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A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

信息与电子工程前沿:英文版 2023年第9期24卷 1316-1331页

作者： Dawen XIA Jian GENG Ruixi HUANG Bingqi SHEN Yang HU Yantao LI Huaqing LI College of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025China Department of Automotive engineering Guizhou Traffic Technician and Transportation CollegeGuiyang 550008China college of Computer science Chongqing UniversityChongqing 400044China college of Electronic and information engineering Southwest UniversityChongqing 400715China

To address the imbalance problem between supply and demand for taxis and passengers,this paper proposes a distributed ensemble empirical mode decomposition with normalization of spatial attention mechanism based bi-directional gated recurrent unit(EEMDN-SABiGRU)model on Spark for accurate passenger hotspot prediction.It focuses on reducing blind cruising costs,improving carrying efficiency,and maximizing incomes.Specifically,the EEMDN method is put forward to process the passenger hotspot data in the grid to solve the problems of non-smooth sequences and the degradation of prediction accuracy caused by excessive numerical differences,while dealing with the eigenmodal EMD.Next,a spatial attention mechanism is constructed to capture the characteristics of passenger hotspots in each grid,taking passenger boarding and alighting hotspots as weights and emphasizing the spatial regularity of passengers in the grid.Furthermore,the bi-directional GRU algorithm is merged to deal with the problem that GRU can obtain only the forward information but ignores the backward information,to improve the accuracy of feature extraction.Finally,the accurate prediction of passenger hotspots is achieved based on the EEMDN-SABiGRU model using real-world taxi GPS trajectory data in the Spark parallel computing framework.The experimental results demonstrate that based on the four datasets in the 00-grid,compared with LSTM,EMDLSTM,EEMD-LSTM,GRU,EMD-GRU,EEMD-GRU,EMDN-GRU,CNN,and BP,the mean absolute percentage error,mean absolute error,root mean square error,and maximum error values of EEMDN-SABiGRU decrease by at least 43.18%,44.91%,55.04%,and 39.33%,respectively.

关键词： Passenger hotspot prediction Ensemble empirical mode decomposition(EEMD) Spatial attention mechanism Bi-directional gated recurrent unit(BiGRU) GPS trajectory Spark

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Robust federated learning for edge-intelligent networks

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science China(information sciences) 2022年第3期65卷 246-254页

作者： Zhihe GAO Xiaoming CHEN Xiaodan SHAO college of information science and Electronic engineering Zhejiang University

The rapid development of machine learning and wireless communication is creating a new paradigm for future networks, namely edge-intelligent networks. Specifically, data generated by terminal devices is processed via machine learning at the edge of wireless networks, but not at the cloud. Owing to the growing concern for privacy information sharing, federated learning, as a new branch of machine learning,is appealing in edge-intelligent networks. For federated learning, the wireless transmission capabilities under practical conditions, e.g., imperfect channel state information(CSI), have a great impact on the accuracy of global aggregation of local model updates. Therefore, it is very important to enhance the robustness of communication for federated learning. In order to realize robust communication in the presence of channel uncertainty, we propose a robust federated learning algorithm for edge-intelligent networks, including device selection, transmit power allocation, and receive beamforming. Simulation results validate the robustness and effectiveness of the proposed robust federated learning algorithm in edge-intelligent networks.

关键词： edge-intelligent network federated learning imperfect CSI robust design

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Tuning-up Learning Parameters for Deep Convolutional Neural Network:A Case Study for Hand-Drawn Sketch Images

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Journal of Electronic science and Technology 2022年第3期20卷 305-318页

作者： Shaukat Hayat Kun She Muhammad Mateen Parinya Suwansrikham Muhammad Abdullah Ahmed Alghaili the School of information and Software engineering University of Electronic Science and Technology of ChinaChengdu 610054 the School of Big data&Software engineering Chongqing UniversityChongqing 400044 the college of information science and engineering Hunan UniversityChangsha 410082

Several recent successes in deep learning(DL),such as state-of-the-art performance on several image classification benchmarks,have been achieved through the improved configuration.Hyperparameters(HPs)tuning is a key factor affecting the performance of machine learning(ML)algorithms.Various state-of-the-art DL models use different HPs in different ways for classification tasks on different datasets.This manuscript provides a brief overview of learning parameters and configuration techniques to show the benefits of using a large-scale handdrawn sketch dataset for classification problems.We analyzed the impact of different learning parameters and toplayer configurations with batch normalization(BN)and dropouts on the performance of the pre-trained visual geometry group 19(VGG-19).The analyzed learning parameters include different learning rates and momentum values of two different optimizers,such as stochastic gradient descent(SGD)and Adam.Our analysis demonstrates that using the SGD optimizer and learning parameters,such as small learning rates with high values of momentum,along with both BN and dropouts in top layers,has a good impact on the sketch image classification accuracy.

