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4th IEEE Global Conference on Life sciences and Technologies, LifeTech 2022

作者： Hanawa, Dai Oguchi, Kimio Nagoya City University Graduate School of Design and Architecture Nagoya Japan National Taiwan University of Science and Technology Department of Electronics and Computer Engineering Taipei Taiwan

ISBN: (纸本)9781665419048

In this research, two experiments were performed to develop a noncontact measurement method for flow velocity in nasal breathing. On the basis of the results of our preliminary study, we first compared the instantaneous flow velocity of nasal breathing and the air temperature of inhalation and exhalation. Next, we compared the air temperature and the nasal regions on facial thermal images. Results showed that the correlation between flow velocity, air temperature, and the nasal region can be improved using a smoothing filter and our modified normalization procedure. © 2022 IEEE.

关键词： Flow velocity

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Automated Discovery of CEP Applications with Evolutionary Computing 25

Automated Discovery of CEP Applications with Evolutionary Co...

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Proceedings of the 19th ACM International Conference on Distributed and Event-based Systems

作者： Giulio Appetito Eric Medvet Vincenzo Gulisano Department of Civil Engineering and Computer Science Engineering (DICII) University of Rome Tor Vergata Roma Italy Department of Engineering and Architecture University of Trieste Trieste Italy Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg Sweden

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Quantitative Evaluation to Detect Crack Depth in Beams Based on CNN-RNN-LSTM Approach

Quantitative Evaluation to Detect Crack Depth in Beams Based...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Gandhi, Mohammad Asif Swaminathen, A.N. Patil, Deepak T. Ravitheja, A. Kamali, R. Rajput, Akshay Anjuman-I-Islam's Kalsekar Technical Campus Department of Mechanical Engineering Maharashtra Panvel India Adi Shankara Institute of Engineering and Technology Kerala India Bharati Vidyapeeth College of Architecture Maharashtra Navi Mumbai India Svr Engineering College Department of Civil Engineering Andhra Pradesh Kurnool India Knowledge Institute of Technology Department of Computer Science and Engineering Tamilnadu Salem India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350300857

In this study, the proposed approach offers a novel hybrid image-based deep learning method for autonomous concrete fracture identification. Cracks are easier to spot and false positives are reduced with hybrid images that include visual and infrared thermography data. In specifically, a Visible and infrared camera as well as a line laser make up a hybrid imaging system for spatially scanning unmanned vehicles can efficiently assess large-scale concrete-made constructions like bridges and dams. Crack detection is a commonplace task in many industrial domains, however the current method of relying on expert judgment is typically laborious, time consuming, and inaccurate. The practical benefits of automated decision-making increase as the size of the target concrete construction grows. Cracks can be automatically identified and seen using the proposed method. Preprocessing, segmentation, feature extraction, and model training are the four components of the proposed method. It performs clever edge recognition, scaling, and greyscale conversion as part of its preparation. Improved K-Means segmentation is used for the separation. It employs GLCM for feature extraction, and CNN-RNN-LSTM for model training. The new method is compared to two established approaches, CNN-RNN and CNN-LSTM, and is found to be superior. © 2023 IEEE.

关键词： Decision making

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Simulation Optimization Method for Intelligent Compaction Construction of Railway Subgrade Oriented Towards Quality Control

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SSRN 2024年

作者： Ma, Zhaoxi Zhao, Qin Jia, Hongfei Sheng, Yiheng Yang, Mingsong Gu, Jingtian Zhu, Yiyun Hei, Xinhong Department of Civil Engineering and Architecture Xi’an University of Technology Xi’an710048 China Department of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

This study, conducted in the context of railway subgrade construction, focuses on quality control and explores an effective method by developing a simulation model and a real-time optimization algorithm. A real-time optimization algorithm was initially utilized to optimize the compaction scheme based on Intelligent Compaction (IC) technology. Subsequently, a comprehensive construction simulation process was established, defining features such as entity arrival patterns, service modes, queueing rules, and service processes, ensuring the accuracy and reliability of the simulation process. Simulation experiments were then conducted to determine the globally optimal compaction construction scheme. Comparison with the original construction scheme revealed that the optimal compaction scheme met quality requirements and improved construction efficiency by nearly 11.4%. Additionally, it facilitated synergistic operations with other construction activities. Thus, this study's quality control method offers a scientific and efficient management tool and decision-making basis for railway subgrade construction, possessing significant practical and promotional value. © 2024, The Authors. All rights reserved.

关键词： Discrete event simulation

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Simulation Optimization Method for Intelligent Compaction Construction of Railway Subgrade Oriented Towards Quality Control

SSRN

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SSRN 2024年

关键词： Discrete event simulation

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A research infrastructure for generating and sharing diversity-aware data 2

A research infrastructure for generating and sharing diversi...

