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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Tang, Zezhi Rossiter, J Anthony Dong, Yi Panoutsos, George University Of Sheffield Department Of Automatic Control And Systems Engineering SheffieldS13JD United Kingdom University Of Southampton Department Of Electronics And Computer Science Southampton United Kingdom

ISBN: (纸本)9798350340266

This paper proposes a new disturbance observer (DO)-based reinforcement learning (RL) control approach for nonlinear systems with unmatched (generalized) disturbances. While a nonlinear disturbance observer (NDO) is utilized to measure the plant uncertainties, disturbances can exist in the plant via distinct channels from those of the control signals;so-called mismatched disturbances are theoretically difficult to attenuate within the channel of the system's states. A generalized disturbance observer-based compensator is implemented to address the uncertainty cancellation problem by removing the influence of uncertainties from the output channels. Con-currently, a composite actor-critic RL scheme is utilized for approximating the optimal control policy as well as the ideal value function pertaining to the compensated system by solving a Hamilton-Jacobi-Bellman (HJB) equation for both online and offline iterations simultaneously. Stability analysis verifies the convergence of the proposed framework. Simulation results are included to illustrate the effectiveness of the proposed scheme. © 2024 IEEE.

关键词： Nonlinear systems

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Sustainable business model in practice: a digital business game training for high school students 15

Sustainable business model in practice: a digital business g...

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15th International Conference on Society and Information Technologies, ICSIT 2024

作者： Fontanella, Sabrina Fraccascia, Luca Nonino, Fabio Scarnicchia, Alessandra Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Roma Lazio Italy Department of High-Tech Business and Entrepreneurship University of Twente Enschede Overijssel Netherlands

ISBN: (纸本)9781950492770

This paper explores the nexus between sustainable business models, education and technology, addressing pressing challenges in economic, social, and environmental spheres. On one hand, education is identified as a key tool for fostering sustainability principles and essential skills for future managers;on the other, businesses, particularly through sustainable business models (SBMs) and evolving digital platforms, play a pivotal role in advancing sustainability goals. The research answer to the need for sustainable development examining the potential of educational business games, blending entertainment and education to engage the 'gamer generation' actively. Considering the growing literature upon sustainable entrepreneurship and business models, the objective of the paper is to implement a digital business game for sustainability education designed to teach high school students how to implement an entrepreneurial activity through a sustainable business model. This study contributes to understanding sustainable business practices and innovative educational approaches, aligning with the global imperative for a sustainable future. In the end of the manuscript some prelaminar result of the game testing phase are presented. © Proceedings ICSIT, International Conference on Society and Information Technologies. All Rights Reserved.

关键词： Blending

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Fault Detection Method of Maintenance Equipment Based on ECAT 5

Fault Detection Method of Maintenance Equipment Based on ECA...

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5th International Conference on Smart Grid and Energy engineering, SGEE 2024

作者： Qin, Shuo Zhang, Yimei Zhang, Yinghao Wang, Hongchao Chang, Bo Feng, Hao State Grid Beijing Electric Power Company Changping Power Supply Company Beijing China North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798331530686

To improve the fault detection performance of power system operation and maintenance equipment, this paper studies the ECAT model through the method of integrating Empirical Mode Decomposition (EMD), Convolutional Neural Networks (CNN), and Attention mechanisms. The results show that: the ECAT model significantly outperforms traditional methods in fault detection accuracy and robustness on multiple public datasets, with an average accuracy improvement of about 10%;the model parameters are globally optimized using the Zebra Optimization Algorithm (ZOA), further enhancing detection performance;and the model effectively captures critical information and long-range dependencies. It can be seen that ECAT provides an efficient and accurate solution for intelligent fault detection of power system operation and maintenance equipment, demonstrating good application prospects. © 2024 IEEE.

关键词： Empirical mode decomposition

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A New Reliability Analysis of RISC-V Soft Processor for Safety-Critical Systems

A New Reliability Analysis of RISC-V Soft Processor for Safe...

