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Annual North-American Power Symposium

作者： Nirasha Bataduvaarachchi Demy Alexander Visvakumar Aravinthan Abigail Devereaux Al Tamimi Department of Electrical and Computer Engineering Wichita State University Wichita Kansas USA Department of Economics Wichita State University Wichita Kansas USA Transmission Planning & Policy Sunflower Electric Power Corporation Wichita Kansas USA

ISBN: (数字)9798331521035

ISBN: (纸本)9798331521042

With the growing adoption of renewable energy resources and the increasing occurrence of high-impact, low-frequency events, maintaining grid reliability has become a significant concern for many countries. Currently, there is no standardized approach for transmission-level emergency load curtailment, as grid emergencies are typically addressed from a distribution system perspective. Consumers' energy conservation efforts, also known as voluntary load reduction, can help mitigate the risk of power outages during grid emergencies. However, the existing curtailment framework does not account for voluntary reductions, leading to unfairness for consumers who proactively reduce power usage to support the grid. This oversight also results in excessive load curtailment. Therefore, predicting voluntary reductions in advance is crucial for effective grid emergency management. This paper proposes a model to predict voluntary load reduction based on collective behaviors of residential consumers. The work is carried out at the transmission level. Micro-level consumer-behaviors are modeled using agent-based modeling with numerical analysis conducted on residential households in Wichita, Kansas. The results quantify voluntary reduction based on selected household attributes and temporal environmental factors, identifying the most crucial attributes that influence significant load reductions.

关键词： Surveys Analytical models Correlation Sensitivity analysis Predictive models Emergency services Data models Numerical models Wind forecasting Load modeling

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Virtual Co-Pilot: Multimodal Large Language Model-enabled Quick-access Procedures for Single Pilot Operations

arXiv

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arXiv 2024年

作者： Li, Fan Feng, Shanshan Yan, Yuqi Lee, Ching-Hung Ong, Yew Soon Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University HKSAR Hong Kong Centre for Frontier AI Research A*STAR Singapore School of Public Policy and Administration Xi'an Jiaotong University Xi’an China School of Computer Science and Engineering Nanyang Technological University Singapore

Advancements in technology, pilot shortages, and cost pressures are driving a trend towards single-pilot and even remote operations in aviation. Considering the extensive workload and huge risks associated with single-pilot operations, the development of a Virtual Co-Pilot (V-CoP) is expected to be a potential way to ensure aviation safety. This study proposes a V-CoP concept and explores how humans and virtual assistants can effectively collaborate. A preliminary case study is conducted to explore a critical role of V-CoP, namely automated quick procedures searching, using the multimodal large language model (LLM). The LLM-enabled V-CoP integrates the pilot’s instruction and real-time cockpit instrumental data to prompt applicable aviation manuals and operation procedures. The results showed that the LLM-enabled V-CoP achieved high accuracy in situational analysis (90.5%) and effective retrieval of procedure information (86.5%). The proposed V-CoP is expected to provide a foundation for future virtual intelligent assistant development, improve the performance of single pilots, and reduce the risk of human errors in aviation. © 2024, CC BY-NC-SA.

关键词： Computational linguistics

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Disturbance Observer-Enhanced Adaptive CBFs for Safety-Critical Motion Control of Autonomous Vehicles

Disturbance Observer-Enhanced Adaptive CBFs for Safety-Criti...

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International Conference on Intelligent Transportation

作者： Yifan Cheng Yuxiang Zhang Yuyao Jiang Hongqing Chu Bingzhao Gao Hong Chen College of Electronic and Information Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering National University of Singapore Singapore School of Customs and Public Administration Shanghai Customs College Shanghai China School of Automotive Studies Tongji University Shanghai China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The standalone adaptability in the penalty parameters of adaptive control barrier functions (ACBFs), may be insufficient to meet the high requirements for safety and robustness of autonomous vehicles (AVs) motion control, especially under system disturbances. This paper integrates the disturbance observer theory into the ACBFs, in order to improve the robustness and adaptability of the corresponding optimization framework. The enhanced performance is mainly realized in the following aspects: firstly, an auxiliary system for safety-critical control design is defined, and the constraints, related to the penalty parameters, are introduced. Secondly, the disturbance observers are introduced to estimate the unknown parts of high-order safety and stability constraints. Thirdly, the estimated error bounds are introduced into the original framework of ACBF-CLF-QPs to guarantee robustness and adaptive safety. The superior performance compared to the original ACBFs is verified in the AVs motion control under the classical scenario, and the simulation results indicate that, the proposed method has stronger robustness and adaptability to system disturbances while meeting the safety constraints.

