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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yi Yuan Guiyun Zhou Zhonghua Su Ziyin Liu Weiwei Sun Xiangchao Meng Jie Wen Zhiqiang Wu School of Resources and Environment University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Department of Geography and Spatial Information Techniques Ningbo University Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Sichuan Provincial Public 7 Security Department Communications and Information Technology Headquarters Chengdu China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Fine mapping of retrogressive thaw slumps (RTSs) holds paramount significance in the study of permafrost degradation and carbon exchange. We propose a lightweight and enhanced semantic segmentation network (LessNet) for automatically mapping the RTSs from Sentinel-2 images. LessNet is constructed on the encoder-decoder framework with innovative incorporation of attention mechanism and dual-level semantic features fusion. The lightweight architecture of LessNet eliminates the need for pre-training, and the network hyperparameters are automatically updated based on the training dataset, which allows for fast convergence of supervised learning. Experiments conducted in the Beiluhe region of the Tibetan Plateau highlight the robustness and competitive performance of the model.

关键词： Training Semantic segmentation Supervised learning Transfer learning Semantics Robustness Permafrost

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Sponge City As A Civil engineering Solution To Climate Change That Improves Urban Flood Resilience 10

Sponge City As A Civil Engineering Solution To Climate Chang...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Shao, Yu-Tong Ma, Kevin Sheng-Kai Department of Bioenvironmental Systems Engineering National Taiwan University Taipei Taiwan Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9781665485579

Climate change and urbanization drive the demand for water management to make cities more water resilient. Climate change brings heavy precipitation and causes urban flooding, with conventional construction of cities no longer suitable given the rising risk of flooding. Characterized by constructing cities as large sponges that absorb and treat water, the idea of sponge cities is a solution to stormwater control and drainage water management, which can be achieved by creating water absorbing areas, increasing permeable surface, and integrating stormwater and drainage systems. To assess flood resilience, evaluation indicators have been introduced to measure the effectiveness of sponge cities. A case study was also provided in this review to demonstrate how the concept of sponge cities was put into practice. © 2022 IEEE.

关键词： Climate change

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Diversifying Transmission Planning of Wind-Heavy Systems by Incorporating Dynamic Line Rating

Diversifying Transmission Planning of Wind-Heavy Systems by ...

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Annual North-American Power Symposium

作者： Sarangan Rajendrane Amirthagunaraj Yogarathnam Tianqiao Zhao Satoshi Endo Visvakumar Aravinthan Al Tamimi Charles R. Yokley Meng Yue Interdisciplinary Science Department Brookhaven National Laboratory Upton NY USA Department of Electrical and Computer Engineering Wichita State University Wichita KS USA Environmental and Climate Sciences Department Brookhaven National Laboratory Upton NY USA Transmission Planning and Policy Sunflower Electric Power Corporation Wichita KS USA

ISBN: (数字)9798331521035

ISBN: (纸本)9798331521042

Expanding electric transmission capacity for renewable energy sources such as wind and solar is essential for decarbonization efforts and meeting increased load demand. This typically requires significant investments in new transmission lines or upgrades to existing ones. However, enhancing the efficiency of current infrastructure can also be highly beneficial. Recently, the use of Dynamic Line Ratings (DLR) has gained significant attention from utilities. DLR allows operators to use lines at their true capacities by considering weather conditions, which can be much higher than the conservative static line ratings. This paper presents a methodology for identifying and implementing DLR on selected lines to enhance capacity and reduce upgrade costs. Line identification is performed using power flow tracing algorithms, where lines carrying more wind power are chosen as candidates for DLR implementation. A criticality score, which is a function of the average wind power flow in the line and the worst-case loading level, is then defined to prioritize the lines that need DLR implementation. The proposed method is tested on a modified system based on the Western Kansas area, with estimated cost savings evaluated. The criticality of these transmission lines during $(n-1)$ contingencies is also evaluated. Additionally, alternative reinvestment strategies for these savings are explored, which show their potential benefits for system security and reliability.

关键词： Wind Renewable energy sources Power transmission lines Costs Wind power generation Low carbon economy Planning Security Reliability North America

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The Effect of Epsilon-Near-Zero (ENZ) Modes on the Casimir Interaction between Ultrathin Films

arXiv

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arXiv 2022年

作者： Gong, Tao Liberal, Iñigo Spreng, Benjamin Camacho, Miguel Engheta, Nader Munday, Jeremy N. Department of Electrical and Computer Engineering University of California Davis United States Department of Materials Science and Engineering University of California Davis United States Institute of Smart Cities Public University of Navarre Spain Department of Electronics and Electromagnetism University of Seville Spain Department of Electrical and Systems Engineering University of Pennsylvania United States

