bluuebunny/crossref_metadata_2025_split huggingface dataset:
Compact quad-channel filtering diplexer using meander hairpin ring resonator aided by grey wolf algorithm
Authors: Liu Haiwen , Wang Sidong , Sun Mingli , Song Zeren , Tian Hongliang 
Published: July 2024

Abstract
In this paper, a compact quad-channel filtering diplexer using meander hairpin ring resonators (M-HRR) aided by grey wolf algorithm (GWA) is proposed. To realize a compact size of the multi-channel diplexer, the form of a meander-line structure is applied to the hairpin ring resonator for the miniaturization of the proposed quad-channel diplexer. Then, using the coupling topology of the 0° feeding structure, a transmission zero is generated between two resonant frequencies of M-HRR to improve the band-edge selectivity and band-to-band isolation of the diplexer. To address the problematic challenge of the complexity in designing the multi-channel diplexer with a time-consuming optimization manually, an intelligent GWA is utilized to optimize the physical layout of the designed circuit to achieve the desired performance of the diplexer rapidly. Furthermore, different kinds of intelligent algorithms are implemented and compared under identical conditions. Finally, a quad-channel filtering diplexer centering at 3.28/3.75/4.32/4.75 GHz with 3 dB fractional bandwidth of 5.4%, 2.9%, 4.9%, and 3.2% respectively is fabricated and measured. The circuit occupies a compact size of 0.22λ
g × 0.20λ
g. Good accordance is observed between the simulated and measured frequency responses.
Green-solvent-processed lead-free perovskite solar cells
Authors: He Linfeng , Zhang Xin , Chen Xinyao , Cheng Jin , Li Zhenjun , Li Junming 
Published: July 2024

Abstract
Tin-based perovskite has been considered as one of the most potential candidates for lead-based perovskite. The solution proceed method was widely utilized in fabricating tin perovskite solar cells. So far, all fabrication processes for tin perovskite solar cells involved toxic organic solvents, which is contrary to the development of environmentally friendly perovskite solar cells. In this study, we report for the first time, by using a mixed green solvent N-diethyl formamide and green 1,3-dimethyl-3,4,5,6-Tetrahydro-2 (1H)-pyrimidinone as precursor solvent, and a green solvent dibutyl ether as antisolvent, a high-quality FA0.75MA0.25SnI3 film was achieved. The optical band gap of the prepared perovskite layer was 1.36 eV, which was close to the ideal band gap. The green-solution-proceed perovskite films showed reduced defect density. As a consequence, the champion green-solution-proceed photovoltaic device achieved a power conversion efficiency of 4.4%. Moreover, it still maintains 80% of the initial efficiency after 600 h of storage in a nitrogen atmosphere. This work would promote the perovskite solar cells from a ‘new’ technique to a ‘new and green’ technique.
Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU
Authors: Liu Xuanyu , Zhang Wenshuai , Mengting Jiang , Wang Yudong , Ma Lili 
Published: July 2024

Abstract
Realizing automatic control of shield machine tunneling attitude is a challenging problem. Realizing multi-step intelligent prediction for attitude and position is an important prerequisite for solving this problem in the tunneling process with complex and varied geological environments. In this paper, a multi-step intelligent predictive scheme based on beluga whale optimization-convolutional neural network-Long Short-term memory-gated recurrent unit (BWO-CNN-LSTM-GRU) is proposed for shield machine position attitude. First, Pearson correlation analysis is utilized to determine the input feature variables from the construction data and temporalize the input features. Subsequently, CNN-LSTM-GRU predictive models are established for the six positional parameters, separately. Among them, CNN performs feature extraction on the input variables, and LSTM-GRU realizes the predictions for the target positional parameters. In the end, the optimization of the convolutional layer dimension, the number of convolutional layers, iterations, the learning rate, the number of neurons in the LSTM layer and GRU layer of each position predictive model is performed on the basis of BWO, separately, and the best hyperparameters found are built into a BWO-CNN-LSTM-GRU position predictive model, which realizes the multi-step intelligent predictions for the shield machine’s position. The proposed approach is examined by utilizing the Beijing Metro Line 10. The results show that the predictive deviation of the position predictive model is within 3 mm, and the positional trajectory points obtained on the basis of the predicted values and the 3D coordinate system are highly coincident with the actual trajectory points. Therefore, the approach provides a more accurate predictive result for shield attitude and position and can provide a decision-making scheme for further realizing the coordinated autonomous control of shield machine.
Online detection method for magnetic suspension concentration based on machine vision
Authors: Yang Yun , Han Baohu , Zuo Jinzhao , Li Long , Li Kenan 
Published: July 2024

