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Geological sampling data often need to be regionally segmented to mark blocks with different geological properties in mathematical geosciences. This paper uses the directed graph model of graph theory to model sampling data and calculate the segmentation of the directed graph by the improved maximum flow minimum cut (max-flow min-cut) algorithm. The proposed energy expressions reflect the continuity of geological properties and the smoothness of segmentation boundaries. A major advantage of this method is that it allows geological experts to share prior knowledge regarding the partitioning of a mineralization area as the seeds of the directed graph. Incorporating this knowledge in the segmentation result via a very intuitive and simple outline operation greatly improves the interactivity and editability of the algorithm. For the integrity of the process framework, this paper also proposes incorporating the characteristic scale of the Hilbert–Huang transformation (HHT) framework as the characteristic parameter to calculate the initial seed, which can achieve automatic regional segmentation. [ABSTRACT FROM AUTHOR]

To determine the precise positions of stars in CCD frames, various centering algorithms have been proposed for astrometry. The effective point spread function (ePSF) and the Gaussian centering algorithms are two representative centering algorithms. This paper compares in detail and investigates these two centering algorithms in performing data reduction. Specifically, synthetic star images in different conditions (i.e. profiles, fluxes, backgrounds and full width at half maximums) are generated and processed. We find that the difference in precision between the two algorithms is related to the profiles of the star images. Therefore, the precision comparison results using an ideal Gaussian-profile star image cannot be extended to other more specific experimental scenarios. Based on the simulation results, the most appropriate algorithm can be selected according to the image characteristics of observations, and the loss of precision of other algorithms can be estimated. The conclusions are verified using observations captured by the 1-m and 2.4-m telescopes at Yunnan Observatory. [ABSTRACT FROM AUTHOR]

In this paper, the load forecasting model is established to increase the precision of meteorological impacts, temperature, short-term power load forecasting, working and holiday factors by considering the power load. Further, IGA-LS-SVM is proposed which is a short-term power load forecasting technique based on AI algorithm. And to increase the forecast accuracy and generalization capability of LS-SVM, we applied the adopted mutation probability and new coding technology to the parameter optimization of LS-SVM. The temperature, load, weather state, working and holiday days be taken as prediction model as input, and load value was predicted output. We selected the sample data from meteorological information and historical load of a city in Yunnan province. By results, the prediction verifies the good prediction effect when associated with existing BP algorithm and the proposed IGA- LS-SVM algorithm yields a value 0.8274 more significant than all others, which is appropriate for short-term power load prediction. [ABSTRACT FROM AUTHOR]

Accurate information concerning crown profile is critical in analyzing biological processes and providing a more accurate estimate of carbon balance, which is conducive to sustainable forest management and planning. The similarities between the types of data addressed with LSTM algorithms and crown profile data make a compelling argument for the integration of deep learning into the crown profile modeling. Thus, the aim was to study the application of deep learning method LSTM and its variant algorithms in the crown profile modeling, using the crown profile database from Pinus yunnanensis secondary forests in Yunnan province, in southwest China. Furthermore, the SHAP (SHapley Additive exPlanations) was used to interpret the predictions of ensemble or deep learning models. The results showed that LSTM's variant algorithms was competitive with traditional Vanila LSTM, but substantially outperformed ensemble learning model LightGBM. Specifically, the proposed Hybrid LSTM-LightGBM and Integrated LSTM-LightGBM have achieved a best forecasting performance on training set and testing set respectively. Furthermore, the feature importance analysis of LightGBM and Vanila LSTM presented that there were more factors that contribute significantly to Vanila LSTM model compared to LightGBM model. This phenomenon can explain why deep learning outperforms ensemble learning when there are more interrelated features. [ABSTRACT FROM AUTHOR]

