bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of optical systems for background oriented schlieren
Authors: Zhou Tanbo , Gaskins Jonathan , Poggie Jonathan , Bane Sally P M 
Published: October 2024

Abstract
Background oriented schlieren (BOS) is a non-intrusive optical method for measuring density gradients in a fluid flow based on variations of the local refractive index. The type of BOS optical system used, i.e. entocentric vs. telecentric, and the system design determine the accuracy and quality of the measurement. This work aims to optimize both types of optical systems to minimize the error for measurements of high-speed compressible turbulent boundary layers. Claims of the advantages offered by telecentric optical systems over entocentric systems are investigated, as well as the out-of-focus effects for types of systems. Numerical ray tracing simulations are performed using density fields from large eddy simulations (LES) of a Mach 2 turbulent boundary layer to generate synthetic but realistic BOS images. The results show that telecentric systems have lower overall error and less sensitivity. Contrary to recommendations by early BOS work, the best accuracy is achieved when density gradient object is placed outside of the depth of field of the optical system, for both entocentric and telecentric systems.
Dynamic range extension method for sinusoidal phase modulating interferometer based on in-phase component signal reconstruction
Authors: Zhang Qiyuan , Zhang Lieshan , Xing Zhaoqiang , Fang Wenjun , Xia Dunzhu 
Published: October 2024

Abstract
The continuous increase of the amplitude of the measured vibration will cause spectrum aliasing in the interference signal of the sinusoidal phase modulating interferometer (SPMI), thereby constraining its measurement dynamic range (DR). To address this issue, a DR extension method of SPMI based on the reconstruction of in-phase component signal is proposed. On the basis of conventional SPMI, an additional reference signal is introduced for estimating the phase modulation depth (PMD) and the major to minor axis ratio R
e of the Lissajous ellipse formed by orthogonal interference signal pair and a phase demodulation algorithm named as phase generated carrier (PGC)- in-phase component signal reconstruction (ICR)- differential-and-cross-multiplying algorithm (DCM) which integrates PGC-DCM algorithm and the ICR is proposed. Initially, the PMD is regulated to approximately 1.8412 rad, ensuring that the quadrature component signal can be accurately extracted from the mixed signal of the interference and carrier signals, even in the presence of spectrum aliasing in the original interference signal. Then, the undistorted quadrature component signal is used to reconstruct an in-phase component signal with the estimated value of R
e. Finally, the reconstructed in-phase component signal and quadrature component signal are normalized, and a DCM approach is applied to obtain the measured vibration. A SPMI measurement system based on Michelson interferometer structure is constructed to experimentally validate the proposed method. The experimental results demonstrate that the proposed method can effectively extend the DR of the SPMI system in the presence of spectrum aliasing. The DR of the system is increased from 97.52 dB @ 55 Hz to 102.6 dB @ 55 Hz, with the maximum measurable amplitude increasing from 7728.75 nm @ 55 Hz to 13 705.16 nm @ 55 Hz.
Remaining useful life prediction of turbofan engines based on dual attention mechanism guided parallel CNN-LSTM
Authors: Han Baokun , Yin Peiwen , Zhang Zongzhen , Wang Jinrui , Bao Huaiqian , Song Lijin , Liu Xinwei , Ma Hao , Wang Dawei 
Published: October 2024

Abstract
Remaining useful life (RUL) prediction is an valuable research task for predictive health management of aero engines, which is crucial to enhance the safety, dependability and economy of the motor. Accurate prediction of remaining engine service life is an important means for the effectively monitoring of engine operating conditions. The forecasting accuracy of turbofan RUL is inadequate with the traditional single-parameter, single-stage mode. To boost the precision of aero-engine RUL anticipation, a novel mode is recommended, based on a parallel convolutional neural network (CNN) with a long and short-term memory (LSTM) neural network and a dual attention mechanism, named PCLD. The degradation information directly from time series sensor data. The advantages of CNN networks and LSTM networks in feature mining and time series processing, respectively, are employed to process time series data, which is conducive to preventing the loss of important element in the data. At last, the experimental results on the aero-engine performance recession dataset C-MAPSS demonstrate that the method outperforms the currently popular models with better robustness and higher prediction accuracy.
Applications of machine learning in real-time control systems: a review
Authors: Zhao Xiaoning , Sun Yougang , Li Yanmin , Jia Ning , Xu Junqi 
Published: October 2024

