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open Set Radar HRRP Recognition Using Confidence through Neural Weight Proximity

Open Set Radar HRRP Recognition Using Confidence through Neu...

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Signal, Information and Data Processing (ICSIDP), ieee International conference on

作者： Yichen Liu Ping-An Liu Liang Zhang Yanhua Wang Zengdi Bao Yinhui Xu Radar Technology Research Institute Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Xi’an Modern Control Technology Research Institute Xi’an China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High-resolution range profiles (HRRP) have become increasingly important in radar automatic target recognition (RATR) due to their ability to capture detailed structural features of targets. In practical RATR scenarios, the operational environment is open and dynamic, often exposing models to targets belonging to previously unseen categories. This underscores the necessity for open-set recognition (OSR) methods that can effectively distinguish both known and unknown targets. Theoretically, there exists a distributional discrepancy between the feature representations of known and unknown categories in deep learning models. By designing a suitable confidence calculation method that leverages this discrepancy, it becomes possible to accurately identify whether a given sample belongs to a known or an unknown category. Inspired by the Neural Collapse theory, we observe that deep features representing known categories tend to cluster tightly around the weight vectors of the final layer, whereas features from unknown categories are typically more scattered and lie farther away. Building on this observation, we propose a confidence measure that is determined by the proximity of a feature to the weight vectors of the final classification layer. Samples with confidence values below a defined threshold are classified as unknown, which forms the fundamental mechanism of our open-set detector. Our approach is versatile, making it applicable to a variety of network architectures and adaptable to different feature distance metrics. Extensive experiments conducted on HRRP data confirm the efficacy of our proposed method for open-set recognition, demonstrating its ability to reliably distinguish between known and unknown targets under diverse conditions.

关键词： Measurement Deep learning Weight measurement Training Target recognition Radar network architecture Vectors Radar signal processing Reliability

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CellNet: An Improved Neural Architecture Search Method for Coal and Gangue Classification

CellNet: An Improved Neural Architecture Search Method for C...

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International Joint conference on Neural networks (IJCNN)

作者： Sun, Lirong Zhu, Wenbo Lu, Qinghua Li, Aiyuan Luo, Lufeng Chen, Jianwen Wang, Jinhai Foshan Univ Sch Mechatron Engn & Automat Foshan Peoples R China Foshan Univ Acad Journal Editorial Off Foshan Peoples R China Foshan Univ Sch Elect Informat Engn Foshan Peoples R China

ISBN: (纸本)9780738133669

This paper uses a neural architecture search (NAS) algorithm based on Bayesian optimization (BO), a version of neural architecture search adapted for network morphism, is proposed to automatically seek an optimized cell-based architecture approximation for CNN to classify the coal and gangue. Besides, we defined the cell-based search space, which has significantly reduced arithmetic operations and computing resources with fewer parameters. In hyperparameter optimization, BO has focused on Euclidean domains that only permit tuning scalar hyper-parameters. However, when BO applies to NAS, a suitable kernel function is needed to build GP, and a distance function is required to describe the differences between two neural network architectures. We define a kernel function suitable for the search space and describe the similarity of network architecture appropriately. We also apply the particle swarm optimization algorithm to optimize the acquisition function. network morphism is combined with Bayesian optimization (BO), so the obtained architecture inherited the knowledge of the parent network, realizing a better trade- off between exploitation and exploration. The adaptability of the GP model is enhanced by self-optimization of network architecture, and dynamic variable model super parameters gradually fit the real data distribution. We can first obtain the cell architecture from a low-resolution and small dataset and then superimpose its number to expand to a more extensive neural network architecture suitable for the feature of coal from different coal washery. The network CellNet outperforms architectures manually designed that is validated on coal and gangue classification problem.

关键词： deep neural network neural architecture search network morphism Bayesian optimization

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Bayesian network and Semidefinite programming Based Wireless Power Transfer Manufacturing System State Estimation and Regulation 23

Bayesian Network and Semidefinite Programming Based Wireless...

