)OTf, Co-I(Pyrox)Br, Co-I(PyBox)Br, Ni-I(Phox)Br complexes. The experimental results support a unified inner-sphere halogen atom abstraction mechanism for these different complexes while also providing the ability to directly compare through multivariate linear regression analyses the subtle differences imparted by metal/ligand combinations. The information gleaned by this study has implications why certain metal complexes are matched to oxidative addition processes relevant in catalytic processes.

关键词： Electrochemistry multivariate linear regression Oxidative addition

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Development of a Machine Learning Algorithm for Drug Screening Analysis on High-Resolution UPLC-MS^E/QTOF Mass Spectrometry

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JOURNAL OF APPLIED LABORATORY MEDICINE 2023年第1期8卷 53-66页

作者： Hao, Ying Lynch, Kara Fan, Pengcheng Jurtschenko, Christopher Cid, Maria Zhao, Zhen Yang, He S. Dept Pathol Lab Med Weill Cornell Med New York NY USA Univ Calif San Francisco Dept Pathol Lab Med San Francisco CA USA Prote & Metabol Core Lab Weill Cornell Med New York NY USA Waters Corp Milford MA USA New York Presbyterian Hosp Weill Cornell Med Ctr Toxicol & Therapeut Drug Monitoring Lab New York NY USA Dept Pathol Lab Med Weill Cornell Med 525 E 68th St F707 New York NY 10065 USA

Background Ultra-performance liquid chromatography (UPLC)-MSE/quadrupole time-of-flight (QTOF) high-resolution mass spectrometry employs untargeted, data-independent acquisition in a dual mode that simultaneously collects precursor ions and product ions at low and ramped collision energies, respectively. However, algorithmic analysis of large-scale multivariate data of comprehensive drug screening as well as the positivity criteria of drug identification have not been systematically investigated. It is also unclear whether ion ratio (IR), the intensity ratio of a defined product ion divided by the precursor ion, is a stable parameter that can be incorporated into the MSE/QTOF data analysis algorithm. Methods IR of 91 drugs were experimentally determined and variation of IR was investigated across 5 concentrations measured on 3 different days. A data-driven machine learning approach was employed to develop multivariate linear regression (MLR) models incorporating mass error, retention time, number of detected fragment ions and IR, accuracy of isotope abundance, and peak response using drug-supplemented urine samples. Performance of the models was evaluated in an independent data set of unknown clinical urine samples in comparison with the results of manual analysis. Results IR of most compounds acquired by MSE/QTOF were low and concentration-dependent (i.e., IR increased at higher concentrations). We developed an MLR model with composite score outputs incorporating 7 parameters to predict positive drug identification. The model achieved a mean accuracy of 89.38% in the validation set and 87.92% agreement in the test set. Conclusions The MLR model incorporating all contributing parameters can serve as a decision-support tool to facilitate objective drug identification using UPLC-MSE/QTOF.

关键词： MSE-QTOF comprehensive drug screening machine learning multivariate linear regression

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Variable selection and collinearity processing for multivariate data via row-elastic-net regularization

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ASTA-ADVANCES IN STATISTICAL ANALYSIS 2022年第1期106卷 79-96页

作者： Chen, Bingzhen Zhai, Wenjuan Kong, Lingchen Renmin Univ China Inst Stat & Big Data 59 Zhongguancun Ave Beijing 100872 Peoples R China Cangzhou Jiaotong Coll Dept Appl Math Cangzhou 061100 Peoples R China Beijing Jiaotong Univ Dept Appl Math Beijing 100044 Peoples R China

multivariate data is collected in many fields, such as chemometrics, econometrics, financial engineering and genetics. In multivariate data, heteroscedasticity and collinearity occur frequently. And selecting material predictors is also a key issue when analyzing multivariate data. To accomplish these tasks, multivariate linear regression model is often constructed. We thus propose row-sparse elastic-net regularized multivariate Huber regression model in this paper. For this new model, we proof its grouping effect property and the property of resisting sample outliers. Based on the KKT condition, an accelerated proximal sub-gradient algorithm is designed to solve the proposed model and its convergency is also established. To demonstrate the accuracy and efficiency, simulation and real data experiments are carried out. The numerical results show that the new model can deal with heteroscedasticity and collinearity well.