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Workshops at the 2nd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2023

作者： Busso, Matteo Abente Acosta, Ronald Chenu de Götzen, Amalia Department of Information Engineering and Computer Science University of Trento Via Sommarive 9 Trento30123 Italy Department of Architecture Design and Media Technology Aalborg University A. C. Meyers Vænge 15 København2450 Denmark

The intensive flow of personal data associated with the trend of computerizing aspects of people’s diversity in their daily lives is associated with issues concerning not only people protection and their trust in new technologies, but also bias in the analysis of data and problems in their management and reuse. Faced with a complex problem, the strategies adopted, including technologies and services, often focus on individual aspects, which are difficult to integrate into a broader framework, which can be of effective support for researchers and developers. Therefore, we argue for the development of an end-to-end research infrastructure (RI) that enables trustworthy diversity-aware data within a citizen science community. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Data privacy

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FMC-CEE: A Collaborative Execution Environment with Flash Memory Controllers in IoT

FMC-CEE: A Collaborative Execution Environment with Flash Me...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Zheng Zhang Jingfeng Xue Thar Baker Tian Chen Yuhang Zhao Weizhi Meng School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Architecture Technology and Engineering University of Brighton Brighton UK School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark

Applications running on an Internet of Things (IoT) device are usually deployed in an untrusted environment. This introduces risks of vulnerability to malware, and loss of intellectual property associated with security sensitive code. Trusted execution environments (TEEs) and TEE-based applications have been widely adopted to run security sensitive workloads and protect the security of applications. However, existing approaches require specialized CPU support or hardware peripherals equipped with co-processors, precluding widely deployment on low-cost IoT devices. In this paper, we propose a flash memory controller-based collaborative execution environment (FMC-CEE), a lightweight security solution constructed on the target flash device to provide code confidentiality and basic security primitives for low-cost IoT devices and embedded devices. FMC-CEE leverages the microprocessor of the target flash device as a co-processor that executes security-sensitive workloads collaboratively with the target system. We implemented a prototype of FMC-CEE on a Trans-Flash (TF) card and executed security-sensitive tasks of the target host. The experimental results show that FMC-CEE takes $590.748 \mu \mathrm{s}$ to execute the remote code (512 bytes), thus incurring very little overhead on the host system.

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Unmilled Rice Moisture Content Sensor for Rice Mill Factory Applications 12

Unmilled Rice Moisture Content Sensor for Rice Mill Factory ...

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12th International Electrical Engineering Congress, iEECON 2024

作者： Seewattanapon, Somporn Chudpooti, Nonchanutt Akkaraekthalin, Prayoot Rajamangala University Of Technology Suvarnabhumi Faculty Of Engineering And Architecture Nonthaburi Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Industrial Physics And Medical Instrumentation Bangkok10800 Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Departmemnt Of Electrical And Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

This research presents a novel electromagnetic-based sensor for inline measurement of unmilled rice paddy moisture content in the rice mill factory. The proposed sensor has been designed based on the modified planar dipole structure at the operating frequency of 440 MHz. The unmilled rice dielectric constant properties have been studied and applied for characterizing the moisture content. The unmilled rice moisture content sensor system then has been designed consisting of the proposed sensor, a microwave signal generator, a microwave signal detector, and a microcontroller. The moisture content sensor system has been implemented and used for measuring the unmilled rice paddy moisture content in a real rice mill factory in Udon Thani province, Thailand. When compared with the commercial rice moisture content tool based on a capacitance technique, the proposed rice moisture content sensor system gives almost same results, which only about 4% difference has been found. © 2024 IEEE.

关键词： Moisture control

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Enhanced Lung Cancer Detection Using CNNs for Accurate and Efficient CT Image Classification 1

Enhanced Lung Cancer Detection Using CNNs for Accurate and E...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Bdair, Mohammad Ruby, E D Kanmani Juyal, Prachi Taseen, Rakheeba Abirami, A. Soujanya, A. School of Architecture Computing & Engineering University of East London London United Kingdom Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of ECE Tamil Nadu Chennai India Uttarakhand Dehradun India School of computer science and Engineering Presidency university Bangalore India Sona College of Technology Department of Mathematics Tamil Nadu Salem India Ibrahimpatnam India

ISBN: (纸本)9798350353648

Lung cancer must be detected as early as possible, but it may be difficult with existing methods since they often depend on human judgment and outdated image processing. These techniques take a lot of time and are prone to errors, which produces uneven outcomes. The research provides a unique deep learning-based method for classifying lung nodules from CT images using convolutional neural networks (CNNs). By automating feature extraction and categorization, the suggested technique greatly increases diagnostic precision. The result shows superior performance over existing systems is demonstrated by evaluation measures such as AUC-ROC, accuracy, sensitivity, and specificity. With a training duration of 12 hours, an AUC-ROC of 0.95, accuracy of 92%, sensitivity of 94%, specificity of 90%, and inference speed of 0.5 seconds per scan, the results demonstrate notable improvements in diagnostic efficiency and accuracy. Deep learning has the potential to revolutionize the diagnosis of lung cancer and enhance patient care, as demonstrated by the study. © 2024 IEEE.

关键词： Lung cancer

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Towards understanding digital support contributing to climate neutral, inclusive, and beautiful cities: A systematic literature review

Towards understanding digital support contributing to climat...

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International Workshop on Green and Sustainable Software (GREENS)

作者： Alicia JW Takaoka Dirk Ahlers Ferdinand Ward Ådlandsvik Eivind Syrdalen Dovland Letizia Jaccheri Department of Computer Science Norwegian University of Science and Technology (NTNU) Trondheim Norway Department of Architecture and Planning Norwegian University of Science and Technology (NTNU) Trondheim Norway

Climate change is an existential threat to Europe and the world, and is impacting and influencing people. At the same time, the urbanization of the world is increasing, meaning that these challenges need to be solved mainly in cities. Cities are also increasingly using digital technologies to become smarter, which will be a crucial part of the future cities. The EU has initiated the European Green Deal in order to overcome these challenges, and to make Europe the first climate neutral continent by 2050. A part of this is the EU Cities Mission aiming for climate-neutral transitions in frontrunner cities by 2030. The EU-funded project Creating Actionable Futures (CrAFt) bridges these ambitions with the New European Bauhaus principles, to ensure that climate-neutral transitions in cities will be sustainable, inclusive, and beautiful. This systematic literature review was performed to understand digital tools in this specific context.

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