Physical Sciences and Mathematics Commons^™

Tourism Review Sentiment Classification Using A Bidirectional Recurrent Neural Network With An Attention Mechanism And Topic-Enriched Word Vectors, Qin Li, Shaobo Li, Jie Hu, Sen Zhang, Jianjun Hu

Faculty Publications

Sentiment analysis of online tourist reviews is playing an increasingly important role in tourism. Accurately capturing the attitudes of tourists regarding different aspects of the scenic sites or the overall polarity of their online reviews is key to tourism analysis and application. However, the performances of current document sentiment analysis methods are not satisfactory as they either neglect the topics of the document or do not consider that not all words contribute equally to the meaning of the text. In this work, we propose a bidirectional gated recurrent unit neural network model (BiGRULA) for sentiment analysis by combining a topic …

End-To-End Convolutional Neural Network Model For Gear Fault Diagnosis Based On Sound Signals, Yong Yao, Honglei Wang, Shaobo Li, Zhongnhao Liu, Gui Gui, Yabo Dan, Jianjun Hu

Faculty Publications

Currently gear fault diagnosis is mainly based on vibration signals with a few studies on acoustic signal analysis. However, vibration signal acquisition is limited by its contact measuring while traditional acoustic-based gear fault diagnosis relies heavily on prior knowledge of signal processing techniques and diagnostic expertise. In this paper, a novel deep learning-based gear fault diagnosis method is proposed based on sound signal analysis. By establishing an end-to-end convolutional neural network (CNN), the time and frequency domain signals can be fed into the model as raw signals without feature engineering. Moreover, multi-channel information from different microphones can also be fused …

An Ensemble Stacked Convolutional Neural Network Model For Environmental Event Sound Recognition, Shaobo Li, Yong Yao, Jie Hu, Guokai Liu, Xuemei Yao, Jianjun Hu

Faculty Publications

Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three …

A Bayesian Network Based Adaptability Design Of Product Structures For Function Evolution, Shaobo Li, Yongming Wu, Yan-Xia Xu, Jie Hu, Jianjun Hu

Faculty Publications

Structure adaptability design is critical for function evolution in product families, in which many structural and functional design factors are intertwined together with manufacturing cost, customer satisfaction, and final market sales. How to achieve a delicate balance among all of these factors to maximize the market performance of the product is too complicated to address based on traditional domain experts’ knowledge or some ad hoc heuristics. Here, we propose a quantitative product evolution design model that is based on Bayesian networks to model the dynamic relationship between customer needs and product structure design. In our model, all of the structural …

Aspie: A Framework For Active Sensing And Processing Of Complex Events In The Internet Of Manufacturing Things, Shaobo Li, Weixing Chen, Jie Hu, Jianjun Hu

Faculty Publications

Rapid perception and processing of critical monitoring events are essential to ensure healthy operation of Internet of Manufacturing Things (IoMT)-based manufacturing processes. In this paper, we proposed a framework (active sensing and processing architecture (ASPIE)) for active sensing and processing of critical events in IoMT-based manufacturing based on the characteristics of IoMT architecture as well as its perception model. A relation model of complex events in manufacturing processes, together with related operators and unified XML-based semantic definitions, are developed to effectively process the complex event big data. A template based processing method for complex events is further introduced to conduct …

A Novel Evolutionary Algorithm For Designing Robust Analog Filters, Shaobo Li, Wang Zou, Jianjun Hu

Faculty Publications

Designing robust circuits that withstand environmental perturbation and device degradation is critical for many applications. Traditional robust circuit design is mainly done by tuning parameters to improve system robustness. However, the topological structure of a system may set a limit on the robustness achievable through parameter tuning. This paper proposes a new evolutionary algorithm for robust design that exploits the open-ended topological search capability of genetic programming (GP) coupled with bond graph modeling. We applied our GP-based robust design (GPRD) algorithm to evolve robust lowpass and highpass analog filters. Compared with a traditional robust design approach based on a state-of-the-art …

Patent Keyword Extraction Algorithm Based On Distributed Representation For Patent Classification, Jie Hu, Shaobo Li, Yong Yao, Liya Yu, Guanci Yang, Jianjun Hu

Faculty Publications

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard …

A Hierarchical Feature Extraction Model For Multi-Label Mechanical Patent Classification, Jie Hu, Shaobo Li, Jianjun Hu, Guanci Yang

Faculty Publications

Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM) for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs) is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM) neural network model is proposed to …

Authenticating Users With 3d Passwords Captured By Motion Sensors, Jing Tian

Theses and Dissertations

Authentication plays a key role in securing various resources including corporate facilities or electronic assets. As the most used authentication scheme, knowledgebased authentication is easy to use but its security is bounded by how much a user can remember. Biometrics-based authentication requires no memorization but ‘resetting’ a biometric password may not always be possible. Thus, we propose study several behavioral biometrics (i.e., mid-air gestures) for authentication which does not have the same privacy or availability concerns as of physiological biometrics.

In this dissertation, we first propose a user-friendly authentication system Kin- Write that allows users to choose arbitrary, short and …