检索结果-内蒙古大学图书馆

Towards spatial integration of qualitative data for urban transformation - Challenges with automated geovisualization of perception of urban places

Towards spatial integration of qualitative data for urban tr...

引用

World Sustainable Built Environment - Beyond 2020, WSBE 2020

作者： Thuvander, L. Latino, F. Zangelidis, C. Adelfio, M. Naserentin, V. Logg, A. Department of Architecture and Civil Engineering Chalmers University of Technology Sven Hultins gata 6 Gothenburg412 96 Sweden Department of Mathematical Sciences Chalmers University of Technology Chalmers tvärgata 3 Gothenburg412 96 Sweden Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece

Urban planning needs to face and integrate ecological, social, and economic aspects of city living. So far, attempts to integrate different urban simulation models into one technical platform have focused on quantitative data. The aim of this paper is to present the preparation for an automated method to spatially integrate and visualize interview-based qualitative data on the perception of urban places into a virtual platform. The Gothenburg suburb of Hammarkullen is used as a case study. Two CAQDAS software, NVivo and ***, were tested. In both software, locations and urban qualities were coded and clustered. Visualization strategies such as information tree structures, geo-coded quotations, spatial word clouds, linked to 2D maps and 3D environments were developed. Results identify the challenges to overcome and show limitations of the software in terms of creating graphs and relationships as well as automated geocoding of data to maps. The project offers a step towards the integration of qualitative (social) data into digital environments that can be scaled up. By that, it contributes with a new dimension of analysis of urban environments which is necessary for sustainable transformation of cities. © Published under licence by IOP Publishing Ltd.

关键词： Metadata

来源：评论

学校读者我要写书评

暂无评论

Sampling methods for efficient training of graph convolutional networks: A survey

arXiv

引用

arXiv 2021年

作者： Liu, Xin Yan, Mingyu Deng, Lei Li, Guoqi Ye, Xiaochun Fan, Dongrui State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100086 China Department of Precision Instrument Center for Brain Inspired Computing Research Tsinghua University Beijing100084 China School of Computer Science and Technology University of Chinese Academy of Sciences

—Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods have been proposed and achieved a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

引用

Heliyon 2024年第6期10卷 e27747页

作者： Latif, Saima Javaid, Nadeem Aslam, Faheem Aldegheishem, Abdulaziz Alrajeh, Nabil Bouk, Safdar Hussain Department of Management Sciences COMSATS University Islamabad Islamabad 44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad 44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin 64002 Taiwan School of Business Administration (SBA) Al Akhawayn University Ifrane 53003 Morocco Department of Urban Planning College of Architecture and Planning King Saud University Riyadh 11574 Saudi Arabia Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh 11633 Saudi Arabia Old Dominion University Hampton Blvd Norfolk 4114 United States

Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into considerable monetary returns. However, the stock markets' prediction is regarded as an intricate research problem for the noise, complexity and volatility of the stocks' data. In recent years, the deep learning models have been successful in providing robust forecasts for sequential data. We propose a novel deep learning-based hybrid classification model by combining peephole LSTM with temporal attention layer (TAL) to accurately predict the direction of stock markets. The daily data of four world indices including those of U.S., U.K., China and India, from 2005 to 2022, are examined. We present a comprehensive evaluation with preliminary data analysis, feature extraction and hyperparameters' optimization for the problem of stock market prediction. TAL is introduced post peephole LSTM to select the relevant information with respect to time and enhance the performance of the proposed model. The prediction performance of the proposed model is compared with that of the benchmark models CNN, LSTM, SVM and RF using evaluation metrics of accuracy, precision, recall, F1-score, AUC-ROC, PR-AUC and MCC. The experimental results show the superior performance of our proposed model achieving better scores than the benchmark models for most evaluation metrics and for all datasets. The accuracy of the proposed model is 96% and 88% for U.K. and Chinese stock markets respectively and it is 85% for both U.S. and Indian markets. Hence, the stock markets of U.K. and China are found to be more predictable than those of U.S. and India. Significant findings of our work include that the attention layer enables peephole LSTM to better identify the long-term dependencies and temporal patterns in the stock markets' data. Profitable and timely tradi