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IEEE Design and Diagnostics of Electronic Circuits and Systems (DDECS)

作者： Giorgio Cora Corrado De Sio Daniele Rizzieri Sarah Azimi Luca Sterpone Department of Control and Computer Engineering Politecnico di Torino Torino Italy

RISC-V soft processors are attractive for various applications, including mission-critical ones, thanks to their reduced costs and high flexibility. Despite their growing popularity, reliability analysis of such platforms is still in an early stage, mainly relying on system-level analysis only, leaving module-level assessment unexplored. Such limitations hinder the development of mitigation strategies that could effectively focus on vulnerabilities within a RISC-V soft processor system. We propose a methodology for evaluating the module-wise reliability of a RISC-V soft processor based on fine-grained fault injection, custom layout placement, and fault analysis. Through this approach, we can provide insights into the critical elements of the processor, identifying the most susceptible to faults, both at the module and system levels. The presented results enhance comprehension of weak points within the processor, paving the way for creating robust and dependable RISC-V systems.

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D4D: An RGBD diffusion model to boost monocular depth estimation

arXiv

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arXiv 2024年

作者： Papa, Lorenzo Russo, Paolo Amerini, Irene The Department of Computer Control and Management Engineering Sapienza University of Rome Italy

Ground-truth RGBD data are fundamental for a wide range of computer vision applications;however, those labeled samples are difficult to collect and time-consuming to produce. A common solution to overcome this lack of data is to employ graphic engines to produce synthetic proxies;however, those data do not often reflect real-world images, resulting in poor performance of the trained models at the inference step. In this paper we propose a novel training pipeline that incorporates Diffusion4D (D4D), a customized 4-channels diffusion model able to generate realistic RGBD samples. We show the effectiveness of the developed solution in improving the performances of deep learning models on the monocular depth estimation task, where the correspondence between RGB and depth map is crucial to achieving accurate measurements. Our supervised training pipeline, enriched by the generated samples, outperforms synthetic and original data performances achieving an RMSE reduction of (8.2%, 11.9%) and (8.1%, 6.1%) respectively on the indoor NYU Depth v2 and the outdoor KITTI dataset. © 2024, CC BY.

关键词： Pipelines

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AI-Enhanced Gait Analysis Decision Support System for Neurodegenerative Disease Patient Care

AI-Enhanced Gait Analysis Decision Support System for Neurod...

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Computing in Natural Sciences, Biomedicine and engineering (COMCONF), International Conference on

作者： Fabian Arun Panaite Monica Leba Computer and System Control Engineering Department University of Petrosani Petrosani Romania

ISBN: (数字)9798350353358

ISBN: (纸本)9798350353365

This paper presents the use of AI-based uncertainty management in the processing of multiple modalities for the purpose of gait motion tracking using Inertial Measurement Units (IMU) sensors. More precisely, it deploys its effort on the assessment of gait abnormalities in Alzheimer disease and other health related illnesses. It can be used to perform a long-term tracking of gait patterns for Alzheimer's patients and identification of the changes that occur allowing for the evaluation of the disease's progression or the outcomes of treatments. Therefore, this research greatly boosts the efficiency of gait analysis systems especially in the identification of mechanical disorders of the musculoskeletal system and the subsequent treatments and the core of diagnosis and rehabilitation. Over the last decade, progress in technology has led to the enhancements in the IMUs that are used in gait analysis where IMUs have shifted from single sensor to multiple sensors where the sensor data is processed through a method known as sensor fusion and machine learning. These developments have made possible their use in both clinical and consumer contexts. Recent developments in gait motion capture enabled by AI are as the continuous trends in deep learning and video-based approaches made marker-less and non-invasive methods possible. Such analyses are crucial for identification work, which in turn positively affects the identification and reintegration into the delivery of health care, of diagnosis and rehabilitation.

关键词： Mechanical sensors Legged locomotion Filters Measurement units Tracking Medical services Inertial navigation Sensor fusion Motion capture Gait recognition

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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Toccacieli, Ali Petti, Manuela Sapienza University of Rome MSc Program in Engineering in Computer Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them. © 2022 IEEE.

