Difference mode decomposition (DMD) is proposed to accurately decompose the signal into health component, fault component and noise. However, DMD is only applicable to vibration signals containing a single fault and is extremely sensitive to interference from other components across all frequencies. In practical operating conditions, bearing damage typically manifests as complex compound faults with numerous intricate disturbances, which makes it challenging for DMD to accurately separate different fault components. To broaden the application prospects of DMD, this paper proposes a difference multi-modes decomposition (DMMD) method, aiming to achieve accurate diagnosis of complex compound-fault signals. Firstly, the center frequencies (CFs) and boundary frequencies (BFs) that indicate fault information are located by the spectral structure information analyzer (SSIA), and the modes containing fault information are selected via correlation kurtosis (CK). Secondly, The initial DMMD weight is set as the average of the difference between two normalized Fourier spectra to improve the efficiency and accuracy of the operation. Finally, Gaussian mixture model (GMM) is used to distinguish the rearranged optimal difference spectrum into three categories accurately and the optimal threshold can be obtained. Simulated and experimental results indicate the effectiveness and accuracy of the proposed method in practical application.

In the practical application of rotating machinery , the change of working conditions can meet different manufacturing requirements. When fault diagnosis is performed on monitoring data with different working conditions, the change of data distribution will bring interference information which is highly related to working conditions and inconsistent matching problems in the process of multi-target domain transfer. In order to solve these problems, a working condition decoupling adversarial network (WCDAN) is proposed for multi-target domain fault diagnosis. Specifically, the prototype discrepancy alignment module is constructed following a weight-shared wavelet convolution feature extractor to ensure a clear prototype representation boundary. Then, the adaptive domain discriminator weight, along with the acquired multi-domain discrepancy, are utilized to decouple the working conditions. This process filters out interference information that highly associated with the source domain working conditions while preserving the inherent fault characteristics. Furthermore, the strategy of multi-domain hybrid alignment aims to minimize the disparity between different domains and solve the inconsistent matching issue. Based on two gearbox fault datasets under stable and unstable conditions, the comparative experimental results show that the WCDAN can be generalized from a single source domain to multiple target domains at the same time and achieve excellent fault diagnosis performance.

Unknown domain shift caused by the unavailability of target domain during training phase degrades the performance of intelligent fault diagnosis models in practical applications. Domain generalization (DG)-based methods have recently emerged to alleviate the influence of domain shift and improve the generalization ability of models toward invisible working conditions. However, most existing studies are conducted on multiple fully labeled source domains. Meanwhile, domain-specific information related to the variations of working conditions is often neglected during model training. Therefore, in order to realize reliable generalization fault diagnosis based on partially labeled source domains, this article proposes a contrast-assisted domain-specificity-removal network (CDSRN) to extract transferable features from domain-specificity-removal perspective. Concretely, a domain-specific feature removal branch is designed to disentangle domain-invariant features and domain-specific features, thus excavating generalized information only in domain-invariance dimension. Simultaneously, proxy-contrastive representation enhancement module is embedded to facilitate the fault class-discriminative and domain-discriminative feature learning, thereby assisting the model in further improvement of generalization capability. Experimental studies confirm the effectiveness and competitiveness of the proposed CDSRN in semi-supervised generalization fault diagnosis.

Rotating machinery often runs under variable working conditions, which results that the working condition of testing samples is unknown for the diagnosis model. The performance of the existed diagnosis methods trained by the samples under the known working condition will be deteriorated when they are used to diagnose the machine under an unknown working condition. The core for solving this issue is to eliminate the influence of working conditions. Inspired by this idea, a mode-decoupling autoencoder (MDAE) with two autoencoders, namely, fault-related mode (FRM) autoencoder and working condition mode (WCM) autoencoder is proposed for machinery fault diagnosis under unknown working conditions. An optimization object with reconstruction loss term, elimination loss term and classification loss term, is custom-tailored for the MDAE to ensure that the FRM autoencoder extracts the FRM and eliminates the WCM as best it can. As a result, the embedding feature extracted by the FRM autoencoder can be directly input into the classifier for the machinery fault diagnosis under unknown working conditions. Experimental results validate the superiority of MDAE in machinery fault diagnosis under unknown working conditions. Moreover, a detailed discussion is performed on the effects of model setting and the interpretability of mode decoupling of MDAE, that is, the stability of MDAE is well at a certain range and the merit of MDAE is given that the WCM autoencoder can drive the trained FRM autoencoder to eliminate the WCM guided by the knowledge of the normal samples.