Abstract
With the intelligent development of magnetic particle inspection, the quality of magnetic indications formed at cracks is closely related to the accuracy of magnetic particle inspection image analysis results. The concentration of magnetic suspension is a key process parameter affecting the quality of magnetic indication formation. Hence, this study presents an online detection method based on machine vision for measuring magnetic suspension concentration. The method initially enhances the contrast of images of the pear-shaped measuring tube containing magnetic suspension and then extracts scale lines through feature analysis and morphological processing. A method for extracting the magnetic particle sedimentation area of magnetic suspension based on a dual-threshold segmentation algorithm is proposed. The contour filtering algorithm and pixel calibration method are used to obtain the magnetic particle concentration of the non-estimation and estimation areas based on scale line extraction, ultimately forming an online accurate detection method for magnetic suspension concentration values. Experiments were conducted to validate the method against different concentrations, turbidity levels, tilting angles of the pear-shaped measuring tube, and ambient brightness. The results show that the error in magnetic suspension concentration detection based on this method is within 5%. This has certain reference value for the stable control of magnetic suspension concentration and for enhancing the reliability of intelligent decision-making results in magnetic particle inspection.
Domain generalized open-set intelligent fault diagnosis based on feature disentanglement meta-learning
Authors: Zhou Xiangdong , Deng Xiao , Liu Zhengwu , Shao Haidong , Liu Bin 
Published: July 2024

Abstract
Existing domain generalization (DG) -based intelligent fault diagnosis methods mainly focus on learning domain-invariant features. However, in practical scenarios, these features are difficult to extract and effectively distinguish from class-related features. Moreover, these methods often assume identical label distributions between the source and target domain, making it challenging to handle scenarios where unknown classes exist in the target domain. To address these issues, this paper proposes a domain generalized open-set intelligent fault diagnosis method based on feature disentanglement meta-learning. A binary mask feature disentanglement module is constructed to overcome the information loss caused by feature reconstruction, enabling the separation of domain-specific and class-related features. Additionally, a meta-purification loss function is defined, incorporating a correlation loss term to remove impurity features from the class-related features, and further purifying class information through feature combination pairing. The method is trained on multiple source domains using a meta-learning strategy and generalized to target domains with unknown classes. The method is utilized for bearing fault diagnosis, designing multi-task experimental scenarios under different rotational speeds, and compared with existing DG methods. Experimental results show that the proposed method exhibits excellent generalization ability and effectively addresses the issue of domain generalized open-set fault diagnosis.
Autonomous ground vehicle gravity anomaly measurement and dynamic error compensation
Authors: Li Xinyu , Zhou Zhaofa , Zhang Zhili , Chang Zhenjun , Hao Shiwen , Duan Hui 
Published: July 2024

