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QUBO Models for the FIFO Stack-Up Problem and Experimental Evaluation on a Quantum Annealer

SN computer Science 2024年第7期5卷 818页

作者： Gebler, Colin Rethmann, Jochen Ueberholz, Peer Department of Electrical Engineering and Computer Science Niederrhein University of Applied Sciences Reinarzstr. 49 Krefeld 47805 Germany

Quantum annealing has been applied to combinatorial optimization problems in recent years. In this paper we study the possibility to use quantum annealing for solving the combinatorial FIFO Stack-Up problem, where bins have to be stacked-up from a conveyor belt onto pallets. The problem is NP-hard and can be solved using linear programming approaches. We developed two QUBO (quadratic unconstrained binary optimization) objective functions based on a bin stack-up solution and a pallet stack-up solution for this problem suitable for a quantum annealer. The number of variables was minimized to increase the performance and their dependence on the number of bins and pallets was discussed. The performances of both methods were studied for various small problem sizes on a D-Wave quantum annealer. We found that only tiny instances could be solved and looked at the terms of the QUBO-formulations, which cause the quantum annealer to fail for larger problem sizes. Furthermore we compare the results to the performance of a classic computer using the same QUBO-formulations. © The Author(s) 2024.

关键词： FIFO Stack-Up problem NP-hard Quantum annealer QUBO formulation

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Decentralized Multi-Armed Bandit Can Outperform Classic Upper Confidence Bound: A Homogeneous Case over Strongly Connected Graphs 63

Decentralized Multi-Armed Bandit Can Outperform Classic Uppe...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhu, Jingxuan Liu, Ji Zhejiang Lab China Stony Brook University Department of Applied Mathematics and Statistics United States Stony Brook University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350316339

This paper studies a homogeneous decentralized multi-armed bandit problem, in which a network of multiple agents faces the same set of arms, and each agent aims to minimize its own regret. A fully decentralized upper confidence bound (UCB) algorithm is proposed for a multi-agent network whose neighbor relations are described by a directed graph. It is shown that the decentralized algorithm guarantees each agent to achieve a lower logarithmic asymptotic regret compared to the classic UCB algorithm, provided the neighbor graph is strongly connected. The improved asymptotic regret upper bound is reciprocally related to the maximal size of a local neighborhood within the network. The roles of graph connectivity, maximum local degree, and network size are analytically elucidated in the expression of regret. © 2024 IEEE.

关键词： Decentralized systems

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Single-Phase Direct PWM Bipolar Buck–Boost AC–AC Converter Without Commutation Problem

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2023年第6期11卷 5940-5953页

作者： Ahmed, Hafiz Furqan Ali Khan, Ashraf Abdoli, Iman Zaabi, Omar Al the Department of Electrical Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan the Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNLA1B 3X7 Canada the Department of Electrical Engineering Shahid Beheshti University Tehran19839-69411 Iran Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

— The direct pulsewidth modulation (PWM) ac–ac converters are seeing rapid development due to their single-stage power conversion with reduced footprints, due to the elimination of intermediate dc-link capacitor. However, they only provide unipolar voltage gain and fixed frequency operation and use the bidirectional voltage-blocking devices without natural inductor current free-wheeling path, restricting their practical applications. This article proposes a direct PWM bipolar and step-variable frequency buck–boost ac–ac converter. The proposed converter is fabricated by connecting input–output LC filters and a small value film capacitor to six unidirectional voltage-blocking devices, which are arranged as a three-leg inverter module. It can provide noninverting and inverting buck–boost voltage operations with a symmetric bipolar voltage gain of ±d/(1 − d) and can also change the frequency of output voltage in discrete steps, same as that of single-phase matrix converters (MCs). It has no open-circuit risk of inductor due to the presence of natural current free-wheeling path during PWM dead time. Therefore, it is unaffected by the commutation problem of conventional direct ac–ac converters. The proposed converter can realize high-quality input and output due to the presence of second-order input–output filters with continuous input–output currents. A detailed analysis is provided based on proposed switch modulation strategies and confirmed through extensive experimental tests on a laboratory-fabricated hardware. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Matrix converters

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Prediction of permeability from well logs using a new hybrid machine learning algorithm

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Petroleum 2023年第1期9卷 108-123页

作者： Morteza Matinkia Romina Hashami Mohammad Mehrad Mohammad Reza Hajsaeedi Arian Velayati Department of Petroleum Engineering Omidiyeh BranchIslamic Azad UniversityOmidiyehIran Department of Applied Mathematics Faculty of Mathematics and Computer SciencesAmirkabir University of TechnologyTehranIran Faculty of Mining Petroleum and Geophysics EngineeringShahrood University of TechnologyShahroodIran Department of Chemical and Materials Engineering University of AlbertaEdmontonCanada

Permeability is a measure of fluid transmissibility in the rock and is a crucial concept in the evaluation of formations and the production of hydrocarbon from the *** techniques such as intelligent methods have been introduced to estimate the permeability from other petrophysical *** efficiency and convergence issues associated with artificial neural networks have motivated researchers to use hybrid techniques for the optimization of the networks,where the artificial neural network is combined with heuristic *** research combines social ski-driver(SSD)algorithm with the multilayer perception(MLP)neural network and presents a new hybrid algorithm to predict the value of rock *** performance of this novel technique is compared with two previously used hybrid methods(genetic algorithm-MLP and particle swarm optimization-MLP)to examine the effectiveness of these hybrid methods in predicting the permeability of the *** results indicate that the hybrid models can predict rock permeability with excellent ***-SSD method yields the highest coefficient of determination(0.9928)among all other methods in predicting the permeability values of the test data set,followed by MLP-PSO and MLP-GA,***,the MLP-GA converged faster than the other two methods and is computationally less expensive.