Single scattering albedo (SSA) is an important parameter affecting the radiative forcing of aerosol. However, current SSA retrieval only relies on several typical aerosol models, limiting the range of SSA variation. This study proposed a new algorithm, and the comparison demonstrates the algorithm has an excellent ability to estimate SSA in pollution over land.

Imported air pollution has a significant impact on urban air quality. Many urban air pollution events are not caused by local emissions, but by the transport of air pollutants from surrounding areas. Therefore, it is very necessary to prevent and control imported air pollution. However, the existing supervision of urban air quality mostly relies on ground monitoring stations, which is extremely limited in time and space. In this paper, MERRA-2 data is used to grasp urban air quality from a more macroscopic perspective, and combined with ground monitoring station data and meteorological data, the transmission route of air pollution is reconstructed. This paper takes Xuzhou City, Jiangsu Province as an example. It is proved that this method is highly feasible and can provide scientific data support for efficient prevention and control of imported air pollution.

Remote sensing inversion of global PM2.5is an important research topic. In the present study, the Aerosol Optical Depth (AOD) dataset was established by four geostationary satellites to estimate global PM2.5concentrations using improved Geographic Time-Weighted Regression model (IGTWR) models. Then a global hourly PM2.5concentration dataset was obtained in May 2020. The estimated result for PM2.5is verified at ground stations with R of 0.71 and RMSE (Root Mean Square Error) of 26.6 μg/m3. The results indicate that PM2.5has obvious spatial and temporal distribution in the world.

Based on the data of air quality monitoring stations and high-resolution remote sensing product data in Xuzhou City, the atmospheric pollutants in 2020 and 2021 were analyzed. The modified A-values method and the model simulation method were used to estimate the remaining atmospheric environmental capacity (RAEC) of PM10and PM2.5pollutants in Xuzhou, Jiangsu Province. The results show that the excessive PM10and PM2.5pollutants are the main problems of atmospheric pollution in Xuzhou, and the annual emissions still need to be reduced are 10.80×104t/a and 5.98×104t/a, respectively. Among them, the situation is the most serious in Tongshan District, which still needs to cut annual PM10and PM2.5emissions by 7.41×104t/a and 3.70×104t/a, respectively. Xinyi City has the smallest annual PM10emission reduction, which is 0.89×104t/a. Suining County needs to cut the smallest PM2.5emissions, at 0.67×104t/a. In addition, there are significant quarterly differences in RAEC, with the first quarter > the fourth quarter > the second quarter > the third quarter. Except for the third quarter, the excess atmospheric environmental capacity was the most serious in Xuzhou urban area, the other three quarters were the highest in Tongshan District.

Rotating machinery often runs under large-speed-fluctuation (LSF) conditions, which results in severe data distribution domain shift for intelligent fault diagnosis methods. However, this challenge is rarely discussed in current studies. Hence, in this article, motivated by the active imagination of a human being, a new tool named actively imaginative data augmentation (AIDA) is constructed to solve machinery intelligent diagnosis under LSF conditions. The two adversarial training steps, namely, knowledge learning and sample imagining, are included in AIDA. In knowledge learning, a deep model is trained to learn the classification knowledge. In sample imagining, the parameters of the deep model are fixed and samples are generated via inversely training the model. As a result, diversified samples and an intelligent deep model adapting to the LSF condition are obtained by alternately carrying out the two steps. Moreover, a detailed discussion is given to interpret the process of actively imagining samples in the proposed AIDA in which some measures are designed, and the feature visualization is conducted. Experimental results show the effectiveness and superiority of AIDA in machinery diagnosis under LSF conditions, and the good performance of AIDA is due to the diversified dataset generated by changing the degrees and directions of each sample imagining.

By learning effective information from unlabeled nodes, node-level graph data-driven diagnosis methods perform better than graph-level methods. However, features of unlabeled nodes, indirectly involved in graph feature learning, are not fully utilized. To overcome aforementioned limitations, a semisupervised machine fault diagnosis fusing unsupervised graph contrastive learning (GCL) is proposed. A new GCL framework, where positive and negative graphs are generated by calculating Pearson correlation coefficient, is fused into the graph transformer network (GTN). Furthermore, a new combined loss, including a supervised cross-entropy loss and a new unsupervised GCL loss, is designed for GTN training. Contrastive learning of positive and negative graphs is guided by the unsupervised GCL loss. While the semisupervised graph feature learning for original graphs is mainly driven by the supervised cross-entropy loss, where the GTN for graph feature learning shares parameters. Experimental results on public and real datasets show the proposed method achieves a competitive performance.

