A novel minority sample fault diagnosis method based on multisource data enhancement

Effective fault diagnosis has a crucial impact on the safety and cost of complex manufacturing systems.However,the complex structure of the collected multisource data and scarcity of fault samples make it difficult to accurately identify multiple fault conditions.To address this challenge,this paper proposes a novel deep-learning model for multisource data augmentation and small sample fault diagnosis.The raw multisource data are first converted into two-dimensional images using the Gramian Angular Field,and a generator is built to transform random noise into images through transposed convolution operations.Then,two discriminators are constructed to evaluate the authenticity of input images and the fault diagnosis ability.The Vision Transformer network is built to diagnose faults and obtain the classification error for the discriminator.Furthermore,a global optimization strategy is designed to upgrade parameters in the model.The discriminators and generator compete with each other until Nash equilibrium is achieved.A real-world multistep forging machine is adopted to compare and validate the performance of different methods.The experimental results indicate that the proposed method has multisource data augmentation and minority sample fault diagnosis capabilities.Compared with other state-of-the-art models,the proposed approach has better fault diagnosis accuracy in various scenarios.

Impact of human settlements quality on urban vitality based on multisource data:A case study of Shahekou District,Dalian,China

Urban geography has always been concerned about the influence of human settlements on urban vitality,but few studies reveal the influence of human settlements on urban vitality at a micro-scale.This paper analyzes the spatial distribution characteristics of human settlements’quality and urban vitality at the micro-scale using Geodetectors and geographic weighted regression to analyze the relationship between human settlements and urban vitality.The results are shown as follows:there is still a significant development space for human settlements quality in Shahekou District,with obvious spatial dependence characteristics and significant gaps between various systems;the urban vitality of Shahekou District has obvious timeliness,and the urban vitality undergoes significant changes over time,which is related to the human settlements quality.The spatial distribution presents a single core spatial distribution structure with strong relative stability.The spatial distribution of cold and hot spots shows a pattern of“high in the north and low in the south,high in the east and low in the west”,with an increasing trend from southwest to northeast;the reachability of public transport has a significant impact on urban vitality.Its synergy with other variables is the leading force forming the spatial distribution of urban vitality.The environmental system,support system and social system are the significant factors affecting the urban vitality of Shahekou District.

Comprehensive drought monitoring in Yunnan Province, China using multisource remote sensing data

Development of drought monitoring techniques is important for understanding and mitigating droughts and for rational agricultural management. This study used data from multiple sources, including MOD13 A3, TRMM 3 B43, and SRTMDEM, for Yunnan Province, China from 2009 to 2018 to calculate the tropical rainfall condition index(TRCI), vegetation condition index(VCI), temperature condition index(TCI), and elevation factors. Principal component analysis(PCA) and analytic hierarchy process(AHP) were used to construct comprehensive drought monitoring models for Yunnan Province. The reliability of the models was verified, following which the drought situation in Yunnan Province for the past ten years was analysed. The results showed that:(1) The comprehensive drought index(CDI) had a high correlation with the standardized precipitation index, standardized precipitation evapotranspiration index, temperature vegetation dryness index, and CLDAS(China Meteorological Administration land data assimilation system), indicating that the CDI was a strong indicator of drought through meteorological, remote sensing and soil moisture monitoring.(2) The droughts from 2009 to 2018 showed generally consistent spatiotemporal changes. Droughts occurred in most parts of the province, with an average drought frequency of 29% and four droughtprone centres.(3) Monthly drought coverage during 2009 to 2014 exceeded that over 2015 to 2018. January had the largest average drought coverage over the study period(61.92%). Droughts at most stations during the remaining months except for October exhibited a weakening trend(slope > 0). The CDI provides a novel approach for drought monitoring in areas with complex terrain such as Yunnan Province.

Integrating multisource RS data and GIS techniques to assist the evaluation of resource-environment carrying capacity in karst mountainous area

