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Designing Truss in Architecture: Method and Applications Based on Clustering algorithms and Principal Stress Lines

COMPUTER-AIDED DESIGN 2022年 151卷

作者： Yu, Zhenya Dai, Hang Shi, Ziying Southeast Univ Sch Architecture Dept Landscape Architecture Nanjing 210096 Peoples R China Southeast Univ Sch Architecture Dept Bldg Technol & Sci Nanjing 210096 Peoples R China

Rationally and effectively balancing the design objectives of architecture and structure is challenging owing to the limitations of existing truss design methods. Inspired by visual design principles, this study proposes a non-black-box, parametric, and integrated solution based on principal stress lines (PSLs). The idea is to mine the potential data features of PSLs by the clustering algorithm. Clustering the endpoints and equipartition points of PSLs is the core operation of this method. The clustering results are then used to reflect the geometric features and distribution characteristics of PSLs. For this purpose, the framework of two clustering algorithms (dbscan and K-means) is adapted with additional functions for sorting, screening, and counting the sample size of specific clusters. Moreover, combined with the layout pattern of linear elements, the truss web number, size, and layout are defined based on the clustering results. In addition, to verify the feasibility of form-finding results, the method is applied to a cantilever truss and two practical projects: Taishan Bridge and the Wing Stairway. Finally, the application potential of this method is demonstrated by comparing its conventional trusses under the same load and support conditions (materials used, maximum displacement, and maximum axial stress).

关键词： Truss design Visual design principle Integrated solution Principal stress lines dbscan algorithm K-means algorithm

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An efficient privacy protection in mobility social network services with novel clustering-based anonymization

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2016年第1期2016卷 1页

作者： Chen, Zhi-Guo Kang, Ho-Seok Yin, Shang-Nan Kim, Sung-Ryul Konkuk Univ Dept Internet & Multimedia Engn Seoul South Korea

A popular means of social communication for online users has become a trend with rapid growth of social networks in the last few years. Facebook, Myspace, Twitter, LinkedIn, etc. have created huge amounts of data about interactions of social networks. Meanwhile, the trend is also true for offline scenarios with rapid growth of mobile devices such as smart phones, tablets, and laptops used for social interactions. These mobile devices enlarge the traditional social network services platform and lead to a greater amount of mobile social network data. These data contain more private information of individuals such as location, habit, and health condition. However, there are many analytical, sociological, and economic questions that can be answered using these data, so the mobility data managers are expected to share the data with researchers, governments, and/or companies. Therefore, mobile social network data is badly in need of anonymization before it is shared or analyzed widely. k-anonymization is a well-known clustering-based anonymization approach. However, the implementation of this basic approach has been a challenge since many of the mobile social network data involve categorical data values. In this paper, we propose an approach for categorical data clustering using rough entropy method with dbscan clustering algorithm to improve the performance of k-anonymization approach. It has the ability to deal with uncertainty in the clustering process and can effectively find arbitrarily shaped clusters. We will report the proposed approach and discuss the credibility by theoretical studies and examples. And experimental results on two benchmark data sets obtained from UCI Machine Learning Repository show that our approach is second to none among the Fuzzy Centroids, MMeR, SDR and ITDR, etc. with respect to the local and global purity of clusters. Since the clustering algorithm is a key point of k-anonymization for clustering mobile social network data, our experiment

关键词： dbscan algorithm Rough entropy Cluster purity Categorical data clustering k-anonymization Mobile Social network

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Modeling data-driven adaptive distributionally robust equilibrium last mile relief network under centrality metric

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APPLIED MATHEMATICAL MODELLING 2023年第1期122卷 614-640页

作者： Yin, Fanghao Zhao, Yi Wang, Dong Hu, Guimin Harbin Inst Technol Shenzhen Sch Sci Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Peoples R China

