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Optimization for Learning and Control 1

2023年

作者： Martin Andersen Anders Hansson

ISBN: (数字)9781119809173;9781119809142

ISBN: (纸本)9781119809135

Optimization for Learning and Control Comprehensive resource providing a masters’ level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems. Several applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. Today, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters’ level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters’ students in a coherent way. The focus is on basic algorithmic principles and trade-offs. Optimization for Learning and Control covers sample topics such as:

Optimization theory and optimization methods, covering classes of optimization problems like least squares problems, quadratic problems, conic optimization problems and rank optimization.
First-order methods, second-order methods, variable metric methods, and methods for nonlinear least squares problems.
Stochastic optimization methods, augmented Lagrangian methods, interior-point methods, and conic optimization methods.
Dynamic programming for solving optimal control problems and its generalization to reinforcement learning.
How optimization theory is used to develop theory and tools of statistics and learning, e.g., the maximum likelihood method, expectation maximization, k-means clustering, and support vector machines.
How calculus of variatio

关键词： Optimization machine learning statistics linear algebra artificial neural networks entropy probability theory signal processing system identification mathematical programming k-means clustering algorithm support vector machine reinforcement learning optimal control dynamic programming

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The FRCk clustering algorithm for determining cluster number and removing outliers automatically

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2021年第5期24卷 485-494页

作者： Guo, Yubin Wu, Yuhang Zhang, Xiaopeng Bo, Aofeng Li, Ximing South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China South China Agr Univ Coll Software Engn Guangzhou 510642 Peoples R China South China Agr Univ Informat & Network Serv Ctr Guangzhou 510642 Peoples R China Guangzhou HolandAI Technol Co Ltd Guangzhou 510006 Peoples R China

clustering algorithm is one of the most popular unsupervised algorithms for data grouping. The k-means algorithm is a popular clustering algorithm for its simplicity, ease of implementation and efficiency. But for k-means algorithm, the optical cluster number is difficult to predict, while it is sensitive to outliers. In this paper, we divide outliers into two types, and then prompt a clustering algorithm to remove the two-type outliers and calculate the optimal cluster number in each clustering iteration. The algorithm is a fusion of rough clustering and k-means, abbreviated as FRCk algorithm. In the FRCk algorithm, outliers are removed precisely, therefore the optical cluster number can be more accurate, and the quality of clustering result can be improved accordingly. And this algorithm is proven effective by experiment.

关键词： clustering k-means clustering algorithm optical cluster number outlier

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Robust PVC Identification by Fusing Expert System and Deep Learning

BIOSENSORS-BASEL

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BIOSENSORS-BASEL 2022年第4期12卷 185页

作者： Cai, Zhipeng Wang, Tiantian Shen, Yumin Xing, Yantao Yan, Ruqiang Li, Jianqing Liu, Chengyu Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 714009 Peoples R China

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for k-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively;P+ was 92.47% and 93.18%, respectively;and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

关键词： electrocardiogram k-means clustering algorithm premature ventricular contraction rule-based decision algorithm

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Analyzing the Urban Hierarchical Structure Based on Multiple Indicators of Economy and Industry: An Econometric Study in China

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1831-1855页

作者： Jing Cheng Yang Xie Jie Zhang School of Architecture Tsinghua UniversityBeijing100084China Beijing Tsinghua Tongheng Urban Planning&Design Institute Beijing100085China

For a city,analyzing its advantages,disadvantages and the level of economic development in a country is important,especially for the cities in China developing at flying *** corresponding literatures for the cities in China have not considered the indicators of economy and industry in *** this paper,based on multiple indicators of economy and industry,the urban hierarchical structure of 285 cities above the prefecture level in China is *** indicators from the economy,industry,infrastructure,medical care,population,education,culture,and employment levels are selected to establish a new indicator system for analyzing urban hierarchical *** factor analysis method is used to investigate the relationship between the variables of selected indicators and obtain the score of each common factor and comprehensive scores and rankings for 285 cities above the prefecture level in *** to the comprehensive scores,285 cities above the prefecture level are clustered into 15 levels by using k-means clustering ***,the hierarchical structure system of the cities above the prefecture level in China is obtained and corresponding policy implications are *** results and implications can not only be applied to the urban planning and development in China but also offer a reference on other developing *** methodologies used in this paper can also be applied to study the urban hierarchical structure in other countries.

