bluuebunny/crossref_metadata_2025_split huggingface dataset:
Complex stochastic optimal control foundation of quantum mechanics
Authors: Yordanov Vasil 
Published: October 2024

Abstract
Recent studies have extended the use of the stochastic Hamilton-Jacobi-Bellman (HJB) equation to include complex variables for deriving quantum mechanical equations. However, these studies often assume that it is valid to apply the HJB equation directly to complex numbers, an approach that overlooks the fundamental problem of comparing complex numbers when finding optimal controls. This paper explores the application of the HJB equation in the context of complex variables. It provides an in-depth investigation of the stochastic movement of quantum particles within the framework of stochastic optimal control theory. We obtain the complex diffusion coefficient in the stochastic equation of motion using the Cauchy-Riemann theorem, considering that the particle’s stochastic movement is described by two perfectly correlated real and imaginary stochastic processes. During the development of the covariant form of the HJB equation, we demonstrate that if the temporal stochastic increments of the two processes are perfectly correlated, then the spatial stochastic increments must be perfectly anti-correlated, and vice versa. The diffusion coefficient we derive has a form that enables the linearization of the HJB equation. The method for linearizing the HJB equation, along with the subsequent derivation of the Dirac equation, was developed in our previous work [V. Yordanov, Scientific Reports 14, 6507 (2024)]. These insights deepen our understanding of quantum dynamics and enhance the application of stochastic optimal control theory to quantum mechanics.
Periodic solutions, breather, lump and interaction solutions of a generalized (2+1)-dimensional Hirota bilinear equation via the bilinear neural network method
Authors: Zhao Zhao , Ren Bo 
Published: October 2024

Abstract
The main focus of this paper is to address a generalized (2+1)-dimensional Hirota bilinear equation utilizing the bilinear neural network method. The paper presents the periodic solutions through a single-layer model of [3-4-1], followed by breather, lump and their interaction solutions by using double-layer models of [3-3-2-1] and [3-3-3-1], respectively. A significant innovation introduced in this work is the computation of periodic cross-rational solutions through the design of a novel [3-(2+2)-4-1] model, where a specific hidden layer is partitioned into two segments for subsequent operations. Three-dimensional and density figures of the solutions are given alongside an analysis of the dynamics of these solutions.
Swarm intelligence-based optimization of machine learning models for diverse industrial problems: a comparative study
Authors: Che Zhongyuan , Peng Chong , Wang Jikun 
Published: October 2024

Abstract
With the rapid development of artificial intelligence, machine learning has emerged as a promising approach to tackle complex problems in various industries. It is particularly evident in situations where limited data samples are available, such as when developing new materials, or in high-speed machining scenarios where computational efficiency and result reliability are equally crucial. In this work, we have selected five widely recognized machine learning models (e-SVR, v-SVR, BP, Random Forest, Extreme Learning Machines) as baseline methods. We employ Snake Optimizer, a recently popular swarm intelligence algorithm, to optimize the parameters of each model individually. The Mean Squared Error is utilized as an evaluation fitness measure to adaptively optimize and determine the optimal hyperparameter combination for each method. Consequently, five hybrid models are established. To thoroughly evaluate the performance and applicability of these models, predictive experiments are conducted using temperature data of permanent magnet synchronous motors, energy consumption data from steel companies and real-estate price data of Chicago, representing three distinct scenarios. The learning ability and generalization capability of these models are tested and assessed using four error measures: Mean Absolute Error, R-Squared, Root Mean Squared Error, and Computation Time. Moreover, comparison and discussion between the hybrid models and their respective basic models are also conducted, analysing the effectiveness of SO algorithm in optimization. Through analysis and discussion, a comprehensive understanding of the characteristics and applicability of them is provided. Our objective is to provide scientific researchers and engineers with valuable insights into different methods and their characteristics.
OriFlexClust denoising method for shallow water ATL03 single-photon point clouds
Authors: Yao Ying , Zhou Guoqing , Zhu Shuaiguang , Song Tianyi , Li Yangleijing , Li Ruixiang 
Published: October 2024

Abstract
Conventional clustering algorithms suffer from misclassification problems caused by uneven density and poor initial parameter choices. This paper introduces an adaptive denoising algorithm called OriFlexClust, based on directional angle continuity. Experimental results demonstrate that the OriFlexClust algorithm outperforms other clustering algorithms, effectively addressing the issues of parameter selection and uneven data density. It enhances denoising accuracy and processing speed. Therefore, the OriFlexClust algorithm proposed in this paper is more superior.
Preface
Authors: Asnafi Nader , Xia Tangbin 
Published: October 2024

