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Parallelization is All System Identification Needs: End-to-end Vibration Diagnostics on a Multi-Core RISC-V Edge Device

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IEEE Internet of Things Journal 2025年第13期12卷 24230-24243页

作者： Kiamarzi, Amirhossein Moallemi, Amirhossein Zonzini, Federica Brunelli, Davide Rossi, Davide Tagliavini, Giuseppe University of Bologna Department of Electrical Electronic and Information Engineering Bologna40136 Italy University of Trento Department of Industrial Engineering Trento38123 Italy University of Bologna Department of Computer Science and Engineering Bologna40136 Italy

The early detection of structural malfunctions requires the installation of real-time monitoring systems ensuring continuous access to the damage-sensitive information;nevertheless, it can generate bottlenecks in terms of bandwidth and storage. Deploying data reduction techniques at the edge is recognized as a proficient solution to reduce the systemfs network traffic. However, the most effective solutions currently employed for the purpose are typically based on memory and power-hungry algorithms, making their embedding on resourceconstrained devices very challenging;this is the case of vibration data reduction based on System Identification (SysId) models. This paper presents PARSY-VDD, a fully optimized PArallel end-to-end software framework based on SYstem identification for Vibration-based Damage Detection, as a suitable solution to perform damage detection at the edge in a time and energyefficient manner, avoiding streaming raw data to the cloud. First, we evaluate the damage detection capabilities of PARSYVDD with two benchmarks: a bridge and a wind turbine blade, showcasing the robustness of the end-to-end approach. Then, we deploy PARSY-VDD on both commercial single-core (STM32 family) and a specific multi-core (GAP9) edge device. We introduce an architecture-agnostic algorithmic optimization for SysId, improving the execution by 90× and reducing the consumption by 85× compared with the state-of-the-art SysId implementation on GAP9. Results show that by utilizing the unique parallel computing capabilities of GAP9, the execution time is 751 μs with the high-performance multi-core solution operating at 370MHz and 0.8V, while the energy consumption is 37 μJ with the low-power solution operating at 240MHz and 0.65V. Compared with other single-core implementations based on STM32 microcontrollers, the GAP9 high-performance configuration is 76× faster, while the low-power configuration is 360× more energy efficient. © 2014 IEEE.

关键词： Structural health monitoring

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A Gaussian Process-Regularized Graphical Learning Method for Distribution System State Estimation Using Extremely Scarce State Variable Labels

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IEEE Transactions on Smart Grid 2025年第4期16卷 3359-3376页

作者： Hu, Jiaxiang Xu, Qianwen KTH Royal Institute of Technology Electric Power and Energy systems School of Electrical Engineering and Computer Science Stockholm11428 Sweden

Learning-based distribution system state estimation (DSSE) methods typically depend on sufficient fully labeled data to construct mapping functions. However, collecting historical labels (state variables) can be challenging and costly in practice, resulting in performance degradation for these methods. To fully leverage low-cost unlabeled historical measurement data, this article proposes a Gaussian process (GP)-regularized semi-supervised learning method for DSSE models, aiming at achieving feasible estimation precision using minimal state variable labels while also providing valuable interval estimation of state variables. Firstly, a structure-informed graphical encoder is established to generate appropriate node embeddings. A tailored GP-regularized learning method is then developed to model the intermediate latent space using these embeddings. It constructs the unlabeled embeddings by a weighted combination of labeled space vectors, with the weights determined by a kernel function, thereby forming additional supervision and regularizing the learning process of the network in the latent space. The regularized embeddings are then fed into a decoder to yield estimation outcomes. This procedure enables the proposed method to model intrinsic correlations across measurement data, as well as capture essential patterns related to DSSE even using extremely limited state labels. The trained DSSE models can thus adapt to the domain of new measurements. Lastly, the decoder outcomes and related latent embeddings are processed through a composite GP kernel to further derive the interval estimation of state variables, enabling uncertainty quantification. Experimental results demonstrate the effectiveness of the proposed method in handling extremely limited historical state labels and accurately quantifying the uncertainty of state variables. © 2010-2012 IEEE.

关键词： Self-supervised learning

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Federated Learning With Meta-Layers Training for Privacy-Preserving in Vehicular Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 621-629页

作者： Shen, Xiaoyang Li, Haibin Li, Yaqian Zhang, Wenming Alenazi, Mohammed J.F. Agarwal, Kadambri Yanshan University College of Electrical Engineering Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia ABES Engineering College Department of Computer Science and Engineering Ghaziabad201009 India

Vehicular consumer electronics, such as autonomous vehicles (AVs), need collecting large amounts of private user information, which face the risk of privacy leakage. To protect the privacy of consumers, researchers have proposed to apply federated learning (FL) to privacy-preserving vehicular consumer electronics, that is, leveraging FL for collaborative training of a decision model without exchanging the sensitive information generated by AVs. However, FL generally needs to exchange the gradients of client models periodically with the central server, which can be attacked by adversaries to infer user information. Thereby, it may still face the risk of privacy leakage. To solve that challenge, we put forward a novel FL framework, called FL with Meta-Layers Training (FL-MLT). Instead of exchanging the gradients of the client models, it exchanges, meta-layers with the central servers. Since meta-layers are only a slice of the client models, exchanging them helps protect privacy. On the other hand, they contain meta-knowledge to help the FL training process. In the experiments, we conduct extensive visual classification simulation to evaluate FL-MLT, and the experimental results demonstrate the superior performance of FL-MLT. © 1975-2011 IEEE.

