The emerging of Digital Twin (DT) technology facilitates the further development of industrial automation. However, real-time and accurate DTs modeling and updating require massive communication and computing resource...
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ISBN:
(纸本)9798350358513;9798350358520
The emerging of Digital Twin (DT) technology facilitates the further development of industrial automation. However, real-time and accurate DTs modeling and updating require massive communication and computing resources, which poses a challenge to limited resources. Edge computing as a distributed computing architecture offers the possibility of high-efficient resource scheduling in DTs. Motivated by this gap, this paper aim to solve the problem of real-time and high fidelity DTs modeling and updating. First, we represent the computing tasks of DTs in the form of Heterogeneous computing Task Graph (HCTG). Then, a Hierarchical Attention Mechanism (HAT) is proposed to obtain the latent representation vectors of the HCTG. Finally, we design Markov Decision Process (MDP), and propose Deep Reinforcement Learning (DRL)-based computing task scheduling approach (HAT-DRL) to satisfy the minimum total completion time requirement of different DTs. Experimental results demonstrate that the proposed algorithm has promising scheduling performance and outperforms other task scheduling algorithms.
Analog Integrated Circuit (IC) design and its automation face challenges due to time-consuming computations and design complexity. Current automation falls short, necessitating a more accurate model and improved datas...
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ISBN:
(纸本)9798350385939;9798350385922
Analog Integrated Circuit (IC) design and its automation face challenges due to time-consuming computations and design complexity. Current automation falls short, necessitating a more accurate model and improved dataset collection techniques. Transmitter and receiver in 6G Communication requires amplifiers like the Differential Amplifier (DiffAmp) and Two-Stage Operational Amplifier (OpAmp). This research utilizes Deep Neural Networks to introduce a novel architecture for enhanced prediction of circuit parameters. The significant characteristics of the proposed architecture includes enhanced circuit automation of both DiffAmp and OpAmp using a singular Machine Learning pipeline. A notable contribution is an efficient dataset acquisition technique. The methodologies achieve high accuracy, with 97.3% for DiffAmp and OpAmp.
Gas leakage occurring in industrial sites is a risk factor that can cause major accidents such as fires or explosions. To mitigate the risk effectively, it is imperative to establish a gas leakage monitoring system ca...
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ISBN:
(纸本)9798331517939;9788993215380
Gas leakage occurring in industrial sites is a risk factor that can cause major accidents such as fires or explosions. To mitigate the risk effectively, it is imperative to establish a gas leakage monitoring system capable of promptly detecting abnormal conditions. In this paper, we propose the anomaly detection method using Hyperdimensional computing (HDC) for detecting gas leakage in pipes. Hyperdimensional computing can reduce the amount of computation through high-dimensional vectors and nested operations, which can solve the computational cost problem of deep learning. In the experiments, we implemented an anomaly detection method with binary classification and multi-class classification, that resulted in an F1 score of 99.97% and 99.85% when using 10,000 vector dimensions, respectively.
Smart contracts have expanded the range of applications for blockchain, enabling the construction of complex business logic on the chain. However, the execution of smart contracts depends on transaction triggers and c...
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ISBN:
(纸本)9798350386066;9798350386059
Smart contracts have expanded the range of applications for blockchain, enabling the construction of complex business logic on the chain. However, the execution of smart contracts depends on transaction triggers and cannot operate autonomously, limiting the flexibility of on-chain applications. Despite the emergence of new smart contract automation solutions, there is a general lack of verifiable execution results and native support for multi-chain operations. In this paper, we propose the Specy Network protocol, which implements verifiable task verification results and is the first publicly available automation solution to support multi-chain operations. To this end, Specy Network first defines a specific domain-specific programming language (DSL) for declaring the execution conditions of tasks, and further designs a trusted runtime for this language based on trusted execution environment (TEE). Secondly, it designs the multi-chain automation task flow using cross-chain technology. Lastly, we implemented the protocol and verified its effectiveness through a loan case study.
This paper presents a novel approach to Echo State Networks (ESNs) by integrating state -feedback with switching systems theory and the computational efficiency of Reservoir computing architectures. We introduce an in...
