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Forecasting public bicycle rental demand using an optimized eXtreme Gradient Boosting model

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期42卷 1783-1801页

作者： Hu, Yuanjiao Sun, Zhaoyun Li, Wei Pei, Lili Changan Univ Sch Informat Engn Xian 710064 Shaanxi Peoples R China

The rational distribution of public bicycle rental fleets is crucial for improving the efficiency of public bicycle programs. The accurate prediction of the demand for public bicycles is critical to improve bicycle utilization. To overcome the shortcomings of traditional algorithms such as low prediction accuracy and poor stability, using the 2011-2012 hourly bicycle rental data provided by the Washington City Bicycle Rental System, this study aims to develop an optimized and innovative public bicycle demand forecasting model based on grid search and eXtreme Gradient Boosting (XGBoost) algorithm. First, the feature ranking method based on machine learning models is used to analyze feature importance on the original data. In addition, a public bicycle demand forecast model is established based on important factors affecting bicycle utilization. Finally, to predict bicycle demand accurately, this study optimizes the model parameters through a grid search (GS) algorithm and builds a new prediction model based on the optimal parameters. The results show that the optimized XGBoost model based on the grid search algorithm can predict the bicycle demand more accurately than other models. The optimized model has an R-Squared of 0.947, and a root mean squared logarithmic error of 0.495. The results can be used for the effective management and reasonable dispatch of public bicycles.

关键词： Bicycle demand forecast feature importance grid search algorithm optimal parameters eXtreme Gradient Boosting

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Applicability comparison of different algorithms for ambient signal based load model parameter identification

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年 111卷 382-389页

作者： Wang, Ying Lu, Chao Zhang, Xinran Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

The time-varying characteristic of the power load can be tracked with the ambient signal based load model parameter identification method proposed recently. Meanwhile, the small fluctuations of the ambient signal bring higher requirement for optimization algorithm as well. The interior-point algorithm (IPA) and the activeset algorithm (ASA) are two typical traditional nonlinear constrained optimization algorithms, while the differential evolution algorithm (DEA) is an excellent heuristic search algorithm. Besides, the grid search algorithm (GSA) is a violent search algorithm, which is also an effective method considering the fact that the real load model parameters are unknown in practical power system. Based on the review of the basic idea of the four algorithms, the applicability of the four algorithms is comparatively assessed for ambient signal based load model parameter identification. Their performance is compared using ambient data from both simulation and field phasor measurement unit (PMU) in China Southern Power grid considering the identification accuracy, calculation time, and the robustness to measurement error and algorithm parameters. Some general conclusions are drawn from the analysis for several cases.

关键词： Ambient signal based load model parameter identification Active-set algorithm Differential evolution algorithm grid search algorithm Interior-point algorithm

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Application of a support vector machine for prediction of piping and internal stability of soils

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GEOMECHANICS AND ENGINEERING 2019年第5期18卷 493-502页

作者： Xue, Xinhua Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Sichuan Peoples R China

Internal stability is an important safety issue for levees, embankments, and other earthen structures. Since a large part of the world's population lives near oceans, lakes and rivers, floods resulting from breaching of dams can lead to devastating disasters with tremendous loss of life and property, especially in densely populated areas. There are some main factors that affect the internal stability of dams, levees and other earthen structures, such as the erodibility of the soil, the water velocity inside the soil mass and the geometry of the earthen structure, etc. Thus, the mechanism of internal erosion and stability of soils is very complicated and it is vital to investigate the assessment methods of internal stability of soils in embankment dams and their foundations. This paper presents an improved support vector machine (SVM) model to predict the internal stability of soils. The grid search algorithm (GSA) is employed to find the optimal parameters of SVM firstly, and then the cross - validation (CV) method is employed to estimate the classification accuracy of the GSA-SVM model. Two examples of internal stability of soils are presented to validate the predictive capability of the proposed GSA-SVM model. In addition to verify the effectiveness of the proposed GSA-SVM model, the predictions from the proposed GSA-SVM model were compared with those from the traditional back propagation neural network (BPNN) model. The results showed that the proposed GSA-SVM model is a feasible and efficient tool for assessing the internal stability of soils with high accuracy.

