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Ordering policies and coordination in a twoechelon supply chain with Nash bargaining fairness concerns

Journal of Management Analytics 2017年第1期4卷 55-79页

作者： Caimin Wei Zhongping Li Zongbao Zou Department of Mathematics Shantou UniversityShantou 515063People’s Republic of China Guangdong Provincial Key Lab of Digital Signals and Image Processing Shantou UniversityShantou 515063People’s Republic of China Sun Yat-Sen Business School Sun Yat-sen UniversityGuangzhou 510275People’s Republic of China

This paper investigates the ordering policies of two competitive retailers,and the coordination status of a two-echelon supply chain by considering the fairness concerns of channel *** consider that two retailers compete with each other over price,where overstock and shortage are *** assume that the demand is stochastic and considered with additive ***,based on the Nash bargaining fairness reference point,we obtain the optimal decisions of the fairness-concerned channel members in both the centralized and the decentralized cases using a two-stage game ***,we analyze the coordination status of the supply chain with Nash bargaining fairness concerns using ideas of ***,numerical experiments are used to illustrate the influence of some parameters,the fairness-concerned behavioral preference of the channel members on the optimal decisions and the coordination status of supply *** managerial insights are obtained.

关键词： supply chain Nash bargaining fairness concern sale price coordination

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Denoising of 3D magnetic resonance images using a residual encoder-decoder wasserstein generative adversarial network

arXiv

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arXiv 2018年

作者： Ran, Maosong Hu, Jinrong Chen, Yang Chen, Hu Sun, Huaiqiang Zhou, Jiliu Zhang, Yi College of Computer Science Sichuan University Chengdu610065 China Department of Computer Science Chengdu University of Information Technology Chengdu610225 China Lab of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing210096 China School of Cyber Science and Engineering Southeast University Nanjing210096 China Ministry of Education Nanjing210096 China Department of Radiology West China Hospital of Sichuan University Chengdu610041 China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou510515 China

Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in medical image analysis. Over the past few years, many algorithms with impressive performances have been proposed. In this paper, inspired by the idea of deep learning, we introduce an MRI denoising method based on the residual encoder-decoder Wasserstein generative adversarial network (RED-WGAN). Specifically, to explore the structure similarity between neighboring slices, a 3D configuration is utilized as the basic processing unit. Residual autoencoders combined with deconvolution operations are introduced into the generator network. Furthermore, to alleviate the oversmoothing shortcoming of the traditional mean squared error (MSE) loss function, the perceptual similarity, which is implemented by calculating the distances in the feature space extracted by a pretrained VGG-19 network, is incorporated with the MSE and adversarial losses to form the new loss function. Extensive experiments are implemented to assess the performance of the proposed method. The experimental results show that the proposed RED-WGAN achieves performance superior to several state-of-the-art methods in both simulated and real clinical data. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Experimental Combined Grouping Analysis Approach for Robust Battery pack design for Electric Vehicles with Higher Performance

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IOP Conference Series: Earth and Environmental Science 2019年第1期268卷

作者： Liu Yun Li Shui Liang Gao Zhun Fan C. Ruhatiya Chin-Tsan Wang Akhil Garg Intelligent Manufacturing Key Laboratory of Ministry of Education Shantou University Guangdong China State Key Lab of Digital Manufacturing Equipment & Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou 515063 China School of Engineering Sciences Mahindra École Centrale Hyderabad Telangana 500043 India Department of Mechanical and Electro-Mechanical Engineering National I Lan University ILan Taiwan

Battery consistency is an important factor for battery pack performance. Excellent battery consistency can make battery packs more energy efficient and electric vehicles can have longer mileage and higher safety. Thus, in this study a comprehensive intelligent clustering methodology for the design of Li-ion battery pack on the basis of uniformity and equalization criteria of the cell was proposed. Firstly, multiple parameters (capacity, voltage, temperature and resistance) test of single cell performance was performed. Secondly, a clustering method combine with self-organizing map neural network (SOM) was proposed. Furthermore, a validation experiment (pack level) was carried out to verify the accuracy of proposed clustering algorithm. It can be concluded that the battery pack formed from SOM sorting results perform better than the battery pack having random cells combination as well as the pack originally purchased from the manufacturer.

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Vehicle Type Classification via Adaptive Feature Clustering for Traffic Surveillance Video

Vehicle Type Classification via Adaptive Feature Clustering ...

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International Conference on Wireless communications and Signal processing

作者： Shu Wang Feng Liu Zongliang Gan Ziguan Cui Jiangsu Province Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781509028610

Vehicle type classification has become an important part of intelligent traffic. However traditional methods can not deal with the varying situations in the reality. In this paper, a novel method is proposed to handle this task in the real road traffic surveillance video. In order to distinguish different vehicles, we categorize vehicles into three types: compact cars, mid-size cars, and heavy-duty vehicles. For a certain video, our method has four steps. First, a deep convolutional neural network is used to detect vehicles in the candidate region and a data set would be generated. Second, the main features of vehicles can be extracted using a fully-connected network. Also, for the sake of higher accuracy, weak labels given by pre-trained extreme learning machine (ELM) are fused into the final features, adding prior information proportionally. Third, K-means is implemented to learn three vehicle-type cluster centers adaptively. Finally, vehicle type will be recognized according to the closest distance principal. Experimental results show that the recognition rate outperforms other traditional methods, verifying the feasibility and effectiveness of the proposed method.

