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538 results on '"Chen, Dongyan"'

4. Determination of the Residues of Three Sulfonamides in Fish by Graphene Foam-Based Electro-Enhanced Solid-Phase Microextraction Coupled with High Performance Liquid Chromatography

Author

SUN Ruixue, FANG Yuwen, CHEN Dongyan, CHEN Quansheng, CHEN Xiaomei

Subjects
graphene foam, electro-enhanced solid-phase microextraction, high performance liquid chromatography, sulfonamide residues, fish, Food processing and manufacture, TP368-456
Abstract

A rapid and sensitive method for the determination of sulfonamide (SA) residues in fish was developed using electro-enhanced solid-phase microextraction (EE-SPME) based on nitrogen-doped three-dimensional graphene foam (NGF) functionalized monolithic column (monolith@NGF, M@NGF) combined with high performance liquid chromatography (HPLC). To improve the enrichment efficiency of SA residues in fish, porous M@NGF was prepared on the surface of stainless-steel wires by in situ polymerization using ionic liquids as the functional monomer and NGF as the conductivity enhancer. The EE-SPME method was applied for the extraction of sulfathiazole (ST), sulfamethazine (SM2), and sulfadimethoxypyrimidine (SMM) from fish. Adsorption time, agitation speed, adsorption voltage, ionic strength, the pH of sample solution, desorption time, desorption voltage, and the composition of desorption solution were optimized. The results showed that the M@NGF-based EE-SPME method enriched ST, SM2 and SMM by 74, 58 and 64 folds, respectively, and shortened the adsorption equilibrium time to 35 min. In the linear range of 5–5 000 μg/kg, the limits of detection (LODs) of this method were 1.78, 3.16 and 1.84 μg/kg for ST, SM2 and SMM, respectively, and the limits of quantification (LOQs) were 5 μg/kg for all analytes. Coefficients of determination (R2) for linear regression analysis of this method were all greater than 0.999 0, and the recoveries from spiked samples ranged from 79.2% to 110.1%, with relative standard deviations (RSDs) ranging from 1.4% to 9.8% (n = 5). This method solved the problems of low extraction efficiency and serious matrix interference of SA residues in fish, and allowed the rapid, sensitive detection of SA residues in fish.

Published
2024
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13. Attribute-guided Feature Learning Network for Vehicle Re-identification

Author

Wang, Huibing, Peng, Jinjia, Chen, Dongyan, Jiang, Guangqi, Zhao, Tongtong, and Fu, Xianping

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Vehicle re-identification (reID) plays an important role in the automatic analysis of the increasing urban surveillance videos, which has become a hot topic in recent years. However, it poses the critical but challenging problem that is caused by various viewpoints of vehicles, diversified illuminations and complicated environments. Till now, most existing vehicle reID approaches focus on learning metrics or ensemble to derive better representation, which are only take identity labels of vehicle into consideration. However, the attributes of vehicle that contain detailed descriptions are beneficial for training reID model. Hence, this paper proposes a novel Attribute-Guided Network (AGNet), which could learn global representation with the abundant attribute features in an end-to-end manner. Specially, an attribute-guided module is proposed in AGNet to generate the attribute mask which could inversely guide to select discriminative features for category classification. Besides that, in our proposed AGNet, an attribute-based label smoothing (ALS) loss is presented to better train the reID model, which can strength the distinct ability of vehicle reID model to regularize AGNet model according to the attributes. Comprehensive experimental results clearly demonstrate that our method achieves excellent performance on both VehicleID dataset and VeRi-776 dataset., Comment: arXiv admin note: text overlap with arXiv:1912.10193

Published
2020

14. Eliminating cross-camera bias for vehicle re-identification

Author

Peng, Jinjia, Jiang, Guangqi, Chen, Dongyan, Zhao, Tongtong, Wang, Huibing, and Fu, Xianping

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Vehicle re-identification (reID) often requires recognize a target vehicle in large datasets captured from multi-cameras. It plays an important role in the automatic analysis of the increasing urban surveillance videos, which has become a hot topic in recent years. However, the appearance of vehicle images is easily affected by the environment that various illuminations, different backgrounds and viewpoints, which leads to the large bias between different cameras. To address this problem, this paper proposes a cross-camera adaptation framework (CCA), which smooths the bias by exploiting the common space between cameras for all samples. CCA first transfers images from multi-cameras into one camera to reduce the impact of the illumination and resolution, which generates the samples with the similar distribution. Then, to eliminate the influence of background and focus on the valuable parts, we propose an attention alignment network (AANet) to learn powerful features for vehicle reID. Specially, in AANet, the spatial transfer network with attention module is introduced to locate a series of the most discriminative regions with high-attention weights and suppress the background. Moreover, comprehensive experimental results have demonstrated that our proposed CCA can achieve excellent performances on benchmark datasets VehicleID and VeRi-776.