关键词： internal stability piping support vector machine grid search algorithm cross-validation

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Generalized Zadoff-Chu Sequences With Low PMEPR Property

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 1174-1178页

作者： Gu, Zhi Zhou, Zhengchun Adhikary, Avik Ranjan Fan, Pingzhi Yang, Yang Southwest Jiaotong Univ Sch Math Chengdu 611756 Peoples R China Southwest Jiaotong Univ Inst Mobile Commun Chengdu 611756 Peoples R China

In this letter, a general class of polyphase sequences called Generalized Zadoff-Chu (GZC) sequences is presented, which is based on a quadratic function with real-valued coefficients. The conventional ZC sequences, P3, and P4 codes are included as special cases of GZC sequences. It is shown that, with the help of grid search algorithm, the optimized GZC sequences have significantly lower PMEPR than the well-known sequences, such as Golay sequences, m-sequences, P3 and P4 codes, and ZC sequences. Numerical simulations suggest that the minimum PMEPR tends to a small constant of less than 1.43 dB as the sequence length approaches infinity.

关键词： grid search algorithm orthogonal frequency-division multiplexing (OFDM) peak-to-mean envelope power ratio (PMEPR) Zadoff-Chu sequence

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Prediction of the strength of concrete radiation shielding based on LS-SVM

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ANNALS OF NUCLEAR ENERGY 2015年 85卷 296-300页

作者： Xu Juncai Ren Qingwen Shen Zhenzhong Hohai Univ Coll Mech & Mat Nanjing 210098 Jiangsu Peoples R China

Radiation-shielding concrete (RSC) and conventional concrete differ in strength because of their distinct constituents. Predicting the strength of RSC with different constituents plays a vital role in radiation shielding (RS) engineering design. In this study, a model to predict the strength of RSC is established using a least squares-support vector machine (LS-SVM) through grid search algorithm. The algorithm is used to optimize the parameters of the LS-SVM on the basis of traditional prediction methods for conventional concrete. The predicted results of the LS-SVM model are compared with the experimental data. The results of the prediction are stable and consistent with the experimental results. In addition, the studied parameters exhibit significant effects on the simulation results. Therefore, the proposed method can be applied in predicting the strength of RSC, and the predicted results can be adopted as an important reference for RS engineering design. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Radiation-shielding concrete grid search algorithm LS-SVM Strength prediction

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Efficient computational approaches to obtain periodic orbits in Hamiltonian systems: application to the motion of a lunar orbiter

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CELESTIAL MECHANICS & DYNAMICAL ASTRONOMY 2016年第1期124卷 51-71页

作者： Dena, Angeles Abad, Alberto Barrio, Roberto Ctr Univ Def Zaragoza Zaragoza Spain Univ Zaragoza Grp Mecan Espacial Zaragoza Spain Univ Zaragoza Dept Matemat Aplicada Zaragoza Spain

In this paper, we study the problem of computing periodic orbits of Hamiltonian systems providing large families of such orbits. Periodic orbits constitute one of the most important invariants of a system, and this paper provides a comprehensive analysis of two efficient computational approaches for Hamiltonian systems. First, a new version of the grid search method, applied to problems with three degrees of freedom, has been considered to find, systematically, symmetric periodic orbits. To obtain non-symmetric periodic orbits, we use a modification of an optimization method based on an evolutionary strategy. Both methods require a great computational effort to find a big number of periodic orbits, and we apply parallelization tools to reduce the CPU time. Finally, we present a strategy to provide initial conditions of the periodic orbits with arbitrary precision. We apply all these algorithms to the problem of the motion of the lunar orbiter referred to the rotating reference frame of the Moon. The periodic orbits of this problem are very useful from the space engineering point of view because they provide low-cost orbits.

关键词： Lunar satellite orbits Periodic orbits (p. o.) Symmetric periodic orbits (s. p. o) Parallelization algorithms grid search algorithm Evolutionary strategy

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A bilevel programming approach to determining tax credits for biofuel production

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EUROPEAN JOURNAL OF OPERATIONAL REsearch 2000年第1期120卷 30-46页

作者： Bard, JF Plummer, J Sourie, JC Univ Texas Dept Mech Engn Grad Program Operat Res Austin TX 78712 USA Univ Texas Coll Business Adm Dept Management Sci & Informat Syst Austin TX 78712 USA INRA Ctr Grignon Dept Econ & Sociol Rurales F-78850 Thiverval Grignon France