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23rd International conference on Advanced Communications Technology (ICACT) - On-Line security in Pandemic Era

作者： Rana, Md Masud Dahotre, Narendra Univ North Texas Dept Mat Sci & Engn Denton TX 76203 USA Univ North Texas Coll Informat Sci Denton TX 76203 USA

ISBN: (纸本)9791188428069

In order to supply energy to the smart objects such as electric vehicles and smart phones using wireless power transfer (WPT) framework, the internet of things (toT) embedded sensors and actuators can play critical roles for sensing, monitoring and regulating the WPT system states. Specifically, the smart sensors are used to transmit sensing state information to the control center, where the state estimation program runs to know the operating and health conditions of WPT system. This study proposes a Bayesian network based WPT system state estimation algorithm. This scheme can estimate the WPT system states in a distributed way using the Bayesian tree structure. Afterwards, the setnidefinite programming based optimal feedback control scheme is proposed to regulate the system states. Simulations results show that the proposed approach provides significant performance improvement compared with the existing scheme.

关键词： Bayesian network controller internet of things sensors senzidefinite programming system state estimation wireless power transfer systems

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AIaaS for ORAN-based 6G networks: Multi-time Scale Slice Resource Management with DRL

AIaaS for ORAN-based 6G Networks: Multi-time Scale Slice Res...

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ieee International conference on Communications (ICC)

作者： Suvidha Mhatre Ferran Adelantado Kostas Ramantas Christos Verikoukis Universitat Politecnica de Catalunya Barcelona Spain Universitat Oberta de Catalunya Barcelona Spain Iquadrat Informatica S.L. Barcelona Spain University of Patras and ISI Athena Greece

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

This paper addresses how to handle slice resources for 6G networks at different time scales in an architecture based on an open radio access network (ORAN). The proposed solution includes artificial intelligence (AI) at the edge of the network and applies two control-level loops to obtain optimal performance compared to other techniques. The ORAN facilitates programmable network architectures to support such multi-time scale management using AI approaches. The proposed algorithms analyze the maximum utilization of resources from slice performance to take decisions at the inter-slice level. Inter-slice intelligent agents work at a non-real-time level to reconfigure resources within various slices. Further than meeting the slice requirements, the intra-slice objective must also include the minimization of maximum resource utilization. This enables smart utilization of the resources within each slice without affecting slice performance. Here, each xApp that is an intra-slice agent aims at meeting the optimal quality of service (QoS) of the users, but at the same time, some inter-slice objectives should be included to coordinate intra- and inter-slice agents. This is done without penalizing the main intra-slice objective. All intelligent agents use deep reinforcement learning (DRL) algorithms to meet their objectives. We have presented results for enhanced mobile broadband (eMBB), ultra-reliable low latency (URLLC), and massive machine type communication (mMTC) slice categories.

关键词： 6G mobile communication Massive machine type communications Quality of service open RAN Ultra reliable low latency communication network architecture Minimization

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Deep Convolutional Neural network Applied to Electroencephalography: Raw Data vs Spectral Features 43

Deep Convolutional Neural Network Applied to Electroencephal...

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43rd Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (ieee EMBC)

作者： Truong, Dung Milham, Michael Makeig, Scott Delorme, Arnaud

ISBN: (纸本)9781728111797

The success of deep learning in computer vision has inspired the scientific community to explore new analysis methods. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictions on EEG data. Research remains open on the network architecture and the feature space that is most effective for EEG decoding. This paper compares deep learning using minimally processed EEG raw data versus deep learning using EEG spectral features using two different deep convolutional neural architectures. One of them from Putten et al. (2018) is tailored to process raw data;the other was derived from the VGG16 vision network (Simonyan and Zisserman, 2015) which is designed to process EEG spectral features. We apply them to classify sex on 24-channel EEG from a large corpus of 1,574 participants. Not only do we improve on state-of-the-art classification performance for this type of classification problem, but we also show that in all cases, raw data classification leads to superior performance as compared to spectral EEG features. Interestingly we show that the neural network tailored to process EEG spectral features has increased performance when applied to raw data classification. Our approach suggests that the same convolutional networks used to process EEG spectral features yield superior performance when applied to EEG raw data.

