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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

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Heart Failure: Machine Learning Prediction Within a 5-Year F...

24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

TSO-DSO Coordinated Operational Planning in the Presence of Shared Resources $i,m,o,t$

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TSO-DSO Coordinated Operational Planning in the Presence of ...

Power Tech Conference

作者： Micael Simoes Andre G. Madureira Filipe Soares Joao Pecas Lopes Center for Power and Energy Systems INESC TEC Porto Portugal Intelligent Clean Energy Systems LIST Belvaux Luxembourg Department of Electrical and Computer Engineering Faculty of Engineering of University of Porto Porto Portugal

Electric power systems are currently experiencing a profound change, as increasing amounts of Renewable Energy Sources (RESs) displace conventional forms of generation. This development has gone hand-in-hand with an increasing share of distributed power generation being connected directly to the Distribution Network (DN), and the widespread of other types of Distributed Energy Resources (DERs), such as Energy Storage Sytems (ESSs), Electric Vehicles (EVs), and active (flexible) consumers. As these trends are expected to continue, this will require a profound revision of the way Transmission System Operators (TSOs) and Distribution System Operators (DSOs) interact with each other to fully benefit from the growing flexibility that is available at the DN level. In this work we propose a new tool for the coordinated operational planning of transmission and distribution systems, considering the existence of shared resources that can be simultaneously used by TSO and DSOs for the optimal operation of their networks. The tool uses advanced distributed optimization techniques, namely the Alternating Direction Method of Multipliers (ADMM) in order to maintain data privacy of the several agents involved in the optimization problem, and keep the tractability of the problem. The proposed tool is applied to modified IEEE test systems, and the results obtained highlight the benefits of the proposed coordination mechanism to solve problems occurring simultaneously at the transmission and DN-levels.

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Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences

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arXiv 2023年

作者： Pataki, Zador Altillawi, Mohammad Kanakis, Menelaos Pautrat, Rémi Shen, Fengyi Liu, Ziyuan Van Gool, Luc Pollefeys, Marc The Computer Vision and Geometry Lab Department of Computer Science ETH Zurich Switzerland The Computer Vision Center CVC-Barcelona The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Computer Vision Lab Department Electrical Engineering ETH Zurich Switzerland The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Center for Processing Speech and Images KU Leuven The Computer Vision Lab ETH Zurich Switzerland

Modern learning-based visual feature extraction networks perform well in intra-domain localization, however, their performance significantly declines when image pairs are captured across long-term visual domain variations, such as different seasonal and daytime variations. In this paper, our first contribution is a benchmark to investigate the performance impact of long-term variations on visual localization. We conduct a thorough analysis of the performance of current state-of-the-art feature extraction networks under various domain changes and find a significant performance gap between intra- and cross-domain localization. We investigate different methods to close this gap by improving the supervision of modern feature extractor networks. We propose a novel data-centric method, Implicit Cross-Domain Correspondences (iCDC). iCDC represents the same environment with multiple Neural Radiance Fields, each fitting the scene under individual visual domains. It utilizes the underlying 3D representations to generate accurate correspondences across different long-term visual conditions. Our proposed method enhances cross-domain localization performance, significantly reducing the performance gap. When evaluated on popular long-term localization benchmarks, our trained networks consistently outperform existing methods. This work serves as a substantial stride toward more robust visual localization pipelines for long-term deployments, and opens up research avenues in the development of long-term invariant descriptors. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature extraction

Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines

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Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

IEEE International Conference on Industrial Technology (ICIT)

作者： Xiao Liu Luefeng Chen Min Wu Weihua Cao Chengda Lu Witold Pedrycz School of Automation China University of Geosciences Wuhan China School of Automation the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.

关键词： Drilling Robust control Uncertainty Robust stability Simulation Coal Resonance

Not so griddy: internal representations of RNNs path integrating more than one agent 24

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Not so griddy: internal representations of RNNs path integra...

Proceedings of the 38th International Conference on Neural Information Processing systems

作者： William T. Redman Francisco Acosta Santiago Acosta-Mendoza Nina Miolane Intelligent Systems Center Johns Hopkins Applied Physics Laboratory Department of Physics University of California Santa Barbara Dynamical Neuroscience University of California Santa Barbara Department of Electrical & Computer Engineering University of California Santa Barbara

ISBN: (纸本)9798331314385

Success in collaborative and competitive environments, where agents must work with or against each other, requires individuals to encode the position and trajectory of themselves and others. Decades of neurophysiological experiments have shed light on how brain regions [e.g., medial entorhinal cortex (MEC), hippocampus] encode the self's position and trajectory. However, it has only recently been discovered that MEC and hippocampus are modulated by the positions and trajectories of others. To understand how encoding spatial information of multiple agents shapes neural representations, we train a recurrent neural network (RNN) model that captures properties of MEC to path integrate trajectories of two agents simultaneously navigating the same environment. We find significant differences between these RNNs and those trained to path integrate only a single agent. At the individual unit level, RNNs trained to path integrate more than one agent develop weaker grid responses, stronger border responses, and tuning for the relative position of the two agents. At the population level, they develop more distributed and robust representations, with changes in network dynamics and manifold topology. Our results provide testable predictions and open new directions with which to study the neural computations supporting spatial navigation.

