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Articles 1 - 26 of 26
Full-Text Articles in Engineering
Transfer Learning With Deep Recurrent Neural Networks For Remaining Useful Life Estimation, Ansi Zhang, Honglei Wang, Shaobo Li, Yuxin Cui, Guanci Yang, Jianjun Hu
Transfer Learning With Deep Recurrent Neural Networks For Remaining Useful Life Estimation, Ansi Zhang, Honglei Wang, Shaobo Li, Yuxin Cui, Guanci Yang, Jianjun Hu
Faculty Publications
Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results …
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
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
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 …
Product Innovation Design Based On Deep Learning And Kansei Engineering, Huafeng Quan, Shaobo Li, Jianjun Hu
Product Innovation Design Based On Deep Learning And Kansei Engineering, Huafeng Quan, Shaobo Li, Jianjun Hu
Faculty Publications
Creative product design is becoming critical to the success of many enterprises. However, the conventional product innovation process is hindered by two major challenges: the difficulty to capture users’ preferences and the lack of intuitive approaches to visually inspire the designer, which is especially true in fashion design and form design of many other types of products. In this paper, we propose to combine Kansei engineering and the deep learning for product innovation (KENPI) framework, which can transfer color, pattern, etc. of a style image in real time to a product’s shape automatically. To capture user preferences, we combine Kansei …
Evaluation Criteria For Selecting Nosql Databases In A Single Box Environment, Ryan D. Engle, Brent T. Langhals, Michael R. Grimaila, Douglas D. Hodson
Evaluation Criteria For Selecting Nosql Databases In A Single Box Environment, Ryan D. Engle, Brent T. Langhals, Michael R. Grimaila, Douglas D. Hodson
Faculty Publications
In recent years, NoSQL database systems have become increasingly popular, especially for big data, commercial applications. These systems were designed to overcome the scaling and flexibility limitations plaguing traditional relational database management systems (RDBMSs). Given NoSQL database systems have been typically implemented in large-scale distributed environments serving large numbers of simultaneous users across potentially thousands of geographically separated devices, little consideration has been given to evaluating their value within single-box environments. It is postulated some of the inherent traits of each NoSQL database type may be useful, perhaps even preferable, regardless of scale. Thus, this paper proposes criteria conceived to …
An Ensemble Stacked Convolutional Neural Network Model For Environmental Event Sound Recognition, Shaobo Li, Yong Yao, Jie Hu, Guokai Liu, Xuemei Yao, Jianjun Hu
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 Building Permit System For Smart Cities: A Cloud-Based Framework, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur, Adwait Kaley, Arpit Patel, Akshar Joshi, Dhvani Shah
A Building Permit System For Smart Cities: A Cloud-Based Framework, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur, Adwait Kaley, Arpit Patel, Akshar Joshi, Dhvani Shah
Faculty Publications
In this paper we propose a novel, cloud-based framework to support citizens and city officials in the building permit process. The proposed framework is efficient, user-friendly, and transparent with a quick turn-around time for homeowners. Compared to existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of both the end user experience and the permitting and urban planning processes. This is enabled through a data mining-powered permit recommendation engine as well as a data analytics process that allow a gleaning of key …
Arrhenius Rate Chemistry-Informed Inter-Phase Source Terms (Arciist), Matthew J. Schwaab, Robert B. Greendyke, Bryan J. Steward
Arrhenius Rate Chemistry-Informed Inter-Phase Source Terms (Arciist), Matthew J. Schwaab, Robert B. Greendyke, Bryan J. Steward
Faculty Publications
Currently, in macro-scale hydrocodes designed to simulate explosive material undergoing shock-induced ignition, the state of the art is to use one of numerous reaction burn rate models. These burn models are designed to estimate the bulk chemical reaction rate. Unfortunately, these burn rate models are largely based on empirical data and must be recalibrated for every new material being simulated. We propose that the use of Arrhenius Rate Chemistry-Informed Interphase Source Terms (ARCIIST) in place of empirically derived burn models will improve the accuracy for these computational codes. A reacting chemistry model of this form was developed for the cyclic …