关键词： Deep learning Neuroimaging Computer vision Neuroscience Computer architecture network architecture Electrophysiology

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network Attack Detection in the PfSense Firewall via Deep Learning

Network Attack Detection in the PfSense Firewall via Deep Le...

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Mechatronic Systems Supervision (IW_MSS), ieee International Workshop on

作者： Merzoug Soltane Aimen Gasmi Laboratory of vision and intelligence (LAVIA) Echahid Cheikh Larbi Tebessi University Tebessa Algeria Department of Mathematics and Computer Science Echahid Cheikh Larbi Tebessi University Tebessa Algeria

ISBN: (数字)9798350378382

ISBN: (纸本)9798350378399

The detection of network attacks is a crucial aspect in ensuring the sustainability and proper functioning of information systems. Complex threat patterns and malicious actors possess the ability to inflict significant damage to cyber systems. In this study, we propose novel approaches utilizing deep learning techniques to identify and respond to threats and alerts found within network logs acquired through pfSense, an open-source firewall software. pfSense incorporates various robust security services including firewall capabilities, URL filtering, and virtual private networking (VPN), among others. The primary objective of this research is to analyze the acquired logs from a local installation of pfSense software, in order to develop a potent and efficient solution that can effectively manage traffic flow by automatically recognizing patterns through the proposed deep learning architectures, which present a significant challenge. To accomplish this, we have devised an attack detection system based on the CICDDOS2019 dataset, leveraging a deep neural network (DNN) model that has been seamlessly integrated into pfSense, enabling automatic identification and prevention of attacks.

关键词： Deep learning Uniform resource locators Firewalls (computing) Prevention and mitigation Artificial neural networks network security Software Virtual private networks Pattern recognition Sustainable development

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network-centric Empirical Analysis of Bitcoins cryptocurrency organization 3

Network-centric Empirical Analysis of Bitcoins cryptocurrenc...

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3rd International conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Joshi, Jaykrishna Sharma, R.P. Mpstme Nmims University Dept. of Data Science Mumbai India Chhatrapati Shivaji Maharaj University Maharashtra Navi Mumbai India Chhatrapati Shivaji Maharaj University Faculty of Science and Registrar Maharashtra Navi Mumbai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Bitcoins framework (also known as Bitcoins cryptocurrency) is a distributed and decentralized cryptocurrency installment framework that utilizes cryptographic money called bitcoins (BTCs) and was delivered as free and open-source as well as free to use programming in the year 2009 [1]. Bitcoins stage has drawn in both several users which can be categorized as social and against social (anti-social or illegal) components. To see and study how the social and against social inclinations in the client community of Bitcoins influence its development, there is an urgent need to break down the Bitcoins framework as an organization. The current research paper plans to investigate the neighborhood geography and math of the Bitcoins cryptocurrency network as it completes its first decade of presence and achieves nearly a trillion dollar brand value. Bitcoins exchange information was handled for this reason to fabricate a Bitcoins client chart. The qualities, neighborhood and worldwide organization properties of the client's chart were investigated at ten spans. Little measurement, slanted circulation of exchanges, power-law dispersed in and out degrees, detached chart, and availability of enormous associated parts were the perceptions from network or graph examination. © 2022 ieee.

关键词： Blockchain

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An IIoT-Based networked Industrial Control System Architecture to Secure Industrial Applications 2

An IIoT-Based Networked Industrial Control System Architectu...

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2nd ieee Industrial Electronics and Applications conference (IEACon)

作者： Sen, Sachin Song, Lei Federat Univ Australia Sch Engn IT & Phys Sci Ballarat Vic 3350 Australia Unitec Inst Technol Sch Comp Elect & Appl Technol Auckland New Zealand

