Localization of CO2_2 gas leakages through acoustic emission multi-sensor fusion based on wavelet-RBFN modeling

CO$_2$ leakage from transmission pipelines in carbon capture and storage systems may seriously endanger the ecological environment and human health. Therefore, there is a pressing need of an accurate and reliable leak localization method for CO$_2$ pipelines. In this study, a novel method based on the combination of a wavelet packet algorithm and a radial basis function network (RBFN) is proposed to realize the leak location. Multiple acoustic emission (AE) sensors are first deployed to collect leakage signals of CO$_2$ pipelines. The characteristics of the leakage signals from the AE sensors under different pressures are then analyzed in both time and frequency domains. Further, leakage signals are decomposed into three layers using wavelet decomposition theory. Wavelet packet energy and maximum value, and time difference calculated by cross-correlation are selected as the input feature vectors of the RBFN. Experiments were carried out on a laboratory-scale test rig to verify the validity and correctness of the proposed method. Leakage signals at different positions under different pressures were obtained on the CO$_2$ pipeline leakage test bench. Compared with the time difference of arrival method, the relative error obtained using the proposed method is less than 2%, which has certain engineering application prospects

Short-term Building Energy Model Recommendation System: A Meta-learning Approach

High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which can recommend appropriate models to forecast the building energy profiles based on building characteristics. To bridge this research gap, we propose a meta-learning based framework, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency

Positive Periodic Solution for Neutral-Type Integral Differential Equation Arising in Epidemic Model

This paper is devoted to investigating a class of neutral-type integral differential equations arising in an epidemic model. By using Mawhin’s continuation theorem and the properties of neutral-type operators, we obtain the existence conditions for positive periodic solutions of the considered neutral-type integral differential equation. Compared with previous results, the existence conditions in this paper are less restricted, thus extending the results of the existing literature. Finally, two examples are given to show the effectiveness and merits of the main results of this paper. Our results can be used to obtain the existence of a positive periodic solution to the corresponding non-neutral-type integral differential equation

Unraveling the Spatio-Temporal Relationship between Ecosystem Services and Socioeconomic Development in Dabie Mountain Area over the Last 10 years

The Dabie Mountain area is a typical poverty-stricken area in China. It is of great significance to evaluate the ecosystem service value and its impact mechanism toward optimizing the ecological structure and coordinating ecological protection and economic development. This study determined the ecosystem service value coefficient and calculated the ecosystem service value (ESV) according to the regional economic development in the past ten years, and the ESV was spatialized based on NPP, which is closely related to ecological function. The temporal and spatial variation of ESV was then analyzed, and an RDE index was proposed to describe its response to land cover change. Further, the relationship between ESV and several parameters that reflect socioeconomic development was researched and analyzed. The results show that the total ESV in the study area first decreased and then increased, with an overall increase of CNY 3.895 billion. Among the land cover types, forest land had the greatest impact, contributing more than 70%. In the ecosystem service functions, the contribution of regulation function exceeded 50%. ESV was found to be sensitive to land cover change. On average, every 1 km2 change leads to an ESV change of about CNY 1 million. Socioeconomic-related parameters were negatively correlated with ESV, among which the correlation with per capita disposable income was the weakest, indicating that there was no obvious contradiction between human well-being and ESV. Therefore, a path for harmonious symbiotic development can be found between man and nature

Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm

Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists “salt-and-pepper phenomenon” in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification

Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops

Non-destructive Evaluation Method of Large-scale Casting Piece Based on Metallographic Structure Statistical Analysis

Aimed at the great metallographic structure differences between every part of the large-scale thin-wall complex integral precise titanium alloy casting, a non-destructive evaluation method based on metallographic structure statistical analysis is proposed. Under the non-destructive condition, the casting skin structure information is given by means of handheld microscope. The casting skin structure statistic model is established. And the result of integral casting structure is given. Through comparative analysis between non-destructive evaluation (Method A) and metallography detection of the dissect sample (Method B), the results show that the non-destructive evaluation evaluates the metallographic structure effectively. The mean value and half-width of the grain size distribution curves fit the characteristic of Gaussian distribution. And the grain size mean value in both two methods increase linearly along with the casting thickness, and the slope deviation is within the limits of 6%; the half-width relative deviation increases exponentially along with the casting thickness, approached 26%

Research on Service Value and Adaptability Zoning of Grassland Ecosystem in Ethiopia

The evaluation of the ecosystem service value (ESV) and its regionalization toward coordinating ecological protection and socioeconomic development is of great significance. In this study, we developed a classification method based on the Random Forest algorithm and a feature optimization method to identify grassland types. Then, we proposed an approach to quantitatively evaluate the ESV of the grassland ecosystem in Ethiopia, in which net primary production derived from remote sensing was used to evaluate organic matter production value (ESV1), promoting nutrient circulation value (ESV2), and gas regulation value (ESV3), the RUSLE model was used to evaluate soil conservation value (ESV4), and cumulative rainfall was used to calculate water conservation value (ESV5). By integrating the mean ESV under various influencing factors, the zoning map of grassland ecosystem service value was obtained. Our study found that more fine grassland types can be well classified with the overall accuracy of 86.52%. And the classification results are the basis of the ESV analysis. The total ESV of grassland ecosystems was found to be USD 105,221.72 million, of which ESV4 was the highest, accounting for 44.09% of the total ESV. The spatial analysis of ESV showed that the differences were due to the impacts of grassland types, elevation, slope, and rainfall. It was found that the grassland is suitable to grow in the elevation zone between approximately 1000 and 2000 m, and the larger the slope and rainfall are, the greater the mean ESV is. The zoning map was used to conclude that the areas from approximately the fourth to sixth level (only 34.78% of the total grassland area, but 65.94% of the total ESV) have better growth status and development potential. The results provide references and bases to support the local coordination and planning of various grassland resources and form reasonable resource utilization and protection measures

Research on Service Value and Adaptability Zoning of Grassland Ecosystem in Ethiopia

The evaluation of the ecosystem service value (ESV) and its regionalization toward coordinating ecological protection and socioeconomic development is of great significance. In this study, we developed a classification method based on the Random Forest algorithm and a feature optimization method to identify grassland types. Then, we proposed an approach to quantitatively evaluate the ESV of the grassland ecosystem in Ethiopia, in which net primary production derived from remote sensing was used to evaluate organic matter production value (ESV1), promoting nutrient circulation value (ESV2), and gas regulation value (ESV3), the RUSLE model was used to evaluate soil conservation value (ESV4), and cumulative rainfall was used to calculate water conservation value (ESV5). By integrating the mean ESV under various influencing factors, the zoning map of grassland ecosystem service value was obtained. Our study found that more fine grassland types can be well classified with the overall accuracy of 86.52%. And the classification results are the basis of the ESV analysis. The total ESV of grassland ecosystems was found to be USD 105,221.72 million, of which ESV4 was the highest, accounting for 44.09% of the total ESV. The spatial analysis of ESV showed that the differences were due to the impacts of grassland types, elevation, slope, and rainfall. It was found that the grassland is suitable to grow in the elevation zone between approximately 1000 and 2000 m, and the larger the slope and rainfall are, the greater the mean ESV is. The zoning map was used to conclude that the areas from approximately the fourth to sixth level (only 34.78% of the total grassland area, but 65.94% of the total ESV) have better growth status and development potential. The results provide references and bases to support the local coordination and planning of various grassland resources and form reasonable resource utilization and protection measures