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Energy efficient building envelope using novel RBF neural network integrated affinity propagation

ENERGY 2020年 209卷

作者： Han, Yongming Fan, Chenyu Geng, Zhiqiang Ma, Bo Cong, Di Chen, Kai Yu, Bin Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing 100029 Peoples R China Guizhou Acad Sci Guiyang 550001 Guizhou Peoples R China Hengli Petrochem Co LTD Dalian 116000 Liaoning Peoples R China

Neural networks have been widely used in energy saving and optimization of construction industries, but neural networks based on K-means clustering needs to set the clustering number, which has poor objectivity on the energy consumption prediction of buildings. Therefore, this paper presents novel radial basis function (RBF) based on affinity propagation (AP) clustering to evaluate the energy performance and save the energy of buildings. The number of hidden layer nodes of the RBF are obtained by the AP. Then main factors affecting the energy consumption of buildings are used as inputs and outputs of the RBF to build the energy performance and saving model of buildings. Compared with other neural networks, the effectiveness of the proposed method is demonstrated though University of California Irvine datasets. Finally, the proposed method is applied in energy saving and emission reduction of construction industries. In the first case, doubling the roof area and halving the overall height of buildings are obtained. And the heating and cooling loads of buildings are reduced by 56.35% and 50.06%, respectively. In the second case, the humidity outside is increased by 12.45%. Meanwhile, the temperature outside and the energy consumption of buildings are reduced by 7.04 degrees C and 31.27 Wh, respectively. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Energy saving Energy efficiency Neural network Radial basis function affinity propagation clustering Buildings

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FedSSC: Joint client selection and resource management for communication-efficient federated vehicular networks

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COMPUTER NETWORKS 2023年 237卷

作者： Liu, Su Guan, Peiyuan Yu, Jiong Taherkordi, Amir Xinjiang Univ Sch Comp Sci & Technol Urumqi 830046 Peoples R China Univ Oslo Dept Informat N-0316 Oslo Norway

As a promising distributed technology, federated learning (FL) has been widely used in vehicular networks involving large amounts of IoT-enabled sensor data, which derives federated vehicular networks (FVNs). However, the efficiency of FVN is generally limited by vehicle selection policy and communication conditions, which leads to high communication costs and latency. The original FVN transmits model parameters from a random subset of vehicles to the roadside unit (RSU) and ignores the diversity of learning quality among vehicles. In addition, a few vehicles with poor wireless channel conditions may prolong communication latency. To address these two issues, we propose a communication-efficient federated learning approach, composed of Vehicle Selection, Student-Project Allocation (SPA) matching model, and Convex Optimization, called FedSSC, to improve the communication efficiency in FVN. The parameter variations for the same vehicle in two consecutive rounds are used to quantify the quality of the learning results by cosine distance and affinity propagation (AP) clustering. Moreover, a subchannel allocation algorithm based on the SPA matching model, as well as a convex optimal power allocation solution are integrated to minimize the communication latency of each training round. According to extensive experiments, the proposed FedSSC reduces the communication overhead by 26.32% on average compared with the benchmarks, whereas the communication latency decreases by 21.84%.

关键词： Federated learning Cosine distance affinity propagation clustering Student-project allocation match Convex optimization

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A Novel Image Retrieval Algorithm Based on K-Neighbor Semi-Supervised affinity propagation Algorithm

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2013年第9期10卷 2282-2287页

作者： Zhao, Jian Ma, Miao Wang, Rui Xie, Yu NW Univ Xian Sch Informat Sci & Technol Xian 710169 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710162 Peoples R China

A novel image retrieval algorithm based on k-neighbor semi-supervised affinity propagation algorithm is proposed in this paper. Aiming at the disadvantages of the widespread popularity k-means, such as choosing the initial exemplars randomly, being weak at speed, low efficiency and so on, it is gradually replaced by affinity propagation. However, large-scale image retrieval, the sample points of the local features of image is very large. In the situation, if we apply affinity propagation algorithm to the image clustering directly, there is no doubt about the low speed and out of memory. According to these problems, we combine the k-neighbor consistent and semi-supervised clustering to add to constraints so that the dimension of similarity matrix can be rebuilt from the part distribution of dataset. This method can reduce the size and computational complexity. The messages during affinity propagation algorithm can be transformed in the partial range. Firstly, we extract the integrated features based on color, shape and texture from each image in the image database, and calculate the distance pairs of image features, then establish a similarity matrix. Secondly, k-neighbor semi-supervised affinity propagation clustering algorithm clusters the image in the image database and creates the index. At last, according to the similarity criterion, similarity retrieval in the cluster is carried out. The experiment results show that, compared with the original algorithm, the speed of affinity propagation is faster, precision and recall are more accurate, aiming at the size of dataset.

