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Asian Control Conference

作者： Chunli Jiang Kuangrong Hao Lei Chen Xin Cai Lihong Ren Engineering Research Center of Digitized Textile & Fashion Technology Ministry of Education College of Information Sciences and Technology Donghua University Shanghai P. R. China

ISBN: (纸本)9781728102634

In this paper, an optimal control system of the whole process of polyester fiber is proposed. Based on the hierarchical Bayesian CTAP model, the system divides fiber production planning into on-demand manufacturing and product development. On-demand manufacturing includes three steps. Firstly, the system collects data from raw material database, equipment database and process database in database cluster. Secondly, immune endocrine neural network (IENN) is used to get the system optimal product process parameters. Finally, a real-time database is used to compensate for the forward error of the production process. In the process of product development, the database of raw materials, equipment and process are regenerated based on the parameter sets of polymerization sub-model, melt transport sub-model and spinning sub-model. Then, IENN is used to get the optimal product process parameters, and a real-time database is used to compensate for the reverse error of the production process. This system can quickly respond to fiber manufacturing needs. On the premise of diversification of enterprise demand and differentiation of competition, it can save energy and reduce consumption, ensure production capacity in a short time, and produce different kinds of polyester fibers with lower cost and higher quality.

关键词： Production Optical fiber devices Spinning Databases Optical fiber networks Polymers Data models

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Recognition of motor imagery EEG based on immune stacked denoising auto-encoders network

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Kongzhi yu Juece/Control and Decision 2018年第4期33卷 719-723页

作者： Hao, Kuang-Rong Yuan, Bo-Wei Chen, Lei Ding, Yong-Sheng Engineering Research Center of Digitized Textile & Apparel Technology of Ministry of Education Shanghai201620 China College of Information Science and Technology Donghua University Shanghai201620 China

In order to solve the problem about the low classification accuracy of the motor imagery electroencephalography(EEG), this paper proposes an improved stacked denoising auto-encoders(ISDAE) network combined with the immune optimization algorithm and decision-making mechanism. The ISDAE network extractes the most effective feature vectors by multi-layer DAE, and then identifies these feature vectors by the neural network. Meanwhile, the decision-making mechanism is obtained, and the immune optimization algorithm is used to optimize parameters of ISDAE network, thus the improved ISDAE model with higher classification accuracy is obtained. The experimental results show that, the proposed ISDAE model has a strong ability for feature learning from raw EEG data and a higher recognition rate, which provides an efficient technique for the recognition of the motor imagery EEG. © 2018, Editorial Office of Control and Decision. All right reserved.

关键词： Electroencephalography

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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： HOU Shaolin WU Yizhi ZHU Mingda College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile ＆Fashion Technology Donghua UniversityShanghai 201620China

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 *** 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 *** 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 *** measured data are analyzed using the BP neural *** 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)

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An overlapping-free leaf segmentation method for plant point clouds

arXiv

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arXiv 2019年

作者： Li, Dawei Cao, Yan Shi, Guoliang Cai, Xin Chen, Yang Wang, Sifan Yan, Siyuan College of Information Sciences and Technology Donghua University Shanghai201620 China Engineering Research Center of Digitized Textile & Fashion Technology Ministry of Education Donghua University Shanghai201620 China

Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation method for plant point clouds using the 3D filtering and facet region growing. In order to separate leaves with different overlapping situations, we develop a new 3D joint filtering operator, which integrates a Radius-based Outlier Filter (RBOF) and a Surface Boundary Filter (SBF) to help to separate occluded leaves. By introducing the facet over-segmentation and facet-based region growing, the noise in segmentation is suppressed and labeled leaf centers can expand to their whole leaves, respectively. Our method can work on point clouds generated from three types of 3D imaging platforms, and also suitable for different kinds of plant species. In experiments, it obtains a point-level cover rate of 97% for Epipremnum aureum, 99% for Monstera deliciosa, 99% for Calathea makoyana, and 87% for Hedera nepalensis sample plants. At the leaf level, our method reaches an average Recall at 100.00%, a Precision at 99.33%, and an average F-measure at 99.66%, respectively. The proposed method can also facilitate the automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering. Copyright © 2019, The Authors. All rights reserved.

关键词： Plants (botany)

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A real-time detection USV algorithm based on bounding box regression

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Journal of Physics: Conference Series 2020年第1期1544卷

作者： Yihong Zhang Sen Wu ZiHao Liu Yijin Yang Di Zhu Qian Chen College of Information Science and Technology Engineering Research Center of Digitized Textile & Fashion Technology Ministry of Education DongHua University Shanghai China

With the rapid development of unmanned surface vessels (USV) applications, USV are widely used in military intelligence collection, target monitoring, and shipping services. However, this also brings security issues to shipping and the country. For more efficient detection of USV, in this paper, an enclosing center distance Intersection over Union (E-CIoU) algorithm is proposed for real-time USV detection, where the normalized distance between center points of the smallest enclosing box and target box is combined with bounding box regression. Meanwhile, we designed a new USV data set. The extensive experiments on USV data set have demonstrated that the proposed approach has achieved better convergence speed during training and a significant accuracy prediction.

