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An adaptive segmentation method combining MSRCR and mean shift algorithm with k-means correction of green apples in natural environment

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Information Processing in Agriculture 2019年第2期6卷 200-215页

作者： Sashuang Sun Huaibo Song Dongjian He Yan Long College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China

During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be *** this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light *** enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent *** that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold ***,the poorly segmented images were corrected using the k-means algorithm according to the shape,color and texture *** users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this *** with the manifold ranking algorithm,the k-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,***,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared *** proposed method accurately recognized the green apples under complex illumination conditions and growth ***,it provided effective references for intelligent growth monitoring and yield estimation of fruits.

关键词： Green fruit Adaptive segmentation MSRCR algorithm Mean shift algorithm k-means clustering algorithm Manifold ranking algorithm

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Privacy Preserving Collaborative Filtering Approach for Recommendation System 3

Privacy Preserving Collaborative Filtering Approach for Reco...

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IEEE International Conference on Wireless Communications, Signal Processing and Networking (IEEE WiSPNET)

作者： Manju, S. Thenmozhi, M. Pondicherry Engn Coll Dept Comp Sci & Engn Pondicherry India

ISBN: (纸本)9781538636244

Recommender Systems use collaborative filtering in order to make recommendations based on similar interest between users or items. In this process, Privacy of users is at severe risk because recommender servers may share user's private data with third parties to make personalized advertisements and in some cases user privacy may be exposed to the public or attacked by malicious users. The existing works are based on encryption based and randomization-based techniques, but they compromise accuracy for privacy and privacy for accuracy. In this project, Privacy Preserving Collaborative Filtering approach has been proposed which solves the limitations in the existing works. This work adopts fuzzy logic to deal with uncertainty among user's interest ratings. The fuzzified data is then perturbated by applying random rotation perturbation technique, thus the user's interest is not directly available for the recommendation server or the third-party. Using the perturbated data, item clusters are formed by utilizing ant-based clustering algorithm. These clusters help the recommendation server to apply item collaborative filtering for the recommendation process. In order to refine the cluster center provided by ant-based clustering process k-means clustering algorithm is applied. The pheromone values obtained during the ant-based clustering is further utilized by the recommender server in order to provide accurate recommendation to the active user.

关键词： Recommender system Collaborative filtering Fuzzy logic Ant-based clustering algorithm k-means clustering algorithm

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Image Segmentation using Firefly algorithm

Image Segmentation using Firefly Algorithm

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International Conference on Information Technology (InCITe) - The Next Generation IT Summit on the Theme - Internet of Things - Connect Your Worlds

作者： Sharma, Akash Sehgal, Smriti Amity Univ Dept CSE Noida India

ISBN: (纸本)9781509026128

Image segmentation is an important step in the domain of image processing in which we segment the image into several parts which carry certain type of information for the user. Image segmentation is very difficult step in the processing of the image which aims at extracting the information from image. clustering is used to segment the image. clustering algorithms are part of data mining algorithm that groups the data into various number of given clusters. All the data points in one cluster have similar properties based on which they are clustered i.e. each cluster has minimum difference between its points and maximum difference from other cluster data points. The proposed algorithm uses k-mean algorithm and firefly to cluster image pixels into k cluster for segmentation. Since k-mean clustering algorithm is gets trapped in local optima it is optimized using firefly algorithm. Swarm intelligence based algorithms forms the basis of the firefly algorithm which has several application and used to solve optimization problems. Firefly algorithm has been applied in many research and optimization areas. Firefly algorithm and its hybridized version have been used to solve various problems successfully. To apply firefly algorithm to wide areas of problem the firefly algorithm must be modified or integrated with other algorithms. Presently metaheuristic nature of algorithm plays an important role and current optimization algorithm include this nature and are very efficient in solving NP-hard problems.

关键词： k-means clustering algorithm Firefly Optimization Image Segmentation Correlation

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Proposing An Evolutionary Method Based On Maximization Precision Of Group Recommender Systems 7

Proposing An Evolutionary Method Based On Maximization Preci...

