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Recognition of green apples based on fuzzy set theory and manifold ranking algorithm

OPTIk 2018年 165卷 395-407页

作者： Sun, Sashuang Wu, Qian Jiao, Lin Long, Yan He, Dongjian Song, Huaibo Northwest A&F Univ Coll Mech & Elect Engn 22 Xinong Road Yangling 712100 Shaanxi Peoples R China Minist Agr Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling 712100 Shaanxi Peoples R China

Accurate recognition of green apple targets is one of the most practical technologies applied to agricultural sciences. The primary goal of this study was to solve the difficulties of distinguishing green apples in similar background areas, such as leaves, by fusing fuzzy set theory and the manifold ranking algorithm (FSMR). First, the original images were roughly enhanced to make apple targets more prominent by using fuzzy set theory in the HSI color space. Second, the specific query nodes were selected for ranking pixels in the whole image to obtain the final recognition results. Then, a series of operations including hole filling, edge smoothing and the mathematical morphology method were carried out to extract the contours of apple targets. Finally, the proposed method was compared with the additional methods based on manifold ranking (MR), saliency detection by self-resemblance (SDSR) and the k-means clustering algorithm. 100 images of green apple targets with different statuses in natural scenes were tested in the experiment. The results showed that the proposed method could effectively extract the real contours and achieve a high degree of coincidence with the edge lines of the targets, indicating that the recognition results were more accurate. Additionally, the average overlapping coefficient was 90.87%, which was 17.34% higher than the SDSR algorithm and 15.98% higher than the k-means clustering algorithm. In conclusion, the proposed method could provide a reference for the developments of growth monitoring, yield estimating and automatic fruit picking. (C) 2018 Elsevier GmbH. All rights reserved.

关键词： Immature green fruit Fuzzy set Manifold ranking Saliency detection Self-resemblance strategy k-means clustering algorithm

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Predicting stability of integrated circuit test equipment using upper side boundary values of normal distribution

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The Journal of China Universities of Posts and Telecommunications 2024年第2期31卷 85-93页

作者： Zhan Wenfa Hu Xinyi Zheng Jiangyun Yu Chuxian Cai Xueyuan Zhang Lihua School of Electronic Engineering and Intelligent Manufacturing Anqing Normal UniversityAnqing 246133China School of Computer and Information Technology Anqing Normal UniversityAnqing 246133China

In response to the growing complexity and performance of integrated circuit(IC),there is an urgent need to enhance the testing and stability of IC test equipment.A method was proposed to predict equipment stability using the upper side boundary value of normal ***,the k-means clustering algorithm classifies and analyzes sample *** accuracy of this boundary value is compared under two common confidence levels to select the optimal threshold.A range is then defined to categorize unqualified test *** experimental verification,the method achieves the purpose of measuring the stability of qualitative IC equipment through a deterministic threshold value and judging the stability of the equipment by comparing the number of unqualified data with the threshold value,which realizes the goal of long-term operation monitoring and stability analysis of IC test equipment.

关键词： k-means clustering algorithm the upper side boundary of normal distribution threshold integrated circuit(IC)test equipment stability analysis

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Robust PVC Identification by Fusing Expert System and Deep Learning

BIOSENSORS-BASEL

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BIOSENSORS-BASEL 2022年第4期12卷 185页

作者： Cai, Zhipeng Wang, Tiantian Shen, Yumin Xing, Yantao Yan, Ruqiang Li, Jianqing Liu, Chengyu Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 714009 Peoples R China

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for k-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively;P+ was 92.47% and 93.18%, respectively;and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

关键词： electrocardiogram k-means clustering algorithm premature ventricular contraction rule-based decision algorithm

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Ranked keyword Search Over Encrypted Cloud Data Through Machine Learning Method

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第1期16卷 525-536页

作者： Miao, Yinbin Zheng, Wei Jia, Xiaohua Liu, Ximeng Choo, kim-kwang Raymond Deng, Robert H. Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China City Univ Hong Kong Hong Kong 999077 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China Fuzhou Univ Coll Math & Comp Sci Key Lab Informat Secur Network Syst Fuzhou 350108 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore

Ranked keyword search over encrypted data has been extensively studied in cloud computing as it enables data users to find the most relevant results quickly. However, existing ranked multi-keyword search solutions cannot achieve efficient ciphertext search and dynamic updates with forward security simultaneously. To solve the above problems, we first present a basic Machine Learning-based Ranked keyword Search (ML-RkS) scheme in the static setting by using the k-means clustering algorithm and a balanced binary tree. ML-RkS reduces the search complexity without sacrificing the search accuracy, but is still vulnerable to forward security threats when applied in the dynamic setting. Then, we propose an Enhanced ML-RkS (called ML-RkS$<^>{+}$+) scheme by introducing a permutation matrix. ML-RkS$<^>{+}$+ prevents cloud servers from making search queries over newly added files via previous tokens, thereby achieving forward security. The security analysis proves that our schemes protect the privacy of indexes, query tokens and keywords. Empirical experiments using the real-world dataset demonstrate that our schemes are efficient and feasible in practical applications.

