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arXiv 2023年

作者： Amorim, Giovanna Santos, María Park, Shinkyu Franci, Alessio Leonard, Naomi Ehrich The Dept. of Mechanical and Aerospace Engineering Princeton Univ. PrincetonNJ08544 United States Computer Electrical and Math. Science and Eng. Div. Thuwal23955-6900 Saudi Arabia The Department of Electrical Engineering and Computer Science University of Liege 10 Allée de la Découverte Liège4000 Belgium The WEL Research Institute Wavre Belgium

We propose a threshold decision-making framework for controlling the physical dynamics of an agent switching between two spatial tasks. Our framework couples a nonlinear opinion dynamics model that represents the evolution of an agent’s preference for a particular task with the physical dynamics of the agent. We prove the bifurcation that governs the behavior of the coupled dynamics. We show by means of the bifurcation behavior how the coupled dynamics are adaptive to the physical constraints of the agent. We also show how the bifurcation can be modulated to allow the agent to switch tasks based on thresholds adaptive to environmental conditions. We illustrate the benefits of the approach through a multi-robot task allocation application for trash collection. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

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Short-Term Load Forecasting via a Multi-Layer Network Based on Feature Weight Optimization

Short-Term Load Forecasting via a Multi-Layer Network Based ...

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IEEE International Conference on Energy Internet (ICEI)

作者： Yan Huang Jieqiong Gan Zhongqi Wang Zhaozhe Zhong Jian Liu Junwei Cao Department of Automation Tsinghua University Beijing China Advanced Engineering Research Div. Toyota Motor Technical Research And Service (Shanghai) Co. Ltd. Shanghai China Beijing National Research Center of Information Science and Technology Tsinghua University Beijing China

Load forecasting is a critical concern for the operation of regulated power systems and electricity markets. It can guarantee the availability of electricity supply, minimize both excess and insufficient utilization of generating capacity and therefore optimize energy prices. In this paper, a novel multi-layer forecasting neural network based on feature weight optimization is developed for general reliable short-term load forecasting. It utilizes historical load data combined with some influencing factors such as seasonal and daily patterns as well as social behavior and user habits. Specifically, the proposed network involves a set of convolutional features extraction, bidirectional gate recurrent unit (BiGRU) and automatic feature weight optimization strategy, which subtly picks the nonlinear relationships and complex dependencies to facilitate the efficient generation of the close-to-true values. Experimental results demonstrate that the network achieves competitive performance with significantly higher accuracy when compared to congeneric methods, which greatly indicates its potential in the load forecasting tasks of electricity resource management.

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An LSTM-based Recurrent Neural Network for Neonatal Sepsis Detection in Preterm Infants

An LSTM-based Recurrent Neural Network for Neonatal Sepsis D...

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IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： A. Honoré H. Siren R. Vinuesa S. Chatterjee E. Herlenius Div. Information Science and Engineering KTH Royal Institute of Technology Stockholm Sweden Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden FLOW Engineering Mechanics KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781665470308

Early and accurate neonatal sepsis detection (NSD) can help reduce mortality, morbidity and antibiotic consumption in premature infants. NSD models are often designed and evaluated in case control setups and using data derived from patient electrocardiogram (ECG) only. In this article, we evaluate our models in a more realistic retrospective cohort study setup. We use data from different modalities, including ECG, chest impedance, pulse oximetry, demographics factors and repetitive measurements of body weights. We study both the vanilla and long-short-term-memory (LSTM) Recurrent Neural Networks (RNN) architectures in a sequence to sequence mapping framework for NSD. We compare the performances of the models with logistic regression (LR) on a variety of classification metrics in a leave-one-out cross validation framework. The population we used contains 118 very low birth weight infants, among which 10 experienced sepsis. We showed that LSTM-based RNNs are both (1) more conservative and (2) more precise than LR or vanilla RNN, with a true negative rate at least +26% higher and a precision score of 0.16 compared to 0.06 for LR. This indicates that LSTM-based RNNs have the potential to reduce the false alarm rate of existing linear models, while providing a reliable diagnostic aid for neonatal sepsis.

关键词： Weight measurement Pediatrics Recurrent neural networks Sociology Electrocardiography Signal processing Sepsis

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Standardization Review on 5G LAN-Type Service

Standardization Review on 5G LAN-Type Service

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International Conference on information and Communication Technology Convergence (ICTC)

作者： Yeunwoong Kyung Donghun Lee Jaewook Lee Youngjun Kim Haneul Ko Seungmin Oh Jaehwan Lee Div. of Info. & Comm. Eng. Kongju National University Cheonan Korea Dep. of Info. and Comm. Engineering Pukyong National University Busan Korea School of Computer Science and Eng. Kyungnam University Changwon Korea Dep. of Electronic Eng. Kyung Hee University Yongin-si Korea Dep. of Computer Science and Eng. Kongju National University Cheonan Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

The 5G LAN-type service refers to a service that supports communication between users within a group through the 5G system. It provides functions similar to those of traditional Local Area Networks (LAN) or Virtual Private Networks (VPN), but it gains attention across various services due to the enhanced performance of 5G such as high throughput, low latency, mobility management, and security. The 3rd Generation Partnership Project (3GPP) introduced the 5G LAN-type service in Release 16 and defined the architecture, protocols, and requirements to support the 5G LAN-type service. In this paper, we briefly examine the architecture and components of the 5G network based on the 3GPP standard and introduce the structure and protocols for supporting 5G LAN-type services in a 5G network.

