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6th Distributed Ledger Technology Workshop, DLT 2024

作者： Barbàra, Fadi Napoli, Emanuele A. Gatteschi, Valentina Schifanella, Claudio Department of Computer Science University of Turin Via Pessinetto 12 Turin10149 Italy Department of Computer and Control Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Turin10129 Italy

Blockchain and Artificial Intelligence (AI) have revolutionized the technology landscape, evolving predominantly along parallel trajectories. This research investigates the feasibility of integrating these two domains, specifically examining the potential of the latest advancements in Large Language Models (LLMs) to assist non-experts in the development of production-ready Solidity smart contracts. Utilizing a lease agreement as a case study, we employ GPT-4 to translate the document into smart contract code. To ensure consistency, we design several distinct prompts with analogous objectives, each employed multiple times. Our evaluation methodology encompasses both automated analysis and expert manual review. The findings indicate a clear limitation: the current iteration of GPT-4 is incapable of generating production-ready smart contracts, primarily due to undetected coding flaws and discrepancies between the prompts and the generated code. This study underscores the challenges and limitations inherent in leveraging LLMs for the autonomous generation of complex, real-world applicable smart contracts. © 2024 Copyright for this paper by its authors.

关键词： Smart contract

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Simplified control Strategies for Power Converters in a Grid-Connected Hybrid DC-AC Microgrid

Simplified Control Strategies for Power Converters in a Grid...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Busarello, Tiago Davi Curi Simões, Marcelo Godoy Laaksonen, Hannu Kauhaniemi, Kimmo Federal University of Santa Catarina Department of Control Automation and Computing Engineering Blumenau Brazil University of Vaasa School of Technology and Innovations Vaasa Finland

ISBN: (纸本)9798350332308

Many typical converter control strategies used in microgrids are complex or require modifications in the power structure. The complexities are due coupling effects among controllers and time-delay caused in exchanging information in the system. This paper proposes simplified control strategies for power converters in a grid-connected hybrid DC-AC microgrid, aiming to ensure precise voltage and current regulation. The used controllers are proportional-resonant, integral-double-lead, proportional-integral and single integral controller. In this paper, the methodology for tuning each controller is carefully described in detail and recommendation for choosing their bandwidth is given. Then, the designed controllers are discretized through the Backward-Euler approximation. The power converters of the microgrid are an inverter, a Buck, a Boost and a bidirectional DC-DC. A case study is conducted through simulations with Matlab/Simulink. Results show that the controlled variables follow their reference signals with negligible steady-state error, proving the efficacy of the simplified control strategies and allowing an stable operation of the microgrid. © 2023 IEEE.

关键词： controllers

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High-Power LED Lamp Driver Modelling 7

High-Power LED Lamp Driver Modelling

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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Chukhraev, Igor V. Sevastyanova, Marina A. Drach, Vladimir E. Torkunova, Julia V. Bauman Moscow State Technical University Computer Science and Control Department Moscow Russia Sochi State University Department of Innovative Engineering and Digital Technologies Sochi Russia Kazan State Power Engineering University Department of Information Technologies and Intelligent Systems Kazan Russia

ISBN: (纸本)9798331531836

The growing demand for energy-saving lamps highlights critical limitations in traditional LED secondary optics, which reduce energy efficiency and complicate heat dissipation - a key factor in LED lamp longevity. This study proposes an optimized circuit design for a 20-watt LED driver that addresses these issues. The proposed driver ensures effective LED control while improving thermal management and eliminating drawbacks of conventional designs. Key principles of modern LED lamp design and current challenges are identified. The electronic schematic of the high-power LED driver was simulated using LTSpice software in order to analyze and optimize the circuit's performance, resulting in a driver solution that enhances efficiency and reliability. © 2025 IEEE.