关键词： Indexes keyword search Cryptography Security Binary trees Complexity theory Servers Ranked keyword search k-means clustering algorithm balanced binary tree permutation matrix forward security

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A task unloading strategy of IoT devices using deep reinforcement learning based on mobile cloud computing environment

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WIRELESS NETWORkS 2024年第5期30卷 3587-3597页

作者： Qi, Hui Mu, Xiaofang Shi, Ying Taiyuan Normal Univ Dept Comp Jingzhong 030619 Shanxi Peoples R China

Aiming at the task unloading mode in cloud computing environment, the task unloading problem for IoT devices is studied. Through theoretical analysis, we can know that in the task unloading problem, it is usually contradictory to improve the utilization of cloud resources and reduce the task delay. In order to solve this problem, a task unloading scheme for Internet of things devices using deep reinforcement learning algorithm is proposed. The deep reinforcement learning algorithm is used to model the task unloading problem. The return value with weight is introduced into the algorithm, and the utilization rate of cloud resources and the delay of unloading task are weighed by adjusting the return value of the weight. First of all, the improved k-means clustering algorithm with weighted density is used to cluster the physical machines. The physical machines of each cluster have similar bandwidth and task waiting time. Then, deep reinforcement learning is used to select the best physical machine cluster from the current unloading tasks. Finally, the improved PSO algorithm is used to select the optimal physical machine from the optimal cluster, and Pareto is used to improve the convergence speed. Experimental results show that compared with the traditional method, the proposed algorithm has a good performance, and can achieve the goal of increasing the utilization of physical machine resources and reducing task delay.

关键词： Task unloading Deep reinforcement learning Mobile cloud computing k-means clustering algorithm Physical machine Convergence speed

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A prototype classification method and its use in a hybrid solution for multiclass pattern recognition

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PATTERN RECOGNITION 2006年第4期39卷 624-634页

作者： Chou, CH Lin, CC Liu, YH Chang, F Acad Sinica Inst Informat Sci Taipei 115 Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan

In this paper, we propose a prototype classification method that employs a learning process to determine both the number and the location of prototypes. This learning process decides whether to stop adding prototypes according to a certain termination condition, and also adjusts the location of prototypes using either the k-means (kM) or the fuzzy c-means (FCM) clustering algorithms. When the prototype classification rnethod is applied, the support vector machine (SVM) method can be used to post-process the top-rank candidates obtained during the prototype learning or matching process. We apply this hybrid solution to handwriting recognition and address the convergence behavior and runtime consumption of the prototype construction process, and discuss how to combine our prototype classifier with SVM classifiers to form an effective hybrid classifier. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： fuzzy c-means clustering algorithm handwritten character recognition hybrid classifier k-means clustering algorithm prototype learning support vector machine

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A self-adaptive mating restriction strategy based on survival length for evolutionary multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 43卷 31-49页

作者： Li, Xin Zhang, Hu Song, Shenmin Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Heilongjiang Peoples R China Beijing Electromech Engn Inst Beijing 100074 Peoples R China

Mating restriction plays an important role in improving the performance of the multiobjective evolutionary algorithms. However, research on mating restriction is still insufficient, and in most cases, the same mating restriction strategy is set for all individuals without consideration of individual quality. To effectively alleviate the above problems, this paper proposes a self-adaptive mating restriction strategy based on survival length (MRSL). Based on the information of neighbourhood relationship extracted by clustering, mating parents are selected from the neighbours for exploitation or from the whole population for exploration, which is controlled by the mating restriction probability. Furthermore, considering different individuals are in need of different search space, each individual has a separate mating restriction probability. Additionally, the survival length of the individual is proposed to define and update the mating restriction probability self-adaptively at each generation. The survival length of the individual represents the number of survival generations over the last certain period of time. A multiobjective differential evolutionary algorithm called MDESL is proposed by using MRSL, and compared with five representative algorithms on several test instances. The experimental results validate the superiority of MDESL over the comparison algorithms.

