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Recursive Fault Estimation With Energy Harvesting Sensors and Uniform Quantization Effects

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 926-929页

作者： Yu-Ang Wang Bo Shen Lei Zou College of Information Science and technology Donghua UniversityShanghai 201620 engineering research center of digitalized textile and fashion technology Ministry of EducationShanghai 201620China IEEE

Dear Editor,In this letter,the recursive fault estimation issue is considered for nonlinear time-varying systems subject to the effects induced by energy harvesting sensors and uniform quantization.Based on the energy harvesting mechanism and stochastic distribution of the absorbed energy,the real-time occurrence probability of missing measurements is calculated recursively.This research intends to develop a recursive estimator for the considered nonlinear time-varying system with energy harvesting sensors,such that,under uniform quantization effects,the state and fault can be jointly estimated.By adopting the induction approach,an upper bound is firstly calculated for the estimation error covariances(EECs)of the state and fault.Then,the value of the time-varying estimator parameter is computed through minimizing such calculated upper bound.In the end,an illustrative example is presented to verify the availability of the developed fault estimation method.

关键词： method estimation estimator

Online Clothing Recommendation and Style Compatibility Learning Based on Joint Semantic Feature Fusion

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Journal of Donghua University(English Edition) 2022年第4期39卷 325-331页

作者：费煜哲尚科科赵鸣博张悦 College of Information Science and technology Donghua UniversityShanghai 201620China engineering research center of digitalized textile&fashion technology Donghua UniversityShanghai 201620China

Clothing plays an important role in humans’social life as it can enhance people’s personal quality,and it is a practical problem by answering the question“which item should be chosen to match current fashion items in a set to form collocational and compatible outfits”.Motivated by this target an end-to-end clothing collocation learning framework is developed for handling the above task.In detail,the proposed framework firstly conducts feature extraction by fusing the features of deep layer from Inception-V3 and classification branch of mask regional convolutional neural network(Mask-RCNN),respectively,so that the low-level texture information and high-level semantic information can be both preserved.Then,the proposed framework treats the collocation outfits as a set of sequences and adopts bidirectional long short-term memory(Bi-LSTM)for the prediction.Extensive simulations are conducted based on Deepfashion2 datasets.Simulation results verify the effectiveness of the proposed method compared with other state-of-the-art clothing collocation methods.

关键词： clothing recommendation style compatibility learning deep learning fashion analysis feature extraction

Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 638-648页

作者： Xiaoting Du Lei Zou Maiying Zhong the College of Electrical engineering and Automation Shandong University of Science and TechnologyQingdao 266590China IEEE the College of Information Science and technology Donghua UniversityShanghai 201620 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

关键词： Dynamic event-triggered mechanism(DETM) fault estimation nonlinear time-varying complex networks set-member-ship filtering unknown input observer

Recursive Filtering for Stochastic Systems With Filter-and-Forward Successive Relays

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1202-1212页

作者： Hailong Tan Bo Shen Qi Li Hongjian Liu School of Mathematics-Physics and Finance Anhui Polytechnic UniversityWuhu 241000China Anhui Future technology research Institute Anhui Polytechnic UniversityWuhu 241000China College of Information Science and technology Donghua UniversityShanghai 201620China engineering research center of digitalized textile and fashion technology Ministry of EducationShanghai 201620China School of Information Science and engineering Hangzhou Normal UniversityHangzhou 311121China

In this paper,the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay(FFSR)networks.An FFSR is located between the sensor and the remote filter to forward the measurement.In the successive relay,two cooperative relay nodes are adopted to forward the signals alternatively,thereby existing switching characteristics and inter-relay interferences(IRI).Since the filter-and-forward scheme is employed,the signal received by the relay is retransmitted after it passes through a linear filter.The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays.First,a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR.Then,novel filter structures with switching parameters are constructed for both FFSR and stochastic systems.With the help of the inductive method,filtering error covariances are presented in the form of coupled difference equations.Next,the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances.Moreover,the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance.Finally,the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.

关键词： filtering successive stochastic

Adaptive Neural Network Control for Euler-Lagrangian Systems with Uncertainties

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Journal of Donghua University(English Edition) 2022年第5期39卷 485-489页

作者：程新卢文科刘华山 College of Information Science and technology Donghua UniversityShanghai 201620China engineering research center of Digitized textile and fashion technology Ministry of EducationShanghai 201620China

An adaptive controller involving a neural network(NN)compensator is proposed to resist the uncertainties in the Euler-Lagrangian system(ELS).Firstly,a proportional-differential(PD)control law is designed for the nominal model.Meanwhile,the uncertainties including model error and external disturbance are separated from the closed-loop system.Then,an adaptive NN compensator based on the online training mode is proposed to eliminate the adverse effect of the uncertainties.In addition,the stability of the closed-loop system is proved by Lyapunov theory.Finally,the effectiveness of the proposed approach is verified on a two-degree-of-freedom robot manipulator.

