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As the demand for detailed load data descriptions in modern power systems continues to increase, challenges such as high computational complexity in load identification tasks and high hardware requirements for devices have significantly hindered progress. Therefore, this paper proposes a non-intrusive load identification method using Densely-connected Bi-directional Long Short-Term Memory (DB-LSTM) with Kernel Principal Component Analysis. Firstly, a bilateral sliding window algorithm is employed for event detection in the data collected by load identification devices, checking for the switching on and off of electrical appliances. Secondly, after detecting the switching of load devices and extracting features, Kernel Principal Component Analysis is used to reduce data dimensions due to the complexity of existing features, selecting more relevant characteristics. Finally, a densely connected Bi-directional Long Short-Term Memory (LSTM) network is utilized. This enhances global and dynamic local features by stacking LSTM units and combining them with dense skip connections, providing additional channels for signal transmission, thereby strengthening feature propagation and reducing the number of parameters. This approach lowers computational complexity and improves the efficiency of the model's load identification. The proposed model is compared and validated against mainstream non-intrusive load identification models through experiments, demonstrating its higher efficiency in load identification. [ABSTRACT FROM AUTHOR]

Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training ( n = 254; median age, 69 years [IQR, 64-74 years]) and testing ( n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article . © RSNA, 2024.