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With the centralization of wind power development, power-prediction technology based on wind power clusters has become an important means to reduce the volatility of wind power, so a large-scale power-prediction method of wind power clusters is proposed considering the prediction stability. Firstly, the fluctuating features of wind farms are constructed by acquiring statistical features to further build a divided model of wind power clusters using fuzzy clustering algorithm. Then the spatiotemporal features of the data of wind power are obtained using a spatiotemporal attention network to train the prediction model of wind power clusters in a large scale. Finally, the stability of predictive performance of wind power is analyzed using the comprehensive index evaluation system. The results show that the RMSE of wind power prediction is lower than 0.079 at large-scale wind farms based on the prediction method of wind power proposed in this paper using experience based on the data of 159 wind farms in the Nei Monggol Autonomous Region in China and the extreme error is better than 25% for the total capacity of wind farms, which indicates high stability and accuracy. [ABSTRACT FROM AUTHOR]

To clarify trends in water quality in Chaohu Lake, 480 surface water samples were collected monthly from 8 sampling sites in the eastern and western Chaohu Lake regions from 2016 to 2020. The content of dissolved oxygen (DO), permanganate index (CODMn), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), and Chlorophyll a (Chl-a) were analyzed, and the water quality of Chaohu Lake was comprehensively evaluated by a comprehensive nutrient status index method and a fuzzy clustering method. The average contents of TP and TN in Chaohu Lake decreased over time. The mean values of TP and TN ranged from 0.049 to 0.16 mg/L and 0.98 to 2.2 mg/L, respectively. The mean value of NH3-N decreased from 0.27 mg/L in 2016 to 0.098 mg/L in 2020. The mean value of CODMn decreased by 22.96% compared with 2016. Based on the fuzzy clustering method and a comprehensive trophic state index, the water quality of the lake was increased from 2018 to 2020, and the comprehensive trophic state value decreased significantly, also indicating that the overall water quality of Chaohu Lake has improved since 2018. The results of this study could provide a reference for the prevention and control of water pollution in the Chaohu Lake Basin. [ABSTRACT FROM AUTHOR]

Academic entrepreneurship performance has become the core research content of academic entrepreneurial activities and the growth of entrepreneurial enterprises as a reflection of the academic entrepreneurship process and essence. Among them, university teachers are playing a social function that is equivalent to the enterprise innovation and entrepreneurship, embedding in the spiral mechanism of regional entrepreneurship, industrial, and value chains. However, studies on the academic entrepreneurship performance of university teachers are limited. Therefore, from the academic entrepreneurship data of 10 university teachers with active academic entrepreneurship behavior in China from 2012 to 2016, this study analyzes the approximate dependence of the academic entrepreneurship performance input and output indicators of university teachers. The rough set fuzzy clustering analysis method is used for the first time to measure the importance of academic entrepreneurship input and output indicators of university teachers in the entire entrepreneurial performance indicator system. In addition, this study analyzes the fuzzy evaluation of the academic entrepreneurship performance of university teachers. The objectives are to eliminate the subjectivity of weight setting, reduce the scale of the index, and establish an objective and effective evaluation index system. Results show that among the attributes of the academic entrepreneurial input and output conditions of university teachers, financial salary input and research funding are the reductions of five entrepreneurial output decision attributes. Moreover, financial salary input and research funding are the most important indicators and have a two-factor relationship with entrepreneurial output. The number of spin-off companies should not be selected as an indicator of entrepreneurial performance due to the almost zero discernable relationship with entrepreneurial input. [ABSTRACT FROM AUTHOR]

Free lime (f-CaO) content is a crucial quality parameter for cement clinkers in rotary cement kiln. Due to lack of hardware sensors, f-CaO content in cement clinker is mostly obtained by offline laboratory measurement, making timely control rather difficult and even impossible. In this work, a soft sensor approach named as support vector machine ensemble (ESVM) model is proposed to estimate f-CaO content. The process data employed to train and test the model were collected from a cement plant in China, covering a time span of about 30 days. The raw data were preprocessed by filters and time-series matching. The processed data were then clustered by fuzzy c-means clustering algorithm to capture process features at different operating conditions. For each individual cluster, a base SVM regressor was trained to estimate f-CaO content. Finally, an ensemble model consisting of four base SVM regressors was established to estimate f-CaO content at multifarious process conditions. The effectiveness of the proposed ESVM model was investigated by comparing it with manual measurements and other models available in literature. The results demonstrate that the proposed ESVM model achieves improvements in model accuracy as well as generalization capability. The proposed ESVM model has a broad application space in cement production process for automatic monitoring of f-CaO content. • A novel SVM ensemble model for cement clinker quality estimation is developed. • Process features at various operating conditions are captured by the model. • The effect of time lag between inputs and output is considered in the model. • The model is compared with soft sensor models available in literature. • The prediction accuracy and generalization ability of the model are improved. [ABSTRACT FROM AUTHOR]

