Based on DEA theory and window analysis method, this paper empirically measures the ecological efficiency of six resource exhausted cities in Jilin old industrial base from 2012 to 2017, and ...investigates their regional differences and dynamic evolution characteristics. The results show that: in the sample period, the overall ecological efficiency of Jilin old industrial base is low, but fluctuates slightly; the difference between different urban areas is obvious, while the industrial structure and low level of science and technology inhibit the improvement of ecological efficiency.
Coal mine safety is crucial to the healthy and sustainable development of the coal industry, and coal mine flood is a major hidden danger of coal mine accidents. Therefore, the processing of coal ...mine water source data is of great significance to prevent mine water inrush accidents. In this experiment, the water source data were obtained by laser induced fluorescence technology with the assistance of laser. The water sample data information was preprocessed by standard normal variable transformation (SNV) and multiple scattering correction (MSC), and then the principal component analysis (PCA) was used to reduce the dimension of the data and ensure the information characteristics of the original data unchanged. In order to identify the water inrush type of coal mine water source, the sparrow search algorithm (SSA) is used to optimize the BP neural network in this study. This is because the SSA algorithm has the advantages of strong optimization ability and fast convergence rate compared with particle swarm optimization (PSO) and other optimization algorithms. Experiments show that under the premise of SNV pretreatment, the R 2 of SSA-BP model is infinitely close to 1, MRE is 0.0017, RMSE is 0.0001, the R 2 of PSO-BP model is 0.9995, MRE is 0.0026, RMSE is 0.0019, the R 2 of BP model is 0.9983, MRE is 0.0140, RMSE is 0.0075. Therefore, SSA-BP model is more suitable for the classification of coal mine water sources.
The intelligent management of weed clearing can improve wheat yield and reduce the use of pesticides. In this paper, our contribution is to propose a method for wheat and weed recognition based on ...multispectral imaging and Membrane Search Algorithm Support Vector Machine (MSA-SVM), which is the basis for intelligent weed clearing. A spectral data acquisition system is set up in the laboratory. Then, a total of 700 groups of wheat and weed spectral data are collected. The classical SVM based on spectral data is applied to identify weeds to study the classification performance of wheat and weeds in different bands. The Membrane Search Algorithm (MSA) algorithm proposed by us was used for SVM parameters optimization, and the dimension reduction algorithm was adopted to reduce the interference of redundant information. After local linear embedding (LLE) dimensionality reduction of spectral data, MSA-SVM has the highest average recognition accuracy for weeds and wheat, which is 91.00%. The experimental results show that our research can distinguish wheat from weeds, which is of great significance for the development of smart agriculture.
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•Construct a multispectral data collection system for wheat and weeds.•A weed recognition method based on spectral imaging and SVM is proposed.•Dimension reduction of spectral data based on LLE was studied.•The MSA algorithm we proposed is used for SVM parameters optimization.•The provided method can distinguish between wheats and weeds.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•The highlights of this paper are as follows: Our team use YOLOv5 algorithm to analyze the spectral images of coal gangue, and we have improved the algorithm based on YOLOv5 algorithm. It improves ...the accuracy of classification and recognition of coal gangue, and is of great significance to the separation of coal gangue industry.
Accurate identification of coal-gangue have great significance for separation of coal-gangue. The traditional coal-gangue identification method has the disadvantages of low accuracy and slow speed. Therefore, an intelligent classification method of coal-gangue based on multispectral imaging technology and target detection is proposed in this paper. According to the model structure of YOLOv5, add scSE module in CSPDarknet and CSP module. The improved YOLOv5 is referred to as YOLOv5.1. To begin with, the multispectral data of coal-gangue are collected, and the collected coal-gangue images are screened. Beside, three bands with high recognition rate and correlation are selected from 25 bands to form pseudo-RGB images. Otherwise, the RGB image of coal-gangue was detected by theYOLOv5.1. By detecting the separated single band, the recognition rate and correlation of band 6, 10 and 12 are higher. The experimental results show that the average accuracy of detecting coal-gangue in the test set reaches 98.34 %, and the detection time is about 3.62 s by using the model of YOLOv5.1 to train the RGB image of coal-gangue. This method can not only accurately identify coal-gangue, but also obtain the relative position of coal-gangue, which can be effectively used for coal-gangue identification.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Based on SBM-Undesirable model, this paper evaluated air environmental efficiency of 8 provinces of the Silk Road Economic Belt of China during the period of 2010-2017, and investigated the ...temporal-variation and energy conservation and emission reduction potential. Empirical analysis shows that the overall atmospheric environmental efficiency of Silk Road Economic Belt is still at low point but presents a mild rising trend over the period 2010-2017, regional differences are remarkable, and it has great potential of energy conservation and emission reduction in the future.
