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  • 1. Aslani, Mohammad
    et al.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. University of Gävle.
    A fast instance selection method for support vector machines in building extraction2020In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 97, article id 106716Article in journal (Refereed)
    Abstract [en]

    Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH.

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  • 2. Feng, Hailin
    et al.
    Qiao, Liang
    Lv, Zhihan
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Arts, Department of Game Design.
    Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins2023In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 136, article id 110082Article in journal (Refereed)
    Abstract [en]

    The research aims to reduce the network resource pressure on cloud centers (CC) and edge nodes, to improve the service quality and to optimize the network performance. In addition, it studies and designs a kind of edge–cloud collaboration framework based on the Internet of Things (IoT). First, raspberry pi (RP) card working machines are utilized as the working nodes, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of three layers, including edge RP (ERP), monitoring & scheduling RP (MSRP), and CC. Among the three layers, collaborative communication can be realized between RPs and between RPs and CCs. Second, a kind of edge–cloud​ matching algorithm is proposed in the time delay constraint scenario. The research results obtained by actual task assignments demonstrate that the task time delay in face recognition on edge–cloud collaboration mode is the least among the three working modes, including edge only, CC only, and edge–CC collaboration modes, reaching only 12 s. Compared with that of CC running alone, the identification results of the framework rates on edge–cloud collaboration and CC modes are both more fluent than those on edge mode only, and real-time object detection can be realized. The total energy consumption of the unloading execution by system users continuously decreases with the increase in the number of users. It is assumed that the number of pieces of equipment in systems is 150, and the energy-saving rate of systems is affected by the frequency of task generation. The frequency of task generation increases with the corresponding reduction in the energy-saving rate of systems. Based on object detection as an example, the system energy consumption is decreased from 18 W to 16 W after the assignment of algorithms. The included framework improves the resource utility rate and reduces system energy consumption. In addition, it provides theoretical and practical references for the implementation of the edge–cloud collaboration framework.

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  • 3.
    Li, Xiaoming
    et al.
    Shenzhen Univ, Sch Architecture & Urban Planning, Res Inst Smart Cities, Shenzhen 518060, Peoples R China.;Shenzhen Key Lab Digital Twin Technol Cities, Shenzhen 518060, Peoples R China.;Guangdong Hong Kong Macau Joint Lab Smart Cities, Shenzhen 518060, Peoples R China.;Shenzhen Key Lab Spatial Smart Sensing & Ser, Shenzhen 518060, Peoples R China.;MNR Technol Innovat Ctr Terr & Spatial Big Data, Shenzhen 518060, Peoples R China..
    Zhang, Dan
    Shenzhen Univ, Sch Architecture & Urban Planning, Res Inst Smart Cities, Shenzhen 518060, Peoples R China.;Shenzhen Key Lab Digital Twin Technol Cities, Shenzhen 518060, Peoples R China.;Guangdong Hong Kong Macau Joint Lab Smart Cities, Shenzhen 518060, Peoples R China.;Shenzhen Key Lab Spatial Smart Sensing & Ser, Shenzhen 518060, Peoples R China.;MNR Technol Innovat Ctr Terr & Spatial Big Data, Shenzhen 518060, Peoples R China..
    Zheng, Ye
    Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo 315211, Peoples R China..
    Hong, Wuyang
    Shenzhen Univ, Sch Architecture & Urban Planning, Res Inst Smart Cities, Shenzhen 518060, Peoples R China.;Shenzhen Key Lab Digital Twin Technol Cities, Shenzhen 518060, Peoples R China.;Guangdong Hong Kong Macau Joint Lab Smart Cities, Shenzhen 518060, Peoples R China.;Shenzhen Key Lab Spatial Smart Sensing & Ser, Shenzhen 518060, Peoples R China.;MNR Technol Innovat Ctr Terr & Spatial Big Data, Shenzhen 518060, Peoples R China..
    Wang, Weixi
    Shenzhen Univ, Sch Architecture & Urban Planning, Res Inst Smart Cities, Shenzhen 518060, Peoples R China.;Shenzhen Key Lab Digital Twin Technol Cities, Shenzhen 518060, Peoples R China.;Guangdong Hong Kong Macau Joint Lab Smart Cities, Shenzhen 518060, Peoples R China.;Shenzhen Key Lab Spatial Smart Sensing & Ser, Shenzhen 518060, Peoples R China.;MNR Technol Innovat Ctr Terr & Spatial Big Data, Shenzhen 518060, Peoples R China..
    Xia, Jizhe
    Shenzhen Univ, Sch Architecture & Urban Planning, Res Inst Smart Cities, Shenzhen 518060, Peoples R China.;Shenzhen Key Lab Digital Twin Technol Cities, Shenzhen 518060, Peoples R China.;Guangdong Hong Kong Macau Joint Lab Smart Cities, Shenzhen 518060, Peoples R China.;Shenzhen Key Lab Spatial Smart Sensing & Ser, Shenzhen 518060, Peoples R China.;MNR Technol Innovat Ctr Terr & Spatial Big Data, Shenzhen 518060, Peoples R China..
    Lv, Zhihan
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Arts, Department of Game Design. Uppsala Univ, Fac Arts, Dept Game Design, Uppsala, Sweden..
    Evolutionary computation-based machine learning for Smart City high-dimensional Big Data Analytics2023In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 133, article id 109955Article in journal (Refereed)
    Abstract [en]

    Science and technology development promotes Smart City Construction (SCC) as a most imminent problem. This work aims to improve the comprehensive performance of the Smart City-oriented high-dimensional Big Data Management (BDM) platform and promote the far-reaching development of SCC. It comprehensively optimizes the calculation process of the BDM platform through Machine Learning (ML), reduces the dimension of the data, and improves the calculation effect. To this end, this work first introduces the concept of SCC and the BDM platform application design. Then, it discusses the design concept of using ML technology to optimize the calculation effect of the BDM platform. Finally, the Tensor Train Support Vector Machine (TT-SVM) model is designed based on dimension reduction data processing. The proposed model can comprehensively optimize the BDM platform, and the model is compared with other models and evaluated. The research results show that the accuracy of the reduced dimension classification of the TT-SVM model is more than 95. The lowest average processing time for the model's reduced dimension classification is about 1ms. The model's highest data processing accuracy is about 98%, and the average processing time is between 1.0- 1.5ms. Compared with traditional models and BDM platforms, the proposed model has a breakthrough performance improvement, so it plays an important role in future SCC. This work has achieved a great breakthrough in big data processing, and innovatively improved the application mode of high-dimensional big data technology by integrating multiple technologies. Therefore, the finding provides targeted technical reference for algorithms in BDM platform and contributes to the construction and improvement of Smart City.& COPY; 2022 Elsevier B.V. All rights reserved.

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