Results 281 to 290 of about 25,867,085 (318)

Planetary gearbox fault diagnosis based on FDKNN-DGAT with few labeled data

Measurement science and technology, 2023
Although data-driven methods have been widely used in planetary gearbox fault diagnosis, the difficulty and high cost of manual labeling leads to little labeled training data, which limits the classification performance of traditional data-driven methods.
Hongfeng Tao, Haojin Shi, Jier Qiu, Guanghu Jin, Vladimir Stojanović   +4 more
semanticscholar   +1 more source

Interinstance and Intratemporal Self-Supervised Learning With Few Labeled Data for Fault Diagnosis

IEEE Transactions on Industrial Informatics, 2023
Recent researches on intelligent fault diagnosis algorithms can achieve great progress. However, considering the practical scenarios, the amount of labeled data is insufficient in face of the difficulty of data annotation, which would raise the risk of ...
Chenye Hu, Jingyao Wu, Chuang Sun, Ruqiang Yan, Xuefeng Chen   +4 more
semanticscholar   +1 more source

FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning

Neural Information Processing Systems, 2023
Semi-Supervised Learning (SSL) has been an effective way to leverage abundant unlabeled data with extremely scarce labeled data. However, most SSL methods are commonly based on instance-wise consistency between different data transformations.
Zhuo Huang, Li Shen, Jun Yu, Bo Han, Tongliang Liu   +4 more
semanticscholar   +1 more source

Multilabel Appliance Classification With Weakly Labeled Data for Non-Intrusive Load Monitoring

IEEE Transactions on Smart Grid, 2023
Non-Intrusive Load Monitoring consists in estimating the power consumption or the states of the appliances using electrical parameters acquired from a single metering point. State-of-the-art approaches are based on deep neural networks, and for training,
Giulia Tanoni, E. Principi, S. Squartini
semanticscholar   +1 more source

SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data

Conference on Empirical Methods in Natural Language Processing, 2023
Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text. In this work, we propose"SQLPrompt", tailored to improve the few-shot prompting capabilities of Text-to-SQL for Large Language Models (LLMs). Our
Ruoxi Sun, Sercan Ö. Arik, Rajarishi Sinha, Hootan Nakhost, Hanjun Dai, Pengcheng Yin, Tomas Pfister   +6 more
semanticscholar   +1 more source

Deep Learning of Partially Labeled Data for Quality Prediction Based on Stacked Target-Related Laplacian Autoencoder

IEEE Transactions on Neural Networks and Learning Systems, 2023
Partially labeled data, which is common in industrial processes due to the low sampling rate of quality variables, remains an important challenge in soft sensor applications.
Bocun He, Xinmin Zhang, Zhihuan Song
semanticscholar   +1 more source

Elastic structural analysis based on graph neural network without labeled data

Comput. Aided Civ. Infrastructure Eng., 2022
Artificial intelligence is gaining increasing popularity in structural analysis. However, at the structural system level, the appropriateness of data representation, the paucity of data, and the physical interpretability of results are rarely studied and
Lingshan Song, Chen Wang, Jian-sheng Fan, Hong-ming Lu   +3 more
semanticscholar   +1 more source

Semisupervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System With Limited Labeled Data

IEEE Transactions on Industrial Informatics, 2021
The labeled monitoring data collected from the electromechanical system is limited in the real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory accurate diagnosis results.
Xiaoli Zhao, M. Jia, Zheng Liu
semanticscholar   +1 more source

Coarsely-labeled Data for Better Few-shot Transfer

IEEE International Conference on Computer Vision, 2021
Few-shot learning is based on the premise that labels are expensive, especially when they are fine-grained and require expertise. But coarse labels might be easy to acquire and thus abundant.
Cheng Perng Phoo, Bharath Hariharan
semanticscholar   +1 more source