Vacuum fluctuation-induced interactions between macroscopic metallic objects result in an attractive force between them, a phenomenon known as the Casimir effect. This force is the result of both plasmonic and photonic modes. For very thin films, field penetration through the films will modify the allowed modes. Here, we investigate the Casimir interaction between two ultrathin films from the perspective of the force distribution over real frequencies for the first time and find pronounced repulsive contributions to the force due to the highly confined and nearly dispersion-free epsilon-near-zero (ENZ) modes that only exist in ultrathin films. These contributions are found to persistently occur around the ENZ frequency of the film and are irrespective of the inter-film separation. We further associate the ENZ modes with a striking thickness dependence in the averaged force density for conductive thin films, a metric signifying a thin-film's acceleration due to Casimir effect. Our results shed light on the role of the unique vacuum fluctuation modes existing in ultrathin ENZ materials, which may offer significant potential for engineering the motion of objects in nanomechanical systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Ultrathin films

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Adapting a Self-Supervised Speech Representation for Noisy Speech Emotion Recognition by Using Contrastive Teacher-Student Learning

Adapting a Self-Supervised Speech Representation for Noisy S...

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

作者： Seong-Gyun Leem Daniel Fulford Jukka-Pekka Onnela David Gard Carlos Busso Department of Electrical and Computer Engineering The University of Texas at Dallas Occupational Therapy and Psychological & Brain Sciences Boston University Department of Biostatistics Harvard T.H. Chan School of Public Health Harvard University Psychology Department San Francisco State University

Studies have shown high performance in the speech emotion recognition (SER) task by fine-tuning a self-supervised speech representation model. Although this model can provide emotionally discriminative embedding in clean conditions, adapting it to a noisy target environment is still required when deployed on real-world applications. For adaptation, it is essential to balance between acquiring new knowledge from noisy speech and keeping the previous knowledge acquired during the pre-training and fine-tuning of the model. Therefore, we propose a contrastive teacher-student learning framework to retrain a self-supervised speech representation model for noisy SER. To keep the knowledge of the original model, we minimize the root mean square error between the clean embeddings from the original SER model and the noisy embeddings from the retrained model. To acquire the discriminative knowledge in the target noisy condition, we also minimize the InfoNCE loss by selecting the corresponding clean embedding as a positive sample and other noisy embeddings with different emotional labels as negative samples. Our experiment with the clean and noisy version of the MSP-Podcast corpus demonstrates that the contrastive teacher-student learning framework can significantly improve the performance of the model only trained with the clean speech in the target noisy condition for all the emotional attributes.

关键词： Adaptation models Emotion recognition Transfer learning Speech recognition Recording Noise measurement Speech processing

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electrically switchable Casimir forces using transparent conductive oxides

arXiv

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arXiv 2022年

作者： Gong, Tao Spreng, Benjamin Camacho, Miguel Liberal, Inigo Engheta, Nader Munday, Jeremy N. Department of Electrical and Computer Engineering University of California Davis United States Department of Materials Science and Engineering University of California Davis United States Department of Electronics and Electromagnetism University of Seville Spain Institute of Smart Cities Public University of Navarre Spain Department of Electrical and Systems Engineering University of Pennsylvania United States

Casimir forces between charge-neutral bodies originate from quantum vacuum fluctuations of electromagnetic fields, which exhibit a critical dependence on material’s electromagnetic properties. Over the years, in-situ modulation of material’s optical properties has been enabled through various means and has been widely exploited in a plethora of applications such as electro-optical modulation, transient color generation, bio- or chemical sensing, etc. Yet Casimir force modulation has been hindered by difficulty in achieving high modulation signals due to the broadband nature of the Casimir interaction. Here we propose and investigate two configurations that allow for in-situ modulation of Casimir forces through electrical gating of a metal-insulator-semiconductor (MIS) junction comprised of transparent conductive oxide (TCO) materials. By switching the gate voltage on and off, a force modulation of > 400 pN is predicted due to substantive charge carrier accumulation in the TCO layer, which can be easily measured using state-of-the-art force measurement techniques in an atomic force microscope (AFM). We further examine the influence of the oxide layer thickness on the force modulation, suggesting the importance of the fine control of the oxide layer deposition. Our work provides a promising pathway for modulating the Casimir effect in-situ with experimentally measurable force contrast. Copyright © 2022, The Authors. All rights reserved.

关键词： Modulation

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Smart Happy Device with IoT : For those who suffer anxiety, stress, and depression

Smart Happy Device with IoT : For those who suffer anxiety, ...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Fitri Yakub Azizul Azizan Inge Dhamanti Ahmad Zahran Md. Khudzari Niranjana Govindan Muljono Muljono Electronic System Engineering Department Malaysia-Japan International Institute of Technology UTM Kuala Lumpur Malaysia Cyberphysical Research Group (Embedded AI & ML Science Lab) Faculty of Artificial Intelligence Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Department of Biosciences & Medical Engineering Faculty of Engineering UTM Johor Bahru Malaysia Department of Computing Technologies Faculty of Engineering & Technology SRMIST Chennai India Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350340204

