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2nd International Workshop on Database Technology and Applications (DBTA)

作者： Yuan, Guo-xiong Wang, Dong Shanghai Jiao Tong Univ Logist Informat Technol & RFID Lab Shanghai 200240 Peoples R China

ISBN: (纸本)9781424469772

as random algorithm can assure fairness, moreover, it can solve some of the problems which the definite algorithm can't solve;the random algorithm has wide spread use. In this paper, it designed an algorithm with time complexity of Theta(NlgN) to solve the probability weight oriented permutation problem, further, the algorithm need no extra space. The probability weight oriented random permutation algorithm does not only guarantee that the permutation is random, but it also guarantees that the element with higher weight has higher probability of ranking in the front of the permutation, and vice versa. Basically, there are two types of algorithms for random permutation, one for the same possibilities and the other for different possibilities. Compare the result of the test and the original probability weight, It shows that the permutation is random and the permutation embodies the probability weight of each element.

关键词： random algorithm probability weight random permutation quick sort algorithm

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random, Deterministic, and Hybrid algorithms for Distributed Beamforming

Random, Deterministic, and Hybrid Algorithms for Distributed...

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5th Advanced Satellite Multimedia Systems Conference / 11th Signal Processing for Space Communications Workshop (ASMS/SPSC)

作者： Thibault, Ilaria Corazza, Giovanni E. Deambrogio, Lina Univ Bologna ARCES DEIS Viale Risorgimento 2 I-40136 Bologna Italy

ISBN: (纸本)9781424468331

Distributed beamforming is a form of cooperation among nodes in a wireless network to efficiently deliver a common message to a distant receiver. The critical requirement to achieve electromagnetic coherence is to synchronize the initial phases of all local oscillators, introducing minimal overhead and complexity. In the paper we introduce random, deterministic and hybrid algorithms, all based on feedback from the common receiver as the driving force, but with different phase update strategies. Our numerical results show that these algorithms achieve faster convergence time than previously proposed approaches.

关键词： array signal processing deterministic algorithm distant receiver distributed beamforming electromagnetic coherence feedback hybrid algorithm local oscillator message delivery radio networks radio receivers random algorithm wireless network

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RET: A random and encounter time based forwarding mechanism for opportunistic network

RET: A random and encounter time based forwarding mechanism ...

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3rd International Symposium on Information Processing, ISIP 2010

作者： Shu, Jian Yu, Kun Liu, Linlan Chen, Yebin Fan, Youlei Internet of Things Technology Institute School of Software Nanchang Hangkong University Nanchang China Internet of Things Technology Institute School of Information Engineering Nanchang Hangkong University Nanchang China

ISBN: (纸本)9780769542614

Based on the analysis of the existing utility-based forwarding mechanisms, for the inefficient forwarding of history encounter time based forwarding mechanism in the initial phase of the node movement, a random and Encounter Time Based algorithm named RET is proposed in this paper. RET divides the nodes moving into two phases - random forwarding phase (initial phase) and utility-based forwarding phase. In random forwarding phase, random algorithm is used to forward packets. And in utility-based forwarding phase, the packets are forwarded by using utility value. The results of simulation experiment show that the RET algorithm can reduce the delivery delay under low node density. © 2010 IEEE.

关键词： RET algorithm computer networks delivery delay forwarding mechanism historical encounter time historical encounter time low node density node movement opportunistic network opportunistic network packet forwarding random algorithm random and encounter time based forwarding mechanism random forwarding phase random processes utility value utility-based utility-based forwarding mechanism utility-based forwarding phase

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Trajectory Optimization and Power Allocation Scheme Based on DRL in Energy Efficient UAV-Aided Communication Networks

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Chinese Journal of Electronics 2022年第3期31卷 397-407页

作者： WANG Chaowei CUI Yuling DENG Danhao WANG Weidong JIANG Fan School of Electronic Engineering Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education School of Telecommunications and Information Engineering Xi'an University of Posts and Telecommunications

With flexibility, convenience and mobility, unmanned aerial vehicles(UAVS) can provide wireless communication networks with lower costs, easier deployment, higher network scalability and larger *** paper proposes the deep deterministic policy gradient algorithm to jointly optimize the power allocation and flight trajectory of UAV with constrained effective energy to maximize the downlink throughput to ground users. To validate the proposed algorithm, we compare with the random algorithm, Q-learning algorithm and deep Q network algorithm. The simulation results show that the proposed algorithm can effectively improve the communication quality and significantly extend the service time of UAV. In addition, the downlink throughput increases with the number of ground users.

