Non-invasive load monitoring based on deep learning to identify unknown loads.

With the rapid development of smart grids, society has become increasingly urgent to solve the problems of low energy utilization efficiency and high energy consumption. In this context, load identification has become a key element in formulating scientific and effective energy consumption plans and reducing unnecessary energy waste. However, traditional load identification methods mainly focus on known electrical equipment, and accurate identification of unknown electrical equipment still faces significant challenges. A new encoding feature space based on Triplet neural networks is proposed in this paper to detect unknown electrical appliances using convex hull coincidence degree. Additionally, transfer learning is introduced for the rapid updating of the pre-classification model's self-incrementing class with the unknown load. In experiments, the effectiveness of our method is successfully tested on the PLAID dataset. The accuracy of unknown load identification reached 99.23%. Through this research, we expect to bring a new idea to the field of load identification to meet the urgent need for the identification of unknown electrical appliances in the development of smart grids.

A NILM load identification method based on structured V-I mapping.

With the increasing number and types of global power loads and the development and popularization of smart grid technology, a large number of researches on load-level non-intrusive load monitoring technology have emerged. However, the unique power characteristics of the load make NILM face the difficult problem of low robustness of feature extraction and low accuracy of classification and identification in the recognition stage. This paper proposes a structured V-I mapping method to address the inherent limitations of traditional V-I trajectory mapping methods from a new perspective. In addition, for the verification of the V-I trajectory mapping method proposed in this paper, the complexity of load characteristics is comprehensively considered, and a lightweight convolutional neural network is designed based on AlexNet. The experimental results on the NILM dataset show that the proposed method significantly improves recognition accuracy compared to existing VI trajectory mapping methods.

Merge Loss Calculation Method for Highly Imbalanced Data Multiclass Classification.

In real classification scenarios, the number distribution of modeling samples is usually out of proportion. Most of the existing classification methods still face challenges in comprehensive model performance for imbalanced data. In this article, a novel theoretical framework is proposed that establishes a proportion coefficient independent of the number distribution of modeling samples and a general merge loss calculation method independent of class distribution. The loss calculation method of the imbalanced problem focuses on both the global and batch sample levels. Specifically, the loss function calculation introduces the true-positive rate (TPR) and the false-positive rate (FPR) to ensure the independence and balance of loss calculation for each class. Based on this, global and local loss weight coefficients are generated from the entire dataset and batch dataset for the multiclass classification problem, and a merge weight loss function is calculated after unifying the weight coefficient scale. Furthermore, the designed loss function is applied to different neural network models and datasets. The method shows better performance on imbalanced datasets than state-of-the-art methods.

Fast environmental sound classification based on resource adaptive convolutional neural network.

Recently, with the construction of smart city, the research on environmental sound classification (ESC) has attracted the attention of academia and industry. The development of convolutional neural network (CNN) makes the accuracy of ESC reach a higher level, but the accuracy improvement brought by CNN is often accompanied by the deepening of network layers, which leads to the rapid growth of parameters and floating-point operations (FLOPs). Therefore, it is difficult to transplant CNN model to embedded devices, and the classification speed is also difficult to accept. In order to reduce the hardware requirements of running CNN and improve the speed of ESC, this paper proposes a resource adaptive convolutional neural network (RACNN). RACNN uses a novel resource adaptive convolutional (RAC) module, which can generate the same number of feature maps as conventional convolution operations more cheaply, and extract the time and frequency features of audio efficiently. The RAC block based on the RAC module is designed to build the lightweight RACNN model, and the RAC module can also be used to upgrade the existing CNN model. Experiments based on public datasets show that RACNN achieves higher performance than the state-of-the-art methods with lower computational complexity.

Environmental sound classification using temporal-frequency attention based convolutional neural network.

Environmental sound classification is one of the important issues in the audio recognition field. Compared with structured sounds such as speech and music, the time-frequency structure of environmental sounds is more complicated. In order to learn time and frequency features from Log-Mel spectrogram more effectively, a temporal-frequency attention based convolutional neural network model (TFCNN) is proposed in this paper. Firstly, an experiment that is used as motivation in proposed method is designed to verify the effect of a specific frequency band in the spectrogram on model classification. Secondly, two new attention mechanisms, temporal attention mechanism and frequency attention mechanism, are proposed. These mechanisms can focus on key frequency bands and semantic related time frames on the spectrogram to reduce the influence of background noise and irrelevant frequency bands. Then, a feature information complementarity is formed by combining these mechanisms to more accurately capture the critical time-frequency features. In such a way, the representation ability of the network model can be greatly improved. Finally, experiments on two public data sets, UrbanSound 8 K and ESC-50, demonstrate the effectiveness of the proposed method.

Simultaneous Prediction of Soil Properties Using Multi_CNN Model.

Soil nutrient prediction based on near-infrared spectroscopy has become the main research direction for rapid acquisition of soil information. The development of deep learning has greatly improved the prediction accuracy of traditional modeling methods. In view of the low efficiency and low accuracy of current soil prediction models, this paper proposes a soil multi-attribute intelligent prediction method based on convolutional neural networks, by constructing a dual-stream convolutional neural network model Multi_CNN that combines one-dimensional convolution and two-dimensional convolution, the intelligent prediction of soil multi-attribute is realized. The model extracts the characteristics of soil attributes from spectral sequences and spectrograms respectively, and multiple attributes can be predicted simultaneously by feature fusion. The model is based on two different-scale soil near-infrared spectroscopy data sets for multi-attribute prediction. The experimental results show that the RP2 of the three attributes of Total Carbon, Total Nitrogen, and Alkaline Nitrogen on the small dataset are 0.94, 0.95, 0.87, respectively, and the RP2 of the attributes of Organic Carbon, Nitrogen, and Clay on the LUCAS dataset are, respectively, 0.95, 0.91, 0.83, And compared with traditional regression models and new prediction methods commonly used in soil nutrient prediction, the multi-task model proposed in this paper is more accurate.

Export trade, embodied carbon emissions, and environmental pollution: An empirical analysis of China's high- and new-technology industries.

China's export trade has been expanding steadily in recent years, significantly increasing resource consumption and environmental pollution. High- and new-technology industries are essential for achieving sustainable economic development and improving environmental quality. This study employs a multi-regional input-output model to estimate the economic benefits and environmental costs of export trade in high- and new-technology industries. Then, it analyzes the impact of economic benefits and technological levels on environmental pollution using the Stochastic Impacts by Regression on Population, Affluence, and Technology model. An input-output multi-objective linear programming model and a non-dominated sorting genetic algorithm II are adopted to combine economic development with environmental pollution and determine the optimal path for export trade. The results show that technological progress in China's high- and new-technology industries is conducive to reducing embodied carbon emissions in developed countries while increasing emissions in developing countries. Moreover, a nonlinear three-stage accompanying relationship exists between the economic benefits and environmental costs of high- and new-technology exports; this is because exports with low economic benefits generate fewer carbon emissions whereas exports with high economic benefits generate significant carbon emissions. An increase in exports with ultra-high economic benefits will generate excessive embodied carbon emissions that hinder coordinated economic-environmental development. Lastly, technological progress in the electrical and optical equipment sector can effectively promote pollution reduction; thus, it should be further developed to improve the comprehensive benefits of exports.