关键词： Deep learning(DL) hand-drawn sketches learning parameters

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Sample Bound Estimate Based Chance-constrained Immune Optimization and Its Applications

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International Journal of Automation and computing 2016年第5期13卷 468-479页

作者： Zhu-Hong Zhang Kai Yang Da-Min Zhang college of Big data & information engineering Guizhou University college of Computer science & Technology Guizhou University

This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.

关键词： Chance-constrained programming immune optimization sample allocation lower bound estimate noise attenuation

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The Analysis of Block Joint Sparse Recovery Using Block Signal Space Matching Pursuit

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Acta Mathematica Sinica,English Series 2024年

作者： Haifeng LI Hao YING Jinming WEN college of information science and Technology and the college of Cyber Security Jinan University Henan engineering Laboratory for Big data Statistical Analysis and Optimal Control College of Mathematics and Information Science Henan Normal University

In many practical applications,we need to recover block sparse signals.In this paper,we encounter the system model where joint sparse signals exhibit block structure.To reconstruct this category of signals,we propose a new algorithm called block signal subspace matching pursuit(BSSMP) for the block joint sparse recovery problem in compressed sensing,which simultaneously reconstructs the support of block jointly sparse signals from a common sensing matrix.To begin with,we consider the case where block joint sparse matrix X has full column rank and any r nonzero rowblocks are linearly independent.Based on these assumptions,our theoretical analysis indicates that the BSSMP algorithm could reconstruct the support of X through at most K-r+■ iterations if sensing matrix A satisfies the block restricted isometry property of order L(K-r)+r+1 with■.This condition improves the existing result.

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A distributed stochastic optimization algorithm with gradient-tracking and distributed heavy-ball acceleration

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Frontiers of information Technology & Electronic engineering 2021年第11期22卷 1463-1476页

作者： Bihao SUN Jinhui HU Dawen XIA Huaqing LI Chongqing Key Laboratory of Nonlinear Circuits and Intelligent information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing400715China College of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang550025China

Distributed optimization has been well developed in recent years due to its wide applications in machine learning and signal processing.In this paper,we focus on investigating distributed optimization to minimize a global objective.The objective is a sum of smooth and strongly convex local cost functions which are distributed over an undirected network of n nodes.In contrast to existing works,we apply a distributed heavy-ball term to improve the convergence performance of the proposed algorithm.To accelerate the convergence of existing distributed stochastic first-order gradient methods,a momentum term is combined with a gradient-tracking technique.It is shown that the proposed algorithm has better acceleration ability than GT-SAGA without increasing the complexity.Extensive experiments on real-world datasets verify the effectiveness and correctness of the proposed algorithm.

关键词： Distributed optimization High-performance algorithm Multi-agent system Machine-learning problem Stochastic gradient

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A NEW SUFFICIENT CONDITION FOR SPARSE RECOVERY WITH MULTIPLE ORTHOGONAL LEAST SQUARES

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Acta Mathematica Scientia 2022年第3期42卷 941-956页

作者：李海锋张静 Henan engineering Laboratory for Big data Statistical Analysis and Optimal Control College of Mathematics and Information ScienceHenan Normal UniversityXinxiang 453007China

A greedy algorithm used for the recovery of sparse signals,multiple orthogonal least squares(MOLS)have recently attracted quite a big of attention.In this paper,we consider the number of iterations required for the MOLS algorithm for recovery of a K-sparse signal x∈R^(n).We show that MOLS provides stable reconstruction of all K-sparse signals x from y=Ax+w in|6K/ M|iterations when the matrix A satisfies the restricted isometry property(RIP)with isometry constantδ_(7K)≤0.094.Compared with the existing results,our sufficient condition is not related to the sparsity level K.