Abstract
Real-time control systems (RTCSs) have become an indispensable part of modern industry, finding widespread applications in fields such as robotics, intelligent manufacturing and transportation. However, these systems face significant challenges, including complex nonlinear dynamics, uncertainties and various constraints. These challenges result in weakened disturbance rejection and reduced adaptability, which make it difficult to meet increasingly stringent performance requirements. In fact, RTCSs generate a large amount of data, which presents an important opportunity to enhance control effectiveness. Machine learning, with its efficiency in extracting valuable information from big data, holds significant potential for applications in RTCSs. Exploring the applications of machine learning in RTCSs is of great importance for guiding scientific research and industrial production. This paper first analyzes the challenges currently faced by RTCSs, elucidating the motivation for integrating machine learning into these systems. Subsequently, it discusses the applications of machine learning in RTCSs from various aspects, including system identification, controller design and optimization, fault diagnosis and tolerance, and perception. The research indicates that data-driven machine learning methods exhibit significant advantages in addressing the multivariable coupling characteristics of complex nonlinear systems, as well as the uncertainties arising from environmental disturbances and faults, thereby effectively enhancing the system’s flexibility and robustness. However, compared to traditional methods, the applications of machine learning also faces issues such as poor model interpretability, high computational requirements leading to insufficient real-time performance, and a strong dependency on high-quality data. This paper discusses these challenges and proposes potential future research directions.
Modeling of an inductive displacement sensor based on 1DCNN-LSTM-AT
Authors: Li Jinlin , Yang Xinyue , Zhong Shuncong , Liang Wei , Guo Qiaoying 
Published: October 2024

Abstract
The input-output relationship of inductive displacement sensors is typically nonlinear, which demands numerous computational resources for precise calculation. Therefore, the traditional analytical methods for the inductive displacement sensors cannot meet the real-time high-precision measurement requirements. In response to the aforementioned issues, this paper proposes an optimized Long Short-Term Memory (LSTM) neural networks based on one-dimensional convolutional neural networks (1DCNN) to modeling the input-output relationship of the inductive displacement sensor. First, the measurement principle of the inductive displacement sensors was analyzed, and the analytical model of the sensor output was derived. And the influences of the key parameters of sensors on the relationship between the induced voltage and the displacement were studied. Then, the 1DCNN-LSTM-AT network for modelling the input-output relationship of the inductive displacement sensor was studied. The spatial features of historical induced electromotive force (EMF) data generated by the induction coil were initially extracted using the 1DCNN network. And these spatial features were then utilized as input to the LSTM neural network to capture the temporal features of the historical induced EMF data. Subsequently, the spatiotemporal features of the induced EMF data were fed into the regression prediction layer to compute the displacement measurement results corresponding to the current input. Moreover, the attention mechanism was used for the 1DCNN-LSTM model to enhance the prediction accuracy and stability of the model. Finally, the experimental results demonstrate that the proposed 1DCNN-LSTM-AT model achieves an average absolute percentage error of 3.1%, significantly outperforming traditional models such as LSTM (29.3%), CNN (4.7%), and ANN (4.3%). This paper provides a new method for modelling the nonlinear relationships of the inductive displacement sensor, and presents a fresh perspective for research in the data processing of nonlinear sensors.
A Novel Demodulator Design for Signal Processing of Inductive Oil Debris Sensors
Authors: Wang Guan , Wu Xianwei , Qian Zhenghua , Liu Dianzi , Li Peng , Kuznetsova Iren 
Published: October 2024

