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The Scheduling Analysis Method of Embedded Real-Time System Based on AADL-IO Annex 26

The Scheduling Analysis Method of Embedded Real-Time System ...

引用

26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Peng, Guo Xiao, Zhang Yang, Wang School of Computer Science and Technology Nuaa Computing Technique Research Institute Avic Xi'an China Computing Technique Research Institute Avic Xi'an China

ISBN: (纸本)9798350345865

The embedded real-time system not only pays attention to the correctness of software task function, but also pays attention to the correctness of software task timing. There are a lot of IO resources in embedded real-time systems. The existing scheduling modeling and analysis methods are mainly aimed at tasks themself, without focusing IO resources. This paper proposes a scheduling analysis method of embedded real-time systems based on AADL-IO Annex. AADL-IO Annex is designed in two forms of meta-model and extended Backus paradigm. Scheduling analysis method of IO resources is based on time automata. Finally, a public embedded real-time system case is selected to verify the correctness and feasibility of the method in this paper. The method in this paper expands the modeling capabilities of AADL in terms of IO resources, and improves the fidelity of Scheduling analysis. © 2023 IEEE.

关键词： Real time systems

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A Systematic Literature Review of the Application of Agile Applied to Large-Scale, Safety-Critical, Cyber-Physical systems

A Systematic Literature Review of the Application of Agile A...

引用

2024 IEEE international conference on Data and Software Engineering, ICoDSE 2024

作者： Yeman, Robin J. Malaiya, Yashwant K. Dept. Systems Engineering Colorado State University Fort Collins United States Dept. Computer Science Colorado State University Fort Collins United States

ISBN: (纸本)9798331506407

The application of Agile methodologies to large-scale, safety-critical cyber-physical systems (LS/SC/CPS) has shown significant interest over the last 5 years. Although there has been limited research into each of the individual attributes for development projects that are large-scale, safety-critical, or cyber-physical systems, there is a scarcity of research when all three attributes exist simultaneously in a development endeavor. This systematic literature review explores the extent of Agile adoption in projects where all three attributes are present. It begins by examining the extent of Agile adoption by domain, identifying the drivers for change, reviewing the challenges encountered, and investigating the augmentations used to address these challenges. The findings indicate that there has been growth in adoption across the medical, aerospace, automotive, and robotics industries, with limited adoption in energy and transportation. Key drivers include speed, adaptability, and cost. Several challenges have been identified, with regulatory/compliance requirements being the most significant. Multiple augmentations to Agile have been applied to overcome the identified challenges, ranging from incorporating safety frameworks and specialized artifacts to leveraging modeling and simulation, as well as reimagining programmatics. This review highlights industry efforts to apply Agile to LS/SC/CPS, while identifying several gaps in the literature. The largest gap is empirical validation in real-world settings. Future research should focus on improving the existing Agile scaling frameworks with guidance and resources to help achieve the benefits of Agile in LS/SC/CPS development while ensuring regulatory and safety compliance. © 2024 IEEE.

关键词： Resource valuation

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Decentralized Subgraph Learning for Spatial-Temporal Data modeling 29

Decentralized Subgraph Learning for Spatial-Temporal Data Mo...

引用

29th IEEE international conference on Parallel and Distributed systems, ICPADS 2023

作者： Wang, Haiquan Yan, Wei Zhao, Jiejie Du, Bowen He, Chenzhi Ma, Yanbo Huang, Runhe Beihang University School of Software Beijing China Zhongguancun Laboratory Beijing China Beihang University SKLSDE Lab Beijing China Pontosense Inc. Canada Hosei University Computer and Information Sciences Tokyo Japan

ISBN: (纸本)9798350330717

Spatial-temporal data modeling has attracted attention due to the massive spatial-temporal data acquired by sensors, as well as its importance in the real world. Most existing methods require transferring a huge volume of data from different parties to a central server, which is impractical due to conflicts of benefit and privacy concerns. A party only possesses a part of the entire spatial-temporal data (i.e., a subgraph), and subgraphs are isolated among parties. Federated Learning (FL) is an emerging framework for training models without sharing data, but it still has a high vulnerability when the central server fails. Besides, naively fusing models in most FL may have a negative impact on performance because of insufficient spatial relations among subgraphs and discrepant spatial-temporal patterns among subgraphs. To this end, we propose a Decentralized Subgraph Learning framework for Spatial-Temporal data modeling, namely DeSL-ST, which can efficiently handle the distributed subgraphs without the need of the central server. Specifically, DeSL-ST uses a cross-subgraph spatial relation learning module to tackle the issue of missing spatial relations between subgraphs. Then, a sparse transfer structure learning module is proposed to produce better-personalized models that are beneficial for each subgraph. Experiments on two traffic forecasting tasks demonstrate that DeSL-ST achieves state-of-the-art performance with lower peer-to-peer communication cost. © 2023 IEEE.

