Ming Huang

Beijing University of Civil Engineering and Architecture

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Xu Liang

Hefei Institutes of Physical Science

Xianglei Liu

Beijing University of Civil Engineering and Architecture

John C. Mosher

The University of Texas Health Science Center at Houston

Wanxin Yang

National Centre for Earth Observation

Richard M. Leahy

University of Southern California

Ziyu Liu

Chinese Academy of Sciences

Xuan Jiao

Beijing Tongren Hospital

Yao Bai

State Key Joint Laboratory of Environment Simulation and Pollution Control

Xiaonan Wu

Guangdong University of Technology

Ramachandra Komera

Health Solutions (Sweden)

Cooperative Institutions

Beijing University of Civil Engineering and Architecture

Health Solutions (Sweden)

National Administration of Surveying, Mapping and Geoinformation of China

Dalian University of Technology

Chinese Academy of Sciences

Zhejiang University

Tongji University

University of Chinese Academy of Sciences

Tianjin University

Dalian Jiaotong University

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A Study of Dipole Localization Accuracy for MEG and EEG using a Human Skull Phantom

NeuroImage (1998)

Richard M. Leahy John C. Mosher Michael E. Spencer Ming Huang J.D. Lewine

Magnetoencephalography

10.1016/s1053-8119(18)31509-x

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Citations (17)

Combinatorial Test Case Suite Generation Based on Differential Evolution Algorithm

Journal of Software (2014)

Xu Liang Shujie Guo Ming Huang Xuan Jiao

The combinatorial testing, an effective way to improve the efficiency of software testing, is an important means to ensure the quality of software. In the combinatorial testing, the key is the combinatorial test case suite generation. According to the characteristics of the combinatorial test case suite generation problem, this paper proposes a differential evolution algorithm based on the one-test-at-a-time strategy for the solution to this problem. Through the experiments, this paper compares optimized performances of different mutations, explores the influence of algorithm parameters on the optimized performance and verifies the effectiveness and advancement of the solution.

Test suite

Differential Evolution

10.4304/jsw.9.6.1479-1484

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Citations (5)

The application of Bee-Genetic Algorithm to Job-Shop Scheduling Problem

Ming Huang Yao Bai Xu Liang

This paper presents a method of solving Job-Shop Scheduling problem, based on the Artificially Bee Colony Algorithm and the Genetic Algorithm. By introducing the genetic operations to the Artificially Bee Colony Algorithm and making the complementary advantages, we can improve the efficiency of solving problem. Using this algorithm to solve JobShop Scheduling Problem, the results show that this algorithm can have good performance, and play the optimization scheduling role.

Genetic algorithm scheduling

10.1109/mic.2012.6273339

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Improved Data-Driven Stochastic Subspace Identification with Autocorrelation Matrix Modal Order Estimation for Bridge Modal Parameter Extraction Using GB-SAR Data

Buildings (2022)

Xianglei Liu Songxue Zhao Peipei Wang Runjie Wang Ming Huang

With the advantage of non-contact measurement, ground-based synthetic aperture radar (GB-SAR) has been widely used to obtain the dynamic deflection of various bridges. Data-driven stochastic subspace recognition (Data-SSI), a popularized time-domain technique, is commonly used for modal parameter identification of bridges. To improve the computational efficiency and accuracy of the Data-SSI method for bridge modal parameter estimation using GB-SAR, this paper proposes an improved Data-SSI method. First, boxplot data filtering is applied to screen out the error points to generate a Hankel matrix. Second, the Hankel matrix compression method is presented to reduce the ill-conditioned vectors in the column vectors of the Hankel matrix to improve calculation efficiency. Finally, the exact modal order (EMO) modal estimation algorithm based on the autocorrelation matrix is adopted to reduce the generation of false modes and improve the calculation efficiency. The results of simulation and field experiments show that the natural frequency values for the improved Data-SSI method are 2.3208 and 2.3189 and the damping ratio coefficient values are 8.10 and 8.08, under windows 1 and 2, respectively. The operation times using the improved Data-SSI method are 2.02 s and 7.61 s under windows 1 and 2, respectively. This proves that the proposed improved Data-SSI method has higher accuracy and computational efficiency.

Autocorrelation matrix

10.3390/buildings12020253

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Citations (8)

An improved morphological edge detection algorithm

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) (2022)

Zehao Yin Ziyu Liu Ming Huang

By constructing multi-scale and multi-directional structural elements and introducing Mahalanobis distance weighting coefficient, the classical morphological algorithm is improved, so as to detect the edge information of digital image with more accurate positioning, richer detail information and fluency. Firstly, three structural elements with different scales are selected for morphological opening and closing operation to remove the noise of the image; Then, the morphological edge detection is carried out by using the structural elements in four different directions: $0^{\circ}, 45^{\circ}, 90^{\circ}, 135^{\circ}$, and the edge image under the structural elements in each direction is obtained; Then the Mahalanobis distance of the edge image in each direction is calculated to determine the weight. The four images are weighted and summed according to the proportion. Finally, the edge image with ideal effect is obtained by experiment. The simulation results show that the improved morphological detection algorithm has smoother and more complete edge information, better retention of edge details, stronger anti noise performance and certain advantages.

