Ye Zhang

South China Agricultural University

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Hexuan Hu

Ministry of Water Resources of the People's Republic of China

Feng Duan

Nankai University

Qiang Hu

Hohai University

Tamio Arai

The University of Tokyo

Jeffrey Too Chuan Tan

University of California, Berkeley

Zhen-Zhou Lin

Hohai University

Nuttapol Pongthanya

The University of Tokyo

Qiang Hu

Tianjin University

Kei Watanabe

Osaka University

Hiroshi Yokoi

University of Electro-Communications

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Institute of Electrical and Electronics Engineers

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Virginia Tech

OrthoVirginia

Sun Yat-sen University

Chinese Academy of Sciences

Hohai University

Shanghai Jiao Tong University

Tsinghua University

Chinese Academy of Medical Sciences & Peking Union Medical College

Southern Medical University

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DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans

arXiv (Cornell University) (2024)

Ye Zhang Yulu Gong Dongji Cui Xinrui Li Xinyu Shen

Gastrointestinal (GI) tract cancers pose a global health challenge, demanding precise radiotherapy planning for optimal treatment outcomes. This paper introduces a cutting-edge approach to automate the segmentation of GI tract regions in magnetic resonance imaging (MRI) scans. Leveraging advanced deep learning architectures, the proposed model integrates Inception-V4 for initial classification, UNet++ with a VGG19 encoder for 2.5D data, and Edge UNet for grayscale data segmentation. Meticulous data preprocessing, including innovative 2.5D processing, is employed to enhance adaptability, robustness, and accuracy. This work addresses the manual and time-consuming segmentation process in current radiotherapy planning, presenting a unified model that captures intricate anatomical details. The integration of diverse architectures, each specializing in unique aspects of the segmentation task, signifies a novel and comprehensive solution. This model emerges as an efficient and accurate tool for clinicians, marking a significant advancement in the field of GI tract image segmentation for radiotherapy planning.

10.48550/arxiv.2401.15354

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Citations (8)

Registration of remote sensing images based on Gaussian fitting

Xiaofei Wang Junping Zhang Ye Zhang

Image registration is one of the critical techniques in multi-sensor image fusion. Classical image registration method using similarity measures can only find Registration Control Point (RCP) on discrete points on images, which makes it difficult to get higher registration precision. This paper presents an image registration method based on Gaussian fitting matching, which takes two dimensional Gaussian fit matching on measurement matrix produced by classical search method of image RCPs. This method has the potential of getting more accurate position of RCPs. Experiment shows that using our method can obtain higher registration accuracy than that of classical method.

Image registration

Control point

Gaussian network model

Point set registration

Similarity (geometry)

Position (finance)

10.1109/iciea.2008.4582543

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The longitudinal response of prestressed cylinder concrete pipe with bell–spigot joints subjected to normal fault

Structural Concrete (2021)

Yanlong Li Weimei Li Lifeng Wen Yang Li Kangping Li

Abstract Buried pipelines inevitably pass through complicated geological areas because of their wide coverage. A three‐dimensional numerical model subjected to normal fault considering the geometrical and mechanical properties of the bell–spigot joint in Prestressed Concrete Cylinder Pipe (PCCP) is proposed to analyze the effects of location, displacement, and dip angle of fault on the rotation angle and axial translation of bell–spigot joints as well as the effects on the deformation of pipe bodies. At a dip angle of 90°, the maximum joint rotation angle and pipe deformation are larger when the relative location of fault and joints is under the odd configuration than under the even configuration. The kinematic characteristics of joints and the mechanical characteristics of pipe bodies on the moving side are opposite to those on the stationary side. The amount of offset has a significant impact on the longitudinal response of the pipeline. The rotation angle and axial translation of the joints on both sides of the fault increase with increasing fault displacement. When the offset reaches 270 mm, the joint rotation angle is 1.05°, which exceeds the specification limit of 1°. On the basis of numerical results, the simplified estimate of the maximum rotation angle is improved, and the error is reduced to 3%. With the increase of fault dip angle, the affected area gradually moves left to the fixed part. When the dip angle is 75°–90°, the pipeline longitudinal response is large, which is unfavorable to the pipeline safety.

