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arXiv 2022年

作者： Soleymani, Maryam Bonyani, Mahdi Attarzadeh, Meghdad Department of Construction and Project Management Art University of Tehran Tehran Iran Department of Computer Engineering University of Tabriz Iran Department of Construction Management School of Architecture & Planning Morgan State University United States Department of Engineering Technology Old Dominion University United States

Resource allocation is one of the most critical issues in planning construction projects, due to its direct impact on cost, time, and quality. There are usually specific allocation methods for autonomous resource management according to the project’s objectives. However, integrated planning and optimization of utilizing resources in an entire construction organization are scarce. The purpose of this study is to present an automatic resource allocation structure for construction companies based on Deep Reinforcement Learning (DRL), which can be used in various situations. In this structure, Data Harvesting (DH) gathers resource information from the distributed Internet of Things (IoT) sensor devices all over the company’s projects to be employed in the autonomous resource management approach. Then, Coverage Resources Allocation (CRA) is compared to the information obtained from DH in which the Autonomous Resource Management (ARM) determines the project of interest. Likewise, Double Deep Q-Networks (DDQNs) with similar models are trained on two distinct assignment situations based on structured resource information of the company to balance objectives with resource constraints. The suggested technique in this paper can efficiently adjust to large resource management systems by combining portfolio information with adopted individual project information. Also, the effects of important information processing parameters on resource allocation performance are analyzed in detail. Moreover, the results of the generalizability of management approaches are presented, indicating no need for additional training when the variables of situations change. © 2022, CC BY-NC-ND.

关键词： Internet of things

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Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

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Psychoradiology 2021年第1期1卷 23-41页

作者： Xi Jiang Tuo Zhang Shu Zhang Keith MKendrick Tianming Liu School of Life Science and Technology MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Automation Northwestern Polytechnical UniversityXi’an 710129China Center for Brain and Brain-Inspired Computing Research School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710129China Cortical Architecture Imaging and Discovery Laboratory Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensGA 30605USA

Folding of the cerebral cortex is a prominent characteristic of mammalian *** or deficits in cortical folding are strongly correlated with abnormal brain function,cognition,and ***,a precise mapping between the anatomy and function of the brain is critical to our understanding of the mechanisms of brain structural architecture in both health and *** and sulci,the standard nomenclature for cortical anatomy,serve as building blocks to make up complex folding patterns,providing a window to decipher cortical anatomy and its relation with brain *** efforts have been devoted to this research topic from a variety of disciplines including genetics,cell biology,anatomy,neuroimaging,and neurology,as well as involving computational approaches based on machine learning and artificial intelligence ***,despite increasing progress,our understanding of the functional anatomy of gyro-sulcal patterns is still in its *** this review,we present the current state of this field and provide our perspectives of the methodologies and conclusions concerning functional differentiation between gyri and sulci,as well as the supporting information from genetic,cell biology,and brain structure *** particular,we will further present a proposed framework for attempting to interpret the dynamic mechanisms of the functional interplay between gyri and ***,this review will provide a comprehensive summary of anatomo-functional relationships in the cortical gyro-sulcal system together with a consideration of how these contribute to brain function,cognition,and behavior,as well as to mental disorders.

关键词： cortical folding gyro-sulcal pattern brain anatomo-function

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Steps: A development of students' employability prediction system using logistic regression model based on principal component analysis 10

Steps: A development of students' employability prediction s...

