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A Comparative Study Between Analytic Hierarchy Process and Its Fuzzy Variants: A Perspective Based on Two Linguistic Models

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第11期29卷 3270-3279页

作者： Zhang, Bowen Li, Cong-Cong Dong, Yucheng Pedrycz, Witold Xidian Univ Sch Econ & Management Xian 710071 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2R3 Canada Southwest Jiaotong Univ Sch Econ & Management Chengdu 610031 Peoples R China Sichuan Univ Business Sch Chengdu 610065 Peoples R China King Abgudulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland

The analytic hierarchy process (AHP) is widely employed to guide the decision-maker to rank or evaluate the alternatives in decision activities. Its fuzzy set-based version, i.e., the fuzzy AHP, has also been widely studied and applied since its inception. The essential distinction between the AHP and fuzzy AHP comes from the diverse transformation methods between the linguistic and numeric judgments. In this article, we conduct a thorough comparative study between the AHP and fuzzy AHP methods in the framework of two linguistic models, i.e., the linguistic model based on the membership functions and two-tuple linguistic model. First, four AHP and three fuzzy AHP methods are revisited with the involvement of two linguistic models. Then, the comparison criteria are involved by calculating the cardinal or ordinal deviation between the original information and decision solutions, and the effects of the transitivity of the reciprocal matrix are also discussed in the comparative study. Finally, the detailed experiments along with a thorough comparative analysis are conducted based on the random and publicly available data to show the difference between the AHP and fuzzy AHP methods.

关键词： Schedules Stochastic processes Clustering algorithms Uncertainty approximation algorithms Programming Personnel Fuzzy analytic hierarchy process (AHP) information granularity linguistic model personalized individual semantics

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Secure Deterministic Wallet and Stealth Address: Key-Insulated and Privacy-Preserving Signature Scheme With Publicly Derived Public Key

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2022年第5期19卷 2934-2951页

作者： Liu, Zhen Yang, Guomin Wong, Duncan S. Khoa Nguyen Wang, Huaxiong Ke, Xiaorong Liu, Yining Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Univ Wollongong Wollongong NSW 2522 Australia CryptoBLK Hong Kong Peoples R China Nanyang Technol Univ Sch Phys & Math Sci Singapore 639798 Singapore Huawei Technol Co Ltd Shenzhen 518129 Peoples R China Univ Southern Calif Los Angeles CA 90007 USA

Deterministic Wallet (DW) and Stealth Address (SA) mechanisms have been widely adopted in the cryptocurrency community, due to their virtues on functionality and privacy protection, which come from a key derivation mechanism that allows an arbitrary number of derived keys to be generated from a master key. However, these algorithms suffer a vulnerability that, when one derived key is compromised somehow, the damage is not limited to the leaked derived key only, but to the master key and in consequence all derived keys are compromised. In this article, we introduce and formalize a new signature variant, called Key-Insulated and Privacy-Preserving Signature Scheme with Publicly Derived Public Key (PDPKS), which fully captures and improves the functionality, security, and privacy requirements of DW and SA. We propose a PDPKS construction and prove its security and privacy in the random oracle model. Furthermore, we implement the construction with parameters for 128-bit security, and the results show that it is practically efficient for the setting of cryptocurrencies. With its solid guarantee on functionality, security and privacy, as well as its practical efficiency, our PDPKS construction provides a practical cryptographic tool that refines DW and SA, without security vulnerability.

关键词： Public key Privacy Companies Bitcoin Digital signatures approximation algorithms Solids Signature scheme publicly derived public key key-insulated security privacy cryptocurrency stealth addresses deterministic wallets

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A FFT-Like MIMO Detection Algorithm

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IEEE ACCESS 2020年 8卷 188233-188242页

作者： Du, Peng Zhang, Yuan Ba, Teer Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

This paper studies the multiple-input-multiple-output (MIMO) detection problem. Existing works model the MIMO detection as a tree or factor graph. This work adds a new member called the clique graph model to the MIMO detection graph model family so that MIMO detection can be transformed into FInding the maximum clique in the constructed graph. Then, a clique based MIMO detection algorithm is developed, which uses an architecture similar to the Fast Fourier Transform (FFT) and can be executed in parallel naturally. The execution time of the algorithm can be proportional to log2 Nt where Nt is the transmit antenna number. Simulation results are provided to evaluate the bit error rate (BER) performance of the proposed algorithm.

