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Online Detection of Turning Tool Wear Based on Machine Vision

JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2022年第5期22卷 050903-050903页

作者： Dong, Xinfeng Li, Yongsheng Weifang Univ Sch Mech & Automat Weifang 261061 Peoples R China Shandong Tianrui Heavy Ind Co Ltd Weifang 261061 Peoples R China Shandong Magnet Suspens Technol Ind Res Inst Weifang 261061 Peoples R China

To identify the tool wear in real time, this article builds the online detection system and proposes an integrated image processing method to measure the wear width for flank face of turning tool. First, the images of flank face wear are collected by the image acquisition system. Second, the collected images are cropped near the flank face wear, and the wear area and background for the cropped images are separated by k-means clustering algorithm. Then, the wear edge is identified by the grayscale transformation and edge detection algorithm, and the wear width is calculated by Hough transformation. Finally, the cutting experiment is carried out on MAG HTC200 CNC lathe to verify the validity of the proposed method, and the results show that the identified wear width by the proposed method and actual measured wear width for flank face of turning tool are in good agreement and show the proposed method is effective and have micron scale calculation accuracy.

关键词： wear width flank face of turning tool k-means clustering algorithm Hough transform online detection machine vision

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Path Planning Based on clustering and Improved ACO in UAV-assisted Wireless Sensor Network

Path Planning Based on Clustering and Improved ACO in UAV-as...

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IEEE International Symposium on Antennas and Propagation (IEEE AP-S) / IEEE USNC-CNC-URSI Radio Science Meeting

作者： Li, Yibing Meng, Xianzhen Ye, Fang Jiang, Tao Li, Yingsong Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9781946815088

UAVs as mobile nodes have been introduced into wireless sensor network (WSN) to assist information transmission and reduce the burden of communication. To minimize the path cost of UAVs for information transmission, this paper focuses on the path planning of UAVs. A multi-UAVs path planning combined k-means clustering algorithm and improved MAX-MIN ant system (MMAS) is proposed. This algorithm apply the k-means clustering algorithm to reduce the problem size and improve the search efficiency of subsequent path planning. By modifying the node search rules and proposing two optimal solution detection rules of the MMAS, the algorithm searching stagnation and failing into local optimal solution are effectively avoided.

关键词： UAV k-means clustering algorithm path planning MAX-MIN ant system

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Multi-objective configuration of an intelligent parking lot and combined hydrogen, heat and power (IPL-CHHP) based microgrid

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SUSTAINABLE CITIES AND SOCIETY 2022年 76卷 103433-103433页

作者： Hosseinnia, Hamed Mohammadi-Ivatloo, Behanm Mohammadpourfard, Mousa Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Aalborg Univ Dept Energy Aalborg Denmark Univ Tabriz Fac Chem & Petr Engn Tabriz Iran

Recently, financial issues have been considered as the main aspects of microgrid (MG) evaluation in the literature. In this study, the optimal configuration of the MG has been calculated by presenting a reliability-constrained optimization model. In this optimization approach, the MG units are considered in full available state and random outage state through the planning horizon. To model a proposed MG in details, its uncertainties are formulated in the main function. A combination of Latin hypercube sampling (LHS) algorithm and k-means clustering algorithm is applied to generate all uncertainties. The proposed model simultaneously optimizes two objectives, namely, economic costs and emission performance. Time of use (TOU) based demand response (DR) program has been employed for optimal management of the demand side. At first, the bi-objective function is converted to a sequence of single-objective constrained problems by employing E-constraints. All Pareto front solutions are obtained by utilizing GAMS for solving the developed mixed-integer linear programming (MILP) model. To make a trade-off among solutions, the max-min fuzzy decision-making method has been used. Due to the positive effect of the DR program on the configuring problem, the total emission and economic costs of MG have been reduced up to 4.01% and 1.72%, respectively.

