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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Shengwei Wang Jianhua Lyu Baili Zhang School of Software Southeast University Nanjing China College of Computer Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

As a significant field within data mining, association rule mining plays a pivotal role in unveiling relationships between items within a dataset. Existing approaches in multivariate time series (MTS) association rule mining primarily emphasize the generation of association rules by combining features from different variables, but neglect to adequately consider the temporal relationship between these features and ignore the trend information of variables, resulting in an incomplete capture of the essential characteristics inherent in MTS data. In this paper, we introduce a novel framework for association rule mining in multivariate time series. First, we discretize the multivariate time series into the symbolic sequence using trend-based symbolic aggregate approximation(TSAX). Then, a rule mining method is introduced to discover association rules that consider the temporal relationship among symbols of different variables. The results of experiments demonstrate the discovery of significant rules within the “Weather&Traffic” dataset which gives the traffic conditions in France depending on the weather. Further, we also provide the outcomes of the sensitivity analysis conducted on our method, illustrating that significant rules can be mined by adjusting the parameters of TSAX to different settings. The experimental results highlight the utility of our proposed framework in discovering temporal association rules within multivariate time series datasets.

关键词： Control engineering Sensitivity analysis Aggregates Time series analysis Symbols Market research approximation algorithms

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A dynamic optimization approach to steady-state input allocation for nonlinear redundant systems

A dynamic optimization approach to steady-state input alloca...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Roberto Masocco Lorenzo Tarantino Sergio Galeani Mario Sassano Department of Civil Engineering and Computer Science Engineering University of Rome Tor Vergata Rome Italy

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper addresses the problem of dynamic input allocation for nonlinear systems that exhibit a redundant input configuration and admit a periodic steady-state behavior. The control task is formulated in a general framework that encompasses a wide variety of practical scenarios, providing greater flexibility in the definition of the operators involved. The peculiar challenges posed by nonlinear control allocation problems are then examined from a different perspective, offered by a formulation in the context of optimal control theory. The similarities and differences between the two formulations are highlighted and discussed, with the aim of establishing a link between the two subjects in order to share their respective tools and results. Finally, an algorithm to determine approximate solutions to the control allocation problem is developed and refined using dynamic optimization tools.

关键词： Machine learning algorithms Heuristic algorithms Optimal control Machine learning Dynamic scheduling approximation algorithms Steady-state Resource management Nonlinear systems Optimization

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Implementation and Performance Measurement of the SUHDSA (Secure, Useful, and High-performance Data-Stream Anonymization)+ Algorithm

Implementation and Performance Measurement of the SUHDSA (Se...

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International Conference on Computer Communications and Networks (ICCCN)

作者： Jiyeon Lee Soonseok Kim Dept. AI Information Security Halla University Wonju Korea

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

This paper focuses on tables in microdata format, where each tuple contains specific individual information collected in real-time and stored at the tuple level. The collected information within each tuple, being personal, necessitates privacy protection. Therefore, we need to anonymize this information, which is the main theme of this paper. In this context, we have proposed the SUHDSA algorithm, an improvement over Sopaoglu and Abul's UBDSA algorithm, and have further enhanced its security with the proposed SUHDSA+ algorithm. This paper aims to validate the results of our previously proposed SUHDSA+ algorithm through empirical experiments. The experiments show that while the existing SUHDSA+ takes between 3.94 to 7.98 seconds longer in execution time compared to the SUHDSA algorithm, it performs about 45.6% faster than the original UBDSA. In terms of information loss, there was an increase of up to 18% compared to the original SUHDSA; however, when the k-value increased from 3 to 10, the loss rate was up to 19% less, indicating a significant impact of the increased k-value.

关键词： Privacy Codes Data integrity Computational modeling approximation algorithms Real-time systems Loss measurement

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Invariant Checking for SMT-based Systems with Quantifiers

arXiv

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arXiv 2024年

作者： Redondi, Gianluca Cimatti, Alessandro Griggio, Alberto McMillan, Kenneth Fondazione Bruno Kessler Universitá di Trento Italy Fondazione Bruno Kessler Italy University of Texas Austin United States

This paper addresses the problem of checking invariant properties for a large class of symbolic transition systems, defined by a combination of SMT theories and quantifiers. State variables can be functions from an uninterpreted sort (finite, but unbounded) to an interpreted sort, such as the the integers under the theory of linear arithmetic. This formalism is very expressive and can be used for modeling parameterized systems, array-manipulating programs, and more. We propose two algorithms for finding universal inductive invariants for such systems. The first algorithm combines an IC3-style loop with a form of implicit predicate abstraction to construct an invariant in an incremental manner. The second algorithm constructs an under-approximation of the original problem, and searches for a formula which is an inductive invariant for this case;then, the invariant is generalized to the original case, and checked with a portfolio of techniques. We have implemented the two algorithms and conducted an extensive experimental evaluation, considering various benchmarks and different tools from the literature. As far as we know, our method is the first capable of handling in a large class of systems in a uniform way. The experiment shows that both algorithms are competitive with the state of the art. © 2024, CC BY.