关键词： MIMO communication Detection algorithms Complexity theory Transmitting antennas Bit error rate approximation algorithms Image edge detection Detection graph model MIMO parallel processing

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Algorithm Parameters Selection Method With Deep Learning for EP MIMO Detector

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第10期70卷 10146-10156页

作者： Chen, Hang Yao, Guoqiang Hu, Jianhao Univ Elect Sci & Technol China Dept Natl Key Lab Sci & Technol Commun Chengdu 611731 Sichuan Peoples R China

Expectation Propagation (EP)-based Multiple-Input Multiple-Output (MIMO) detector achieves exceptional performance in high-dimensional systems with high-order modulations and flexible antenna configurations. However, based on our studies, the EP MIMO detector cannot achieve superior performance due to the empirical parameter selection, including initial variance and damping factors. According to the influence of the moment matching and parameter selection on the performance of the EP MIMO detector, we propose a modified EP MIMO detector (MEPD). To obtain the initial variance and damping factors which lead to better performance, we adopt a deep learning scheme, the iterative process of MEPD is unfolded to establish MEPNet for parameters training. The simulation results show that MEPD with off-line trained parameters outperforms the original one in various MIMO scenarios. Besides, the proposed MEPD with deep learning parameters selection is more robust than EPD in practical scenarios.

关键词： Detectors Training Deep learning Complexity theory approximation algorithms Massive MIMO Simulation MIMO detection expectation propagation parameters selection deep learning

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Back-Stepping Experience Replay With Application to Model-Free Reinforcement Learning for a Soft Snake Robot

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第9期9卷 7517-7524页

作者： Qi, Xinda Chen, Dong Li, Zhaojian Tan, Xiaobo Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA

In this letter, we propose a novel technique, Back-stepping Experience Replay (BER), that is compatible with arbitrary off-policy reinforcement learning (RL) algorithms. BER aims to enhance learning efficiency in systems with approximate reversibility, reducing the need for complex reward shaping. The method constructs reversed trajectories using back-stepping transitions to reach random or fixed targets. Interpretable as a bi-directional approach, BER addresses inaccuracies in back-stepping transitions through a purification of the replay experience during learning. Given the intricate nature of soft robots and their complex interactions with environments, we present an application of BER in a model-free RL approach for the locomotion and navigation of a soft snake robot, which is capable of serpentine motion enabled by anisotropic friction between the body and ground. In addition, a dynamic simulator is developed to assess the effectiveness and efficiency of the BER algorithm, in which the robot demonstrates successful learning (reaching a 100% success rate) and adeptly reaches random targets, achieving an average speed 48% faster than that of the best baseline approach.

关键词： Standards approximation algorithms Snake robots Task analysis Trajectory Training Navigation Deep reinforcement learning experience replay soft robot snake robot locomotion navigation

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Distributed Nesterov Gradient and Heavy-Ball Double Accelerated Asynchronous Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第12期32卷 5723-5737页

作者： Li, Huaqing Cheng, Huqiang Wang, Zheng Wu, Guo-Cheng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Neijiang Normal Univ Coll Math & Informat Sci Data Recovery Key Lab Sichuan Prov Neijiang 641100 Peoples R China

In this article, we come up with a novel Nesterov gradient and heavy-ball double accelerated distributed synchronous optimization algorithm, called NHDA, and adopt a general asynchronous model to further propose an effective asynchronous algorithm, called ASY-NHDA, for distributed optimization problem over directed graphs, where each agent has access to a local objective function and computes the optimal solution via communicating only with its immediate neighbors. Our goal is to minimize a sum of all local objective functions satisfying strong convexity and Lipschitz continuity. Consider a general asynchronous model, where agents communicate with their immediate neighbors and start a new computation independently, that is, agents can communicate with their neighbors at any time without any coordination and use delayed information from their in-neighbors to compute a new update. Delays are arbitrary, unpredictable, and time-varying but bounded. The theoretical analysis of NHDA is based on analyzing the interaction among the consensus, the gradient tracking, and the optimization processes. As for the analysis of ASY-NHDA, we equivalently transform the asynchronous system into an augmented synchronous system without delays and prove its convergence through using the generalized small gain theorem. The results show that NHDA and ASY-NHDA converge to the optimal solution at a linear convergence as long as the largest step size is positive and less than an explicitly estimated upper bound, and the largest momentum parameter is nonnegative and less than an upper bound. Finally, we demonstrate the advantages of ASY-NHDA through simulations.

关键词： Optimization Convergence Delays Acceleration Directed graphs approximation algorithms Computational modeling Asynchrony directed graph distributed optimization heavy ball linear convergence rate Nesterov

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Adaptive Partial Reinforcement Learning Neural Network-Based Tracking Control for Wheeled Mobile Robotic Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第7期50卷 2512-2523页

作者： Ding, Liang Li, Shu Gao, Haibo Chen, Chao Deng, Zongquan Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China

In this paper, a dynamic model of a wheeled mobile robotic (WMR) system with coupled control input is developed, which will increase the complexity of its tracking control with time-varying advance angle. To deal with this problem, a partial reinforcement learning neural network (PRLNN)-based tracking algorithm is proposed for the WMR systems. The main contributions of the PRLNN adaptive tracking control method is that it is the first control method to introduce the PRLNN adaptive control to the WMR system, which determines to solve the WMR tracking control with the time-varying advance angle. The critic neural network (NN) and action NN adaptive laws for the decoupled controllers are designed using the standard gradient-based adaptation method. According to the Lyapunov stability analysis theorem, the uniform ultimate boundedness of all signals in the WMR system can be guaranteed with the design parameters chose properly, and the tracking error converge to a small compact set nearby zero. A numerical simulation is presented to verify the effectiveness of the proposed control algorithm.

