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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Alonso, Maria Justo Jørgensen, Rikke Bramming Madsen, Henrik Mathisen, Hans Martin NTNU Dep. Energy and Process Engineering Trondheim Norway NTNU Dep. Industrial Economics and Technology Management Trondheim Norway DTU Applied Mathematics and Computer Science Kgs. Lyngby Denmark

Recently many indoor air quality (IAQ) low-cost sensors (LCS) have arrived in the market. Being cost-effective encourages sensor installation in many different situations for IAQ-monitoring and sometimes for control of HVAC systems. However, very often these sensors are delivered with incomplete information and there are many doubts about their performance, reliability, and response to aging or drift. In this article, an evaluation of the performance of eight equal in-house mounted IAQ stations is presented. Pre-calibrated LCS measuring CO2, temperature, RH, and particulate matter were connected to a RaspberryPi. Some tests were repeated with 6 months of difference and some with one year. In general, all stations responded similarly to the events to which they were exposed, the inter-sensor correlations values were higher than 0.95. LCS reacted similarly to the sources (trends) but with quantitative disparities. Sensor calibration is needed but when included, the tested LCS seem fit for IAQ measurements. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Carbon dioxide

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Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

Planing It by Ear: Convolutional Neural Networks for Acousti...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Anthony Deschênes Rémi Georges Cem Subakan Bruna Ugulino Antoine Henry Michael Morin Dep. of Wood and Forest Sciences Université Laval Canada Dep. of Computer Science and Software Engineering Université Laval Canada FORAC Research Consortium Université Laval Canada Mila-Quebec AI Institute Canada Concordia University Canada FPInnovations Canada Dep. of Operations and Decision Systems Université Laval Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities.

关键词： Support vector machines Convolution Autoencoders Forestry Transformers Acoustics Sensors Convolutional neural networks Speech processing Anomaly detection

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Pollutant correlation analysis in measurements at four classrooms in four Norwegian schools 17

Pollutant correlation analysis in measurements at four class...

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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Alonso, Maria Justo Jørgensen, Rikke Bramming Madsen, Henrik Mathisen, Hans Martin Dep. Energy and Process Engineering Trondheim Norway Dep. Industrial Economics and Technology Management Trondheim Norway Technical University of Denmark Applied Mathematics and Computer Science Kgs. Lyngby Denmark

Demand-controlled ventilation (DCV) is often used in school buildings to save energy by modulating the airflow rates based on the demand. However, other pollutants and indoor air parameters may affect health and are not necessarily correlated to CO2. In this study investigation of correlations between PM2.5, formaldehyde, absolute humidity, CO2 and temperature are performed. Measurements of these parameters were performed between two months and one year, positioned in the supply air in four CO2-based DCV classrooms at four different schools. The time series of measurements were de-trended to avoid overestimating cross-correlation functions. From the measurements we have concluded the need to measure: 1) pollutants related to human activities (CO2) 2) pollutants that infiltrate from processes like combustion or traffic outdoors (PM2.5), and 3) parameters related to other "non-combustion-related activities" indoors, heat sources and moisture loads (temperature). Formaldehyde from building materials seems to be mostly degassed in these older than one-year buildings, but this study should be repeated on newer buildings. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Formaldehyde

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Intelligent Recognition of Immune Cells: Analysis and Research 19

Intelligent Recognition of Immune Cells: Analysis and Resear...

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19th IEEE International Conference on e-Business engineering, ICEBE 2023

作者： Li, Chih-En Liu, Wei-Lun Lai, Kun-Yi Chiang, Yu-Hsiang Yan, Xiaowei Chen, Hao-Xiang Li, Hsu-Ching Cheng, Wan-Shu National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung Taiwan Institute for Systems Biology Seattle Washington United States Providence University Dep. of Computer Science and Information Management Taichung Taiwan

