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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Iryna Aznaurian Yurii Pohrebniak Yuliia Riabchun Maksym Delembovskyi Nataliia Poltorachenko Oleksiy Panko Department of Physics Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Cybersecurity and Computer Engineering Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Information Technology Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Information Systems and Technologies Taras Shevchenko National University of Kyiv Kyiv Ukraine Department of Information Technology Design and Applied Mathematics Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Architectural Structures Kyiv National University of Construction and Architecture Kyiv Ukraine

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

The modern rapid development of means and technologies for education is aimed at reducing classroom classes and remote control of knowledge and skills of education seekers. The adaptation of the education system to the remote regime has become especially relevant throughout the world during the period of quarantine restrictions caused by COVID-19. In Ukraine, the urgency of developing and implementing info-communication systems in the education process has been intensified by the martial law since February 24, 2022. The urgent need to rebuild a large number of buildings and structures destroyed and damaged by hostilities makes special demands on graduates of construction educational institutions. However, the fundamental studying of specialists in this field in remote mode requires specialized info-communication systems, creating of which need taking part of specialists in the field. On-line testing is the main means of guaranteeing the quality of the modern educational process. Therefore, the article presents the specialized on-line testing system "Fast Knowledge Test", which was developed at the Kyiv National University of Construction and architecture with the participation of stakeholders. The system operates during 2021-2023 and while the monitoring of students’ knowledge and skills remains with the teacher, but the service provides for its automation.

关键词： COVID-19 Automation Education Mathematics Stakeholders Task analysis Physics

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SOCIO-SPATIAL SEGREGATION AND HUMAN MOBILITY: A REVIEW OF EMPIRICAL EVIDENCE

arXiv

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arXiv 2024年

作者： Liao, Yuan Gil, Jorge Yeh, Sonia Pereira, Rafael H.M. Alessandretti, Laura Department of Space Earth and Environment Chalmers University of Technology Gothenburg Sweden Department of Architecture and Civil Engineering Chalmers University of Technology Gothenburg Sweden - Brazil Data Science Division Brazil Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation. © 2024, CC BY-SA.

关键词： Leisure

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Exploring Gate-Diversity Enabled by Reconfigurable Memristive technology

Exploring Gate-Diversity Enabled by Reconfigurable Memristiv...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Sebastian Brandhofer Ziang Chen Li-Wei Chen Xianyue Zhao Nan Du Ilia Polian Institute of Computer Architecture and Computer Engineering University of Stuttgart Stuttgart Germany Institute for Solid State Physics Friedrich Schiller University Jena Jena Germany Department of Quantum Detection Leibniz Institute of Photonic Technology (IPHT) Jena Germany

While memristive devices are highly attractive as memory cells, they are also capable of performing computations, paving the way to futuristic in-memory computing architecture. Several memristive logic families have been proposed, and approaches to map gate-level logic circuits to such memristive implementations have been introduced. In this paper, we focus on the CRS logic family that offers several advantages compared to the more often considered IMPLY and MAGIC families. A central feature of CRS is the ability of one physical memristive device to realize varying logic gates over several clock cycles. Our method computes a schedule, i.e., an assignment which logic gate of a given circuit is executed on which memristive device during which clock cycles. Using an optimal MaxSAT model, it can minimize the resulting schedule’s duration (depth), the cost of the used memristors, or the cost of additional cache register cells, while satisfying all dependencies needed for correct computation. In addition to results of the scheduling procedure itself, we report a physical experiment that demonstrates one of the schedules and discuss the energy benefits of the CRS family.

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Low-Cost Method for Effective Conductivity Improvement of Additively Manufactured All-Metal Waveguide Components

Low-Cost Method for Effective Conductivity Improvement of Ad...

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European Conference on Microwave

作者： Jakub Sorocki Ilona Piekarz Michal Baranowski Adam Lamecki Alberto Cattenone Stefania Marconi Gianluca Alaimo Nicolo Delmonte Lorenzo Silvestri Maurizio Bozzi Institute of Electronics AGH University of Krakow Poland Department of Microwave and Antenna Engineering Gdansk University of Technology Poland Department of Civil Engineering and Architecture University of Pavia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

ISBN: (数字)9782874870774

ISBN: (纸本)9798350385892

In this paper, a low-cost method of 3D printed all-metal waveguide effective conductivity improvement is proposed and studied. The approach is a combination of internal surface polishing to reduce the roughness followed by coating a high-conductivity layer through electroplating. Both methods allow to reduce total power losses within the waveguide which are impacted by the conductivity of the metal. A set of mm-wave test vehicles was developed in WR-28 geometry (26.5 GHz to 40 GHz) being a straight and twisted transmission line section along with a narrowband filter to experimentally validate the approach. The models were 3D printed using Powder Bed Fusion out of stainless-steel powder, dry polished using glass microbeads, and then coated with copper. Up to 40% power loss reduction was obtained with respect to raw prints proving the performance of the approach.