This paper studies a variant of the two-stage last mile relief network problem by introducing a centrality metric objective including the distance centrality metric, the demand centrality value-at-risk and the residual supplies centrality metric. Under the scenario-based design, the demand in each scenario is described as a random fuzzy variable whose probability distribution is ambiguous. In order to characterize its ambiguousness, this paper designs a Density-Based Spatial Clustering of Applications with Noise (dbscan) algorithm based on sample data to construct a data-driven ambiguity set containing the support and mean, which not only realizes the visualization of the ambiguous probability distribution, but also provides a finite set of scenarios for the uncertain demand. Given the constructed data-driven ambiguity set, this paper proposes a data-driven adaptive distributionally robust equilibrium programming (DDADREP) method for the last mile relief network problem (LMRNP). In view of its computational difficulty, the safe approximation, duality technique and credibility measure theory are used to approximate it as a mixed integer second order cone programming with significant computational advantages. Finally, the proposed model is validated with two real-world cases, and its effectiveness as well as advantages is demonstrated by comparison with the classical robust optimization and the nominal equilibrium optimization, respectively.& COPY;2023 Elsevier Inc. All rights reserved.

关键词： Last mile relief network Data-driven adaptive distributionally robust equilibrium programming dbscan algorithm Centrality metric objective Random fuzzy variable

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Automatic multi-needle localization in ultrasound images using large margin mask RCNN for ultrasound-guided prostate brachytherapy

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PHYSICS IN MEDICINE AND BIOLOGY 2020年第20期65卷 205003-205003页

作者： Zhang, Yupei Tian, Zhen Lei, Yang Wang, Tonghe Patel, Pretesh Jani, Ashesh B. Curran, Walter J. Liu, Tian Yang, Xiaofeng Emory Univ Dept Radiat Oncol Atlanta GA 30322 USA Emory Univ Winship Canc Inst Atlanta GA 30322 USA

Multi-needle localization in ultrasound (US) images is a crucial step of treatment planning for US-guided prostate brachytherapy. However, current computer-aided technologies are mostly focused on single-needle digitization, while manual digitization is labor intensive and time consuming. In this paper, we proposed a deep learning-based workflow for fast automatic multi-needle digitization, including needle shaft detection and needle tip detection. The major workflow is composed of two components: a large margin mask R-CNN model (LMMask R-CNN), which adopts the lager margin loss to reformulate Mask R-CNN for needle shaft localization, and a needle based density-based spatial clustering of application with noise algorithm which integrates priors to model a needle in an iteration for a needle shaft refinement and tip detections. Besides, we use the skipping connection in neural network architecture to improve the supervision in hidden layers. Our workflow was evaluated on 23 patients who underwent US-guided high-dose-rate (HDR) prostrate brachytherapy with 339 needles being tested in total. Our method detected 98% of the needles with 0.091 +/- 0.043 mm shaft error and 0.330 +/- 0.363 mm tip error. Compared with only using Mask R-CNN and only using LMMask R-CNN, the proposed method gains a significant improvement on both shaft error and tip error. The proposed method automatically digitizes needles per patient with in a second. It streamlines the workflow of transrectal ultrasound-guided HDR prostate brachytherapy and paves the way for the development of real-time treatment planning system that is expected to further elevate the quality and outcome of HDR prostate brachytherapy.

关键词： multi-needle localization mask RCNN dbscan algorithm ultrasound images US-guided HDR prostate brachytherapy deep learning

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Colour FAST (CFAST) match: fast affine template matching for colour images

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ELECTRONICS LETTERS 2016年第14期52卷 1220-+页

作者： Jia, Di Cao, Jun Song, Wei-dong Tang, Xiao-liang Zhu, Hong Liaoning Tech Univ Sch Elect & Informat Engn Liaoning 125105 Peoples R China Liaoning Tech Univ Sch Geomat Liaoning 125105 Peoples R China Liaoning Tech Univ Sch Software Liaoning 125105 Peoples R China Intel Corp 4500 S Dobson Rd Chandler AZ 85224 USA

Fast-match is a fast and effective algorithm for template matching. However, when matching colour images, the images are converted into greyscale images. The colour information is lost in this process, resulting in errors in areas with distinctive colours but similar greyscale values An improved fast-match algorithm that utilises all three RGB channels to construct colour sum-of-absolute-differences (CSAD) is proposed, thus improving the sum-of-absolute-differences distance used in fast-match. In this algorithm, each pixel in the image is categorised by clustering them using density-based spatial clustering of applications with noise (dbscan) algorithm over the RGB vector, then the number of pixels in each category and the cumulative RGB values for each RGB channel are calculated to identify the centroid of each category. The RGB vector centroid is used as the CSAD decision criteria, and inverse of number of pixels in each category is used as the differentiating coefficient to construct a new similarity measure. Experiment results demonstrate that this algorithm has significant higher accuracy for matching colour images than the original fast-match algorithm.

