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Miniaturization of High Voltage Power Supply for Portable Electrostatic Filtration

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Miniaturization of High Voltage Power Supply for Portable El...

Annual Conference of Industrial Electronics Society

作者： Jia Hao Joe Wang Cynthia Yu Cameron Urquhart Zane Hernke Jan Leuca Espelien Christopher Simpson Igor Novosselov Alexander Mamishev SpectTree LLC Seattle WA USA School of Public Health University of Washington Seattle WA USA Department of Electrical and Computer Engineering University of Washington Seattle WA USA Dept. of Electrical Engineering Columbia University New York NY USA Department of Mechanical Engineering University of Washington Seattle WA USA

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Electrostatic Precipitation (ESP) is an effective method of removing particulates from the air. It is currently used in many large-scale industrial applications but sees limited use in smaller-scale cases due to the difficulty of generating high voltage in small packages. Additionally, safety concerns prevent the widespread use of this effective technique. This paper presents the motivation, design, and evaluation of a miniature high-voltage DC (HVDC) power supply for applications in small-scale ESP systems. The power supply’s potential for portable ESP application is tested on a personal protective device, the so-called Smart, Individualized, Noncontact, Extended-Wear (SINEW) mask. The results show that the power supply operates with acceptable efficiency and generates high voltages and power necessary to drive ESP setups in practical applications.

关键词： Electric potential Power supplies Filtration Electromagnetic interference High-voltage techniques Transformers Topology Electrostatics Wireless fidelity Testing

Understanding the Transit Gap: A Comparative Study of On-Demand Bus Services and Urban Climate Resilience in South End, Charlotte, NC and Avondale, Chattanooga, TN

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arXiv 2024年

作者： Hosseini, Sanaz Sadat Ardabili, Babak Rahimi Azarbayjani, Mona Pulugurtha, Srinivas Tabkhi, Hamed Department of Civil and Environmental Engineering University of North Carolina at Charlotte CharlotteNC United States Department of Public Policy University of North Carolina at Charlotte CharlotteNC United States School of Architecture University of North Carolina at Charlotte CharlotteNC United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC United States

Urban design significantly impacts sustainability, particularly in the context of public transit efficiency and carbon emissions reduction. This study explores two neighborhoods with distinct urban designs: South End, Charlotte, NC, featuring a dynamic mixed-use urban design pattern, and Avondale, Chattanooga, TN, with a residential suburban grid layout. Using the TRANSIT-GYM tool, we assess the impact of increased bus utilization in these different urban settings on traffic and CO2 emissions. Our results highlight the critical role of urban design and planning in transit system efficiency. In South End, the mixed-use design led to more substantial emission reductions, indicating that urban layout can significantly influence public transit outcomes. Tailored strategies that consider the unique urban design elements are essential for climate resilience. Notably, doubling bus utilization decreased daily emissions by 10.18% in South End and 8.13% in Avondale, with a corresponding reduction in overall traffic. A target of 50% bus utilization saw emissions drop by 21.45% in South End and 14.50% in Avondale. At an idealistic goal of 70% bus utilization, South End and Avondale witnessed emission reductions of 37.22% and 27.80%, respectively. These insights are crucial for urban designers and policymakers in developing sustainable urban landscapes. Copyright © 2024, The Authors. All rights reserved.

关键词： Urban transportation

Anomaly Detection of Patient Data in public Hospital used Internet of Things Sensors

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Anomaly Detection of Patient Data in Public Hospital used In...

International Conference on Inventive Computation Technologies (ICICT)

作者： Evizal Abdul Kadir Lenni Dianna Putri Sri Listia Rosa Apri Siswanto Farhan Assidiqi Muhammad Fikri Evizal Department of Informatics Engineering Universitas Islam Riau Pekanbaru Indonesia Department of Public Health Universitas Prima Indonesia Medan Indonesia Department of Electrical Engineering Telkom University Bandung Indonesia Computer Science and Information Eng National Dong Hwa University Hualien County Taiwan

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Increasing number of patients doing treatment in a public hospital become as issue for the management. Limited number of equipment to detect abnormal patient and support from medical staff. This research aims to detect on the anomaly detection of hospital patient data through the utilization of Internet of Things (IoT) sensors. With the increasing integration of IoT devices in healthcare settings, such as sensors monitoring vital signs like temperature, blood pressure, and heart rate, the need for robust anomaly detection mechanisms becomes imperative. The research explores advanced algorithms and machine learning techniques to identify irregular patterns or outliers in patient data, aiming to enhance the early detection of potential health issues or abnormalities. Leveraging the vast amount of data generated by IoT sensors in hospital environments, the research aims to contribute to the development of more efficient and accurate anomaly detection systems, ultimately improving the quality of patient care and facilitating timely intervention by healthcare professionals. The findings of this research have significant implications for the evolving landscape of healthcare technologies, emphasizing the importance of ensuring data integrity and patient safety in the era of IoT-driven healthcare solutions. Patient data shows in graph to see how the anomaly indication for the patient. Multi sensor with parallel system will develop in future research plan.