Published
2019

15. Graph-based Multi-view Binary Learning for Image Clustering

Author

Jiang, Guangqi, Wang, Huibing, Peng, Jinjia, Chen, Dongyan, and Fu, Xianping

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Hashing techniques, also known as binary code learning, have recently gained increasing attention in large-scale data analysis and storage. Generally, most existing hash clustering methods are single-view ones, which lack complete structure or complementary information from multiple views. For cluster tasks, abundant prior researches mainly focus on learning discrete hash code while few works take original data structure into consideration. To address these problems, we propose a novel binary code algorithm for clustering, which adopts graph embedding to preserve the original data structure, called (Graph-based Multi-view Binary Learning) GMBL in this paper. GMBL mainly focuses on encoding the information of multiple views into a compact binary code, which explores complementary information from multiple views. In particular, in order to maintain the graph-based structure of the original data, we adopt a Laplacian matrix to preserve the local linear relationship of the data and map it to the Hamming space. Considering different views have distinctive contributions to the final clustering results, GMBL adopts a strategy of automatically assign weights for each view to better guide the clustering. Finally, An alternating iterative optimization method is adopted to optimize discrete binary codes directly instead of relaxing the binary constraint in two steps. Experiments on five public datasets demonstrate the superiority of our proposed method compared with previous approaches in terms of clustering performance.

Published
2019

24. Pricing Strategy of Dual-Channel Supply Chain for Alcoholic Products with Platform Subsidy

Author

Chen, Dongyan, Zhang, Yi, Zhao, Shouting, Barbosa-Povoa, Ana Paula, Editorial Board Member, de Almeida, Adiel Teixeira, Editorial Board Member, Gans, Noah, Editorial Board Member, Gupta, Jatinder N. D., Editorial Board Member, Heim, Gregory R., Editorial Board Member, Hua, Guowei, Editorial Board Member, Kimms, Alf, Editorial Board Member, Li, Xiang, Editorial Board Member, Masri, Hatem, Editorial Board Member, Nickel, Stefan, Editorial Board Member, Qiu, Robin, Editorial Board Member, Shankar, Ravi, Editorial Board Member, Slowiński, Roman, Editorial Board Member, Tang, Christopher S., Editorial Board Member, Wu, Yuzhe, Editorial Board Member, Zhu, Joe, Editorial Board Member, Zopounidis, Constantin, Editorial Board Member, Shi, Xianliang, editor, Bohács, Gábor, editor, Ma, Yixuan, editor, Gong, Daqing, editor, and Shang, Xiaopu, editor

Published
2022
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34. Early stages of adult L2 acquisition of Mandarin : an organic grammar approach

Author

Chen, Dongyan

Abstract

There has been debate over whether the theory of Organic Grammar (OG) (Vainikka and Young-Scholten 1994, 1996a and 2011) can predict L2 development in languages other than European ones. Specifically, OG argues that second-language learners move from an initial bare VP stage to intermediate IP-level stages to a final CP stage, regardless of their L1 or L2 status. Moreover, the order of acquisition of the relevant elements is the result of the interaction between Universal Grammar and the target language input. Counter-arguments have been found in Schwartz and Sprouse (1994, 1996), Epstein, Flynn and Martohardjono (1996), Haznedar (1997, 2003), Lardiere (1998, 2008) etc. with discussion focused on L1 functional transfer and the stage-like development of functional projections. This thesis investigates the extent to which the theory of OG can explain aspects of Mandarin Chinese acquisition by native English-speakers. Extending existing proposals, I hypothesise a clausal structure model for Mandarin, which includes the functional projections AspPl, BaP, BeiP, AspPm, NegP, AspPh, headed respectively by lel/guo/zhe, ba, bei, zai, bu/mei and leh respectively. Based on OG, L2 Mandarin learners are predicted to start with a bare VP and demonstrate word order consistent with their first language, and to develop over time in the following functional sequence: AspPl > BaP [lower IP stage] > BeiP > AspPm> NegpPh > AspPh [upper IP stage] based on access to UG and the input. Oral production and grammaticality judgement data were collected through administering repeated multi-task tests to eight instructed English-speaking learners of Mandarin during their first and their second years of university study between October 2014 and June 2015. Emergence and accuracy criteria were used to analyse the production data, while the grammaticality judgement data were analysed through the repeated measures of the SPSS General Linear Model. The thesis presents three main findings. Firstly, when the word order in the verb phrase is different in English and Mandarin, the learners in this study use the order similar to their L1 English, thereby indicating the learners' transfer of their L1 word order. Secondly, there was largely a distinctive stage-like development based on hypothesised hierarchical functional projection structure; nonetheless, variability was also found in the development of individual learners. Thirdly, the acquisition results could be attributed to a great extent to the interaction between Universal Grammar and target language input; although the role of cognitive factors like memorisation should not be ignored. Overall, the results revealed the predicted stages; thereby supporting OG's explanatory value by extending it to the analysis of L2 Mandarin.