关键词： Vehicle type classification Adaptive clustering Feature learning Deep learning Extreme learning machine

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Edge directed single image super resolution through the learning based gradient regression estimation

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ICIC Express Letters, Part B: Applications 2016年第2期7卷 337-344页

作者： Wang, Guogang Gan, Zongliang Zhu, Xiuchang Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing University of Posts and Telecommunications No. 66 Xinmofan Road Nanjing China

Single image super resolution (SR) aims to estimate high resolution (HR) image from the low resolution (LR) one, and estimating accuracy of HR image gradient is very important for edge directed image SR methods. In this paper, we propose a novel edge directed image SR method by learning simple priors by mapping functions. Recognizing that the training samples of the given sub-set for regression should have similar local geometric structure based on clustering, we employ high frequency of LR image patches with removing the mean value to perform such clustering. Then we learn effective mapping functions of gradient of bicubic image patches and that of HR image patches for each cluster. Experimental results suggest that the proposed method can achieve better gradient estimation of HR image and competitive SR quality compared with other SR methods. © 2016 ICIC International.

关键词： Optical resolving power

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

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arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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Visibility Graph Analysis on EEG Signal

Visibility Graph Analysis on EEG Signal

引用

International Congress on image and Signal processing, BioMedical Engineering and Informatics

作者： Hongda Ji Tian Xu Wei Wu Jun Wang Image Processing and Image Communications Key Lab Nanjing University of Posts & Telecomm School of Public Health Southeast Univ.

ISBN: (纸本)9781509037117

We use the visibility graph analysis to research EEG signal and construct the complex network of EEG signal. In the paper, we construct the network by using EEG signal of the people with occupational stress and the normal people. Some properties of the complex network are found in this paper. The results of our experiment show that the properties of the normal people and the people with occupational stress are different.

关键词： EEG visibility graph occupational stress

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Attention State Assessment by Analyzing Electroencephalograph Signals

Attention State Assessment by Analyzing Electroencephalograp...

引用

International Congress on image and Signal processing, BioMedical Engineering and Informatics

作者： Junchang Zhao Xiaolin Huang Jun Wang Image Processing and Image Communications Key Lab Nanjing University of Posts & Telecomm Institute of Biomedical Electronic Engineering School of Electronic Science and Engineering Nanjing University

ISBN: (纸本)9781509037117

For brain function study, it is very important to assess the state of different attention conditions. In this paper, we study a cross-correlation between different attention electroencephalograph (EEG) signals by detrended cross-correlation analysis (DCCA). We use the method to study attention α wave EEG. And, we found that is possible to discriminate the cross-correlation between the meditation state of closing eyes and the absence of mind. It was found that the DCCA values of idle subjects' α wave EEG signals increased compared the meditating participants' α wave EEG signals. And it can be particularly helpful for treatment and brain development research.

关键词： meditation activity DCCA a wave

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Analysis of alpha rhythm epileptic Electroencephalogram based on Inner composition alignment

Analysis of alpha rhythm epileptic Electroencephalogram base...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Yiyi He Danqin Xing Jiaqin Wang Jia-Fei Dai Jun Wang Feng-Zhen Hou Image Processing and Image Communications Key Lab Nanjing Univ.of Posts & Telecomm. Nanjing General Hospital of Nanjing Military Command School of Science China Pharmaceutical University

ISBN: (纸本)9781510823808

The EEG signal is an important tool for the diagnosis and prediction of epilepsy due to EEG containing a large number of physiological and pathological *** on alpha rhythm multi-channel EEG(electroencephalogram),this paper applied inner composition alignment(IOTA) algorithm to construct brain functional network and visualize the network *** is to apply the algorithm to calculate and analyze IOTA coefficient,the node average degree and clustering coefficient of epileptic brain network for studying if epileptic brain network is significantly different from those of *** results show that IOTA coefficient of epileptic brain network obviously differs from the normal by calculating T testing with SPSS software,which proved that the effectiveness of the algorithm to distinguish IOTA coefficient of epileptic brain network.

关键词： alpha rhythm Electroencephalogram epileptic Inner composition alignment

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Beta wave of sleep electroencephalogram analysis based on sign series entropy

Beta wave of sleep electroencephalogram analysis based on si...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Min Zhao Yuting Li Lu Yang Jia-Fei Dai Jun Wang Feng-Zhen Hou Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm. Nanjing General Hospital of Nanjing Military Command School of Science China Pharmaceutical University

ISBN: (纸本)9781510823808

Sleep and wake EEG have some *** studying their brain waves and calculating the sign series entropy,we use the T test for the detection of sleep and wake EEG data to figure out whether they are *** beta waves being filtered out by the filter,we calculate the entropy of the sign *** results of T test show that the beta waves in the state of sleep and wake are different.

关键词： sleep EEG sign series entropy

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