Urban rail transit (URT) is an essential part of urban public transportation. Accurate ridership prediction is increasingly important for the safe operation and efficient management of URT. However, existing studies regard the URT stations with different intersecting subway lines as a whole, which ignores the internal spatial connections within the stations. In fact, URT stations are embodiments of spatial coupling between subway lines. Additionally, the intrinsic patterns of ridership are also neglected. To further improve the prediction accuracy, this study proposes a deep learning model based on graph convolutional network (GCN) and bidirectional long short-term memory network (Bi-LSTM) with a non-parallel structure (D-BLGCN). At the beginning, this study decouples the URT stations according to the intersecting subway lines. On the basis of spatial decoupling, different patterns of ridership are diverged into tributaries. Then, a non-parallel structure in the proposed model is designed to capture the intrinsic spatio-temporal correlations of ridership. To the best of our knowledge, this is the first time that the integration of internal spatial connections and ridership diverging is employed for URT ridership prediction. Extensive experiments are conducted on Beijing URT ridership data with different time granularities. The results demonstrate that the proposed model achieves better prediction performance compared with baselines.

Single scattering albedo (SSA) is an important parameter affecting the radiative forcing of aerosol. However, current SSA retrieval only relies on several typical aerosol models, limiting the range of SSA variation. This study proposed a new parameterization scheme of the aerosol model, which optimized the independent SSA value into mixed combination of three basic aerosol components, then constructed lookup table to obtain hourly SSA from the Advanced Himawari Image (AHI) sensor. The comparison is encouraging the retrieved SSA results agree well with AERONET, especially for high aerosol loading (AOD > 0.3 at 470nm), with correlation coefficient of R = 0.50, RMSE = 0.02, and approximately 93% of retrieval results falling within the 5% EE envelope. Under normal conditions, R = 0.46, RMSE = 0.04, and approximately 68% of retrieved SSA fall in within the uncertainty of$\Delta$SSA = ±0.05. The comparison demonstrates the algorithm has the excellent ability to estimate SSA in pollution over land.

The Greenhouse Gases Observing Satellite (GOSAT) is the world's first spacecraft to measure the concentrations of CO2from space and it has high-precision hyperspectral atmospheric CO2monitoring from 2009. Several atmospheric CO2products, such as ACOS, NIES, OCFP and SRFP products from full physics retrieval algorithm, both provide XCO2(the column-average dry-air mole fraction of atmospheric CO2) of GOSAT. These products have different characteristics and advantages and have different performance in different regions. In order to obtain the XCO2data set with high precision, low uncertainty and high coverage, the maximum likelihood estimation (MLE) method is used to fuse GOSAT XCO2products. The algorithm takes into account the uncertainty of each product on each pixel, and is applied from April 2009 to December 2015. The validation result between fusion XCO2and Total Carbon Column Observing Network XCO2shows R = 0.843 and RMSE = 3.248.

This paper calculates the Air Quality Index (AQI) in Xuzhou from 2018 to 2020 and analyzes its annual and monthly changes in air quality to determine the month of severe pollution. Through AQI calculation and interpolation analysis of different locations in Xuzhou, the air quality conditions in different regions are determined. Through the HYSPLIT backward trajectory model, cluster analysis of the air masses in December 2020 is carried out. Combined with the PM2.5 concentration data released every hour by the Xuzhou State Control Station, PSCF and CWT methods are used to determine potential sources of PM2.5 pollution in different regions. This paper also uses the hourly PM2.5 data in Xuzhou area obtained from the retrieval of the Himawari-8/AHI AOD through the IGTWR model for the traceability analysis of pollutants. The results show that there are large potential pollution sources in Henan, northern Anhui, northern Hubei and other places. At the same time, pollutant emissions in parts of northern Jiangsu, southern Shanxi and Shandong province also contribute to PM2.5 in Xuzhou. This paper also traced the source of the PM2.5 heavy pollution days in Xuzhou in December 2020 and determined the location of the specific pollution source based on the actual situation.

