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Optimization of Seasonal Geographically and Temporally Weighted Regression Model for Accurate Estimation of Seasonal PM2.5 Concentrations in Beijing-Tianjin-Hebei Region

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SENSORS AND MATERIALS 2020年第12期32卷 4393-4412页

作者： Zhou, Lei Wang, Yani Du, Mingyi Jing, Changfeng Wang, Siyu Zhu, Yinuo Luo, Ting He, Congcong Gao, Ting Yang, Kun Beijing Univ Civil Engn & Architecture Sch Geomat & Urban Spatial Informat Beijing 102616 Peoples R China Gansu Inst Nat Resources Planning & Res Lanzhou 730030 Gansu Peoples R China

Particulate matter with a diameter of less than 2.5 mu m (PM2.5) has a significant impact on air pollution, atmospheric visibility, and human health. The most basic and important step of regional air pollution control is to obtain air pollution data in different seasons from both satellite sensors and ground-level observations. The aim of this paper is to accurately estimate the PM2.5 concentration in the Beijing-Tianjin-Hebei urban area in different seasons by establishing a seasonal geographically and temporally weighted regression (S-GTWR) model that integrates multiple complex factors. Using a greedy algorithm, the model results were optimized by selecting the characteristic variables that contributed to the accuracy of the model in different seasons. The measured and estimated PM2.5 concentrations were compared and the cross-validation results were used as a basis for evaluating the accuracy of the model. The results showed that the accuracy of the S-GTWR model that combined the optimal characteristic variables was higher than that of the geographically weighted regression (GWR) model and the kriging method. The mean prediction error (ME), relative prediction error (RPE), and root mean square error (RMSE) of the S-GTWR model were small, and the coefficient of determination (R-2) of the model exceeded 0.86 for each season. The accuracy of the S-GTWR model in estimating the PM2.5 concentration was highest in summer and lowest in winter. In addition, the proposed model can accurately estimate PM2.5 concentrations in areas without monitoring sites. The results can provide a scientific basis for the study of pollution control and PM2.5 exposure in large urban agglomerations.

关键词： Beijing-Tianjin-Hebei urban agglomeration PM2.5 S-GTWR greedy algorithm model optimization

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Survey of 8 UAV Set-Covering algorithms for Terrain Photogrammetry

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REMOTE SENSING 2020年第14期12卷 2285-2285页

作者： Hammond, Joshua E. Vernon, Cory A. Okeson, Trent J. Barrett, Benjamin J. Arce, Samuel Newell, Valerie Janson, Joseph Franke, Kevin W. Hedengren, John D. Brigham Young Univ Ira A Fulton Coll Engn & Technol Dept Chem Engn 350 Clyde Bldg Provo UT 84602 USA Brigham Young Univ Ira A Fulton Coll Engn & Technol Dept Civil & Environm Engn 368 Clyde Bldg Provo UT 84602 USA

Remote sensing with unmanned aerial vehicles (UAVs) facilitates photogrammetry for environmental and infrastructural monitoring. Models are created with less computational cost by reducing the number of photos required. Optimal camera locations for reducing the number of photos needed for structure-from-motion (SfM) are determined through eight mathematical set-covering algorithms as constrained by solve time. The algorithms examined are: traditional greedy, reverse greedy, carousel greedy (CG), linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization. Coverage and solve time are investigated for these algorithms. CG is the best method for choosing optimal camera locations as it balances number of photos required and time required to calculate camera positions as shown through an analysis similar to a Pareto Front. CG obtains a statistically significant 3.2 fewer cameras per modeled area than base greedy algorithm while requiring just one additional order of magnitude of solve time. For comparison, linear programming is capable of fewer cameras than base greedy but takes at least three orders of magnitude longer to solve. A grid independence study serves as a sensitivity analysis of the CG algorithms alpha(iteration number) and beta (percentage to be recalculated) parameters that adjust traditional greedy heuristics, and a case study at the Rock Canyon collection dike in Provo, UT, USA, compares the results of all eight algorithms and the uniqueness (in terms of percentage comparisons based on location/angle metadata and qualitative visual comparison) of each selected set. Though this specific study uses SfM, the principles could apply to other instruments such as multi-spectral cameras or aerial LiDAR.