This paper presents a bilevel programming formulation of a leader-follower game that can be used to help decision makers arrive at a rational policy for encouraging biofuel production. Zn the model, the government is the leader and would like to minimize the annual tax credits it allows the petro-chemical industry for producing biofuels. The crops grown for this purpose are on land now set aside and subsidized through a different support program. The agricultural sector is the follower. Its objective is to maximize profits by selecting the best mix of crops to grow as well as the percentage of land to set aside. Two solution algorithms are developed. The first involves a grid search over the tax credit variables corresponding to the two biofuels under consideration, ester and ethanol. Once these values are fixed, nonfood crop prices can be determined and the farm sector linear program solved. The second algorithm is based on an approximate nonlinear programming (NLP) formulation of the bilevel program. An "engineering" approach is taken where the discontinuities in the government's problem are ignored and the farm model is treated as a function that maps nonfood crop prices into allocation decisions. Results are given for an agricultural region in the northern part of France comprising 393 farms. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： nonlinear bilevel programming government regulation subsidies grid search algorithm agriculture

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Intelligent multiple fault diagnosis for predictive maintenance of induction motor 10

Intelligent multiple fault diagnosis for predictive maintena...

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10th IEEE Power India International Conference (PIICON)

作者： Jigyasu, Rajvardhan Shrivastava, Vivek Singh, Sachin Natl Inst Technol Dept Elect Engn Delhi India

ISBN: (纸本)9781665459303

This work proposes a machine learning-based bearing and rotor fault detection technique with an improved feature set and processing time. Predictive maintenance uses intelligent artificial learning techniques, which consist of problems such as high predictive time due to a massive amount of data and improper classifier parameters, which lead to less reliable and lower accuracy fault detection intelligent models. Both problems can lead to huge losses because of the high false positive results. The problems of massive data and more extended detection periods have been solved using the Univariate Feature Selection technique. The improper classifier parameter problem is solved using the grid search optimization technique. In this work, two majorly occurring faults, the broken rotor bar (BRB) and Bearing faults, have been considered. The acquired current signal for different conditions, such as healthy, bearing inner cage fault, bearing outer cage fault, one broken rotor bar, and three broken rotor bars, is divided using the Average Frequency Band Decomposition (AFBD) approach. An experimentally acquired data set is used to validate the proposed methodology. The data set is provided for training and testing the Support Vector Machine (SVM) classifier, whose parameters include 'regularization factor (C), ''gamma,' 'kernel,' and 'decision function shape' are tuned using grid search algorithm. A resulting model with high accuracy of 99.7% was obtained with a 235 ms drop in detection time.

关键词： Fault Diagnosis SVM Feature Selection grid search algorithm Induction Motor Rotor faults Bearing Fault

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The Study of Characteristics of the Hybrid Particle Swarm algorithm in Solution of the Global Optimization Problem 5

The Study of Characteristics of the Hybrid Particle Swarm Al...

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5th Mediterranean Conference on Embedded Computing (MECO)

作者： Demidova, Liliya Klyueva, Irina Pylkin, Alexander Ryazan State Radio Engn Univ Ryazan Russia

ISBN: (纸本)9781509022212

The present paper considers convergence characteristics of the particle swarm algorithm and its modification - the hybrid PSO-GS algorithm obtained under combination of the PSO algorithm and grid search algorithm. Comparison of quality indices of the particle swarm algorithm and the steepest descent algorithm has been carried out for evaluation of advantages of the PSO algorithm in comparison with classical optimization algorithms.

关键词： particle swarm optimization algorithm grid search algorithm hybridization solution of problems of the global unconstrained optimization

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Face Recognition algorithm Based on Weighted Intensity PCNN 8

Face Recognition Algorithm Based on Weighted Intensity PCNN

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8th International Conference on Advanced Cloud and Big Data (CBD)

作者： Deng, Hongxia Feng, Zijian Liu, Ying Luo, Dongsheng Yang, Xiaofeng Li, Haifang Taiyuan Univ Technol Coll Informat & Comp Taiyuan Peoples R China

ISBN: (纸本)9781665423137

Face recognition is an important identification technology in daily life and one of the research hotspots in the field of computer vision and intelligent image processing. However, many algorithms based on Pulse Coupled Neural Network (PCNN) do not adequately describe features, same parameters are used to process the entire face image, ignoring the difference between the local recognition rate and gray distribution of each part of the face image. This paper proposes a block face recognition algorithm based on the weighted intensity PCNN model, we have refined the description of the features through parameter adaptation, partitioned the face according to the degree of contribution in the recognition process, and set the corresponding weight parameters through the improved grid search algorithm, which can effectively improve the PCNN model's ability to recognize faces. Experiments show that in different face databases, the performance of this model is greatly improved compared with common methods such as PCA and ICA.

关键词： face recognition PCNN weighted intensity grid search algorithm

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