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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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Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding

arXiv

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arXiv 2022年

作者： Xiu, Haoyi Liu, Xin Wang, Weimin Kim, Kyoung-Sook Shinohara, Takayuki Chang, Qiong Matsuoka, Masashi Department of Architecture and Building Engineering Tokyo Institute of Technology Tokyo Japan Artificial Intelligence Research Center AIST Tokyo Japan DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China Innovation Technology Office Research Center PASCO Corporation Tokyo Japan Department of Computer Science Tokyo Institute of Technology Tokyo Japan

Learning point clouds is challenging due to the lack of connectivity information, i.e., edges. Although existing edge-aware methods can improve the performance by modeling edges, how edges contribute to the improvement is unclear. In this study, we propose a method that automatically learns to enhance/suppress edges while keeping the its working mechanism clear. First, we theoretically figure out how edge enhancement/suppression works. Second, we experimentally verify the edge enhancement/suppression behavior. Third, we empirically show that this behavior improves the performance. In general, we observe that the proposed method achieves competitive performance in point cloud classification and segmentation tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution

arXiv

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arXiv 2022年

作者： Xiu, Haoyi Liu, Xin Wang, Weimin Kim, Kyoung-Sook Shinohara, Takayuki Chang, Qiong Matsuoka, Masashi Department of Architecture and Building Engineering Tokyo Institute of Technology Tokyo Japan Artificial Intelligence Research Center AIST Tokyo Japan DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science Tokyo Institute of Technology Tokyo Japan Innovation Technology Office Research Center PASCO Corporation Tokyo Japan

Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information. Most prior works model local geometry using various convolution operations. We observe that the convolution can be equivalently decomposed as a weighted combination of a local and a global component. With this observation, we explicitly decouple these two components so that the local one can be enhanced and facilitate the learning of local surface geometry. Specifically, we propose Laplacian Unit (LU), a simple yet effective architectural unit that can enhance the learning of local geometry. Extensive experiments demonstrate that networks equipped with LUs achieve competitive or superior performance on typical point cloud understanding tasks. Moreover, through establishing connections between the mean curvature flow, a further investigation of LU based on curvatures is made to interpret the adaptive smoothing and sharpening effect of LU. The code will be available. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks

arXiv

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arXiv 2022年

作者： Han, Husheng Hu, Xing Xu, Kaidi Dang, Pucheng Wang, Ying Zhao, Yongwei Du, Zidong Guo, Qi Yang, Yanzhi Chen, Tianshi SKL of Processors Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Cambricon Technologies Beijing China Department of Computer Science College of Computing & Informatics Drexel University Philadelphia19104 United States SKL of Computer Architecture Institute of Computing Technology CAS Beijing100190 China Department of Electrical and Computer Engineering Northeastern University Boston02115 United States

DNN-based video object detection (VOD) powers autonomous driving and video surveillance industries with rising importance and promising opportunities. However, adversarial patch attack yields huge concern in live vision tasks because of its practicality, feasibility, and powerful attack effectiveness. This work proposes Themis, a software/hardware system to defend against adversarial patches for real-time robust video object detection. We observe that adversarial patches exhibit extremely localized superficial feature importance in a small region with non-robust predictions, and thus propose the adversarial region detection algorithm for adversarial effect elimination. Themis also proposes a systematic design to efficiently support the algorithm by eliminating redundant computations and memory traffics. Experimental results show that the proposed methodology can effectively recover the system from the adversarial attack with negligible hardware overhead. © 2022, CC BY.

关键词： Object detection

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RumorLens: Interactive Analysis and Validation of Suspected Rumors on Social Media

arXiv

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arXiv 2022年

作者： Wang, Ran Zhao, Yifei Wang, Shipan Du, Kehan Tang, Mojie Li, Yiyao Chen, Qianhe Tao, Hongxi Wang, Yong School of Journalism and Information Communication Huazhong University of Science and Technology China School of Artificial Intelligence and Automation Huazhong University of Science and Technology China School of Computer Science United Kingdom University of Bristol United Kingdom School of Architecture and Urban Planning Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China Computer Science Department University College London United Kingdom School of Computing and Information Systems Singapore Management University Singapore

With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society. Thus, it has become a critical task for social media platforms to deal with suspected rumors. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual analytics system, RumorLens, to help them deal with the rumor efficiently and gain an in-depth understanding of the patterns of rumor spreading. RumorLens integrates natural language processing (NLP) and other data processing techniques with visualization techniques to facilitate interactive analysis and validation of suspected rumors. We propose well-coordinated visualizations to provide users with three levels of details of suspected rumors: an overview displays both spatial distribution and temporal evolution of suspected rumors;a projection view leverages a metaphor-based glyph to represent each suspected rumor and further enable users to gain a quick understanding of their overall characteristics and similarity with each other;a propagation view visualizes the dynamic spreading details of a suspected rumor with a novel circular visualization design, and facilitates interactive analysis and validation of rumors in a compact manner. By using a real-world dataset collected from Sina Weibo, one case study with a domain expert is conducted to evaluate RumorLens. The results demonstrated the usefulness and effectiveness of our approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Social networking (online)

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Unsupervised Deep Learning Approaches for Anomaly Detection in IoT Data Streams

Unsupervised Deep Learning Approaches for Anomaly Detection ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Helina Rajini Suresh R. Neelaveni Bonda Kiran Kumar R. Arunadevi Indhumathi G M Varun Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of CSE SRM institute of science and technology Chennai Tamilnadu India KL School of Architecture KL Deemed to be University Andhrapradesh India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Department of ECE Rajalakshmi Engineering College Chennai Tamilnadu India Department of ECE Saveetha Engineering College Chennai Tamilnadu India