关键词： Sparse signal recovery multiple orthogonal least squares(MOLS) sufficient condition restricted isometry property(RIP)

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APFD:an effective approach to taxi route recommendation with mobile trajectory big data

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Frontiers of information Technology & Electronic engineering 2022年第10期23卷 1494-1510页

作者： Wenyong ZHANG Dawen XIA Guoyan CHANG Yang HU Yujia HUO Fujian FENG Yantao LI Huaqing LI College of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025China Department of Automotive engineering Guizhou Traffic Technician and Transportation CollegeGuiyang 550008China college of Computer science Chongqing UniversityChongqing 400044China college of Electronic and information engineering Southwest UniversityChongqing 400715China The Affiliated Hospital of Guizhou Medical University Guiyang 550001China

With the rapid development of data-driven intelligent transportation systems,an efficient route recommendation method for taxis has become a hot topic in smart cities.We present an effective taxi route recommendation approach(called APFD)based on the artificial potential field(APF)method and Dijkstra method with mobile trajectory big data.Specifically,to improve the efficiency of route recommendation,we propose a region extraction method that searches for a region including the optimal route through the origin and destination coordinates.Then,based on the APF method,we put forward an effective approach for removing redundant nodes.Finally,we employ the Dijkstra method to determine the optimal route recommendation.In particular,the APFD approach is applied to a simulation map and the real-world road network on the Fourth Ring Road in Beijing.On the map,we randomly select 20 pairs of origin and destination coordinates and use APFD with the ant colony(AC)algorithm,greedy algorithm(A*),APF,rapid-exploration random tree(RRT),non-dominated sorting genetic algorithm-II(NSGA-II),particle swarm optimization(PSO),and Dijkstra for the shortest route recommendation.Compared with AC,A*,APF,RRT,NSGA-II,and PSO,concerning shortest route planning,APFD improves route planning capability by 1.45%–39.56%,4.64%–54.75%,8.59%–37.25%,5.06%–45.34%,0.94%–20.40%,and 2.43%–38.31%,respectively.Compared with Dijkstra,the performance of APFD is improved by 1.03–27.75 times in terms of the execution efficiency.In addition,in the real-world road network,on the Fourth Ring Road in Beijing,the ability of APFD to recommend the shortest route is better than those of AC,A*,APF,RRT,NSGA-II,and PSO,and the execution efficiency of APFD is higher than that of the Dijkstra method.

关键词： Big data analytics Region extraction Artificial potential field Dijkstra Route recommendation GPS trajectories of taxis

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An Incremental Model Transfer Method for Complex Process Fault Diagnosis

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1268-1280页

作者： Xiaogang Wang Xiyu Liu Yu Li college of information science and engineering Northeastern University

Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.

关键词： Complex process fault diagnosis incremental learning model transfer

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Performance Analysis of ACO on the Quadratic Assignment Problem

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Chinese Journal of Electronics 2018年第1期27卷 26-34页

作者： XIA Xiaoyun ZHOU Yuren college of Mathematics Physics and information engineering Jiaxing University School of data and Computer science Sun Yat-sen University

The Quadratic assignment problem(QAP)is to assign a set of facilities to a set of locations with given distances between the locations and given flows between the facilities such that the sum of the products between flows and distances is minimized, which is a notoriously difficult NP-hard combinatorial optimization problem. A lot of heuristics have been proposed for the QAP problem, and some of them have proved to be efficient approximation algorithms for this problem. Ant colony optimization(ACO) is a general-purpose heuristic and usually considered as an approximation algorithms for NP-hard optimization problems. However, we know little about the performance of ACO for QAP from a theoretical perspective. This paper contributes to a theoretical understanding of ACO on the QAP problem. The worst-case bound on a simple ACO algorithm called(1+1) Max-min ant algorithm((1+1) MMAA) for the QAP problem is presented.It is shown that a degenerate(1+1) MMAA finds an approximate solution on the QAP problem. Finally, we reveal that ACO can outperform the 2-exchange local search algorithm on an instance of the QAP problem.

关键词： Ant colony optimization(ACO) Quadratic assignment problem(QAP) Approximation algorithms Local search Algorithms analysis Runtime analysis

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