Abstract
Metal debris from bearing wear occurred in the lubrication system of aircraft engines represents valuable information, which can be utilized to assess the current engine condition and further enable the life-cycle prediction. The demodulation of debris signals consists of two parts: mixing and filtering. In traditional methods, the mixing process is complex to adjust and prone to failure, while the filtering employs the fixed low-pass filtering, which significantly affects the demodulation performance when the debris flow rate changes. To address these issues, this paper proposes a novel demodulation model to enhance the demodulation performance and increase the signal-to-noise ratio of chip signals. Firstly, the circuit innovatively adopts a self-frequency mixing method to avoid the complex phase adjustment required by traditional reference-frequency mixing methods for the improved stability of the circuit. Following that, a digital potentiometer is employed to implement the flow rate filtering, enabling precise control over the filter type and range, thus mitigating signal distortion caused by changes in flow rate and increasing the signal-to-noise ratio. Throughout extensive experiments and data analysis, the self-frequency mixing method improves the signal-to-noise ratio by 0.72 dB and reduces the signal standard deviation by 59.12% as compared to the reference-frequency mixing method. Considering the debris flow rate in the range of 0.2 to 0.5 m/s, the flow rate filtering method improves the signal-to-noise ratio by 19.71 dB and reduces the amplitude standard deviation by 66.18%, as compared to results by the traditional fixed low-pass filtering method. Results demonstrate that the aforementioned circuit design effectively enhances the signal amplitude and stability of inductive oil debris sensors in the wear assessment of mechanical systems and provides useful insights into the development of advanced sensors used in the field of structural health monitoring.&#xD;
Segmentation of tunnel water leakage based on a lightweight DeepLabV3+ model
Authors: Wang Dandan , Hou Gongyu , Chen Qinhuang , Li Weiyi , Fu Huanhuan , Sun Xiaorong , Yu Xunan 
Published: October 2024

Abstract
The accurate and efficient detection of water leakage with complex backgrounds is crucial for the safety of metro operations. A lightweight segmentation method for metro tunnel water leakage based on transfer learning is proposed. Firstly, this is based on the Deeplabv3+ model and adopts MobileNetv3-Large as the backbone feature extraction network, which significantly reduces the network parameters and improves the detection speed; secondly, it incorporates the efficient channel attention mechanism, which enables the model to adaptively adjust the weights of the channel features and capture the inter-channel relationships in the image, which significantly improves the model’s ability for feature extraction ability; furthermore, for the problem of severe imbalance between positive and negative samples in the dataset, the recognition accuracy of complex samples is increased by optimizing the loss function; finally, the training method of transfer learning is utilized to solve the problem of scarcity of water leakage dataset, and to improve the model’s accuracy and generalization ability. The results show that the model has more significant detection accuracy and segmentation speed advantages than today’s mainstream semantic segmentation model. With strong generalization ability in complex environments (e.g. low illumination and multiple obstructions), model can be used for intelligent operation and maintenance in metro tunnel projects.
Adaptive hybrid prediction model for adapting to data distribution shifts in machining quality prediction
Authors: Li Feng , Yang Xu , Gao Jie , Du Jinsong , Wang Wei 
Published: October 2024

Abstract
The accuracy of data-driven intelligent prediction for machining quality relies on the training samples. However, in actual applications, the continuous operation of machining equipment leads to gradual distribution shifts between the process data and the training samples for modeling. The shifts result in a degradation in the performance of predictive model, previous studies have often overlooked this issue. To tackle with the intricate problem, this research proposes a real-time model optimization approach. Firstly, a method for detecting machining data distribution shifts based on the two-sample Kolmogorov–Smirnov test is proposed. Then, an adaptive hybrid prediction model (AHPM) capable of real-time optimization is developed. This model consists of a deep neural network (DNN) and a broad learning system (BLS). DNN plays a primary role in prediction within the hybrid model with excellent generalization capability. BLS quickly completes optimization prior to DNN with its unique parameter update mechanism to compensate for prediction loss. Experimental results indicate that AHPM achieves the shortest optimization time while maintaining high accuracy, with post-optimization error reduction rates for mean squared error, mean absolute error, and mean absolute percentage error all exceeding 10%. In the test of application to actual machining cases, accuracy improved by 8.88% compared to traditional methods without optimization.
MOF-derived three-dimensional porous dodecahedral structured bimetallic Mn/Co-C-N composite for high-performance durable oxygen reduction reaction electrocatalysts
Authors: Qiao Yelin , Guo Yuanyuan , Zhao Yueqi , Chang Caiyun , Wang Shuo , Zhang Xiaoting , Gao Faming , Chen Rongna , Hou Li 
Published: October 2024

