Space fission power systems can enable ambitious solarsystem and deep-space science missions. The heat pipe cooled reactor is one of the most potential candidates for near-term space power supply, featured with safety...
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Correction to:Waste Disposal&Sustainable Energy(2022)4:117-129 https://***/10.1007/s42768-022-00101-7 The section Confict of interest'has been amended:'Jian-hua Yan is the Editor in-Chief of Waste Disposal...
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Correction to:Waste Disposal&Sustainable Energy(2022)4:117-129 https://***/10.1007/s42768-022-00101-7 The section Confict of interest'has been amended:'Jian-hua Yan is the Editor in-Chief of Waste Disposal&Sustain-able Energy.'The revised'Confict of interest'is as follows:Jianhua Yan is the Editor-in-Chief of Waste Disposal&Sustainable *** behalf of all authors,the corresponding author states that there is no conflict of interest.
Marine diesel engine has been the main power of civil ships, small and medium-sized ships and conventional submarines. Anomaly detection on diesel engines is very important for the safety of marine systems. Diesel eng...
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In real-world scenarios, the rotational speed of bearings is variable. Due to changes in operating conditions, the feature distribution of bearing vibration data becomes inconsistent, which leads to the inability to d...
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In real-world scenarios, the rotational speed of bearings is variable. Due to changes in operating conditions, the feature distribution of bearing vibration data becomes inconsistent, which leads to the inability to directly apply the training model built under one operating condition (source domain) to another condition (target domain). Furthermore, the lack of sufficient labeled data in the target domain further complicates fault diagnosis under varying operating conditions. To address this issue, this paper proposes a spatiotemporal feature fusion domain-adaptive network (STFDAN) framework for bearing fault diagnosis under varying operating conditions. The framework constructs a feature extraction and domain adaptation network based on a parallel architecture, designed to capture the complex dynamic characteristics of vibration signals. First, the Fast Fourier Transform (FFT) and Variational Mode Decomposition (VMD) are used to extract the spectral and modal features of the signals, generating a joint representation with multi-level information. Then, a parallel processing mechanism of the Convolutional Neural Network (SECNN) based on the Squeeze-and-Excitation module and the Bidirectional Long Short-Term Memory network (BiLSTM) is employed to dynamically adjust weights, capturing high-dimensional spatiotemporal features. The cross-attention mechanism enables the interaction and fusion of spatial and temporal features, significantly enhancing the complementarity and coupling of the feature representations. Finally, a Multi-Kernel Maximum Mean Discrepancy (MKMMD) is introduced to align the feature distributions between the source and target domains, enabling efficient fault diagnosis under varying bearing conditions. The proposed STFDAN framework is evaluated using bearing datasets from Case Western Reserve University (CWRU), Jiangnan University (JNU), and Southeast University (SEU). Experimental results demonstrate that STFDAN achieves high diagnostic accuracy ac
Climate models are vital for understanding and projecting global climate change and its associated ***,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of fut...
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Climate models are vital for understanding and projecting global climate change and its associated ***,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future *** these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning ***,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth system Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST ***,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to ***,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to *** enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales *** study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.
Objective In recent years, with increasing demands for imaging optical systems and continuous advancements in manufacturing technologies, dual-band infrared zoom optical systems have been widely applied in various fie...
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Objective In recent years, with increasing demands for imaging optical systems and continuous advancements in manufacturing technologies, dual-band infrared zoom optical systems have been widely applied in various fields such as military reconnaissance, precision guidance, airborne electro-optical pods, aerospace, security, and night vision surveillance. Continuous zoom infrared lenses enable target searching over a wide field of view and rapid switching to a narrow field of view for precise tracking and observation. This capability is particularly advantageous for tracking high-speed moving targets, addressing the issue of losing targets during field-of-view switching in stepped zoom lenses. However, dual-band infrared zoom systems face challenges such as complex structures, difficult aberration correction, suboptimal image quality, and limited material selection. Therefore, developing a high-performance dual-band infrared zoom optical system is crucial for advancing imaging optical systems. Methods First, in the dual-band infrared range of 3.7‒4.8 μm and 8‒10 μm, a double-layer diffractive optical element (DOE) is designed using a method that maximizes polychromatic integrated diffraction efficiency (PIDE) considering angular-wavelength characteristics. The wavelengths corresponding to the maximum PIDE values are selected as the design wavelengths, and the corresponding microstructure height parameters are calculated. The designed double-layer DOE achieves a comprehensive PIDE of 98.29%, demonstrating high diffraction efficiency across the dual-band infrared range. Second, a dual-group linked zoom model is derived. Using this model, the component distances at different focal lengths are calculated. These parameters are then input into optical design software to develop a dual-band infrared zoom optical system with a continuous zoom range of 30‒300 mm. However, the initial system exhibits suboptimal image quality. Finally, the high-efficiency double-layer DOE is in
The large variation in target scales within steel surface defect images often results in low detection accuracy. To address this issue, we have developed a multi-scale defect detection method named SGNet. Initially, w...
