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Fault section diagnosis (FSD) is significant for the power system dispatching. Artificial neural network (ANN)‐based FSD method has strong fault tolerance but it looks like a black box and lacks the interpretability to the diagnosis outputs. In addition, when the topology of power systems changes, the ANN structure needs to be reconstructed and retrained, and thus has low adaptive capability. In order to tackle these challenges, in this paper, an ANN‐based FSD method by constructing universal transparent diagnosis models is proposed. The diagnosis models are constructed for the transmission line, transformer, and bus types rather than for a specific power system section. They can express the logical relations among sections, protective relays (PRs) and circuit breakers (CBs) clearly and intuitively. In addition, fuzzy values are used to model the uncertainties of PRs and CBs, and to determine the inputs of diagnosis models. Furthermore, the differential evolution algorithm is employed to optimize the network parameters of diagnosis models. The proposed method is verified on the IEEE 30‐bus test system and an actual local power system in Jilin Province of China. [ABSTRACT FROM AUTHOR]

Ice clouds are mostly composed of different ice crystal habits. It is of great importance to classify ice crystal habits seeing as they could greatly impact single‐scattering properties of ice crystal particles. The single‐scattering properties play an important role in the study of cloud remote sensing and the Earth's atmospheric radiation budget. However, there are countless ice crystals with different shapes in ice clouds, and the task of empirical classification based on naked‐eye observations is unreliable, time consuming and subjective, which leads to classification results having obvious uncertainties and biases. In this paper, the images of ice crystals observed from airborne Cloud Particle Imager in China are used to establish an ice crystal data set called Ice Crystals Database in China, which consists of 10 habit categories containing over 7,000 images. We propose an automatic classification model of ice crystal habits, called TL‐ResNet152, which is a deep convolutional neural network based on the newly developed method of transfer learning. The results show that the TL‐ResNet152 model could achieve reliable performance in ice crystal habits classification with the accuracy of 96%, which is far more accurate than traditional classification methods. Achieving high‐precision automatic classification of ice crystal habits will help us better understand the radiation characteristics of ice clouds. Plain Language Summary: In recent years, Convolutional Neural Networks (CNNs), as one of the representative algorithms used in deep learning, have been widely applied in the field of image classification and have achieved remarkable results. However, to the best of our knowledge, there are few studies regarding the application of deep CNNs to ice crystal image classification. In this paper, we propose an automatic classification model of ice crystal habits called TL‐ResNet152, which is a deep CNN based on a newly developed method of transfer learning. Using the TL‐ResNet152 model, high precision and automatic classification of ice crystal habits could be achieved, furthering our understanding of radiation characteristics of ice clouds. We have set up an ice crystal data set, called Ice Crystals Database in China, which consists of 10 habit categories with 7,282 images. As far as we know, it is the first publicly available ice crystals data set in China, pertaining to ice crystals observed in natural ice clouds. The publication of this database will promote more and more researches into understanding the physical process of ice clouds based on ice crystal habits classification. Key Points: An ice crystal data set called Ice Crystals Database in China (ICDC) containing over 7,000 images has been first establishedWe propose an automatic classification model of ice crystal habits, TL‐ResNet152, which is a convolutional neural network created using transfer learningThe high‐precision automatic classification of ice crystal habits is achieved by using this classification model [ABSTRACT FROM AUTHOR]

Learning analytics (LA) has been widely adopted in research on education. However, most studies in the area have conducted LA after computer‐supported collaborative learning (CSCL) activities rather than during CSCL. To address this problem, this study proposed a LA‐based real‐time feedback approach based on a deep neural network model to improve CSCL performance. In total, 72 university students participated in the study and were randomly assigned to an experimental or control group. The students in the experimental group learned with the LA‐based real‐time feedback approach, whereas the students in the control group learned with the conventional online collaborative learning approach. To analyse the data, both quantitative and qualitative methods were adopted. The results indicated that the LA‐based real‐time feedback approach significantly promoted knowledge convergence, knowledge elaboration, interactive relationships and group performance. The interview results also confirmed the effectiveness of the proposed approach. Practitioner notesWhat is already known regarding this topic Learning analytics (LA) has been widely used in education.Most studies in the area have presented LA results only after collaborative learning and have lacked real‐time analysis and feedback.What this paper adds A LA‐based real‐time feedback approach was proposed and validated in the computer‐supported collaborative learning (CSCL) context.The experimental results indicated that the LA‐based real‐time feedback approach significantly promoted knowledge elaboration, knowledge convergence, interactive relationships and group performance.Implications for practice and/or policy To shed light on progress in CSCL, real‐time LA are recommended.Deep neural network models, such as bidirectional encoder representations from transformers, can be adopted to automatically analyse online discussion transcripts.Real‐time feedback based on LA results can promote CSCL performance. [ABSTRACT FROM AUTHOR]