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Self-Healing First-Order Distributed Optimization with Packet Loss

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arXiv 2023年

作者： Donato Ridgley, Israel L. Freeman, Randy A. Lynch, Kevin M. Northwestern University EvanstonIL60208 United States Department of Electrical & Computer Engineering Department of Mechanical Engineering Northwestern Institute on Complex Systems United States Center for Robotics and Biosystems United States

We describe SH-SVL, a parameterized family of first-order distributed optimization algorithms that enable a network of agents to collaboratively calculate a decision variable that minimizes the sum of cost functions at each agent. These algorithms are self-healing in that their convergence to the correct optimizer can be guaranteed even if they are initialized randomly, agents join or leave the network, or local cost functions change. We also present simulation evidence that our algorithms are self-healing in the case of dropped communication packets. Our algorithms are the first single-Laplacian methods for distributed convex optimization to exhibit all of these characteristics. We achieve self-healing by sacrificing internal stability, a fundamental trade-off for single-Laplacian methods. © 2023, CC BY-SA.

关键词： Cost functions

Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

The Validity of a Machine Learning-Based Video Game in the Objective Screening of Attention Deficit Hyperactivity Disorder in Children Aged 5 to 12 Years

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arXiv 2023年

作者： Zakani, Zeinab Moradi, Hadi Ghasemzadeh, Sogand Riazi, Maryam Mortazavi, Fatemeh Department of Psychology University of Tehran Iran Department of Robotics and Artificial Intelligence University of Tehran Iran Intelligent Systems Research Institute SKKU Iran Department of Electrical and Computer Engineering Boston University United States Department of Robotics and Artificial Intelligence University of Tehran Tehran Iran

Objective: Early identification of ADHD is necessary to provide the opportunity for timely treatment. However, screening the symptoms of ADHD on a large scale is not easy. This study aimed to validate a video game (FishFinder) for the screening of ADHD using objective measurement of the core symptoms of this disorder. Method: The FishFinder measures attention and impulsivity through in-game performance and evaluates the child's hyperactivity using smartphone motion sensors. This game was tested on 26 children with ADHD and 26 healthy children aged 5 to 12 years. A Support Vector Machine was employed to detect children with ADHD. results: This system showed 92.3% accuracy, 90% sensitivity, and 93.7% specificity using a combination of in-game and movement features. Conclusions: The FishFinder demonstrated a strong ability to identify ADHD in children. So, this game can be used as an affordable, accessible, and enjoyable method for the objective screening of ADHD. © 2023, CC BY-NC-ND.

关键词： Serious games

A K -Band Four-Beam Transmitter with Decoupled Phase/Gain Control and Enhanced Power Back off Efficiency for SATCOM

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IEEE Transactions on Microwave Theory and Techniques 2024年第11期72卷 6443-6459页

作者： Gao, Huiyan Li, Nayu Xie, Xinhong Wang, Shaogang Lu, Hang Kuan, Yen-Cheng Yu, Xiaopeng Song, Chunyi Jane Gu, Qun Xu, Zhiwei Institute of Marine Electronics and Intelligent Systems Ocean College Zhejiang University Zhoushan316021 China Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Zhoushan316021 China Donghai Laboratory Zhoushan316021 China National Yang Ming Chiao Tung University International College of Semiconductor Technology Hsinchu30010 Taiwan Institute of VLSI Design and Research College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China University of California at Davis Department of Electrical and Computer Engineering DavisCA95616 United States

This article presents a 17.7-20.2 GHz eight-element four-beam RF-beamforming transmitter in 65-nm CMOS for satellite communication (SATCOM). The transmitter utilizes an analog scheme in the variable-gain amplifier (VGA) to achieve dB-in-linear gain control with a high dynamic range (DR), together with a current combining crossbar for a compact footprint. The vector-modulation-based (VM-based) phase shifter (PS) adopts the cancellation-based Gm units for low switching variation. Furthermore, eight PSs of each beam are split into two groups, with each group sharing the same tunable I/Q generator for low power consumption. Considering the increased peak-to-average power ratio (PAPR) caused by high-order modulation schemes and multibeam operation, the transmitter incorporates compact transformer-based Doherty power amplifiers (DPAs) with adaptive bias control to improve the power efficiency at back-off. The chip achieves a 31-dB gain-tuning range with a 0.283-dB step and a 360° phase-shifting range with a calibrated 7-bit resolution. The measured root-mean-square (RMS) phase/gain errors induced by the VGA/PS are below 2.05°/0.147 dB, respectively, which decouples phase/gain control. The transmitter achieves a maximum output 1-dB gain compression point (OP1dB) of 17.9 dBm and a maximum saturated power (Psat) of 18.9 dBm. The output element exhibits 29.3% drain efficiency (DE) at 6-dB power back off (PBO) from Psat , which is 1.56/3.12 times over the normalized class-B/class-A implementations, demonstrating Doherty PBO efficiency enhancement. The chip occupies an area of 8.59 × 4.17 mm2 and consumes 398 mW per element at OP1dB. Additionally, a 16-element linear array has been developed to showcase the capabilities of the multibeam operation and gain tapering. © 1963-2012 IEEE.

关键词： Efficiency