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5th IEEE Conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)

作者： Xue, Weixing Hua, Xianghong Li, Qingquan Qiu, Weining Peng, Xuesheng Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Hubei Peoples R China Shenzhen Univ Dept Shenzhen Key Lab Spatial Smart Sensing & Ser Shenzhen 518060 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China

ISBN: (纸本)9781538637555

Reference points (RP) clustering methods such as k-means are frequently used to reduce the region of search in most of fingerprint clustering algorithms. However, traditional clustering algorithms analysis the geometric proximity of RP only in the offline phase, which has nothing to do with the test point. Meanwhile, both the clustering pattern and the number of clusters need to be predefined directly or indirectly, which means an unsuitable clustering pattern or an unsuitable number of clusters would lead to poor estimation accuracy. In this letter, in order to improve the performance of kNN algorithm with the neighboring RP selection, we utilize the k-means clustering algorithm to analysis the geometric proximity between RP and test point in the online phase. In the proposed algorithm, k-means clustering algorithm groups M (M<k) nearest neighboring RPs according to their real physical distances to the test point instead of their signal distances. Then these M nearest neighboring RPs are used to estimate the location of the test point. Experiments were conducted in the fourteenth floor within an office building and the results demonstrate that the proposed method considerably outperforms the kNN algorithms in terms of positioning accuracy.

关键词： reference points clustering method geometric proximity physical real distance k-means clustering algorithm indoor location

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Determining the Best clustering Number of k-means Based on Bootstrap Sampling 2

Determining the Best Clustering Number of K-means Based on B...

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2nd International Conference on Data Science and Business Analytics (ICDSBA)

作者： Yu, Lianmin Zhou, Changyin Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao Shandong Peoples R China

ISBN: (纸本)9781538684313

In this paper, the selection of clustering number in k-means clustering algorithm is studied, based on the Bootstrap sampling, a new method is proposed to determine the best clustering number based on the between the actual value of total within-cluster sum of squares and its estimated interval. By UCI Machine Learning Repository and randomly generated artificial simulated test data sets, the experimental results show that using clustering has obvious improvement, the method can overcome falls into local optimum caused by unreasonable clustering number to select. k-means algorithm.

关键词： k-means clustering algorithm the best clustering number within-cluster sum of squares Bootstrap sampling

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Data and analytics for heating energy consumption of residential buildings: The case of a severe cold climate region of China

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ENERGY AND BUILDINGS 2018年第Aug.期172卷 104-115页

作者： Chang, Chen Zhu, Neng Yang, kun Yang, Fan Tianjin Univ Sch Environm Sci & Engn Key Lab Efficient Utilizat Low & Medium Grade Ene MOE Tianjin 300072 Peoples R China

Building energy conservation has been a globally important issue not only because of the greenhouse gas emissions that occur during the construction and operation of a building, but also due to the increasing emphasis on indoor thermal quality, which is directly related to the quality of life, especially in northern China. The behaviors of heating companies and building users are considered to play important roles in the reduction of building energy consumption. However, it is necessary to understand the relationship between the heating fee, which represents the heating policy, and the energy performance, which considers both the actual energy consumption and thermal ability of a building. With this goal in sight, this study focuses on residential buildings in a climate zone that experiences quite low temperatures, and the heating energy consumption of 40 residential buildings from five typical regions in Inner Mongolia were monitored and analyzed. The thermal comfort, occupant behavior, and heating energy consumption of typical households and buildings in these regions were quantified. In view of the monitored results, building models were built and validated, with the results treated as training samples in clustering and classification procedures. For the sake of the further formulation of a heating energy cost allocation policy, the k-means clustering algorithm and discriminant analysis (DA) were used in this work to evaluate and group residential buildings according to their heating energy consumption and thermal-physical properties of the envelopes The results of this study indicated the existence of regional heating energy consumption differences. Moreover, the main influential factors for grouping residential buildings and the number of clusters were suggested based on the k-means clustering algorithm and DA method. Meanwhile, the classification accuracy rate obtained from the DA method not only verified the clustering results, but also confirmed the further

关键词： Severe cold climate zone of China Residential buildings Heating energy consumption k-means clustering algorithm Discriminant analysis

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Study on urban road network traffic district division based on clustering analysis

Study on urban road network traffic district division based ...

