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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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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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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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Empirical Analysis of Vulnerabilities Life Cycle in Golang Ecosystem 24

Empirical Analysis of Vulnerabilities Life Cycle in Golang E...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Hu, Jinchang Zhang, Lyuye Liu, Chengwei Yang, Sen Huang, Song Liu, Yang College of Command and Control Engineering Army Engineering University of Pla Nanjing China Continental-NTU Corporate Lab Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Academy of Military Science Nanjing China

ISBN: (纸本)9798400702174

Open-source software (OSS) greatly facilitates program development for developers. However, the high number of vulnerabilities in open-source software is a major concern, including in Golang, a relatively new programming language. In contrast to other commonly used OSS package managers, Golang presents a distinctive feature whereby commits are prevalently used as dependency versions prior to their integration into official releases. This attribute can prove advantageous to users, as patch commits can be implemented in a timely manner before the releases. However, Golang employs a decentralized mechanism for managing dependencies, whereby dependencies are upheld and distributed in separate repositories. This approach can result in delays in the dissemination of patches and unresolved vulnerabilities. To tackle the aforementioned concern, a comprehensive investi-gation was undertaken to examine the life cycle of vulnerability in Golang, commencing from its introduction and culminating with its rectification. To this end, a framework was established by gathering data from diverse sources and systematically amalgamating them with an algorithm to compute the lags in vulnerability patching. It turned out that 66.10% of modules in the Golang ecosystem were affected by vulnerabilities. Within the vulnerability life cycle, we found two kinds of lag impeding the propagation of vulnerability fixing. By analyzing reasons behind non-lagged and lagged vul-nerabilities, timely releasing and indexing patch versions could significantly enhance ecosystem security. © 2024 ACM.

关键词： Open source software

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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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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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Fault-Tolerant Quantum Computation Using Large Spin-Cat Codes

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PRX Quantum 2024年第2期5卷 020355-020355页

作者： Sivaprasad Omanakuttan Vikas Buchemmavari Jonathan A. Gross Ivan H. Deutsch Milad Marvian Center for Quantum Information and Control Department of Physics and Astronomy Venice California 90291 USA Department of Electrical and Computer Engineering

We construct a fault-tolerant quantum error-correcting protocol based on a qubit encoded in a large spin qudit using a spin-cat code, analogous to the continuous-variable cat encoding. With this, we can correct the dominant error sources, namely processes that can be expressed as error operators that are linear or quadratic in the components of angular momentum. Such codes tailored to dominant error sources can exhibit superior thresholds and lower resource overheads when compared to those designed for unstructured noise models. A key component is the cnot gate that preserves the rank of spherical tensor operators. Categorizing the dominant errors as phase and amplitude errors, we demonstrate how phase errors, analogous to phase-flip errors for qubits, can be effectively corrected. Furthermore, we propose a measurement-free error-correction scheme to address amplitude errors without relying on syndrome measurements. Through an in-depth analysis of logical cnot gate errors, we establish that the fault-tolerant threshold for error correction in the spin-cat encoding surpasses that of standard qubit-based encodings. We consider a specific implementation based on neutral-atom quantum computing, with qudits encoded in the nuclear spin of 87Sr, and show how to generate the universal gate set, including the rank-preserving cnot gate, using quantum control and the Rydberg blockade. These findings pave the way for encoding a qubit in a large spin with the potential to achieve fault tolerance, high threshold, and reduced resource overhead in quantum information processing.

关键词： Quantum computation Quantum control Quantum error correction Quantum information theory

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A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

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Journal of Bionic engineering 2022年第1期19卷 224-239页

作者： Sen Tian Jin Zhang Xuanyu Shu Lingyu Chen Xin Niu You Wang School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou310058China Science and Technology on Parallel and Distributed Laboratory College of ComputerNational University of Defense TechnologyChangsha410199China Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhou310027China

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and ***,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive ***,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the ***,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed *** analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in ***,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic ***,the network model is quantitatively analyzed by evaluation indexes from the perspective of *** experimental results show that the DBN network,LeNet5 network,and BP network have synchronous *** the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural *** KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic *** with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

关键词： Artificial neural network(ANN) Back Propagation(BP)network Deep Belief Network(DBN) LeNet5 network Olfactory bionic model(KIII model) Small world Chaos Synchronous

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RiscDaqExt – Digital Acquisition and control RISC-V Extension

RiscDaqExt – Digital Acquisition and Control RISC-V Extensi...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Cosmin-Andrei Popovici Andrei Stan Vasile-Ion Manta Department of Computer Engineering Faculty of Automatic Control and Computer Engineering Technical University "Gheorghe Asachi" Iași România

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This paper presents a RISC-V extension, called RiscDaqExt designed for Digital Acquisition and control over 32 individually managed digital channels. Each channel can be configured as input or output, inputs can be negated or filtered before being recorded using specific masks and output channels source can be chosen between values given with a mask, result of a multi-signal operation on specified inputs or a PWM signal generated by the SigWavy extension. The extension is also capable of measuring PWMs frequencies and duty cycles. The extension is controlled using a dedicated ISA extension containing instructions for setting/getting configuration/values and for controlling the storing records of 32 signals values with 32-bit timestamps to an attached DDR3 memory or directly to hosting PC’s RAM via Ethernet. The proposed solution, embedded into the RISC-V CPU named RiscDaq manages to initiate an Ethernet transmission of digital records 9.78x times faster than a MicroBlaze microcontroller, although our SoC operates with a 4x lower frequency, and outputs the result of logical operation performed over maximum 31 digital channels, with a constant 20 ns propagation delay, 49.2x times faster than a Cortex-M7 clocked at a 6x times higher frequency.

关键词： Time-frequency analysis Digital systems Process control Random access memory Ethernet Pulse width modulation Frequency measurement Time factors Logic Propagation delay

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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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