Appliance-level data is a prerequisite for establishing friendly two-way interactions between customers and the power company, and this data is now mainly obtained by non-intrusive load monitoring. However, as the number of loads increases, the number of possible appliances state combinations tends to grow exponentially, leading to a significant increase in the time of load identification. In order to reduce the search range of the load state combinations and shorten the algorithm response time, a non-intrusive load monitoring method based on the time-segmented state probability is proposed in this paper. Firstly, the affinity propagation (AP) clustering algorithm is introduced to obtain the power templates of the load, and then the power templates are used to count the time-segmented state probabilities. Secondly, a number of appliance state matrices are generated using the probabilities, and the optimal matrix is selected by the function as the identification result of the appliance state. Finally, the performance of the algorithm is tested on the public NILM dataset and compared to several state-of-the-art techniques. The results illustrate that the proposed method achieves an accuracy of 96% for load state identification and 89% for power decomposition of the load, and is able to meet the real-time application requirements.
Objective. Skin lesion segmentation plays an important role in the diagnosis and treatment of melanoma. Existing skin lesion segmentation methods have trouble distinguishing hairs, air bubbles, and blood vessels around lesions, which affects the segmentation performance. Approach. To clarify the lesion boundary and raise the accuracy of skin lesion segmentation, a joint attention and adversarial learning network (JAAL-Net) is proposed that consists of a generator and a discriminator. In the JAAL-Net, the generator is a Local Fusion Network (LF-Net) utilizing the encoder-decoder structure. The encoder contains a convolutional block attention module to increase the weight of lesion information. The decoder involves a contour attention to obtain edge information and locate the lesion. To aid the LF-Net generate higher confidence predictions, a discriminant Dual Attention Network (DA-Net) is constructed with channel attention and position attention. Main results. The JAAL-Net is evaluated on three datasets ISBI2016, ISBI2017 and ISIC2018. The intersection over union (IoU) of the JAAL-Net on the three datasets are 90.27%, 89.56% and 80.76%, respectively. Experimental results show that the JAAL-Net obtains rich lesion and boundary information, enhances the confidence of the predictions, and improves the accuracy of skin lesion segmentation. Significance. The proposed approach effectively improves the performance of the model for skin lesion segmentation, which can assist physicians in accurate diagnosis well.
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