关键词： Control design Simulation Robustness Stability analysis Disturbance observers Safety Motion control Optimization Autonomous vehicles Intelligent transportation systems

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Learning on Forecasting HIV Epidemic Based on Individuals’ Contact Networks

Learning on Forecasting HIV Epidemic Based on Individuals’ ...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Sun, Chaoyue Liu, Yiyang Parisi, Christina Fisk-Hoffman, Rebecca Salemi, Marco Fang, Ruogu Danforth, Brandi Prosperi, Mattia Marini, Simone Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions and College of Medicine University of Florida GainesvilleFL United States Department of Pathology Immunology and Laboratory Medicine College of Medicine University of Florida GainesvilleFL United States J. Crayton Pruitt Family Department of Biomedical Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Center for Cognitive Aging and Memory McKnight Brain Institute University of Florida GainesvilleFL United States Florida Department of Health 4025 Esplanade Way TallahasseeFL United States Emerging Pathogens Institute University of Florida GainesvilleFL United States

Improving the diagnosis of HIV is a fundamental objective of the Ending the HIV Epidemic initiative, as it represents the initial step toward treatment and achieving undetectable status, thereby reducing transmission. To attain these objectives effectively, it is crucial to identify the groups most susceptible to HIV, allowing interventions to be tailored to their specific needs. In this study, we developed a predictive model designed to assess individual HIV risk within a high-risk contact network – predicting treatment or at-risk – leveraging surveillance data collected through routine HIV case interviews in Florida. Unique to our analysis, we explored the incorporation of behavioral network information with Graph Neural Networks to enhance the predictive capacity for identifying individuals within the treatment or intervention categories, when compared to models that mainly consider conventional HIV risk factors. Our deployed Graph Isomorphism Network achieved 77.3% and 73.2% balanced accuracy in inductive and trans ductive learning scenarios respectively, outperforming the traditional prediction algorithms that do not leverage the network structure. We then usedour model to further investigate the importance of demographic and behavioral factors in the HIV risk prediction process. Our findings provide valuable insights for healthcare practitioners and policymakers in their efforts to combat HIV infection. © 2024 by SCITEPRESS – Science and Technology publications, Lda.

关键词： Contrastive Learning

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Label-free impedimetric immunosensor for point-of-care detection of COVID-19 antibodies

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Microsystems & Nanoengineering 2023年第1期9卷 161-176页

作者： Lian C.T.Shoute Gaser N.Abdelrasoul Yuhao Ma Pedro A.Duarte Cole Edwards Ran Zhuo Jie Zeng Yiwei Feng Carmen L.Charlton Jamil N.Kanji Shawn Babiuk Jie Chen Department of Electrical and Computer Engineering University of AlbertaEdmontonABT6G 2V4Canada Public Health Laboratory Alberta Precision LaboratoriesEdmontonABCanada Department of Laboratory Medicine and Pathology University of AlbertaEdmontonABT6G 2B7Canada Li Ka Shing Institute for Virology University of AlbertaEdmontonABCanada Division of Infectious Diseases Department of MedicineCumming School of MedicineUniversity of CalgaryCalgaryABCanada Department of Pathology&Laboratory Medicine Cumming School of MedicineUniversity of CalgaryCalgaryABCanada National Centre for Foreign Animal Disease Canadian Food Inspection AgencyWinnipegMBCanada Department of Immunology University of ManitobaWinnipegMBCanada Department of Biomedical Engineering University of AlbertaEdmontonABT6G 2R3Canada

The COVID-19 pandemic has posed enormous challenges for existing diagnostic tools to detect and monitor ***,there is a need to develop point-of-care(POC)devices to perform fast,accurate,and accessible diagnostic methods to detect infections and monitor immune *** most amenable to miniaturization and suitable for POC applications are biosensors based on electrochemical *** have developed an impedimetric immunosensor based on an interdigitated microelectrode array(IMA)to detect and monitor SARS-CoV-2 antibodies in human *** chemistry was applied to functionalize and covalently immobilize the spike protein(S-protein)of SARS-CoV-2 on the surface of the IMA to serve as the recognition layer and specifically bind anti-spike *** bound to the S-proteins in the recognition layer result in an increase in capacitance and a consequent change in the impedance of the *** impedimetric immunosensor is label-free and uses non-Faradaic impedance with low nonperturbing AC voltage for *** sensitivity of a capacitive immunosensor can be enhanced by simply tuning the ionic strength of the sample *** device exhibits an LOD of 0.4 BAU/ml,as determined from the standard curve using WHO IS for anti-SARS-CoV-2 immunoglobulins;this LOD is similar to the corresponding LODs reported for all validated and established commercial assays,which range from 0.41 to 4.81 BAU/*** proof-of-concept biosensor has been demonstrated to detect anti-spike antibodies in sera from patients infected with COVID-19 within 1 h.