Over the last 6 years, MEIE has been held in various locations, including in-person conferences in Hangzhou (2018 & 2019), an online conference for COVID-19 (2020), a hybrid event in Kunming (2021), an online conference (2022) and a hybrid event in Sanya (2023), attracted participants from more than 14 countries and regions including Sweden, Canada, the United States, Australia, Singapore, South Korea, and Malaysia.
The 7th International Conference on Mechanical, Electric, and Industrial Engineering (MEIE2024) was organized by the College of Mechanical Engineering, Donghua University, China, co-organized by Chinese Institute for Quality Research, the School of Mechanical Engineering and Mechanics, Ningbo University, China, the Shanghai Society for Modern Design Theory and Methodology Research, and Shanghai Graphics Society. It was successfully held during May 21-23, 2024 in Yichang, China and online.
This event attracted over 100 participants from all over the world from China, Sweden, USA, Canada, Germany, Pakistan, and etc., and addressed both basic research and the societal/industrial-technological needs within mechanical, electric and industrial engineering. Conference program was divided into 4 sessions: keynote speeches, invited speeches, oral presentations and poster presentations. During these sessions, presenters shared their latest achievements and audiences were actively participated as well.
The MEIE organizing committee extend their sincerest gratitude to all who have supported the conference in their ways, to the authors who have chosen this platform to publish their works and communicate with peers, to the participants who took an interest and attended the conference in person and/or online, to the chairs and committee members who have been indispensable in lending their professional expertise and judgment, to the keynote speakers who generously shared their vision and passion, and to the reviewers who held up the faith of being a scholar and contributed their experience and honest opinions. It has been a pleasure and honor working alongside them, and we look forward to future cooperation with them at future MEIE conferences to come.
Finally, our sincere gratitude goes also to the IOP Publishing editors and managers for their helpful cooperation during the preparation of the conference proceedings.
On behalf of the Organizing Committee of MEIE 2024.
List of MEIE2024 Committee members are available in this Pdf.
Optimal operation strategy for renewable power plants based on 5G base stations response
Authors: Li Lijuan , Zheng Xinhui , Xiao Chuang , Liu Renai 
Published: October 2024

Abstract
The integration of large-scale new energy sources has led to a significant challenge in electricity supply and demand balancing within the power system. To address this issue, it is crucial to establish an optimization strategy of renewable power plant based on 5G base stations response. Firstly, according to the operating state of the base station, the energy storage schedulable strategy of the base station was designed. Then, through the regulation function of the base station’s reserve energy storage and the operation principle of the renewable energy power plant, the operation model of the renewable power plant considering the base station’s participation and response was established. Finally, evaluation indexes such as the absolute peak-to-valley difference, the standard deviation of daily load and the peak-valley difference rate can be used to measure system optimization results. The results show that the proposed strategy improves the source load imbalance in the power system while enhancing reliability and economy of new energy.
Mechanical simulation and electrical performance analysis of high current connectors
Authors: Wang Saichen , Li Hui , Kong Zhigang 
Published: October 2024

Abstract
Automotive high-current connectors are one of the key components in new energy vehicles, used for transmitting high currents within the vehicle. However, vibration is unavoidable during the actual operation of connectors. This vibration directly affects the contact pressure between the connector’s contact points, thereby influencing the electrical contact performance of the connector. This paper simplifies the contact components of the electrical connector using a cantilever beam model and conducts theoretical calculations of the contact pressure. Utilizing Ansys Workbench simulation software, the study investigates the contact pressure of the electrical connector under static and various sinusoidal vibration conditions, followed by sinusoidal frequency sweep vibration experiments. The research findings indicate that under vibration conditions, the contact pressure is lower than that under static conditions, and the electrical resistance fluctuates during the vibration process.
A hybrid driven framework for power data recovery
Authors: Li Lijuan , Zhang Qingsong , Liu Renai , Liu Pan 
Published: October 2024

Abstract
In addressing the limitations of unclear mechanisms in data driven methods and the restricted applicability of inference interpolation methods in power data recovery research, this paper proposes a hybrid driven framework that combines both data driven and model driven approaches. This framework integrates data driven and model driven components through linear recombination, while explicitly demonstrating the interpretability of the model. By fitting three nonlinear transformation processes and comparing effective and ineffective mechanisms, the efficacy of model driven enhancement in improving data driven performance is validated. Furthermore, the effectiveness of the proposed framework is demonstrated through a data recovery task in IEEE-39 node system.
Physics-informed neural network for simulating magnetic field of permanent magnet
Authors: Hou Shubo , Wu Wenchao , Hao Xiuhong 
Published: October 2024

