bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leakage monitoring and diagnosis of LNG storage tanks with temperature sensing network integration and artificial intelligence algorithm
Authors: Wu Yujian , Yang Gang , Sun Jiangang , Cui Lifu , Wang Mengzhu 
Published: February 2024

Abstract
Temperature is the most important safety monitoring indicator for leakage diagnosis during the operation phase of liquefied natural gas (LNG) storage tanks. Timely monitoring and accurate identification of LNG leakage events are crucial for accident prevention, loss reduction, and facility safety maintenance. This study integrates artificial intelligence (AI) algorithms and temperature sensing data to achieve intelligent monitoring and diagnosis of leakage in LNG storage tanks. Firstly, a comprehensive temperature sensing network is constructed by combining numerical simulation of the temperature field and temperature sensing experiments using fiber Bragg grating sensors. Secondly, Python is used to perform linear grid interpolation and flattening on the sensing network, generating 2D temperature nephogram samples that are conducive to AI algorithm recognition. Finally, sample features are extracted using machine vision, and leakage location calculation, leakage diagnosis and leakage volume calculation are implemented with the help of machine learning algorithms, achieving satisfactory accuracy on the test set. In addition, the ConvLSTM framework is introduced for deep learning and recurrent neural network training, enabling spatiotemporal prediction of the leakage area.
A single-stage anchor-free rotating target visual detection algorithm suitable for flexible body vibration displacement measurement
Authors: Li Mao , Wang Sen , Liu Chang , Liu Tao 
Published: February 2024

Abstract
The phenomenon of angular inclination of flexible structures during vibration poses a significant challenge to the applicability of visual vibration measurement methods because the target locked in the captured image will produce unknown geometric deformations such as scale, displacement, and angle in the time domain space, and the horizontal rectangular frame used for matching during target detection will also increase the false detection rate of the target due to the introduction of more background information. Such subtle geometric deformations and false detections can lead to severe fit errors in the displacement curves regressed by the visual vibration measurement algorithm. To effectively improve the accuracy and robustness of vibration image target recognition, this article takes the flexible body captured by a high-speed camera as the target of vibration displacement measurement. It introduces the rotating target detection method based on deep learning into the field of visual vibration measurement, which verifies the feasibility of the deep learning method in flexible body vibration measurement, and based on the deep convolutional neural network framework, a high-precision displacement measurement algorithm based on single-stage anchor-free rotating target detection is proposed. The algorithm in this article first uses the CSPDarknet backbone network to extract multi-scale features of flexible structural image sequences. It then uses PANet to fuse the top-down and bottom-up bidirectional feature maps of the four bridge target feature maps obtained through the backbone network. The shallow and deep information is used for semantic feature fusion and combined with the Coordinate Attention mechanism to achieve target finding and fine positioning on the feature map. Finally, we use the coordinates of the bounding box obtained from the test to regress the position offset of the object’s center point. To verify the accuracy of the algorithm in this article, we conducted experimental validation on the cable-stayed bridge model and the actual bridge and compared the performance with the traditional template matching algorithm, differential optical flow method, and various deep learning algorithms with different localization principles, as well as the displacement signals collected and processed by accelerometers. The experimental results of time-frequency characteristics analysis show that the vibration displacement trajectories regressed by the algorithm in this paper have the best overlap with the displacement measurements collected by the accelerometer, which verifies that the algorithm in this article has good application potential and implementation space in the field of condition monitoring of flexible structural bodies.
A fault diagnosis method based on label-wise density-domain space learning
Authors: Su Shuzhi , Hou Yakui , Zhu Yanmin , Zhang Zhipeng 
Published: February 2024