ISBN: (纸本)9798350325553

Immunotherapy utilizes the potential of the patient's immune system to fight disease. T-lymphocytes have become the central focus of the immune system in the fight against cancer. One of the fundamental studies in immunotherapy is how to accurately identify various types of immune cells. Therefore, the study of immune-related cells will be an important reference for the treatment of COVID19 or other related diseases. In this paper, a series of experiments were conducted with COVID-19 patients as the starting point to design a strategy to identify the important subtypes of CD$4^{+}$T cells. In this paper, an intelligent machine learning algorithm is proposed according to the results of different experiments of linear and nonlinear dimension reduction algorithms, integrating the important characteristics of these two algorithms and improving the performance. Our experimental results show that the algorithm designed based on the front-end experiments can effectively distinguish the important lymphocytes. The proposed algorithm not only improves the performance, but also the dimension reduction procedure keeps the important features of both global and regional data. The experimental results of this paper will be beneficial for the subsequent analysis of the clustering in identifying important subtypes of CD$4^{+}$T cells. Most importantly, this wm significantly improve the procedure of manually integrating and labeling the recorded data after several tedious and monotonous analyses with external software. © 2023 IEEE.

关键词： COVID-19

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Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

arXiv

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arXiv 2025年

作者： Deschênes, Anthony Georges, Rémi Subakan, Cem Ugulino, Bruna Henry, Antoine Morin, Michael Dep. of Wood and Forest Sciences QC Canada Dep. of Computer Science and Software Engineering Dep. of Operations and Decision Systems QC Canada FORAC Research Consortium Université Laval QC Canada Mila-Quebec AI Institute QC Canada FPInnovations QC Canada Concordia University QC Canada

关键词： Competition

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From near real-time urban data to an explainable city-scale model to help reduce the Urban Heat Island (UHI) effect

From near real-time urban data to an explainable city-scale ...

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2023 International Conference on the Built Environment in Transition, CISBAT 2023

作者： Eslamirad, Nasim De Luca, Francesco Yahia, Sadok Ben Lylykangas, Kimmo FinEst Centre for Smart Cities Tallinn University of Technology Tallinn Estonia Dep. of Civil Engineering & Architecture Tallinn University of Technology Tallinn Estonia Department of Software Science Tallinn University of Technology Tallinn Estonia

Urbanization is associated with increasing the temperature of urban areas and phenomena like the Urban Heat Island (UHI). The latter produces a sensible impact on people's health, quality of life, urban liveability, and even the mortality rate. As a result, reducing high temperatures in cities will improve the quality of life in cities. Overall, the understanding of the relationship between UHI and urban features is not well captured due to a lack of accessibility to data and cost implications related to long-term monitoring and simulation processes. Machine Learning (ML) offers significant opportunities to develop approaches that lead to high accuracy in discovering the significant features of buildings and urban areas, improving the conditions of the inhabitants. The paper aims to describe how the actual spatial data of Tallinn, Estonia, were assessed and applied to build an Explainable ML-based model. The study outcomes include the ML-based models that help understand the importance of urban features and their value in developing strategies and solutions to mitigate the UHI effect. The study's findings show that changes in the most important features of urban areas and buildings, such as the built area in the neighborhood zone and the area and orientation of buildings, play the most significant role in transforming an urban area from one with no UHI effect to one that experiences heat waves. The results provide essential information for urban planners who implement built environments. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Machine learning

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Prediction of Electrical Characteristics of a-IGZO TFT Based on Transfer Learning-Based Variational Autoencoder

Prediction of Electrical Characteristics of a-IGZO TFT Based...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bea, Khean-Thye Wei-Hsuan Lin, Shih-Shin Hu Lin, Da-Zheng Hu, Hsin-Hui Chen, Xiu-Zhi Lin, Ting-Ru Chen, Yen-Lin Chen, Kun-Ming Cheng, Wai Khuen National Taipei University of Technology Dep. of Electronic Engineering Taipei106 Taiwan National Taipei University of Technology Dep. of Computer Science and Information Engineering Taipei106 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar31900 Malaysia

ISBN: (纸本)9798350337020

In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a high R2 score of 0.9704 with a low-computing-power hardware-friendly method that reduced time consumption significantly compared to prior approaches. The findings have practical implications for mitigating the time-consuming nature of TCAD simulations, and the method can expand to various types of input data while ensuring high performance and generalization. We demonstrated significant improvement in generalization and accuracy through a k-fold validation. © 2023 IEEE.