关键词： Protocols 5G mobile communication Robot control Standardization Throughput Virtual private networks Security 3GPP Low latency communication Local area networks

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PDU Set-Based Traffic Handling for XR Service Support in 5G User Plane

PDU Set-Based Traffic Handling for XR Service Support in 5G ...

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International Conference on information and Communication Technology Convergence (ICTC)

作者： Yeunwoong Kyung Donghun Lee Jaewook Lee Haneul Ko Seungmin Oh Jaehwan Lee Youngjun Kim Div. of Info. & Comm. Eng. Kongju National University Cheonan Korea Dep. of Info. and Comm. Engineering Pukyong National University Busan Korea Dep. of Electronic Eng. Kyung Hee University Yongin-si Korea Dep. of Computer Science and Eng. Kongju National University Cheonan Korea School of Computer Science and Eng. Kyungnam University Changwon Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

To support XR services, the 3GPP standards intro-duce a protocol data unit (PDU) set, which is the minimum group of packets needed to carry a video frame, and also define the relative importance of different PDU sets. This allows the network operator to handle network traffic based on the importance of PDU sets. 3GPP standards define two options for PDU set-based traffic handling in the user plane: 1) gNB-based and 2) UPF-based. This paper introduces a basic concept of PDU set and specifies two procedures for PDU set-based traffic handling in the user plane.

关键词： 3GPP Standards Protocols 5G mobile communication Telecommunication traffic information and communication technology Convergence

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Development of a wide-band compact diplexer using a redistribution layer for 5G application 23

Development of a wide-band compact diplexer using a redistri...

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23rd IEEE Electronics Packaging Technology Conference, EPTC 2021

作者： Fujii, Yuki Nagano, Hiroaki Ishiguro, Yuya Horiguchi, Teruo Akita, Naoya Sakamoto, Koichi Kanaya, Haruichi Kyushu University Graduate School of Information Science and Electrical Engineering Fukuoka Japan Analog LSI Division Sony Semiconductor Solutions Co. MMIC Product Department Kanagawa Japan LSI Products Div. LSI Product Engineering Dept.3 Sony Semiconductor Manufacturing Co. Kagoshima Japan

ISBN: (纸本)9781665416191

This paper presents a design of a wide-band compact diplexer using a redistribution layer (RDL) for 5G application. The proposed diplexer is combined a low pass filter (LPF) which has designed frequency at 4.1 GHz for n77 band and a high pass filter (HPF) which has designed frequency at 4.5 GHz. for n79 band. Moreover, spiral inductor is attached to enhance the band rejection characteristics. In the circuit simulation, input reflection coefficient (S11) is less than -10dB from 3.3 to 5.0 GHz. Insertion loss in low band (S21) is lower than -3dB ffom 3.3 to 4.1 GHz and that in high band (S31) is lower than -3dB from 4.5 to 5.0 GHz. © 2021 IEEE.

关键词： High pass filters

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Bayesian modeling of effective and functional brain connectivity using hierarchical vector autoregressions

arXiv

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arXiv 2021年

作者： Wegmann, Bertil Lundquist, Anders Eklund, Anders Villani, Mattias Div. of Statistics and Machine Learning Dept. of Computer and Information Science Linköping University Sweden Umeå University Sweden Div. of Medical Informatics Dept. of Biomedical Engineering Linköping University Sweden Dept. of Statistics Stockholm University Sweden

Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression (VAR) hierarchical model for analyzing brain connectivity in a resting-state fMRI data set with autism spectrum disorder (ASD) patients and healthy controls. Our approach models functional and effective connectivity simultaneously, which is new in the VAR literature for brain connectivity, and allows for both group- and single-subject inference as well as group comparisons. We combine analytical marginalization with Hamiltonian Monte Carlo (HMC) to obtain highly efficient posterior sampling. The results from more simplified covariance settings are, in general, overly optimistic about functional connectivity between regions compared to our results. In addition, our modeling of heterogeneous subject-specific covariance matrices is shown to give smaller differences in effective connectivity compared to models with a common covariance matrix to all subjects. © 2021, CC BY.

关键词： Covariance matrix

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Dual-Polarized Microstrip Antenna with Cross Grid Structure Radiating Towards the Edge of the Substrate in the D-Band

Dual-Polarized Microstrip Antenna with Cross Grid Structure ...

