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Hongyue Sun

Bio: Hongyue Sun is an academic researcher from University at Buffalo. The author has contributed to research in topics: Computer science & Wearable computer. The author has an hindex of 12, co-authored 37 publications receiving 347 citations. Previous affiliations of Hongyue Sun include Beijing Institute of Technology & Virginia Tech.

Papers
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Journal ArticleDOI
Shuai Luo1, Hongyue Sun1, Qingyun Ping1, Ran Jin1, Zhen He1 
18 Feb 2016-Energies
TL;DR: In this paper, the authors discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters.
Abstract: Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.

64 citations

Journal ArticleDOI
TL;DR: An unsupervised learning method for studying the flow pattern of the droplet, which does not require well-defined ground-truth labels is proposed and Experimental results demonstrate that the proposed method can learn latent representations of thedroplet jetting process video data, which is very useful for the prediction of theDroplet behavior.
Abstract: Droplet jetting behavior largely determines the final drop deposition quality in the inkjet printing process. Forming such behavior is governed by the fluid flow pattern. Therefore, a measurement of the flow pattern is of great importance for improving the printing quality of the inkjet printing process. Most of the current works use static images for the study of the drop evolution process. The problem of the static images is that the images cannot recognize the motion information (i.e., temporal transformation) of the droplet. Thus the information of the jetting process in the temporal domain will be lost. Instead of using the images, this paper takes the video data as the study subject to investigate the droplet evolution behavior in the inkjet printing process. Moreover, this paper introduces a deep learning method for the study of such video data. Compared to most of the current learning approaches conducted in a supervised/semi-supervised manner for manufacturing process data, we propose an unsupervised learning method for studying the flow pattern of the droplet, which does not require well-defined ground-truth labels. Regarding the spatial and temporal transformation of the droplet in video data, we apply a deep recurrent neural network (DRNN) to implement the proposed unsupervised learning. To verify the hypothesis that the proposed method can learn a latent representation for reproducing original data, the proposed DRNN is trained and tested on both simulation and experimental datasets. Experimental results demonstrate that the proposed method can learn latent representations of the droplet jetting process video data, which is very useful for the prediction of the droplet behavior. Furthermore, through latent space decoding, the learned representations can infer the droplet forming stimulus parameters such as material properties, which would be very helpful for further understanding of the process dynamics and achieving real-time in-situ droplet deposition quality monitoring and control.

45 citations

Journal ArticleDOI
TL;DR: In this paper, the authors explored the aspects of entropy generation for magnetohydrodynamic (MHD) mixed convective flow of cross-nanoliquid, where the similarity transformation helps to simplify the complex model in the form of nonlinear PDEs into nonlinear ODEs.
Abstract: Here modeling and computations are performed to explore the aspects of entropy generation for magnetohydrodynamic (MHD) mixed convective flow of Cross nanoliquid. Heat transfer process comprises thermal radiation and Joule heating. Moreover, phenomenal aspect of current review is to consider the characteristics of activation energy. The idea of combined convective conditions and zero mass flux relation is introduced first time. The similarity transformation helps to simplify the complex model in the form of nonlinear PDEs into nonlinear ODEs. Numerical algorithm leads to solution computations. The numerical solutions of temperature, nanoparticle concentration fields, Nusselt number and coefficient of skin friction are exhibited via plots. It is noticed that radiation factor increases the thermal field and related layer thickness. Moreover, the obtained data reveal that profiles of Bejan number intensify for augmented values of radiation parameter. Intensifies

44 citations

Journal ArticleDOI
TL;DR: The QQ quality response variables are modeled by offline process setting variables and in situ process variables via functional QQ models, which provides the basis for real-time monitoring and control for AM processes.
Abstract: Additive manufacturing (AM) enables flexible part geometry and functionality, and reduces product development life cycle by direct layer-wise fabrication from CAD files. In the last decade, great achievements are made on AM materials, machines, processes, etc. However, the quality of the AM parts is still questionable for industrial specifications. On the one hand, AM part quality variables can be either quantitative, such as dimensional accuracy, or qualitative, such as binary indicators for voids, missing features, or surface roughness. On the other hand, both offline process setting variables and functional in situ process variables can be measured and modeled with both quantitative and qualitative (QQ) quality response variables. In this paper, the QQ quality response variables are modeled by offline process setting variables and in situ process variables via functional QQ models. The modeling of these in situ process variables provides the basis for real-time monitoring and control for AM processes. Simulation studies and experimental data from a fused deposition modeling process are performed to demonstrate the effectiveness of the proposed method. Note to Practitioners —Additive manufacturing (AM) processes have attracted much attention and showed many advantages over the traditional subtractive manufacturing processes. However, the product quality issues make AM intractable for high-quality parts in industrial applications. This paper aims to address the quality issues by modeling both quantitative quality variables, such as dimensional accuracy, and qualitative quality variables, such as the binary (go/no-go) indicator for surface conditions. Both offline process setting variables and in situ process variables are used in the model as predictors. Such a model is important for systematically quality evaluation of AM parts, and provides the basis for future process monitoring and control. The merits of the proposed method are demonstrated with simulation studies and a case study in a fused deposition modeling process.