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2015 7th Conference on Information and knowledge Technology (IkT)

作者： Loveymi, Shakib Hamzeh, Ali Shiraz Univ Dept Comp Sci & Engn & IT Shiraz Iran

ISBN: (纸本)9781467374859

in this paper we proposed an evolutionary algorithm to maximize precision of group recommender systems and reducing the online calculations. In this method we try to build a transition matrix that's made by an evolutionary algorithm and then we multiply this transition matrix with user-item rate matrix. By this action we go to a reduced dimension space. The characteristic of this space is that the users that are really more similar, would be closer to each other. Also because the dimension of the user-item matrix has been reduced, the online calculations are hugely reduced and if we have a new user, we can easily multiply his rates on the transition matrix and find out that he has to be in which group. We used the average real rate of group users to an item as a metric to evaluate how much this item is suitable for this specific group. At end we compare this method with other methods that also reduce the dimension on the various datasets. Then we show that our method works better. Finally we have a discussion about weakness and strength of our method.

关键词： Group Recommender Systems Evolutionary algorithm Transition matrix k-means clustering algorithm Average Precision

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Internet Public Opinion Hotspot Detection Research Based on k-means algorithm

Internet Public Opinion Hotspot Detection Research Based on ...

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1st International Conference on Swarm Intelligence

作者： Liu, Hong Li, Xiaojun Zhejiang Gongshang Univ Coll Comp & Informat Engn Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9783642134975

Internet is becoming a spreading platform for the public opinion. It is important to grasp the internet public opinion (IPO) in time and understand the trends of their opinion correctly. Text mining plays a fundamental role in a number of information management and retrieval tasks. This paper studies internet public opinion hotspot detection using text mining approaches. First, we create an algorithm to obtain vector space model for all of text document. Second, this algorithm is combined with k-means clustering algorithm to develop unsupervised text mining approach. We use the proposed text mining approach to group the internet public opinion into various clusters, with the center of each representing a hotspot public opinion within the current time span. Through the result of the experiment, it shows that the efficiency and effectiveness of the algorithm using.

关键词： Internet public opinion k-means clustering algorithm vector space model text classification

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Lithium battery surface defect detection based on the YOLOv3 detection algorithm 10

Lithium battery surface defect detection based on the YOLOv3...

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10th International Symposium on Precision Mechanical Measurements

作者： Lang, Xianli Zhang, Yu Shu, Shuangbao Liang, Huajun Zhang, Yuzhong Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Anhui Prov Key Lab Measuring Theory & Precis Inst Hefei 230009 Peoples R China

ISBN: (数字)9781510649941

ISBN: (纸本)9781510649941;9781510649934

With the continuous development of science and technology, cylindrical lithium batteries, as new energy batteries, are widely used in many fields. In the production process of lithium batteries, various defects may occur. To detect the defects of lithium batteries, a detection algorithm based on convolutional neural networks is proposed in this paper. Firstly, image preprocessing is introduced on the collected lithium battery dataset. Secondly, the k-means clustering algorithm is used on the preprocessed dataset to generate anchor boxes for lithium battery defect detection. Then the detection network YOLOv3 is trained with the given dataset. Finally, the detection network YOLOv3 is applied to output the type and location information of the defect. The experimental results show that the mean average precision (mAP) value of the detection algorithm on the lithium battery validation dataset reaches 94% and the detection speed is 25 frames per second. The proposed algorithm can effectively locate and classify the bottom defects of the lithium battery.

关键词： Defect detection Convolutional neural network Image processing k-means clustering algorithm YOLOv3

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RETRACTED: Pressure source of athletes in field based on improved hierarchical k-means algorithm (Retracted Article)

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INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION 2021年第1-Sup期60卷 2300-2314页

作者： Wu, Qian Jilin Sport Univ Coll Changchun 130000 Jilin Peoples R China

Nowadays, with the development of economy, sports industry attracts more and more people's attention, and the number and scale of sports events continue to expand. But the fierce competition, for athletes, is not only physical competition, psychological competition is particularly important. Psychological pressure is an important factor that affects athletes to play at a high level in the competition field. How to analyze the pressure source of athletes in the competition field is very important for athletes to overcome the competition pressure. Therefore, this paper designs a method based on the improved hierarchical k-means clustering algorithm (kMCA) to analyze the pressure source of athletes in the competition field, so as to help athletes overcome the pressure on the competition field and promote their high-level performance. In this method, firstly, 182 athletes were investigated by questionnaire to obtain the data of related psychological pressure sources. Secondly, because kMCA is sensitive to the selection of initial class center, the performance of kMCA is directly related to the selection of initial class. Aiming at the problem of kMCA, this paper proposes an improved hierarchical kMCA. Finally, the improved hierarchical kMCA is applied to the clustering analysis of sports competition pressure source data. Through simulation analysis, compared with kMCA, the improved layered kMCA proposed in this paper has a good performance improvement. The improved hierarchical kMCA proposed in this paper can be applied to the analysis of the pressure source of athletes in the competition field, which can analyze the pressure of athletes and get the characteristics of the pressure of athletes, so as to help athletes overcome the pressure in the competition field and achieve the mental health of the competition field?

