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Extraction of Multiple Diseases in Apple Leaf Using Machine Learning

INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2022年第3期22卷

作者： Singh, Swati Gupta, Sheifali Tanta, Ankush Gupta, Rupesh Himachal Pradesh Univ Elect & Commun Engn Univ Inst Technol Shimla 171005 India Chitkara Univ Inst Engn & Technol Chandigarh 140401 Punjab India Chitkara Univ Sch Engn & Technol Chandigarh 174103 Himachal Prades India

This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4% is achieved using k nearest neighbor classifier.

关键词： Image segmentation brightness preserving dynamic fuzzy histogram equalization k-means clustering algorithm support vector machine k nearest neighbor Naive Bayes

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Real-Time Fire Detection Method for Electric Vehicle Charging Stations Based on Machine Vision

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第2期13卷 23页

作者： Zhang, Shiyu Yang, Qing Gao, Yuchen Gao, Dexin Qingdao Univ Sci Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China Qingdao Univ Sci Technol Coll Informat Sci & Technol Qingdao 266061 Peoples R China

During the charging process of electric vehicles (EV), the circuit inside the charger plug is connected in series, the charger input voltage does not match the rated input voltage, the temperature caused by the severe heating of the charging time is too high for too long, and other factors are very likely to trigger a fire in the vehicle charging pile. In this paper, an improved You Only Look Once v4 (YOLOv4) real-time target detection algorithm based on machine vision is proposed to monitor the site based on existing monitoring equipment, transmit live video information in real-time, expand the monitoring range, and significantly reduce the cost of use. During the experiment, the improved neural network model was trained by a homemade fire video image dataset, and a k-means clustering algorithm iwasintroduced to recalculate the anchor frame size for the specific object of flame;the existing dataset was used to perform multiple divisions by using a tenfold cross-validation algorithm, thus avoiding the selection of chance hyperparameters and models that do not have generalization ability because of special divisions. The experimental results show that the improved algorithm is fast and accurate in detecting large-size flames in real-time and small-size flames at the beginning of a fire, with a detection speed of 43 fps/s, mAP value of 91.53%, and F1 value of 0.91. Compared with YOLOv3 and YOLOv4 models, the improved model is sensitive to detecting different sizes of flames. It can suppress false alarms well in a variety of complex lighting environments. The prediction frame size fits the area where the target is located, the detection accuracy remains stable, and the comprehensive performance of the network model is significantly improved to meet the demand of real-time monitoring. It is significant for developing the EV industry and enhancing emergency response capability.

关键词： electric vehicle charging stations machine vision fire detection YOLOv4 k-means clustering algorithm

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PRkAR2A-derived circular RNAs promote the malignant transformation of colitis and distinguish patients with colitis-associated colorectal cancer

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CLINICAL AND TRANSLATIONAL MEDICINE 2022年第2期12卷 e683页

作者： Wan, Daiwei Wang, Sentai Xu, Zhihua Zan, Xinquan Liu, Fei Han, Ye Jiang, Min Wu, Airong Zhi, Qiaoming Soochow Univ Dept Gen Surg Affiliated Hosp 1 Suzhou 215006 Peoples R China Soochow Univ Dept Gastroenterol Affiliated Hosp 1 Suzhou 215006 Peoples R China Soochow Univ Dept Oncol Affiliated Hosp 1 Suzhou 215006 Peoples R China

Background Emerging studies have proved that colonic inflammation caused by refractory inflammatory bowel disease (IBD) can initiate the colitis-associated cancer (CAC), but the transition from inflammation to carcinoma is still largely unknown. Methods In this study, mouse colitis and CAC models were established, and the RNA-seq by circRNA microarray was employed to identify the differentially expressed circRNAs and mRNAs in different comparisons (DSS vs. NC and AOM/DSS vs. DSS). The bioinformatics analyses were used to search the common characteristics in mouse colitis and CAC. Results The k-means clustering algorithm packaged these differential expressed circRNAs into subgroup analysis, and the data strongly implied that mmu_circ_0001109 closely correlated to the pro-inflammatory signals, while mmu_circ_0001845 was significantly associated with the Wnt signalling pathway. Our subsequent data in vivo and in vitro confirmed that mmu_circ_0001109 could exacerbate the colitis by up-regulating the Jak-STAT3 and NF-kappa B signalling pathways, and mmu_circ_0001845 promoted the CAC transformation through the Wnt signalling pathway. By RNA blasting between mice and humans, the human RTEL1- and PRkAR2A-derived circRNAs, which might be considered as homeotic circRNAs of mmu_circ_0001109 and mmu_circ_0001845, respectively, were identified. The clinical data revealed that RTEL1-derived circRNAs had no clinical significance in human IBD and CAC. However, three PRkAR2A-derived circRNAs, which had the high RNA similarities to mmu_circ_0001845, were remarkably up-regulated in CAC tissue samples and promoted the transition from colitis to CAC. Conclusions Our results suggested that these human PRkAR2A-derived circRNAs could be novel candidates for distinguishing CAC patients and predicted the prognosis of CAC.