关键词： Artificial neural networks Adaptive systems Mobile robots Learning (artificial intelligence) Nonlinear systems approximation algorithms Adaptive control neural network (NN) partial reinforcement learning tracking wheeled mobile robots (WMRs)

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Effective Keyword Search Over Weighted Graphs

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第2期34卷 601-616页

作者： Kargar, Mehdi Golab, Lukasz Srivastava, Divesh Szlichta, Jaroslaw Zihayat, Morteza Ryerson Univ Ted Rogers Sch Management Toronto ON M5B 2K3 Canada Univ Waterloo Waterloo ON N2L 3G1 Canada A&T Labs Res Middletown NJ 07748 USA Ontario Tech Univ Oshawa ON L1G 0C5 Canada

Real graphs often contain edge and node weights, representing, for instance, penalty, distance or uncertainty. We study the problem of keyword search over weighted node-labeled graphs, in which a query consists of a set of keywords and an answer is a subgraph whose nodes contain the keywords. We evaluate answers using three ranking strategies: optimizing edge weights, optimizing node weights, and a bi-objective combination of both node and edge weights. We prove that optimizing node weights and the bi-objective function are NP-hard. We propose an algorithm that optimizes edge weights and has an approximation ratio of two for the unique node enumeration paradigm. To optimize node weights and the bi-objective function, we propose transformations that distribute node weights onto the edges. We then prove that our transformations allow our algorithm to also optimize node weights and the bi-objective function with the same approximation ratio of two. Notably, the proposed transformations are compatible with existing algorithms that only optimize edge weights. We empirically show that in many natural examples, incorporating node weights (both keyword holders and middle nodes) produces more relevant answers than ranking methods based only on edge weights. Extensive experiments over real-life datasets verify the effectiveness and efficiency of our solution.

关键词： Proteins Companies Databases Keyword search Security Uncertainty approximation algorithms Graph databases keyword search approximation algorithms

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Energy-efficient scheduling: classification, bounds, and algorithms

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2021年第1期46卷 1-13页

作者： Agrawal, Pragati Rao, Shrisha Maulana Azad Natl Inst Technol Dept Comp Sci & Engn Bhopal India Int Inst Informat Technol Bangalore Bangalore Karnataka India

The problem of attaining energy efficiency in distributed systems is of importance, but a general, non-domain-specific theory of energy-minimal scheduling is far from developed. In this paper, we classify the problems of energy-minimal scheduling and present theoretical foundations of the same. We derive results concerning energy-minimal scheduling of independent jobs in a distributed system with functionally similar machines with different working and idle power ratings. The machines considered in our system can have identical as well as different speeds. If the jobs can be divided into arbitrary parts, we show that the minimum-energy schedule can be generated in linear time and give exact scheduling algorithms. For the cases where jobs are non-divisible, we prove that the scheduling problems are NP-hard and also give approximation algorithms for the same along with their bounds.

关键词： Scheduling energy efficiency approximation algorithms distributed systems computational complexity

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Minimizing the Deployment Cost of UAVs for Delay-Sensitive Data Collection in IoT Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第2期30卷 812-825页

作者： Xu, Wenzheng Xiao, Tao Zhang, Junqi Liang, Weifa Xu, Zichuan Liu, Xuxun Jia, Xiaohua Das, Sajal K. Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Peoples R China South China Univ Technol Coll Elect & Informat Engn Guangzhou 510641 Peoples R China Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the `freshness' of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay B, e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour of any UAV is no greater than B. Specifically, we study two variants of the problem: one is that a UAV needs to fly to the location of each IoT device to collect its data;the other is that a UAV is able to collect the data of an IoT device if the Euclidean distance between them is no greater than the wireless transmission range of the IoT device. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio 4 4/7 for it so far. For the second variant, we devise the very first constant factor approximation algorithm. We also evaluate the performance of the proposed algorithms via extensive experiment simulations. Experimental results show that the numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms on average.

关键词： approximation algorithms Data collection Sensors Wireless communication Unmanned aerial vehicles Monitoring Dispatching Mobile data collection multiple UAV scheduling minimum numbers of UAV deployments minimum cycle cover with neighborhoods approximation algorithms

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