关键词： Decentralized Learning Spatial-Temporal Data modeling

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Accurate Classification of Ovarian Cyst Using Topic modeling 4

Accurate Classification of Ovarian Cyst Using Topic Modeling

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4th IEEE international conference on Computing, Communication, and Intelligent systems, ICCCIS 2023

作者： Gupta, Aditi Fatima, Hoor Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350306118

Ovaries are an essential female organ in the reproductive and endocrine system, responsible for healthy development and foster fertility. Occasionally, they are affected by vesicles that are filled with fluid and are referred to as cyst. Most of the ovarian cysts are benign in nature, but in few cases, they can be harmful for the patient and need to be treated at an early phase. Globally, ovarian cyst is a prevalent problem among females. The identification and classification of ovarian cyst is a major task in medical data analysis for healthcare providers. Manual classification of ovarian cyst may lead to delay in the diagnosis of the patient. In this paper, three Topic modeling algorithms are utilized to classify ultrasound textual reports into cystic and normal ovary reports. The results of the algorithms are analyzed on the basis of six evaluation metrics like, accuracy, precision, recall, etc. Latent Dirichlet Allocation with accuracy of 90.71% is proven to be a better algorithm for ovarian cyst classification compared to Non-negative Matrix Factorization and Latent Semantic Analysis with accuracies of 83.79% and 90.34% respectively. © 2023 IEEE.

关键词： Patient treatment

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Formal modeling and Verification of Industrial Robotic Arm - A Case Study 23

Formal Modeling and Verification of Industrial Robotic Arm -...

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23rd IEEE international conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Zia, Ilmaan Basit Ur Rahim, Muhammad Abdul Liu, Tairan He, Zhangying California State University Long Beach Department of Computer Science and Engineering CA United States California State University Long Beach Department of Mechanical and Aerospace Engineering CA United States

ISBN: (纸本)9798350359398

Robotic arm systems are revolutionizing industries by automating operations and extending capabilities beyond human limitations, prioritizing accuracy and precision. These systems redefine boundaries across various sectors, including healthcare, mobile automation, and space exploration. Ensuring the safety of the robotic arm and its surrounding environment is paramount during its operations. This paper presents a detailed investigation of formal modeling and verification in the context of an industrial robotic arm, specifically a multi-joint mechanism equipped with advanced capabilities such as object manipulation, obstacle detection, and avoidance. The study uses the model-checking tool to capture the complex behavior of the system precisely. In-depth simulation and verification techniques are used for the formal model, focusing on its crucial properties, including safety, liveness, fairness, and deadlock-freeness. © 2023 IEEE.

关键词： Accident prevention

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MSFSAN: A Novel Multi-Scale Spatio-Temporal Feature Screening Attention Network for Urban Carbon Emission Prediction

MSFSAN: A Novel Multi-Scale Spatio-Temporal Feature Screenin...

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2024 IEEE international conference on systems, Man, and Cybernetics, SMC 2024

作者： Wang, Ben Qin, Xizhong Qin, Jiwei Zhang, Xiaoyu Ma, Haodong College of Computer Science and Technology Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing Ürümqi830046 China

ISBN: (纸本)9781665410205

In order to cope with the increasingly severe global energy conservation and emission reduction problems, research on urban carbon emission prediction is of great significance. The existing methods mainly use time series analysis to predict urban carbon emission, but there is a strong spatial correlation between the carbon emission data of several cities. Therefore, this paper designs a multi-scale spatial-temporal feature screening attention network to predict target cities' future carbon emission data. Firstly, this paper combines the daily carbon emission data of the near-neighbouring cities and the daily homologous emission data of the target city to analyze the urban carbon emission data from a spatio-temporal perspective. Then, this paper designs a multi-scale spatial interactive convolution module and a multi-scale temporal convolution module to extract multi-scale spatio-temporal features effectively. In addition, the feature screening module is designed to reduce the adverse effects of redundant features. Finally, the multi-scale features are used to predict the future carbon emissions of the target city through a predictor. The experimental results show that our prediction model is superior to the existing methods in six datasets. © 2024 IEEE.

关键词： Low emission

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Quantitative Study on the Color of Mineral Materials Based on Cielab and Munsell systems 21

Quantitative Study on the Color of Mineral Materials Based o...

引用

21st international computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Zhihui, Yue Xinshuo, Li Sujun, Wang Limei, Yu Honglin, Yu Meishan Vocational and Technical College Meishan620010 China Chengdu Vocational& Technical College of Industry Chengdu610213 China

ISBN: (纸本)9798331519254

The color of mineral materials is an important indicator for determining characteristics such as the formation environment, mineral composition, and elemental composition of the materials. Currently, the methods for obtaining the color of mineral materials (such as rocks and soils) primarily rely on manual identification, which presents limitations such as instability in results due to differences in the professional levels of observers and low efficiency. Therefore, there is an urgent need to develop objective and standardized methods for quantitatively characterizing the color of mineral materials. This study proposes the use of two internationally widely adopted color systems (CIELab and Munsell color system) for characterization. Firstly, the CIELab color values of mineral materials are measured using a spectrophotometer, while the Munsell color values can be quantitatively obtained through relevant models and methods. The Munsell color values determined by specialized technicians are used to verify the quantitative accuracy. The results indicate that the CIELab color standard database can be quickly and directly established using the spectrophotometer, allowing for preliminary judgments and comparisons of the differences in related components among different samples based on the data results. Additionally, the quantitative results of the Munsell color values are relatively ideal, with an average color difference of only 0.67 units among 25 samples. This study provides a highly operable solution for the objective and standardized quantitative characterization of the color of mineral materials, with mature measurement techniques and a simplified implementation process, demonstrating certain application prospects. © 2024 IEEE.