Enabling Autonomous Navigation For Affordable Scooters, Kaikai Liu, Rajathswaroop Mulky
Enabling Autonomous Navigation For Affordable Scooters, Kaikai Liu, Rajathswaroop Mulky
Faculty Publications
Despite the technical success of existing assistive technologies, for example, electric wheelchairs and scooters, they are still far from effective enough in helping those in need navigate to their destinations in a hassle-free manner. In this paper, we propose to improve the safety and autonomy of navigation by designing a cutting-edge autonomous scooter, thus allowing people with mobility challenges to ambulate independently and safely in possibly unfamiliar surroundings. We focus on indoor navigation scenarios for the autonomous scooter where the current location, maps, and nearby obstacles are unknown. To achieve semi-LiDAR functionality, we leverage the gyros-based pose data to compensate …
Identifying Prevalent Mathematical Pathways To Engineering In South Carolina, Eliza Gallagher, Christy Brown, D. Andrew Brown, Kristin Kelly Frady, Patrick Bass, Michael A. Matthews, Thomas T. Peters, Robert J. Rabb, Ikhalfani Solan, Ronald W. Welch, Anand K. Gramopadhye
Identifying Prevalent Mathematical Pathways To Engineering In South Carolina, Eliza Gallagher, Christy Brown, D. Andrew Brown, Kristin Kelly Frady, Patrick Bass, Michael A. Matthews, Thomas T. Peters, Robert J. Rabb, Ikhalfani Solan, Ronald W. Welch, Anand K. Gramopadhye
Faculty Publications
National data indicate that initial mathematics course placement in college is a strong predictor of persistence to degree in engineering, with students placed in calculus persisting at nearly twice the rate of those placed below calculus. Within the state of South Carolina, approximately 95% of engineering-intending students who initially place below calculus are from in-state. In order to make systemic change, we are first analyzing system-wide data to identify prevalent educational pathways within the state, and the mathematical milestones along those pathways taken by students in engineering and engineering-related fields. This paper reports preliminary analysis of that data to understand …
Uncertainty Evaluation In The Design Of Structural Health Monitoring Systems For Damage Detection, Christine M. Schubert Kabban, Richard P. Uber, Kevin J. Lin, Bin Lin, M. Bhuiyan, Victor Giurgiutiu
Uncertainty Evaluation In The Design Of Structural Health Monitoring Systems For Damage Detection, Christine M. Schubert Kabban, Richard P. Uber, Kevin J. Lin, Bin Lin, M. Bhuiyan, Victor Giurgiutiu
Faculty Publications
The validation of structural health monitoring (SHM) systems for aircraft is complicated by the extent and number of factors that the SHM system must demonstrate for robust performance. Therefore, a time- and cost-efficient method for examining all of the sensitive factors must be conducted. In this paper, we demonstrate the utility of using the simulation modeling environment to determine the SHM sensitive factors that must be considered for subsequent experiments, in order to enable the SHM validation. We demonstrate this concept by examining the effect of SHM system configuration and flaw characteristics on the response of a signal from a …
Social Recommendations For Personalized Fitness Assistance, Saumil Dharia, Magdalini Eirinaki, Vijesh Jain, Jvalant Patel, Iraklis Varlamis, Jainikkumar Vora, Rizen Yamauchi
Social Recommendations For Personalized Fitness Assistance, Saumil Dharia, Magdalini Eirinaki, Vijesh Jain, Jvalant Patel, Iraklis Varlamis, Jainikkumar Vora, Rizen Yamauchi
Faculty Publications
Wearable technology allows users to monitor their activity and pursue a healthy lifestyle through the use of embedded sensors. Such wearables usually connect to a mobile application that allows them to set their profile and keep track of their goals. However, due to the relatively “high maintenance” of such applications, where a significant amount of user feedback is expected, users who are very busy, or not as self-motivated, stop using them after a while. It has been shown that accountability improves commitment to an exercise routine. In this work, we present the PRO-Fit framework, a personalized fitness assistant aiming at …
A Bayesian Network Based Adaptability Design Of Product Structures For Function Evolution, Shaobo Li, Yongming Wu, Yan-Xia Xu, Jie Hu, Jianjun Hu
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 …
Comparison Of Plume Dynamics For Laser Ablated Metals: Al And Ti, William A. Bauer, Glen P. Perram, Timothy Haugan
