散开
发表于 2025-3-23 11:12:32
Class, Surplus, and the Division of Labourhine learning techniques for condition monitoring in power transformers. Our objective is to classify the three different types of Partial Discharge (PD), the identify of which is highly correlated with insulation failure. Measurements from Acoustic Emission (AE) sensors are used as input data. Two
Reservation
发表于 2025-3-23 16:39:15
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周年纪念日
发表于 2025-3-23 19:50:28
https://doi.org/10.1057/9781137287731and location-based social networks has become a serious challenge for data management and analysis systems. In urban micro-climate, we need to deal with various types of data such as: environmental data measurements, Wi-Fi data and so on. The format and the nature of data coming from different senso
严峻考验
发表于 2025-3-24 01:17:13
Data Analytics for Renewable Energy Integration. Technologies, Systems and Society978-3-030-04303-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
开头
发表于 2025-3-24 04:11:31
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LEERY
发表于 2025-3-24 09:24:43
978-3-030-04302-5Springer Nature Switzerland AG 2018
watertight,
发表于 2025-3-24 12:00:23
Conference proceedings 2018 in Dublin, Ireland, in September 2018...The 9 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response, and many others..
传授知识
发表于 2025-3-24 18:42:29
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潜伏期
发表于 2025-3-24 21:30:47
Fused Lasso Dimensionality Reduction of Highly Correlated NWP Features,models, and the high correlation of the features, which can affect the performance of learning machines as Multilayer Perceptron (MLP). In this work we propose to reduce the dimension of the problem using a supervised Fused Lasso model, which generates meta-features corresponding to the average of t
消极词汇
发表于 2025-3-25 00:03:55
Sampling Strategies for Representative Time Series in Load Flow Calculations,grid. By using time series with high temporal resolution, information is getting more detailed, but at the same time, the computational costs of the algorithms increase. With the help of our algorithm, we create representative time series that have similar characteristics to the original time series