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An integrated image filter for enhancing change detection results

arXiv

引用

arXiv 2019年

作者： Li, Dawei Yan, Siyuan Cai, Xin Cao, Yan Wang, Sifan College of Information Sciences and Technology Donghua University Shanghai201620 China Engineering Research Center of Digitized Textile & Fashion Technology Ministry of Education Donghua University Shanghai201620 China

Change detection is a fundamental task in computer vision. Despite significant advances have been made, most of the change detection methods fail to work well in challenging scenes due to ubiquitous noise and interferences. Nowadays, post-processing methods (e.g. MRF, and CRF) aiming to enhance the binary change detection results still fall short of the requirements on universality for distinctive scenes, applicability for different types of detection methods, accuracy, and real-time performance. Inspired by the nature of image filtering, which separates noise from pixel observations and recovers the real structure of patches, we consider utilizing image filters to enhance the detection masks. In this paper, we present an integrated filter which comprises a weighted local guided image filter and a weighted spatiotemporal tree filter. The spatiotemporal tree filter leverages the global spatiotemporal information of adjacent video frames and meanwhile the guided filter carries out local window filtering of pixels, for enhancing the coarse change detection masks. The main contributions are three: (i) the proposed filter can make full use of the information of the same object in consecutive frames to improve its current detection mask by computations on a spatiotemporal minimum spanning tree;(ii) the integrated filter possesses both advantages of local filtering and global filtering;it not only has good edge-preserving property but also can handle heavily textured and colorful foreground regions;and (iii) Unlike some popular enhancement methods (MRF, and CRF) that require either a priori background probabilities or a posteriori foreground probabilities for every pixel to improve the coarse detection masks, our method is a versatile enhancement filter that can be applied after many different types of change detection methods, and is particularly suitable for video sequences. Experiments demonstrate that our method are suitable to be applied after a wide range of change d

关键词： Pixels

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A Piecewise Internal Model Control of an Automatic Weighting Scale System for Mixing Process of Plastic Granule-Materials

A Piecewise Internal Model Control of an Automatic Weighting...

引用

IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Ruili Dong Lei Wu Yonghong Tan Engineering Research Center of Digitized Textile and Fashion Technology Ministry of Education College of Information Science and Technology Donghua University Shanghai China College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

A Piecewise Internal Model Control (PIMC) Strategy is applied to an automatic weighting scale system for mixing process of plastic granule-materials. In this system, a parallel of twin screws are installed to extrude the plastic granule in exact proportion to a mixing tank. As the backlash-like-hysteresis exists in the gearbox-parallel screws and the weight of granule material in the system is changed with time, and it increases the difficulties of control. Thus, in this paper, a piecewise internal model controller is designed for handle to the effect of backlash-like-hysteresis to obtain accurate weighting. Both simulation study and experiment on a weighing scale system for mixing process of plastic granule-materials are implemented for evaluating the PIMC method.

关键词： Conferences Handheld computers Real-time systems Robots Europe Mechatronics Dynamical systems

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A Nonsmooth Multi-Step Predictive Control Strategy for A Sandwich System With Backlash

A Nonsmooth Multi-Step Predictive Control Strategy for A San...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Huiyu Wang Ruili Dong Yonghong Tan Xiaoli Li Ministry of Education Engineering Research Center of Digitized Textile and Fashion Technology College of Information Science and Technology Donghua University Shanghai China College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

A multi-step predictive control strategy for a sandwich system with backlash is proposed. Backlash is a nonsmooth multi-value mapping, so a classical predictive control method cannot be used in the system directly. In the proposed control approach, a multi-step predictive controller based on nonsmooth optimization technique is designed. Moreover, simulated results are illustrated the validation of the proposed method.

关键词： Handheld computers Conferences Real-time systems Robots Silicon

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Identification of ARX Model with Multi-Gaussian Noises

Identification of ARX Model with Multi-Gaussian Noises

引用

International Conference on Control, Automation and Systems ( ICCAS)

作者： Wentao Bai Fan Guo Lei Chen Kuangrong Hao Biao Huang College of Information Sciences and Technology Donghua University Shanghai China University of Alberta Edmonton AB Canada Engineering Research Center of Digitized Textile & Fashion Technology Ministry of Education Shanghai China

In most industrial environments, the real process data points are usually subject to contamination from a variety of noises. Most of the traditional methods assume that the process noise is Gaussian white noise, which can lead to poor robustness of the model to abnormal data. In this paper, we consider an ARX model with multi-Gaussian white noises, assuming that the collected data is affected by different noise models and switching of noise model follows Markov chain probability. The parameters of the model are estimated by Expectation-Maximization (EM) algorithm. A numerical example and a continuous stirred tank reactor are employed to verify the effectiveness of the proposed algorithm.

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An improved long short-term memory neural network for stock forecast 2

An improved long short-term memory neural network for stock ...

引用

2nd International Conference on Electronic Information technology and Computer engineering, EITCE 2018

作者： Lv, Liujia Kong, Weijian Qi, Jie Zhang, Jue College of Information Science and Technology Donghua University Shanghai201620 China Engineering Research Center of Digitized Textile and Fashion Technology Ministry of Education Donghua University Shanghai201620 China

This paper presents an improved long short-term memory (LSTM) neural network based on particle swarm optimization (PSO), which is applied to predict the closing price of the stock. PSO is introduced to optimize the weights of the LSTM neural network, which reduces the prediction error. After preprocessing the historical data of the stock, including opening price, closing price, highest price, lowest price, and daily volume these five attributes, we train the LSTM by employing time series of the historical data. Finally, we apply the proposed LSTM to predict the closing price of the stock in the last two years. Compared with typical algorithms by simulation, we find the LSTM has better performance in reliability and adaptability, and the improved PSO-LSTM algorithm has better accuracy. © The Authors, published by EDP Sciences, 2018.

关键词： Long short-term memory

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