关键词： adaptive control neural network(NN) uncertainty compensator Euler-Lagrangian system(ELS)

BP Neural Network Based on Microwave Method for Measuring the Moisture Content of textile

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Journal of Donghua University(English Edition) 2018年第3期35卷 215-219页

作者：侯绍林吴怡之朱明达 College of Information Science and technology Donghua University engineering research center of Digitized textile &fashion technology Donghua University

The moisture content of yarn and fabric is an important factor in textiles industry.A novel microwave method used for material moisture content measurements is described in this paper.It can estimate the moisture content of the yarn roll with a standard deviation of 1.58% in the range of 0% to 90.00%.According to the actual size of the yarn,the yarn roll simulation model is established.The microwave attenuation variations arising from the changes in the conductivity and dielectric constant of the wet cone yarn from1.8 GHz to 5.0 GHz frequency are obtained by ultra-wideband antenna.The measured data are analyzed using the BP neural network.The result shows that it is a non-contact and online method to solve the moisture content of the yarn in the wide moisture content range.

关键词： microwave moisture content BP neural network yarn computer simulation technology microwave studio(CST)

Application Layer Multicast Model with Low Delay and High Stability

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Journal of Donghua University(English Edition) 2023年第1期40卷 74-79页

作者：廖小飞宣益亮宿轩策李德敏 College of Information Sciences and technology Donghua UniversityShanghai 201620China engineering research center of Digitized textile&fashion technology Ministry of EducationDonghua UniversityShanghai 201620China

As an alternative of Internet protocol(IP)multicast,application layer multicast(ALM)is widely used with the advantage of simplicity and flexibility.However,the existing problems of large transmission delay and poor stability limit the application and development.In this article,to solve these problems,an ALM model based on node potential(NP)and topological index(TI)is proposed.The proposed model considers the factors of node capability and node distance in constructing and maintaining multicast tree to reduce transmission delay and increase stability,and thus it improves the application level in real-time multimedia multicast.The computer simulations prove that the proposed model reduces the ALM transmission delay,increases multicast tree stability effectively,and improves the ALM performance,and therefore it is suitable to apply in large-scale real-time multimedia environment.

关键词： application layer multicast(ALM) high stability low delay node potential(NP) topological index(TI)

Smart Clothing Fabric Color Matching with Reference to Popular Colors

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Journal of Donghua University(English Edition) 2022年第4期39卷 317-324页

作者：张亚妮庄剑强黄荣董爱华袁浩东 College of Information Science and technology Donghua UniversityShanghai 201620China engineering research center of Digitized textile&fashion technology Ministry of EducationDonghua UniversityShanghai 201620China

Color economy and market fashion trend have an increasing impact on clothing fabric color matching.Therefore,a smart clothing fabric color matching system with reference to popular colors is designed to realize the diversification of clothing color matching,which includes a palette generation module and a clothing fabrics-palette color matching network(CF-PCN).Firstly,palette generation module generates palettes referring popular colors while maintains color styles of clothing fabrics.Secondly,CF-PCN generates color matching images containing color information of palettes.The experimental results show that the color matching system has a higher average pixel ratio of palette colors and contains more palette color information.It demonstrates that the system achieves color matching innovation referring popular colors while retaining color style of clothing brands and provides designers with appropriate color matching solutions.

关键词： popular color clothing fabric color matching support vector machine(SVM) discrete particle swarm optimization algorithm generative adversarial network

Improved Spectral Clustering Clothing Image Segmentation Algorithm Based on Sparrow Search Algorithm

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Journal of Donghua University(English Edition) 2022年第4期39卷 340-344页

作者：黄文谙钱素琴 College of Information Science and technology Donghua UniversityShanghai 201620China engineering research center of Digitized textile&fashion technology Ministry of EducationDonghua UniversityShanghai 201620China

In the process of clothing image researching,how to segment the clothing quickly and accurately and retain the clothing style details as much as possible is the basis of subsequent image analysis.Spectral clustering clothing image segmentation algorithm is a common method in the process of clothing image extraction.However,the traditional model requires high computing power and is easily affected by the initial center of clustering.It often falls into local optimization.Aiming at the above two points,an improved spectral clustering clothing image segmentation algorithm is proposed in this paper.The Nystrom approximation strategy is introduced into the spectral mapping process to reduce the computational complexity.In the clustering stage,this algorithm uses the global optimization advantage of the particle swarm optimization algorithm and selects the sparrow search algorithm to search the optimal initial clustering point,to effectively avoid the occurrence of local optimization.In the end,the effectiveness of this algorithm is verified on clothing images in each environment.

关键词： clothing segmentation spectral clustering particle swarm optimization algorithm intelligent fashion design