关键词： unmanned aerial vehicles remotely operated vehicles trajectory optimization Q-learning algorithm power allocation scheme flight trajectory communication quality optimisation mobile robots random algorithm deep Q network algorithm learning systems constrained effective energy ground users autonomous aerial vehicles network scalability resource allocation UAVS energy efficient UAV-aided communication networks gradient methods deep deterministic policy gradient algorithm trajectory control radio networks wireless communication networks

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Generalized self-profit maximization and complementary-profit maximization in attribute networks

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2023年第1期45卷 1-27页

作者： Du, Liman Gao, Suixiang Yang, Wenguo Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

Profit Maximization problem is an extension of Influence Maximization problem and aims to find a small set of initial adopters to maximize the expected profit generated by all the adopters after information dissemination. To the best of our knowledge, almost all the related works about Profit Maximization focus on one-entity diffusion model. In addition, emotion tendency and interest classification also play a role in real marketing process while their importance are underestimated in a lot of research. In this paper, we propose two novel nonsubmodular optimization problems, Generalized Self-profit Maximization in Attribute networks (GSMA) and Generalized Complementary-profit Maximization in Attribute networks (GCMA). Based on attribute networks' community structure, these two problems consider the influence of both emotion tendency and interest classification on information dissemination process. GSMA problem focuses on supplement relationship and aims to maximize the expected value of profit. Whereas GCMA problem concentrates on the dependency relationship between entities and its goal is boosting the expected value of profit generated by adopter for an entity by selecting some initial adopters for its complementing entities. To solve these two problems, the R-GPMA algorithm framework which is inspired by sampling method and martingale analysis is designed. We evaluate our proposed algorithm by conducting experiments on randomly generated and real data sets and show that R-GPMA is superior in effectiveness and accuracy comparing with other baseline algorithms.

关键词： Profit maximization Nonsubmodularity Attribute networks random algorithm Martingale

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Influence maximization in mobile social networks based on RWP-CELF

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COMPUTING 2024年第6期106卷 1913-1931页

作者： Xu, Zhenyu Zhang, Xinxin Chen, Mingzhi Xu, Li Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou Fujian Peoples R China Fujian Normal Univ Fujian Prov Key Lab Network Secur & Cryptol Fuzhou Fujian Peoples R China Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou Peoples R China

Influence maximization (IM) problem for messages propagation is an important topic in mobile social networks. The success of the spreading process depends on the mechanism for selection of the influential user. Beside selection of influential users, the computation and running time should be considered in this mechanism to ensure the accurecy and efficient. In this paper, considering that the overhead of exact computation varies nonlinearly with fluctuations in data size, random algorithm with smoother complexity change was designed to solve the IM problem in combination with greedy algorithm. Firstly, we proposed a method named two-hop neighbor network influence estimator to evaluate the influence of all nodes in the two-hop neighbor network. Then, we developed a novel greedy algorithm, the random walk probability cost-effective with lazy-forward (RWP-CELF) algorithm by modifying cost-effective with lazy-forward (CELF) with random algorithm, which uses 25-50 orders of magnitude less time than the state-of-the-art algorithms. We compared the influence spread effect of RWP-CELF on real datasets with a theoretically proven algorithm that is guaranteed to be approximately optimal. Experiments show that the spread effect of RWP-CELF is comparable to this algorithm, and the running time is much lower than this algorithm.

关键词： Influence maximization Mobile social network Two-hop neighbor network influence estimator random algorithm Greedy algorithm 9103D

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Federated Gradient Matching Pursuit

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第6期70卷 4512-4537页

作者： Jeong, Halyun Needell, Deanna Qin, Jing Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA Univ Kentucky Dept Math Lexington KY 40506 USA

Traditional machine learning techniques require centralizing all training data on one server or data hub. However, with the development of communication technologies and a huge amount of decentralized data on many clients, collaborative machine learning has become the main interest while providing privacy-preserving frameworks. Federated learning (FL) provides such a solution to learn a shared model while keeping training data at local clients. On the other hand, in a wide range of machine learning and signal processing applications, the desired solution naturally has a certain structure that can be framed as sparsity with respect to a certain dictionary. This problem can be formulated as an optimization problem with sparsity constraints and solving it efficiently has been one of the primary research topics in the traditional centralized setting. In this paper, we propose a novel algorithmic framework, federated gradient matching pursuit (FedGradMP), to solve the sparsity constrained minimization problem in the FL setting. We also generalize our algorithms to accommodate various practical FL scenarios when only a subset of clients participate per round, when the local model estimation at clients could be inexact, or when the model parameters are sparse with respect to general dictionaries. Our theoretical analysis shows the linear convergence of the proposed algorithms. A variety of numerical experiments are conducted to demonstrate the great potential of the proposed framework - fast convergence both in communication rounds and computation time for many important scenarios without intricate parameter tuning.