关键词： antibodies impedance free

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ConfliLPC: Logits and Parameter Calibration for Political Conflict Analysis in Continual Learning

ConfliLPC: Logits and Parameter Calibration for Political Co...

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IEEE International Conference on Big Data

作者： Xiaodi Li Niamat Zawad Patrick T. Brandt Javier Osorioc Vito D’Orazio Latifur Khan Computer Engineering The University of Texas at Dallas Richardson USA Computer Science The University of Texas at Dallas Richardson USA Economic Political and Policy Science The University of Texas at Dallas Richardson USA School of Government and Public Policy The University of Arizona Tucson USA Department of Political Science West Virginia University Morgantown USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The ConfliLPC framework introduces an innovative integration of Logits and Parameter Calibration (LPC) with the ConfliBERT model, tailored specifically for the nuanced analysis of political conflict and violence. This paper details the development and application of ConfliLPC, highlighting its robust capability to adapt to evolving data landscapes without succumbing to catastrophic forgetting (CF), a common challenge in machine learning models applied to dynamic domains such as political science. ConfliLPC enhances accuracy and adaptability by continually adjusting its parameters to accommodate new information while retaining valuable historical insights. The framework has been rigorously tested across various conflict scenarios, demonstrating superior performance in real-time analysis and predictive tasks. This work serves as a significant contribution to the fields of political science, conflict research, and applied machine learning, providing a powerful tool for analysts and policymakers engaged in the understanding and resolution of political conflict. The experimental results highlight the efficiency of the ConfliLPC method and its capability to minimize CF. Our code is publicly available 1

关键词： Continuing education Adaptation models Analytical models Accuracy Machine learning Data models Real-time systems Question answering (information retrieval) Calibration Streams

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Empowering Healthcare with AI: Brain Tumor Detection Using MRI and Multiple Algorithms

Empowering Healthcare with AI: Brain Tumor Detection Using M...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Micheal Olaolu Arowolo Williams Oluwatobiloba Ajayi Prisca Olawoye Oluwatomilola Babajide Happiness Eric Aigbogun Mukhtar Damola Salawu Marion Olubunmi Adebiyi Ayodele Adebiyi Department of Electrical Engineering and Computer Science University of Missouri Columbia USA Department of Computer Science Landmark University Omu-Aran Nigeria Department of Public Health Western Illinois University USA Stowers Institute for Medical Research Kansas City MO USA Department of Computer Science Kwara State University Malete Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Artificial intelligence (AI) has revolutionized neuroimaging, especially in the identification of brain cancers, by giving patients greater control and providing highly accurate diagnostic imaging. utilizing MRI data, this study investigates the feasibility of AI-driven brain tumor diagnosis utilizing methods including K-nearest neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). Careful data preprocessing allowed us to access Kaggle's brain MRI datasets labeled with cancer information, which allowed us to obtain accurate results. While Naive Bayes achieved 60% accuracy and 84% sensitivity, SVM achieved 82% sensitivity and 62% accuracy. Enhanced sensitivity and precision, in addition to 63% accuracy, were shared by Decision Tree and KNN. These findings suggest that AI could be helpful in the multi-method detection strategy that is essential for the correct diagnosis of brain tumors. This study provides clinicians with flexible diagnostic tools powered by artificial intelligence, opening a new chapter in the fight against neurological disorders and for better patient outcomes. The combination of artificial intelligence with magnetic resonance imaging (MRI) holds great promise for the better early detection and treatment planning of brain cancers. As AI keeps changing the healthcare landscape, these studies show how AI can revolutionize diagnostics and the fight against neurological illnesses.