关键词： Federated learning

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An Efficient Method to Estimate Admittance of Black-boxed Inverter-based Resources for Varying Operating Points

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CSEE Journal of Power and Energy systems 2024年第1期10卷 421-426页

作者： Weihua Zhou Bin Liu Nabil Mohammed Behrooz Bahrani Department of Electrical and Computer Systems Engineering Monash UniversityMelbourne 3800Australia Energy Centre Commonwealth Scientific and Industrial Research Organisation(CSIRO)Newcastle 2300Australia.He is now with the Network Planning DivisionTransgridSydney 2000Australia

Traditional analytical approaches for stability assessment of inverter-based resources(IBRs),often requiring detailed knowledge of IBR internals,become impractical due to IBRs’proprietary *** measurements,relying on electromagnetic transient simulation or laboratory settings,are not only time-intensive but also operationally inflexible,since various non-linear control loops make IBRs’admittance models operating-point ***,such admittance measurements must be performed repeatedly when operating point *** avoid time-consuming and cumbersome measurements,admittance estimation for arbitrary operating points is highly ***,existing admittance estimation algorithms usually face challenges in versatility,data demands,and *** this challenge,this letter presents a simple and efficient admittance estimation method for blackboxed IBRs,by utilizing a minimal set of seven operating points to solve a homogeneous linear equation *** studies demonstrate this proposed method ensures high accuracy across various types of *** accuracy is satisfying even when non-negligible measurement errors exist.

关键词： Admittance estimation black box inverterbased resource measurement error operating point

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A hybrid machine-learning model for solar irradiance forecasting

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Clean Energy 2024年第1期8卷 100-110页

作者： Ameera M.Almarzooqi Maher Maalouf Tarek H.M.El-Fouly Vasileios E.Katzourakis Mohamed S.El Moursi Constantinos V.Chrysikopoulos Department of Industrial and Systems Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE Electrical Engineering and Computer Science Department Khalifa University Abu DhabiUAE Department of Civil Infrastructure and Environmental Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE School of Chemical and Environmental Engineering Technical University of CreteChaniaGreece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power *** plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power *** the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather *** study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast *** proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression *** proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable *** evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least *** on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

关键词： solar power generation kernel ridge regression hybrid model forecasting

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The Lynchpin of In-Memory Computing: A Benchmarking Framework for Vector-Matrix Multiplication in RRAMs

The Lynchpin of In-Memory Computing: A Benchmarking Framewor...

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2024 International Conference on Neuromorphic systems, ICONS 2024

作者： Rahman Chowdhury, Md Tawsif Nguyen Vo, Huynh Quang Ramanan, Paritosh Yildirim, Murat Tutuncuoglu, Gozde Wayne State University Electrical and Computer Engineering Detroit United States Oklahoma State University Industrial Engineering and Management Stillwater United States Wayne State University Industrial and Systems Engineering Detroit United States

ISBN: (纸本)9798350368659

The Von Neumann bottleneck, a fundamental challenge in conventional computer architecture, arises from the inability to execute fetch and data operations simultaneously due to a shared bus linking processing and memory units. This bottleneck significantly limits system performance, increases energy consumption, and exacerbates computational complexity. Emerging technologies such as Resistive Random Access Memories (RRAMs), leveraging crossbar arrays, offer promising alternatives for addressing the demands of data-intensive computational tasks through in-memory computing of analog vector-matrix multiplication (VMM) operations. However, the propagation of errors due to device and circuit-level imperfections remains a significant challenge. In this study, we introduce MELISO (In-Memory Linear Solver), a comprehensive end-to-end VMM benchmarking framework tailored for RRAM-based systems. MELISO evaluates the error propagation in VMM operations, analyzing the impact of RRAM device metrics on error magnitude and distribution. This paper introduces the MELISO framework and demonstrates its utility in characterizing and mitigating VMM error propagation using state-of-the-art RRAM device metrics. © 2024 IEEE.

关键词： RRAM

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Performance and Cost Analysis of Sensors: A Comparative Study in a Testbed Automation System 12

Performance and Cost Analysis of Sensors: A Comparative Stud...