关键词： Color computer language Mineral materials Quantification

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Factor Graph Accelerator for LiDAR-Inertial Odometry (Invited Paper) 22

Factor Graph Accelerator for LiDAR-Inertial Odometry (Invite...

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IEEE/ACM 41st international conference on computer Aided-Design (ICCAD)

作者： Hao, Yuhui Yu, Bo Liu, Qiang Liu, Shaoshan Zhu, Yuhao Tianjin Univ Tianjin Peoples R China PerceptIn Santa Clara CA USA Univ Rochester Rochester MN USA

ISBN: (纸本)9781450392174

Factor graph is a graph representing the factorization of a probability distribution function, and has been utilized in many autonomous machine computing tasks, such as localization, tracking, planning and control etc. We are developing an architecture with the goal of using factor graph as a common abstraction for most, if not, all autonomous machine computing tasks. If successful, the architecture would provide a very simple interface of mapping autonomous machine functions to the underlying compute hardware. As a first step of such an attempt, this paper presents our most recent work of developing a factor graph accelerator for LiDAR-Inertial Odometry (LIO), an essential task in many autonomous machines, such as autonomous vehicles and mobile robots. By modeling LIO as a factor graph, the proposed accelerator not only supports multi-sensor fusion such as LiDAR, inertial measurement unit (IMU), GPS, etc., but solves the global optimization problem of robot navigation in batch or incremental modes. Our evaluation demonstrates that the proposed design significantly improves the real-time performance and energy efficiency of autonomous machine navigation systems. The initial success suggests the potential of generalizing the factor graph architecture as a common abstraction for autonomous machine computing, including tracking, planning, and control etc.

关键词： factor graph autonomous machine computing computer architecture robotics

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ARIMA-LP: A Hybrid Model for Air Pollution Forecasting with Uncertainty Data 8

ARIMA-LP: A Hybrid Model for Air Pollution Forecasting with ...

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8th IEEE international conference on Software Engineering and computer systems, ICSECS 2023

作者： Che Lah, Muhammad Shukri Arbaiy, Nureize Lin, Pei-Chun Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Malaysia Feng Chia University Department of Information Engineering and Computer Science Taiwan

ISBN: (纸本)9798350310931

Public health is seriously threatened by air pollution. systems for early warning are crucial for preventing its negative impacts on humans. But predicting air quality is difficult because it needs precise data from time series gathered at air monitoring sites. Due to things like measurement mistakes, this data may be ambiguous. Using fuzzy symmetry triangular fuzzy numbers, we suggest a strategy in this study for preparing data that contains uncertain information. Linear programming is utilized to obtain the midpoint value for defuzzification. The fuzzy pre-processed data is then used to build a predictive model using ARIMA. Our findings imply that the linear program can greatly lower prediction errors, resulting in more precise predictions. © 2023 IEEE.

关键词： Linear programming

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Development of Skin Cancer Detection Using Deep Spiking Neural Networks 10

Development of Skin Cancer Detection Using Deep Spiking Neur...

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10th IEEE international conference on Smart Instrumentation, measurement and Applications, ICSIMA 2024

作者： Habaebi, Mohamed Hadi Zulkarnain, Muhammad Amin Gunawan, Teddy Surya Fitriawan, Helmy Kartiwi, Mira Abd.rahman, Faridah International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia Universitas Lampung Department of Electrical Engineering Bandar Lampung35145 Indonesia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia

ISBN: (纸本)9798350365283

The limitations of conventional techniques necessitate the development of new diagnostic strategies for early detection of skin cancer, the most prevalent form of cancer. This investigation investigates the application of Spiking Neural Networks (SNNs) in creating a dependable and automated skin cancer detection system. The primary goal is to evaluate the feasibility and efficacy of SNNs for this purpose. The methodology entails using the SpikingJelly framework to create and assess an SNN-based model, specifically a modified Spiking VGG-11. Focusing on images classified as melanoma or non-melanoma, the investigation employs the ISIC 2019 dataset. The Spiking VGG-11 model, which features approximately 15 million parameters, achieved a test accuracy of 81.3%, specificity of 81.7%, and precision of 81.5%. This performance is noteworthy in light of its reduced complexity compared to models such as the Spiking VGG-13, which has approximately 134 million parameters. The model's high specificity is especially advantageous in clinical environments, as it minimizes false positives and unnecessary testing. These results emphasize the potential of SNNs to offer diagnostic tools that are both efficient and effective in the detection of skin cancer. These findings underscore the potential of SNNs to enhance the early detection and diagnosis of skin cancer, offering a promising alternative to conventional diagnostic methods. © 2024 IEEE.

关键词： Lung cancer

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