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Short - Term Prediction of Coke Pushing Current Peak Based on Improved ARIMA Model

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Short - Term Prediction of Coke Pushing Current Peak Based o...

Industrial Cyber-Physical Systems (ICPS)

作者： Haiyang Wei Luefeng Chen Jie Hu Yi Ren Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan

Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. Then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. The experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. The scheme is applied to actual coking production to guide production of coke.

关键词： Resistance Ovens Time series analysis Noise reduction Production Predictive models Prediction algorithms

Deep Learning Models for Intelligent Healthcare: Implementation and Challenges 7th

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Deep Learning Models for Intelligent Healthcare: Implementat...

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Rehman, Sadaqat ur Tu, Shanshan Shah, Zubair Ahmad, Jawad Waqas, Muhammad Rehman, Obaid ur Kouba, Anis Abbasi, Qammer H. Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Namal Institute Mianwali42250 Pakistan Department of Computer Science and Engineering Hammad Bin Khalifa University Doha Qatar School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Electrical Engineering Sarhad University of Science and IT Peshawar25000 Pakistan Department of Computer Science Prince Sultan University Riyadh Saudi Arabia James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9783030786083

The rapid developments of artificial intelligent (AI) is being transformed for its extensive use-cases, people-centered intelligent systems focusing on care delivery, research encounter complex problems related to improve the overall infrastructure and management of intelligent delivery service;for instance, bringing transformation in healthcare sector for monitoring patients with chronic disease. Most of these systems are driven by state-of-the-art learning algorithms i.e., Convolution Neural Network. The CNN algorithm is considered to be one of the most prominent architectures of DL. Recently, due to enormous growth in the amount of annotated data and the development of CNN hardware accelerator, further, boost the research on CNN and accomplished benchmark enactment on different applications. This paper presents cutting-edge applications of CNN for an intelligent healthcare system. We provide useful findings of different CNN features such as optimization, fast computation, design, activation function, and loss function. To our knowledge, this is the first comprehensive work to address the recent trends in the architecture of CNN, which offers insight to the underlying problems and provides the potential solutions for any given intelligent healthcare applications. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

A novel deep learning method for textual sentiment analysis

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arXiv 2021年

作者： Sadr, Hossein Solimandarabi, Mozhdeh Nazari Pedram, Mir Mohsen Teshnehlab, Mohammad Department of Computer Engineering Rasht Branch Islamic Azad University Rasht Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Industrial Control Center of Excellence Faculty of Electrical and Computer Engineering K. N. Toosi University Tehran Iran

Sentiment analysis is known as one of the most crucial tasks in the field of natural language processing and Convolutional Neural Network (CNN) is one of those prominent models that is commonly used for this aim. Although convolutional neural networks have obtained remarkable results in recent years, they are still confronted with some limitations. Firstly, they consider that all words in a sentence have equal contributions in the sentence meaning representation and are not able to extract informative words. Secondly, they require a large number of training data to obtain considerable results while they have many parameters that must be accurately adjusted. To this end, a convolutional neural network integrated with a hierarchical attention layer is proposed which is able to extract informative words and assign them higher weight. Moreover, the effect of transfer learning that transfers knowledge learned in the source domain to the target domain with the aim of improving the performance is also explored. Based on the empirical results, the proposed model not only has higher classification accuracy and can extract informative words but also applying incremental transfer learning can significantly enhance the classification performance. © 2021, CC BY.

关键词： Sentiment analysis

Policy Evaluation in Distributional LQR

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arXiv 2023年

作者： Wang, Zifan Gao, Yulong Wang, Siyi Zavlanos, Michael M. Abate, Alessandro Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology Digital Futures StockholmSE-10044 Sweden Department of Computer Science University of Oxford OxfordOX13QD United Kingdom The School of Computation Information and Technology Technical University of Munich Munich80333 Germany The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard reinforcement learning. Meanwhile, a challenge in DRL is that the policy evaluation typically relies on the representation of the return distribution, which needs to be carefully designed. In this paper, we address this challenge for the special class of DRL problems that rely on a discounted linear quadratic regulator (LQR), which we call distributional LQR. Specifically, we provide a closed-form expression for the distribution of the random return, which is applicable for all types of exogenous disturbance as long as it is independent and identically distributed (i.i.d.). We show that the variance of the random return is bounded if the fourth moment of the exogenous disturbance is bounded. Furthermore, we investigate the sensitivity of the return distribution to model perturbations. While the proposed exact return distribution consists of infinitely many random variables, we show that this distribution can be well approximated by a finite number of random variables. The associated approximation error can be analytically bounded under mild assumptions. When the model is unknown, we propose a model-free approach for estimating the return distribution, supported by sample complexity guarantees. Finally, we extend our approach to partially observable linear systems. Numerical experiments are provided to illustrate the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Improving the speed of variational quantum algorithms for quantum error correction