关键词： Combined hydrogen Heat and power (CHHP) Reliability constraint Latin hypercube sampling (LHS) k-means clustering algorithm Eco-emission function

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Production planning and scheduling problem of continuous parallel lines with demand uncertain and different production capacities

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2020年第6期7卷 761-774页

作者： Bhosale, kailash Changdeorao Pawar, Padmakar Jagannath Nasik Savitribai Phule Pune Univ KK Wagh Inst Engn Educ & Res Dept Prod Engn Pune Maharashtra India

Production planning and scheduling problems are highly interdependent as scheduling provides optimum allocation of resources and planning is an optimum utilization of these allocated resources to serve multiple customers. Researchers have solved production planning and scheduling problems by the sequential method. But, in this case, the solution obtained by the production planning problem may not be feasible for scheduling method. Hence, production planning and scheduling problems must be solved simultaneously. Therefore, in this work, a mathematical model is developed to integrate production planning and scheduling problems. The solution to this integrated planning and scheduling problem is attempted by using a discrete artificial bee colony (DABC) algorithm. To speed up the DABC algorithm, a k-means clustering algorithm is used in the initial population generation phase. This k-means clustering algorithm will help to converge the algorithm in lesser time. A real-life case study of a soap manufacturing industry is presented to demonstrate the effectiveness of the proposed approach. An objective function to minimize overall cost, which comprises the processing cost, material cost, utility cost, and changeover cost, is considered. The results obtained by using DABC algorithm are compared with those obtained by CPLEX software. There is a saving of (sic)2 23 324 for weeks 1-4 in overall cost compared with the results obtained by using CPLEX software.

关键词： Integrated production planning and scheduling problem discrete artificial bee colony (DABC) k-means clustering algorithm Monte Carlo simulation

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Automatic identification of the critical slip surface of slopes

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ENGINEERING GEOLOGY 2020年 273卷 105672-105672页

作者： Wang, Yankun Huang, Jinsong Tang, Huiming China Univ Geosci Fac Engn Wuhan 430074 Hubei Peoples R China Univ Newcastle Prior Res Ctr Geotech Sci & Engn Discipline Civil Surveying & Environm Engn Callaghan NSW 2308 Australia China Univ Geosci Three Gorges Res Ctr Geohazards Minist Educ Wuhan 430074 Hubei Peoples R China

Although numerical methods based on strength reduction are becoming popular in slope stability analysis, they fail to provide a distinct critical slip surface and only provide a shear band. The widely used visualization techniques for defining the critical slip surface are susceptible to subjective judgment and are inefficient for batch analysis and three-dimensional analysis. When a slope fails, the displacements on the two sides of the critical slip surface will be substantially different. Based on this observation, an automatic identification method for locating the critical slip surface is proposed. The k-means clustering algorithm is first applied to automatically separate the nodal displacements into two categories representing the sliding mass and the stable block. Then, the scatters near the separation surface are obtained by constructing the alpha shape of the sliding mass. Finally, the critical slip surface is obtained by fitting the extracted scatters. A homogeneous slope, a slope with a thin weak layer and a real landslide are used to test the effectiveness of the proposed method. The results show that the proposed method can automatically and accurately identify two-dimensional and three-dimensional critical slip surfaces.

关键词： Critical slip surface k-means clustering algorithm Automatic identification Slope stability Strength reduction method

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Typical current modelling and feature extraction of high voltage circuit breaker towards condition analysis and fault diagnosis

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第8期14卷 1521-1527页

作者： Ji, Tianyao Ye, Xiuzhen Shi, Mengjie Li, Mengshi Wu, Qinghua SCUT Sch Elect Power Engn Guangzhou 510641 Peoples R China

This study proposes a coil current model and an energy storage motor current (ESMC) model of circuit breakers (CBs) with spring operated mechanism. To make sure the signals generated by the models are identical to the actual ones, this study proposes a stochastic optimisation algorithm to optimise the model parameters. Based on the data produced by the optimised models, two fault diagnosis methods are proposed to assess operational condition and detect faults. The first method is based on fast template matching, which adopts k-means clustering algorithm to cluster the data and form a template library. The second one combines deep belief network and Softmax classifier, which can not only extract high level information of the characteristic signals, but also avoid the negative impact of the large dimension on classification results. In the simulation studies, the two methods are tested on various scenarios and their merits are demonstrated, respectively, where the latter one shows superior performance.