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10th IEEE International Conference on System Engineering and technology, ICSET 2020

作者： Casuat, Cherry D. Castro, Julius C. Evangelista, Deanne Cameren P. Merencilla, Niño E. Atal, Christina P. Department of Computer Engineering Technological Institute of the Philippines Manila Philippines College of Engineering and Architecture Bohol Island State University Bohol Philippines Department of Computer Engineering FEU Institute of Technology Manila Philippines Department of Computer Engineering Universidad De Manila Manila Philippines

ISBN: (纸本)9781728199108

Predicting students' employability prior to graduation can be a great tool for every HEI's career center to intervene timely and to take steps on how to improve the weaknesses of the students to become more employable. At present, there is no tool that can be used to determine undergraduate students who are at risk of unemployment or becoming disadvantaged because vulnerabilities are not detected early. In this study the principal component analysis (PCA) and logistic regression were used to determine the most predictive features in the students' employability prediction system (STEPS). The Dataset used consist of 1000 information of engineering students who took their on-the Job training from School year 2017- 2018 to School year 2019. The features used were professionalism and branding, confidence, comprehension, communication skills, growth potential, student performance rating. Upon using PCA,the experiments resulted to communication skills growth potential and student performance rating obtained the most predictive attributes that affects the employability prediction. © 2020 IEEE

关键词： Principal component analysis

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Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors 36

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph...

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36th Annual Conference on Neural Information Processing Systems - Competition Track, NeurIPS 2022

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab. China Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023 M. Neun et al.

关键词： Travel time

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A Non-contact Microwave Sensor System for Characterizing a Binary-liquid Mixed Concentration

A Non-contact Microwave Sensor System for Characterizing a B...

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International Electrical Engineering Congress (iEECON)

作者： Somporn Seewattanapon Apirada Namsang Reungyot Lerdwanittip Prayoot Akkaraekthalin Chawalit Rakluea Nattapong Duangrit Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Aeronautical Engineering Division Civil Aviation Training Center Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electronics and Telecommunication Engineering Rajamangala University of Technology Thanyaburi Pathumthani Thailand Centre of Observatory Operation and Engineering National Astronomical Research Instituite of Thailand Chiangmai Thailand

This research presents a design and analysis of an open-loop microstrip resonator which is integrated with a microwave sensor system. It is used for measuring a binary-liquid mixed concentration of sodium chloride (NaCl) and DI-water to detect salinity level. The open-loop resonator is designed to provide a band-stop bandwidth of 1 GHz, which has operated from 2 GHz to 3 GHz. The proposed microwave sensor system consists of a microwave synthesizer, a voltage detector, and an open-loop resonator that is designed to operate at 3 GHz to characterize the salinity level of the sample liquid mixture. A liquid mixture samples of NaCl and Distill-water with 5 different concentration levels of 0%, 2%, 5%, 10%, and 20% were used to assess the salinity level for the proposed microwave sensor system.

关键词： Chlorine compounds Microwave measurement Microstrip resonators Liquids Voltage measurement Salinity (geophysical) Synthesizers

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The analysis of intelligent real-time image recognition technology based on mobile edge computing and deep learning

The analysis of intelligent real-time image recognition tech...

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作者： Shen, Tao Gao, Chan Xu, Dawei College of Design and Innovation Tongji University Shanghai China Architecture Department Huzhou University Huzhou China School of Knowledge Science Japan Advanced Institute of Science and Technology NomiIshikawa Japan College of Computer Science Qinghai Nationalities University XiningQinghai China

This article aims to improve the accuracy of real-time image recognition in the context of the Internet of Things (IoT), reduce the core network pressure of the IoT and the proportion of IoT broadband, and meet people’s demand for internet image transmission. An intelligent image fusion system based on mobile edge computing (MEC) and deep learning is proposed, which can extract the features of images and optimize the sum of intra-class distance and inter-class distance relying on the hierarchical mode of deep learning, and realize distributed computing with the edge server and base station. Through comparison with other algorithms and strategies on the text and character data sets, the effectiveness of the constructed system is verified in the performance of the algorithm and the IoT. The results reveal that the application of the unsupervised learning hierarchical discriminant analysis (HDA) has better accuracy and recall in various databases compared with conventional image recognition algorithms. When the sum intra-class and inter-class distance K is 2, the accuracy of the algorithm can be as high as 98%. The combination of MEC and layered algorithms effectively reduces the pressures of core network and shortens the response time, greatly reduces the broadband occupancy ratio. The performance of IoT is increased by 37.03% compared with the general extraction and common cloud computing. Image recognition based on the MEC architecture can reduce the amount of network transmission and reduce the response time under the premise of ensuring the recognition rate, which can provide a theoretical basis for the research and application of user image recognition under the IoT. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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Integrating Simulation and AI for Optimal Electric Vehicle Charging Infrastructure: Achievements and Future Directions 16th

Integrating Simulation and AI for Optimal Electric Vehicle...