Published
2018

35. Set-membership state estimation for time-varying complex networks: two zonotopic design methods.

Author

Yao, Mengyuan, Chen, Dongyan, Hu, Jun, and Yang, Ning

Subjects
*TIME-varying networks, *TIME perception, *FUZZY sets, *MEMBERSHIP functions (Fuzzy logic)
Abstract

This article studies the zonotopic set-membership state estimation problem for linear time-varying complex networks with unknown-but-bounded (UBB) noises, where the UBB noises are contained by a set of zonotopes. The objective of the addressed problem is to give two design methods, namely the correction matrix method and the state observer method, where a time-varying zonotopic sequence containing all possible states of the system is obtained. The expressions of the correction matrix and the observer gain are given under the F-radius criterion, and the desired minimum zonotopes are obtained. In addition, a state observer based on the measured output at the current moment is designed to analyze the equivalence between the above two methods. Finally, in order to demonstrate the effectiveness of the proposed state estimation algorithms, two simulation examples are presented. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Disturbance observer‐based PID synchronization control of complex networks with probabilistic interval time‐varying delays and multiple disturbances.

Author

Shi, Yujing, Lv, Hongyang, and Chen, Dongyan

Subjects
SYNCHRONIZATION, DISTRIBUTION (Probability theory)
Abstract

In this article, the disturbance observer‐based proportional–integral–derivative (PID) control protocol is investigated for synchronization of complex networks with probabilistic interval time‐varying delays and multiple disturbances. For the sake of reducing conservatism, a method of probabilistic interval time‐varying delay is proposed by introducing two certain intervals with known probability distributions. Moreover, two different disturbances are taken into account. One of the disturbances is produced by an exogenous system which acts through the input channel, while the other is usual norm‐bounded disturbance. Simultaneously, a disturbance observer is designed to estimate and compensate the disturbance generated by the exogenous system. By constructing an appropriate Lyapunov–Krasovskii function, a sufficient criterion is obtained to ensure both the exponential synchronization and prescribed H∞$$ {H}_{\infty } $$ performance index. Finally, the simulation examples are employed to demonstrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Noise-to-state and delay-dependent stability of highly nonlinear hybrid stochastic differential equation with multiple delays.

Author

Yang, Ning, Hu, Jun, and Chen, Dongyan

Subjects
STOCHASTIC differential equations, STOCHASTIC analysis, FUNCTIONAL analysis, DELAY differential equations, WHITE noise
Abstract

This study is intended to discuss the noise-to-state stability (NSS) and the delay-dependent stability (DDS) of the highly nonlinear hybrid stochastic differential equation (HNHSDE) with multiple delays. Distinct from the white noise considered in the existing literature, we introduce the coloured noise characterised by a finite second-order process into the HNHSDE. Under some mild assumption conditions, we demonstrate that the HNHSDE exists a unique global solution (GS). Moreover, some criteria regarding the NSS in the qth moment of the GS are established. Furthermore, several theorems are provided for guaranteeing the pth moment asymptotic stability and the almost surely asymptotic stability of the GS with the aid of the delay-dependent Lyapunov functional and stochastic analysis theory. At last, two illustrative examples are given to show the feasibility of our developed theory findings. [ABSTRACT FROM AUTHOR]

Published
2024
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