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12th IEEE Conference on systems, Process and Control, ICSPC 2024

作者： Srimuk, Pachara Daengsi, Therdpong Prajong, Amnaj Computer Engineering RMUTT Pathum Thani Thailand Sustainable Industrial Management Engineering RMUTP Bangkok Thailand Industrial Electrical Engineering NSRU Nakhonsawan Thailand

ISBN: (纸本)9798350391398

This paper presents a performance and cost analysis of sensors to determine the most cost-effective choice for use in an automation system. Four sensors-two inductive proximity sensors, a capacitive proximity sensor, and an optical sensor-were tested in a testbed system at the NSRU laboratory. The study measured the duration from the starting point to the endpoint as a metal object passed through. Each sensor was tested multiple times, but only the 30 valid results per sensor were reported and analyzed. The results show that the optical proximity sensor recorded the lowest average time of 7.14 s. Statistical analysis using ANOVA and t-tests confirmed significant differences in the average times among the four sensors. Additionally, a cost-effectiveness analysis indicates that one of the inductive proximity sensors was the best option, even though it may not be the most sensitive sensor. © 2024 IEEE.

关键词： Inductive sensors

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Lasso Regression Model of HSV Color Space Masking Vision Based Whole Coffee Bean Processing Agtron Measurement

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Journal of Institute of Control, Robotics and systems 2024年第11期30卷 1245-1253页

作者： Lee, Ye Chan Noh, Yi Hyun Noh, Yun Seo Kim, Ju Song Ryu, Kwangyeol Kim, Byeong Hak Department of Industrial Engineering Pusan National University Korea Republic of Department of Computer Science and Engineering Pusan National University Korea Republic of AI Research Center for Manufacturing Systems Korea Institute of Industrial Technology Korea Republic of

The rapid increase in coffee consumption has led to a significant expansion in production scale and variety within the agricultural regions of the global coffee belt. Recent coffee harvested in varies specious and processed in various ways, and the functionality of processing equipment is advanced to optimize its quality. In particular, coffee roasting equipment has evolved to incorporate complex structures and advanced electronic, mechanical, and heat/air control technologies for processing harvested green coffee beans. One of the most critical quality factors in the roasting process is the degree of roast, which is measured by the bean color (Agtron) value. The conventional method for Agtron measurement involves grinding the beans and using vision sensors. However, the conventional method shows difficult and time-consuming. The method for measuring the color of whole beans without grinding is simpler, but it has limitations in accuracy due to variations in shading and background images. This study proposes a method to accurately measure the Agtron color of whole coffee beans without grinding. The proposed method employs a preprocessing technique that separates the measurement area from the background area through color histogram analysis. To enhance measurement accuracy, we suggest using a Lasso regression model, which mitigates overfitting problems. Experimental results demonstrate that the proposed method achieves an improved accuracy compared with traditional regression methods and neural network-based approaches. © ICROS 2024.

关键词： Grinding (machining)

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Wheat Field Fire Smoke Detection from UAV Images using CNN-CBAM 2

Wheat Field Fire Smoke Detection from UAV Images using CNN-C...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Alsalem, Ahmed Zohdy, Mohamed Oakland University Rochester Department of Industrial and Systems Engineering MI United States Oakland University Rochester Department of Electrical and Computer Engineering MI United States

ISBN: (纸本)9798331529529

Wheat field fires pose a significant threat to wildlife habitats, agricultural resources, and especially farmer incomes. The frequency of these fires has increased noticeably in recent years, likely due to human activity in agricultural areas and the exacerbating effects of climate change. Rapid action is crucial once smoke is detected, as it marks the first step in early fire detection. To address this challenge, we propose an enhanced CNN model specifically designed to identify smoke in wheat field fires. As part of this approach, a dataset of 3900 UAV images was compiled to depict smoke from wheat field fires. The proposed work also integrates the CBAM into the CNN to improve the model's ability to extract features for smoke patch characteristics of wheat field fires. Furthermore, the CNN architecture includes decoupled heads to extract relevant data from various sources. Testing on our wheat field fire smoke dataset has demonstrated the effectiveness of this proposed method, achieving an accuracy of up to 0.91 and outperforming the previous model. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A deep convolutional neural network for COVID-19 detection using chest X-rays

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Research on Biomedical engineering 2022年第1期38卷 139-148页

作者： Bassi, Pedro R. A. S. Attux, Romis Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Campinas Brazil

Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia, and normal. Methods: We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-ray14 dataset as an intermediate step. We also suggested a novelty called output neuron keeping, which changes the twice transfer learning technique. In order to clarify the modus operandi of the models, we used Layer-wise Relevance Propagation (LRP) to generate heatmaps. Results: We were able to reach test accuracy of 100% on our test dataset. Twice transfer learning and output neuron keeping showed promising results improving performances, mainly in the beginning of the training process. Although LRP revealed that words on the X-rays can influence the networks’ predictions, we discovered this had only a very small effect on accuracy. Conclusion: Although clinical studies and larger datasets are still needed to further ensure good generalization, the state-of-the-art performances we achieved show that, with the help of artificial intelligence, chest X-rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis. Heatmaps generated by LRP improve the interpretability of the deep neural networks and indicate an analytical path for future research on diagnosis. Twice transfer learning with output neuron keeping improved DNN performance. © 2021, Sociedade Brasileira de Engenharia Biomedica.

关键词： COVID-19

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