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This paper presents a novel approach to Echo State Networks (ESNs) by integrating state -feedback with switching systems theory and the computational efficiency of Reservoir computing architectures. We introduce an innovative architecture for ESNs, featuring a switching input weight and state -feedback gain, applicable to both linear and non-linear reservoirs. The stability of this architecture is rigorously analysed using the standard Lyapunov function construction method. Additionally, we explore the dynamical properties of a state -feedback ESN architecture used in the literature. The advantages of the proposed switching ESN is demonstrated using electrophysiological recordings in fruitfly photoreceptors.
Quantum computing is one of the shifts in paradigm with the potential to break most cryptographic systems. Quantum computers will run complex problems with superposition and entanglement much faster exponentially than...
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ISBN:
(纸本)9798331517519;9798331517526
Quantum computing is one of the shifts in paradigm with the potential to break most cryptographic systems. Quantum computers will run complex problems with superposition and entanglement much faster exponentially than classical computers. This poses a solid threat to cryptographic security by efficiently factoring large integers using quantum algorithms like Shor's algorithm, possibly breaking public-key cryptosystems like RSA and ECC. Moreover, Grover's algorithm speeds up symmetric key algorithms brute-force search. This paper discusses these vulnerabilities, surveys the development and feasibility of quantum-resistant algorithms, and addresses practical challenges from their implementations. Finally, it points out standardization processes underway and computation paths for further research. Our findings underline the need to urgently migrate to quantumresistant cryptographic solutions for robust security in the presence of quantum computing.
DevOps is a practice that integrates and automates the work of software development and IT Operations. DevSecOps emphasizes automating security within CI-CD pipelines to streamline the swift deployment of developmenta...
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ISBN:
(纸本)9798350385939;9798350385922
DevOps is a practice that integrates and automates the work of software development and IT Operations. DevSecOps emphasizes automating security within CI-CD pipelines to streamline the swift deployment of developmental changes and counter the threats and vulnerabilities that arise in a financial web application. This study utilizes comprehensive threat modeling to identify potential vulnerabilities early in development, allowing timely remediation. It explores integrating SAST and DAST tools like Codacy, OWASP ZAP, and GitHub Advanced Security Scanning into an Azure DevOps pipeline to enhance the security of financial web applications.
This paper presents a memristor-based reservoir computing (RC) framework for waveform classification tasks, including synthetic signal and electrocardiogram (ECG) datasets. Leveraging the nonlinear dynamic characteris...
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ISBN:
(纸本)9798350377040;9798350377033
This paper presents a memristor-based reservoir computing (RC) framework for waveform classification tasks, including synthetic signal and electrocardiogram (ECG) datasets. Leveraging the nonlinear dynamic characteristics of memristors and the processing capabilities of the input layer, the model effectively extracts time-series features from input signals. Experimental results demonstrate that as the number of memristors in the reservoir increases, the classification performance significantly improves and stabilizes after reaching a saturation point. Furthermore, the framework exhibits advantages in both low power consumption and high performance, achieving classification accuracies of 100% and 89% for synthetic waveform and ECG classification tasks, respectively. The findings suggest that this approach strikes a favorable balance between energy efficiency and classification performance, showing great potential for widespread applications in low-power scenarios such as wearable devices and edge computing.
This paper presents a system for the acquisition and processing of phonocardiogram (PCG) signals. The aim was to develop a system with the following facilities: automatic control of input signal amplitude, signal filt...
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This paper presents a system for the acquisition and processing of phonocardiogram (PCG) signals. The aim was to develop a system with the following facilities: automatic control of input signal amplitude, signal filtering, digital processing for heart beat rate (HBR) detection, and storage of signal samples for further automatic classification and pathology identification.
In this contribution a new discrete event control scheme is proposed to ensure a defined and a reproducible process in reactive sputtering plants. The control scheme is based on a novel hybrid model that describes the...
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ISBN:
(纸本)9798331518509;9798331518493
In this contribution a new discrete event control scheme is proposed to ensure a defined and a reproducible process in reactive sputtering plants. The control scheme is based on a novel hybrid model that describes the pressure behaviour and the electrical behaviour of such processes. A new automation system is developed to allow the experimental validation of the control scheme, which consists of two parts. An interlock control prevents prohibited process states and a sequence control ensures a desired sequence of process states. Experimental data are shown to demonstrate the applicability of the discrete event control scheme and of the automation system.
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