ISBN: (纸本)9781728192536

Behind the great success of the current internet, open Systems Interconnect (OSI) and Transport Control Protocol/Internet Protocol (TCP/IP) standards play the most important role. Whereas, due to a lack of standard architectures, industrial internet is lagging behind. This makes industrial internet applications experience increased security risks due to their integration with the information technology and exposure to the public internet. In this research, we propose a layered architecture for industrial internet of things (IIoT) based networked industrial control systems (n-ICS). Layer-wise functionality of this architecture could be useful in identifying necessary security protocols for each layer. Subsequently, this might assist in allocating resources towards the secure operation of industrial applications. To validate the proposed architecture, we modelled a water flow control system, where we demonstrated a data deception attack on its operation at the physical layer. This demonstration validates that from within the close proximity of networked control systems, threat actors can launch possible attacks to deceive physical industrial applications. Our proposed system includes a network communication architecture and a corresponding security architecture aligning with the network architecture. This will facilitate the design of security suites and/or the allocation of security resources on the basis of layered network functionalities.

关键词： IIoT IoT ICS n-ICS security cybersecurity architectural modeling industrial applications

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Ubiquitous System Integration as a Service in Smart Factories

Ubiquitous System Integration as a Service in Smart Factorie...

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ieee International conference on Internet of Things and Intelligence Systems (IoTaIS)

作者： Soderi, Mirco Kamath, Vignesh Morgan, Jeff Breslin, John G. Natl Univ Ireland Galway Data Sci Inst Galway Ireland

ISBN: (纸本)9781665420358

The state-of-the-art in manufacturing technology identifies a data rich, horizontally and vertically integrated environment, with collaborative control and intelligence systems, to enable what is called a smart factory. Similar to digital factories, smart factories signify a greater digital convergence between manufacturing Operational Technology (OT), and wider enterprise Integrated Technology (IT). With these new capabilities comes new challenges in scale, complexity, and the skills needed to enable ubiquitous system integration. While in the past, engineers have utilized a variety of proprietary graphical configuration and programming tools to intuitively create simple to complex manufacturing systems, presently, the standard technology ecosystem which engineers design, control, and maintain, is observably expanding, and/or evolving to incorporate more open-source technologies, network services, and distributed devices. Therefore, this paper will examine the emerging next generation technologies and tools which will support engineers in designing and interacting with the new digital manufacturing ecosystem in smart factories. To achieve this: a state-of-the-art survey of commercial Industrial Internet of Things (IIoT) technology is presented;three key ubiquitous technology trends are examined;and a use-case is presented with a universal framework. Uniquely, this framework demonstrates the collective capabilities of the technologies to overcome ubiquitous system integration problems.

关键词： Digital Factory Smart Factory Internet of Things (IoT) Software Containers Graphical programming Interfaces (GPI) Enterprise Integration Micro-services NodeRED Cyber Devices

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A Scalable Framework for Automation of Quantum network Experiments

A Scalable Framework for Automation of Quantum Network Exper...

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Quantum Computing and Engineering (QCE), ieee International conference on

作者： Amin Taherkhani Kentaro Teramoto Andrew Todd Rodney Van Meter Shota Nagayama Graduate School of Media and Governance Keio University Shonan Fujisawa Campus Kanagawa Japan Mercari R4D Mercari Inc. Tokyo Japan Faculty of Environment and Information Studies Keio University Shonan Fujisawa Campus Kanagawa Japan

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

As quantum computing continues to progress, there is an increasing need for standardized quantum network technology [1], [2] to connect quantum computers together. Experimental testbeds are necessary in order to develop quantum network software and hardware. Building a scalable testbed for data center-level quantum networks requires overcoming numerous engineering and technical challenges in quantum communications, quantum processing and quantum storage. In this work, we describe a software framework for use with an all-photonic quantum networking testbed. This framework is designed for rapid development and capable of automated execution of quantum experiments. We introduce PnPQ, an open-source, unified driver library for hardware commonly used in quantum optical experiments. On top of this low-level hardware control library, we present software modules for controlling and coordinating quantum network nodes using remote procedure calls (RPC). Finally, we describe high-level software for performing and analyzing quantum photonic experiments using these software modules. As a whole, these software components comprise the first version of a framework that will enable us to implement and test quantum network architectures. This framework is intended to help us move forward from building simple experiments to complex Quantum systems.

关键词： Computers Quantum system Protocols Automation Optical fiber networks Software Libraries Hardware Quantum networks Photonics

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