Abstract
Nonlinear space learning of fault samples is a category of common fault diagnosis methods, which usually use Euclidean distances to describe manifold structures among fault samples. However, in nonlinear space, Euclidean distances lead to a potential manifold loss problem. Aiming these issues, we propose a novel fault diagnosis method based on label-wise density-domain space learning. The label-wise density-domain space learns more intrinsic manifold structures from four density-constrained order graphs. Density-constrained order graphs constructed by our method integrate different discriminative relationships from original fault samples with the help of density-domain information, and the density-domain information can effectively capture potential density information and global structure between fault samples. By density Laplacian of the graphs, we further construct a label-wise density-domain manifold space learning model, and the analytical solutions of space projections can be obtained by solving the model. Fault features directly obtained by the space projections possess good class separability. Extensive experiments on the Case Western Reserve University fault dataset and a roll-bearing fault dataset from our roll-bearing test platform show the effectiveness and robustness of our method.
Mg incorporation induced microstructural evolution of reactively sputtered GaN epitaxial films to Mg-doped GaN nanorods
Authors: Monish Mohammad , Major S S 
Published: February 2024

Abstract
Mg-doped GaN films/nanorods were grown epitaxially on c-sapphire by reactive co-sputtering of GaAs and Mg at different N2 percentages in Ar–N2 sputtering atmosphere. Energy dispersive x-ray spectroscopy revealed that the Mg incorporation increases with increase of Mg area coverage of GaAs target, but does not depend on N2 percentage. In comparison to undoped GaN films, Mg-doped GaN displayed substantial decrease of lateral conductivity and electron concentration with the initial incorporation of Mg, indicating p-type doping, but revealed insulating behaviour at larger Mg content. Morphological investigations by scanning electron microscopy have shown that the films grown with 2%–4% Mg area coverages displayed substantially improved columnar structure, compared to undoped GaN films, along with rough and voided surface features at lower N2 percentages. With increase of Mg area coverage to 6%, the growth of vertically aligned and well-separated nanorods, terminating with smooth hexagonal faces was observed in the range of 50%–75% N2 in sputtering atmosphere. High-resolution x-ray diffraction studies confirmed the epitaxial character of Mg-doped GaN films and nanorods, which displayed complete c-axis orientation of crystallites and a mosaic structure, aligned laterally with the c-sapphire lattice. The catalyst-free growth of self-assembled Mg-doped GaN nanorods is attributed to increase of surface energy anisotropy due to the incorporation of Mg. However, with further increase of Mg area coverage to 8%, the nanorods revealed lateral merger, suggesting enhanced radial growth at larger Mg content.
Spectrum learning for super-resolution tomographic reconstruction
Authors: Li Zirong , An Kang , Yu Hengyong , Luo Fulin , Pan Jiayi , Wang Shaoyu , Zhang Jianjia , Wu Weiwen , Chang Dingyue 
Published: February 2024

Abstract

Objective. Computed Tomography (CT) has been widely used in industrial high-resolution non-destructive testing. However, it is difficult to obtain high-resolution images for large-scale objects due to their physical limitations. The objective is to develop an improved super-resolution technique that preserves small structures and details while efficiently capturing high-frequency information. Approach. The study proposes a new deep learning based method called spectrum learning (SPEAR) network for CT images super-resolution. This approach leverages both global information in the image domain and high-frequency information in the frequency domain. The SPEAR network is designed to reconstruct high-resolution images from low-resolution inputs by considering not only the main body of the images but also the small structures and other details. The symmetric property of the spectrum is exploited to reduce weight parameters in the frequency domain. Additionally, a spectrum loss is introduced to enforce the preservation of both high-frequency components and global information. Main results. The network is trained using pairs of low-resolution and high-resolution CT images, and it is fine-tuned using additional low-dose and normal-dose CT image pairs. The experimental results demonstrate that the proposed SPEAR network outperforms state-of-the-art networks in terms of image reconstruction quality. The approach successfully preserves high-frequency information and small structures, leading to better results compared to existing methods. The network’s ability to generate high-resolution images from low-resolution inputs, even in cases of low-dose CT images, showcases its effectiveness in maintaining image quality. Significance. The proposed SPEAR network’s ability to simultaneously capture global information and high-frequency details addresses the limitations of existing methods, resulting in more accurate and informative image reconstructions. This advancement can have substantial implications for various industries and medical diagnoses relying on accurate imaging.
Radioport: a radiomics-reporting network for interpretable deep learning in BI-RADS classification of mammographic calcification
Authors: Pang Ting , Wong Jeannie Hsiu Ding , Ng Wei Lin , Chan Chee Seng , Wang Chang , Zhou Xuezhi , Yu Yi 
Published: February 2024