关键词： structure-from-motion unmanned aerial vehicles uav remote sensing set-covering problem algorithms greedy algorithm carousel greedy algorithm grid independence photogrammetry optimization

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Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

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IEEE ACCESS 2020年 8卷 200129-200145页

作者： Ali, Zuhura J. Noordin, Nor K. Sali, Aduwati Hashim, Fazirulhisyam Univ Putra Malaysia Fac Engn Dept Comp & Commun Syst Engn Serdang 43400 Malaysia Univ Putra Malaysia Fac Engn Wireless & Photon Networks Res Ctr Excellence WiP Serdang 43400 Malaysia

The increasing in energy consumptions of the current wireless networks, leads towards designing energy-efficient 5G networks. The application of non-orthogonal multiple access (NOMA) in the heterogeneous networks (HetNets) improves the spectrum utilization with the cost of efficient resource allocation. Hence, this article proposes optimal user-pairing and power allocation solutions towards achieving fair energy-efficient resource allocation in downlink femtocell NOMA-HetNets. In the proposed optimization process, the considered constraints are the user's transmission rate, transmit power budget at the base station (BS), and the interference. The energy consumption of both the transmitter and the receiver are considered to simulate the real system design. The greedy algorithm (GA) is used to achieve a low-complex optimal solution during the user-pairing process. Simultaneously, the max-min energy efficiency optimization approach is employed to maximize the minimum energy efficiency of the femtocell users to achieve the optimal power allocation solution. The mathematical formulation of the max-min energy efficiency is a non-convex fractional programming problem and is intractable. Thus, the fractional programming theory is adopted to transform the problem into a sequence of subtractive form, followed by the Sequential Convex Programming (SCP) approach to determine the optimal solution. Simulation results show that the proposed NOMA with optimal power allocation method using SCP and GA (NOMA-SCP-GA) achieves fair energy efficiency performance with lower complexity compared to the benchmark methods. Moreover, the minimum energy efficiency of the femtocell user is 38.22% higher than NOMA with Difference of Convex programming (NOMA-DC). The NOMA-SCP-GA method can assure 5G capability demands.

关键词： greedy algorithm heterogeneous network non-orthogonal multiple access power allocation sequential convex programming

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Functional martingale residual process for high-dimensional Cox regression with model averaging

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8553-8589页

作者： Baihua He Yanyan Liu Yuanshan Wu Guosheng Yin Xingqiu Zhao School of Mathematics and Statistics Wuhan University Wuhan China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan China Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Regularization methods for the Cox proportional hazards regression with high-dimensional survival data have been studied extensively in the literature. However, if the model is mis-specified, this would result in misleading statistical inference and prediction. To enhance the prediction accuracy for the relative risk and the survival probability, we propose three model averaging approaches for the high-dimensional Cox proportional hazards regression. Based on the martingale residual process, we define the delete-one cross-validation (CV) process, and further propose three novel CV functionals, including the end-time CV, integrated CV, and supremum CV, to achieve more accurate prediction for the risk quantities of clinical interest. The optimal weights for candidate models, without the constraint of summing up to one, can be obtained by minimizing these functionals, respectively. The proposed model averaging approach can attain the lowest possible prediction loss asymptotically. Furthermore, we develop a greedy model averaging algorithm to overcome the computational obstacle when the dimension is high. The performances of the proposed model averaging procedures are evaluated via extensive simulation studies, demonstrating that our methods achieve superior prediction accuracy over the existing regularization methods. As an illustration, we apply the proposed methods to the mantle cell lymphoma study.