关键词： approximation algorithms

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Deep-Unfolded Joint Activity and Data Detection for Grant-Free Transmission in Cell-Free Systems

Deep-Unfolded Joint Activity and Data Detection for Grant-Fr...

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International Symposium on Wireless Communication Systems (ISWCS)

作者： Gangle Sun Wenjin Wang Wei Xu Christoph Studer National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

Massive grant-free transmission and cell-free wireless communication systems have emerged as pivotal enablers for massive machine-type communication. This paper proposes a deep-unfolded joint activity and data detection (DU-JAD) algorithm for massive grant-free transmission in cell-free systems. We first formulate a joint activity and data detection optimization problem, which we solve approximately using forward-backward splitting (FBS). We then apply deep unfolding to FBS to optimize algorithm parameters using machine learning. In order to improve data detection (DD) performance, reduce algorithm complexity, and enhance active user detection (AUD) accuracy, we employ a momentum strategy, an approximate posterior mean estimator, and a novel soft-output AUD module, respectively. Simulation results confirm the efficacy of DU-JAD for AUD and DD.

关键词： Wireless communication Machine learning algorithms Accuracy Simulation Machine learning approximation algorithms Complexity theory

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An Approximate Least-Squares Method for Fast Gridless Radar Imaging

An Approximate Least-Squares Method for Fast Gridless Radar ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Longhui Wang Zhanyang Ai Jian Wang Department of Electronic Engineering Tsinghua University Beijing P.R.China Aerospace Information Research Institute Chinese Academy of Sciences Beijing P.R.China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This paper presents a fast method called approximate least-squares (ALS) algorithm to address the off-grid problem in tomographic synthetic aperture radar (TomoSAR) imaging. First, we give the model of TomoSAR imaging and formulate the off-grid optimization problem. Next, we propose ALS algorithm to solve the optimization problem. ALS uses the second-order approximation of Taylor expansion to represent the off-grid optimization problem, and gives approximate closed-form solution for fast processing. Finally, numerical simulations confirm that ALS achieves gridless TomoSAR imaging faster while ensuring performance.

关键词： Imaging Radar imaging Tomography approximation algorithms Taylor series Numerical simulation Real-time systems

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A Low-Complexity and High-Quality Reconstruction Algorithm for Terahertz Single-Pixel Imaging

A Low-Complexity and High-Quality Reconstruction Algorithm f...

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International Conference on Signal Processing and Communication Systems, ICSPCS

作者： Chieh-Li Wang Yuan-Hao Huang Department of Electric Engineering Natioinal Tsing Hua University Hsinchu Taiwan R.O.C

ISBN: (数字)9798350389630

ISBN: (纸本)9798350389647

Single-pixel imaging is a promising technique for Terahertz (THz) imaging, because it can largely reduce the numbers of expensive THz emitter and detector. Single-pixel imaging uses compressive sensing (CS) to measure the compressed THz signal and to recover the object image. This work presents a low-complexity and high-quality total variation augmented Lagrangian method (2D-TVALM) algorithm based on the well-known total variation augmented Lagrangian alternating-direction algorithm (TVAL3). TVAL3 is a convex optimization algorithm used to solve the problem of total variation (TV) regularization. By designing the sampling matrix and some mathematic approximation, this work largely reduces the computational complexity and speed up the convergence. The simulation results show that the reconstruction quality of the proposed algorithm is about 7 dB higher than TVAL3 in PSNR and the performance is much better than that of the other CS algorithms. In the computation time, the proposed algorithm is about 31 times faster than TVAL3. Finally, the average PSNR and elapsed time of the proposed algorithm are 38.43 dB and 0.0558 s respectively with 200 iterations in the 15 test patterns.

关键词： Terahertz wave imaging TV Simulation Signal processing algorithms Signal processing approximation algorithms Sensors Computational complexity Image reconstruction Terahertz communications

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Throughput Enhancement of Full-Duplex CSMA Networks Using Multiplayer Bandits

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IEEE INTERNET OF THINGS JOURNAL 2021年第15期8卷 11807-11821页

作者： Tong, Jingwen Fu, Liqun Han, Zhu Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Xiamen Univ Minist Educ Key Lab Underwater Acoust Commun & Marine Informa Xiamen 361005 Peoples R China Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

