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Koopman Theory Inspired Neural Network for State of Charge Estimation

Koopman Theory Inspired Neural Network for State of Charge E...

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ieee international conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Vedika Gadia Adarsh Jaju P V Subrahmanyam Debashisha Jena EEE Dept. NITK Surathkal India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Koopman theory offers a potent framework for mode-by-mode analysis of system dynamics. Inspired by this theory, this work introduces a deep-learning framework utilizing autoencoders and a customized loss function for time series prediction. The work demonstrates the effectiveness of the model through practical application on state of charge (SoC) estimation of a battery. Model based SoC estimation techniques like Extended Kalman Filter(EKF) require complex models and technical data to estimate the SoC of a battery. The proposed model is able to predict the state of charge upto 10 time stamps in the future. This model is able to generalize the system dynamics such that a model trained for T time stamps is seen to give RMSE lesser than 0.01 for all tested temperatures at a future time stamp lesser than T. These findings demonstrate the superior performance of the proposed Koopman-inspired neural network(KoNN) compared to the traditional time series estimation technique EKF, Multivariate Linear Regression(MVLR), Extra Tree Regression(ETR), and Neural Network(NN) showcasing its versatility for various predictive tasks.

关键词： System dynamics Time series analysis Neural networks Estimation Predictive models Reliability theory data models

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Paving the Way for an Urban Intelligence OpenStack-Based Architecture

Paving the Way for an Urban Intelligence OpenStack-Based Arc...

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international conference on Smart computing (SMARTCOMP)

作者： Giuseppe Tricomi Luca D'Agati Francesco Longo Giovanni Merlino Antonio Puliafito Stefano Silvestri CINI: National Interuniversity Consortium for Informatics Rome Italy Department of Engineering University of Messina Italy Institute for High Performance Computing and Networking of ICAR National Research Council (CNR) Naples Italy

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

This paper describes a novel architecture aiming to create an open-source template for implementing a platform enabling support for the deployment and integration of services and workflows that compose, manage, and continuously evolve a complex system: the Urban Intelligence. More specifically, the proposed Smart City IT architecture is an evolution of a concrete urban intelligence architecture meant mostly to support data scientist activities and, obviously, the exploitation of Smart City services by citizens and users. The proposed solution, due to its open-source nature, is fully replicable and improves its ancestor by provisioning new characteristics: i) managing facilities of urban Cyber-Physical System via exploitation of a combination of Administrator/data scientist workflows and IoT management platform, ii) the definition of workflow directly on the Edge (even through the exploitation of FaaS paradigm directly on IoTs), and iii) enhancement of Smart City infrastructure security via the reduction of external attack surface. The prototype of the proposed IT solution has been implemented leveraging open-source frameworks and technologies belonging to the OpenStack ecosystem.

关键词： Smart cities Distributed ledger Scalability data security Ecosystems Prototypes Computer architecture

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Joint Edge Aggregation and Association for Cost-Efficient Multi-Cell Federated Learning 42

Joint Edge Aggregation and Association for Cost-Efficient Mu...

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42nd ieee international conference on Computer Communications, INFOCOM 2023

作者： Wu, Tao Qu, Yuben Liu, Chunsheng Jing, Yuqian Wu, Feiyu Dai, Haipeng Dong, Chao Cao, Jiannong National University of Defense Technology China Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Nanjing University State Key Laboratory for Novel Software Technology China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9798350334142

Federated learning (FL) has been proposed as a promising distributed learning paradigm to realize edge artificial intelligence (AI) without revealing the raw data. Nevertheless, it would incur inevitable costs in terms of training latency and energy consumption, due to periodical communication between user equipments (UEs) and the geographically remote central parameter server. Thus motivated, we study the joint edge aggregation and association problem to minimize the total cost, where the model aggregation over multiple cells just happens at the network edge. After proving its hardness with complex coupled variables, we transform it into a set function optimization problem and prove the objective function is neither submodular nor supermodular, which further complicates the problem. To tackle this difficulty, we first split it into multiple edge association subproblems, where the optimal solution to the computation resource allocation can be efficiently obtained in the closed form. We then construct a substitute function with the supermodularity and provable upper bound. On this basis, we reformulate an equivalent set function minimization problem under a matroid base constraint. We then propose an approximation algorithm to the original problem based on the two-stage search strategy with theoretical performance guarantee. Both extensive simulations and field experiments are conducted to validate the effectiveness of our proposed solution. © 2023 ieee.

关键词： Approximation algorithms

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A large model method for fine-tuning segmentation of all abdominal tumors based on the AdaLoRA approach

A large model method for fine-tuning segmentation of all abd...

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ieee international conference on Signal and Image Processing (ICSIP)

作者： Hongliang Wang Hu Li Lijun Fu Xiaozhou Liu Jing Xu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Stomatological Hospital Affiliated to China Medical University Shenyang China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Medical image segmentation is an important task in the field of medical image processing, and deep learning-based methods have made significant progress in this area. However, traditional segmentation models have limitations when dealing with complex and diverse medical images, and they lack interactivity. To overcome these limitations, the SAM adapter is introduced into the SAM algorithm to provide greater flexibility and scalability. We also introduce an object detection interactive algorithm to enhance the interactivity of the model. The SAM adapter allows for customized model adjustments to adapt to different medical image segmentation tasks and datasets. By finetuning the SAM adapter using the AdaLORA method, we propose the AdaSAMed algorithm, which enables researchers to conveniently develop adaptive and accurate segmentation models, thus promoting further advancement in medical image segmentation of gastrointestinal tumors.

