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Energy consumption of high-rise double skin façade buildings, a machine learning analysis

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Journal of Building Engineering 2024年 89卷

作者： Rezaie, Maedeh Kariminia, Shahab Band, Shahab S. Ameri, Rasoul Farokhi, Maryam Pai, Hao-Ting Gocer, Ozgur Rismanchi, Behzad Shooshtarian, Salman Department of Architecture Advancement in Architecture and Urban Planning Research Center Najafabad Branch Islamic Azad University Najafabad Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Information Management National Yunlin University of Science and Technology Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan School of Architecture Design and Planning The University of Sydney SydneyNSW Australia Renewable Energy and Energy Efficiency Group Department of Infrastructure Engineering Faculty of Engineering and Information Technology The University of Melbourne Australia School of Property Construction and Project Management RMIT University Melbourne Australia

Most energy exchanges take place through the building skin. The skin characteristics play a decisive role in the extent of these exchanges, but they are somewhat more varied in the double skin façade (DSF). Among these characteristics, cavity segmentation has a noticeable effect on the implementation of the DSF in different directions during the hot and cold seasons. The aim of this study was to investigate the role of DSF segmentation in energy consumption and natural ventilation of high-rise buildings in hot and dry climates. This study used designBuilder software to study sixty-four segmentation component scenarios in an eight-story residential building in Isfahan, Iran. Moreover, a proposed hybrid model utilizing the hybridization of the Hunger Game Search and Gradient Boosting (HGS-GB) algorithm was employed to estimate energy consumption in various scenarios involving lighting, heating, cooling, and total scenarios. The available outcomes revealed that the HGS-GB model had a better performance in comparison with other individual models, such as Gradient Boosting (GB), Random Forest (RF), and K-nearest neighbors (KNN). The R2 values for lighting, heating, cooling, and total energy estimation were 0.9993, 0.9958, 0.9991, and 0.9922, respectively. The findings of this study suggest the significance of DSF segmentation in energy consumption and natural ventilation in high-rise buildings in hot and dry climates. © 2024 Elsevier Ltd

关键词： Forestry

Analysis of the dispersion of carbon nanotubes in soil-cement composites for different types and concentrations of polycarboxylate superplasticizers

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Journal of Building Engineering 2025年 111卷

作者： Ana Raina Carneiro Vasconcelos Ryan Araújo Matos Nádia Pitombeira Mariana Vella Silveira Luís Paulo Mourão Laís Cristina Barbosa Costa Alain Ledoux Esequiel Mesquita Post-Graduate Program in Civil Engineering Department of Hydraulic and Environmental Engineering Federal University of Ceara 60400-900 Fortaleza Brazil Laboratory of Buildings Durability and Digitalization Campus Russas Federal University of Ceara 62900-000 Russas Brazil Post-Graduate Program in Chemistry Department of Organic and Inorganic Chemistry Federal University of Ceara 60440-900 Fortaleza Brazil Post-Graduate Program in Materials Engineering and Science Federal University of Ceara Department of Metallurgical and Materials Engineering 60440-554 Fortaleza Brazil Laboratoire de Sécurité des Procédés Chimiques - LSPC INSA 76000 Rouen France Laboratory of Digital Experience Institute of Architecture and Urbanism and Design Campus Benfica Federal University of Ceara 60020-181 Fortaleza Brazil

This study investigates the dispersion of multi-walled carbon nanotubes (MWCNTs) in soil-cement composites using different concentrations of three polycarboxylate ether superplasticizer admixtures. Physicochemical characterization of superplasticizer admixtures (SP), MWCNT suspensions, and their application in soil-cement composites was performed to identify stable and optimized MWCNT dispersions with SP concentrations. Over four weeks, analyses of hydrodynamic diameter indicated the efficacy of selected admixtures in dispersing MWCNTs compared to aqueous solutions. Ultraviolet–visible (UV–vis) spectroscopy confirmed that the most efficient suspension maintained its stability over 60 days without significant changes in the composite's behavior. The incorporation of superplasticizer admixtures led to a reduction in zeta potential and an increase in steric stability in the MWCNT suspensions. Scanning electron microscopy illustrated MWCNTs interconnect microparticles, emphasizing the crucial role of dispersants in achieving proper dispersion. Remarkably, the superplasticizer admixture at 0.3 % concentration emerged as the most effective dispersant. Raman and AFM analyses agreed that the concentration of superplasticizers directly impacts dispersion and defect density in MWCNTs. The results demonstrate the efficient dispersion of MWCNTs in soil-cement composites with the selected superplasticizer admixtures, ensuring stability and uniform distribution.

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Ant Colony Optimization based Medical Image Preservation and Segmentation

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Ant Colony Optimization based Medical Image Preservation and...

Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Priya Nandihal Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Madhumala R B Ashish Khanna V Sunil Kumar Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceara Brazil Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

ISBN: (纸本)9781665493970

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work.