关键词： Bayesian optimization Contractive autoencoder Ensemble empirical mode decomposition Peephole LSTM Stock market prediction Temporal attention layer Time series Urban planing

来源：评论

学校读者我要写书评

暂无评论

Handwritten Digit Recognition using Deep Learning Networks

Handwritten Digit Recognition using Deep Learning Networks

引用

Telecommunications, Optics and computer Science (TOCS), IEEE Conference on

作者： Shiying Yu Jinmei Liu Hang Shu Zhiwei Cheng School of Geodesy and Geomatics Wuhan University Wuhan China School of Arts and Sciences Rutgers University New Jersey NJ United States School of Computer and Information Science South West University Chongqing China Department of Architecture and Civil Engineering Xi’an University of Science and Technology Xi’an China

ISBN: (纸本)9781665470544

Handwritten digit recognition has been widely researched. Numerous classification methods have been proposed. This article improves the recognition of handwritten numbers method based on CNN neural network. Recognition of handwritten digits has made great progress, but due to insufficient recognition accuracy, it will have a certain impact on work efficiency. In this paper, a two-layer CNN network with two fully connected layers can achieve a good prediction performance. In our framework, the activation function used is the ReLU function, to some extent, this avoids the difficulties of gradient disappearance and gradient saturation. At the same time, dropout is added to the full connection layer to discard the neurons in the network with a certain probability, which can avoid certain accidents, and the final trained network will be more robust and the generalization performance will be stronger. This method effectively improves the accuracy of recognition in the final experiment, which achieves 98.75% accuracy on theMNIST dataset and 99.25% on the CIFAR-100 dataset.

关键词： Industries Deep learning Handwriting recognition Text recognition Neurons Predictive models Optics

来源：评论

学校读者我要写书评

暂无评论

Dataset on energy consumption in buildings within tropical climate based on design aspects of courtyards

引用

Data in Brief 2025年 61卷

作者： Abdulbasit Almhafdy Ashjan Al-Mutairi Asma Al-Shargabi Amal Al-Shargabi Department of Architecture College of Architecture and Planning Qassim University Buraydah Saudi Arabia Independent Researcher Riyadh 11461 Saudi Arabia Department of Information Technology College of Computer Qassim University Buraydah 51452 Saudi Arabia Institute of Artificial Intelligence School of Computer Science and Informatics De Montfort University Leicester LE1 9BH United Kingdom

Sustainability and energy efficiency have become fundamental objectives for modern society. Green roofs and facades are increasingly recognized as innovative and sustainable strategies to improve the energy performance of buildings. This paper introduces a dataset about buildings thermal performance and energy consumption in tropical climate depending on adjacent outdoor enclosed courtyards design features with different architectural shapes U, L, and O. The core data has been collected in public building in Kuala Lumpur, Malaysia. Then it expanded using simulation. The core measured raw data is the temperature and the other data is simulated and/or calculated. The dataset includes detailed design features of courtyards such as plan aspect ratio, number of floors, and orientation. Measurement instruments were calibrated against real-world measurements to ensure accuracy and reliability. The simulated data is tested and validated based on the statistical aspects of the raw data using Pearson correlation coefficient, with a value of 0.882. The dataset includes total 8,685 records across the different courtyard' shapes. This dataset captures intricate relationships between architectural design parameters and energy consumption, making it a valuable resource for architects, engineers, and researchers interested in optimizing building designs for improved energy efficiency. It also allows in-depth analysis and potential reuse in studies related to sustainable architecture and urban planning.