The carbon trading market has become a powerful weapon in alleviating carbon emissions in China, and the carbon price is at the core of its operation. Hence, the carbon trading market serves as an indispensable component in forecasting the carbon price accurately in advance. This paper innovatively explores an ensemble‐driven long short‐term memory network (LSTM) model based on complementary ensemble empirical mode decomposition (CEEMD) for carbon price forecasting, applying it to all eight carbon trading pilots in China. The CEEMD was initially implemented for mode transformation in order to decompose the original complicated mode into a set of simple modes. Then, the partial autocorrelation function selected time‐lagged features as inputs for each mode. Subsequently, the LSTM was used to model the mapping between time‐lagged factors as well as each mode's target values, constructing multiple LSTM models for ensemble learning. Finally, the inverse CEEMD computation was introduced to integrate the anticipated results of the multi‐mode into the final results. Its practical application simultaneously embraced all eight carbon pilots in China, covering their corresponding carbon price data over a considerably long period. The obtained results illustrated that the proposed model driven by ensemble learning possessed sufficient accuracy in carbon price forecasting in China compared with the single LSTM model as well as other conventional artificial neural network models. Furthermore, according to the scope of its application, the innovative model exhibited strong stability and universality. [ABSTRACT FROM AUTHOR]

Objects: This study aims to explore the Cancer antigen 724 (CA724) reference values spatial distribution characteristics in healthy Chinese adults. The study can provide regional reference for medical diagnosis. Study Design: The relationship between CA724 and 25 geographical environmental factors was analyzed firstly. Artificial neural network simulation training was used to construct the prediction model. The national forecast distribution map of the CA724 reference values was obtained by the geostatistical mapping method. Analyzing and exploring the influence mechanism of geographical environment factors on CA724 reference values. Methods: Collecting 34470 cases from more than 106 cities healthy adults CA724 reference values via several paper databases in 10 recent years. Correlation analysis, RBF artificial neural networks and trend surface analysis were applied to explore if there was any tendency of spatial variation. The Kriging interpolation of geostatistical analysis was developed to reveal the spatial distribution characteristics of the CA724 reference values. Results: The distribution of CA724 reference values of Chinese healthy adults shows a downward trend from south to north. CA724 reference values have negative correlations with latitude, annual sunshine duration and topsoil cation exchange capacity in clay. CA724 have positive correlations with annual mean air temperature, annual mean relative humidity, and annual precipitation amount. High temperature and high humidity environment will reduce gastrointestinal function and breeze various mold bacteria. Lack of sunshine can easily lead to vitamin C deficiency in the body. These will increase the incidence of gastrointestinal diseases and gastric cancer, then increase the CA724 value. Conclusion: CA724 reference values show spatial autocorrelation and regional variation. There are some geographical environment factors effected Chinese healthy adults CA724 reference values. Geographic factors such as sunshine, temperature, and humidity have effects on CA724 reference values can provide new ideas and directions of prevention and clinical diagnosis in the future. [ABSTRACT FROM AUTHOR]

The self-adaptive intelligence gray predictive model (SAIGM) has an alterable-flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the Particle Swarm Optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models. [ABSTRACT FROM AUTHOR]

Opinion words are crucial information for sentiment analysis. In some text, however, opinion words are absent or highly ambiguous. The resulting implicit opinions are more difficult to extract and label than explicit ones. In this paper, cutting-edge machine-learning approaches - deep neural network and word-embedding - are adopted for implicit opinion mining at the snippet and clause levels. Hotel reviews written in Chinese are collected and annotated as the experimental data set. Results show the convolutional neural network models not only outperform traditional support vector machine models, but also capture hidden knowledge within the raw text. The strength of word-embedding is also analyzed. [ABSTRACT FROM AUTHOR]

In recent years, research on intelligent systems is becoming more and more popular in academe. Intelligent systems research mainly involves the field of artificial intelligence. There are many research groups engaged in the research of intelligent systems in the world, and a variety of achievements have been made. In this paper, the main research institutions in China as well as their main research topics are introduced. [ABSTRACT FROM AUTHOR]