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Chinese Automation Congress (CAC)

作者： Zhao, Luhua Li, Yang Shandong Univ Sci & Technol Coll Transportat Qingdao Peoples R China

ISBN: (纸本)9781728113128

Aiming at the problems of road congestion and complex road network information during peak commuting period, a spatial statistical method for traffic district division based on k-means algorithm is proposed. This method uses Jinan taxi GPS data, uses Python software to realize the k-means clustering division of taxi departure and departure points, and uses Moran's I model to analyze the spatial statistical characteristics of the partition results. Then, the taxi speed data is analyzed by ArcGIS software to further divide the community according to the clustering results. The results w ill he displayed on the map. The results show that this method can more reasonably and accurately classify traffic districts by collecting and analyzing a large number of historical data

关键词： traffic area k-means clustering algorithm Python spatial statistics ArcGIS

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Research on weeds identification based on k-means feature learning

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SOFT COMPUTING 2018年第22期22卷 7649-7658页

作者： Tang, JingLei Zhang, ZhiGuang Wang, Dong Xin, Jing He, LiJun Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Xian Univ Technol Shaanxi Key Lab Complex Syst Control & Intelligen Xian 710048 Shaanxi Peoples R China Hebei Normal Univ Sci & Technol Sch Math & Informat Sci & Technol Qinhuangdao 066004 Peoples R China

This paper aims to overcome the unstable identification results and weak generalization ability in feature extraction based on manual design to realize the automatic weeds identification. On the basis of unsupervised feature learning identification model, k-means clustering algorithm after data preprocessing is used to realize feature learning and construct feature dictionary. Then this feature dictionary is used to extract features from labeled data and train the classification model to realize the automatic weeds identification. In this process, this paper focuses on the effect of parameters such as the clustering number to identification accuracy under single-layer network structure, and the identification accuracy between the single-layer and the two-layer network structure was compared and analyzed. Experimental results show that identification rate can be improved by increasing the network levels, as well as fine-tuning the parameters under the premise of selecting reasonable parameters.

关键词： Feature learning k-means clustering algorithm Feature dictionary Weeds identification

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Rapid Detection of Rice Disease Based on FCM-kM and Faster R-CNN Fusion

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IEEE ACCESS 2019年 7卷 143190-143206页

作者： Zhou, Guoxiong Zhang, Wenzhuo Chen, Aibin He, Mingfang Ma, Xueshuo Cent South Univ Forestry & Technol Coll Comp & Informat Engn Changsha 410004 Hunan Peoples R China

In this paper, a method for detecting rapid rice disease based on FCM-kM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the k-means clustering algorithm (FCM-kM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-kM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification.

关键词： Chaos theory faster R-CNN firefly algorithm Otsu threshold segmentation k-means clustering algorithm rice disease detection weighted multistage median filter

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

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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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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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Evaluation of taxi carpooling feasibility in different urban areas through the k-means mattereelement analysis method

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TECHNOLOGY IN SOCIETY 2018年 53卷 135-143页

作者： Xiao, Qiang He, Ruichun Yu, Jianning Lanzhou Jiao Tong Univ Sch Traff & Transportat Lanzhou Gansu Peoples R China

A k-means mattereelement model for the evaluation of taxi carpooling feasibility was proposed. The model was established on the basis of the influential factors and characteristics of urban taxi carpooling and data of taxi traffic. The first step taken to improve the shortcomings of the existing element model is to determine the index interval of the mattereelement characteristic quantity using the k-means clustering algorithm. The entropy method is used to determine index weight. Taxi carpooling feasibility can be evaluated when passenger point density, taxi passenger rate, taxi distribution density, average carpool kilometres, average carpool return and average successful carpooling index information are described. Results showed that the algorithm can be used to effectively evaluate the effects feasibility of taxi carpooling. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Traffic engineering Taxi carpooling Matter-eelement evaluation k-means clustering algorithm Entropy weight method

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Steady-State Interval Detection and Nonlinear Modeling for Automatic Generation Control Systems

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IEEE ACCESS 2019年 7卷 139592-139600页

作者： Cao, Pengfei Wang, Jiandong Zhang, Chao Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Shandong Peoples R China

Automatic generation control systems are designed to adjust electric power outputs of multiple generators simultaneously in accordance with the current load. However, the control instruction and the main steam pressure have significant impacts on the resulting active power generation of a conventional thermal generator, and the impacts may be associated with nonlinear characteristics. As a result, the control instruction requires an accurate modeling of the relationship between these three variables for a satisfactory control performance. This paper proposes a method to build a piecewise linear model for the nonlinear relationship from steady-state data hidden in historical data samples. The proposed method is composed by two main steps of steady-state interval detection and steady-state data segmentation. Historical data samples are grouped using the k-means clustering algorithm, and the time domains of each cluster are merged in a specific way to obtain the steady-state intervals. The steady-state data are taken as the samples means of data in the steady-state intervals. A bottom-up algorithm is utilized to partition the steady-state data into numbers of sets iteratively, and the parameters of the piecewise linear model for each data set are estimated by the least squares algorithm. The effectiveness of the proposed method is illustrated via industrial applications to two thermal power generation units.

关键词： Automatic generation control system piecewise linear model steady-state interval detection k-means clustering algorithm bottom-up algorithm

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