An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine ...engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Oxide-/hydroxide-derived copper electrodes exhibit excellent selectivity toward C2+ products during the electrocatalytic CO2 reduction reaction (CO2RR). However, the origin of such enhanced ...selectivity remains controversial. Here, we prepared two Cu-based electrodes with mixed oxidation states, namely, HQ-Cu (containing Cu, Cu2O, CuO) and AN-Cu (containing Cu, Cu(OH)2). We extracted an ultrathin specimen from the electrodes using a focused ion beam to investigate the distribution and evolution of various Cu species by electron microscopy and electron energy loss spectroscopy. We found that at the steady stage of the CO2RR, the electrodes have all been reduced to Cu0, regardless of the initial states, suggesting that the high C2+ selectivities are not associated with specific oxidation states of Cu. We verified this conclusion by control experiments in which HQ-Cu and AN-Cu were pretreated to fully reduce oxides/hydroxides to Cu0, and the pretreated electrodes showed even higher C2+ selectivity compared with their unpretreated counterparts. We observed that the oxide/hydroxide crystals in HQ-Cu and AN-Cu were fragmented into nanosized irregular Cu grains under the applied negative potentials. Such a fragmentation process, which is the consequence of an oxidation–reduction cycle and does not occur in electropolished Cu, not only built an intricate network of grain boundaries but also exposed a variety of high-index facets. These two features greatly facilitated the C–C coupling, thus accounting for the enhanced C2+ selectivity. Our work demonstrates that the use of advanced characterization techniques enables investigating the structural and chemical states of electrodes in unprecedented detail to gain new insights into a widely studied system.
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IJS, KILJ, NUK, PNG, UL, UM
A precipitation-based regionalization for the Tibetan Plateau (TP) was investigated for regional precipitation trend analysis and frequency analysis using data from 1113 grid points covering the ...period 1900–2014. The results utilizing self-organizing map (SOM) network suggest that four clusters of precipitation coherent zones can be identified, including the southwestern edge, the southern edge, the southeastern region, and the north central region. Regionalization results of the SOM network satisfactorily represent the influences of the atmospheric circulation systems such as the East Asian summer monsoon, the south Asian summer monsoon, and the mid-latitude westerlies. Regionalization results also well display the direct impacts of physical geographical features of the TP such as orography, topography, and land-sea distribution. Regional-scale annual precipitation trend as well as regional differences of annual and seasonal total precipitation were investigated by precipitation index such as precipitation concentration index (PCI) and Standardized Anomaly Index (SAI). Results demonstrate significant negative long-term linear trends in southeastern TP and the north central part of the TP, indicating arid and semi-arid regions in the TP are getting drier. The empirical mode decomposition (EMD) method shows an evolution of the main cycle with 4 and 12 months for all the representative grids of four sub-regions. The cross-wavelet analysis suggests that predominant and effective period of Indian Ocean Dipole (IOD) on monthly precipitation is around ∼12 months, except for the representative grid of the northwestern region.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
A novel sampling pool selection scheme is proposed for the online sequential extreme learning machine (OS-ELM) based on improved Gath–Geva (IGG) fuzzy segmentation algorithm. Tidal change is a ...time-varying process whose dynamics vary with changes of internal and environmental factors such as celestial bodies movements, coastal topology and environmental disturbances. When OS-ELM is implemented for identifying time-varying system dynamics, it usually sequentially selects samples with fixed number. Under such circumstance, samples representing different system dynamics are mixed together so that the online representation and prediction abilities of OS-ELM may be deteriorated. To consciously select samples with most representing ability and construct appropriate sampling pool for OS-ELM, in this study, a dynamic sampling pool selection scheme is proposed based on IGG fuzzy segmentation approach. Time series of input and output variables are segmented as per their dynamics characteristics. The change points split up the time series into several segments and the change points themselves represent the changes of system dynamics. Samples within the same segment are considered as possessing homogeneous characteristics. To achieve best representing abilities for current system dynamics, the proposed IGG-based sampling scheme is implemented for selecting sampling pool. The OS-ELM selects homogeneous samples from sampling pool thus possesses better representing ability for current dynamics. In the meantime, conventional harmonic analysis is also applied to represent the influences of celestial bodies and coastal topology. The harmonic method and IGG-based OS-ELM are combined together and the resulted modular prediction scheme is applied for online tidal level prediction of ports of King Point, Mokuoloe and Old Port Tampa in the United States. Simulation results demonstrate the feasibility and effectiveness of the proposed sampling scheme and the modular tidal prediction approach.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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