Abstract

Objective. Generally, due to a lack of explainability, radiomics based on deep learning has been perceived as a black-box solution for radiologists. Automatic generation of diagnostic reports is a semantic approach to enhance the explanation of deep learning radiomics (DLR). Approach. In this paper, we propose a novel model called radiomics-reporting network (Radioport), which incorporates text attention. This model aims to improve the interpretability of DLR in mammographic calcification diagnosis. Firstly, it employs convolutional neural networks to extract visual features as radiomics for multi-category classification based on breast imaging reporting and data system. Then, it builds a mapping between these visual features and textual features to generate diagnostic reports, incorporating an attention module for improved clarity. Main results. To demonstrate the effectiveness of our proposed model, we conducted experiments on a breast calcification dataset comprising mammograms and diagnostic reports. The results demonstrate that our model can: (i) semantically enhance the interpretability of DLR; and, (ii) improve the readability of generated medical reports. Significance. Our interpretable textual model can explicitly simulate the mammographic calcification diagnosis process.
Bridging the gap between surface physics and photonics
Authors: Laukkanen Pekka , Punkkinen Marko , Kuzmin Mikhail , Kokko Kalevi , Liu Xiaolong , Radfar Behrad , Vähänissi Ville , Savin Hele , Tukiainen Antti , Hakkarainen Teemu , Viheriälä Jukka , Guina Mircea 
Published: February 2024

Abstract
Use and performance criteria of photonic devices increase in various application areas such as information and communication, lighting, and photovoltaics. In many current and future photonic devices, surfaces of a semiconductor crystal are a weak part causing significant photo-electric losses and malfunctions in applications. These surface challenges, many of which arise from material defects at semiconductor surfaces, include signal attenuation in waveguides, light absorption in light emitting diodes, non-radiative recombination of carriers in solar cells, leakage (dark) current of photodiodes, and light reflection at solar cell interfaces for instance. To reduce harmful surface effects, the optical and electrical passivation of devices has been developed for several decades, especially with the methods of semiconductor technology. Because atomic scale control and knowledge of surface-related phenomena have become relevant to increase the performance of different devices, it might be useful to enhance the bridging of surface physics to photonics. Toward that target, we review some evolving research subjects with open questions and possible solutions, which hopefully provide example connecting points between photonic device passivation and surface physics. One question is related to the properties of the wet chemically cleaned semiconductor surfaces which are typically utilized in device manufacturing processes, but which appear to be different from crystalline surfaces studied in ultrahigh vacuum by physicists. In devices, a defective semiconductor surface often lies at an embedded interface formed by a thin metal or insulator film grown on the semiconductor crystal, which makes the measurements of its atomic and electronic structures difficult. To understand these interface properties, it is essential to combine quantum mechanical simulation methods. This review also covers metal-semiconductor interfaces which are included in most photonic devices to transmit electric carriers to the semiconductor structure. Low-resistive and passivated contacts with an ultrathin tunneling barrier are an emergent solution to control electrical losses in photonic devices.
Study of bending angle and shadow in a new Schwarzschild-like black hole affected by plasma and non-plasma mediums*
Authors: Ali Riasat , Xia Tiecheng , Awais Muhammad , Babar Rimsha 
Published: February 2024