关键词： Energy efficiency

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Multi-Trajectory Prediction of Pedestrian Based on Deep Neural Network Architecture 6

Multi-Trajectory Prediction of Pedestrian Based on Deep Neur...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Astuti, Lia Lin, Yu-Chen Chen, Wen-Hui Feng Chia University Department of Automatic Control Engineering Taichung Taiwan National Taipei University of Technology Department of Graduate Institute of Automation Technology Taipei Taiwan

ISBN: (纸本)9798350323535

Multimodal learning input has been used to predict the single trajectory of pedestrians. However, the single-trajectory is not efficient in making the vehicle avoid the sudden direction changes of the pedestrian in the drivable area. Therefore, we proposed a multi-trajectory prediction model with a Deep Neural Network (DNN) using an egocentric perspective. First, we input unimodal learning i.e., the historical trajectory sequences into the temporal encoder module. Second, the vision DNN-based network module was used to process the trajectory predictions through the temporal attention mechanism. Third, the linear projection was used for the decoder module to generate single- and multi-trajectory predictions for collision avoidance of vehicles and pedestrians. Finally, the proposed model outperformed previous state-of-the-art models in the experiments on publicly available Pedestrian Intention Estimation (PIE) and Joint Attention in Autonomous Driving (JAAD) datasets. © 2023 IEEE.

关键词： Trajectories

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On the Stabilization of a Class of OSL Conformable Fractional Order Nonlinear Systems 11

On the Stabilization of a Class of OSL Conformable Fractiona...

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11th IEEE International Conference on Systems and control, ICSC 2023

作者： Alsharif, Ali Omar M. Jmal, A. Naifar, O. Ahmed, Ausama H. Gassara, Hamdi Makhlouf, A. Ben Electrical Engineering Department BP 1173 Sfax3038 Tunisia Elmergib University Faculty of Engineering Department of Electrical & Computer Engineering Alkhums Libya National School of Engineering of Sfax University of Sfax Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering Tunisia Faculty of Sciences of Sfax Department of Mathematics Route Soukra BP 1171 Sfax3000 Tunisia

ISBN: (纸本)9798350304886

One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes to fractional calculus. By providing a stabilizing strategy for a certain type of OSL fractional order nonlinear systems, this study fills the gap. A numerical example demonstrating the correctness of the suggested approach and demonstrating its efficacy for the tested class. © 2023 IEEE.

关键词： Stabilization

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Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 512-523页

作者： Jie Chen Kaiyi Luo Changbing Tang Zhao Zhang Xiang Li the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004 Yuxi Middle School Taizhou 317399China IEEE the College of Physics and Electronics Information Engineering Zhejiang Normal UniversityJinhua 321004China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004China the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433 the Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 201210China

Weighted vertex cover(WVC)is one of the most important combinatorial optimization *** this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted *** first model the WVC problem as a general game on weighted *** the framework of a game,we newly define several cover states to describe the WVC ***,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the ***,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial ***,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the ***,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.

关键词： Game-based asynchronous algorithm(GAA) game optimization polynomial time strict Nash equilibrium(SNE) weighted vertex cover(WVC)

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Fault Diagnosis of Electric Drive Systems Based on Dynamic Independent Component Analysis and A Hidden Markov Model 8

Fault Diagnosis of Electric Drive Systems Based on Dynamic I...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Wang, Jingkun Wang, Hongmei Cheng, Chao Chen, Hongtian School of Computer Science and Engineering Changchun University of Technology Changchun China Shanghai Jiao Tong University Department of Automation Shanghai China

ISBN: (纸本)9798350300178

Due to the rapid development of high-speed trains, the fault diagnosis of electrical drive systems is a hot issue. A fault diagnosis method for electrical drive systems based on dynamic inner independent component analysis and the hidden Markov model is proposed. First, the offline data of the electric drive systems under different operating states are processed by using the dynamic inner independent component analysis algorithm. The dynamic features in the offline data are extracted by establishing equations that maximize the non-Gaussianity of the predicted latent variables. Second, these dynamic features are used to train a Hidden Markov Model to represent various states of the electric drive systems. Finally, the online data are injected into the Hidden Markov Model for fault diagnosis, and the experimental results on the electric drive systems simulation platform show the effectiveness of the method. © 2023 IEEE.

关键词： Failure analysis

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Design and Analysis of Weather Parameters Using MEMS Technology with Suitable Characteristics for Integration into a Single Chip for Environment Monitoring 3

Design and Analysis of Weather Parameters Using MEMS Technol...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Hassan, Abdurrashid S. Juliet, Vimala A. Pius Agbulu, G. Joselin Retna Kumar, G. Raghavendra Reddy, N.V. Gunasekar, S. SRM Institute of Science and Technology Department of Electronics and Control/Instrumentation Engineering Kancheepuram Chennai. Tamil Nadu Kattankulathur India Department of Mechatronic Engineering School of Engineering Sciences and Technology Chinhoyi University of Technology Chinhoyi Zimbabwe Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Kattankulathur Kancheepuram Tamil Nadu Chennai India RV Institute of Technology and Management Department of Mechanical Engineering Karnataka India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