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16th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2024

作者： Guisado-Lizar, José-Luis Ragel-Díaz-Jara, David Navas-Orozco, Antonio Morera-Figueroa, José Diaz-del-Rio, Fernando Morón-Fernández, María José Cerezuela-Escudero, E. Cardenas-Montes, Miguel Jiménez-Moreno, Gabriel Department of Computer Architecture and Technology Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain I3US Research Institute Universidad de Sevilla Seville Spain SCORE Lab Universidad de Sevilla Seville Spain CIEMAT Centro Inv. Energéticas Medioambientales y Tecnológicas Madrid Spain

ISBN: (纸本)9783031873447

Electric vehicles (EVs) are the most prominent technology for reducing road transport emissions. Their rapid adoption necessitates efficient deployment of charging infrastructure. This study presents works in progress in an innovative approach integrating simulation and artificial intelligence (AI) to optimize EV charging station placement. We develop a microscopic traffic simulation model combining cellular automata and agent-based techniques to capture the dynamic interactions between vehicles, charging stations, and urban environments. Genetic algorithms and neural networks are employed to navigate the complex solution space and identify optimal configurations. The model considers multiple factors including traffic patterns, air quality, grid impacts, and urban mobility. The objective of this holistic approach is to improve charging infrastructure planning, leading to reduced congestion, lower emissions, and enhanced urban mobility. We discuss achievements to date and outline future research directions. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Genetic algorithms

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A Quantum Generative Adversarial Network-based Intrusion Detection System

A Quantum Generative Adversarial Network-based Intrusion Det...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Md Abdur Rahman Hossain Shahriar Victor Clincy Md Faruque Hossain Muhammad Rahman Department of Mathematics Jahangirnagar University Savar Dhaka Bangladesh Department of Information Technology Kennesaw State University Georgia United States Department of Computer Science Kennesaw State University Georgia United States College of Architecture and Construction Management Kennesaw State University Georgia United States College of Information and Mathematical Sciences Clayton State University Georgia United States

Machine learning has become widely accepted because of its diverse approaches to deal with a variety of cyber security issues. However, their capricious nature of security threats makes classical machine learning cyber systems vulnerable. Moreover, more samples in a big data dataset in classical machine learning approaches could produce the security defence systems weaken. It may create accurate outcomes by processing information which takes longer than expected, or observe poor accuracy because of inefficient training as well as other issues. However, quantum systems have the potential to produce atypical patterns which can not be possible to produce efficiently by classical systems, so we can postulate that quantum computers could use these advantages in that it could outperform the capabilities of classical computers on machine learning tasks. To be specific, an intrusion detection system can detect attack packets or sequence of attack packets at TCP/IP or other protocol level data based on certain patterns present or by profiling to detect anomalies. O(poly(n) gates are required to enable the use of potentially advantageous quantum algorithms with quantum states using he Quantum generative adversarial networks (qGAN) implemented by Qiskit which is a quantum computing tool of IBM.

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Designing EV charging stations deployment through holistic simulations: the SANEVEC project

Designing EV charging stations deployment through holistic s...