Abstract
Investigating high-efficiency oxygen reduction reaction (ORR) catalysts is one of the most effective methods for addressing the sluggish kinetics at the fuel cell cathode. Bimetallic three-dimensional porous materials have garnered significant attention due to their diverse structures, large specific surface area and synergistic catalytic effects. Herein, we synthesized a bimetallic three-dimensional porous dodecahedral structure, Mn/Co-C-N, derived from MOF using a straightforward approach. Experimental reults confirm that the strategic incorporation of Mn enhances the electrocatalytic activity for ORR. Meanwhile, the synergistic effects of Mn and Co, as well as the advantages of the dodecahedral structure for expediting electron transfer, all contribute to the exceptional ORR performance. Arc testing in an alkaline electrolyte reveals that the initial potential (E
onset) and the half-wave potential (E
1/2) are 0.89 V and 0.80 V, closely approximating those of commercial Pt/C (20 wt%). Following 10 000 stability test cycles, the half-wave potential exhibits a mere 8 mV change, confirming its remarkable stability.
Exploring nonlinear correlations among transition metal nanocluster properties using deep learning: a comparative analysis with LOO-CV method and cosine similarity
Authors: Nasiri Mahd Zahra , Kokabi Alireza , Fallahzadeh Maryam , Naghibi Zohreh 
Published: October 2024

Abstract
A novel approach is introduced for the rapid and accurate correlation analysis of nonlinear properties in Transition Metal (TM) clusters utilizing the Deep Leave-One-Out Cross-Validation technique. This investigation demonstrates that the Deep Neural Network (DNN)-based approach offers a more efficient predictive method for various properties of fourth-row TM nanoclusters compared to conventional Density Functional Theory methods, which are computationally intensive and time-consuming. The feature space, also known as descriptors, is established based on a broad spectrum of electronic and physical characteristics. Leveraging the similarities among these clusters, the DNN-based model is employed to explore the correlations among TM cluster properties. The proposed method, in conjunction with cosine similarity, achieves remarkable accuracy up to 10−9 for predicting total energy, lowest vibrational mode, binding energy, and HOMO-LUMO energy gap of TM2, TM3, and TM4 nanoclusters. By analyzing correlation errors, the most closely coupled TM clusters are identified. Notably, Mn and Ni clusters exhibit the highest and lowest levels of energy coupling with other TMs, respectively. Generally, energy prediction for TM2, TM3, and TM4 clusters exhibit similar trends, while an alternating behavior is observed for vibrational modes and binding energies. Furthermore, Ti, V, and Co demonstrate the highest binding energy correlations with TM2, TM3, and TM4 sets, respectively. Regarding energy gap predictions, Ni exhibits the strongest correlation in the smallest TM2 clusters, while Cr shows the highest dependence in TM3 and TM4 sets. Lastly, Zn displays the largest error in HOMO-LUMO energy gap across all sets, indicating distinctive independent energy gap characteristics.
Simulation study of a novel GaN on Si quasi-vertical reverse-conducting insulated gate bipolar transistor
Authors: Zhao Wen , Zhang Peng , Xu Jiarui , Du Jiale , Hou Bin , Zhu Jiejie , Ma Xiaohua , Hao Yue 
Published: October 2024