关键词： image colour analysis image matching image resolution pattern clustering vectors CFAST-match CSAD decision criteria dbscan algorithm RGB channels RGB vector centroid colour image matching colour sum-of-absolute-differences cumulative RGB values density-based spatial clustering-of-applications-with-noise fast affine template matching similarity measure

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Efficient 3D Junction Detection in Biomedical Images Based on a Circular Sampling Model and Reverse Mapping

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第5期25卷 1612-1623页

作者： Shen, Lan Liu, Min Wang, Chao Guo, Changhao Meijering, Erik Wang, Yaonan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Natl Engn Lab Robot Visual Percept & Control Tech Changsha 410082 Hunan Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Univ New South Wales Grad Sch Biomed Engn Sydney NSW 2052 Australia

Detection and localization of terminations and junctions is a key step in the morphological reconstruction of tree-like structures in images. Previously, a ray-shooting model was proposed to detect termination points automatically. In this paper, we propose an automatic method for 3D junction points detection in biomedical images, relying on a circular sampling model and a 2D-to-3D reverse mapping approach. First, the existing ray-shooting model is improved to a circular sampling model to extract the pixel intensity distribution feature across the potential branches around the point of interest. The computation cost can be reduced dramatically compared to the existing ray-shooting model. Then, the Density-Based Spatial Clustering of Applications with Noise (dbscan) algorithm is employed to detect 2D junction points in maximum intensity projections (MIPs) of sub-volume images in a given 3D image, by determining the number of branches in the candidate junction region. Further, a 2D-to-3D reverse mapping approach is used to map these detected 2D junction points in MIPs to the 3D junction points in the original 3D images. The proposed 3D junction point detection method is implemented as a build-in tool in the Vaa3D platform. Experiments on multiple 2D images and 3D images show average precision and recall rates of 87.11% and 88.33% respectively. In addition, the proposed algorithm is dozens of times faster than the existing deep-learning based model. The proposed method has excellent performance in both detection precision and computation efficiency for junction detection even in large-scale biomedical images.

关键词： Junctions Three-dimensional displays Two dimensional displays Biomedical imaging Neurons Biological system modeling Solid modeling Junction point detection tree-like structure reconstruction circular sampling model dbscan algorithm reverse mapping approach

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Network Site Optimization and Clustering Study Based on Simulated Annealing algorithm

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IEEE ACCESS 2023年 11卷 108167-108177页

作者： Yang, Lin-Shen Wen, Bin Yan, Jie-Jun Tiangong Univ Sch Artificial Intelligence Tianjin 300387 Peoples R China

Nowadays, communication networks are becoming increasingly complex. This paper aims to demonstrate an effective method to achieve the intelligent planning for network base stations (BSs). The various parameters such as BS coordinates (x, y), the collaboration of multiple types of BS, and the density of BS construction are taken as design parameters for BS placement. We construct the objective function using the lowest total cost and the total minimum workload of BS to 90%. To solve the problem of siting planning with large data volume and mixed placement of multiple BS, we propose a new practical three-step model for BS siting planning: (I) roughly selecting the alternative coordinates for the BS using the dbscan algorithm;(II) correcting and further refining the alternative BS coordinates using the K-means algorithm;(III) determining the optimal BS construction solution to meet the requirements using simulated annealing algorithm (SAA). The real data of a 2500x 2500 area have been used for the simulation test. The simulation result shows that BS placement covers 90.03% of the workload, confirming that the proposed method can handle site planning for large orders of magnitude of data and use a mix of BS to achieve the best economics for the demand. This paper provides basic support for future research on network site optimization.

