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Task Scheduling Mechanism for Crowdsourcing in Mobile Social Networks

IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Jiale Yi, Longhao Gao, Xiaofeng Bhatti, Shahzad Sarwar Yuan, Ting Chen, Guihai Shanghai Jiao Tong University Department of Computer Science and Engineering China Emerson University Faculty of Computing and Emerging Technologies Multan Pakistan Shanghai Jiao Tong University Department of Automation China

With the popularization of smart phones, mobile crowdsourcing emerged and gained growing attention in the recent years. Mobile users are now able to conduct complex tasks with the communication between each other. In this paper, we study the task scheduling problem in the mobile crowdsourcing systems based on the spontaneously formed mobile social networks (MSNs). We introduce two crowdsourcing task scheduling problems under this system model, with one problem aiming to minimize the total cost of some crowdsourcing tasks and the other focusing on minimizing the final completion time of the tasks belonging to the same project. We introduce a unified framework to solve the problems and propose two approximation algorithms for these two problems in the offline versions respectively and prove Their approximation ratios accordingly. Based on the two algorithms, we further design two online algorithms to deal with the workers' dynamism and also analyze their competitive ratios. Finally, we verify the effectiveness and efficiency of the proposed methods through numerical experiments on real and synthetic datasets. © 2002-2012 IEEE.

关键词： approximation algorithms

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H-VLO: Hybrid LiDAR-Camera Fusion For Self-Supervised Odometry

H-VLO: Hybrid LiDAR-Camera Fusion For Self-Supervised Odomet...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Aydemir, Eren Fetic, Naida Unel, Mustafa Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Ford Otosan Prod Dev Istanbul Turkey

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

In this paper, we propose a hybrid visual-LiDAR odometry (H-VLO) framework that fuses predicted visual depth map and completed LiDAR map. Compared to the previous visual-LiDAR odometry methods, our approach leverages 2D feature matching and 3D association by utilizing deep depth map, deep flow map and deep LiDAR depth completion networks. Rather than extraction of the depth values from LiDAR measurements for each visual feature, our method first densifies a LiDAR scan with a deep depth completion network and then fuses it with visual deep depth map estimation in a Bayesian framework. This method reduces pose estimation drift by improving feature-to-feature and point-to-feature matching, as well as scale recovery. The evaluations on the public KITTI odometry benchmark show that our technique achieves better or at least comparable estimates than the state-of-the-art visual-LiDAR and monocular visual odometry approaches.

关键词： Visualization Laser radar Three-dimensional displays Fuses approximation algorithms Feature extraction Cameras

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An approximation algorithm for unifying adjacencies by double cut and joins in unsigned genomes

An approximation algorithm for unifying adjacencies by doubl...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Sun, Chengcheng Jiang, Haitao Shandong University School of Computer Science and Technology Qingdao China

ISBN: (纸本)9781665401265

Computing the genome rearrangement distance is an important subject in computational biology. The double cut and join operation(abbreviated as DCJ) has been extensively used for genome rearrangement over the last few years. When the genomes contain duplicate genes or segments, computing the DCJ distance becomes NP-hard, and there are scarcely positive results besides exploiting the algorithms for solving the minimum common string partition problem. As a twist, the DCJ adjacency distance, defined as the minimum number of DCJs to unify all the adjacencies between two genomes with duplicate genes or segments, reveals the similarity of the two genomes to a large extent. In this paper, we formally show that computing the DCJ adjacency distance is NP-hard, and present an approximation algorithm, which guarantees an approximation factor of frac{13}{9}+varepsilon(approx 1.44+varepsilon) and runs in Oleft(left(n cdot 8{operatorname{logn}}right){24 cdot 7} fracapproximation{varepsilon}right) time, where n is the length of each genome. We also design an integer linear programming for computing the exact DCJ adjacency distance. Then, we apply our algorithm to simulated genomic data with bounded duplicate number, the solution of our algorithm is even better than the isochronal solution from the integer linear programming. Moreover, our algorithm approximates the DCJ adjacency distance between the genomes of Human and Gorilla with a real ratio of less than 1.12. © 2021 IEEE.

关键词： approximation algorithms

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Efficient Phase Shift Migration for MIMO 2-D Imaging in Millimeter-Wave Band

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IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS 2021年第2期31卷 215-218页

作者： Yang, Guan Li, Chao Gao, Hang Li, Hongwei Zheng, Shen Fang, Guangyou Chinese Acad Sci Aerosp Informat Res Inst Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Electromagnet Radiat & Sensing Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

The real-time capability becomes more and more important for the millimeter-wave imaging systems in variety of applications. To achieve this purpose, the imaging scheme with multiple-input-multiple-output (MIMO) is one of the potential ways, while still limited by the high computational complexity of the conventional MIMO image reconstruction algorithm, which is commonly based on the back-projection theorem. In this letter, an efficient phase shift migration (PSM) image reconstruction algorithm was proposed to improve the computation efficiency for MIMO systems 2-D imaging. By decomposing and approximating the PSM operator reasonably, only complex multiplication and Fourier transform (FT) operations are employed which can be easily carried out to reduce computation cost. An MIMO side-looking prototype system was designed for proof experiments in the 100-GHz band. Although the proposed algorithm introduces a negligible phase error, the experimental results show that the error can be ignored and the algorithm can achieve high-precision real-time imaging.