关键词： Urban planning hierarchical structure prefecture-level city factor analysis method k-means clustering algorithm China

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Democracy, military expenditure and economic growth: A heterogeneous perspective

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DEFENCE AND PEACE ECONOMICS 2023年第8期34卷 1039-1070页

作者： Wang, Xinyi Hou, Na Chen, Bo Cent Univ Finance & Econ Inst Def Econ & Management Beijing Peoples R China

Countries with different democratic levels tend to show various degrees of emphasis on military expenditure and a consensus can hardly be reached on the economic effect of democracy and military expenditure. By applying a panel vector autoregressive (PVAR) approach, this article examines the potential dynamic and endogenous relationships among democracy, military expenditure and economic growth of 126 countries from 1990 to 2020. Furthermore, the k-means clustering algorithm is employed to account for the heterogeneous interaction between democracy and military expenditure in different countries. The empirical results reveal that the strong positive impact of democracy and the negative effect of military spending on economic growth exists in the full sample and the cluster of countries with low democratic levels and a high military burden. For democratic countries with low military expenditure, there is a more significant and negative impact both of military expenditure on democracy and vice versa.

关键词： Democracy military expenditure economic growth panel vector autoregression k-means clustering algorithm

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A Cluster Analysis of Power Load Forecasting Based on Power Consumption Data and Meteorological Data 4

A Cluster Analysis of Power Load Forecasting Based on Power ...

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4th International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2024

作者： Wei, Yi Tang, Zhong-Sheng College of Intelligent Networking and New Energy Vehicles Geely University of China Sichuan Province Chengdu City China

ISBN: (纸本)9798350378542

With the increasing of the big data volume for power consumption data and the complexity of meteorological data, it has become more difficult to cope with the power load forecasting in the recent energy crisis. To address this issue, we analyze the power consumption data and meteorological data in this paper, which would be applied in developing a more effective power load management for the intelligent power systems. We develop a hybrid day-ahead load forecasting models based on k-means clustering algorithm well-known neural networks Long Short-Term Memory (LSTM)). To study the impact of meteorological factors on power load forecasting, a cluster analysis is also conducted in this work to classify temperature data and rainfall data, which aims to mine the relationship between power consumption data and meteorological data. In the experiments on the public data sets, the performance evaluation of the proposed method is compared with the commonly used algorithms for power load forecasting. In the numerical results, it is shown that the proposed method performs higher accuracy and applicability compared with the traditional methods. © 2024 IEEE.

关键词： k-means clustering algorithm load forecasting Neural network Smart grid

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Adaptive POD surrogate model method for centrifugal pump impeller flow field reconstruction based on clustering algorithm

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MODERN PHYSICS LETTERS B 2021年第7期35卷

作者： Chen, Xuebing Zhang, Renhui Jiang, Lijie Yang, Weifeng Lanzhou Univ Technol Coll Energy & Power Engn Lanzhou 730050 Gansu Peoples R China Key Lab Fluid Machinery & Syst Lanzhou 730050 Gansu Peoples R China

To reduce the calculation cost and improve the accuracy of flow field prediction, an adaptive proper orthogonal decomposition (APOD) surrogate model based on k-means clustering algorithm was proposed to reconstruct the flow field of impeller. The experiment samples were designed by introducing the perturbation of the blade control parameters such as blade wrap angle and blade angle of outlet. k-means clustering algorithm was used to classify the sample blade shapes, and find out the cluster of the objective blade. The snapshot set, which consisted of the blade shape and the flow field data of impeller, can be described as a linear combination of orthogonal basis by POD method. The radial basis function (RBF) was used to fit the orthogonal basis coefficients of the objective blade, and then the flow field of objective impeller was reconstructed. The traditional fixed sample POD (FPOD) method and the proposed APOD method were used to reconstruct the flow field in impeller, respectively, and the prediction results of the two methods were compared and analyzed. The results show that the proposed APOD method could quickly and accurately reconstruct the objective flow field. The flow field prediction accuracy of the APOD method is significantly higher than the FPOD method, and the calculation time for the flow field prediction is less than 1/360 of the CFD.