关键词： affinity propagation clustering k-Neighbor Consistent Semi-Supervised clustering Image Retrieval Feature Exaction

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Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks

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ENERGIES 2019年第7期12卷

作者： Leiva, Javier Carmona Pardo, Ruben Aguado, Jose A. Endesa Madrid 28042 Spain Univ Malaga Dept Elect Engn Malaga 29016 Spain

A growing presence of distributed energy resources (DER) and the increasingly diverse nature of end users at low-voltage (LV) networks make the operation of these grids more and more challenging. Particularly, congestion and voltage management strategies for LV grids have usually been limited to some elemental criteria based on human experience, asset oversizing, or grid reinforcement. However, with the current massive deployment of sensors in modern LV grids, new approaches are feasible for distribution network assets operation. This article proposes a multi-objective particle swarm optimization (MOPSO) approach, combined with data analytics through affinity propagation clustering, for congestion threshold determination in LV grids. A real case study from the smart grid of Smartcity Malaga Living Lab is used to illustrate the proposed approach. Within this approach, distribution system operators (DSOs) can take decisions in order to prevent situations of risk or potential failure at LV grids.

关键词： congestion management low-voltage networks multi-objective particle swarm optimization affinity propagation clustering optimal congestion threshold

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An Efficient Crossing-Line Crowd Counting Algorithm with Two-Stage Detection

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CMC-COMPUTERS MATERIALS & CONTINUA 2019年第3期60卷 1141-1154页

作者： Xiao, Zhenqiu Yang, Bin Tjahjadi, Desy Jiaying Univ Sch Comp Meizhou 514015 Peoples R China Jiangnan Univ Sch Design Wuxi 214122 Jiangsu Peoples R China Assumption Univ Sch Comp Sci Bangkok 351078 Thailand

Crowd counting is a challenging task in crowded scenes due to heavy occlusions, appearance variations and perspective distortions. Current crowd counting methods typically operate on an image patch level with overlaps, then sum over the patches to get the final count. In this paper we describe a real-time pedestrian counting framework based on a two-stage human detection algorithm. Existing works with overhead cameras is mainly based on visual tracking, and their robustness is rather limited. On the other hand, some works, which focus on improving the performances, are too complicated to be realistic. By adopting a line sampling process, a temporal slice image can be obtained for pedestrian counting without the need for visual tracking. Only ten low level features are extracted from the input image to establish a feature vector. As a result, our algorithm is more efficient and accurate than existing methods. Pedestrians in the temporal slice image are then located by the two-stage detection algorithm, which is largely based on support vector machine and affinity propagation clustering. Moreover, a novel algorithm is proposed to determine the moving directions of pedestrians by comparing the centers of them in two temporal slice images. Extensive experiments reveal that our system achieves satisfaction performances in terms of both robustness and efficiency.

关键词： Pedestrian counting crowd counting temporal slice image affinity propagation clustering

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Nonparametric catchment clustering using the data depth function

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2016年第15期61卷 2649-2667页

作者： Singh, Shailesh Kumar McMillan, Hilary Bardossy, Andras Fateh, Chebana Natl Inst Water & Atmospher Res Natl Inst Dept Hydrol Proc Christchurch New Zealand San Diego State Univ Dept Geog San Diego CA 92182 USA Univ Stuttgart Inst Modelling Hydraul & Environm Syst Dept Hydrol & Geohydrol Stuttgart Germany Inst Natl Rech Sci Ctr Eau Terre Environm Quebec City PQ Canada

The clustering of catchments is important for prediction in ungauged basins, model parameterization and watershed development and management. The aim of this study is to explore a new measure of similarity among catchments, using a data depth function and comparing it with catchment clustering indices based on flow and physical characteristics. A cluster analysis was performed for each similarity measure using the affinity propagation clustering algorithm. We evaluated the similarity measure based on depth-depth plots (DD-plots) as a basis for transferring parameter sets of a hydrological model between catchments. A case study was developed with 21 catchments in a diverse New Zealand region. Results show that clustering based on the depth-depth measure is dissimilar to clustering on catchment characteristics, flow, or flow indices. A hydrological model was calibrated for the 21 catchments and the transferability of model parameters among similar catchments was tested within and between clusters defined by each clustering method. The mean model performance for parameters transferred within a group always outperformed those from outside the group. The DD-plot based method was found to produce the best in-group performance and second-highest difference between in-group and out-group performance.