This article studies the network-level throughput of a full-duplex (FD)-enabled CSMA network, considering the link's transmit power (TP) control, carrier-sensing threshold (CST) adjustment, and logarithm access intensity (LAI) adaptation. With the FD technique, a transmitter-receiver pair can transmit and receive simultaneously in the same frequency band. We aim to find the best combination of TP, CST, and LAI for each link to maximize the FD-CSMA network throughput. However, adjusting each link's TP and CST will change the network's carrier-sensing relation or contention graph, consequently leading to a computationally intractable network optimization problem. On the other hand, it is difficult to jointly optimize these three parameters in a fully distributed network. To overcome these, we first decompose this network optimization problem into two subproblems: 1) a joint control and scheduling problem in the transport- and media access control (MAC)-layer and 2) a parameter selection problem in the PHY-layer. Then, the multiplayer multiarmed bandit (MPMAB) framework has been introduced to address this problem by solving the two subproblems alternately. We put forth a fully distributed algorithm, named the stochastic and adversarial optimal FD-CSMA (SAO-FD-CSMA) algorithm, to solve the MPMAB problem by taking advantage of the optimization tool and the bandit theory. The numerical results show that the proposed algorithm outperforms the state-of-the-art bandit algorithms and can improve the network throughput by 43% compared with the random selection method.

关键词： Multiaccess communication Throughput Optimization Stochastic processes approximation algorithms Tuning Internet of Things CSMA full duplex (FD) multiplayer multiarmed bandit (MPMAB) stochastic and adversarial optimal FD-CSMA (SAO-FD-CSMA) algorithm

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Contiguous Allocation of Indivisible Items on a Path

arXiv

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arXiv 2024年

作者： Kawase, Yasushi Roy, Bodhayan Sanpui, Mohammad Azharuddin The University of Tokyo Japan Indian Institute of Technology Kharagpur India

We study the problem of allocating indivisible items on a path among agents. The objective is to find a fair and efficient allocation in which each agent’s bundle forms a contiguous block on the line. We say that an instance is (a, b)-sparse if each agent values at most a items positively and each item is valued positively by at most b agents. We demonstrate that, even when the valuations are binary additive, deciding whether every item can be allocated to an agent who wants it is NP-complete for the (4, 3)-sparse instances. Consequently, we provide two fixed-parameter tractable (FPT) algorithms for maximizing utilitarian social welfare, with respect to the number of agents and the number of items. Additionally, we present a 2-approximation algorithm for the special case when the valuations are binary additive, and the maximum utility is equal to the number of items. Also, we provide a 1/a-approximation algorithm for the (a, b)-sparse instances. Furthermore, we establish that deciding whether the maximum egalitarian social welfare is at least 2 or at most 1 is NP-complete for the (6, 3)-sparse instances, even when the valuations are binary additive. We present a 1/a-approximation algorithm for maximizing egalitarian social welfare for the (a, b)-sparse instances. Besides, we give two FPT algorithms for maximizing egalitarian social welfare in terms of the number of agents and the number of items. We also explore the case where the order of the blocks of items allocated to the agents is predetermined. In this case, we show that both maximum utilitarian social welfare and egalitarian social welfare can be computed in polynomial time. However, we determine that checking the existence of an EF1 allocation is NP-complete, even when the valuations are binary additive. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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Real-Time Obstacle Detection and Localization for USV Using Stereo Vision and Edge Computing

Real-Time Obstacle Detection and Localization for USV Using ...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Di Wang Haiming Chen MD Asif Hasan Weihao Zhang Yuqing Hu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Zhejiang China Information Center Ningbo Polytechnic Ningbo Zhejiang China Ningbo Industrial Internet Research Institute Ningbo Zhejiang China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

Unmanned Surface Vessel (USV) is widely used in various fields, and obstacle avoidance is a key technology for USV. Accurately detecting the position and type of obstacles is crucial for obstacle avoidance. Active sensors can accurately detect the position of obstacles but cannot distinguish their types, necessitating the use of vision sensors. Therefore, this paper employs stereo vision and proposes a stereo obstacle detection and localization method, based on SGBM and YOLOv5. The SGBM algorithm generates depth information, while the YOLOv5 algorithm detects the position of obstacles in the image. To address the issue of errors in measuring the continuous position of obstacles, this paper uses a Kalman filter to smooth the trajectory. Experimental results show that the proposed method improves obstacle localization accuracy by approximately 38% compared to raw measurements. Besides, considering the energy constraints and computing capabilities of lightweight USV, which cannot meet the demands of processing vision tasks, this paper introduces an edge-device collaboration architecture, by offloading the USV computing tasks to edge nodes, thereby extending the computing capacity and energy efficiency of the USV. Experimental results indicate that the proposed architecture meets the real-time obstacle avoidance requirements of USV.

关键词： Location awareness YOLO Accuracy Image edge detection Computer architecture approximation algorithms Stereo vision Trajectory Collision avoidance Underactuated surface vessels

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