关键词： Centrifugal pump impeller surrogate model proper orthogonal decomposition adaptive k-means clustering algorithm

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Study of Seepage Properties of Fractured Rock Mass Based on Improved k-means clustering algorithm

Study of Seepage Properties of Fractured Rock Mass Based on ...

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2nd International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2013)

作者： Guo, Haiqing Wen, Bo Bai, Xiaofeng Hohai Univ Minist Educ Geomech & Embankment Engn Key Lab Nanjing 210098 Jiangsu Peoples R China

ISBN: (纸本)9783037858578

Seepage properties of fractured rock mass are of prime importance for hydiaulic engineering and accurate description of rock fracture geometry parameters is an important and basic task in rock hydraulics. In this paper, an improved k-means clustering algorithm for structural plane of fractured rock mass was first brought forward and the corresponding Matlab program for discontinuity orientations partitioning was compiled and then used in the fitting analysis of dominant orientations of certain dam foundation rock mass. On this basis, combining calculation formulas of multi-group fractures, the permeability tensor and principle value was calculated for the actual dam foundation. The results provide a theoretical and computational reference for other similar projects.

关键词： k-means clustering algorithm discontinuities dominant orientation permeability tensor

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Increasing flexibility of self-piercing riveting by reducing tool-geometry combinations using cluster analysis in the application of multi-material design

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS 2022年第6期236卷 1310-1320页

作者： kappe, Fabian Schadow, Luca Bobbert, Mathias Meschut, Gerson Paderborn Univ Lab Mat & Joining Technol LWF D-33098 Paderborn Germany

A frequently used mechanical joining process that enables the joining of dissimilar materials is self-piercing riveting. Nevertheless, the increasing number of materials as well as material-thickness combinations leads to the need for a large number of rivet-die combinations as the rigid tool systems are not able to react to changing boundary conditions. Therefore, tool changes or system conversions are needed, resulting in longer process times and inflexibility of the joining processes. In this investigation, the flexibility of the self-piercing riveting process by reducing the required tool-geometry combinations is examined. For this purpose, various joints consisting of similar as well as dissimilar materials with different material thickness are sampled and analysed. Subsequently, a cluster algorithm is used to reduce the number of rivet-die combinations required. Finally, the effect of the changed tool geometries on both the joint formation and the joint load-bearing capacity is investigated. The investigation showed that a reduction by 55% of the required rivet-die combinations was possible. In particular, the rivet length influences the joint formation and the joint load-bearing capacity. An exclusive change of the die (e.g. die depth or die diameter) did not show a significant influence on these parameters.

关键词： Self-piercing riveting flexibility cluster analysis k-means clustering algorithm

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Software Technology Immersive Education Research and Practice Based on Spark Platform 3

Software Technology Immersive Education Research and Practic...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Genrang, Zheng Chunping, Huang Zhongshan Polytechnic Department of Information Engineering Guangdong Zhongshan China

ISBN: (纸本)9798350380989

From the perspective of education informatization, a teaching resource sharing system with huge storage space and computation amount is established. When the user accesses the teaching materials, it constructs the database with a large number of users through instant access to the data. This project intends to study an optimization scheme that does not require any intermediary output based on Hadoop and combined with Spark's parallel computing mode, so that it can make better use of Spark's iterative computing capability. It realizes the efficient recommendation of teaching resources. A hierarchical resource model based on collaborative filtering is proposed. The unrated resources predicted in this way are imprecise. A time function is added to the prediction process for non-rated resources to reflect the change in user interest over time. The index function is used to describe the change of time. The practicability of the Spark-based in the recommendation of teaching resources are verified by the research. © 2024 IEEE.

关键词： collaborative filtering Educational resources recommendation k-means clustering algorithm recommendation system spark

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