Abstract
In this study, we analyze the models of the deflection angle of a new Schwarzschild-like black hole (BH) and employ the optical metric of the BH. To achieve this, we use the Gaussian curvature of the optical metric and the Gauss-Bonnet theorem, known as the Gibbons-Werner technique, to determine the deflection angle. Furthermore, we examine the deflection angle in the presence of a plasma medium and the effect of the plasma medium on the deflection angle. The deflection angle of the BH solution in the gauged super-gravity is computed using the Keeton-Petters approach. Utilizing the ray-tracing technique, we investigate the shadow of the corresponding BH and analyze the plots of the deflection angle and shadow to verify the influence of the plasma and algebraic thermodynamic parameters on the deflection angle and shadow.
Brownian oscillator with time-dependent strength: a delta function protocol
Authors: Plyukhin Alex V 
Published: February 2024

Abstract
We consider a classical Brownian oscillator of mass m driven from an arbitrary initial state by varying the stiffness k(t) of the harmonic potential according to the protocol

k
(
t
)
=

k
0

+
a
δ
(
t
)

, involving the Dirac delta function. The microscopic work performed on the oscillator is shown to be

W
=
(

a
2

/

2
m
)

q
2

−
a
q
v

, where q and v are the coordinate and velocity in the initial state. If the initial distribution of q and v is the equilibrium one with temperature T, the average work is

⟨
W
⟩
=

a
2

T

/

(
2
m

k
0

)

and the distribution f(W) has the form of the product of exponential and modified Bessel functions. The distribution is asymmetric and diverges as W → 0. The system’s response for t > 0 is evaluated for specific models.
Electric vehicle charging optimization strategy based on the mopso algorithm
Authors: Hu Haopeng , Wei Yunbing 
Published: February 2024

Abstract
Aiming at the problems of electric vehicle disorderly charging on grid load stability and charging cost, this study considers the grid load pressure and presents a multi-faceted optimized model for electric vehicle charging. The model is addressed by utilizing a particle swarm optimization algorithm designed for multiple objectives. The outcomes demonstrate that the charging framework built upon the multi-objective particle swarm algorithm has a fast convergence speed and can avoid the limitation of the local optimal solutions. Under the premise of reducing by managing the grid load fluctuation, the model effectively curbs the expense of vehicle charging while also minimizing peak-to-valley disparities in grid load.
Fuel Combination Optimization Model of Thermal Power Plant Based on New Particle Swarm Optimization Algorithm
Authors: Cui Hailin 
Published: February 2024

Abstract
Influenced by the unbalanced state of particle swarm in the process of fuel combustion in thermal power plants, the fuel cost in the thermal power generation stage is relatively high. Therefore, a new particle swarm optimization model for fuel combination in thermal power plants is proposed. Combined with the combustion properties of different fuels, from the point of view of particle swarm optimization, in the process of carrying out specific particle swarm optimization simulation, the original particle swarm optimization algorithm is improved adaptively. A new particle swarm optimization algorithm is constructed by coupling it with a multiphase turbulence model. The fuel combustion performance of the power plant is analyzed by using its coupling. The optimization model of fuel combination in the thermal power plant is constructed by maximizing the energy release of fuel as the core, and the state of the fluid-particle field is taken as the constraint condition. In the test results, the design of the fuel combination optimization model can fully improve the energy release degree of fuel and reduce fuel consumption under the same power generation demand, which has a positive effect on power generation cost control.
Simulation Analysis of Electric Field on the Body Surface of Personnel Engaged in Interphase Live Working on 750 kV Double Circuit Transmission Lines on the Same Tower
Authors: Xingdong Bai , Minsheng Du , Zhangpeng Zhou , Feng Liu , Qiang Lv , Yongxin Zhang 
Published: February 2024