关键词： Learning systems Image segmentation Adaptation models Scalability Object detection Robustness Gastrointestinal tract

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A corrected method for parameters in the signal propagation model

A corrected method for parameters in the signal propagation ...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yan Liang Xin Dong Song Chen Zhengda Li Zhimin Chang School of Computer Engineering Chengdu Technological University Chengdu China Research and Development Center HAN Networks Corporation Limited Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The received signal strength (RSS) is an important indicator of the strength of a received signal. The signal propagation model is crucial for calculating the RSS. However, the RSS can vary and be unstable even in the same location, so it requires a more flexible calculation method to adapt to complex scenarios. In this article, we propose an automatic calculation method that can adjust the parameters according to the measured RSS. First, some outliers are removed from the measurements. Second, to ensure the accurate calculation of indoor signals, the propagation parameters are adjusted based on the actual measurement data from different indoor scenarios. Finally, segmental correction of the RSS is used to account for faster signal attenuation at closer ranges and slower attenuation at farther ranges. The performance of the corrected algorithm has been verified under various conditions. Experiments demonstrate that the proposed method can enhance the accuracy of signal calculations.

关键词： Hands Adaptation models Privacy Accuracy Computational modeling Attenuation data models Security

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A data-driven approach for context definition in situation-aware wearable computing systems

A data-driven approach for context definition in situation-a...

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ieee international conference on Automation Science and Engineering (CASE)

作者： Giuseppe D’Aniello Giancarlo Fortino Matteo Gaeta Zia Ur Rehman Dept. Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy Dept. Computer Engineering Modeling Electronics and Systems University of Calabria Rende Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

A situation-aware wearable computing system is defined as a wearable device with the capability to perceive, comprehend, and project the situations occurring in the environment in order to adapt its behavior accordingly. For this kind of devices, the identification of complex situations related to the activities performed by users in various contexts is of great interest. This requires the capability to identify context states. A well-known technique for this task is the Context Space Theory (CST), which provides a multidimensional space representing contexts. Although powerful and lightweight, this technique has the drawback of requiring manual definition of such a space, a time-consuming process that involves domain experts. To address this issue, this work proposes a data-driven approach for defining context spaces in CST using kernel density estimation. This approach is compared with a state-of-theart expert-based CST technique and a fuzzy inference system for context representation, demonstrating superior performance on the Extrasensory dataset.

关键词： Performance evaluation Fuzzy logic Computer aided software engineering Estimation Manuals Computer architecture Feature extraction Object recognition Wearable devices Kernel

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Real-Time Multimodal cognitive Assistant for Emergency Medical Services

Real-Time Multimodal Cognitive Assistant for Emergency Medic...

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ieee international conference on Internet-of-Things Design and Implementation (IoTDI)

作者： Keshara Weerasinghe Saahith Janapati Xueren Ge Sion Kim Sneha Iyer John A. Stankovic Homa Alemzadeh Electrical and Computer Engineering University of Virginia Computer Science University of Virginia

ISBN: (数字)9798350370256

ISBN: (纸本)9798350370263

Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thinking and rapid decision-making. This paper presents cognitiveEMS, an end-to-end wearable cognitive assistant system that can act as a collaborative virtual partner engaging in the real-time acquisition and analysis of multimodal data from an emergency scene and interacting with EMS responders through Augmented Reality (AR) smart glasses. cognitiveEMS processes the continuous streams of data in real-time and leverages edge computing to provide assistance in EMS protocol selection and intervention recognition. We address key technical challenges in real-time cognitive assistance by introducing three novel components: (i) a Speech Recognition model that is fine-tuned for real-world medical emergency conversations using simulated EMS audio recordings, augmented with synthetic data generated by large language models (LLMs); (ii) an EMS Protocol Prediction model that combines state-of-the-art (SOTA) tiny language models with EMS domain knowledge using graph-based attention mechanisms; (iii) an EMS Action Recognition module which leverages multimodal audio and video data and protocol predictions to infer the intervention/treatment actions taken by the responders at the incident scene. Our results show that for speech recognition we achieve superior performance compared to SOTA (WER of 0.290 vs. 0.618) on conversational data. Our protocol prediction component also significantly outperforms SOTA (top-3 accuracy of 0.800 vs. 0.200) and the action recognition achieves an accuracy of 0.727, while maintaining an end-to-end latency of 3.78s for protocol prediction on the edge and 0.31s on the server.