关键词： Energy consumption Sustainable architectural design Optimization Courtyard design Smart cities Big data

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Review on Big Data Challenges

A Comprehensive Review on Big Data Challenges

引用

Business Analytics for technology and Security (ICBATS), International Conference on

作者： Salil Bharany Nasser Taleb M. Tariq Sadiq Nayab Kanwal Mohamed Abdelhakim Taher M. Ghazal Manas Pradhan Ateeq Ur Rehman CET Department Tech Amritsar India UK Faculty of Management Canadian University Dubai Dubai UAE School of Architecture Engineering University of Brighton School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Engineering and IT University of Dubai School of Information Technology Skyline University College Sharjah UAE

Increased hardware capability has allowed many businesses to store massive amounts of data in the previous few years. Streams of data may go on forever, so they are used in so many different applications. Analyzing and querying these streams is challenging for a stream of dynamic information. The term "big data" refers to a collection of methods and tools that must be combined in novel ways to unearth the vast amounts of previously unseen information buried within vast, diverse, and intricate databases. There are several ways to obtain, organize and analyze data in the big data world. In order to deal with streaming data, efficient data mining procedures must be in place. In this article, we examine a variety of Big Data-related concerns and challenges. This paper introduces the features, and challenges of managing big data.

关键词： Planets Transforms Big Data Media Telecommunications Security Surges

来源：评论

学校读者我要写书评

暂无评论

Big data-oriented energy prosumption service in smart community districts: a multi-case study perspective

引用

Energy Informatics 2019年第1期2卷 1-26页

作者： Anthony, Bokolo Petersen, Sobah Abbas Ahlers, Dirk Krogstie, John Livik, Klaus Department of Computer Science Norwegian University of Science and Technology NTNU TrondheimNO-7491 Norway Department of Architecture and Planning Norwegian University of Science and Technology Trondheim Norway Powel AS Klæbuveien 194 TrondheimNO-7037 Norway

The smart grid achieves bidirectional information and energy flow between energy consumer and utility grid, aiding energy users not only to utilize energy, but also to produce, sell, and share energy with other consumers or with the utility grid. This type of energy user is referred to as the "prosumer". Thus, prosumer management structures are important within energy market. However, prior studies on energy sustainability has paid little attention on prosumer involvement and management. Likewise, the continuous growth of cities has increased data processing complexity. Consequently, processing and analysis of historical, online, and real-time streaming data from energy sensors and metering devices has become a major issue in smart cities. Therefore, this research aims to present an architecture based on big data to improve energy prosumption in smart community districts by applying enterprise architecture approach grounded on The Open Group architecture Framework (TOGAF). Accordingly, qualitative methodology is adopted to collect data by employing case study by focus group interview from two energy companies in Norway to preliminarily validate the architecture. Findings from the case studies was demonstrated in ArchiMate modeling language to evaluate the applicability of the architecture. Moreover, findings from this study provides practical scenario that energy service providers can refer to in designing their own energy data platforms. Essentially, the architecture can be utilized as a guide to help municipalities and policy makers in creating approach for energy data analytics in smart community districts towards making decisions for future energy prosumption planning. © 2019, The Author(s).

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment

arXiv

引用

arXiv 2022年

作者： Ni, Haomiao Xue, Yuan Ma, Liya Zhang, Qian Li, Xiaoye Huang, Xiaolei College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Shenzhen Baoan Women’s and Children’s Hospital Jinan University Shenzhen China School of Information and Control Engineering Xi’an University of Architecture and Technology Xi’an China

General movement assessment (GMA) of infant movement videos (IMVs) is an effective method for early detection of cerebral palsy (CP) in infants. We demonstrate in this paper that end-to-end trainable neural networks for image sequence recognition can be applied to achieve good results in GMA, and more importantly, augmenting raw video with infant body parsing and pose estimation information can significantly improve performance. To solve the problem of efficiently utilizing partially labeled IMVs for body parsing, we propose a semi-supervised model, termed SiamParseNet (SPN), which consists of two branches, one for intra-frame body parts segmentation and another for inter-frame label propagation. During training, the two branches are jointly trained by alternating between using input pairs of only labeled frames and input of both labeled and unlabeled frames. We also investigate training data augmentation by proposing a factorized video generative adversarial network (FVGAN) to synthesize novel labeled frames for training. FVGAN decouples foreground and background generation which allows for generating multiple labeled frames from one real labeled frame. When testing, we employ a multi-source inference mechanism, where the final result for a test frame is either obtained via the segmentation branch or via propagation from a nearby key frame. We conduct extensive experiments for body parsing using SPN on two infant movement video datasets;on these partially labeled IMVs, we show that SPN coupled with FVGAN achieves state-of-the-art performance. We further demonstrate that our proposed SPN can be easily adapted to the infant pose estimation task with superior performance. Last but not least, we explore the clinical application of our method for GMA. We collected a new clinical IMV dataset with GMA annotations, and our experiments show that our SPN models for body parsing and pose estimation trained on the first two datasets generalize well to the new clinical dataset

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Smart Prediction Models for Energy Efficiency in Smart Residential Buildings: A Deep Learning Approach

Smart Prediction Models for Energy Efficiency in Smart Resid...