关键词： asymptotic optimality censored data cross validation greedy algorithm martingale residual process prediction survival analysis

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On the Heapability of Finite Partial Orders

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DISCRETE MATHEMATICS AND THEORETICAL COMPUTER SCIENCE 2020年第1期22卷 1页

作者： Balogh, Janos Bonchis, Cosmin Dinis, Diana Istrate, Gabriel Todinca, Ioan Univ Szeged Inst Informat Szeged Hungary West Univ Timisoara Dept Comp Sci Timisoara Romania E Austria Res Inst Timisoara Romania Univ Orleans LIFO Orleans France INSA Ctr Val de Loire Blois France

Define a sequence of elements from a partially ordered set to be heapable if it can be successively inserted as the leaves of a binary tree, not necessarily complete, such that children nodes are always greater or equal than parent nodes. This is a natural binary analog of the notion of a chain in a poset and an easy extension of a definition for integers due to Byers et al. (2011). A set of elements is called heapable if some permutation of its elements is. We investigate the partitioning of sequences from a poset into a minimal number of heapable subsequences. We give an extension of Fulkerson's proof of Dilworth's theorem to decomposition into heapable subsequences which yields as a byproduct a flow-based algorithm for computing such a minimal decomposition. On the other hand, for sets and sequences of intervals and for trapezoid partial orders we prove that such minimal decompositions can be computed via simple greedy-type algorithms. Second, while the complexity of computing a maximal heapable subsequence of integers is still open, we show that this problem has a polynomial time algorithm for sequences of intervals. The paper concludes with a couple of open problems related to the analog of the Ulam-Hammersley problem for sets and sequences of random intervals.

关键词： partial order heapable sequence Dilworth's theorem greedy algorithm random intervals

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Steel plate scheduling optimization in shipbuilding based on storage area partition

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Procedia CIRP 2020年 93卷 1001-1006页

作者： Xu Liyun Shu Zhongyu Yang Liansheng School of Mechanical Engineering Tongji University Shanghai 201804 China Shanghai Waigaoqiao Shipbuilding Co. Ltd Shanghai 200137 China

Steel plate yard in shipbuilding, followed by the Separated-Strategy, is divided into Temporary-Area, Storage-Area, and Delivery-Area. When a target plate is not at the top of the stack, we must relocate the plates on it, which is called Plate-Shift. This paper develops a bilevel combinatorial optimization model and a multi-stage decision-making process of the delivery operation, and applies a greedy algorithm combined with a genetic algorithm to optimize the total amount of shifting. Compared with the current schedule based on a series of production rules, this method can get a better solution with less computing time and shifts.

关键词： Shipbuilding Plate-Shift scheduling optimization Combinatorial optimization model Multi-stage decision-making process greedy algorithm

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greedy Nonintrusive Reduced Order Model for Fluid Dynamics

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AIAA JOURNAL 2018年第12期56卷 4927-4943页

作者： Wang Chen Hesthaven, Jan S. Bai Junqiang Qiu, Yasong Zhang Yang Yang Tihao Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China Ecole Polytech Fed Lausanne Swiss Fed Inst Technol Lausanne Chair Computat Math & Simulat Sci MCSS CH-1015 Lausanne Switzerland Xi An Jiao Tong Univ Sch Aerosp Xian 710018 Shaanxi Peoples R China

A greedy nonintrusive reduced order method (ROM) is proposed for parameterized time-dependent problems with an emphasis on problems in fluid dynamics. The nonintrusive ROM (NIROM) bases on a two-level proper orthogonal decomposition to extract temporal and spatial reduced basis from a set of candidates, and adopts the radial basis function to approximate undetermined coefficients of extracted reduced basis. Instead of adopting uniform or random sampling strategies, the candidates are determined by an adaptive greedy approach to minimize the overall offline computational cost. Numerical studies are presented for a two-dimensional diffusion problem as well as a lid-driven cavity problem governed by incompressible Navier-Stokes equations. The results demonstrate that the greedy nonintrusive ROM (GNIROM) predicts the flow field accurately and efficiently.