Abstract
With the rapid development of deep learning, its application in physical field simulation has been widely concerned, and it has begun to lead a new model of meshless simulation. In this paper, research based on physics-informed neural networks is carried out to solve partial differential equations related to the physical laws of electromagnetism. Then the magnetic field simulation is realized. In this method, the governing equation and the boundary conditions containing physical information are embedded into the neural network loss function as constraints, and the backpropagation of neural networks is realized based on automatic differentiation to solve partial differential equations. The high-precision simulation of tile-shaped and rectangular permanent magnet magnetic fields of permanent magnet motors based on physical information neural network is studied, and the error is within 5%. We consider the simulation of magnetic field in two coordinate systems, and realize the joint training of multiple neural networks in multiple sub-domains and different media.
Mathematical modelling of a flexible steering robot for vehicle trajectory control
Authors: Ali Furqan , Yu Liangyao 
Published: October 2024

Abstract
This paper presents a mathematical modelling framework for a novel design of a steering robot to control vehicle trajectory. A systematic approach is adopted to capture the dynamics of a belt transmission mechanical system of the robot. The steering robot is powered by a Brushless Direct Current (BLDC) servo motor which employs a Field Oriented Current (FOC) control for motor position control. The system’s accuracy is demonstrated through simulations conducted using MATLAB SIMULINK, showcasing its capability to effectively track the reference steering angle.
A novel TD3 for solving multi-level imperfect maintenance optimization problem
Authors: An Youjun , Chen Duwanqi , Zhao Ziye , Liu Cheng , Zhang Jun 
Published: October 2024

Abstract
As we all know, multi-level imperfect maintenance strategy is usually more effective than single-level maintenance strategy for the actual production machines. At the same time, in the previous multi-level maintenance strategies, the majority of maintenance models only consider the constant maintenance thresholds, while variable maintenance thresholds are usually ignored. Under these contexts, a novel multi-level imperfect maintenance model with variable preventive maintenance (PM) thresholds, variable overhaul maintenance (OM) thresholds and variable number of PMs in each OM cycle is established. In order to deal with the concerned problem, a novel twin delayed deep deterministic policy gradient (TD3) algorithm that is a kind of reinforcement learning is designed, renamed as NTD3. Finally, through numerical simulation, we can find that (1) the average improvements between the proposed maintenance strategy and other three traditional strategies in the average cost rate (ACR) are 11.50%, 595.91% and 5.16%, respectively; and (2) the average improvement between the proposed NTD3 and other random search method is 5.53%. Thus, the effectiveness of the proposed maintenance strategy and the superiority of proposed NTD3 are all demonstrated.
Design of an FPGA-based short-circuit fault diagnosis algorithm for DC current-limiting fuses
Authors: Min Guangyao , Lei Yuanlin , Luo Qiquan , Huang Yuanfeng 
Published: October 2024

Abstract
In recent years, DC current-limiting fuses have played a crucial role in the comprehensive power systems of ships, rail transport, and new energy sectors, ensuring the safe and reliable operation of DC power systems. However, with the increase in the capacity of DC power systems, short-circuit faults can lead to a rise in short-circuit current at rates of up to 40 A/μs, with peak short-circuit currents reaching up to 105KA. This poses higher demands on the accuracy and speed of fuse diagnostics to interrupt short-circuit currents. To address this issue, this paper introduces a fault diagnostic algorithm based on FPGA that can calculate the rise rate of short-circuit currents in real-time: employing FPGA with its high integration, abundant logic resources, and parallel data processing capabilities as the core of the measurement and control system to enhance the speed of short-circuit fault diagnosis from a hardware perspective; replacing the common large current trip protection and DDL protection algorithms with the Newton interpolation algorithm. This improvement calculates the rise rate of each current data point instead of the average rise rate over a period, thus enhancing the speed and accuracy of short-circuit fault diagnosis from a software perspective. The paper also discusses the design of the system’s hardware and software and the construction of the experimental platform. Simulation and experimental results show that the fault diagnosis time of the algorithm has been reduced from 98μs to 36μs, with the numerical error in the short-circuit current rise rate within 2%, and a significant reduction in the peak value of short-circuit current, meeting performance requirements and effectively ensuring the safe and reliable operation of DC power systems.
Laboratory x-ray nano-computed tomography for biomedical research
Authors: Dreier Till , Krüger Robin , Bernström Gustaf , Tran-Lundmark Karin , Gonçalves Isabel , Bech Martin 
Published: October 2024