关键词： Integrated optics Image segmentation Ant colony optimization Biomedical optical imaging Optical design Image edge detection Metaheuristics

Multimodal deep learning framework for image popularity prediction on social media

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arXiv 2021年

作者： Abousaleh, Fatma S. Cheng, Wen-Huang Yu, Neng-Hao Tsao, Yu Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan The Institute of Electronics Department of Electronics Engineering National Chiao Tung University Hsinchu30010 Taiwan The College of Design Department of Design National Taiwan University of Science and Technology Taipei10607 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a dataset of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman’s Rho, mean absolute error, and mean squared error, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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Entropic Herding

arXiv 2021年

作者： Yamashita, Hiroshi Suzuki, Hideyuki Aihara, Kazuyuki Intelligent Mobility Society Design Social Cooperation Program Graduate School of Information Science and Technology the University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Graduate School of Information Science and Technology Osaka University 1-5 Yamadaoka Suita-shi Osaka Japan International Research Center for Neurointelligence the University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan

Herding is a deterministic algorithm used to generate data points that can be regarded as random samples satisfying input moment conditions. The algorithm is based on the complex behavior of a high-dimensional dynamical system and is inspired by the maximum entropy principle of statistical inference. In this paper, we propose an extension of the herding algorithm, called entropic herding, which generates a sequence of distributions instead of points. Entropic herding is derived as the optimization of the target function obtained from the maximum entropy principle. Using the proposed entropic herding algorithm as a framework, we discuss a closer connection between herding and the maximum entropy principle. Specifically, we interpret the original herding algorithm as a tractable version of entropic herding, the ideal output distribution of which is mathematically represented. We further discuss how the complex behavior of the herding algorithm contributes to optimization. We argue that the proposed entropic herding algorithm extends the application of herding to probabilistic modeling. In contrast to original herding, entropic herding can generate a smooth distribution such that both efficient probability density calculation and sample generation become possible. To demonstrate the viability of these arguments in this study, numerical experiments were conducted, including a comparison with other conventional methods, on both synthetic and real data. © 2021, CC BY.

关键词： Dynamical systems

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review

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arXiv 2024年

作者： Jin, Rui Li, Derun Xiang, Dehui Zhang, Lei Zhou, Hailing Shi, Fei Zhu, Weifang Cai, Jing Peng, Tao Chen, Xinjian School of Future Science and Engineering Soochow University Suzhou China Department of Urology Peking University First Hospital Xicheng District Beijing100034 China MIPAV Lab The School of Electronic and Information Engineering Soochow University Jiangsu China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan215316 China Department of Mechanical Engineering and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States The State Key Laboratory of Radiation Medicine and Protection Soochow University Jiangsu China

Prostate cancer represents a major threat to health. Early detection is vital in reducing the mortality rate among prostate cancer patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the prostate region. However, prostate segmentation is challenging due to imperfections in the images and the prostate’s complex tissue structure. The advent of precision medicine and a significant increase in clinical capacity have spurred the need for various data-driven tasks in the field of medical imaging. Recently, numerous machine learning and data mining tools have been integrated into various medical areas, including image segmentation. This article proposes a new classification method that differentiates supervision types, either in number or kind, during the training phase. Subsequently, we conducted a survey on artificial intelligence (AI)-based automatic prostate segmentation methods, examining the advantages and limitations of each. Additionally, we introduce variants of evaluation metrics for the verification and performance assessment of the segmentation method and summarize the current challenges. Finally, future research directions and development trends are discussed, reflecting the outcomes of our literature survey, suggesting high-precision detection and treatment of prostate cancer as a promising avenue. Impact Statement — Prostate cancer remains a significant health threat, necessitating early detection to reduce mortality rates. Current diagnostic approaches, particularly in medical imaging, face challenges in accurate prostate segmentation due to the organ's complex structure and imaging imperfections. Our review paper delves into AI-based automatic segmentation methods across various imaging modalities (CT, MRI, US), emphasizing their strengths, weaknesses, and the specific challenges they address. By classifying segmentation techniques based on supervision levels, we provide a comprehensive analys

关键词： Computer aided diagnosis

Secure Handwritten Protocol for Protecting Integrity of Digital Evidences

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Secure Handwritten Protocol for Protecting Integrity of Digi...

IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Chien-Lung Hsu Wei-Qian Lin Sheng-Hsin Tso Graduate Institute of Management Chang Gung University (R.O.C) Taoyuan Taiwan Department of Information Management Chang Gung University (R.O.C) Taoyuan Taiwan Department of Master of Science Degree Program in Innovation for Smart Medicine Chang Gung University (R.O.C) Taoyuan Taiwan Healthy Aging Research Center Chang Gung University (R.O.C) Taoyuan Taiwan Department of Visual Communication Design Ming-Chi University of Technology (R.O.C) Taoyuan Taiwan Adjunct Research Fellow Chang-Gung Memorial Hospital Department of Information Management National Central University (R.O.C) Taoyuan Taiwan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

With the advancement of technology, people constantly use IT through the Internet. In this modern living environment, many individuals exploit this technological landscape for illegal gains and criminal activities. Judicial investigation units gather relevant evidence from the crime scene and process it when a crime occurs. When the medium used in criminal activity involves information technology, it is referred to as digital evidence. Unlike traditional evidence, which leaves traces if moved or altered, digital evidence can be copied without any detectable changes, posing challenges to maintaining its integrity and accuracy. This article explores how digital identification can ensure that the content of a report matches the evidence seized at the scene. It also addresses how the validity of digital evidence, as presented by the investigating authority, can be upheld in court. Additionally, it discusses the use of digital evidence and electronic handwritten signatures to enhance evidence preservation measures.

关键词： Biometrics Protocols Accuracy Public key Authentication Signal processing Non-repudiation Internet Security Information technology

BPS2025 - Structural and functional characterization of integrin α5-targeting antibodies for anti-angiogenic therapy

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Biophysical Journal 2025年第3期124卷 12a-12a页

作者： Adam Nguyen Joel B. Heim Gabriele Cordara Matthew C. Chan Hedda Johannesen Cristine Charlesworth Ming Li Caleigh M. Azumaya Benjamin Madden Ute Krengel Alexander Meves Melody G. Campbell Basic Sciences Fred Hutchinson Cancer Center Seattle WA USA Biological Physics Structure and Design Graduate Program University of Washington Seattle WA USA Department of Dermatology Mayo Clinic Rochester MN USA Department of Chemistry University of Oslo Oslo Norway Medical Genome and Proteomics Core Mayo Clinic Rochester MN USA