关键词： Convergence Servers Dictionaries Matching pursuit algorithms Federated learning Tuning Distributed databases sparse recovery gradient matching pursuit random algorithm

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Network-on-chip heuristic mapping algorithm based on isomorphism elimination for NoC optimisation

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IET COMPUTERS AND DIGITAL TECHNIQUES 2020年第6期14卷 272-280页

作者： Weng Xiaodong Liu Yi Yang Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China

With the development of network-on-chip (NoC) theory, lots of mapping algorithm have been proposed to solve the application mapping problem which is an NP-hard (non-polynomial hard) problem. Most algorithms are based on a heuristic algorithm. They are trapped by iterations limited, not by the distance between iterations, because of the isomorphism of mapping sequence. In this study, the authors define and analyse the isomorphism with the genetic algorithm (GA) which is a heuristic algorithm. Then, they proposed an approach called density direction transform algorithm to eliminate the isomorphism of mapping sequence and accelerate the convergence of population. To verify this approach, they developed a density-direction-based genetic mapping algorithm (DDGMAP) and make a comparison with genetic mapping algorithm (GMA). The experiment demonstrates that compared to the random algorithm, their algorithm (DDGMAP) can achieve on an average 23.48% delay reduction and 7.15% power reduction. And DDGMAP gets better performance than GA in searching the optimal solution.

关键词： network-on-chip computational complexity genetic algorithms iterative methods transforms mapping sequence heuristic algorithm density-direction-based genetic mapping algorithm random algorithm heuristic mapping algorithm isomorphism elimination NoC optimisation network-on-chip application mapping problem NP-hard problem nonpolynomial hard genetic mapping algorithm density direction transform algorithm

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Link Flow Estimation in Traffic Networks on the Basis of Link Flow Observations

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JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2011年第4期15卷 205-222页

作者： Castillo, Enrique Gallego, Inmaculada Maria Menendez, Jose Jimenez, Pilar Univ Cantabria Dept Appl Math & Computat Sci E-39005 Santander Spain Univ Castilla La Mancha Dept Civil Engn E-13071 Ciudad Real Spain

Given a traffic network, the problem of identifying the smallest subset of links on which to locate sensors that allow the exact estimation of a given subset of links flows is dealt with, and methods for solving this partial link-observability problem are given. As sources of information, the authors consider 2 separate types of link sensors: counters and scanners. The first type leads to a method, which is an alternative to the previous method of Hu, Peeta, and Chu. First, the authors show how the previous method can be directly used for solving the partial observability problem. Next, the authors present a simple alternative algorithm, based on the pivoting strategy, that can include information about route and Origin-Destination (OD) flows observability. The observability problem based on scanners leads to a more difficult problem but supplies much more information about traffic flows. The authors give 2 simple algorithms for solving this problem in this case. The first algorithm allows checking that a given subset of links supplies the required information to estimate the flows in the selected subset of links, and provides information about route and OD observability. The second, which is a random algorithm, permits reducing the number of links of an initial subset of scanned links that solves the problem, and when a further reduction is not possible, modifies the initial set of scanning links for a new trial randomly. The proposed methods are illustrated using the parallel highway network of Hu, Peeta, and Chu. Last, the authors apply methods to two examples of applications-the Nguyen-Dupuis and the real Cuenca network-and provide some conclusions.

关键词： Link Flow Estimation random algorithm Optimal Sensor Location OD and Route Observability

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RLS: An efficient time series clustering method based on u-shapelets

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INTELLIGENT DATA ANALYSIS 2018年第4期22卷 767-785页

作者： Meng, Qinghong Pu, Peng East China Normal Univ Sch Comp Sci & Software Engn Shanghai 200062 Peoples R China

Time series clustering has been attracted great interest in the last decade. Most time series clustering works focus on clustering algorithms and similarity measures. Recently a u-shapelet-based time series clustering method has been proposed which can not only hold a high performance of clustering but also offer an acceptable interpretation of clustering result. Time complexity is the major issue in the process of discovering u-shapelets, particularly for huge datasets. In this paper, we propose a random Local Search algorithm that reduces the time to discover u-shapelets, meanwhile keeps or even improves the quality of clustering. Our algorithm first randomly samples subsequences from time series to reduce the time cost of the u-shapelet search problem. It then uses a local search strategy to make clustering result more excellent and stable. We test our approach extensively on 27 UCR time series datasets, and obtain improved clustering accuracies over existing approaches. The experiment shows that our method outperforms the primitive algorithm by up to 2 orders of magnitude on some datasets in runtime. Because the method allows a fast u-shapelet discovery, it is feasible to apply u-shapelets clustering on large datasets.

关键词： Data mining time series clustering u-shapelet random algorithm local search method

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