39 citations


Cited by
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Journal ArticleDOI
TL;DR: Chapman and Miller as mentioned in this paper, Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40, 1990) and Section 5.8.
Abstract: 8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

1,154 citations

Journal ArticleDOI
TL;DR: A comprehensive literature review is presented to provide an overview of how machine learning techniques can be applied to realize manufacturing mechanisms with intelligent actions and points to several significant research questions that are unanswered in the recent literature having the same target.
Abstract: Manufacturing organizations need to use different kinds of techniques and tools in order to fulfill their foundation goals. In this aspect, using machine learning (ML) and data mining (DM) techniques and tools could be very helpful for dealing with challenges in manufacturing. Therefore, in this paper, a comprehensive literature review is presented to provide an overview of how machine learning techniques can be applied to realize manufacturing mechanisms with intelligent actions. Furthermore, it points to several significant research questions that are unanswered in the recent literature having the same target. Our survey aims to provide researchers with a solid understanding of the main approaches and algorithms used to improve manufacturing processes over the past two decades. It presents the previous ML studies and recent advances in manufacturing by grouping them under four main subjects: scheduling, monitoring, quality, and failure. It comprehensively discusses existing solutions in manufacturing according to various aspects, including tasks (i.e., clustering, classification, regression), algorithms (i.e., support vector machine, neural network), learning types (i.e., ensemble learning, deep learning), and performance metrics (i.e., accuracy, mean absolute error). Furthermore, the main steps of knowledge discovery in databases (KDD) process to be followed in manufacturing applications are explained in detail. In addition, some statistics about the current state are also given from different perspectives. Besides, it explains the advantages of using machine learning techniques in manufacturing, expresses the ways to overcome certain challenges, and offers some possible further research directions.

237 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed that biofuel from crop residues can be promoted by government subsidies to reduce the fuel price and meet the requirement of industries, transportation and agricultural sectors.
Abstract: Energy demands, pollution and global warming induced by globalization are rising, thus calling for alternative sources of energies. In particular, biofuels are increasingly used for transportation, electric power and heat energy generation. Biofuels can mitigate greenhouse gas emissions by up to 50%. Biofuels are produced from organic matter and waste such as dry lignocellulose, algae, yeast, restaurant greases, food grain, non-food grain and animal fats. Biofuel from crop residues can be promoted by government subsidies to reduce the fuel price and meet the requirement of industries, transportation and agricultural sectors.

134 citations

01 Jan 2007
TL;DR: Under certain mutual incoherence conditions analogous to those imposed in previous work on linear regression, it is proved that consistent neighborhood selection can be obtained as long as the number of observations n grows more quickly than 6d6 log d + 2d5 log p, thereby establishing that logarithmic growth in thenumber of samples n relative to graph size p is sufficient to achieve neighborhood consistency.
Abstract: We focus on the problem of estimating the graph structure associated with a discrete Markov random field We describe a method based on l1-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an l1-constraint Our framework applies to the high-dimensional setting, in which both the number of nodes p and maximum neighborhood sizes d are allowed to grow as a function of the number of observations n Our main result is to establish sufficient conditions on the triple (n, p, d) for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously Under certain mutual incoherence conditions analogous to those imposed in previous work on linear regression, we prove that consistent neighborhood selection can be obtained as long as the number of observations n grows more quickly than 6d6 log d + 2d5 log p, thereby establishing that logarithmic growth in the number of samples n relative to graph size p is sufficient to achieve neighborhood consistency

121 citations

Journal ArticleDOI
Shiqiang Zou1, Zhen He1
TL;DR: This is an in-depth analysis of energy performance of various BES and expected to encourage more thinking, analysis, and presentation of energy data towards appropriate research and development of BES technology for resource recovery from wastewater.

112 citations