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A Review of Grid-Connected Inverters and Control Methods Under Unbalanced Grid Conditions

A Review of Grid-Connected Inverters and Control Methods Und...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Milad Ghavipanjeh Marangalu Fazal Ur Rehman Naser Vosoughi Kurkdjian Dep. Of Electrical and Computer Engineering Computer Queen’s University Kingston Ontario Canada Dep. Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Faculty of Electrical and Computer Engineering San Diego State University San Diego CA USA

ISBN: (数字)9798331533946

ISBN: (纸本)9798331533953

Grid-connected inverters play a pivotal role in integrating renewable energy sources into modern power systems. However, the presence of unbalanced grid conditions poses significant challenges to the stable operation of these inverters. This review paper provides a comprehensive overview of grid-connected inverters and control methods tailored to address unbalanced grid conditions. Beginning with an introduction to the fundamentals of grid-connected inverters, the paper elucidates the impact of unbalanced grid voltages on their performance. Various control strategies, including voltage and current control methods, are examined in detail, highlighting their strengths and limitations in mitigating the effects of grid imbalance. Lastly, the review identifies emerging trends and future research directions in the field, emphasizing the need for innovative solutions to address the evolving challenges posed by unbalanced grid conditions in the context of renewable energy integration.

关键词： Current control Photovoltaic systems Renewable energy sources Technological innovation Reviews Pressing Market research Inverters Power systems Voltage control

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Detecting 5G Signal Jammers with Autoencoders Based on Loose Observations

Detecting 5G Signal Jammers with Autoencoders Based on Loose...

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IEEE Globecom Workshops

作者： Matteo Varotto Stefan Valentin Stefano Tomasin Dep. of Information Engineering University of Padova Padova Italy Dep. of Computer Science Darmstadt University of Applied Sciences Darmstadt Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Padova Italy

Fifth-generation (5G) networks are prone to jamming attacks, which are particularly dangerous in mission-critical applications such as factory automation. In this paper, we present a simple method to detect jamming attacks with machine learning techniques operating on in-phase and quadrature (IQ) modulated signals. In particular, a convolutional autoencoder (CAE) learns the structure of the clean signal to distinguish it from jammed signals in real time. This approach requires only a loose synchronization to the OFDM symbol, while equalization and decoding are not necessary. Despite its simplicity, our technique has shown high detection rates in experiments on a 5G testbed.

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Studying Risks of Hybrid Reengineering on Air-Transportation Systems Management “A Case Study on Jordanian Civil Aviation Regulatory Commission (CARC)”

Studying Risks of Hybrid Reengineering on Air-Transportation...

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Information Technology (ACIT)

作者： Al Refai Mohammed N. Taani M.K. K. Ahmed Ali Otoom Ghassan Samara Hanandeh E. S Hayel Khafajeh Software Engineering Dep. Zarqa University Zarqa Jordan Cybersecurity and Cloud Computing Dep. Applied Science Private University Amman Jordan Department of Computer Science Zarqa University Zarqa Jordan Artificial Intelligence Dep. Zarqa University Zarqa Jordan

This study examines decision-making in software re-engineering within businesses, focusing on the challenges and considerations involved. Financial constraints, time, and staff efforts are critical factors in these decisions. The study identifies difficulties faced by top-level management with legacy systems, existing systems, underused systems, and packaged software due to user resistance and customer dissatisfaction. The primary goal of the study is to assess the importance of software quality factors, especially from the users' perspective, in making re-engineering decisions. It uses the ISO 9126 Model to analyze quality elements in the case study software, aiming to provide guidance for deciding whether to proceed with re-engineering. The study explores functionality, reliability, usability, efficiency, maintainability, and portability as factors influencing re-engineering decisions, based on the ISO 9126 model. To understand user interactions and program performance, the study used unstructured participant observation in the case study software, highlighting the challenges faced by top management due to user resistance. These findings were further confirmed through face-to-face interviews with software users and dep.rtment employees, offering insights into their perspectives on software quality elements. The results reveal that, despite some flaws, the system has valuable features and capabilities. Most employees express a desire to undergo re-engineering to enhance service quality, align with ISO 9126 standards, and leverage the expertise of competent staff for re-engineering activities.

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