检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Research on weeds identification based on k-means feature learning

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Privacy Preserving Collaborative Filtering Approach for Recommendation System 3

Privacy Preserving Collaborative Filtering Approach for Reco...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Study on the Grouping of Patients with Chronic Infectious Diseases Based on Data Mining

Study on the Grouping of Patients with Chronic Infectious Di...

引用

作者： Min Li Shanghai Public Health Clinical Center Fudan University College of Economics and Management Nanjing University of Aeronautics and Astronautics

Objective:According to RFM model theory of customer relationship management,data mining technology was used to group the chronic infectious disease patients to explore the effect of customer segmentation on the management of patients with different ***:170,246 outpatient data was extracted from the hospital management information system(HIS) during January 2016 to July 2016,43,448 data was formed after the data cleaning.k-means clustering algorithm was used to classify patients with chronic infectious diseases,and then C5.0 decision tree algorithm was used to predict the situation of patients with chronic infectious ***:Male patients accounted for 58.7%,patients living in Shanghai accounted for 85.6%.The average age of patients is 45.88 years old,the high incidence age is 25 to 65 years *** was gathered into three categories:1) Clusters 1—Important patients(4786 people,11.72%,R = 2.89,F = 11.72,M = 84,302.95);2) clustering 2—Major patients(23,103,53.2%,R = 5.22,F = 3.45,M = 9146.39);3) Cluster 3—Potential patients(15,559 people,35.8%,R = 19.77,F = 1.55,M = 1739.09).C5.0 decision tree algorithm was used to predict the treatment situation of patients with chronic infectious diseases,the final treatment time(weeks) is an important predictor,the accuracy rate is 99.94% verified by the confusion ***:Medical institutions should strengthen the adherence education for patients with chronic infectious diseases,establish the chronic infectious diseases and customer relationship management database,take the initiative to help them improve treatment *** governments at all levels should speed up the construction of hospital information,establish the chronic infectious disease database,strengthen the blocking of mother-to-child transmission,to effectively curb chronic infectious diseases,reduce disease burden and mortality.

关键词： Data Mining k-means clustering algorithm C5.0 Decision Tree algorithm Customer Relationship Management Patients with Chronic Infectious Disease

来源：评论

学校读者我要写书评

暂无评论

Aerial Disaster Relief Response System Research

Aerial Disaster Relief Response System Research

引用

作者： Hongyang He Zhenlin Chen Electronic and Information Engineering Guangdong Normal University of Technology

In order to solve the problem of medical supply in the affected areas, we designed a disaster response system to provide medical package transportation and related route detection for five regions. To solve the first problem, we built two optimization models for 3 D boxing. By considering the size of the medical package and cargo bay, the first model of the number of medical packages required in the affected area was established. Solve the distribution plan of medical package by MATLAB. Then, by considering the size of ISO containers and shipping containers and the number of cargo containers, a three-dimensional packing optimization model is established. Solve in MATLAB to determine the type and number of drones needed. This resulted in a complete disaster response system consisting of a drone fleet and a medical packag To solve the second problem, we use cluster analysis. First, the five disaster areas were classified into three categories to determine the three best cluster centers as the best locations for the three ISO containers. Then, the Euclidean distance was taken as the similarity between the two points, and the three optimal positions were obtained by k-means clustering algorithm as x6=(18.45,-66.40),x7=(18.40,-66.16),x8=(18.27,-68.84)Finally, according to the demand characteristics of the disaster area and the video reconnaissance capability of the drone, the corresponding ISO container was allocated.

关键词： Three-dimensional packing Nonlinear optimization model k-means clustering algorithm 0-1 programming model MATLAB

来源：评论

学校读者我要写书评

暂无评论

Analysis of space-time adaptive processing performance using k-means clustering algorithm for normalisation method in non-homogeneity detector process

引用

IET SIGNAL PROCESSING 2011年第2期5卷 113-120页

作者： kang, S. Ryu, J. Lee, J. Jeong, J. Korea Univ Dept Comp & Radio Commun Engn Seoul 136713 South Korea Korea Univ Dept Brain & Cognit Engn Seoul 136713 South Korea

This study describes the performance analysis of the non-homogeneity detector (NHD) with various normalisation methods for the space-time adaptive processing (STAP) of airborne radar signals under the non-homogeneous clutter environments. The authors can calculate a threshold value from the statistical analysis of generalised inner product (GIP) using the normalisation method using mean, median and the k-means clustering algorithm of training data snapshots in the NHD process. The selected homogeneous data using the threshold value are used to recalculate covariance matrix of the total interference. To evaluate the performance of the covariance matrix, the authors calculated the eigenspectra and signal to interference noise ratio (SINR) loss. The accuracy of the recalculated covariance matrix is verified by the modified sample matrix inversion (MSMI) test statistic for the target detection. Projection statistics (PS) based on GIP is also used to compare the performance of detecting single and multiple targets. The authors' simulation results demonstrate that the k-means clustering algorithm as a normalisation method for both GIP and GIP-based PS can improve the STAP performance in the severe non-homogeneous clutter environment even under the multiple targets scenarios, compared to the other normalisation methods.

关键词： airborne radar signals Radar theory statistical analysis covariance matrix airborne radar target detection space-time adaptive processing generalised inner product normalisation method k-means clustering algorithm non-homogeneity detector Radar equipment, systems and applications modified sample matrix inversion test statistic projection statistics Signal processing and detection Other topics in statistics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：