关键词： Fluid Dynamics Navier Stokes Equations Kernel Functions Aerodynamics Computing greedy algorithm Collocation Method Hilbert Space Velocity Profiles Numerical Simulation

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Data Aggregation using Compressive Sensing for Energy Efficient Routing Strategy

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Procedia Computer Science 2020年 171卷 2242-2251页

作者： Deepa Puneeth Muralidhar Kulkarni National Institute of Technology Karnataka Surathkal Mangalore-575025 India

Data gathering in an energy efficient way, is one of the crucial concerns in Wireless Sensor Networks (WSNs). With incorporation of Compressive Sensing (CS), as a data aggregation scheme the information contained in the signal, is safely maintained through its projections, which can be reconstructed later. Inclusion of CS, for an energy efficient routing technique further enhances the lifetime of the network. To improve network lifetime, CS has been employed at level 1 (at the leaf nodes). The dimensionality reduction at the transmitter is done, using a measurement matrix. At the receiver, data is recovered using greedy based methods. greedy based method offers low complexity, and low implementation cost. The success rate of greedy method, depends on the sparsity of the data. Performance evaluation of greedy based method is analyzed, by considering varying sparsity and plotted against number of measurements, required to reconstruct the signal. At the same time, reconstruction error of every method has been evaluated by varying sparsity of the signal. The same is analyzed by considering real temperature, and humidity data sets.

关键词： Wireless Sensor Network’s (WSN’s) Compressive Sensing (CS) greedy algorithm data aggregation multi-path routing algorithm network life-time

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Stochastic conditional gradient methods: from convex minimization to submodular maximization

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 4232-4280页

作者： Aryan Mokhtari Hamed Hassani Amin Karbasi Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA Department of Electrical Engineering and Computer Science Yale University New Haven CT

This paper considers stochastic optimization problems for a large class of objective functions, including convex and continuous submodular. Stochastic proximal gradient methods have been widely used to solve such problems; however, their applicability remains limited when the problem dimension is large and the projection onto a convex set is computationally costly. Instead, stochastic conditional gradient algorithms are proposed as an alternative solution which rely on (i) Approximating gradients via a simple averaging technique requiring a single stochastic gradient evaluation per iteration; (ii) Solving a linear program to compute the descent/ascent direction. The gradient averaging technique reduces the noise of gradient approximations as time progresses, and replacing projection step in proximal methods by a linear program lowers the computational complexity of each iteration. We show that under convexity and smoothness assumptions, our proposed stochastic conditional gradient method converges to the optimal objective function value at a sublinear rate of O(1/t1/3). Further, for a monotone and continuous DR-submodular function and subject to a general convex body constraint, we prove that our proposed method achieves a ((1 - 1/e)OPT - ε) guarantee (in expectation) with O(1/ε3) stochastic gradient computations. This guarantee matches the known hardness results and closes the gap between deterministic and stochastic continuous submodular maximization. Additionally, we achieve ((1/e)OPT - ε) guarantee after operating on O(1/ε3) stochastic gradients for the case that the objective function is continuous DR-submodular but nonmonotone and the constraint set is a down-closed convex body. By using stochastic continuous optimization as an interface, we also provide the first (1 - 1/e) tight approximation guarantee for maximizing a monotone but stochastic submodular set function subject to a general matroid constraint and (1/e) approximation guarantee for the non-monotone

关键词： stochastic optimization conditional gradient methods convex minimization submodular maximization gradient averaging Frank-Wolfe algorithm greedy algorithm

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Real-time reversible data hiding based on multiple histogram modification

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2019年第3期16卷 661-671页

作者： Zhang, Tong Li, Xiaolong Qi, Wenfa Li, Wei Guo, Zongming Peking Univ Inst Comp Sci & Technol Beijing 100871 Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China Capital Normal Univ Sch Math Sci Beijing 100048 Peoples R China

Prediction-error expansion (PEE) has been widely utilized in reversible data hiding (RDH) due to its advantage of high-quality marked image. Recently, a RDH method based on multiple histogram modification (MHM) has been proposed, achieving good performance at low capacity. However, its exhaustive expansion-bin-selection mechanism is time consuming, so that it can only choose a single pair of expansion bins in each histogram for embedding data. As an extension of MHM, a novel RDH scheme for high-capacity embedding is proposed in this paper. In the proposed method, multiple pairs of expansion bins are utilized in each histogram and a greedy search algorithm is designed to determine the nearly optimal expansion bins. The results of experiments demonstrate that the proposed scheme has real-time performance and high-quality marked image, and it outperforms the original MHM-based RDH method and some other state-of-the-art works.

关键词： Reversible data hiding Prediction-error expansion Real-time embedding greedy algorithm

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