关键词： optimisation coils circuit breakers pattern clustering fault diagnosis feature extraction belief networks stochastic processes signal classification power engineering computing electric motors feature extraction high voltage circuit breaker condition analysis coil current model energy storage motor current model spring operated mechanism stochastic optimisation algorithm fault diagnosis methods fast template matching k-means clustering algorithm template library characteristic signals ESMC CBs deep belief network Softmax classifier

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Custom optical imaging system for ex-vivo breast cancer detection based on spectral signature

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SURGICAL ONCOLOGY-OXFORD 2020年 35卷 547-555页

作者： Aref, Mohamed Hisham Aboughaleb, Ibrahim H. El-Sharkawy, Yasser H. Mil Tech Coll Biomed Engn Dept El Fangary St Cairo Egypt

Background and purpose: Breast cancer is a popular well-known tumor in women globally and the subsequent driving reason for malignancy death. The purpose of the present study is to develop Low cost, commercial, and affordable system that discriminates malignant from normal breast tissues by exploiting the unique properties of Hyperspectral (HS) Imaging. Materials and methods: The difference in the optical properties of the investigated breast tissues gives various reactions to light transmission, absorption, and especially the reflection over the spectral range. A custom optical imaging system (COIS) was designed to assess variable responses to monochromatic LEDs (415, 565, 660 nm) to highlight the differences in the reflectance properties of malignant/normal tissue. Statistical analysis was computed for determining the ideal wavelength to differentiate between normal and malignant regions. The experiment was repeated using the same LEDs, and low-cost CCD camera to examine the capability of such a system to discriminate between normal and malignant tissue. Results: Spectral images obtained by Hyperspectral camera, have been analyzed to reveal the difference of reflectance malignant and normal breast tissue. Superficial spectral reflection image with blue LED (415 nm) showed high variance (10.11). However, a more-depth reflection image with red LED (660 nm) showed low variance (4.44). So the optimum contrast image was produced by combining the three spectral information images from blue, green, and red LED. The COIS using a commercial CCD camera was in agreement with the HS camera. Conclusions: The novel COIS of the commercial Low-cost CCD Camera is reliable and can be used with endoscopy technique as an assistant tool for surgical doctor to make decision and assess the resection edges in real time during surgery.

关键词： Breast cancer Hyperspectral imaging Low-cost CCD camera Monochromatic LED k-means clustering algorithm Spectral signature

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High-velocity impact behaviors and post-impact tension performance of 2D-C/SiC composite beams

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CERAMICS INTERNATIONAL 2020年第9期46卷 13282-13291页

作者： Hu, Wei Guan, Tianhao Gao, Mengting Ren, Tengfei Huang, Jinzi Suo, Tao Wang, Bo Zhang, Chao Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Joint Int Res Ctr Impact Dynam & Engn Applicat Xian 710072 Peoples R China Northwestern Polytech Univ Shaanxi Key Lab Impact Dynam & Engn Applicat IDEA Xian 710072 Peoples R China

2D-C/SiC composites have widely been used in aeronautical and aerospace engineering, but their mechanical behaviors under small-mass and high-speed impact have not been thoroughly studied yet. In this paper, 2D-C/SiC beam specimens were impacted by a single-stage light-gas gun and the fracture processes were captured by a high-speed camera. Post-impact internal and surface damage morphologies were scanned by a CT and a SEM, respectively. Similar damage modes were revealed by high-speed images. Subsequently, quasi-static post-impact tension tests were conducted to understand the residual mechanical properties. Acoustic emission (AE) signals of specimens were detected during the tests and then classified by the k-means algorithm. Therefore, evolutions of matrix damage, interfacial delamination and fiber fracture were recognized. At the same time, strain value was obtained by digital image correlation (DIC) method and main crack propagations were obvious in strain contours. A combination of the AE and DIC methods very well monitored the real-time damage during post-impact testing, which further revealed the damage during impact phase.