关键词： circular RNA colitis-associated colorectal cancer PRkAR2A RNA sequencing k-means clustering algorithm

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Robust Static Transmission Expansion Planning Considering Contingency and Wind Power Generation

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第2期31卷 461-470页

作者： de Paula, Arthur N. de Oliveira, Edimar J. de Oliveira, Leonardo W. Honorio, Leonardo M. Univ Fed Juiz de Fora Rua Jose Lourenco Kelmer S-N BR-36036900 Juiz De Fora MG Brazil

This paper proposes a framework based on the Benders decomposition to obtain a scenario-based robust static transmission expansion planning by considering N-1 security criterion, transmission losses and uncertainties in wind power generation. The model is solved by a bi-level approach that seeks to minimize investment cost as well as penalty costs of wind spill and load curtailment. The wind uncertainty is modeled by grouped historical wind series through k-means clustering technique maintaining the wind correlation between different geographic regions. Case studies are performed in the well-known power systems: IEEE-RTS 24-bus test system and an equivalent Brazilian Southern 46-bus system. In addition, a detailed tutorial case is also presented with a modified version of Garver 6-bus test system.

关键词： Transmission expansion planning Transmission active losses Benders decomposition N-1 security criterion Wind power uncertainty k-means clustering algorithm

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Automatic data clustering using nature-inspired symbiotic organism search algorithm

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kNOWLEDGE-BASED SYSTEMS 2019年第Jan.1期163卷 546-557页

作者： Zhou, Yongquan Wu, Haizhou Luo, Qifang Abdel-Baset, Mohamed Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Key Lab Guangxi High Sch Complex Syst & Computat Nanning 530006 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China Zagazig Univ Fac Comp & Informat El Zera Sq Zagazig 44519 Sharqiyah Egypt

The symbiotic organism search (SOS) is a recently proposed metaheuristic optimization algorithm that simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in an ecosystem. clustering is a popular data analysis and data mining technique, and k-means clustering is one of the most commonly used methods. However, its effectiveness is highly dependent on the initial solution, and the algorithm may become trapped around local optima. In view of these drawbacks of the k-means method, this paper describes the use of the SOS algorithm to solve clustering problems. Ten standard datasets from the UCI Machine Learning Repository are used to evaluate the effectiveness of SOS against that of optimization algorithms including differential evolution, cuckoo search, flower pollination, particle swarm optimization, artificial bee colony, multi-verse optimizer, and k-means. Experimental results show that the SOS algorithm not only achieves superior accuracy, but also exhibits a higher level of stability. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Symbiotic organism search k-means clustering algorithm clustering analysis Metaheuristic optimization

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A comprehensive obstacle analysis framework on dispersed wind power: A case of China

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JOURNAL OF CLEANER PRODUCTION 2021年 297卷 126684-126684页

作者： Wu, Yunna Liu, Fangtong Deng, Zhongqing He, Jiaming Xu, Chuanbo Tao, Yao ke, Yiming North China Elect Power Univ Sch Econ & Management Beijing Peoples R China Beijing Key Lab New Energy & Low Carbon Dev Beijing Peoples R China Jinan Univ Energy & Elect Res Ctr Zhuhai Peoples R China

Dispersed wind power is undoubtedly assisting sustainable development of society but hindered by various factors. To promote the development of dispersed wind power, this paper is devoted to obstacles identification and analysis to provide effective development proposals. Firstly, a three-stage process (namely collection, screening and improvement) is developed to extract common obstacles from literature and explore unique obstacles according to the characteristics and development status of dispersed wind power. Then, DANP method is improved to analyze interaction, interrelation and relative importance among filtered obstacles, where Pythagorean fuzzy numbers and k-means clustering algorithm are introduced to modify incomplete information collection and arbitrary threshold determination, respectively. Three reasonable thresholds are determined according to clustering results, which divide the influence degree among obstacles into four categories (namely, high, moderate, low and no influence). The analysis results indicate that lagged unified planning ranks the first of obstacles, followed by financial difficulty and restricted grid access. On that basis, some practical proposals on regional coordinated development are suggested, including building unified coordination platforms and forming dispersed wind power project group. This paper does not only enrich material database for scholars, but also provide reference and guidance for government and related enterprises. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Dispersed wind power Obstacles analysis Pythagorean fuzzy numbers DANP k-means clustering algorithm

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Intelligent analysis technology of bamboo structure. Part II: The variability of radial distribution of fiber volume fraction

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INDUSTRIAL CROPS AND PRODUCTS 2021年 174卷 114164-114164页