关键词： Imaging MIMO communication Image reconstruction approximation algorithms Real-time systems Radar imaging Microwave imaging Image reconstruction algorithm millimeter-wave (MMW) multiple-input– multiple-output (MIMO) radar phase shift migration (PSM)

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A Population-Membrane-System-Inspired Evolutionary Algorithm for Distribution Network Reconfiguration

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Chinese Journal of Electronics 2025年第3期23卷 437-441页

作者： Gexiang Zhang Haina Rong Jixiang Cheng Yanhui Qin School of Electrical Engineering Southwest Jiaotong University Chengdu China

This paper proposes a Population-membrane-system-inspired evolutionary algorithm (PM-SIEA), which is designed by using a population P system and a Quantum-inspired evolutionary algorithm (QIEA). PMSIEA uses the population P system with three cells to organize three variants of QIEAs, where communications between cells are performed at the level of genes, instead of the level of individuals reported in the existing membrane algorithms. This work provides a useful frame-work for synthesizing different algorithms at macro level and exchanging genic information at micro scale. Experimental results conduced on knapsack problems show that PMSIEA is superior to four representative QIEAs and our previous work with respect to the quality of solutions and the elapsed time. We also use PMSIEA to solve the optimal distribution system reconfiguration problem in power systems for minimizing the power loss.

关键词： Computational modeling Evolutionary computation Distribution networks approximation algorithms Search problems Power systems Optimization

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Robust Beamforming Design for Integrated Sensing and Communication Systems

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 114-123页

作者： Yongjun Xu Na Cao Yi Jin Haibo Zhang Chongwen Huang Qianbin Chen Chau Yuen School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China China Academy of Space Technology-Xi'an Xi'an China Information Communication and Network Engineering Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Integrated sensing and communication (ISAC) can improve spectral, energy, and transmission efficiency. To overcome the impact of channel uncertainties, we investigate a robust beamforming design problem for a multiple-input single-output based ISAC system with imperfect channel state information (CSI), where a multiantenna base station (BS) serves multiple wireless users and obtains state information of a point target. Based on bounded CSI error models, a total throughput maximization problem is formulated under the constraints of the minimum rate threshold of each communication user, sensing performance based on Cramér–Rao lower bound thresholds, and the maximum transmit power of the BS. The formulated problem with parameter perturbations belongs to a nonconvex one that is challenging to solve. To address this complexity, an iterative robust beamforming algorithm is designed by employing S-procedure, semidefinite relaxation technique, Schur complementarity conditions, and successive convex approximation. Simulation results demonstrate that the proposed algorithm exhibits better convergence and stronger robustness.

关键词： Sensors Array signal processing Radar Interference Signal to noise ratio approximation algorithms Vectors

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Better approximation algorithms for Grasp-and-Delivery Robot Routing Problems

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2013年第3期E96D卷 450-456页

作者： Shurbevski, Aleksandar Nagamochi, Hiroshi Karuno, Yoshiyuki Kyoto Univ Grad Sch Informat Doctoral Program Dept Appl Math & Phys Kyoto 6068501 Japan Kyoto Univ Grad Sch Informat Dept Appl Math & Phys Kyoto 6068501 Japan Kyoto Inst Technol Grad Sch Sci & Technol Dept Mech & Syst Engn Kyoto 6068585 Japan

In this paper, we consider a problem of simultaneously optimizing a sequence of graphs and a route which exhaustively visits the vertices from each pair of successive graphs in the sequence. This type of problem arises from repetitive routing of grasp-and-delivery robots used in the production of printed circuit boards. The problem is formulated as follows. We are given a metric graph G* = (V*, E*), a set of m + 1 disjoint subsets C-i subset of V* of vertices with vertical bar C-i vertical bar = n, i = 0,1, ..., m, and a starting vertex s is an element of C-0. We seek to find a sequence pi = (C-i1, C-i2, ..., C-im) of the subsets of vertices and a shortest walk P which visits all (m + 1)n vertices in G* in such a way that after starting from s, the walk alternately visits the vertices in Cik-1 and C-ik, for k = 1, 2, ..., m (i(0) = 0). Thus, P is a walk with m(2n - 1) edges obtained by concatenating m alternating Hamiltonian paths between Cik-1 and C-ik, k = 1, 2, ..., m. In this paper, we show. that an approximate sequence of subsets of vertices and an approximate walk with at most three times the optimal route length can be found in polynomial time.