关键词： Platelets

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Predicting the Time from Diagnosis to Treatment in Breast Cancer

Predicting the Time from Diagnosis to Treatment in Breast Ca...

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E-Health and Bioengineering Conference (EHB)

作者： Sergiu Ceică Ştefan-Daniel Achirei Dept. of Computer Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Recent advancements in machine learning (ML) have significantly impacted the medical field, particularly in diagnosing and treating breast cancer. This study models the time from diagnosis to treatment for breast cancer patients, focusing on delay factors and ML-based solutions. The goal is to develop precise ML models that predict treatment delays, thereby improving the quality and efficiency of medical services. We investigated socio-economic disparities affecting treatment access and developed models to predict these delays. The predictive models used include K-Nearest Neighbors, Decision Trees, Linear Regression, and Boosting Algorithms such as AdaBoost and Gradient Boosting Regressor. By accurately predicting the interval from diagnosis to the first treatment, our work aims to promote equity in healthcare, ensuring timely treatment for all patients. Our findings highlight the potential of ML in optimizing treatment timelines and resource allocation, contributing to improved patient outcomes and advancing the medical system.

关键词： Medical services Predictive models Boosting Prediction algorithms Breast cancer Delays Resource management Socioeconomics Medical diagnostic imaging Regression tree analysis

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Lightweight PCB defect detection algorithm and deployment based on ASF-YOLO

Lightweight PCB defect detection algorithm and deployment ba...

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computer Information Science and Application Technology (CISAT), International Conference on

作者： Zhuqi Li Jingbo Zhan Chenpeng Qu Xiangping Chen Lei Zhang College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

With the advancement in the precision of printed circuit boards (PCBs), their impact on electronic products has become increasingly significant. PCBs often exhibit various surface defects such as short circuits, open circuits, and burrs, which can have devastating effects on electronic products. To address these defects, this study proposes a lightweight detection model, ASC-YOLO. The dataset used was sourced from a public dataset on Roboflow, comprising 9,669 images. The Adown module enhances the model’s ability to recognize defects of different sizes and types while effectively reducing computation and parameter volume. SENetV2 improves the network’s learning capacity through multi-branch fully connected layers. Finally, PConv is introduced to reduce redundant computation and memory access, efficiently extracting spatial features. Compared to the original model, the parameters were reduced by 28%, and the accuracy reached 94.8%. Moreover, the model was deployed on mobile terminals, achieving high detection accuracy and efficiency. This advancement makes fully automated, high-precision AI-based detection possible.

关键词： Solid modeling Accuracy Computational modeling Printed circuits Redundancy Feature extraction Real-time systems Computational efficiency Integrated circuit modeling Defect detection

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Considering Approaches to Enhancing Power System Flexibility Through Distributed Energy Storage

Considering Approaches to Enhancing Power System Flexibility...

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China International Electrical and Energy Conference (CIEEC)

作者： Mingfei Ban Zhiwei Xue College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

In pursuit of the goal of reducing the wastage of renewable energy resources and enhancing the flexibility of the power system, this paper introduces a coordinated optimization scheduling strategy, incorporating distributed energy storage systems as a key component. This strategy comprehensively considers the real-time supply and demand dynamics of the power system, the energy storage status of distributed energy storage stations, and the generation forecasts of renewable energy. Based on these factors, a scheduling model for distributed energy storage systems is constructed. In the proposed scheduling strategy, optimize the allocation of energy resources to meet power demand by utilizing the operational costs of generating units, energy storage station operations, and load as objective functions. The primary aim of the model is to enhance power system flexibility through peak-shifting of renewable energy generation. To validate the effectiveness of the model, utilized MATLAB for its solution and, through practical case studies, verified the model's performance. The case study results demonstrate that the proposed distributed energy storage system strategy effectively reduces wind curtailment rates while simultaneously enhancing the flexibility of power system scheduling.

关键词： Renewable energy sources Costs Absorption Power system dynamics Wind power generation Mathematical models Batteries

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