Abstract
To address the issue that dynamic gravity anomaly measurement is overly dependent on GNSS and cannot be measured autonomously at this stage, this paper proposes an autonomous ground vehicle dynamic gravity anomaly measurement method based on a strapdown inertial navigation system (SINS), odometer (OD), barometer and platform gravimeter. The SINS/OD/barometer integrated navigation solution delivers high-precision navigation parameters, completes the calculation of correction terms, and performs the autonomous dynamic gravity anomaly measurement combined with the primary measurement results of the platform gravimeter. Numerical calculations provide the requirements for the application of the proposed method, and the cut-off frequency for extracting gravity anomalies is 0.02 Hz, as determined by power spectral density analysis. In order to further improve the measurement accuracy and account for dynamic errors caused by vehicle maneuvering, a long-short-term memory (LSTM) model of recurrent neural network is introduced. A series of experiments under multiple circumstances with repeated lines were conducted in Tianjin, China, and the static measurements along the line were taken using CG-5 to provide true values of gravity anomalies. The results demonstrate that the autonomous measurement scheme can achieve accuracy comparable to GNSS-assisted, and that dynamic error compensation algorithm based on LSTM improves the dynamic gravity measurements accuracy significantly without sacrificing the spatial resolution of gravity anomalies.
Systematic investigation on the rational design and optimization of bi-based metal oxide semiconductors in photocatalytic applications
Authors: Song Yuan-Jun , Xia Peng , Zhang Xiao-Yang , Zhang Tong 
Published: July 2024

Abstract
To address the global energy shortage and mitigate greenhouse gas emissions on a massive scale, it is critical to explore novel and efficient photocatalysts for the utilization of renewable resources. Bi-based metal oxide (Bi
x
MO
y
) semiconductors composed of bismuth, transition metal, and oxygen atoms have demonstrated improved photocatalytic activity and product selectivity. The vast number of element combinations available for Bi
x
MO
y
materials provides a huge compositional space for the rational design and isolation of promising photocatalysts for specific applications. In this study, we have systematically investigated the electronic and optical properties over Bi2O3 and a series of selected Bi
x
MO
y
group materials (BiVO4, BiFeO3, BiCoO3, and BiCrO3) by calculating band structure, basic optical property features, mobility and separation of charge carriers. It is clearly noted that the band gap and band edge position of the Bi
x
MO
y
group materials can be tuned in a wide range in comparison to Bi2O3. Similarly, the light response of Bi
x
MO
y
also can be broadened from the ultraviolet to the visible light region by adjusting the selection of transition metals. Additionally, the analysis of the effective mass of charge carriers of these materials further confirms their possibility in photocatalytic reaction applications because of the appropriate separation efficiency and mobility of carriers. A selection of experimental investigations on the crystal structure, composition, and optical properties of Bi2O3, BiVO4, and BiFeO3 as well as their catalytic performance in the degradation of methylene blue over was also conducted, which agree well with the theoretical predictions.
Monte Carlo simulation of initial and volume ion recombination correction factor in plane parallel ionization chambers exposed to ion beams
Authors: Orts Marina , Rossomme Severine , Souris Kevin , Sterpin Edmond 
Published: July 2024

Abstract

Objective. Accurate reference dosimetry with ionization chambers (ICs) relies on correcting for various influencing factors, including ion recombination. Theoretical frameworks, such as the Boag and Jaffe theories, are conventionally used to describe the ion recombination correction factors. Experimental methods are time consuming, the applicability may be limited and, in some cases, impractical to be used in clinical routine. The development of simulation tools becomes necessary to enhance the understanding of recombination under circumstances that may differ from conventional use. Before progressing, it is crucial to benchmark novel approaches to calculate ion recombination losses under known conditions. In this study, we introduce and validate a versatile simulation tool based on a Monte Carlo scheme for calculating initial and volume ion recombination correction factors in air-filled ICs exposed to ion beams with clinical dose rates. Approach. The simulation includes gaussian distribution of ion positions to model the distribution of charge carriers along the chamber volume. It accounts for various physical transport effects, including drift, diffusion, space charge screening and free electron fraction. To compute ion recombination, a Monte Carlo scheme is used due to its versatility in multiple geometries, without exhibiting convergence problems associated with numerically solved procedures. Main results. The code is validated in conventional dose rates against Jaffe’s theory for initial recombination and Boag’s theory for volume recombination based on parameters derived from experimental data including proton, helium and carbon ion beams measured with a plane parallel IC. Significance. The simulation demonstrates excellent agreement, typically 0.05% or less relative difference with the theoretical and experimental data. The current code successfully predicts ion recombination correction factors, in a large variety of ion beams, including different temporal beam structures.
FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool
Authors: Li Suya , Hamdi Mahdjoub , Dutta Kaushik , Fraum Tyler J , Luo Jingqin , Laforest Richard , Shoghi Kooresh I 
Published: July 2024