关键词： combinatorial optimization routing and scheduling industrial robots approximation algorithms metric traveling salesperson problem

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Multi-Kernel Online Reinforcement Learning for Path Tracking Control of Intelligent Vehicles

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第11期51卷 6962-6975页

作者： Liu, Jiahang Huang, Zhenhua Xu, Xin Zhang, Xinglong Sun, Shiliang Li, Dazi Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China Rocket Engn Design & Res Inst Beijing 100011 Peoples R China East China Normal Univ Dept Comp Sci & Technol Shanghai 200241 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

Path tracking control of intelligent vehicles has to deal with the difficulties of model uncertainties and nonlinearities. As a class of adaptive optimal control methods, reinforcement learning (RL) has received increasing attention in solving difficult control problems. However, feature representation and online learning ability are two major problems to be solved for learning control of uncertain dynamic systems. In this article, we propose a multi-kernel online RL approach for path tracking control of intelligent vehicles. In the proposed approach, a multiple kernel feature learning framework is designed for online learning control based on dual heuristic programming (DHP) and the new online learning control algorithm is called multi-kernel DHP (MKDHP). In MKDHP, instead of the expert knowledge for selecting and fine-tuning of a suitable kernel function, only a set of basic kernel functions is required to be predefined and the multi-kernel features can be learned for value function approximation in the critic. The simulation studies on path tracking control for intelligent vehicles have been conducted under S-curve and urban road conditions. The results demonstrated that compared with other typical path tracking controllers for intelligent vehicles, such as the linear quadratic regulator (LQR), the pure pursuit controller and the ribbon-based controller, the proposed multi-kernel learning controller can achieve better performance in terms of tracking precision and smoothness.

关键词： Kernel Intelligent vehicles Vehicle dynamics Adaptation models Optimal control Heuristic algorithms approximation algorithms Intelligent vehicle multiple kernel learning (MKL) path tracking reinforcement learning (RL) steering control

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Accuracy Versus Simplification in an Approximate Logic Neural Model

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第11期32卷 5194-5207页

作者： Ji, Junkai Tang, Yajiao Ma, Lijia Li, Jianqiang Lin, Qiuzhen Tang, Zheng Todo, Yuki Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Cent South Univ Forestry & Technol Coll Econ Changsha 410004 Peoples R China Univ Toyama Fac Engn Toyama 9308555 Japan Kanazawa Univ Sch Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

An approximate logic neural model (ALNM) is a novel single-neuron model with plastic dendritic morphology. During the training process, the model can eliminate unnecessary synapses and useless branches of dendrites. It will produce a specific dendritic structure for a particular task. The simplified structure of ALNM can be substituted by a logic circuit classifier (LCC) without losing any essential information. The LCC merely consists of the comparator and logic NOT, AND, and OR gates. Thus, it can be easily implemented in hardware. However, the architecture of ALNM affects the learning capacity, generalization capability, computing time and approximation of LCC. Thus, a Pareto-based multiobjective differential evolution (MODE) algorithm is proposed to simultaneously optimize ALNM's topology and weights. MODE can generate a concise and accurate LCC for every specific task from ALNM. To verify the effectiveness of MODE, extensive experiments are performed on eight benchmark classification problems. The statistical results demonstrate that MODE is superior to conventional learning methods, such as the backpropagation algorithm and single-objective evolutionary algorithms. In addition, compared against several commonly used classifiers, both ALNM and LCC are capable of obtaining promising and competitive classification performances on the benchmark problems. Besides, the experimental results also verify that the LCC obtains the faster classification speed than the other classifiers.

关键词： Neurons Synapses Biological system modeling Training approximation algorithms Computational modeling Integrated circuit modeling Classification dendrite differential evolution (DE) logic circuit multiobjective neural network

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Two-Stage Dual Dynamic Programming With Application to Nonlinear Hydro Scheduling

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2021年第1期29卷 96-107页

作者： Flamm, Benjamin Eichler, Annika Warrington, Joseph Lygeros, John Swiss Fed Inst Technol Automat Control Lab CH-8092 Zurich Switzerland

We present an approximate method for solving nonlinear control problems over long time horizons, in which the full nonlinear model is preserved over an initial part of the horizon, while the remainder of the horizon is modeled using a linear relaxation. As this approximate problem may still be too large to solve directly, we present a Benders decomposition-based solution algorithm that iterates between solving the nonlinear and linear parts of the horizon. This extends the dual dynamic programming approach commonly employed for optimization of linearized hydro power systems. We prove that the proposed algorithm converges after a finite number of iterations, even when the nonlinear initial stage problems are solved inexactly. We also bound the suboptimality of the split-horizon method with respect to the original nonlinear problem, in terms of the properties of a map between the linear and nonlinear state-input trajectories. We then apply this method to a case study concerning a multiple reservoir hydro system, approximating the nonlinear head effects in the second stage using McCormick envelopes. We demonstrate that near-optimal solutions can be obtained in a shrinking horizon setting when the full nonlinear model is used for only a short initial section of the horizon. For this example, the approach is shown to be more practical than both conventional dynamic programming and a multi-cell McCormick envelope approximation from the literature.

关键词： approximation algorithms Dynamic programming Computational modeling Optimal control Energy storage Heuristic algorithms Optimization Dual dynamic programming (DDP) hydro optimization nonlinear model predictive control (NMPC) optimal control

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