Abstract
In this paper, a novel GaN on Si quasi-vertical reverse conducting insulated gate bipolar transistor (RC-IGBT) with NPN structure is proposed. The distinctive feature of this device structure is that the collector, emitter and gate are all fabricated on the wafer surface, eliminating the two major challenges associated with conventional PNPN structures: the difficulty in forming ohmic contacts due to the activation difficulty of the bottom p-GaN layer and the necessity of creating backside via holes for the collector electrode. Additionally, the heavily doped n+ GaN in the emitter and RC region can be selectively regrown by Metal-organic Chemical Vapor Deposition (MOCVD) /MBE. It avoids the deterioration of ohmic contact characteristics caused by etching of p-GaN on the top layer of n+ GaN during p-collector electrode fabrication. Compared to fully vertical GaN-based IGBT devices, the device has the feasibility of wafer level preparation technology. In this paper, Silvaco TCAD is used to simulate the static and dynamic characteristics of this novel device. The simulation results reveal that the device has a high saturation current density of 10 528.16 A cm−2 at V
g = 14 V, a threshold voltage (V
th) of 5 V, a breakdown voltage of 1103 V, and a total turn-on time of 3.14 ns, total turn-off time of 9.19 ns. The calculated turn-on and turn-off conduction losses for the RC-IGBT device are 0.438 mJ cm−2 and 9.12 mJ cm−2. Compared to other GaN-based IGBT devices, the novel GaN-based IGBT device has faster switching speed, lower switching loss and smaller chip area. Furthermore, we investigated the effects of the doping concentration of the field stop layer, drift region doping concentration, and collector region p/n+ length ratio on the ‘snapback’ phenomenon of the RC-IGBT device. We also studied the impact of stage etching width and depth on the device’s output current capability and latch-up characteristics.
Impact of facet degree heterogeneity on phase transitions in infectious disease spread
Authors: Xi Yuxia , Hu Jianghong , Luo Jianfeng , Li Xing , Li Yihong , Luo Xiaofeng , Guo Zunguang 
Published: October 2024

Abstract
Complex dynamical behaviors, such as bistable and periodic phenomena, have been shown to emerge due to group interactions in higher-order networks. Traditionally, the transitions between these behaviors are primarily driven by changing model parameters that represent the transmission characteristics of a single infectious disease, while maintaining a fixed network structure. However, for newly emerging infectious diseases, modifying network structures is crucial as transmission characteristics are generally fixed. This study investigates transitions between these dynamical behaviors by altering the network structure, specifically facet degree heterogeneity in the simplicial complex, under same model parameters. We develop a dynamical model incorporating facet degree distributions and derive the corresponding outbreak thresholds. Firstly, we validated the rationality of the model using Monte Carlo simulation. Subsequently, by comparing the general model and the base model under the same and different network structures, we demonstrate the advantage of the general model in capturing dynamical behavior. Furthermore, it was discovered that variations in the facet degree heterogeneity lead to phase transitions of the stability region of equilibrium. We finally provide the distribution of the stable equilibrium regions with varying facet degree heterogeneity. These findings offer valuable insights for the prevention and control of infectious diseases.
Periodic, quasi-periodic, chaotic waves and solitonic structures of coupled Benjamin-Bona-Mahony-KdV system
Authors: Hussain Amjad , Abbas Naseem 
Published: October 2024

Abstract
In this paper, we mainly focus on studying the dynamical behaviour and soliton solution of the coupled Benjamin-Bona-Mahony-Korteweg–de Vries (BBM-KdV) system, which characterizes the propagation of long waves in weakly nonlinear dispersive media. The paper utilizes different tools to detect chaos, such as time series analysis, bifurcation diagrams, power spectra, phase portraits, Poincare maps, and Lyapunov exponents. This analysis helps in more accurate predictive modeling of the systems. This understanding can aid in the design of control strategies, resulting in enhancements in prediction, control, optimization, and design. Additionally, we construct the system’s solitary wave structures using the Jacobi elliptic function (JEF) method. We identify periodic wave solutions expressed in terms of rational, hyperbolic, and trigonometric functions. Certain parameter values can lead to periodic wave solutions, solitary waves (bell-shaped solitons), shock wave solutions (kink-shaped soliton solutions), and double periodic wave solutions.