Deep-learning-based earth fault detection using continuous wavelet transform and convolutional neural network in resonant grounding distribution systems
MF Guo, XD Zeng, DY Chen, NC Yang - IEEE Sensors Journal, 2017 - ieeexplore.ieee.org
Feature extraction for fault signals is critical and difficult in all kinds of fault detection
schemes. A novel simple and effective method of faulty feeder detection in resonant …
schemes. A novel simple and effective method of faulty feeder detection in resonant …
Recent advances in the discipline of text based affect recognition
Sentiment analysis is a part of natural language processing, along with text mining. Over the
years, sentiment analysis has become a key study area for researchers and industries all …
years, sentiment analysis has become a key study area for researchers and industries all …
Board-level functional fault diagnosis using multikernel support vector machines and incremental learning
F Ye, Z Zhang, K Chakrabarty… - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
Advanced machine learning techniques offer an unprecedented opportunity to increase the
accuracy of board-level functional fault diagnosis and reduce product cost through …
accuracy of board-level functional fault diagnosis and reduce product cost through …
A weighted SVM ensemble predictor based on AdaBoost for blast furnace ironmaking process
S Luo, Z Dai, T Chen, H Chen, L Jian - Applied Intelligence, 2020 - Springer
As one of the most complex industrial reactors, there remain some urgent issues for blast
furnace (BF), such as BF automation, prediction of the inner thermal state, etc. In this work …
furnace (BF), such as BF automation, prediction of the inner thermal state, etc. In this work …
irrelevant attribute resistance approach to binary classification for imbalanced data
J Zheng, X Hu - Information Sciences, 2024 - Elsevier
Imbalanced data distribution is a common feature in real-world datasets. For imbalanced
data, the imbalanced characteristics of the classes have two negative effects on …
data, the imbalanced characteristics of the classes have two negative effects on …
Incremental support vector machine algorithm based on multi-kernel learning
Z Li, J Zhang, S Hu - Journal of Systems Engineering and …, 2011 - ieeexplore.ieee.org
A new incremental support vector machine (SVM) algorithm is proposed which is based on
multiple kernel learning. Through introducing multiple kernel learning into the SVM …
multiple kernel learning. Through introducing multiple kernel learning into the SVM …
基于概率密度估计的增量支持向量机算法
潘世超， 王文剑， 郭虎升 - 南京大学学报(自然科学版), 2014 - jns.nju.edu.cn
增量支持向量机(Incremental Support Vector Machine, ISVM) 模型通过每次加入一个或者一批
样本进行学习, 将大规模问题分解成一系列子问题, 以提高支持向量机(Support Vector Machine …
样本进行学习, 将大规模问题分解成一系列子问题, 以提高支持向量机(Support Vector Machine …
LDA boost classification: boosting by topics
L Lei, G Qiao, C Qimin, L Qitao - EURASIP Journal on Advances in Signal …, 2012 - Springer
AdaBoost is an efficacious classification algorithm especially in text categorization (TC)
tasks. The methodology of setting up a classifier committee and voting on the documents for …
tasks. The methodology of setting up a classifier committee and voting on the documents for …
Self-learning and adaptive board-level functional fault diagnosis
F Ye, K Chakrabarty, Z Zhang… - The 20th Asia and South …, 2015 - ieeexplore.ieee.org
Functional fault diagnosis is necessary for board-level product qualification. However,
ambiguous diagnosis results can lead to long debug times and wrong repair actions, which …
ambiguous diagnosis results can lead to long debug times and wrong repair actions, which …
Diagnosis using support vector machines (SVM)
F Ye, Z Zhang, K Chakrabarty, X Gu, F Ye… - … -driven Board-level …, 2017 - Springer
Diagnosis of functional failures at the board level is critical for improving product yield and
reducing manufacturing cost. State-of-the-art board-level diagnostic software is unable to …
reducing manufacturing cost. State-of-the-art board-level diagnostic software is unable to …