New Appliance Detection for Nonintrusive Load Monitoring

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Cost-sensitive weighting and imbalance-reversed bagging for streaming imbalanced and concept drifting in electricity pricing classification

National Natural Science Foundation of China Grants 61572201 and 51707041; Guangzhou Science and Technology Plan Project 201804010245; Fundamental Research Funds for the Central Universities 2017ZD052; Guangdong University of Technology Grant from the Financial and Education Department of Guangdong Province 2016[202]; Education Department of Guangdong Province project number 2016KCXTD022; State Grid Technology Project Grant 5211011600RJ

Deep autoencoder with localized stochastic sensitivity for short-term load forecasting

National Natural Science Foundation of China; Guangdong Province Science and Technology Plan Project; Brunel University London; UK BRIEF Funding; Department of Finance and Education of Guangdong Province 2016; Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Grou

Urban Regeneration in a Restructuring Executive-led Polity: A Case Study of Hong Kong

organized by Centre of Asian Studies, the University of Hong Kon

HELP: An LSTM-based approach to hyperparameter exploration in neural network learning

Hyperparameter selection is very important for the success of deep neural network training. Random search of hyperparameters for deep neural networks may take a long time to converge and yield good results because the training of deep neural networks with a huge number of parameters for every selected hyperparameter is very time-consuming. In this work, we propose the Hyperparameter Exploration LSTM-Predictor (HELP) which is an improved random exploring method using a probability-based exploration with an LSTM-based prediction. The HELP has a higher probability to find a better hyperparameter with less time. The HELP uses a series of hyperparameters in a time period as input and predicts the fitness values of these hyperparameters. Then, exploration directions in the hyper-parameter space yielding higher fitness values will have higher probabilities to be explored in the next turn. Experimental results for training both the Generative Adversarial Net and the Convolution Neural Network show that the HELP finds hyperparameters yielding better results and converges faster. (c) 2021 Elsevier B.V. All rights reserved

Diversified Sensitivity-Based Undersampling for Imbalance Classification Problems

none5siopenNg WWY; Hu J; Yeung DS; Yin S; ROLI, FABIONg, Wwy; Hu, J; Yeung, Ds; Yin, S; Roli, Fabi

Gene expression studies of the dikaryotic mycelium and primordium of Lentinula edodes by serial analysis of gene expression

Lentinula edodes (Shiitake mushroom) is a common edible mushroom that has high nutritional and medical value. Although a number of genes involved in the fruit of the species have been identified, little is known about the process of differentiation from dikaryotic mycelium to primordium. In this study, serial analysis of gene expression (SAGE) was applied to determine the gene expression profiles of the dikaryotic mycelium and primordium of L. edodes in an effort to advance our understanding of the molecular basis of fruit body development. A total of 6363 tags were extracted (3278 from the dikaryotic mycelium and 3085 from the primordium), 164 unique tags matched the in-house expressed sequence tag (EST) database. The difference between the expression profiles of the dikaryotic mycelium and primordium suggests that a specific set of genes is required for fruit body development. In the transition from the mycelium to the primordium, different hydrophobins were expressed abundantly, fewer structural genes were expressed, transcription and translation became active, different genes became involved in intracellular trafficking, and stress responses were expressed. These findings advance our understanding of fruit body development. We used cDNA microarray hybridization and Northern blotting to verify the SAGE results, and found SAGE to be highly efficient in the performance of transcriptome analysis. To our knowledge, this is the first SAGE study of a mushroom.link_to_subscribed_fulltex

A Deep Learning based Hybrid Method for Hourly Solar Radiation Forecasting

National Natural Science Foundation of China; Guangzhou Science and Technology Plan; Department of Finance and Education of Guangdong Province 2016; Key Discipline Construction Program, China; the Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group; Brunel University London BRIEF Funding, U