Comparison Of Plume Dynamics For Laser Ablated Metals: Al And Ti, William A. Bauer, Glen P. Perram, Timothy Haugan
Faculty Publications
Emissive plumes from pulsed laser ablation of bulk Ti and Al from KrF laser irradiation at laser fluence up to 3.5 J/cm2 and argon background pressures of 0–1 Torr have been observed using gated intensified charged-coupled device imagery. Mass loss for Ti increases from 0.1 to 0.8 μg/pulse as pulse energy increase from 174 to 282 mJ/pulse (35–170 photons/atom) and decreases by ∼30% as pressure increases from vacuum to 1 Torr. Early plume energies are described by the free expansion velocities of 1.57 ± 0.02 and of 1.81 ± 0.07 cm/μs for Ti and Al, respectively, …
Demonstration Of Versatile Whispering-Gallery Micro-Lasers For Remote Refractive Index Sensing, Lei Wan, Hengky Chandrahalim, Jian Zhou
Demonstration Of Versatile Whispering-Gallery Micro-Lasers For Remote Refractive Index Sensing, Lei Wan, Hengky Chandrahalim, Jian Zhou
Faculty Publications
We developed chip-scale remote refractive index sensors based on Rhodamine 6G (R6G)-doped polymer micro-ring lasers. The chemical, temperature, and mechanical sturdiness of the fused-silica host guaranteed a flexible deployment of dye-doped polymers for refractive index sensing. The introduction of the dye as gain medium demonstrated the feasibility of remote sensing based on the free-space optics measurement setup. Compared to the R6G-doped TZ-001, the lasing behavior of R6G-doped SU-8 polymer micro-ring laser under an aqueous environment had a narrower spectrum linewidth, producing the minimum detectable refractive index change of 4 x 10−4 RIU. The maximum bulk refractive index sensitivity (BRIS) …
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
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
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 …
Monte Carlo Simulations Of Three-Dimensional Electromagnetic Gaussian Schell-Model Sources, Milo W. Hyde Iv, Santasri Bose-Pillai, Olga Korotkova
Monte Carlo Simulations Of Three-Dimensional Electromagnetic Gaussian Schell-Model Sources, Milo W. Hyde Iv, Santasri Bose-Pillai, Olga Korotkova
Faculty Publications
This article presents a method to simulate a three-dimensional (3D) electromagnetic Gaussian-Schell model (EGSM) source with desired characteristics. Using the complex screen method, originally developed for the synthesis of two-dimensional stochastic electromagnetic fields, a set of equations is derived which relate the desired 3D source characteristics to those of the statistics of the random complex screen. From these equations and the 3D EGSM source realizability conditions, a single criterion is derived, which when satisfied guarantees both the realizability and simulatability of the desired 3D EGSM source. Lastly, a 3D EGSM source, with specified properties, is simulated; the Monte Carlo simulation …
Patent Keyword Extraction Algorithm Based On Distributed Representation For Patent Classification, Jie Hu, Shaobo Li, Yong Yao, Liya Yu, Guanci Yang, Jianjun Hu
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 …
Copper-Doped Lithium Triborate (Lib3o5) Crystals: A Photoluminescence, Thermoluminescence, And Electron Paramagnetic Resonance Study, Brant E. Kananen, John W. Mcclory, Nancy C. Giles, Larry E. Halliburton
Copper-Doped Lithium Triborate (Lib3o5) Crystals: A Photoluminescence, Thermoluminescence, And Electron Paramagnetic Resonance Study, Brant E. Kananen, John W. Mcclory, Nancy C. Giles, Larry E. Halliburton
Faculty Publications
When doped with copper ions, lithium borate materials are candidates for use in radiation dosimeters. Copper-doped lithium tetraborate (Li2B4O7) crystals have been widely studied, but little is known thus far about copper ions in lithium triborate (LiB3O5) crystals. In the present investigation, Cu+ ions (3d10) were diffused into an undoped LiB3O5 crystal at high temperature. These ions occupy both Li+ and interstitial positions in the crystal. A photoluminescence (PL) band peaking near 387 nm and a photoluminescence excitation (PLE) band peaking near 273 nm verify that a portion of these Cu+ ions are located at regular Li+ sites. After an …
Metastable Ar(1s5) Density Dependence On Pressure And Argon-Helium Mixture In A High Pressure Radio Frequency Dielectric Barrier Discharge, Daniel J. Emmons, David E. Weeks, Ben Eshel, Glen P. Perram
Metastable Ar(1s5) Density Dependence On Pressure And Argon-Helium Mixture In A High Pressure Radio Frequency Dielectric Barrier Discharge, Daniel J. Emmons, David E. Weeks, Ben Eshel, Glen P. Perram
Faculty Publications