The proliferation of IoT devices is primarily responsible for the data deluge that has engulfed multiple industries. Nevertheless, due to the high dimensionality and complexity of IoT data streams, anomaly detection remains a significant challenge. This suggests that an unsupervised deep learning strategy could be used to identify anomalies in IoT data streams. Instead of relying on labeled data, this method employs deep neural networks, which can be trained to recognize anomalies and comprehend complex patterns. Experiments are conducted on a real-world IoT dataset to evaluate the efficacy and accuracy of the proposed method. The results demonstrate its proficiency in detecting anomalies in IoT data streams, paving the way for a safer, more reliable Internet of Things. Using unsupervised deep learning techniques, the method provides a practicable solution to the age-old problem of IoT anomaly identification.

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Evaluating Large Language Models on Spatial Tasks: A Multi-Task Benchmarking Study

arXiv

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arXiv 2024年

作者： Xu, Liuchang Zhao, Shuo Lin, Qingming Chen, Luyao Luo, Qianqian Wu, Sensen Ye, Xinyue Feng, Hailin Du, Zhenhong School of Mathematics and Computer Science Zhejiang Agriculture and Forestry University Hangzhou311300 China School of Earth Sciences Zhejiang University Hangzhou310058 China Department of Landscape Architecture and Urban Planning Center for Geospatial Sciences Applications and Technology Texas A&M University College StationTX77843 United States Financial Big Data Research Institute Sunyard Technology Co. Ltd Hangzhou310053 China

The advent of large language models such as ChatGPT, Gemini, and others has underscored the importance of evaluating their diverse capabilities, ranging from natural language understanding to code generation. However, their performance on spatial tasks has not been comprehensively assessed. This study addresses this gap by introducing a novel multi-task spatial evaluation dataset, designed to systematically explore and compare the performance of several advanced models on spatial tasks. The dataset encompasses twelve distinct task types, including spatial understanding and simple route planning, each with verified, accurate answers. We evaluated multiple models, including OpenAI’s gpt-3.5-turbo, gpt-4-turbo, gpt-4o, ZhipuAI’s glm-4, Anthropic’s claude-3-sonnet-20240229, and MoonShot’s moonshot-v1-8k, through a two-phase testing approach. Initially, we conducted zero-shot testing, followed by categorizing the dataset by difficulty and performing prompt tuning tests. Results indicate that gpt-4o achieved the highest overall accuracy in the first phase, with an average of 71.3%. Although moonshotv1-8k slightly underperformed overall, it surpassed gpt-4o in place name recognition tasks. The study also highlights the impact of prompt strategies on model performance in specific tasks. For example, the Chain-of-Thought (CoT) strategy increased gpt-4o’s accuracy in simple route planning from 12.4% to 87.5%, while a one-shot strategy enhanced moonshot-v1-8k’s accuracy in mapping tasks from 10.1% to 76.3%. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Pathways for Efficient Transition into Net Zero Energy Buildings (Nzeb) in Sub-Saharan Africa. Case Study: Cameroon, Senegal, and Côte D’Ivoire

SSRN

引用

SSRN 2023年

作者： Umaru Mohammed, Bongwirnso Wiysahnyuy, Yufenyuy Severine Ashraf, Noman Mempouo, Blaise Mengata, Ghislain Mengounou Department of Building Engineering College of Architecture and Planning Imam Abdulrahman Bin Faisal University Dammam31451 Saudi Arabia Laboratory of Computer Science Engineering and Automation University of Douala Cameroon Smart Green Shift Ltd Rotherham United Kingdom Laboratory of Technology and Applied Sciences University of Douala Cameroon

The combustion of fossil fuels to generate electricity to meet up with high energy demands of buildings, increases pollution;deteriorating the environment and accelerating climate change. The idea of net zero energy buildings (nZEB) was developed to reduce building energy use and pollution. However, there are still many obstacles to implementing the nZEB idea, notably in sub-Saharan Africa, where energy consumption and pollutant emissions from buildings are particularly high. This article focuses on Cameroon, Senegal, and Côte d'Ivoire and proposes guidelines for making a smooth transition to nZEB in sub-Saharan Africa, as are examples of strategies that might be used to minimize energy consumption in buildings and pollution. The path has been shown to consist of the following steps: conducting a baseline assessment;creating building energy codes and standards;conducting a stakeholder analysis;carrying out capacity building and awareness-raising;setting up financing mechanisms and incentives;creating a strong supply chain;setting up a monitoring and evaluation framework;piloting nZEB projects;disseminating best practices;and scaling up adoption. With this plan in place, sub-Saharan Africa can make the most of its resources while preserving the environment as it makes the move to nZEB © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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Reliable Multihop Routing and Congestion Aware Chicken Swarm Optimization in Vehicular Ad-Hoc Network

Reliable Multihop Routing and Congestion Aware Chicken Swarm...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Cuddapah Anitha Dalia Al-Tarazi GD. Vignesh A. Vinotha Vasuki Jayant Giri M. Dinesh Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati India Department of Architecture Engineering Faculty of Engineering Zarqa university Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering St. Joseph's College of Engineering Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security.

关键词： Vehicular ad hoc networks Routing Throughput Synchronization Resource management Dedicated short range communication Particle swarm optimization Artificial intelligence Vehicle dynamics Testing

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