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arXiv 2023年

作者： Zoratti, Fabio De Palma, Giacomo Kiani, Bobak Nguyen, Quynh T. Marvian, Milad Lloyd, Seth Giovannetti, Vittorio Scuola Normale Superiore PisaI-56126 Italy Department of Mathematics University of Bologna Bologna40126 Italy Department of Electrical Engineering and Computer Science MIT 77 Massachusetts Avenue CambridgeMA02139 United States Department of Electrical and Computer Engineering Center for Quantum Information and Control University of New Mexico United States Department of Mechanical Engineering MIT 77 Massachusetts Avenue CambridgeMA02139 United States Turing Inc. CambridgeMA02139 United States NEST Scuola Normale Superiore Istituto Nanoscienze-CNR PisaI-56126 Italy

We consider the problem of devising a suitable Quantum Error Correction (QEC) procedures for a generic quantum noise acting on a quantum circuit. In general, there is no analytic universal procedure to obtain the encoding and correction unitary gates, and the problem is even harder if the noise is unknown and has to be reconstructed. The existing procedures rely on Variational Quantum Algorithms (VQAs) and are very difficult to train since the size of the gradient of the cost function decays exponentially with the number of qubits. We address this problem using a cost function based on the Quantum Wasserstein distance of order 1 (QW1). At variance with other quantum distances typically adopted in quantum information processing, QW1 lacks the unitary invariance property which makes it a suitable tool to avoid to get trapped in local minima. Focusing on a simple noise model for which an exact QEC solution is known and can be used as a theoretical benchmark, we run a series of numerical tests that show how, guiding the VQA search through the QW1, can indeed significantly increase both the probability of a successful training and the fidelity of the recovered state, with respect to the results one obtains when using conventional approaches. © 2023, CC BY-SA.

关键词： Cost functions

The SWAP Imposter: Bidirectional Quantum Teleportation and its Performance

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arXiv 2022年

作者： Siddiqui, Aliza U. Wilde, Mark M. Division of Computer Science and Engineering Louisiana State University Baton RougeLA70803 United States Hearne Institute for Theoretical Physics Department of Physics and Astronomy Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

Bidirectional quantum teleportation is a fundamental protocol for exchanging quantum information between two parties. Specifically, the two individuals make use of a shared resource state as well as local operations and classical communication (LOCC) to swap quantum states. In this work, we concisely highlight the contributions of our companion paper [Siddiqui and Wilde, arXiv:2010.07905]. We develop two different ways of quantifying the error of nonideal bidirectional teleportation by means of the normalized diamond distance and the channel infidelity. We then establish that the values given by both metrics are equal for this task. Additionally, by relaxing the set of operations allowed from LOCC to those that completely preserve the positivity of the partial transpose, we obtain semidefinite programming lower bounds on the error of nonideal bidirectional teleportation. We evaluate these bounds for some key examples-isotropic states and when there is no resource state at all. In both cases, we find an analytical solution. The second example establishes a benchmark for classical versus quantum bidirectional teleportation. Another example that we investigate consists of two Bell states that have been sent through a generalized amplitude damping channel (GADC). For this scenario, we find an analytical expression for the error, as well as a numerical solution that agrees with the former up to numerical precision. Copyright © 2022, The Authors. All rights reserved.

关键词： Errors

Limitations of Variational Quantum Algorithms: A Quantum Optimal Transport Approach

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PRX Quantum 2023年第1期4卷 010309-010309页

作者： Giacomo De Palma Milad Marvian Cambyse Rouzé Daniel Stilck França Department of Mathematics University of Bologna Bologna 40126 Italy Department of Electrical & Computer Engineering and Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA Zentrum Mathematik Technische Universität München Garching 85748 Germany QMATH Department of Mathematical Sciences University of Copenhagen Universitetsparken 5 Copenhagen 2100 Denmark ENS Lyon Inria F-69342 Lyon Cedex 07 France

The impressive progress in quantum hardware of the last years has raised the interest of the quantum computing community in harvesting the computational power of such devices. However, in the absence of error correction, these devices can only reliably implement very shallow circuits or comparatively deeper circuits at the expense of a nontrivial density of errors. In this work, we obtain extremely tight limitation bounds for standard noisy intermediate-scale quantum proposals in both the noisy and noiseless regimes, with or without error-mitigation tools. The bounds limit the performance of both circuit model algorithms, such as the quantum approximate optimization algorithm, and also continuous-time algorithms, such as quantum annealing. In the noisy regime with local depolarizing noise p, we prove that at depths L=O(p−1) it is exponentially unlikely that the outcome of a noisy quantum circuit outperforms efficient classical algorithms for combinatorial optimization problems like max-cut. Although previous results already showed that classical algorithms outperform noisy quantum circuits at constant depth, these results only held for the expectation value of the output. Our results are based on newly developed quantum entropic and concentration inequalities, which constitute a homogeneous toolkit of theoretical methods from the quantum theory of optimal mass transport whose potential usefulness goes beyond the study of variational quantum algorithms.