作者： Xu, Haocheng Li, Jing Ma, Xinxin Yi, Wukun Wang, Hankun Int Ctr Bamboo & Rattan Inst New Bamboo & Rattan Based Biomat Key Lab Natl Forestry & Grassland Adm Beijing Bam Beijing 100102 Peoples R China Chinese Acad Forestry Res Inst Wood Ind Key Lab Natl Forestry & Grassland Adm Wood Sci & Beijing 100091 Peoples R China Xiaoxiang Res Inst Big Data Changsha 410000 Peoples R China

Scientific and reasonable utilization of bamboo is strongly related to the radial distribution of the fiber volume fraction. In this paper, the k-means clustering algorithm was applied to set separate thresholds for individual vascular bundles, to solve the problem that the bamboo ring cannot be binarized well. We introduced a layered model based on the WEB coordinate system, programmed via JavaScript, to layer the cross-section within the entire bamboo conveniently and accurately. It allowed uniform layering of sample areas of any thickness, any width, and any number of layers. Furthermore, this research systematically studied the bamboo rings at the breast height of Moso bamboo [Phyllostachys edulis (Carr)H. de Lebaie] grown in 12 major producing areas of China and the 50-internode sections from base to top of one bamboo with the method mentioned above. The results indicated that the thickness of a single layer should not be higher than 0.4 mm while examining the gradient structure of bamboo. The radial distribution of fiber volume fraction from 12 areas all followed the ExpDec1 function, and the mean goodness of fit (R2) was 0.988. The radial distribution of fiber volume fraction in the first 45 sections from the base to the top corresponded to the ExpDec1 function. It provided a theoretical basis for the functional improvement of bamboo and the development of new bamboo-based smart materials.

关键词： Fiber volume fraction Radial distribution k-means clustering algorithm Regional differences Axial variation ExpDec1 function

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Research on glass classification and identification based on heritage samples

Research on glass classification and identification based on...

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作者： Zhiyuan Liu Institute of electric power South China University of Technology

The study of ancient glass has been intensified in archaeological circles today,but its susceptibility to weathering can affect archaeologists' ability to classify *** order to classify glass artefacts,this paper establishes a 'subclass classification model based on the sum of the absolute values of relative changes' and uses the k-means clustering algorithm to classify the *** order to identify different classes of glass objects,the chemical composition of the glass is extracted from the centroids of the different subclasses,and the Euclidean distance between the class centres is solved for,and the class represented by the class centre with the lowest Euclidean distance is taken as the class corresponding to the unknown object.

关键词： k-means clustering algorithm Euclidean distance glass artefacts

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Chemical composition analysis of ancient glass products based on decision tree

Chemical composition analysis of ancient glass products base...

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作者： Zhihao He Weiduo Qin Changping Duan School of Food Science and Bioengineering Tianjin Agriculture University School of Humanities Tianjin Agriculture University School of Mechanical and Electrical Engineering Lanzhou University of Technology

Due to the effects of prolonged burial,freshly unearthed ancient glass is often weathered to varying degrees,and it is difficult to identify the type of *** introduce machine learning into the composition analysis and type identification of ancient glass *** objective is to build a reliable ancient glass classification model based on decision trees and two different k-means clustering *** performance of the decision tree is measured by the ROC *** performance of its clustering algorithm was evaluated by the Calinski-Harabasz *** results show that the area of AUC in the decision tree is 1 and the highest Calinski-Harabasz index of the two clustering algorithms is *** predictive ability of the model was verified well.

关键词： Machine learning Decision tree k-means clustering algorithm Mini-batch k-means

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MULTIMODAL OPTIMIZATION OF JOB-SHOP SCHEDULING PROBLEMS USING A clustering-GENETIC algorithm BASED APPROACH

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2019年第5期26卷 651-662页

作者： Zou, Pan Rajora, Manik Liang, Steven Y. Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Donghua Univ Coll Mech Engn Shanghai Peoples R China

Though ample work has been performed on the optimization of job-shop scheduling problems (JSSPs), very few techniques can satisfy the requirement of modern-day workshops, i.e., providing multiple schedules that achieve the desired goal. These techniques also do not provide a good balance between converging to the optimal solution while finding multiple optimal solutions. To overcome this obstacle, a new algorithm is proposed by combining the k-means clustering algorithm and genetic algorithm (GA) for multimodal optimization of JSSPs. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters based on some machine sequence-related features under the assumption that different global optima will have different features. Next, the adapted genetic operators are applied to the individuals belonging within the same cluster with the aim of independently searching for global optima within each cluster. The performance of the proposed algorithm is measured by its application to the multimodal optimization of benchmark JSSPs and comparing its performance against other multimodal optimization algorithms. The results of the case studies show that the algorithm has a better average optimal value and is also capable of finding multiple optimal solutions.

关键词： multimodal optimization job-shop scheduling problem (JSSP) k-means clustering algorithm genetic algorithm

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