关键词： Geometry Powders Three-dimensional displays Surface waves Masers Waveguide lasers Conductivity Surface roughness Rough surfaces Microwave filters

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Modulation Recognition of the OFDM Signals in the UAV-RIS Based AI-enabled 6G Network for Smart Agriculture

Modulation Recognition of the OFDM Signals in the UAV-RIS Ba...

引用

International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Adil Sheraz Muhammad Farrukh Shahid Zuhaib Ashfaq Khan Tariq Jamil. S. Khanzada Department of Computer Science FAST-NUCES Karachi Pakistan Department of Electronic and Communication Engineering (ECE) School of Architecture Technology and Engineering (ATE) University of Brighton UK Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Computer Systems Engineering Department Mehran UET Jamshoro Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The integration of artificial intelligence (AI) and 6G networks aims to enhance agricultural applications by enabling adaptive and efficient communication systems. Fusing the potential and strengths of machine learning (ML) technology and the high-speed, low-latency communication capabilities of 6G networks, we can create intelligent farming systems that can enhance productivity, reduce resource wastage, and ensure sustainable agricultural practices. To tackle resource wastage and power efficiency of 6G-enabled devices, we present an AI-based solution that utilizes pre-trained Deep Learning (DL) models to classify Orthogonal Frequency Division Multiplexing (OFDM) modulated Quadrature Amplitude Modulation (QAM) signals. The proposed approach involves recognizing signals based on the classification results at the receiving nodes, prompting them to switch their radio modulation schemes to options such as 4-QAM, 16-QAM, and 64-QAM. Taking advantage of DL models for QAM classification and dynamically adjusting modulation schemes, the proposed solution can optimize resource utilization, improve data transmission quality, and enhance overall network performance in agricultural applications. We used pre-trained ResNet50, MobileNetV2, and a scratch CNN model. Our results show that pretrained models performed better than scratch CNN models and demonstrate the feasibility of using pretrained DL models for modulation scheme classification, offering promising opportunities for the development of adaptive and intelligent agricultural systems.

关键词： 6G mobile communication Smart agriculture Adaptation models Adaptive systems Quadrature amplitude modulation OFDM Modulation Switches Resource management Residual neural networks

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Plant Disease Detection and Classification Using Transfer Learning

Plant Disease Detection and Classification Using Transfer Le...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Karthigaiveni Madhubala. C G. Avinash E. Saranya P. Rohini G. Yogapriya Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamilnadu India School of Environment and Architecture Design SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science Engineering Sri Eshwar College of Engineering Chennai India Department of Computer Science and Engineering Saranathan College of Engineering Trichy Tamilnadu Chettinad School of Architecture Chettinad Academy of Research and Education Chennai Tamilnadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to forecast and identify plant leaf diseases may be tedious and inaccurate. Challenges may occur while endeavoring to manually forecast illness kinds. The failure to promptly identify and categorize plant diseases may lead to the devastation of crops, causing a substantial reduction in yield. Farmers using automated image processing techniques in their fields may mitigate losses and enhance productivity. This study proposes using a deep learning framework utilizing the latest convolutional neural network model EfficientNet to identify tomato illnesses based on 18,161 plain and segmented photos of tomato leaves. The efficacy of two segmentation theories, namely U-net and Modified U-net, for leaf segmentation is shown. The proposed methodology for plant disease identification is straightforward and computationally efficient, necessitating less time for predictions compared to previous deep learning methodologies. This document presents the computed accuracies for several plant and leaf diseases.