Abstract
This article focuses on the assistance of electric lifting devices for phase-to-phase live working. Taking a typical 750 kV double circuit vertical arrangement line on the same tower as the research object, a three-dimensional simulation model of a real tower conductor simulated human is established. The field strength on the human body surface is calculated and analyzed during the process of operators entering the equipotential (conductor) while riding the electric lifting device in order to determine the risk points of the operation and determine the safety protection measures for the human body. Through simulation, it is found that when simulating human entry (exit) and phase-down operations, the human body is located at different distances of 0.4 m-1.5 m directly below the conductor. When not wearing shielding clothing, the maximum field strength of the human head is much greater than the allowable value. If using shielding clothing with a shielding efficiency of 60 dB for safety protection, the surface field strength inside the worker’s clothing can be reduced to 2.25 kV/m, meeting the requirements of live working. Operators should pay attention to the electric field protection at each phase wire when using an electric lifting device to enter (exit) the interphase wire. Equipotential operators must wear a complete set of shielding clothing for 750 kV live working.
Study of high-power breakdown accompanied by sliding discharge mode in a three-dimensional sliding arc plasma igniter
Authors: Yongjian Ning , Qian Zhang , Tiantian Zhang , Ruilei Yang , Xueke Che 
Published: February 2024

Abstract
In order to improve the ignition reliability of a small-thrust rocket engine, a three-dimensional sliding arc igniter discharge characteristic research experimental system was constructed. The work gas flow rate and excitation parameters were changed, the plasma discharge characteristic and spectral characteristic parameters were measured, and the characteristics of the igniter discharge modes were investigated. The influence of the excitation parameters on the discharge modes was explored, and the effect of the discharge modes on the ignition performance of the igniter was analyzed as well as the mechanism of the discharge modes. The results show that the igniter has three different discharge modes during the discharge process, i.e., breakdown with sliding mode, high-power breakdown with sliding mode, and the transition state between the two modes, the shift of the discharge mode is mainly affected by the excitation power, and the igniter is in the breakdown with sliding mode when the excitation power is low, and in the high-power breakdown with sliding mode when the power is high. High-power breakdown with sliding mode has both breakdown with sliding and stable sliding characteristics in the early stage of the sliding arc movement for the breakdown with sliding and then transformed into sliding. Compared with the breakdown with sliding mode and high-power mode, the sliding arc discharge area is wider, the concentration of O * than the breakdown with sliding mode increased by 2.86 times, the electron temperature increased by 31.76%, and it can significantly improve plasma ignition effect, reduce the activation energy and enhance the performance of the igniter.
An Improved MP Algorithm for Power System Mode Identification
Authors: Meng Xuanren , Liu Mosi , Li Jinzhao , Zheng Kun 
Published: February 2024

Abstract
An improved matrix pencil (MP) algorithm for power system mode identification has been proposed in this paper. Aiming at the problem of inaccurate order determined by the singular value decomposition (SVD) step in traditional MP algorithms and the lack of Hankel structure in the reconstructed matrix, which leads to inaccurate identification under low signal-to-noise ratio (SNR) condition, singular entropy order determination is introduced. An iterative algorithm is proposed to calculate a reconstructed matrix with both Hankel structure and rank-deficient attributes. This reconstruction matrix has a better approximation effect and improves the identification accuracy of the MP algorithm under low SNR. The effectiveness of the proposed algorithm was verified by modal parameter identification of numerical signals and oscillation signals of the 4-machine and 2-area systems. The algorithm proposed in low SNR has higher accuracy than the traditional MP algorithm, and the reconstructed signal has a better fitting effect.
Zero crossing detection algorithm based on an MLP neural network for differential confocal microscopy
Authors: Wang Zhiyi , Wang Tingyu , Yang Yongqiang , Mi Xiaotao , Wang Jianli 
Published: February 2024

Abstract
Differential confocal microscopy is widely used because of its ultra-high axial resolution. The surface gradient results in light loss, which decreases the slope of the differential response signal at zero crossing. At this point, when the signal-to-noise ratio is fixed, the traditional linear fitting method to determine the position of zero crossing is subject to significant error influence. To solve these issues, this paper proposes a zero crossing detection algorithm based on a multilayer perceptron (MLP) neural network. Experimental results reveal that the proposed algorithm is more robust and capable of better zero crossing extraction. When numerical aperture (NA)=0.4, the average error is 16.9 nm, which is 55.4 % higher than that of the traditional linear fitting algorithm. The proposed algorithm has a high potential for use with the differential confocal sensor to measure unknown steep surfaces.