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An integrated approach to the selection of municipal solid waste landfills through GIS, k-means and multi-criteria decision analysis

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ENVIRONMENTAL RESEARCH 2020年 185卷 109348-109348页

作者： Eghtesadifard, Mahmood Afkhami, Payam Bazyar, Arash Shiraz Univ Technol Fac Ind Engn Shiraz Iran

Selecting the proper site for disposing of solid waste is one of the serious environmental and public health concerns in metropolises. This multifaceted issue encompasses environmental, economic, social, geographical, technical, and legal criteria. Some of these criteria, however, are less influenced by experts' subjective judgments because they display scientific specifications. This study drew on a novel, integrated method for the selection of municipal solid waste (MSW) landfills in the Iranian metropolis, Shiraz;the study relied on the GIS and multi-criteria decision-making methods, which helped to reduce the number of comparisons in collecting experts' opinions, simplified the selection process, and enhanced the assessment method. The method proposed was regulated by DEMATEL and ANP. Primarily 13 criteria were identified in five groups through the Delphi method. Next, using the integrated method, the weight of each criterion was determined and was assigned to the corresponding layer in ArcGIS 10.5. By combining these layers through a fuzzy logic, the sites satisfying the disposal conditions were identified. The sites were then divided into six areas through the k-means clustering algorithm, while MOORA, WASPAS and COPRAS methods were used to discover the best sites based on their priorities. Finally, to confirm the reliability of the results, compare and verify them, and conduct sensitivity analysis on them, 13 scenarios were used.

关键词： Sustainable municipal solid waste management Landfill siting Geographic information system (GIS) Multi criteria decision analysis k-means clustering algorithm

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A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images

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REMOTE SENSING 2022年第15期14卷 3600-3600页

作者： Wei, Yanbo Liu, Yalin Lei, Yifei Lian, Ruiyao Lu, Zhizhong Sun, Lei Luoyang Normal Univ Coll Phys & Elect Informat 6 Jiqing Rd Luoyang 471934 Peoples R China Zhengzhou Railway Vocat & Tech Coll Sch Elect Engn 56 Pengcheng Ave Zhengzhou 451460 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn 145 Nantong St Harbin 150001 Peoples R China

To control the quality of X-band marine radar images for retrieving information and improve the inversion accuracy, the research on rainfall detection from marine radar images is investigated in this paper. Currently, the difference in the correlation characteristic between the rain-contaminated radar image and the rain-free radar image is utilized to detect rainfall. However, only the correlation coefficient at a position in the lagged azimuth is utilized, and a statistical hard threshold is adopted. By deeply investigating the difference between the calculated correlation characteristic and the marine radar images, the correlation coefficient in the lagged azimuth can be used to constitute the correlation coefficient feature vector (CCFV). Then, an unsupervised k-means clustering learning method is used to obtain the clustering centers. Based on the constituted CCFV and the k-means clustering algorithm, a new method of rainfall detection from the collected X-band marine radar images is proposed. The acquired X-band marine radar images are utilized to verify the effectiveness of the proposed rainfall detection method. Compared with the zero-pixel percentage (ZPP) method, the correlation coefficient difference (CCD) method, the support vector machine (SVM) method and the wave texture difference (WTD) method, the experimental results demonstrate that the proposed method could finish the task of rainfall detection, and the detection accuracy increases by 10.0%, 6.3%, 2.0% and 0.6%, respectively, for the proportion of the 25% training dataset.

关键词： correlation coefficient feature vector (CCFV) k-means clustering algorithm marine radar images rainfall detection

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Spilled load detection based on lightweight YOLOv4 trained with easily accessible synthetic dataset

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 100卷 1页

作者： Li, Feng Jiang, Zhongwang Zhou, Siqi Deng, Yutong Bi, Yufeng Beihang Univ Beijing Key Lab Cooperat Vehicle Infrastruct Syst Sch Transportat Sci & Engn Beijing 100191 Peoples R China China Univ Geosci Fac Engn Beijing 100083 Peoples R China Shandong Prov Commun Planning & Design Inst CO LT Jinan 250031 Shandong Peoples R China

The automated detection of spilled loads on the highway can help quick finding of offending vehicles and prevent traffic congestion. A real-time spilled loads detection algorithm based on the improved You Only Look Once v4 (YOLOv4) is proposed in this paper. Image synthesis method is used to manually generate dataset to solve the problem of insufficient spilled loads images. Six categories of spilled loads are divided into 35,468 samples as a dataset (80% for training and 20% for testing). k-means clustering and Convolutional Block Attention Module (CBAM) were introduced to YOLOv4 Channel pruning was carried out to reduce the computational complexity and resource consumption of the model. The proposed model, of which average precision=98.3%, recall= 0.962, precision=0.98, performs better than other 5 types classifiers and is applied to estimate spilled loads, where the model size and GFLOPs is 134.535 MB and 70 respectively. Compared with the original model, the reduction is 47.6% and 34%, saving computing resources and speeding up the detection speed on the premise of ensuring the detection accuracy. This paper paves a new way for automated spilled load detection with deep learning method, improving the efficiency and accuracy of spilled load detection.

关键词： Spilled load Deep learning k-means clustering algorithm Attention module Channel pruning

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Online Detection of Turning Tool Wear Based on Machine Vision

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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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MOSS SOFTWARE: A NEW TOOL FOR MULTI-OBJECTIVE GREEN SUPPLIER SELECTION

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2022年第2期29卷 244-266页

作者： Toktas-Palut, Peral Onan, kivanc Gurbuz, Mustafa Zahid Gulden-Ozdemir, Birsen Dogus Univ Dept Ind Engn Istanbul Turkey Dogus Univ Dept Comp Engn Istanbul Turkey

Competition between companies is getting more intense by the day. Corporations need to decrease costs while improving the quality and reliability of their deliveries. Meanwhile, the demand for environmentally sustainable products and operations is increasing. These issues are all compounded by the supplier relations between companies and their need to develop better economic and environmental supply chain contracts. This research thus aims to propose a tool, i.e., the MOSS Software, concerning the total cost, rate of defected materials, rate of late deliveries, and environmental improvement potential for suppliers. MOSS is a two-stage software. First, the objectives are included in a multi-objective evolutionary mathematical model, and the model is solved using the NSGA-III algorithm. In the second stage, as a post-Pareto analysis approach, the k means algorithm is used for selecting representative solutions among Pareto sets by comparing silhouette values for different k values. An application of the MOSS software is also presented in this paper.

关键词： Green supplier selection Lot-sizing Quantity discount Multi-objective optimization Genetic algorithm k-means clustering algorithm

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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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