关键词： Deep learning Productivity Plant diseases Image segmentation Accuracy Computational modeling Transfer learning Crops Agriculture Robustness

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Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly

arXiv

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arXiv 2024年

作者： Kyaw, Alexander Htet Jeon, Se Hwan Smith, Miana Gershenfeld, Neil Department of Architecture The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Center for Bits and Atoms Massachusetts Institute of Technology CambridgeMA02139 United States

We present a system that transforms speech into physical objects by combining 3D generative Artificial Intelligence (AI) with robotic assembly. The system leverages natural language input to make design and manufacturing more accessible, enabling individuals without expertise in 3D modeling or robotic programming to create physical objects. We propose utilizing discrete robotic assembly of lattice-based voxel components to address the challenges of using generative AI outputs in physical production—such as design variability, fabrication speed, structural integrity, and material waste. The system interprets speech to generate 3D objects, discretizes them into voxel components, computes an optimized assembly sequence, and generates a robotic toolpath. The results are demonstrated through the assembly of various objects, ranging from chairs to shelves, which are prompted via speech and realized within 5 minutes using a 6-axis robotic arm. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic assembly

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A Many-Objective Evolutionary Algorithm Based on Indicator Selection and Adaptive Angle Estimation

SSRN

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SSRN 2024年

作者： Wang, Qian Gu, Qinghua Zhou, Qing Xiong, Neal Naixue Liu, Di School of Management Xi’an University of Architecture and Technology Shaanxi Xi’an China School of Resources Engineering Xi’an University of Architecture and Technology Shaanxi Xi’an China Xi’an Key Laboratory for Intelligent Industrial Perception Calculation and Decision Xi’an University of Architecture and Technology Xi’an710055 China Department of Mathematics and Computer Science Northeastern State University TahlequahOK74464 United States

Currently, real-world complex engineering problems often have more than three objectives that need to be optimized simultaneously. Existing algorithms for solving them mostly rely on the shape of the Pareto front to maintain diversity, which makes it difficult to effectively balance the convergence and diversity of the solutions. To address the above problems, a many-objective evolutionary algorithm based on indicator selection and adaptive angle estimation (MaOEA-ISAE) is proposed in this paper. Firstly, a unit vector metric selection strategy is presented, which uses the favorable physical properties of the metric to identify dominant individuals, thus striking a balance between convergence and diversity. Then, to preserve the diversity of the population, an adaptive angle estimation strategy based on Pareto front curvature prediction is developed. The angular distances of individuals are adaptively measured according to the predicted curvature, thereby selecting individuals with good diversity in the dominant region. Besides, the algorithm has a simple evolutionary process, and no additional parameters are involved. Finally, the proposed MaOEA-ISAE is compared with five other representative many-objective evolutionary algorithms on test instances of 3-20 objectives with different Pareto front shapes. The experimental results show the strong competitiveness of the proposed algorithm on different Pareto front problems. © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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3D visualization of hurricane storm surge impact on urban infrastructure

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Urban Informatics 2024年第1期3卷 1-14页

作者： Ye, Xinyue Li, Shoujia Gao, Ge Retchless, David Cai, Zhenhang Newman, Galen Du, Jiaxin Duffield, Nick Department of Landscape Architecture & Urban Planning Texas A&M University College Station USA Department of Computer Science & Engineering Texas A&M University College Station USA Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Marine and Coastal Environmental Science Texas A&M University Galveston USA Department of Electrical and Computer Engineering Texas A&M University College Station USA

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.

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Research on Practical Byzantine Fault Tolerant Algorithm Based on Trust Mechanism

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Journal of computers (Taiwan) 2022年第2期33卷 11-23页

作者： Pang, Hui Li, Yanan Wen, Xiumei Mu, Yingxue Department of Computer Science and Technology Hebei University of Architecture Big Data Technology Innovation Center of Zhangjiakou Hebei Zhangjiakou China

The consensus algorithm is one of the core technologies of the blockchain, which determines how the nodes in the blockchain network reach a consensus and enable them to jointly maintain a piece of data. This paper focuses on the Practical Byzantine Fault Tolerant Algorithm (PBFT Algorithm), and proposes a Practical Byzantine Fault Tolerant Algorithm based on Trust Mechanism (TM-PBFT Algorithm) to solve the problems of its low fault-tolerant rate and high communication cost. This algorithm supports voting for trusted nodes before the consensus cycle starts. In order to make nodes actively vote and vote for reliable nodes, this paper models the trust degree of nodes. In addition, the time factor is added to the Shapley value to design a new reward and punishment scheme, which makes the distribution of income of each node more reasonable and further encourages nodes to vote for reliable nodes. As the system runs for a long time, the probability of malicious nodes becoming master nodes decreases. Finally, a comparison experiment between the proposed algorithm and the PBFT algorithm shows that the fault tolerance and throughput of the TM-PBFT algorithm are higher than that of the PBFT algorithm, and the communication bandwidth overhead is lower than that of the PBFT algorithm. © 2022 computer Society of the Republic of China. All rights reserved.

关键词： Blockchain

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