关键词： Training Protocols Accuracy Collaboration Medical services Speech recognition Oral communication

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Predicting Chess Puzzle Difficulty with Transformers

Predicting Chess Puzzle Difficulty with Transformers

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ieee international conference on Big data

作者： Szymon Miłosz Paweł Kapusta Institute of Applied Computer Science Lodz University of Technology Lodz Poland

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study addresses the challenge of quantifying chess puzzle difficulty - a complex task that combines elements of game theory and human cognition and underscores its critical role in effective chess training. We present GlickFormer, a novel transformer-based architecture that predicts chess puzzle difficulty by approximating the Glicko-2 rating system. Unlike conventional chess engines that optimize for game outcomes, GlickFormer models human perception of tactical patterns and problem-solving *** proposed model utilizes a modified ChessFormer backbone for spatial feature extraction and incorporates temporal information via factorized transformer techniques. This approach enables the capture of both spatial chess piece arrangements and move sequences, effectively modeling spatio-temporal relationships relevant to difficulty *** evaluation was conducted on a dataset of over 4 million chess puzzles. Results demonstrate GlickFormer’s superior performance compared to the state-of-the-art ChessFormer baseline across multiple metrics. The algorithm’s performance has also been recognized through its competitive results in the ieee Bigdata 2024 Cup: Predicting Chess Puzzle Difficulty competition, where it placed *** insights gained from this study have implications for personalized chess training and broader applications in educational technology and cognitive modeling.

关键词： Training Measurement Accuracy Games Predictive models Transformers Feature extraction data models Problem-solving Context modeling

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Cybersecurity Using Hybrid Type Model for Classification Through SCO Optimization Technique

Cybersecurity Using Hybrid Type Model for Classification Thr...

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computing, Power and Communication Technologies (IC2PCT), ieee international conference on

作者： Jothi Prabha Appadurai Lekshmi S Raveendran R Latha E. Gangadevi M.Lawanya Shri Shilpa Gite Department of CSE(Networks) Kakatiya Institute of Technology and Science Warangal Telangana India Sathyabama Institute of Science and Technology Chennai India Electronics & Communication Engineering Saveetha Engineering College Saveetha Nagar Thandalam Chennai India Department of Computer Science Loyola College Chennai India SITE Vellore Institute of Technology Vellore India AIML department Symbiosis Institute of Technology Pune. Symbiosis International (Deemed University) Pune India

The effectiveness of Network Intrusion Detection Systems (NIDS) in recognizing complex attacks is hindered by evasion strategies like polymorphic malware and encrypted traffic. Vulnerabilities in NIDS algorithms are regularly exploited by cybercriminals, highlighting the dynamic relationship between changing attack tactics and cybersecurity defenses. An enhanced approach to strengthen Network Intrusion Detection Systems is suggested by this study. The methodology begins with a rigorous preprocessing phase that includes normalization functions to progress the accuracy and consistency of the input data. Optimizing the NIDS’s efficacy is the aim of the Single Candidate Optimization (SCO) algorithm for feature selection. We utilize a hybrid model that incorporates ANN, Wavelet Transform, and Long Short-Term Memory (LSTM) for the classification phase. This model is designed to identify sequential dependencies in the network traffic data that we have. A second iteration of the SCO is devoted to hyperparameter optimization in order to attain optimal model performance and to further optimize the model. For our review, we opted to use the BoT-IoT dataset because it is a gold standard in the field. The suggested approach demonstrates how SCO can improve hyperparameter optimization and feature selection, leading to a more accurate and cyberattack-resistant NIDS. Consequences from experiments and evaluations of performance demonstrate that the proposed strategy is effective; it achieves a remarkable 99.6 percent accuracy. This proves how successful it is in judgement to current representations, which is a significant step towards consolidation cybersecurity defenses against changing threat landscapes.

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Co-Evolution DTs: Achieving Value-Added cognitive Digital Twins Across the Entire Lifecycle

Co-Evolution DTs: Achieving Value-Added Cognitive Digital Tw...

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ieee international conference on Automation Science and Engineering (CASE)

作者： Xiaodong Tong Jinsong Bao Tianyuan Liu College of Mechanical Engineering Donghua University Shanghai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

The comprehensive information generated from manufacturing activities throughout the lifecycle plays a vital role in enhancing cognitive intelligence, as it provides cognitive Digital Twin (CDT) with extensive historical and real-time status information, thereby facilitating value-driven and sustainable product lifecycle development. However, due to the highly complex interactions between modern industrial subsystems, integrating multiple relevant DTs from different lifecycle stages poses a challenging task. To address this, this paper presents the Co-Evolution DTs (CoEDTs), which providing a more comprehensive insight for future product development by constructing a parallel world that simulates the lifecycle. In the CoEDTs, we tackle the behavioral identification and structure of these evolving DTs throughout lifecycle through the following approaches. Initially, drawing inspiration from biological cytology, we have conceived a framework to establish the collective behavioral of multi-DT. We then went on to further create CoEDTs, which support the co-evolutionary architecture and behavioral patterns of numerous DTs over the course of the product lifetime, by fusing digital threads and (MBSE) technology. Finally, the viability of the proposed CoEDTs framework is demonstrated through the development of a solid rocket engine.

关键词： Rockets Computer aided software engineering data security Machine learning Solids Real-time systems Product development Digital twins Manufacturing Engines

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