ISBN: (纸本)9798350340211

Due to the rising cases of Covid-19 in the world especially in Malaysia, the impact had been hit hard for many people. Due to this all people must be isolated in their home. It has affected the well-being and working life of millions of people across the world. Due to this disease, the mental health among people has increased significantly for the past 2 years. According to MoH, out of 983 Malaysian students, about 20.4%, 6.6%, and 2.8% are experienced minimal to moderate, marked to severe, and most extreme levels of anxiety, respectively. The main stressors are the remote online teaching, and uncertainty about the future regarding study and career which can affect mental health. Thus, the main objective of this project is to develop a product that may provide different medication methods to its users by helping people who suffer anxiety, stress, and depression. We used aromatherapy, positive quotes, and music therapy in order to release stress. The user can also check his or her pulse rate by only touching the fingerprint through the sensor. The microcontrollers used in this project are Arduino Uno and Arduino Mega to build the project circuit. The experimental results can improve body health, mind and spirit which enhances both physical and emotional health.

关键词： COVID-19 Uncertainty Pandemics Microcontrollers Anxiety disorders Medical treatment Mental health Fingerprint recognition Biomedical imaging IEEE Regions

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Measurement of paracetamol concentration using a fiber laser system

Measurement of paracetamol concentration using a fiber laser...

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Liliana Soares Rosa Ana Perez-Herrera Susana Novais António Ferreira Orlando Frazão Susana Silva Faculty of Engineering INESC TEC - Institute for Systems and Computer Engineering Technology and Science University of Porto Porto Portugal Department of Electrical Electronic and Communication Engineering Public Univ. of Navarra ISC - Institute of Smart Cities Public Univ. of Navarra Pamplona Spain INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal LEPABE-Laboratory for Process Engineering Environment Biotechnology and Energy University of Porto Porto Portugal ALiCE - Associate Laboratory in Chemical Engineering University of Porto Porto Portugal

A linear fiber laser system for measurements of paracetamol concentration is experimentally demonstrated. The cavity is based on a fiber loop mirror and an FBG centered at 1567.8 nm. The sensing head corresponds to a refractometric sensor, whose which principle of operation is based on Fresnel reflection in the fiber tip (FBG side). The system works at detected variations of paracetamol concentrations with a sensitivity of $[(8.74\pm 0.34)\times 10^{-5}]\ \ \mu \mathrm{W}/(\mathrm{g}/\text{kg})$ and a resolution of 2.77 g/kg. The results prove that the fiber laser system could be an asset for processing industries, specifically for non-invasive and real-time measurements of concentration.

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MABSplit: faster forest training using multi-armed bandits 22

MABSplit: faster forest training using multi-armed bandits

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Mo Tiwari Ryan Kang Je-Yong Lee Sebastian Thrun Chris Piech Ilan Shomorony Martin Jinye Zhang Department of Computer Science Stanford University Oxford University Electrical and Computer Engineering University of Illinois at Urbana-Champaign Department of Epidemiology Harvard T.H. Chan School of Public Health

ISBN: (纸本)9781713871088

Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine, dubbed MABSplit, used to efficiently find split points when constructing decision trees. Our algorithm borrows techniques from the multi-armed bandit literature to judiciously determine how to allocate samples and computational power across candidate split points. We provide theoretical guarantees that MABSplit improves the sample complexity of each node split from linear to logarithmic in the number of data points. In some settings, MABSplit leads to 100x faster training (an 99% reduction in training time) without any decrease in generalization performance. We demonstrate similar speedups when MABSplit is used across a variety of forest-based variants, such as Extremely Random Forests and Random Patches. We also show our algorithm can be used in both classification and regression tasks. Finally, we show that MABSplit outperforms existing methods in generalization performance and feature importance calculations under a fixed computational budget.

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Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study

arXiv

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arXiv 2024年

作者： Du, Hongru Zhao, Jianan Zhao, Yang Xu, Shaochong Lin, Xihong Chen, Yiran Gardner, Lauren M. Yang, Hao Center for Systems Science and Engineering Johns Hopkins University BaltimoreMD United States Department of Civil and Systems Engineering Johns Hopkins University BaltimoreMD United States Mila - Quebec AI Institute MontréalQC Canada Department of Computer Science Universite de Montréal MontréalQC Canada Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Department of Statistics Harvard University CambridgeMA United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Epidemiology Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States

Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior. Existing forecasting model frameworks struggle with the multifaceted nature of relevant data and robust results translation, which hinders their performances and the provision of actionable insights for public health decision-makers. Our work introduces PandemicLLM, a novel framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information—such as textual policies and genomic surveillance data—previously unattainable in traditional forecasting models. This approach, through a unique AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. By redefining the forecasting process as an ordinal classification task, PandemicLLM yields more robust and trustworthy predictions, facilitating public health decision-making. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is subsequently tested across all 50 states of the U.S. for a duration of 16 weeks. Empirically, PandemicLLM is shown to be a high-performing pandemic forecasting framework that effectively captures the impact of emerging variants and can provide timely and accurate predictions. The proposed PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models. This study illuminates the potential of adapting LLMs and representation learning to enh

关键词： Genome

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