Abstract

Objective. Simulation of positron emission tomography (PET) images is an essential tool in the development and validation of quantitative imaging workflows and advanced image processing pipelines. Existing Monte Carlo or analytical PET simulators often compromise on either efficiency or accuracy. We aim to develop and validate fast analytical simulator of tracer (FAST)-PET, a novel analytical framework, to simulate PET images accurately and efficiently. Approach. FAST-PET simulates PET images by performing precise forward projection, scatter, and random estimation that match the scanner geometry and statistics. Although the same process should be applicable to other scanner models, we focus on the Siemens Biograph Vision-600 in this work. Calibration and validation of FAST-PET were performed through comparison with an experimental scan of a National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Further validation was conducted between FAST-PET and Geant4 Application for Tomographic Emission (GATE) quantitatively in clinical image simulations in terms of intensity-based and texture-based features and task-based tumor segmentation. Main results. According to the NEMA IQ phantom simulation, FAST-PET’s simulated images exhibited partial volume effects and noise levels comparable to experimental images, with a relative bias of the recovery coefficient RC within 10% for all spheres and a coefficient of variation for the background region within 6% across various acquisition times. FAST-PET generated clinical PET images exhibit high quantitative accuracy and texture comparable to GATE (correlation coefficients of all features over 0.95) but with ∼100-fold lower computation time. The tumor segmentation masks comparison between both methods exhibited significant overlap and shape similarity with high concordance CCC > 0.97 across measures. Significance. FAST-PET generated PET images with high quantitative accuracy comparable to GATE, making it ideal for applications requiring extensive PET image simulations such as virtual imaging trials, and the development and validation of image processing pipelines.
MANTA: a negative-triangularity NASEM-compliant fusion pilot plant
Authors:   The MANTA Collaboration, Rutherford G , Wilson H S , Saltzman A , Arnold D , Ball J L , Benjamin S , Bielajew R , de Boucaud N , Calvo-Carrera M , Chandra R , Choudhury H , Cummings C , Corsaro L , DaSilva N , Diab R , Devitre A R , Ferry S , Frank S J , Hansen C J , Jerkins J , Johnson J D , Lunia P , van de Lindt J , Mackie S , Maris A D , Mandell N R , Miller M A , Mouratidis T , Nelson A O , Pharr M , Peterson E E , Rodriguez-Fernandez P , Segantin S , Tobin M , Velberg A , Wang A M , Wigram M , Witham J , Paz-Soldan C , Whyte D G 
Published: July 2024

Abstract
The MANTA (Modular Adjustable Negative Triangularity ARC-class) design study investigated how negative-triangularity (NT) may be leveraged in a compact, fusion pilot plant (FPP) to take a ‘power-handling first’ approach. The result is a pulsed, radiative, ELM-free tokamak that satisfies and exceeds the FPP requirements described in the 2021 National Academies of Sciences, Engineering, and Medicine (NASEM) report ‘Bringing Fusion to the U.S. Grid’ (2021 Bringing Fusion to the U.S. Grid). A self-consistent integrated modeling workflow predicts a fusion power of 450 MW and a plasma gain of 11.5 with only 23.5 MW of power to the scrape-off layer (SOL). This low P
SOL together with impurity seeding and high density at the separatrix results in a peak heat flux of just 2.8 MW m−2. MANTA’s high aspect ratio provides space for a large central solenoid (CS), resulting in ∼15 minute inductive pulses. In spite of the high B fields on the CS and the other REBCO-based magnets, the electromagnetic stresses remain below structural and critical current density limits. Iterative optimization of neutron shielding and tritium breeding blanket yield tritium self-sufficiency with a breeding ratio of 1.15, a blanket power multiplication factor of 1.11, toroidal field coil lifetimes of