关键词： dbscan algorithm K-means algorithm simulated annealing algorithm base station (BS) planning

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A deep reinforcement learning based searching method for source localization

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INFORMATION SCIENCES 2022年 588卷 67-81页

作者： Zhao, Yong Chen, Bin Wang, XiangHan Zhu, Zhengqiu Wang, Yiduo Cheng, Guangquan Wang, Rui Wang, Rongxiao He, Ming Liu, Yu Natl Univ Def Technol Coll Syst Engn 109 Deya Rd Changsha Hunan Peoples R China Peoples Liberat Army Engn Univ Command & Control Engn Coll Zhengzhou Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China

The localization of hazardous sources (e.g. poisonous gas sources) is an important task regarding the security of human society. To find the unknown source in time, various autonomous source searching methods have mushroomed and been employed over the past decade. This paper designs a fresh source searching approach, namely particle clustering-deep Q-network, PC-DQN, which applies the deep reinforcement learning (DRL) techniques as a source searching approach for the first time. Specifically, the search process is formulated as the partially observable Markov decision process, then converted into the Markov decision process based on the belief state (represented by the particle fil-ter). PC-DQN leverages the density-based spatial clustering of applications with noise (dbscan) algorithm to extract the feature of belief state, and employ the deep Q-network (DQN) algorithm to find the optimal policy for the source searching task. Through the comparison with two baseline methods (i.e. RANDOM and Entrotaxis algo-rithm) under various experimental conditions, the viability of our proposed PC-DQN is tes-tified. Results explicitly reveal that the success rate of the PC-DQN maintains at a high level (beyond 99.6%) in all scenarios in this paper, and the mean search step shows evident supe-riority over baseline methods in most scenarios. Significantly, we also introduce the trans-fer learning concept to reuse the well-trained Q-network into new scenarios. These findings show important implications of the DRL-based approach as an alternative and more effective source searching approach.(c) 2021 Elsevier Inc. All rights reserved.

关键词： Autonomous searching method Deep reinforcement learning dbscan algorithm Entrotaxis algorithm

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Identification and Optimization Models for a Freight-Integrated Transportation Corridor With Line Importance and Freight Communication Capability

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IEEE ACCESS 2019年 7卷 11114-11126页

作者： Wang, Zhanzhong Chu, Ruijuan Wu, Wenjing Li, Qiuxia Cai, Zhouling Cao, Ningbo Gu, Mingxiang Jilin Univ Transportat Coll Changchun 130022 Jilin Peoples R China Liaocheng Univ Sch Mech & Automot Engn Liaocheng 252059 Peoples R China Changan Univ Sch Econ & Management Xian 710064 Shaanxi Peoples R China

Identifying and optimizing a freight-integrated transportation corridor is a complicated problem. Currently, corridor structure optimization approaches, such as the mode split rate of freight transportation in the corridor, are topics of discussion. However, identifying the corridor lines and clustering the lines into the same corridor are the keys to reasonably optimizing the corridor. To this end, this paper establishes a corridor selection model from the importance of routes and freight communication capacity point of view and designs a corridor-clustering algorithm based on the dbscan clustering algorithm to optimize the transportation corridor. This method is applied to the example of freight transportation in Jilin Province, China, to demonstrate the performance of this model and algorithm.

关键词： Transportation Freight transportation corridor Data processing dbscan algorithm

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Typed topology and its application to data sets

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TOPOLOGY AND ITS APPLICATIONS 2024年 342卷

作者： Hu, Wanjun Albany State Univ Dept Math CS & Phys 504 Coll Dr Albany GA 31705 USA

The concept of typed topology is introduced. In a typed topological space, some open sets are assigned "types", and topological concepts such as closure, connectedness can be defined using types. A finite data set in R2 is a typically typed topological space. Clusters calculated by the dbscan algorithm for data clustering can be well represented in a finite typed topological space. Other concepts such as tracks, port (starting points), type-p-connectedness, p-closure-connectedness, indexing, branches are also introduced for finite typed topological spaces. Finally, left - r and up - left - r type open sets are introduced for finite data sets in R2, so that tracks, port, branches can be calculated.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Typed topology Data mining dbscan algorithm

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