Simulations of an α-mode radio frequency dielectric barrier discharge are performed for varying mixtures of argon and helium at pressures ranging from 200 to 500 Torr using both zero and one-dimensional models. Metastable densities are analyzed as a function of argon-helium mixture and pressure to determine the optimal conditions, maximizing metastable density for use in an optically pumped rare gas laser. Argon fractions corresponding to the peak metastable densities are found to be pressure dependent, shifting from approximately 15% Ar in He at 200 Torr to 10% at 500 Torr. A decrease in metastable density is observed as pressure …
A Hierarchical Feature Extraction Model For Multi-Label Mechanical Patent Classification, Jie Hu, Shaobo Li, Jianjun Hu, Guanci Yang
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 …
Effects Of Edge Inclination Angles On Whispering-Gallery Modes In Printable Wedge Microdisk Lasers, Cong Chen, Lei Wan, Hengky Chandrahalim
Effects Of Edge Inclination Angles On Whispering-Gallery Modes In Printable Wedge Microdisk Lasers, Cong Chen, Lei Wan, Hengky Chandrahalim
Faculty Publications
The ink-jet technique was developed to print the wedge polymer microdisk lasers. The characterization of these lasers was implemented using a free-space optics measurement setup. It was found that disks of larger edge inclination angles have a larger free spectral range (FSR) and a lower resonance wavelength difference between the fundamental transverse electric (TE) and transverse magnetic (TM) whispering-gallery modes (WGMs). This behavior was also confirmed with simulations based on the modified Oxborrow’s model with perfectly matched layers (PMLs), which was adopted to accurately calculate the eigenfrequencies, electric field distributions, and quality parameters of modes in the axisymmetric microdisk resonators. …
Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes
Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes
Faculty Publications
Social networks have become very important for networking, communications, and content sharing. Social networking applications generate a huge amount of data on a daily basis and social networks constitute a growing field of research, because of the heterogeneity of data and structures formed in them, and their size and dynamics. When this wealth of data is leveraged by recommender systems, the resulting coupling can help address interesting problems related to social engagement, member recruitment, and friend recommendations.In this work we review the various facets of large-scale social recommender systems, summarizing the challenges and interesting problems and discussing some of the …
Wavelet Anova Bisection Method For Identifying Simulation Model Bias, Andrew D. Atkinson, Raymond R. Hill, Joseph J. Pignatiello Jr., G. Geoffrey Vining, Edward D. White, Eric Chicken
Wavelet Anova Bisection Method For Identifying Simulation Model Bias, Andrew D. Atkinson, Raymond R. Hill, Joseph J. Pignatiello Jr., G. Geoffrey Vining, Edward D. White, Eric Chicken
Faculty Publications
High-resolution computer models can simulate complex systems and processes in order to evaluate a solution quickly and inexpensively. Many simulation models produce dynamic functional output, such as a set of time-series data generated during a process. These computer models require verification and validation (V&V) to assess the correctness of these simulations. In particular, the model validation effort evaluates if the model is an appropriate representation of the real-world system that it is meant to simulate. However, when assessing a model capable of generating functional output, it is useful to learn more than simply whether the model is valid or invalid. …
Unmasking Cost Growth Behavior: A Longitudinal Study, Cory N. D'Amico, Edward D. White, Jonathan D. Ritschel, Scott R. Kozlak
Unmasking Cost Growth Behavior: A Longitudinal Study, Cory N. D'Amico, Edward D. White, Jonathan D. Ritschel, Scott R. Kozlak
Faculty Publications
This article examines how cost growth factors (CGF) change over a program’s acquisition life cycle for 36 Department of Defense aircraft programs. Starting from Milestone B, the authors examine CGFs at five gateways: Critical Design Review, First Flight (FF), the end of Developmental Test and Evaluation (DT&E), Initial Operational Capability, and Full Operational Capability. Each CGF is assigned a color rating based upon the program’s cost growth: Green (low), Amber (moderate), or Red (high). Significant findings include dependencies among similar CGF color ratings and cost growth occurring primarily between FF and the end of DT&E during a program’s life cycle.