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ISBN:
(数字)9798331520861
ISBN:
(纸本)9798331520878
The large variation in target scales within steel surface defect images often results in low detection accuracy. To address this issue, we have developed a multi-scale defect detection method named SGNet. Initially, we introduced a Self-Guided Bi-Level Routing Transformer (SG-BiFormer) as the backbone network of our model. SG-BiFormer utilizes a self-guided strategy to model global information and dynamically reallocates tokens based on the importance of image regions, increasing token quantity in significant areas to preserve fine-grained details and reducing it in less critical areas. This strategy significantly enhances the accuracy and efficiency of the model in handling multi-scale targets. Subsequently, we incorporated Grouped Separable Convolutions (GSConv) to manage local information of multi-scale features. GSConv automatically adjusts the size and shape of the convolution kernels according to different input features. This design not only improves global information processing capabilities but also ensures sensitive and precise detail capture. Lastly, we employed MPDIoU as the loss function for our model, which optimizes corner positioning to tackle the challenges of regression tasks, substantially improving the accuracy of the model in handling complex defects. Experimental evidence confirms the effectiveness of this method. Our SGNet achieved a 77.1% mAP on the NEU-DET dataset, demonstrating outstanding performance.
Field-road mode mining (FRMM) has gained increasing attention because of its crucial role in machinery management. As the Global Navigation Satellite system (GNSS) is widely applied in agricultural machinery, many met...
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ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
Field-road mode mining (FRMM) has gained increasing attention because of its crucial role in machinery management. As the Global Navigation Satellite system (GNSS) is widely applied in agricultural machinery, many methods based on motion and spatial-temporal features of GNSS trajectory data have been proposed for FRMM. However, these methods ignore the density and parallel features of GNSS points. The two features are useful for FRMM because field points usually have a higher density and more parallel points than road points. Therefore, a neural network driven by density and parallel features is proposed for accurate FRMM. Firstly, a statistical method is designed to extract the density feature (i.e., the number of its neighbor points) and parallel feature (i.e., the number of approximately parallel points in its neighbors) of each point. Then, the two features and eight motion features (e.g., speed, direction, etc.) are fed into a neural network to extract valuable latent features. Finally, a linear classifier is used to identify the field and road categories of GNSS points based on the latent features. Experimental results show that our method outperforms state-of-the-art methods and achieves the accuracy of 91.69% and 86.44% on public Wheat and Paddy datasets, respectively.
Engineering advanced S-scheme heterojunction photocatalysts represents a prospective strategy for efficient antibiotic-contaminated wastewater decontamination. However, the practical realization of such systems is hin...
Engineering advanced S-scheme heterojunction photocatalysts represents a prospective strategy for efficient antibiotic-contaminated wastewater decontamination. However, the practical realization of such systems is hindered by difficulties in achieving seamless interfacial integration and precise control over charge-carrier dynamics. Herein, we proposed a shell-core 0D/2D Mn 0.5 Cd 0.5 S/C 3 N 5 S-scheme heterojunction with compact interfacial contact, synthesized by in-situ solvothermal growth of Mn 0.5 Cd 0.5 S nanodots on C 3 N 5 nanosheets. This optimized Mn 0.5 Cd 0.5 S/C 3 N 5 heterojunction performs extraordinary catalytic performance and enables approximately 1.3- and 3.2-fold tetracycline abatement rate greater than that for Mn 0.5 Cd 0.5 S and C 3 N 5 , respectively, which arises from the synergy of efficient spatial photo-carrier separation and well preserved great redox capacity of the heterojunction enabled by the S-scheme mechanism. Mechanistic validation was achieved through systematic characterizations and computational analyses. This study advances the rational design of shell-core S-scheme heterojunctions for photocatalytic antibiotic wastewater treatment.
Smartphone,as a smart device with multiple built-in sensors,can be used for collecting information(e.g.,vibration and location).In this paper,we propose an approach for using the smartphone as a sensing platform to ob...
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Smartphone,as a smart device with multiple built-in sensors,can be used for collecting information(e.g.,vibration and location).In this paper,we propose an approach for using the smartphone as a sensing platform to obtain real-time data on vehicle acceleration,velocity,and location through the development of the corresponding application software and thereby achieve the green concept based monitoring of the track condition during subway rail *** tests are conducted to verify the accuracy of smartphones in terms of the obtained data’s standard deviation(SD),Sperling index(SI),and International Organization for Standardization(ISO)-2631 weighted acceleration index(WAI).A vehicle-positioning method,together with the coordinate alignment algorithm for a Global Positioning system(GPS)free tunnel environment,is *** the time-domain integration method,the relationship between the longitudinal acceleration of a vehicle and the subway location is established,and the distance between adjacent stations of the subway is calculated and compared with the actual *** effectiveness of the method is verified,and it is confirmed that this approach can be used in the GPS-free subway tunnel *** is also found that using the proposed vehicle-positioning method,the integral error of displacement of a single subway section can be controlled to within 5%.This study can make full use of smartphones and offer a smart and eco-friendly approach for human life in the field of intelligent transportation systems.
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