The karst mountainous area is an ecologically fragile region with prominent humanland contradictions.The resource-environment carrying capacity(RECC)of this region needs to be further clarified.The development of remote sensing(RS)and geographic information system(GIS)provides data sources and processing platform for RECC monitoring.This study analyzed and established the evaluation index system of RECC by considering particularity in the karst mountainous area of Southwest China;processed multisource RS data(Sentinel-2,Aster-DEM and Landsat-8)to extract the spatial distributions of nine key indexes by GIS techniques(information classification,overlay analysis and raster calculation);proposed the methods of index integration and fuzzy comprehensive evaluation of the RECC by GIS;and took a typical area,Guangnan County in Yunnan Province of China,as an experimental area to explore the effectiveness of the indexes and methods.The results showed that:(1)The important indexes affecting the RECC of karst mountainous area are water resources,tourism resources,position resources,geographical environment and soil erosion environment.(2)Data on cultivated land,construction land,minerals,transportation,water conservancy,ecosystem services,topography,soil erosion and rocky desertification can be obtained from RS data.GIS techniques integrate the information into the RECC results.The data extraction and processing methods are feasible on evaluating RECC.(3)The RECC of Guangnan County was in the mid-carrying level in 2018.The midcarrying and low-carrying levels were the main types,accounting for more than 80.00%of the total study area.The areas with high carrying capacity were mainly distributed in the northern regions of the northwest-southeast line of the county,and other areas have a low carrying capacity comparatively.The coordination between regional resource-environment status and socioeconomic development is the key to improve RECC.This study explores the evaluation index system of RECC in karst mountainous area and the application of multisource RS data and GIS techniques in the comprehensive evaluation.The methods can be applied in related fields to provide suggestions for data/information extraction and integration,and sustainable development.

Construction and implementation of multisource spatial data management system of China's coastal zone and offshore

To meet the increasing demand of national spatial database infrastructure construction and application, a concept model of China's coastal zone scientific data platform is established based on the information feature analysis of a compound dataset, consisting of remote sensing data and conventional data. Based on this concept model, the detailed logical database structure and the storage strategy of remote sensing data and their metadata using ArcSDE are designed. The complicated technology of multisources data combination in this research is crucial to the future coastal zone and offshore database construction and practical running, which will provide intelligent information analysis and technological service for coastal zone and offshore investigation, research, development and management.

Forest type identification by random forest classification combined with SPOT and multitemporal SAR data

We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.

A Comprehensive Observational Analysis for the Effects of Gas Cannons on Clouds and Precipitation

To analyze the effects of gas cannons on clouds and precipitation,multisource observational data,including those from National Centers for Environmental Prediction(NCEP)reanalysis,Hangzhou and Huzhou new-generation weather radars,laser disdrometer,ground-based automatic weather station,wind profiler radar,and Lin'an C-band dualpolarization radar,were adopted in this study.Based on the variational dual-Doppler wind retrieval method and the polarimetric variables obtained by the dual-polarization radar,we analyzed the microphysical processes and the variations in the macro-and microphysical quantities in clouds from the perspective of the synoptic background before precipitation enhancement,the polarization echo characteristics before,during and after enhancement,and the evolution of the fine three-dimensional kinematic structure and the microphysical structure.The results show that the precipitation enhancement operation promoted the development of radar echoes and prolonged their duration,and both the horizontal and vertical wind speeds increased.The dual-polarization radar echo showed that the diameter of the precipitation particles increased,and the concentration of raindrops increased after precipitation enhancement.The raindrops were lifted to a height corresponding to 0 to-20℃due to vertical updrafts.Based on the disdrometer data during precipitation enhancement,the concentration of small raindrops(lgN_(w))showed a significant increase,and the mass-weighted diameter D_(m)value decreased,indicating that the precipitation enhancement operation played a certain“lubricating”effect.After the precipitation enhancement,the concentration of raindrops did not change much compared with that during the enhancement process,while the Dm increased,corresponding to an increase in rain intensity.The results suggest the positive effect of gas cannons on precipitation enhancement.

Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling

Based on station precipitation observations,radar quantitative precipitation estimates(QPE), and radar fusion data during Typhoon Fitow(2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China,as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows:(1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%.(2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details.(3)One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result-all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.

Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing,crowdsourced,and Auxiliary Datasets

Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products.Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping,which leads to high spatial discre-pancies among remote sensing cropland products.Since no dataset could cope with all limitations,multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers.Here,in the current study,four cropland products,produced initially from multiple sensors(e.g.Landsat-8 OLI,Sentinel-2 MSI,and PROBA-V)to cover the period(2015-2017),were integrated based on their cropland mapping accuracy to build a more accurate cropland layer.The four cropland layers’accuracy was assessed at Agro-ecological zones units via an inten-sive reference dataset(17,592 samples).The most accurate crop-land layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa.As a result,the new layer was produced in higher cropland mapping accuracy(overall accuracy=91.64%and cropland’s F-score=0.75).The layer mapped the African cropland area as 282 Mha(9.38%of the Continent area).Compared to earlier crop-land synergy layers,the constructed cropland mask showed a considerable improvement in its spatial resolution(30 m instead of 250 m),mapping quality,and closeness to official statistics(R^(2)=0.853 and RMSE=2.85 Mha).The final layer can be down-loaded as described under the“Data Availability Statement”section.