Magnetic dip

10.1002/suco.202100553

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Analysis of Travel Demand between Transportation Hubs in Urban Agglomeration Based on Mobile Phone Call Detail Record Data

Journal of Transportation Engineering Part A Systems (2022)

Yanyan Chen Zifan Wang Haodong Sun Ye Zhang Zhengbing He

With the growth of a city’s economy, neighboring cities are gradually integrated to form an urban agglomeration. As travel activities driven by various travel demands frequently take place within an urban agglomeration, it is essential to understand the travel demand between cities and improve the intercity transportation system, which promotes the coordinated development of cities in an urban agglomeration. This paper presents our investigation of the travel demand characteristics of urban agglomeration cities. The Beijing-Tianjin-Hebei area and its passenger flows between transportation hubs in the different cities, which are part of a typical urban agglomeration in China and representative travel demand, are taken as the empirical study objects. First, we introduce a method to extract trip data using mobile phone call detail record (CDR) data, which carry rich geographical information on travelers and has been extensively used in recent transportation research. Based on trip data, directed weighted travel demand networks were constructed, with the nodes representing the transportation hubs and the edges representing passenger flows. The results showed that edge weights can be characterized by power-law distribution, which reveals the phenomenon that most passenger flows are concentrated among a few hubs, implying unbalanced travel demand in the urban agglomeration. Our empirical findings contribute to a method for applying large-scale location-based data to extract human mobility information and to understanding the nature of travel demand on the scale of an urban agglomeration. They also provide guidance to government agencies in developing appropriate transportation policies and enhancing infrastructure in the urban agglomeration.

Urban agglomeration

Mobile phone

Empirical Research

10.1061/jtepbs.0000693

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Citations (4)

Database-Driven Safe Flight-Envelope Protection for Impaired Aircraft

Journal of Aerospace Information Systems (2020)

Ye Zhang Yingzhi Huang Qiping Chu Coen C. de Visser

In this paper, an online flight envelope protection system is developed and implemented on impaired aircraft with structural damage. The whole protection system is designed to be a closed loop of several subsystems, including system identification, damage classification, flight-envelope prediction, and fault-tolerant control. Based on the information given by damage classification, the flight envelopes are explicitly retrieved, processed online from the database, and fed into the fault-tolerant controller, which makes the protection system adaptive to a wide range of abnormal conditions. Simulation results show that with envelope protection, loss-of-control accidents are more likely to be prevented, since excessive commands to the controller are restricted based on the updated information of the changed flight envelopes. In this way, the fault tolerance of the impaired aircraft can be effectively enhanced.

Flight envelope

Envelope (radar)

Identification

10.2514/1.i010846

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Citations (4)

A Fault Diagnosis and Predictive Maintenance Algorithm for Mechanical Systems Based on Deep Learning

Ye Zhang Fumin Chen

10.1109/edpee61724.2024.00146

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Citations (1)

Study on intelligent syndrome differentiation neural network model of stomachache in traditional Chinese medicine based on the real world

Medicine (2020)