关键词： Sports events psychological stress stress source analysis k-means clustering algorithm

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Research on the Relationship between Fuel Economy and Driving Behavior of Heavy-Duty Vehicles 23

Research on the Relationship between Fuel Economy and Drivin...

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23rd COTA International Conference of Transportation Professionals (CICTP) - Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation

作者： Zhang, Shuai-Qi Xu, Ting Peng, Chong Wang, Jing-Tao Wang, Shu-Zhen Changan Univ Xian Coll Transportat Engn Xian Peoples R China Changan Univ Xian Coll Transportat Engn Dept Transportat Xian Peoples R China

ISBN: (数字)9780784484869

ISBN: (纸本)9780784484869

In order to study the influence of heavy-duty vehicle driving behavior on fuel economy, this paper collected the on-board diagnostics (OBD) data of vehicle driving on a circular urban arterial road. After data preprocessing, the kinematic segments were divided, and the characteristic parameter values were defined. Principal component analysis was used to reduce the dimension of the values. Then k-means clustering algorithm was carried out to classify driving behavior based on traffic and operating conditions. The cumulative fuel consumption and running time under the two conditions were analyzed. Finally, the abnormal driving behavior is defined, and the regression analysis of its impact on fuel consumption was carried out. The research shows that different traffic and operating conditions have a direct impact on vehicle fuel consumption per hundred kilometers, which can comprehensively evaluate the relationship between vehicle driving behavior and fuel economy.

关键词： Driving behavior Fuel economy kinematic segment characteristic value Principal component analysis k-means clustering algorithm

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A DD-means clustering algorithm for Intrusion Behavior Analysis

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电脑学刊 2019年第3期30卷 224-230页

作者： Yongxin Feng Yingyun kang Yuntao Zhao Wenbo Zhang

With the rapid development of Internet technology, network security is becoming more and more severe. Data transferred on the Internet can be divided into two categories, normal data and intrusion data. clustering is a popular data analysis technology to classify data according to the characteristics of the data and clustering can also be used for intrusion behavior analysis on the Internet. k-means is a simple and an efficient data clustering algorithm, but it has a tendency to converge to local optima and the optimization results depend on the initial values of cluster centers. Therefore, DD-means clustering algorithm, which selects the initial center points based on the density and the maximum distance of different data points, has been proposed to improve the performance of classical k-means. The performance of DD-means clustering algorithm is significantly better than that of classical k-means clustering algorithm in silhouette coefficient. The experimental results show that the proposed algorithm is effective and efficient for analyzing the intrusion behavior on the Internet.

关键词： classification DD-means k-means clustering algorithm network security

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Optimal Locations of Remote Radio Units in CoMP Systems for Energy Efficiency

Optimal Locations of Remote Radio Units in CoMP Systems for ...

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IEEE 72nd Vehicular Technology Conference Fall 2010

作者： Zhang, Congqing Zhang, Tiankui Zeng, Zhimin Cuthbert, Laurie Xiao, Lin Beijing Univ Posts & Telecommun Sch Informat & Telecommun Engn Beijing 100088 Peoples R China Univ London Sch Elect Engn & Comp Sci London WC1E 7HU England

ISBN: (纸本)9781424435746

In coordinated multi-point transmission (CoMP) systems, the optimal remote radio unit (RRU) location is analyzed theoretically and a RRU location design scheme for energy efficiency in practical scenarios is given. An average minimum access distance criterion is given for RRU location optimization. By minimizing the average distance between users and RRU, the optimal RRU distribution can be obtained when users are located uniformly in the cell. Taking into account the fact that user distribution will not be completely uniform in a practical environment, the k-means clustering algorithm is used to get the optimized RRU deployment in a practical user distribution. Simulation results show that the uplink transmission power can be greatly reduced with the RRU optimized location design in both the uniform and non-uniform user distribution.

关键词： CoMP energy efficiency RRU location optimization site design k-means clustering algorithm

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