Mahalanobis distance

Canny edge detector

10.1109/icgmrs55602.2022.9849347

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Citations (3)

3D Digital protection and representation of burial ruins based on LiDAR and UAV survey

Measurement and Control (2022)

Ming Guo Zexin Fu Deng Pan Yuquan Zhou Ming Huang

To improve the efficiency and accuracy of digital protection of burial sites, the digital protection of burial sites was studied by the collaborative observation technology of LiDAR and unmanned aerial vehicle (UAV) mapping. Multi-feature constrained iterative global registration algorithm is used to realize fast registration of multi-site cloud, the Structure from motion (SFM)-based algorithm is used to generate dense point cloud from UAV image, and then the Iterative Closest Point(ICP) algorithm is used to realize the fusion of point cloud data on-site and image. According to the fused data, a variety of measurable results, such as high-precision burial model and contour map, are produced, and an orthophoto generation system is written independently, which can fully automatically generate high-precision orthophoto map and realize the digitalization of the burial sites. Taking the protection application of the burial site in Yaoheyuan, Ningxia, one of the top 10 archeological discoveries in China in 2017, with guaranteed data accuracy and integrity, the surveying and mapping data of the tomb site was obtained quickly. The global registration accuracy of the proposed algorithm is within 1 cm, and the registration efficiency was superior to the current mainstream open source algorithm, which verified the feasibility and efficiency of this method. It provides an effective scheme for the digital protection of burial cultural heritage, and is also suitable for the digital protection of other heritage sites in the World Heritage List such as Panlongcheng Site and Sanxingdui.

Orthophoto

Iterative closest point

Feature (linguistics)

Structure from Motion

10.1177/00202940221110949

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Citations (8)

An Improved Second-Order Blind Identification (SOBI) Signal De-Noising Method for Dynamic Deflection Measurements of Bridges Using Ground-Based Synthetic Aperture Radar (GBSAR)

Applied Sciences (2019)

Xianglei Liu Hui Wang Ming Huang Wanxin Yang

Ground-based synthetic aperture radar (GBSAR) technology has been widely used for bridge dynamic deflection measurements due to its advantages of non-contact measurements, high frequency, and high accuracy. To reduce the influence of noise in dynamic deflection measurements of bridges using GBSAR—especially for noise of the instantaneous vibrations of the instrument itself caused by passing vehicles—an improved second-order blind identification (SOBI) signal de-noising method is proposed to obtain the de-noised time-series displacement of bridges. First, the obtained time-series displacements of three adjacent monitoring points in the same time domain are selected as observation signals, and the second-order correlations among the three time-series displacements are removed using a whitening process. Second, a mixing matrix is calculated using the joint approximation diagonalization technique for covariance matrices and to further obtain three separate signal components. Finally, the three separate signal components are converted in the frequency domain using the fast Fourier transform (FFT) algorithm, and the noise signal components are identified using a spectrum analysis. A new, independent, separated signal component matrix is generated using a zeroing process for the noise signal components. This process is inversely reconstructed using a mixing matrix to recover the original amplitude of the de-noised time-series displacement of the middle monitoring point among three adjacent monitoring points. The results of both simulated and on-site experiments show that the improved SOBI method has a powerful signal de-noising ability.

SIGNAL (programming language)

10.3390/app9173561

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Citations (6)

Using speech recognition to analyze the physical condition of the elderly and its application in telemedicine

Emerging Science (2020)

Jianyu Wang Lingjing Yu Ming Huang Runchuan Feng Yi-Yang He

10.47917/j.es.20203005

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Citations (0)

Predicting Endometriosis Onset Using Machine Learning Algorithms

Research Square (Research Square) (2020)

Ewa J. Kleczyk Aparna Peri Tarachand Yadav Ramachandra Komera Maruthi Peri

Abstract Background Endometriosis is a common progressive female health disorder in which tissues similar to the lining of the uterus grow on other parts of the body like ovaries, fallopian tubes, bowel, and other parts of reproductive organs. In women, it is one of the most common causes of pelvic pain and infertility. In the US, one in every ten women of reproductive age group has endometriosis. The actual cause of endometriosis is still unknown, and it is quite difficult to diagnose. There are several theories regarding the cause; however, not a single theory has been scientifically proven. Methods In this paper, we try to identify the drivers of endometriosis’ diagnoses via leveraging advanced Machine Learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a great extent, if likelihood of endometriosis can be predicted well in advance. As a result, the proper medical care and treatment can be given to the impacted patients. To demonstrate the feasibility, Logistic Regression (LR) and eXtreme Gradient Boosting (XGB) algorithms were trained on 36 months of medical history data. Results The machine learning models were used to predict the likelihood of disease on qualified patients from the healthcare claims patient level database. Several directly and indirectly features were identified as important in accurate prediction of the condition onset, including selected diagnosis and procedure codes. Conclusions Leveraging the machine learning approaches can aid early prediction of the disease and offer an opportunity for patients to receive the needed medical treatment earlier in the patient journey. Creating a typing tool that can be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers could further aid the objective of improving the diagnosis activities and inform the diagnostic processes that would result in timely and precise diagnosis, ultimately increasing patient care and quality of life.

10.21203/rs.3.rs-135736/v1

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Citations (9)

A Tunnel Crack Extraction Method Based on Point Cloud

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) (2022)

Xiaonan Wu Ziyu Liu Ming Huang

Tunnel cracks, water seepage, stage-shift deformation and other problems will have a great impact on the safe use of the tunnel, among which the accidents caused by tunnel cracks account for a large proportion. Therefore, regular detection of tunnel cracks is needed. The traditional manual detection method is less efficient and easy to produce missed detection. With the rapid development of 3D laser scanning technology, it can be used in the extraction of tunnel cracks. In this paper, the tunnel point cloud processing, using CTA algorithm combined with edge detection to achieve rapid detection of tunnel cracks, finally using the method of connected domain analysis to extract the location of the disease.

Tunnel Construction

10.1109/icgmrs55602.2022.9849275

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Citations (1)