关键词： Permeability Artificial neural network Multilayer perceptron Social ski driver algorithm

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Performance-Guaranteed Finite-Time Tracking of Strict-Feedback Systems with Unknown Control Directions: A Novel Switching Mechanism

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IEEE Transactions on Automatic Control 2025年第6期70卷 4061-4068页

作者： Mao, Bing Wu, Xiaoqun Fan, Ziye Lu, Jinhu Chen, Guanrong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Wuhan University School of Mathematics and Statistics Hubei430072 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong

The problem of achieving performance-guaranteed finite-time exact tracking for uncertain strict-feedback nonlinear systems with unknown control directions is addressed. A novel logic switching mechanism with monitoring functions is designed to deal with unknown control directions. This approach is different from existing methods like the Nussbaum gain technique and smooth orientation functions, which are inapplicable to finite-time exact tracking. The main challenge lies in designing monitoring functions and determining switching rules, which is well resolved through three steps. Most significantly, in contrast to existing works that solely guarantee bounded tracking or asymptotic exact tracking, the new mechanism incorporates novel integral sliding mode techniques to achieve finite-time exact tracking with guaranteed performance. Finally, simulations are presented with comparison to demonstrate the effectiveness of the proposed methods. © 1963-2012 IEEE.

关键词： Feedback

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All-photonic artificial-neural-network processor via nonlinear optics

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Physical Review applied 2024年第1期22卷 014009-014009页

作者： Jasvith Raj Basani Mikkel Heuck Dirk R. Englund Stefan Krastanov Department of Electrical and Electronics Engineering Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics and Department of Electrical Engineering and Computer Science Department of Electrical and Photonics Engineering College of Information and Computer Sciences

Optics and photonics have recently captured interest as a platform to accelerate linear matrix processing, otherwise a bottleneck in traditional digital electronics. In this paper we propose an all-photonic computational accelerator wherein information is encoded in the amplitudes of frequency modes stored in a single ring resonator. Interaction among these modes is enabled by nonlinear optical processes. Both the matrix multiplication and elementwise activation functions on these modes (the artificial neurons) are performed through coherent processes, enabling the direct representation of negative and complex numbers without having to pass through digital electronics, a common limitation in today’s photonic architectures. This design also has a drastically lower hardware footprint compared with today’s electronic and optical accelerators, as the entirety of the matrix multiplication happens in a single multimode resonator on chip. Our architecture is unique in providing a completely unitary, reversible mode of computation, enabling on-chip analog Hamiltonian-echo backpropagation for gradient descent and other self-learning tasks. Moreover, the computational speed increases with the power of the pumps to arbitrarily high rates, as long as the circuitry can sustain the higher optical power. Lastly, the design presented here is a less demanding version of a future room-temperature quantum computational device. Therefore, while this architecture is already viable today, direct reinvestments in it would be enabling its evolution into quantum computational hardware.

关键词： Machine learning Neuromorphic computing Nonlinear optics Photonics Artificial neural networks Deep learning Neural network simulations Optical computing

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Unsupervised Anomaly Detection in Predictive Maintenance using Sound Data 31

Unsupervised Anomaly Detection in Predictive Maintenance usi...

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31st Symposium of Advanced Database Systems, SEBD 2023

作者： Ferraro, Antonino Galli, Antonio Gatta, Valerio La Moscato, Vincenzo Postiglione, Marco Sperlì, Giancarlo Moscato, Francesco Department of Electrical Engineering and Information Technology University of Naples Federico II Italy Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Italy

This paper represent an extended abstract of a recent proposal ([1]), in which the authors present a new methodology for unsupervised anomaly detection in predictive maintenance using sound data. In particular, the methodology leverages LSTM and CNN-based autoencoders to process continuous audio streams from different audio sources in real-world factories based on a customized sliding window. The novelties of the proposed approach include a general methodology for unsupervised anomaly detection, machine ID encoding using one-hot encoding, and conditioning an autoencoder by jointly analyzing the relationships between the mel-spectrogram and the machine ID to compute an anomaly score. The methodology achieves good performances in terms of effectiveness and in addition low inference time and memory requirements. © 2023 CEUR-WS. All rights reserved.

关键词： Anomaly detection

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Rapid computational algorithm with minimum user input for reconstructing phase images in structured illumination Digital Holographic Microscopy

Rapid computational algorithm with minimum user input for re...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress

作者： Obando-Vásquez, Sofía Castaneda, Raúl Restrepo, René Trujillo, Carlos Doblas, Ana Applied Optics Group School of Applied Science and Engineering EAFIT University Medellin Colombia Electrical and Computer Engineering Department University of Massachusetts Dartmouth United States

A rapid computational algorithm is presented for Structured Illumination in Digital Holographic Microscopy. The proposed algorithm is based on the minimization of two cost functions to reconstruct improved resolution ... 详细信息

关键词： Cost functions

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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