关键词： Support vector machines Accuracy Sensitivity Magnetic resonance imaging Brain cancer Nearest neighbor methods Classification algorithms

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Developing Digital Therapeutics: The University Health Network Experience

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Journal of Commercial Biotechnology 2022年第1期27卷 7-11页

作者： Pham, Quynh Cafazzo, Joseph The Centre for Global eHealth Innovation Techna Institute University Health Network Canada Healthcare Human Factors Techna Institute University of Toronto Institute of Health Policy Canada Dalla Lana School of Public Health University of Toronto Canada Telfer School of Management University of Ottawa Canada Institute of Biomedical Engineering University of Toronto Department of Computer Science Canada

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Towards Quantifying Stress in Patients with a History of Myocardial Infarction: Validating ECG-Derived Patch Features

Towards Quantifying Stress in Patients with a History of Myo...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Afra Nawar Asim H. Gazi Michael Chan Jesus Antonio Sanchez-Perez Farhan N. Rahman Carrie Ziegler Obada Daaboul George Haddad Omar A. Al-Abboud Hashir Ahmed Nancy Murrah Viola Vaccarino Amit J. Shah Omer T. Inan School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Department of Epidemiology Rollins School of Public Health Emory University Atlanta GA USA

Patients with prior myocardial infarction (MI) have an increased risk of experiencing a secondary event which is exacerbated by mental stress. Our team has developed a miniaturized patch with the capability to capture electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals which may provide multimodal information to characterize stress responses within the post-MI population in ambulatory settings. As ECG-derived features have been shown to be informative in assessing the risk of MI, a critical first step is to ensure that the patch ECG features agree with gold-standard devices, such as the Biopac. However, this is yet to be done in this population. We, thus, performed a comparative analysis between ECG-derived features (heart rate (HR) and heart rate variability (HRV)) of the patch and Biopac in the context of stress. Our dataset contained post-MI and healthy control subjects who participated in a public speaking challenge. Regression analyses for patch and Biopac HR and HRV features (RMSSD, pNN50, SD1/SD2, and LF/HF) were all significant (p<0.001) and had strong positive correlations (r>0.9). Additionally, Bland-Altman analyses for most features showed tight limits of agreement: 0.999 bpm (HR), 11.341 ms (RMSSD), 0.07% (pNN50), 0.146 ratio difference (SD1/SD2), 0.750 ratio difference (LF/HF).Clinical relevance— This work demonstrates that ECG-derived features obtained from the patch and Biopac are in agreement, suggesting the clinical utility of the patch in deriving quantitative metrics of physiology during stress in post-MI patients. This has the potential to improve post-MI patients' outcomes, but needs to be further evaluated.

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High-Precision Positioning in GNSS-Challenged Environments: A LiDAR-Based Multi-Sensor Fusion Approach with 3D Digital Maps Registration

TechRxiv

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TechRxiv 2023年

作者： Mounier, Eslam Elhabiby, Mohamed Korenberg, Michael Noureldin, Aboelmagd The Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada The Department of Computer and Systems Engineering Ain Shams University Cairo11535 Egypt Micro Engineering Tech. Inc. Canada The Public Works Department Ain Shams University Cairo11535 Egypt The Department of Electrical and Computer Engineering Royal Military College of Canada KingstonONK7K 7B4 Canada

Autonomous vehicles (Avs) necessitate highly accurate and robust positioning capabilities. This calls for developing innovative algorithms that leverage the diversity of sensors and systems available in AVs to fulfill such demands across various environmental and operational conditions. This paper presents a multi-sensor positioning and navigation system that leverages cost-effective commercial-grade sensors for GNSS-challenging urban and indoor environments. The system fuses onboard motion sensor data with LiDAR point clouds registered to high-accuracy 3D digital maps to achieve sustained decimeter-level positioning. Key contributions include accurate LiDAR scans geo-referencing with motion compensation, efficient map-to-map registration, and an effective decentralized fusion. Real-world driving data from downtown Kingston, Ontario, Canada, and a high-accuracy 3D city geodatabase were used to examine the proposed methods’ performance and benefits. Results demonstrate the efficacy of the proposed technique, achieving accurate positioning with an average RMSE of 20cm horizontally and 13cm vertically, and a sustainable positioning sub-meter level of positioning accuracy 100% of the time. The proposed method was also able to sustain high precision positioning in such GNSS-denied environments with position errors of less than 50cm for 96.8% of the time and less than 30cm for 91% of the time. The performance achieved demonstrates that the proposed system is a practical option for the positioning and navigation of self-driving cars and has the potential for cooperative mapping and updating 3D city maps. © 2023, CC BY.

关键词： Autonomous vehicles

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