关键词： Ceramic matrix composites Foreign object damage k-means clustering algorithm Digital image correlation

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An Efficient Content Based Image Retrieval using an Optimized Neural Network for Medical Application

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第31-32期79卷 22277-22292页

作者： kumar, R. Biji Marikkannu, P. Pentagram Res Ctr Pvt Ltd Hyderabad India Anna Univ Dept Informat Technol Chennai Tamil Nadu India

Content Based Image Retrieval (CBIR) is a popular method to search and retrieve the similar images. For medical applications, it plays an important role to find the diseasessuch as breast cancer in human body. Many existing methods were presented for improving the performance of CBIR method. Nevertheless, retrieval time and accuracy of CBIR are further to be improved. To solve this issue, an optimal classifier is to be used in CBIR. In this paper, Artificial Neural Network based on Particle Swarm Optimization based (PSO-ANN) is presented as an optimized classifier. Also, the features of images such as shape, texture, mean and standard deviation are extracted. To increase the speed of the classification, these extracted features are to be clustered using k-means clustering algorithm. From the clustered features, similar images of query image are retrieved using the proposed PSO-ANN classifier. Simulation results prove that performance of this proposed CBIR outperforms than that of existing methods in terms of accuracy and CBIR time.

关键词： CBIR Particle Swarm Optimization Artificial Neural Network PSO-ANN k-means clustering algorithm Gray Level Co-occurrence Matrix

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Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI

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NEUROIMAGE-CLINICAL 2020年 28卷 102411-102411页

作者： Martinez-Heras, Eloy Solana, Elisabeth Prados, Ferran Andorra, Magi Solanes, Aleix Lopez-Soley, Elisabet Montejo, Carmen Pulido-Valdeolivas, Irene Alba-Arbalat, Salut Sola-Valls, Nuria Sepulveda, Maria Blanco, Yolanda Saiz, Albert Radua, Joaquim Llufriu, Sara Hosp Clin Barcelona Inst Invest Biomed August Pi & Sunyer IDIBAPS Ctr Neuroimmunol Lab Adv Imaging Neuroimmunol Dis Barcelona Spain Univ Barcelona Barcelona Spain Univ Oberta Catalunya E Hlth Ctr Barcelona Spain UCL Dept Med Phys & Bioengn Ctr Med Image Comp London England UCL Dept Neuroinflammat UCL Inst Neurol NMR Res UnitQueen Sq MS Ctr London England IDIBAPS Imaging Mood & Anxiety Related Disorders IMARD Gr Barcelona Spain CIBERSAM Barcelona Spain Karolinska Inst Dept Clin Neurosci Ctr Psychiat Res Stockholm Sweden Kings Coll London Inst Psychiat Psychol & Neurosci Dept Psychosis Studies Early Psychosis Intervent & Clin Detect EPIC Lab London England

Diffusion magnetic resonance imaging can reveal quantitative information about the tissue changes in multiple sclerosis. The recently developed multi-compartment spherical mean technique can map different microscopic properties based only on local diffusion signals, and it may provide specific information on the underlying microstructural modifications that arise in multiple sclerosis. Given that the lesions in multiple sclerosis may reflect different degrees of damage, we hypothesized that quantitative diffusion maps may help characterize the severity of lesions "in vivo" and correlate these to an individual's clinical profile. We evaluated this in a cohort of 59 multiple sclerosis patients (62% female, mean age 44.7 years), for whom demographic and disease information was obtained, and who underwent a comprehensive physical and cognitive evaluation. The magnetic resonance imaging protocol included conventional sequences to define focal lesions, and multi-shell diffusion imaging was used with b-values of 1000, 2000 and 3000 s/mm(2) in 180 encoding directions. Quantitative diffusion properties on a macro- and micro-scale were used to discriminate distinct types of lesions through a kmeans clustering algorithm, and the number and volume of those lesion types were correlated with parameters of the disease. The combination of diffusion tensor imaging metrics (fractional anisotropy and radial diffusivity) and multi-compartment spherical mean technique values (microscopic fractional anisotropy and intra-neurite volume fraction) differentiated two type of lesions, with a prediction strength of 0.931. The B-type lesions had larger diffusion changes compared to the A-type lesions, irrespective of their location (P < 0.001). The number of A and B type lesions was similar, although in juxtacortical areas B-type lesions predominated (60%, P < 0.001). Also, the percentage of B-type lesion volume was higher (64%, P < 0.001), indicating that these lesions were larger. The number

关键词： MRI Diffusion magnetic resonance imaging k-means clustering algorithm MS lesion types Multiple Sclerosis

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