关键词： Temperature sensors Hospitals Data integrity Sensor systems Sensors Safety Internet of Things

Low Complexity Heuristics for Multi-Objective Sensor Placement in Traffic Networks

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Low Complexity Heuristics for Multi-Objective Sensor Placeme...

International Conference on Intelligent Transportation

作者： Marina Sofokleous Yiolanda Englezou Aristotelis Savva Stelios Timotheou Christos G. Panayiotou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Public Works Department of the Ministry of Transport Communications & Works Nicosia Cyprus

Monitoring the traffic state of the road network is very important for a plethora of reasons, such as the prevention of traffic congestion and the development of estimation and control policies. In order to efficiently obtain high-quality information on traffic, the sensors must be installed at optimal locations in the road network under study. This problem is known as the Network Sensor Location Problem (NSLP). In this work, a multi-objective NSLP is proposed for the installation of a pre-defined number of sensors to maximise (i) the covered traffic flow volume and (ii) the minimum distance between candidate links for sensor installation, while taking into account pre-installed sensors. We reformulate the problem into a single-objective mixed-integer linear program (MILP) that yields the optimal sensor locations. In addition, we propose four low-complexity heuristics for the solution of the problem. The performance of the proposed algorithms is evaluated for the traffic network of the Republic of Cyprus under real-life conditions and traffic data. Results show that the four low-complexity approaches yield a different trade-off between execution speed and solution quality.

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Integration of 5G and Motion Sensors for Vehicular Positioning: A Loosely-Coupled Approach

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arXiv 2024年

作者： Saleh, Sharief Bader, Qamar Karaim, Malek Elhabiby, Mohamed Noureldin, Aboelmagd Lab Department of Electrical and Computer Engineering Royal Military College of Canada KingstonONK7K 7B4 Canada The Department of Electrical and Computer Engineering Queen's University KingstonONK7L 3N6 Canada Micro Engineering Tech. Inc The Public Works Department Ain Shams University Cairo11566 Egypt

Autonomous vehicles (AVs) are poised to revolutionize the transportation industry by enhancing traffic efficiency and road safety. However, achieving optimal vehicular autonomy demands an uninterrupted and precise positioning solution, especially in deep urban environments. 5G mmWave holds immense potential to provide such a service due to its accurate range and angle measurements. Yet, as mmWave signals are prone to signal blockage, severe positioning errors will occur. Most of the 5G positioning literature relies on constant motion models to bridge such 5G outages, which do not capture the true dynamics of the vehicle. Few proposed methodologies rely on inertial measurement units (IMUs) to bridge such gaps, where they predominantly use tightly coupled (TC) integration schemes, introducing a nonlinear 5G measurement model. Such approaches, which rely on Kalman filtering, necessitate the linearization of the measurement model, leading to pronounced positioning errors. In this paper, however, we propose a loosely coupled (LC) sensor fusion scheme to integrate 5G, IMUs, and odometers to mitigate linearization errors. Additionally, we propose a novel method to design the process covariance matrix of the extended Kalman filter (EKF). Moreover, we propose enhancements to the mechanization of the IMU data to enhance the standalone IMU solution. The proposed methodologies were tested using a novel setup comprising 5G measurements from Siradel's S_5G simulation tool and real IMU and odometer measurements from an hour-long trajectory. The proposed method resulted in 14 cm of error for 95% of the time compared to 1 m provided by the traditional constant velocity model approach. © 2024, CC BY.

关键词： Linearization

VERVE: Template-based ReflectiVE Rewriting for MotiVational IntErviewing

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arXiv 2023年

作者： Min, Do June Pérez-Rosas, Verónica Resnicow, Kenneth Mihalcea, Rada Department of Electrical Engineering and Computer Science United States School of Public Health University of Michigan Ann ArborMI United States

Reflective listening is a fundamental skill that counselors must acquire to achieve proficiency in motivational interviewing (MI). It involves responding in a manner that acknowledges and explores the meaning of what the client has expressed in the conversation. In this work, we introduce the task of counseling response rewriting, which transforms non-reflective statements into reflective responses. We introduce VERVE, a template-based rewriting system with paraphrase-augmented training and adaptive template updating. VERVE first creates a template by identifying and filtering out tokens that are not relevant to reflections and constructs a reflective response using the template. Paraphrase-augmented training allows the model to learn less-strict fillings of masked spans, and adaptive template updating helps discover effective templates for rewriting without significantly removing the original content. Using both automatic and human evaluations, we compare our method against text rewriting baselines and show that our framework is effective in turning non-reflective statements into more reflective responses while achieving a good content preservation-reflection style trade-off. © 2023, CC BY.