关键词： Quantum algorithms

Variational approach to the dynamics of dissipative quantum impurity models

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Physical Review B 2025年第15期111卷 155113-155113页

作者： Yi-Fan Qu Martino Stefanini Tao Shi Tilman Esslinger Sarang Gopalakrishnan Jamir Marino Eugene Demler Institute for Theoretical Physics ETH Zurich 8093 Zurich Switzerland Institut für Physik Johannes Gutenberg-Universität Mainz D-55099 Mainz Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China CAS Center for Excellence in Topological Quantum Computation & School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China Institute for Quantum Electronics & Quantum Center ETH Zurich 8093 Zurich Switzerland Department of Electrical and Computer Engineering Princeton University Princeton New Jersey 08540 USA

Recent experiments with quantum simulators using ultracold atoms and superconducting qubits have demonstrated the potential of controlled dissipation as a versatile tool for realizing correlated many-body states. However, determining the dynamics of dissipative quantum many-body systems remains a significant analytical and numerical challenge. In this paper, we focus on a dissipative impurity problem as a testbed for new methodological developments. We introduce an efficient nonperturbative framework that combines the superposition of Gaussian states variational ansatz with the quantum trajectory approach to simulate open systems featuring a dissipative impurity. Applying this method to a spinful impurity subject to two-body losses and embedded in a bath of noninteracting fermions, we explore the full crossover from weak to strong dissipation regimes. The nonperturbative nature of the SGS ansatz allows us to thoroughly examine this crossover, providing comprehensive insights into the behavior of the system. In the strong dissipation regime, our approach reproduces the finding that localized two-body losses can induce the Kondo effect [Stefanini et al., arXiv:2406.03527], characterized by a slowdown of spin relaxation and an enhancement of charge conductance. Furthermore, we reveal an exotic negative conductance phenomenon at zero potential bias—a counterintuitive single-body effect resulting from intermediate dissipation and finite bandwidth. Finally, we investigate the formation of ferromagnetic domains and propose an extension to realize a higher-spin Kondo model using localized dissipation.

关键词： Variational techniques

Macroproperties vs. Microstates in the Classical Simulation of Critical Phenomena in Quench Dynamics of 1D Ising Models

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arXiv 2023年

作者： Mitra, Anupam Albash, Tameem Blocher, Philip Daniel Takahashi, Jun Miyake, Akimasa Biedermann, Grant W. Deutsch, Ivan H. Center for Quantum Information and Control Department of Physics and Astronomy University of New Mexico New Mexico87131 United States Department of Electrical and Computer Engineering University of New Mexico New Mexico87131 United States Center for Quantum Research and Technology Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Oklahoma73019 United States

We study the tractability of classically simulating critical phenomena in the quench dynamics of one-dimensional transverse field Ising models (TFIMs) using highly truncated matrix product states (MPS). We focus on two paradigmatic examples: a dynamical quantum phase transition (DQPT) that occurs in nonintegrable long-range TFIMs, and the infinite-time correlation length of the integrable nearest-neighbor TFIM when quenched to the critical point, where the quantities of interest involve equal time one- and two- point correlation functions, which we associate with macroproperties. For the DQPT, we show that the order parameters can be efficiently simulated with heavy truncation of the MPS bond dimension. This can be used to reliably extract critical properties of the phase transition, including critical exponents, even when the full many-body state is not simulated with high fidelity. The long-time correlation length near the critical point is more sensitive to the full many-body state fidelity, and generally requires a large bond dimension MPS. Nonetheless, this can still be efficiently simulated with strongly truncated MPS because it can be extracted from the short-time behavior of the dynamics where entanglement is low. Our results provide illustrations of scenarios where accurate calculation of the full many-body state (microstate) is intractable due to the volume-law growth of entanglement, yet a precise specification of an exact microstate may not be required when simulating macroproperties that play a role in phases of matter of many-body systems. We also study the tractability of simulation using truncated MPS based on quantum chaos and equilibration in the models. We find a counterintuitive inverse relationship, whereby local expectation values are most easily approximated for chaotic systems whose exact many-body state is most intractable. Copyright © 2023, The Authors. All rights reserved.

关键词： Ising model