关键词： DD-plot catchment similarity data depth affinity propagation clustering

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The CAM Software for Nonnegative Blind Source Separation in R-Java

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JOURNAL OF MACHINE LEARNING RESEARCH 2013年 14卷 2899-2903页

作者： Wang, Niya Meng, Fan Chen, Li Madhavan, Subha Clarke, Robert Hoffman, Eric P. Xuan, Jianhua Wang, Yue Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Arlington VA 22203 USA NCI Pediat Oncol Branch NIH Gaithersburg MD 20850 USA Georgetown Univ Innovat Ctr Biomed Informat Washington DC 20007 USA Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC 20057 USA Childrens Natl Med Ctr Res Ctr Genet Med Washington DC 20010 USA

We describe a R-Java CAM (convex analysis of mixtures) package that provides comprehensive analytic functions and a graphic user interface (GUI) for blindly separating mixed nonnegative sources. This open-source multiplatform software implements recent and classic algorithms in the literature including Chan et al. (2008), Wang et al. (2010), Chen et al. (2011a) and Chen et al. (2011b). The CAM package offers several attractive features: (1) instead of using proprietary MATLAB, its analytic functions are written in R, which makes the codes more portable and easier to modify;(2) besides producing and plotting results in R, it also provides a Java GUI for automatic progress update and convenient visual monitoring;(3) multi-thread interactions between the R and Java modules are driven and integrated by a Java GUI, assuring that the whole CAM software runs responsively;(4) the package offers a simple mechanism to allow others to plug-in additional R-functions.

关键词： convex analysis of mixtures blind source separation affinity propagation clustering compartment modeling information-based model selection

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Consensus reaching process with noncooperative behaviors in large-scale group social network environment

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APPLIED SOFT COMPUTING 2023年 144卷

作者： You, Xinli Hou, Fujun Chiclana, Francisco Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England

Large-scale group decision-making (LSGDM) problems have drawn general attention from scholars. The distribution linguistic preference relation (DLPR) is a flexible and practical tool to describe the preferences of decision makers (DMs) in the decision-making process. Opinion conflict is inevitable among large-scale DMs due to self-interest and individual perception. Thus, it is essential to establish a consensus reaching process (CRP), but there may be non-cooperative behaviors for DMs when they are required to accept the opinions adjustment. As the social network becomes more ubiquitous, moreover, the trust relationship among decision members has implications for the consensus reaching process. Hence, this study proposes a new consensus model that manages non-cooperative behaviors from the cooperative degree, trust relationship, and individual self-confidence level three aspects, and discusses the specific influence of these factors on the penalty for non-cooperative behaviors. In addition, hesitancy-based similarity measure of linguistic distributed assessment is proposed for clustering and measuring consensus level. Finally, a numerical example of a price hearing system demonstrates the feasibility and efficacy of the proposed method, and a comparative analysis illustrates its features and advantages.

关键词： Distribution linguistic preference relation Large-scale group decision making affinity propagation clustering Social network Consensus reaching process Non -cooperative behaviors

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Data-driven process decomposition and robust online distributed modelling for large-scale processes

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2018年第3期49卷 449-463页

作者： Zhang Shu Li Lijuan Yao Lijuan Yang Shipin Zou Tao Nanjing Tech Univ Coll Elect Engn & Control Sci Ind Syst & Automat Dept Nanjing Jiangsu Peoples R China Chinese Acad Sci Shenyang Inst Automat Ind Control Networks & Syst Dept Shenyang Liaoning Peoples R China

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.

关键词： Canonical correlation analysis affinity propagation clustering block-wise RPLS model reduction model-predictive control process control parameter identification

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A niching backtracking search algorithm with adaptive local search for multimodal multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 69卷

作者： Hu, Zhongbo Zhou, Ting Su, Qinghua Liu, Mianfang Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Hunan Univ Sci & Technol Sch Math & Comp Sci Xiangtan Hunan Peoples R China Hunan Software Vocat & Tech Univ Sch Software & Informat Engn Xiangtan Hunan Peoples R China

Multimodal multiobjective optimization problems,which widely exist in real-world applications, have multiple Pareto optimal sets in decision space corresponding to the same Pareto front in objective space. The key to handling such problems is locating and maintaining all Pareto optimal solutions in decision space simultaneously. The distribution of Pareto optimal solutions obtained by some existing multimodal multiobjective evolutionary algorithms is still not satisfactory. This paper proposes a niching backtracking search algorithm with adaptive local search to solve such problems. In the proposed algorithm, the affinity propagation clustering method as a parameter-free automatic niching technique is adopted to form multiple niches. A novel mutation based on affinity propagation clustering is then developed to search for more Pareto solutions within each niche. In addition, an adaptive local search strategy is designed in each niche to improve the search efficiency and the accuracy of Pareto optimal solutions. The proposed algorithm is compared with seven state-of-the-art multimodal multiobjective evolutionary algorithms on a multimodal multiobjective optimization test suite with 22 benchmark functions from CEC 2019 competition and a map-based practical application problem. The experimental results show that the proposed algorithm outperforms its competitors on 14 out of 22 benchmark functions in terms of the reciprocal of Pareto sets proximity and inverted generational distance in decision space metrics. Also, the proposed method is more effective and competitive than its competitors when solving the map-based practical application problem.

关键词： affinity propagation clustering Backtracking search algorithm Multimodal multiobjective optimization problems

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