关键词： Robust estimator multivariate linear regression Collinearity Row-sparsity Sub-gradient algorithm

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A fully Bayesian approach to sparse reduced-rank multivariate regression

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STATISTICAL MODELLING 2022年第3期22卷 199-220页

作者： Yang, Dunfu Goh, Gyuhyeong Wang, Haiyan Kansas State Univ Dept Stat 101 Dickens Hall1116 Midcampus Dr N Manhattan KS 66506 USA

In the context of high-dimensional multivariate linear regression, sparse reduced-rank regression (SRRR) provides a way to handle both variable selection and low-rank estimation problems. Although there has been extensive research on SRRR, statistical inference procedures that deal with the uncertainty due to variable selection and rank reduction are still limited. To fill this research gap, we develop a fully Bayesian approach to SRRR. A major difficulty that occurs in a fully Bayesian framework is that the dimension of parameter space varies with the selected variables and the reduced-rank. Due to the varying-dimensional problems, traditional Markov chain Monte Carlo (MCMC) methods such as Gibbs sampler and Metropolis-Hastings algorithm are inapplicable in our Bayesian framework. To address this issue, we propose a new posterior computation procedure based on the Laplace approximation within the collapsed Gibbs sampler. A key feature of our fully Bayesian method is that the model uncertainty is automatically integrated out by the proposed MCMC computation. The proposed method is examined via simulation study and real data analysis.

关键词： bayesian reduced-rank regression fully Bayesian inference high-dimensional variable selection low-rank matrix estimation multivariate linear regression

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Co-occurrence of organophosphate esters and phosphorus fractions in river sediments: Implications for pollution prediction and environment risk assessment

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JOURNAL OF HAZARDOUS MATERIALS 2024年 465卷 133262页

作者： Fu, Lingfang Liu, Yuxin Lin, Shu Xiao, Jieer Li, Weijie Yu, Yang Zeng, Hailong Li, Ping Fang, Huaiyang Minist Ecol & Environm South China Inst Environm Sci Natl Key Lab Water Environm Simulat & Pollut Contr Guangdong Key Lab Water & Air Pollut Control Guangzhou 510655 Peoples R China Guangdong Univ Technol Sch Environm Sci & Engn Guangzhou Key Lab Environm Catalysis & Pollut Cont Guangdong Key Lab Environm Catalysis & Hlth Risk C Guangzhou 510006 Peoples R China

Organophosphate esters (OPEs) and phosphorus (P) are widespread pollutants in aquatic ecosystems, presenting potential ecological risks. However, there is still a lack of comprehensive understanding of their relationships in sediments. In this study, we investigated the co-occurrence and behaviors of the OPEs and P in urban river sediments. The results indicated serious OPE and P pollution in the study area, with substantial spatial variations in the contents and compositions. The OPE congeners and P fractions exhibited different correlations, particularly more significant linear relationships (R = 0.455 - 0.816, p < 0.05) were observed between the aryl-OPEs and P fractions, potentially due to the influence from sources, physicochemical properties, and total organic carbon. About 56 to 71% of variability in predicting the concentrations of aryl-OPE can be explained by the multiple linear regression model using the Fe/Al- and Ca-bound P contents. The study regions exhibited greater aryl-OPEs ecological risks were consistent with the regions with more serious Total P pollution levels. This study represents the first report demonstrating the potential of Fe/Al-P and Ca-P contents in predicting aryl-OPE contents in heavily polluted sediments, providing a useful reference to comprehensively assess the occurrence and environmental behaviors of aryl-OPEs in anthropogenic polluted sediments.