Abstract
High-resolution x-ray tomography is a common technique for
biomedical research using synchrotron sources. With advancements in
laboratory x-ray sources, an increasing number of experiments can be
performed in the lab. In this paper, the design, implementation, and
verification of a laboratory setup for x-ray nano-computed
tomography is presented using a nano-focus x-ray source and high
geometric magnification not requiring any optical
elements. Comparing a scintillator-based detector to a photon
counting detector shows a clear benefit of using photon counting
detectors for these applications, where the flux of the x-ray source
is limited and samples have low contrast. Sample contrast is
enhanced using propagation-based phase contrast. The resolution of
the system is verified using 2D resolution charts and using Fourier
Ring Correlation on reconstructed CT slices. Evaluating noise and
contrast highlights the benefits of photon counting detectors and
the contrast improvement through phase contrast. The implemented
setup is capable of reaching sub-micron resolution and satisfying
contrast in biological samples, like paraffin embedded tissue.
Readout electronics of a muon beam monitor for the phase-α of the COMET experiment
Authors: Qin Zhizhen , Feng Changqing , Teng Yao , Xu Yu , Ning Yunsong , Tang Jian , Wen Mingjian , Ge Ke , Liu Shubin 
Published: October 2024

Abstract
A 256-channel readout electronics system has been developed
for a SciFi (scintillating fiber)-based muon beam monitor, which is
utilized in the COMET (COherent Muon to Electron Transition)
experiment. The muon beam monitor allows for the monitoring of muon
beams by collecting optical signals generated when muons hit plastic
scintillating fibers. The 256-channel readout electronics, including
two SiPM carrier boards, two front-end boards (FEB), two data
acquisition boards (DAQ) and one clock and trigger unit (CTU),
measures the arrival time of each signal from the detector. The
developed 256-channel readout electronics, with a timing precision
of 3.3 ns and an event rate exceeding 500 kHz, has successfully met
the requirements of the muon beam monitor during the phase-α
of the COMET experiment.
A closed-form solution for thermally induced affine deformation in unbounded domains with a temporally accelerated anomalous thermal conductivity
Authors: Awad Emad , Samir Noha 
Published: October 2024

Abstract
One of the successful mathematical tools in the 21st century is the distributed-order fractional derivative, particularly, the bi-fractional diffusion equation of natural form (Chechkin et al 2002 Phys. Rev. E 66 046129) and the bi-fractional diffusion equation of the modified form (Sokolov 2004 Acta Phys. Pol. B 35). The present study adopts the bi-fractional diffusion heat equation of the modified form that uses two Riemann–Liouville time-fractional derivatives with different fractional orders and the Riesz space-fractional derivative, and bears the property of variable thermal conductivity with temporal acceleration; i.e. the material thermal conduction transits from a low value at the small values of time (t → 0) to a larger value at the long-time domain (

t
→
∞

). The corresponding fractional thermoelasticity theory, that uses the bi-fractional diffusion heat equation of the modified form, is exclusively considered in this work. For a quasi-static unbounded Hookean domain, exact solutions for the temperature, hydrostatic stress, and the displacement fields are derived in both short-time and long-time domains, in terms of the Fox H-function.The exact solutions for the linearized problem are derived using the inversion of problem variables from the Laplace-Fourier space. Adding a zero initial condition on the normal stress of the unbounded space transforms the conventional Cauchy problem to a mixed initial-boundary value problem in order to derive a generalized form of the displacement. Additionally, thermal energy is not conserved in the presence of space fractality. The effect of such an accelerating transition in the thermal conduction on the displacement component is focused on, and it is found that the displacement inherits a similar transition behavior.
Conservation laws of the one-dimensional relativistic magnetogasdynamics equations
Authors: Nakpim W , Meleshko S V , Mukdasanit P 
Published: October 2024

AbstractThe present paper offers a comprehensive analysis of the one-dimensional relativistic magnetogasdynamics equations in Lagrangian coordinates. These equations, describing the behavior of a relativistic magnetized gas, are reformulated in variational form to facilitate further analysis. A complete group analysis of the resulting Euler–Lagrange equation is performed, allowing us to systematically identify the symmetries inherent in the system. Using Noether’s theorem, we derive conservation laws in Lagrangian coordinates, offering critical insights into the invariants and conserved quantities of the system. This work expands the theoretical understanding of relativistic magnetogasdynamics.
Classifying coherence with a finite set of witnesses
Authors: Zhu Xue-Na , Jin Zhi-Xiang , Bao Gui , Fei Shao-Ming 
Published: October 2024

Abstract
Coherence is a fundamental resource in quantum information processing, which can be certified by a coherence witness. In order to detect all the coherent states, we introduce a useful concept of coherence witness and structure the set of coherence witnesses

C

[
m
,
M
]

d

. We present necessary and sufficient conditions of detecting quantum coherence of

d
−

dimensional quantum states based on

C

[
m
,
M
]

d

. Moreover, we show that each coherent state can be detected by one of the coherence witnesses in a finite set. The corresponding finite set of coherence witnesses is presented explicitly, which detects all the coherent states.