Mining cities are an essential part of China's urban agglomerations, and as mining cities continue to develop, ecological and environmental pollution has become a primary problem. In the present study, the Aerosol Optical Depth (AOD) retrieval of major mining urban agglomerations in China from the Gaofen-1 satellite data. Then a new hybrid model based on CTM (chemical transport model) Transport Model 5 (TM5) and GTWR (Geographic Time-Weighted Regression model) is proposed for PM2.5 and PM10mass concentration estimation. According to the different transformation stages and urban structure of mining cities, the temporal and spatial analysis of particulate matter characteristics is carried out in mining urban agglomerations. The estimated result for PM2.5 is verified at ground stations with R2 of 0.956 and RMSE (Root Mean Square Error) of 10.377 μg/m3, Moreover, the estimated result for PM10is verified at ground stations with R2 of 0.926 and RMSE of 16.669 μg/m3, The results indicate that PM2.5 and PM10have distinct spatial and temporal distribution patterns as Chinese mining cities are undergoing different types of transformation processes.

The advent of the era of Remote Sensing Big Data has produced a large number of processing and analysis tasks, which require powerful computing capabilities to support. The computational efficiency of distributed computer clusters which are the most commonly used parallel computing architecture for high performance computing can be significantly improved through an effective task scheduling strategy. In this paper, in order to improve data computing efficiency, we propose a dynamic load balancing strategy for remote sensing data processing workflow tasks based on the Hungarian algorithm for heterogeneous distributed computing clusters. We also compare this strategy with the classic load balancing algorithm. We find that the speed-up effect of the strategy proposed in this paper is better, and the speedups become more pronounced as the number of tasks increases.

Aerosol optical depth (AOD) is an important factor to estimate the effect of aerosol on light, and an accurate retrieval of it can make great contribution to monitor atmosphere. Therefore, retrieval of AOD has been a frontier topic and attracted much attention from researchers at home and abroad. In 2013, China launched Gaofen-1 satellite, improving the scale and timeliness of remote sensing data acquisition and making up for the shortcomings of lacking multi-spectral satellite with medium and high spatial resolution. In this paper, we calculated AOD at 100m from Gaofen-1 and AQUA data based on the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm over Beijing, China. The experimental results are compared with the Aerosol Robotic Network (AERONET) for preliminary validation. The correlation coefficient is about 0.9 and a root-mean-square error (RMSE) of about 0.13. The experimental results show that the method have higher accuracy, and further validation work is continuing.

Aerosols playa significant role in earth-atmospheric radiant balance and global climate changes. FengYun-4A(FY-4A) is the first three-axis stabilized geostationary satellite in China, the Advanced Geosynchronous Radiation Imager (AGRI) is one of the four payloads onboard the satellite. In fact, FY-4A AOD products is not officially available so far, and there are few researches on AOD retrieval of FY-4A. Therefore, AGRI data was used to develop a new algorithm for retrieval of AOD over land in this paper. We used MCD43C2 datasets to obtain the band surface reflectance, established the surface reflectance ratio database. Next used 6SV model to build lookup table (LUT), calculated hourly AOD eventually. To validate our algorithm, the AGRI -derived AOD was quantitatively compared with AERONET ground-based measurements. It shows the better accuracy and coverage than JMA-AOD.

The global long-term aerosol optical depth (AOD) dataset has a great significance for the study of global climate change. Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) satellites can provide global observation from 1978 to present. In this paper, we have improved the algorithm for the retrieval of the AOD over land proposed by Xue et al in 2017 [1]. We obtain 0.64μm band surface reflectance by utilizing a linear relationship between the surface reflectance at the wavelength of3.75μm and 0.64μm, which has been verified in the Moderate Resolution Imaging Spectroradiometer (MODIS). Considering difference of spectral response between AVHRR's bands and MODIS's bands, we calibrate this empirical relationship by fitting the simulated surface reflectance of the relative AVHRR and MODIS bands. A radiative transfer model for Lambertian surface and the look-up table (LUT) method are applied to NOAA-7, 9,11,14, 18 over China mainland (15° - 60° N, 70° - 140° E) from 1982 to 2011. Comparison of retrieval AVHRR AOD against AErosol RObotic NETwork (AERONET) data shows good consistency with more than 60% points within uncertainty of$\pm$(0.05+0.25xAOD).

In this paper, PM2.5 and PM10 in mainland China were estimated by using the Geographically and Temporally Weighted Regression model and FY-4 AOD data. Based on the GTWR model, the PM was estimated by BLH, RH, time, space and AOD. Taking June 2, 2019 as an example, the feasibility of FY-4 data in estimating PM2.5 and PM10 mass concentrations was analyzed and confirmed.