The monitoring of drainage pipes is an important task in urban stormwater management. The selection of suitable monitoring points can guarantee the reliability of the data. Based on a scenario simulation, a time series of each node was obtained and a method using fuzzy clustering to automatically set drainage pipe monitoring points was established. This automatic method will reduce the workload of staff who need to develop and adjust the monitoring plan. It also avoids the problem of workers not being fully aware of all the operational laws of the drainage system, especially under certain extreme conditions. Moreover, the methodology is useful for the quantitative assessment of the monitoring project. The automatic setting method was applied in Qian’an, a city in the north of China. Consideration was given to monitoring indexes, different levels of rainfall, and the number of monitoring points. [ABSTRACT FROM AUTHOR]

Groundwater chemistry data are normally scarce in remote inland areas. Effective statistical approaches are highly desired to extract important information about hydrochemical processes from the limited data. This study applied a clustering approach based on the Gaussian Mixture Model (GMM) to a hydrochemical dataset of groundwater collected in the middle Heihe River Basin (HRB) of northwestern China. Independent hydrological data were introduced to examine whether the clustering results led to an appropriate interpretation on the hydrochemical processes. The main findings include the following. First, in the middle HRB, although groundwater chemistry reflects primarily a natural salinization process, there are evidence for significant anthropogenic influence such as irrigation and fertilization. Second, the regional hydrological cycle, particularly surface water-groundwater interaction, has a profound and spatially variable impact on groundwater chemistry. Third, the interaction between the regional agricultural development and the groundwater quality is complicated. Overall, this study demonstrates that the GMM clustering can effectively analyze hydrochemical datasets and that these clustering results can provide insights into hydrochemical processes, even with a limited number of observations. The clustering approach introduced in this study represents a cost-effective way to investigate groundwater chemistry in remote inland areas where groundwater monitoring is difficult and costly. [ABSTRACT FROM AUTHOR]

Classification of Beijing aerosol is carried out based on clustering optical properties obtained from three Aerosol Robotic Network (AERONET) sites. The fuzzy c-mean (FCM) clustering algorithm is used to classify fourteen-year (2001–2014) observations, totally of 6,732 records, into six aerosol types. They are identified as fine particle nonabsorbing, two kinds of fine particle moderately absorbing (fine-MA1 and fine-MA2), fine particle highly absorbing, polluted dust, and desert dust aerosol. These aerosol types exhibit obvious optical characteristics difference. While five of them show similarities with aerosol types identified elsewhere, the polluted dust aerosol has no comparable prototype. Then the membership degree, a significant parameter provided by fuzzy clustering, is used to analyze internal variation of optical properties of each aerosol type. Finally, temporal variations of aerosol types are investigated. The dominant aerosol types are polluted dust and desert dust in spring, fine particle nonabsorbing aerosol in summer, and fine particle highly absorbing aerosol in winter. The fine particle moderately absorbing aerosol occurs during the whole year. Optical properties of the six types can also be used for radiative forcing estimation and satellite aerosol retrieval. Additionally, methodology of this study can be applied to identify aerosol types on a global scale. [ABSTRACT FROM AUTHOR]

With the patent co-inventing data of top 9 ICT firms with the highest patent application in China, this study establishes the co-inventing network and examines the moderate role of network connectivity, measured by classifying the individuals into two cohorts: inventors in the largest connected component and inventors in other isolated components. The network stability and innovation output demonstrate strong positive interaction, which is significant in not only the largest but also other isolated components. The clustering and centrality demonstrate significant effect on network stability and innovation output in the largest connected component, which is generally the same as that of extant studies. This impact is not significant in the other isolated components, which confirms the moderate role of network connectivity, i.e., fully connected networks constitute the basis for the network structure to be functioning. However, the significantly positive role of the structural hole is not moderated by the network connectivity. The contributions and implications of our findings is discussed at the end of this study. [ABSTRACT FROM AUTHOR]

Aiming to improve the algorithm of the classic fuzzy C-means model (FCM), a double fuzzy C-means model (DFCM) was presented in this paper. A new fuzzy cluster validity index (RWW) and the DFCM algorithm were proposed, simultaneously. Then, the double fuzzy C-means model was applied for the clustering analysis of the regional technology innovation level in China. The validity of the double fuzzy C-means model was tested using the wine data set of UCI. The comparison results of different cluster validity indexes validated the fuzzy cluster validity index (RWW) proposed in this paper. The application example and wine data set clustering results indicated that the DFCM model enhanced the intra-class compactness and inter-class separation, making the classification more accurate. [ABSTRACT FROM AUTHOR]

