Entropy

9 pages, 246 KiB

Open AccessArticle

Entropy Analysis of Implicit Heat Fluxes in Multi-Temperature Mixtures

by A. D. Kirwan, Jr. and Mehrdad Massoudi

Entropy 2024, 26(9), 723; https://doi.org/10.3390/e26090723 (registering DOI) - 24 Aug 2024

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We propose new implicit constitutive relations for the heat fluxes of a two-temperature mixture of fluids. These relations are frame-indifferent forms. However, classical explicit forms of the stress tensors and the interaction forces (specified as explicit forms of constitutive relations) as given in [...] Read more.

We propose new implicit constitutive relations for the heat fluxes of a two-temperature mixture of fluids. These relations are frame-indifferent forms. However, classical explicit forms of the stress tensors and the interaction forces (specified as explicit forms of constitutive relations) as given in mixture theory are used. The focus here is to establish constraints imposed on the implicit terms in the heat fluxes due to the Second Law of Thermodynamics. Our analysis establishes that the magnitude of the explicit entropy production is equal to or greater than that of the implicit entropy production. Full article

(This article belongs to the Special Issue Thermodynamic Constitutive Theory and Its Application)

11 pages, 1803 KiB

Open AccessArticle

Estimating Molecular Thermal Averages with the Quantum Equation of Motion and Informationally Complete Measurements

by Daniele Morrone, N. Walter Talarico, Marco Cattaneo and Matteo A. C. Rossi

Entropy 2024, 26(9), 722; https://doi.org/10.3390/e26090722 (registering DOI) - 23 Aug 2024

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Abstract

By leveraging the Variational Quantum Eigensolver (VQE), the “quantum equation of motion” (qEOM) method established itself as a promising tool for quantum chemistry on near-term quantum computers and has been used extensively to estimate molecular excited states. Here, we explore a novel application [...] Read more.

By leveraging the Variational Quantum Eigensolver (VQE), the “quantum equation of motion” (qEOM) method established itself as a promising tool for quantum chemistry on near-term quantum computers and has been used extensively to estimate molecular excited states. Here, we explore a novel application of this method, employing it to compute thermal averages of quantum systems, specifically molecules like ethylene and butadiene. A drawback of qEOM is that it requires measuring the expectation values of a large number of observables on the ground state of the system, and the number of necessary measurements can become a bottleneck of the method. In this work, we focus on measurements through informationally complete positive operator-valued measures (IC-POVMs) to achieve a reduction in the measurement overheads by estimating different observables of interest through the measurement of a single set of POVMs. We show with numerical simulations that the qEOM combined with IC-POVM measurements ensures satisfactory accuracy in the reconstruction of the thermal state with a reasonable number of shots. Full article

(This article belongs to the Special Issue Simulation of Open Quantum Systems)

22 pages, 4525 KiB

Open AccessArticle

Research on a Transformer Vibration Fault Diagnosis Method Based on Time-Shift Multiscale Increment Entropy and CatBoost

by Haikun Shang, Tao Huang, Zhiming Wang, Jiawen Li and Shen Zhang

Entropy 2024, 26(9), 721; https://doi.org/10.3390/e26090721 (registering DOI) - 23 Aug 2024

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Abstract

A mechanical vibration fault diagnosis is a key means of ensuring the safe and stable operation of transformers. To achieve an accurate diagnosis of transformer vibration faults, this paper proposes a novel fault diagnosis method based on time-shift multiscale increment entropy (TSMIE) combined [...] Read more.

A mechanical vibration fault diagnosis is a key means of ensuring the safe and stable operation of transformers. To achieve an accurate diagnosis of transformer vibration faults, this paper proposes a novel fault diagnosis method based on time-shift multiscale increment entropy (TSMIE) combined with CatBoost. Firstly, inspired by the concept of a time shift, TSMIE was proposed. TSMIE effectively solves the problem of the information loss caused by the coarse-graining process of traditional multiscale entropy. Secondly, the TSMIE of transformer vibration signals under different operating conditions was extracted as fault features. Finally, the features were sent into the CatBoost model for pattern recognition. Compared with different models, the simulation and experimental results showed that the proposed model had a higher diagnostic accuracy and stability, and this provides a new tool for transformer vibration fault diagnoses. Full article

(This article belongs to the Special Issue Entropy Applications in Condition Monitoring and Fault Diagnosis)

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12 pages, 1670 KiB

Open AccessArticle

Estimation of the Impulse Response of the AWGN Channel with ISI within an Iterative Equalization and Decoding System That Uses LDPC Codes

by Adriana-Maria Cuc, Florin Lucian Morgoș, Adriana-Marcela Grava and Cristian Grava

Entropy 2024, 26(9), 720; https://doi.org/10.3390/e26090720 (registering DOI) - 23 Aug 2024

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Abstract

In this paper, new schemes have been proposed for the estimation of the additive white Gaussian noise (AWGN) channel with intersymbol interference (ISI) in an iterative equalization and decoding system using low-density parity check (LDPC) codes. This article explores the use of the [...] Read more.