引用

Technological Engineering and its Applications in Sustainable Development (ICTEASD), International Conference for

作者： A. Vasantharaj M. Irshad Ahamed K. Prema Aditya Mudigonda J. B. Sapaev Kavitha Thandapani Electronics and Communication Engineering Kalaignar Karunanidhi Institute of Technology Information for Department of Electronics and Communication Engineerin E.G.S. Pillay Engineering College Nagapattinam Tamilnadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India JNIAS School of Planning and Architecture Hyderabad Telangana India Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and vgy Technology Avadi Chennai Tamil Nadu

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

Using power in houses has given rise to several ecological issues, including air pollution. Predicting building energy use is essential for better regulating or reducing energy usage. Machine Learning (ML) techniques have been used to estimate the power use of operating buildings. However, there has been little research on using ML algorithms to anticipate the prospective energy usage of buildings during the initial design phase. When lowering energy-inefficient buildings, it is crucial to tackle the underlying issue: accurately anticipating energy consumption before construction. This is necessary to prevent the future development of environmentally detrimental buildings. This research introduces a new and innovative Hybrid Deep Learning Model for Predicting Energy Efficiency in Smart Residential Buildings (HDL-PEE-SRB). The suggested framework has two steps: cleaning the information and constructing the model. The information cleansing step utilizes pre-processing methods to manipulate the original information and incorporates lag values as supplementary characteristics. During model-building, the hybrid model undergoes training using the processed data. The HDL model combines fully linked layers, Unidirectional Long Short-Term Memory (Uni-LSTM), and bi-directional LSTMs (Bi-LSTM), which are stacked together. The suggested model is assessed using standard energy usage databases, demonstrating higher accuracy than commonly used HDL models. The proposed model surpasses other HDL models, demonstrating the most favorable performance with a MAPE of 1.94% and an accuracy of 98.06%.

关键词： Deep learning Training Energy consumption Accuracy Biological system modeling Buildings Predictive models

来源：评论

学校读者我要写书评

暂无评论

Emotional evaluation of architectural interior forms based on personality differences using virtual reality

引用

Frontiers of Architectural Research 2020年第1期9卷 138-147页

作者： Maryam Banaei Ali Ahmadi Klaus Gramann Javad Hatami School of Architecture and Environmental Design Iran University of Science and TechnologyTehranIran Faculty of Computer Engineering K.N.Toosi UniversityTehranIran Department of Psychology and Ergonomics Biological Psychology and NeuroergonomicsBerlin Institute of TechnologyBerlinGermany Center for Advanced Neurological Engineering University of CaliforniaSan DiegoCAUSA School of Software University of Technology SydneySydneyAustralia Department of Psychology University of TehranTehranIran

Recent studies have shown that architectural interior forms could impact the affective state of ***,the direct relation of specific forms with specific affective states is difficult to *** addition,no systematic categorization of architectural forms and their relation to emotional states *** investigation of the impact of architectural features on inhabitants'emotions is further complicated by the use of two-dimensional images of forms in laboratory investigations,which cannot perceive real-world ***,the interior form consists of a combination of different forms rather than only pure forms,which was considered in previous *** study aimed to rill these gaps by evaluating interior forms on the basis of clustering different images of built living rooms throughout history as well as their impact on *** study used pleasure,arousal,and dominance ratings with an emphasis on individual differences in *** sample rooms were created based on formal clusters of architectural *** showed a relationship between forms and emotional states for different personality *** work provided a novel approach on the influence of architecture on emotion by considering systematic form categorization and combinations,personality differences,and a virtual reality setup.

关键词： Architectural interior form PAD Self-assessment manikin Personality Virtual reality

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：