3100
±
400

MW · yr, and poloidal field coil lifetimes of at least

890
±
40

MW · yr. Following balance of plant modeling, MANTA is projected to generate 90 MW of net electricity at an electricity gain factor of

∼

2.4

. Systems-level economic analysis estimates an overnight cost of US$3.4 billion, meeting the NASEM FPP requirement that this first-of-a-kind be less than US$5 billion. The toroidal field coil cost and replacement time are the most critical upfront and lifetime cost drivers, respectively.
Parsimonious statistical techniques for the detection of drifts toward dangerous operational conditions in tokamaks
Authors: Craciunescu Teddy , Murari Andrea ,   on behalf of JET Contributors,   the EUROfusion Tokamak Exploitation Team
Published: July 2024

Abstract
The approaching initial operation of major new tokomaks is rendering more pressing the need for effective disruption prediction techniques. The required tools should be not only accurate but also capable of operating with a minimum number of signals, because in the first campaigns of new devices typically only a very limited number of diagnostics is available. The present work reports the results obtained with two advanced statistical techniques, chaos onset and concept drift (CD). These methods are capable of detecting the plasma drifting towards dangerous regions of the operational space using only global signals such as the plasma current or the locked mode amplitude. The obtained performances meet the requirements of mitigation in devices such as JT60-SA and ITER at the beginning of their operation. The computational times of the chaos onset approach are already of the order of 1 ms on laptop computers, whereas the CD technique requires implementation on FPGAs or GPUs to be compatible with real time requirements.
2D axisymmetric electromagnetic modeling of HTS coils based on T-A formulation with modified Newman boundary conditions
Authors: Chen Yong , Wang Qiuliang , Wang Kangshuai , Zhou Benzhe , Liu Shixian , Ji Xiaoyu , Wang Lei , Liu Jianhua 
Published: July 2024

Abstract
The T-A formulation based on thin film approximation has been widely used in electromagnetic modeling of high temperature superconducting (HTS) coated conductors (CCs). However, with the emergence of no-insulation (NI) HTS coils and its variant HTS coils, the electrical connection of HTS coils has become increasingly complex, and the traditional T-A formulation is challenging to handle the problems of conductors with non-negligible thickness and current sharing. This paper firstly describes the Neumann boundary condition of the T-A formulation under 2D axisymmetric symmetry in detail, as well as the conversion of different boundary conditions. And additional voltage variable is added to correct the Newman boundary condition from the perspective of circuit. Then, considering HTS CCs series or parallel stacking to carry current, the effectiveness of this method is verified by comparing with benchmark model. Finally, we extend the application range of the T-A formulation with modified Newman boundary conditions to simulate thick superconductors, and naturally process current sharing of azimuthal and radial current in circular NI HTS coils.
Complex Branch Pipe Layout Optimization Based on Machine Learning and Improved Grey Wolf Algorithm
Authors: Ming Y R , Liu S B 
Published: July 2024

Abstract
Branch pipes are widely found in complex equipment, and its layout planning and design has an important impact on the quality and efficiency of equipment design. In this paper, a branch pipe layout planning method based on machine learning and improved grey wolf algorithm is proposed. Firstly, the branch pipe layout problem is decomposed into a series of “between two points” pipe layout and “point to line” pipe layout problems to reduce the complexity of the problem. Secondly, the Q-learning algorithm is applied to solve the “between two points” pipe layout path solving problem, and then combined with the improved grey wolf algorithm to optimize the “point to line” pipe path solving. Finally, a simulation system is built based on Siemens NX and MATLAB platform, and the validity of the proposed method is verified by simulation cases.