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European Conference on Antennas and Propagation, EuCAP

作者： Ryosuke Hasaba Ken Takahashi Tomoki Abe Tomohiro Murata Yoichi Nakagawa Koji Takinami Hiroshi Taneda Takashi Tomura Issei Watanabe Engineering Division Panasonic Industry Co. Ltd. Yokohama Japan Development Center Panasonic System Networks R&D Lab. Co Kanazawa Japan Research & Development Div. Shinko Electric Industries Co. Ltd. Nagano Japan Department of Electrical and Electronic Engineering Institute of Science Tokyo Tokyo Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Tokyo Japan

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This paper proposes a dual-polarized antenna for D-band applications, which radiates in the substrate end direction. The antenna's radiating elements and ground are formed using vias, via lands, and copper foil. The radiating element features a cross-grid design, which combines both cross and grid shapes. This allows for the adjustment of the resonant frequency for each polarization, even under manufacturing rule constraints. The characteristics and operation of a singleelement antenna were demonstrated through simulations. Subsequently, the gain of an 8-element array was compared between measured and calculated values, demonstrating the effectiveness of the proposed antenna.

关键词： Antenna measurements Shape Resonant frequency Microstrip antennas Directive antennas Gain measurement Manufacturing Microstrip Substrates Antenna arrays

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Renal Cell Type and State Estimation in Brightfield Histology Images: A Pilot Study on Diabetic Nephropathy

Renal Cell Type and State Estimation in Brightfield Histolog...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Fermin, Jamie L. Border, Samuel Naglah, Ahmed Shickel, Benjamin La Rosa, Patricio S. Tomaszewski, John E. Jain, Sanjay El-Achkar, Tarek M. Eadon, Michael T. Sarder, Pinaki Dept. of Electrical and Computer Engineering Univ. of Florida GainesvilleFL United States Dept. of Biomedical Engineering Univ. of Florida GainesvilleFL United States Univ. of Florida Intelligent Critical Care Center GainesvilleFL United States Seed Production Innovation Crop Science Division Bayer Company St. LouisMO United States Dept. of Pathology & Anatomical Sciences Univ. at Buffalo BuffaloNY United States Div. of Nephrology Dept. of Medicine Washington Univ. School of Medicine St. LouisMO United States Div. of Nephrology Dept. of Medicine. Indiana Univ School of Medicine IndianapolisIN United States Div. of Nephrology Hypertension and Renal Transplantation Quantitative Health Section Dept. of Medicine Univ. of Florida GainesvilleFL United States

ISBN: (纸本)9781510686045

Multi-omics data, such as 10X Genomics Visium (spatial transcriptomics), measure gene expressions, molecular pathway activities, and can predict cell types/states but are often expensive and inaccessible in clinical settings. Thus, despite the emergence of multi-omics technologies, histopathological assessments under brightfield microscopy remain the diagnostic gold standard. In this work, we examine machine learning-based pipelines for predicting cell types/states from brightfield h i stology i m ages u s ing s t ate-of-the-art (SOTA) d e ep l e arning (DL) models, aiming to enhance diagnostics and prognostics in clinical medicine. Our proposed pipeline consists of two stages: (1) an Image-To-Text retrieval Network (ITTN) that leverages the CONtrastive learning from Captions for Histopathology (CONCH) model to assign histopathological text prompt from brightfield h i stology i m age, a n d (2) a V i sion L a nguage M odel (V LM), w h ich i s b u ilt o n t h e same CONCH model used in ITTN but incorporates a regression head to predict cell type/state proportions based on the paired image and text inputs. During training, we classify the image into one of four structural types (glomerulus, tubules, vessels, and interstitium) using the ITTN. These classification l a bels a r e t h en u s ed to construct a new text prompt with a suitable histopathological description for each image in the test set. The new text prompt and raw image are used as paired inputs to the VLM to predict cell types/states. We also utilize SOTA models, such as CONCH (using only the vision encoder), ViT, and ResNet, which employ image-only inputs in separate regression pipelines. We experimented and tested our proposed pipelines on a set of 10X Visium formalin-fixed paraffin-embedded whole slides images of diabetic nephropathy samples collected at Indiana University. Our experiments yielded a mean squared error of 0.0027 for the proposed pipeline, showing improvements of 20.59%, 27.03%, and 32.50

关键词： Genes

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Minimization of wind power curtailment using dynamic line rating 10

Minimization of wind power curtailment using dynamic line ra...

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10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020

作者： Chakrapani Manakari, Vageesh Thengius, Sandra Tammanur Ravi, Akshaya Morozovska, Kateryna Hilber, Patrik KTH Royal Institute of Technology Div. of Electric Power and Energy Systems School of Electrical Engineering and Computer Science Stockholm Sweden Heimdall Power AS Oslo Norway KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Dep. of Electromagnetic Engineering Stockholm Sweden

ISBN: (数字)9781728171005

ISBN: (纸本)9781728171005

Large scale penetration of wind power to the grid and unevenly spread power demand can result in the transmission system not being able to dispatch all the produced wind power, causing in wind power *** line rating (DLR) is a technology which uses thermal properties of overhead conductors and weather data to determine the real-time ampacity limits of transmission lines. In general, dynamic rating allows extending capacity limits of power lines and helps to remove congestion in the grid.(/p)(p This study looks into the possibility of using dynamic line rating for removing congestion in the power system to allow dispatching more wind power and minimize the need for curtailment. The results of case-studies have shown that DLR allows to significantly reduce the curtailment of generation, especially during winter, when wind production is highest and day time, when the power demand is the highest. © 2020 IEEE.

关键词： Wind power

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