Water quality forecasting can provide useful information for public health protection and support water resources management. In order to forecast water quality more accurately, this paper proposes a novel hybrid model by combining data decomposition, fuzzy C-means clustering and bidirectional gated recurrent unit. Firstly, the original water quality data is decomposed into several subseries by empirical wavelet transform, and then, the decomposed subseries are recombined by fuzzy C-means clustering. Next, for each clustered series, bidirectional gated recurrent unit is applied to develop prediction model. Finally, the forecast result is obtained by the summation of the predictions for the subseries. The proposed forecast model is evaluated by the water quality data of Poyang Lake, China. Results show that the proposed forecast model provides highly accurate forecast result for all of the six water quality data: the average of MAPE of the forecast results for the six water quality datasets is 4.59% for 7 day ahead prediction. Furthermore, our model shows better forecast performance than the other models. Particularly, compared with the single BiGRU model, MAPE decreased by 32.86% in average. Results demonstrate that the proposed forecast model can be used effectively for water quality forecasting. [Display omitted] • A new real-time data decomposition strategy is proposed. • More accurate prediction result is obtained by clustering the decomposed subseries. • Bidirectional gated recurrent unit has a great simulation capacity. • Proposed model is a powerful tool to predict water quality. [ABSTRACT FROM AUTHOR]

In this paper, in order to explore the main factors affecting grain production in China systematically : use the methods of fuzzy clustering analysis and fuzzy comprehensive evaluation to make an empirical analysis on 3 main factors. The weight of each influence factor is given respectively, and the results on how to improve food production provide a reliable theoretical basis. [ABSTRACT FROM AUTHOR]

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations. Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data, which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects. After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals, an interval-valued fuzzy c-means (FCM) clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study. Such a process can significantly improve the clustering effect; specifically, the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results. Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region, China, and the TM sensor for Yushu, Qinghai, China, were conducted, as well as experiments involving the conventional FCM algorithm, the results of which were used for comparative analysis. Next, a supervised classification method was used to validate the clustering results. The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery. [ABSTRACT FROM AUTHOR]

Abstract: The better to explore the regional characteristics of inter-provincial CO2 emissions and the rational distribution of the reduction of emission intensity reduction in China, this paper proposes an improved PSO-FCM clustering algorithm. This method can obtain the optimal cluster number and membership grade values by utilizing the global capacity of Particle Swarm Optimization (PSO) on Fuzzy C-means (FCM). The clustering results of CO2 emissions indicate that the 30 provinces of China are divided into five clusters and each has its own significant characteristics. Compared with other clustering methods, the results of PSO-FCM are more explanatory. The most important indicators affecting regional emission characteristics are CO2 emission intensity and per capita emissions, whereas CO2 emission per unit of energy is not obvious in clustering. Furthermore, some policy recommendations on setting emission reduction targets according to the emission characteristics of different clusters are made. [Copyright &y& Elsevier]

The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions. [ABSTRACT FROM AUTHOR]

Recently, support vector machines, a supervised learning algorithm, have been widely used in the scope of credit risk management. However, noise may increase the complexity of the algorithm building and destroy the performance of classifier. In our work, we propose an ensemble support vector machine model to solve the risk assessment of supply chain finance, combined with reducing noises method. The main characteristics of this approach include that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noise and class noise to achieve an optimal clean set, and (2) support vector machine classifiers, based on the improved particle swarm optimization algorithm, are seen as component classifiers. Then, we obtained the final classification results by combining finally individual prediction through AdaBoosting algorithm on the new sample set. Some experiments are applied on supply chain financial analysis of China's listed companies. Results indicate that the credit assessment accuracy can be increased by applying this approach. [ABSTRACT FROM AUTHOR]

• A fuzzy c-means algorithm-based time series segmentation approach is proposed. • A decision making method is proposed to determine the appropriate number of segments. • The proposed approach has promising applications to TBM time series. Tunnel boring machine (TBM) is a complex engineering system widely used for tunnel construction. In recent years, massive in-situ time series data of TBM has been recorded, which can provide important references and useful information for TBM designers and operators. In this work, a new fuzzy c-means clustering-based time series segmentation approach is proposed for TBM time series data, where the prior information of attributes is incorporated to facilitate effective segmentation. In this approach, the segmentation objective function is formed by multiplying the time distance and the spatial distance between data. The prior information, i.e. the torque of cutterhead, is correlated with the penetration rate, is described by a linear model and included in the part of spatial distance between data. A new decision making method based on the distance between the joint segment prototypes is proposed to determine the appropriate number of segments. The application on TBM time series data from a tunnel in China shows that the proposed approach can accurately identify different excavation status of the TBM, and help the other data mining tasks of TBM as well. The proposed approach also has promising applications to other complex engineering systems. [ABSTRACT FROM AUTHOR]

Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can obtain accurate forecasting results though efficient linearization. The proposed method employs meteorological measurements as input. Next, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means clustering algorithm and the recursive least squares method. From these components, the T–S fuzzy model is obtained. Wind farms located in China (Shanxi Province) and in Ireland (County Kerry) are considered as cases with which to validate the proposed forecasting method. The forecasting results are compared with results from the contemporary machine learning-based models including support vector machine (SVM), the combined model of SVM and empirical mode decomposition, and back propagation neural network methods. The results show that the proposed T–S fuzzy model can effectively improve the precision of the short-term wind power forecasting. [ABSTRACT FROM AUTHOR]