关键词： Economic and social effects

Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System 1

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Using Arduino Iot Modules As A Low Cost Environmental Resear...

1st International Conference on computer Science and Emerging Technologies, CSET 2023

作者： Magableh, Murad Marie, Ziyad Mohamed, Rajina R. Bin Ibrahim, Mohd Hafizuddin Jusoh, Julaily Aida Kumar, Rohit Irbid National University Faculty of Business and Financial Science MIS BI and Data Analysis Department Irbid Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Public Administration Department Palestine College of Computing Dan Informatics Universiti Tenaga Nasional Malaysia Politeknik Kuala Terenngganu Department of Electrical Engineering Kuala Terengganu20200 Malaysia UniSZA Faculty of Informatics and Computing Malaysia School of Computer Science and Engineering Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

关键词： Internet of things

ENHANCING SYMBOLIC REGRESSION AND UNIVERSAL PHYSICS-INFORMED NEURAL NETWORKS WITH DIMENSIONAL ANALYSIS

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arXiv 2024年

作者： Podina, Lena Darooneh, Diba Grewal, Joshveer Kohandel, Mohammad Cheriton School of Computer Science University of Waterloo Canada Department of Electrical and Computer Engineering University of Waterloo Canada Northville High School Public school in Northville MI United States Department of Applied Mathematics University of Waterloo Canada

We present a new method for enhancing symbolic regression for differential equations via dimensional analysis, specifically Ipsen’s and Buckingham π methods. Since symbolic regression often suffers from high computational costs and overfitting, non-dimensionalizing datasets reduces the number of input variables, simplifies the search space, and ensures that derived equations are physically meaningful. As our main contribution, we integrate Ipsen’s Method of dimensional analysis with Universal Physics-Informed Neural Networks. We also combine dimensional analysis with the AI Feynman symbolic regression algorithm to show that dimensional analysis significantly improves the accuracy of the recovered equation. The results demonstrate that transforming data into a dimensionless form significantly decreases computation time and improves accuracy of the recovered hidden term. For algebraic equations, using the Buckingham π Theorem reduced complexity, allowing the AI-Feynman model to converge faster with fewer data points and lower error rates. For differential equations, Ipsen’s Method was combined with Universal Physics-Informed Neural Networks (UPINNs) to identify hidden terms more effectively. These findings suggest that integrating dimensional analysis with symbolic regression can significantly lower computational costs, enhance model interpretability, and increase accuracy, providing a robust framework for automated discovery of governing equations in complex systems when data is limited. © 2024, CC BY.

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Ripeness Classification of Fresh Oil Palm Using Deep Learning

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Ripeness Classification of Fresh Oil Palm Using Deep Learnin...

International electrical engineering Congress (iEECON)

作者： Nathaphat Srithai Krittiporn Rittireung Nongnoot Sungthong Kanabadee Srisomboon Wilaiporn Lee Vera Sa-Ing Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Broadcasting and Telecommunications Research and Development Fund for Public Interest Office of The National Broadcasting and Telecommunications Commission Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The challenge of accurately ripeness level classifying from fresh oil palm fruit, this research investigated the problems and methods that were used in ripeness level classification. The goal of this research is to present a method for classifying the ripeness levels of oil palm fruits using deep learning techniques applied to image data. This research applies a deep learning model for classifying the ripeness level of fresh oil palm fruit and aiming for real-time application or mobile usage. The proposed model, Yolov9n (nano), adapts principles from convolutional neural networks that was tuned the learning parameters consist of the Adam optimizer, a learning rate of 0.001, and a batch size of 32, result in the most accurate ripeness level classification model. The research demonstrates the Yolov9n (nano) improved model with optimized parameters to achieve the highest accuracy. Training and validation datasets represent precision of 99.4%, recall of 99.6%, mean average precision at 50% (mAP50) of 99.5%, and mAP50-95 of 92.6%. These findings confirm the model’s effectiveness in accurately classifying the ripeness level of oil palm fruit from images. The study provides guidance for the practical deep learning implementation of ripeness level classification on fresh oil palm imaging for efficient harvesting by farmers cultivating oil palm.

关键词： Deep learning Training YOLO Measurement Adaptation models Accuracy Oils Imaging Real-time systems Convolutional neural networks