Hua Ye Yuan Gao Ye Zhang Yue Cao Liang Zhao

Abstract Stomachache is not only disease name of Traditional Chinese medicine (TCM) but also the clinical symptom. It is a common and multiple diseases. TCM has its particular advantage in clinical treatment of stomachache. Syndrome differentiation is an important concept in TCM practice. The therapeutic process is virtually a nonlinear mapping process from clinical symptom to syndrome diagnosis with processing and seeking rules from mass data. Artificial neutral network has strong learning ability for nonlinear relationship. Artificial neutral network has been widely used to TCM area where the multiple factors, multilevel, nonlinear problem accompanied by a large number of optimization exist. We present an original experimental method to apply the improved third-order convergence LM algorithm to intelligent syndrome differentiation for the first time, and compare the predicted ability of Levenberg-Marquardt (LM) algorithm and the improved third-order convergence LM algorithm in syndrome differentiation. In this study, 2436 cases of stomachache electronic medical data from hospital information system, and then the real world data were normalized and standardized. Afterwards, LM algorithm and the improved third-order convergence LM algorithm were used to build the Back Propagation (BP) neural network model for intelligent syndrome differentiation of stomachache on Matlab, respectively. Finally, the differentiation performance of the 2 models was tested and analyzed. The testing results showed that the improved third-order convergence LM algorithm model has better average prediction and diagnosis accuracy, especially in predicting “liver-stomach disharmony” and “stomach yang deficiency”, is above 95%. By effectively using the self-learning and auto-update ability of the BP neural network, the intelligent syndrome differentiation model of TCM can fully approach the real side of syndrome differentiation, and shows excellent predicted ability of syndrome differentiation.

Backpropagation

10.1097/md.0000000000020316

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Citations (5)

IEEE Transactions on Intelligent Transportation Systems publication information

IEEE Transactions on Intelligent Transportation Systems (2019)

Daniel Zeng Miguel Ángel Sotelo Azim Eskandarian Brendan Morris Meng Lu

10.1109/tits.2018.2884842

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Semisupervised support vector machine classification for hyperspectral imagery

Fereidoun A. Mianji Ye Zhang

Variety of techniques with the capability of being applied on original data spaces has been developed for hyperspectral (HS) classification. Among them, support vector machine (SVM) presents a high classification accuracy, however, its performance for too small ratios of number of available training samples to number of features is relatively low due to the Hughes effect. This paper proposes a new semisupervised approach through combining appropriate discriminant data transforms such as principal component analysis with SVM to tackle the above mentioned drawback. The experiments on real HS data validate the superiority of the proposed combined approach over the traditional pure SVM technique.

Training set

10.1109/iccsp.2011.5739328

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Citations (1)

MFVAE: A Multi-Scale Fuzzy Variational Autoencoder for Big Data-Based Fault Diagnosis in Gearbox

IEEE Transactions on Fuzzy Systems (2024)

Hexuan Hu Yicheng Cai Qing Meng Han Cui Qiang Hu

Gearboxes are widely used in various types of mechanical equipment and have become an essential part of connecting various components in the machine and transmitting power. A fault in the gearbox will cause the entire mechanical equipment to stop working, causing economic losses and safety hazards. Previous fault diagnosis models for gearbox used the vibration signals of the gearbox as input features. They designed a variety of feature extraction modules to learn the information contained in the vibration signals. However, the latent features learned by previous models have poor interpretability and robustness, thus affecting model performance. In addition, the prediction results of previous fault diagnosis models used for gearbox using single-scale features are often less robust. Moreover, the fault diagnosis models used for gearbox are usually end-to-end black box models, which cannot provide interpretive information about the predicted values, and it is difficult to handle the uncertain information in the vibration signals. Therefore, we propose a multi-scale fuzzy variational autoencoder (MFVAE) using a fuzzy neural network for Big Data-based fault diagnosis in gearbox. The Big Data technology can automatically collect vibration signals from sensors and provide high-quality training samples for fault diagnosis models. The MFVAE model first uses two variational autoencoders of different sizes to extract the latent features of the gearbox vibration signal. Subsequently, the MFVAE model fuses low-level latent features and high-level latent features in proportion to form the final head features, which are used to diagnose gearbox faults. Finally, the MFVAE model inputs the head features into the prediction layer to diagnose the fault of the gearbox. The prediction layer comprises a fully connected network and a fuzzy neural network. Experimental results on the real gearbox dataset verify the outstanding performance of the MFVAE model.

Autoencoder

10.1109/tfuzz.2024.3406001

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