关键词： Multiobjective optimization Evolutionary algorithm Mating restriction k-means clustering algorithm Differential evolution

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Combined FATEMD-based band selection method for hyperspectral images

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IET IMAGE PROCESSING 2019年第2期13卷 287-298页

作者： Yu, Wenbo Zhang, Miao Shen, Yi Harbin Inst Technol Dept Control Sci & Engn Harbin Heilongjiang Peoples R China

Feature selection, which is called band selection for hyperspectral data, is widely used for hyperspectral images. A novel hyperspectral band selection method based on combined fast and adaptive tridimensional empirical mode decomposition (cFATEMD) is proposed in this study. The hyperspectral data is decomposed into a set of tridimensional intrinsic mode functions (TIMFs) and a residual (RES) by FATEMD, which can reduce high-frequency noise and signal. A stop condition of the decomposition is proposed based on the k-means clustering algorithm and the Dunn validity index, which can prevent excessive decomposition and make generated RES contain as much useful information as possible. In consideration of the useful information in decomposition results, these TIMFs and the RES are combined into a new data based on the spectral similarity between themselves and the original data. Four state-of-the-art band selection methods, cooperating with the proposed cFATEMD, are used to select bands by the new combined data. Several experiments are conducted on three publicly available hyperspectral datasets and the results are compared with corresponding methods' results using the original data. Experimental results demonstrate that the proposed method yields great classification appearance.

关键词： hyperspectral imaging image classification feature selection remote sensing geophysical image processing pattern clustering combined FATEMD-based band selection method hyperspectral images feature selection hyperspectral data hyperspectral band selection method combined fast-and-adaptive tridimensional empirical mode decomposition cFATEMD tridimensional intrinsic mode functions TIMF k-means clustering algorithm Dunn validity index RES spectral similarity residual publicly available hyperspectral datasets

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Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm

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kNOWLEDGE-BASED SYSTEMS 2011年第3期24卷 378-385页

作者： Shen, Wei Guo, Xiaopen Wu, Chao Wu, Desheng Univ Toronto RiskLab Toronto ON M5S 3G3 Canada N China Elect Power Univ Sch Business & Adm Beijing 102206 Peoples R China Univ Waterloo Waterloo ON N2L 3G1 Canada Reykjavik Univ Menntavegur 1 IS-101 Reykjavik Iceland

Stock index forecasting is a hot issue in the financial arena. As the movements of stock indices are non-linear and subject to many internal and external factors, they pose a great challenge to researchers who try to predict them. In this paper, we select a radial basis function neural network (RBFNN) to train data and forecast the stock indices of the Shanghai Stock Exchange. We introduce the artificial fish swarm algorithm (AFSA) to optimize RBF. To increase forecasting efficiency, a k-means clustering algorithm is optimized by AFSA in the learning process of RBF. To verify the usefulness of our algorithm, we compared the forecasting results of RBF optimized by AFSA, genetic algorithms (GA) and particle swarm optimization (PSO), as well as forecasting results of ARIMA. BP and support vector machine (SVM). Our experiment indicates that RBF optimized by AFSA is an easy-to-use algorithm with considerable accuracy. Of all the combinations we tried in this paper, BIAS6 + MA5 + ASY4 was the optimum group with the least errors. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Artificial fish swarm algorithm Radial basis function neural network k-means clustering algorithm Data mining Shanghai Stock Exchange Index

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Analyzing the Urban Hierarchical Structure Based on Multiple Indicators of Economy and Industry: An Econometric Study in China

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1831-1855页

作者： Jing Cheng Yang Xie Jie Zhang School of Architecture Tsinghua UniversityBeijing100084China Beijing Tsinghua Tongheng Urban Planning&Design Institute Beijing100085China

For a city,analyzing its advantages,disadvantages and the level of economic development in a country is important,especially for the cities in China developing at flying *** corresponding literatures for the cities in China have not considered the indicators of economy and industry in *** this paper,based on multiple indicators of economy and industry,the urban hierarchical structure of 285 cities above the prefecture level in China is *** indicators from the economy,industry,infrastructure,medical care,population,education,culture,and employment levels are selected to establish a new indicator system for analyzing urban hierarchical *** factor analysis method is used to investigate the relationship between the variables of selected indicators and obtain the score of each common factor and comprehensive scores and rankings for 285 cities above the prefecture level in *** to the comprehensive scores,285 cities above the prefecture level are clustered into 15 levels by using k-means clustering ***,the hierarchical structure system of the cities above the prefecture level in China is obtained and corresponding policy implications are *** results and implications can not only be applied to the urban planning and development in China but also offer a reference on other developing *** methodologies used in this paper can also be applied to study the urban hierarchical structure in other countries.

关键词： Urban planning hierarchical structure prefecture-level city factor analysis method k-means clustering algorithm China

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