174 research outputs found
A Novel Transfer Learning Method Utilizing Acoustic and Vibration Signals for Rotating Machinery Fault Diagnosis
Fault diagnosis of rotating machinery plays a important role for the safety
and stability of modern industrial systems. However, there is a distribution
discrepancy between training data and data of real-world operation scenarios,
which causing the decrease of performance of existing systems. This paper
proposed a transfer learning based method utilizing acoustic and vibration
signal to address this distribution discrepancy. We designed the acoustic and
vibration feature fusion MAVgram to offer richer and more reliable information
of faults, coordinating with a DNN-based classifier to obtain more effective
diagnosis representation. The backbone was pre-trained and then fine-tuned to
obtained excellent performance of the target task. Experimental results
demonstrate the effectiveness of the proposed method, and achieved improved
performance compared to STgram-MFN
Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Automatically extracting useful information from electronic medical records
along with conducting disease diagnoses is a promising task for both clinical
decision support(CDS) and neural language processing(NLP). Most of the existing
systems are based on artificially constructed knowledge bases, and then
auxiliary diagnosis is done by rule matching. In this study, we present a
clinical intelligent decision approach based on Convolutional Neural
Networks(CNN), which can automatically extract high-level semantic information
of electronic medical records and then perform automatic diagnosis without
artificial construction of rules or knowledge bases. We use collected 18,590
copies of the real-world clinical electronic medical records to train and test
the proposed model. Experimental results show that the proposed model can
achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using
convolutional neural network to automatically learn high-level semantic
features of electronic medical records and then conduct assist diagnosis is
feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling
Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in
achieving representation learning and generation for natural language at the
same time. Nevertheless, existing VAE-based language models either employ
elementary RNNs, which is not powerful to handle complex works in the
multi-task situation, or fine-tunes two pre-trained language models (PLMs) for
any downstream task, which is a huge drain on resources. In this paper, we
propose the first VAE framework empowered with adaptive GPT-2s (AdaVAE).
Different from existing systems, we unify both the encoder\&decoder of the VAE
model using GPT-2s with adaptive parameter-efficient components, and further
introduce Latent Attention operation to better construct latent space from
transformer models. Experiments from multiple dimensions validate that AdaVAE
is competent to effectively organize language in three related tasks (language
modeling, representation modeling and guided text generation) even with less
than activated parameters in training. Our code is available at
\url{https://github.com/ImKeTT/AdaVAE}
A New Sentence Extraction Strategy for Unsupervised Extractive Summarization Methods
In recent years, text summarization methods have attracted much attention
again thanks to the researches on neural network models. Most of the current
text summarization methods based on neural network models are supervised
methods which need large-scale datasets. However, large-scale datasets are
difficult to obtain in practical applications. In this paper, we model the task
of extractive text summarization methods from the perspective of Information
Theory, and then describe the unsupervised extractive methods with a uniform
framework. To improve the feature distribution and to decrease the mutual
information of summarization sentences, we propose a new sentence extraction
strategy which can be applied to existing unsupervised extractive methods.
Experiments are carried out on different datasets, and results show that our
strategy is indeed effective and in line with expectations
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