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EVI: Charging Ahead (EVI 2023)

作者： J.-L. Guisado-Lizar G. Jimenez-Moreno F. Diaz-del-Rio J. L. Sevillano-Ramos D. Cagigas-Muñiz M. J. Morón-Fernández D. Cascado-Caballero F. Jimenéz-Morales M. Cárdenas-Montes G. A. Wainer D. Ragel-Díaz-Jara J. Rodríguez-Leal E. Cerezuela-Escudero J. M. Montes-Sánchez D. Casanueva-Morato A. Ayuso-Martínez Research Institute of Computer Engineering (I3US) Universidad de Sevilla Avenida Reina Mercedes s/n 41012 Sevilla Spain Department of Condensed Matter Physics Universidad de Sevilla Avenida Reina Mercedes s/n 41012 Sevilla Spain Department of Basic Research Centro de Investigaciones Energeticas Medioambientales y Tecnologicas Madrid Spain Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Computer Architecture and Technology Universidad de Sevilla Avenida Reina Mercedes s/n 41012 Sevilla Spain

To determine the optimal location of the large number of urban public charging stations that will have to be deployed in the following years to transition to electric vehicles (EVs) is a challenging task, as it is intimately interconnected with the complexity of urban traffic and electric grid operation. The city is a complex system in which emerging patterns such as traffic jams, and instabilities or even outages in the electric grid are difficult to predict. The layout of charging stations can alter those patterns in traffic and electric grid operation, with consequences to mobility, air quality, and electric system. The present article presents the concept under the research project SANEVEC. It will build a framework for the optimal determination of the location of EV charging stations in a city based on simulation, which will be able to reproduce the feedback effects between all the variables involved: locations of the stations, traffic patterns, characteristics, and operation of the electricity grid, charging fees and air quality in the city. It will employ artificial intelligence methods to find high-quality station location solutions from the simulations and to predict the air quality in the city.

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Energy Mapping of Existing Building Stock in Cambridge using Energy Performance Certificates and Thermal Infrared Imagery

arXiv

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arXiv 2024年

作者： He, Yinglong Pan, Jiayu Debnath, Ramit Bardhan, Ronita Cullen, Luke Jenkins, Marco Gomez Mackie, Erik Hawker, George Parry, Ian Institute of Astronomy University of Cambridge CambridgeCB3 0HA United Kingdom Department of Architecture University of Cambridge CambridgeCB2 1PX United Kingdom EPRG Cambridge Judge Business School University of Cambridge CambridgeCB2 1AG United Kingdom Cambridge Zero Department of Computer Science and Technology Department of Architecture CambridgeCB3 OFD United Kingdom Division of Humanities and Social Science California Institute of Technology Pasadena91125 United States School of Mechanical Engineering Sciences University of Surrey SurreyGU2 7XH United Kingdom

Energy performance certificate (EPC) and thermal infrared (TIR) images both play a key role in the energy performance mapping of the urban building stock. In this paper, we developed parametric building archetypes using an EPC database and conducted temperature clustering on TIR images acquired through drones and satellite datasets. We evaluated 1725 EPCs of existing building stock in Cambridge, UK to generate energy consumption profiles. Drone-based TIR images of individual buildings in two Cambridge University colleges were processed using a machine learning pipeline for thermal anomaly detection and investigated the influence of two specific factors that affect the reliability of TIR for energy management applications: ground sample distance (GSD) and angle of view (AOV). The EPC-level results suggest that the construction year of the buildings influences their energy consumption. For example, modern buildings were over 30% more energy-efficient than older ones. Parallelly, older buildings were found to show almost double the energy savings potential through retrofitting than newly constructed buildings. TIR imaging results showed that thermal anomalies can only be properly identified in images with a GSD of 1m/pixel or less. A GSD of 1-6m/pixel can detect hot areas of building surfaces. We found that a GSD > 6m/pixel cannot characterise individual buildings but does help identify urban heat island effects. Additional sensitivity analysis showed that building thermal anomaly detection is more sensitive to AOV than to GSD. Our study informs newer approaches to building energy diagnostics using thermography and supports decision-making for retrofitting at a large scale. Copyright © 2024, The Authors. All rights reserved.

关键词： Drones

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