In this paper, new schemes have been proposed for the estimation of the additive white Gaussian noise (AWGN) channel with intersymbol interference (ISI) in an iterative equalization and decoding system using low-density parity check (LDPC) codes. This article explores the use of the least squares algorithm in various scenarios. For example, the impulse response of the AWGN channel h was initially estimated using a training sequence. Subsequently, the impulse response was calculated based on the training sequence and then re-estimated once using the sequence estimated from the output of the LDPC decoder. Lastly, the impulse response was calculated based on the training sequence and re-estimated twice using the sequence estimated from the output of the LDPC decoder. Comparisons were made between the performances of the three mentioned situations, with the situation in which a perfect estimate of the impulse response of the channel is assumed. The performance analysis focused on how the bit error rate changes in relation to the signal-to-noise ratio. The BER performance comes close to the scenario of having a perfect estimate of the impulse response when the estimation is performed based on the training sequence and then re-estimated twice from the sequence obtained from the output of the LDPC decoder. Full article

(This article belongs to the Special Issue New Advances in Error-Correcting Codes)

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20 pages, 3038 KiB

Open AccessArticle

Quantum Dynamical Interpretation of the Mean Strategy

by Fang Wang, Peng Wang and Yuwei Jiao

Entropy 2024, 26(9), 719; https://doi.org/10.3390/e26090719 (registering DOI) - 23 Aug 2024

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Abstract

The method of quantum dynamics is employed to investigate the mean strategy in the swarm intelligence algorithm. The physical significance of the population mean point is explained as the location where the optimal solution with the highest likelihood can be found once a [...] Read more.

The method of quantum dynamics is employed to investigate the mean strategy in the swarm intelligence algorithm. The physical significance of the population mean point is explained as the location where the optimal solution with the highest likelihood can be found once a quantum system has reached a ground state. Through the use of the double well function and the CEC2013 test suite, controlled experiments are conducted to perform a comprehensive performance analysis of the mean strategy. The empirical results indicate that implementing the mean strategy not only enhances solution diversity but also yields accurate, efficient, stable, and effective outcomes for finding the optimal solution. Full article

(This article belongs to the Section Quantum Information)

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26 pages, 11603 KiB

Open AccessArticle

A Federated Adversarial Fault Diagnosis Method Driven by Fault Information Discrepancy

by Jiechen Sun, Funa Zhou, Jie Chen, Chaoge Wang, Xiong Hu and Tianzhen Wang

Entropy 2024, 26(9), 718; https://doi.org/10.3390/e26090718 (registering DOI) - 23 Aug 2024

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Abstract

Federated learning (FL) facilitates the collaborative optimization of fault diagnosis models across multiple clients. However, the performance of the global model in the federated center is contingent upon the effectiveness of the local models. Low-quality local models participating in the federation can result [...] Read more.

Federated learning (FL) facilitates the collaborative optimization of fault diagnosis models across multiple clients. However, the performance of the global model in the federated center is contingent upon the effectiveness of the local models. Low-quality local models participating in the federation can result in negative transfer within the FL framework. Traditional regularization-based FL methods can partially mitigate the performance disparity between local models. Nevertheless, they do not adequately address the inconsistency in model optimization directions caused by variations in fault information distribution under different working conditions, thereby diminishing the applicability of the global model. This paper proposes a federated adversarial fault diagnosis method driven by fault information discrepancy (FedAdv_ID) to address the challenge of constructing an optimal global model under multiple working conditions. A consistency evaluation metric is introduced to quantify the discrepancy between local and global average fault information, guiding the federated adversarial training mechanism between clients and the federated center to minimize feature discrepancy across clients. In addition, an optimal aggregation strategy is developed based on the information discrepancies among different clients, which adaptively learns the aggregation weights and model parameters needed to reduce global feature discrepancy, ultimately yielding an optimal global model. Experiments conducted on benchmark and real-world motor-bearing datasets demonstrate that FedAdv_ID achieves a fault diagnosis accuracy of 93.09% under various motor operating conditions, outperforming model regularization-based FL methods by 17.89%. Full article

(This article belongs to the Special Issue Entropy Applications in Condition Monitoring and Fault Diagnosis)

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19 pages, 9600 KiB

Open AccessArticle

A Hierarchical Neural Network for Point Cloud Segmentation and Geometric Primitive Fitting

by Honghui Wan and Feiyu Zhao

Entropy 2024, 26(9), 717; https://doi.org/10.3390/e26090717 (registering DOI) - 23 Aug 2024

Viewed by 223

Abstract

Automated generation of geometric models from point cloud data holds significant importance in the field of computer vision and has expansive applications, such as shape modeling and object recognition. However, prevalent methods exhibit accuracy issues. In this study, we introduce a novel hierarchical [...] Read more.

Automated generation of geometric models from point cloud data holds significant importance in the field of computer vision and has expansive applications, such as shape modeling and object recognition. However, prevalent methods exhibit accuracy issues. In this study, we introduce a novel hierarchical neural network that utilizes recursive PointConv operations on nested subdivisions of point sets. This network effectively extracts features, segments point clouds, and accurately identifies and computes parameters of regular geometric primitives with notable resilience to noise. On fine-grained primitive detection, our approach outperforms Supervised Primitive Fitting Network (SPFN) by 18.5% and Cascaded Primitive Fitting Network (CPFN) by 11.2%. Additionally, our approach consistently maintains low absolute errors in parameter prediction across varying noise levels in the point cloud data. Our experiments validate the robustness of our proposed method and establish its superiority relative to other methodologies in the extant literature. Full article

(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing II)

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Entropy, Volume 26, Issue 9 (September 2024) – 7 articles

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