According to the characteristics of traditional clothing, clothing identification is studied, and clothing identification and clothing culture learning are effectively combined to find a new method for the inheritance of national culture and strive to make contributions to the inheritance of national culture. According to the requirement of the traditional garment identification watermark monitoring system, a self-synchronous digital watermarking algorithm is designed and implemented. Watermark is embedded in the time domain, and feature information is extracted from traditional clothing by means of mean filtering and replaced by watermark to achieve the purpose of embedding information. Blind detection is realized without the participation of the original image. The difference between the traditional costume embedded with watermark and the original traditional costume is almost imperceptible. It can effectively resist synchronous attacks including clipping and time shifting, showing good robustness. Imperceptibility and robustness can be adjusted freely by embedding strength. The HOG + SVM algorithm is applied to minority clothing classification and recognition. By comparing different classifiers, it is concluded that the classifier trained by the support vector machine algorithm has the best classification effect on ethnic clothing. In order to improve the classification effect, the classical algorithm of color, texture, and shape feature extraction was combined with SVM to conduct experiments on the clothing database collected and sorted out in Yunnan ethnic minority communities, and finally, we verified that the HOG feature combined with the SVM classification algorithm achieved good results in the classification of ethnic clothing. [ABSTRACT FROM AUTHOR]

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A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products. [ABSTRACT FROM AUTHOR]

Yunnan Province, residing in the eastern segment of the Qinghai–Tibet Plateau and the western part of the Yunnan–Guizhou Plateau, faces significant challenges due to its intricate geological structures and frequent geohazards. These pose monumental risks to community safety and infrastructure. Unfortunately, conventional spatial indexing methods struggle with the enormous influx of geohazard data, exhibiting inadequacies in efficient spatio-temporal querying and failing to meet the swift response imperatives for real-time geohazard monitoring and early warning mechanisms. In response to these challenges, this study proffers a cutting-edge spatio-temporal indexing model, the BCHR-index, undergirded by data stream clustering algorithms. The operational schema of the BCHR-index model is bifurcated into two stages: real-time and offline. The real-time phase proficiently uses micro-clusters shaped by the CluStream algorithm in unison with a B+ tree to construct indices in memory, thereby satisfying the exigent response necessities for geohazard data streams. Conversely, the offline stage employs the CluStream algorithm and the Hilbert curve to manage heterogeneously distributed spatial objects. Paired with a B+ tree, this framework promotes efficient spatio-temporal querying of geohazard data. The empirical results indicate that the indexing model implemented in this study affords millisecond-level responses when faced with query requests from real-time geohazard data streams. Moreover, in aspects of spatial query efficiency and data-insertion performance, it demonstrates superior results compared to the R-tree and Hilbert-R tree models. [ABSTRACT FROM AUTHOR]

Understanding the spatial and temporal distribution patterns of fire is of ecological, social and economic importance. The purpose of this study is to examine the spatial distribution of high fire risk using machine learning algorithms and early warning weather in high-risk areas. Take the satellite monitored fire point data in Yunnan Province during 2015–2019 as an example. The spatial distribution law of high-density parts is found using hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm, and the correlation degree analysis of high-density fire areas based on clustering and annual mean meteorological factors is carried out using grey correlation analysis (GCA) method. Results illustrates that within five years, fires frequently occurred in the seven regions of Wenshan Zhuang and Miao Autonomous Prefecture, Honghe Hani and Yi Autonomous Prefecture, Lijiang City, Pu'er City, and Xishuangbanna Dai Autonomous Prefecture. Among them, the fire in Lijiang had the greatest relationship with precipitation, Pu'er and Xishuangbanna had the greatest correlation with temperature, and Honghe Hani and Wenshan Zhuang were most affected by wind speed. This article acclaims fire prevention in key periods, key areas, key weather and reinforces the protection of transmission lines in the risk area. [ABSTRACT FROM AUTHOR]

Wind power forecasting (WPF) plays an important role in the planning, efficient operation, and security maintenance of power systems. A large number of hybrid models have been applied to WPF in the past two decades. Due to the rapid development of swarm intelligence algorithms, there is great potential for forecasting performance improvements by combining them with basic data-driven models for parameter optimization. In this study, a hybrid WPF method is proposed, which combines an extreme learning machine (ELM) and improved teaching-learning-based optimization (iTLBO), and incorporates a recursive feature elimination (RFE) method for feature selection. For WPF, appropriate feature combination is recognized from original input data using the RFE method, which helps facilitate understanding of the data pattern and defy the curse of dimensionality. To enhance the convergence speed and learning ability of the basic TLBO, four improvements are performed, and the obtained iTLBO algorithm is applied to optimize the parameters of the ELM model. Case study data came from a wind farm in Yunnan, China. The E RMSE , E MAE , and MAPE values of the proposed hybrid method are all lower than those of the comparison methods. The results demonstrate the superior forecasting performance that makes the hybrid method more applicable in real WPF applications. [ABSTRACT FROM AUTHOR]