This study designs and simulates weather parameters using MEMS technology with suitable characteristics for integration into a single chip for environment monitoring. The sensing and heating filament of the temperature sensor was designed using a thin film platinum resistor, examining its ability to experience stability, increase in resistivity, accuracy, and free-contamination. The temperature sensing and heating element are based on platinum RTD (Resistance Temperature Detector) with meander shape and aluminum as the bonding pad connecting the sensor to the external circuit. A capacitive humidity sensor is implemented in consolidation with a piezoresistive pressure sensor with a square membrane. The capacitive humidity sensor with hole pattern at the upper plate and a polyimide serves as water absorbent sandwiched between the electrodes. The simulation analysis was conducted using finite element analysis in COMSOL Multiphysics version 6.0. It was observed that the temperature increases from -100oC to - 20oC, and 10oC to 50oC respectively as a result of deviation in relative humidity in the troposphere, and atmosphere. The average changes in capacitances were found at 0.0713/%RH, while the pressure was examined to vary from 1050hPa to 5hPa in a step of 200hPa with a sensitivity of 11.50mv/bar. © 2024 IEEE.

关键词： Humidity sensors

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Optimizing DNN Inference on Multi-Accelerator SoCs at Training-time

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Risso, Matteo Burrello, Alessio Pagliari, Daniele Jahier Politecnico di Torino Department of Control and Computer Engineering Turin10129 Italy

The demand for executing Deep Neural Networks (DNNs) with low latency and minimal power consumption at the edge has led to the development of advanced heterogeneous Systems-on-Chips (SoCs) that incorporate multiple specialized computing units (CUs), such as accelerators. Offloading DNN computations to a specific CU from the available set often exposes accuracy vs efficiency trade-offs, due to differences in their supported operations (e.g., standard vs. depthwise convolution) or data representations (e.g., more/less aggressively quantized). A challenging yet unresolved issue is how to map a DNN onto these multi-CU systems to maximally exploit the parallelization possibilities while taking accuracy into account. To address this problem, we present ODiMO, a hardware-aware tool that efficiently explores fine-grain mapping of DNNs among various on-chip CUs, during the training phase. ODiMO strategically splits individual layers of the neural network and executes them in parallel on the multiple available CUs, aiming to balance the total inference energy consumption or latency with the resulting accuracy, impacted by the unique features of the different hardware units. We test our approach on CIFAR-10, CIFAR-100, and ImageNet, targeting two open-source heterogeneous SoCs, i.e., DIANA and Darkside. We obtain a rich collection of Paretooptimal networks in the accuracy vs. energy or latency space. We show that ODiMO reduces the latency of a DNN executed on the Darkside SoC by up to 8× at iso-accuracy, compared to manual heuristic mappings. When targeting energy, on the same SoC, ODiMO produced up to 50.8× more efficient mappings, with minimal accuracy drop ( © 1982-2012 IEEE.

关键词： System-on-chip

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A Cooperative Feature Removal Mechanism for Cell Outage Detection in Wireless Telecommunication Networks 19th

A Cooperative Feature Removal Mechanism for Cell Outage Det...

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19th International Conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Wrona, Andrea Gentile, Simone De Santis, Emanuele Giuseppi, Alessandro Pietrabissa, Antonio Delli Priscoli, Francesco Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome via Ariosto 25 Lazio Rome00185 Italy Department of Electrical and Information Engineering Polytechnic of Bari via Re David 200 Puglia Bari70125 Italy

ISBN: (纸本)9783031842597

In the domain of crisis management for telecommunications infrastructures, the autonomous detection of cell outages within cellular networks is of paramount importance for prompt identification and resolution in ensuring uninterrupted connectivity to users. Traditional methods usually involve data aggregation at the core network, which is responsible for identifying cell failures. Proposing a novel approach, we leverage a Machine Learning-based distributed and cooperative feature removal mechanism in order to preserve the privacy of data and avoid any degradation in classification performance. Simulations carried out on a dataset retrieved from a real 4G-LTE rollout demonstrate that the proposed approach, through a cooperation among agents, maintains or even slightly improves accuracy, precision, recall, and F1-score in outage prediction compared to other conventional methods, showcasing its efficacy for cell outage detection purposes while maintaining data privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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