关键词： Organophosphate esters Phosphorus fractions Sediments Ecological risk assessment multivariate linear regression

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Housing satisfaction and migrant health in urban China: empirical evidence and implications for housing policy

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JOURNAL OF HOUSING AND THE BUILT ENVIRONMENT 2022年第3期37卷 1199-1220页

作者： You, Heyuan Guo, Xuxu Wu, Xin Wu, Mengshuang Zhejiang Univ Finance & Econ Sch Publ Adm Hangzhou Peoples R China Univ N Carolina Dept City & Reg Planning Chapel Hill NC 27515 USA

Substandard housing is considered a public health issue worldwide. Migrants are more likely than other individuals to experience poor housing conditions in urban China, and these challenges in housing conditions have deep effects on migrant health. This study characterizes the relationship between housing satisfaction and migrant health in Hangzhou, China. In particular, 17 indicators for assessing housing satisfaction, which include the indoor environment (IE), outdoor environment (OE) and safety (SE), are integrated. In order to reveal the physical health and mental health status of migrants, a physical component summary (PCS) and mental component summary (MCS) were assessed by using a Twelve-Item Short-Form Health Survey. multivariate linear regression was further employed to quantify the associations between housing satisfaction and migrant health. The results showed that high satisfaction with the indoor environment, outdoor environment and safety can improve the physical health of migrants. High housing satisfaction, in addition to satisfaction with the outdoor environment, has a significant relationship with the mental health of migrants. Income is associated with migrant health since it plays a key role in determining migrants' socioeconomic and demographic characteristics and subjective aspirations. However, per capita living space does not have a significant association with migrant health because most of the migrants live in small living spaces in the study area. According to the findings, implications for housing policies that can better integrate housing conditions and health are proposed.

关键词： Housing satisfaction Migrant health multivariate linear regression Urban area China

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Research on the Coupling Relationship between Teaching Style and Classroom Management Based on Grey Correlation Analysis and multivariate linear regression Method

Research on the Coupling Relationship between Teaching Style...

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2013 2nd International Conference on Management Science and Industrial Engineering(MSIE 2013)

作者： Lihua Yang Dongying Vocational College

ISBN: (纸本)9781629939919

With the reform and the deepening of education modernization, the traditional teacher- centered classroom management methods can not meet the current needs of teaching, teachers classroom management concept and teaching style occur rapid change with students learning style and personality characteristics, this paper used grey correlative degree analysis and multivariate linear regression method to analyze teaching style and orientation of each dimension of classroom management methods, it is concluded that the matching degree of corresponding characteristics of teachers’ teaching style and students can effect teacher’s teaching performance and teaching satisfaction and students’ learning achievement, and the students’ evaluation on the teachers. Through the research it tries to provide theoretical basis and explore ways to education workers in making accurate technique and style to manage the teaching.

关键词： Variance analysis Behavior sequence Gray correlation analysis multivariate linear regression Matching degree Precise technique

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Dual-stage color calibration of UAV imagery using multivariate regression and deep learning

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 224卷

作者： Abdalla, Alwaseela Karn, Rupak Adedeji, Oluwatola Guo, Wenxuan Texas Tech Univ Dept Plant & Soil Sci Lubbock TX 79409 USA Texas A&M AgriLife Res Dept Soil & Crop Sci Lubbock TX 79403 USA

Accurate color representation in UAV-based imagery is critical in high-throughput plant phenotyping and precision agriculture for monitoring crop health and identifying nutrient deficiencies or water stress. However, achieving color accuracy is challenging due to varying lighting conditions and flight altitudes in UAV operations. To overcome these challenges, we propose an automated dual-phase approach for consistent color calibration in UAV imagery, integrating traditional color calibration methods with cutting-edge deep learning algorithms. In the first phase, we derived a color calibration matrix (M) for four color boards placed within the imaging area. This involved mapping the observed color values in the UAV images to their true color values, as measured by a colorimeter, using multivariate linear regression (MLR). The individual matrices from each board were then merged into a single, weighted average matrix, which was then applied to the UAV images for color correction, resulting in standardized color outputs. In the second phase, we developed a deep learning model, trained on the MLR-corrected images as a ground truth. This model assigns varying weights for different pixels across the image scene, translating the image from a distorted to a color-corrected image using image-to-image regression without manual intervention. We validated this approach using 184 UAV images of various sizes, ranging from 3969 x 2589 to 15257 x 15098 pixels, captured under a wide array of lighting conditions and altitudes. The results show that our deep learning model significantly outperforms existing methods, with a marginal color error of 1.52 %. This two-phase color correction method greatly improves operational efficiency by removing the necessity for physical color boards in each image and allowing for automated processing after training.

关键词： color calibration deep learning multivariate linear regression image-to-image regression residual image.

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