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the past, the present and the future of online road vehicle diagnosis from car and scan tool communication perspective 6

The past, the present and the future of online road vehicle ...

引用

6th international conference on Material Strength and Applied Mechanics, MSAM 2023

作者： Nagy, Jozsef Lakatos, Istvan Head of Corporate Quality Vehicle Technology Functions/Geometry AUDI HUNGARIA Zrt. Audi Hungaria ut 1 Győr9027 Hungary Head of Department of Road and Rail Vehicles Audi Hungaria Faculty of Automotive Engineering Szechenyi Istvan University Egyetem ter 1 H-9026 Győr Hungary

ISBN: (纸本)9781643684345

In the luxury vehicle class, online internet connection has become a standard feature in the recent years. this process is likely to spread completely in all vehicle categories in the coming years. In the past, computer supported vehicle diagnostics started with On-Board Diagnostic (OBD) I standard in the late 1960's. this has been followed by OBD II in 1996, which allows a fully standardization regardless of vehicle manufacturer and models, based on a generic scan tool. Even its communication with the vehicle electronic architecture has been developed rapidly followed by the new technologies. this way of car analysis has been extended also for non-emission related functions. Online data collecting and analysis provides not only solutions in the car production and service but in its development process too. For example, it can help you to set realistic thresholds by defining an OBD strategy for many in-car systems and functions to avoid customer complaints, warranty costs and recalls afterwards. From predictive maintenance / repair perspective it is possible to monitor material properties, vibrations, damages potentials also in processes e.g. welding and soldering. the dynamic development of telecommunication and automotive technologies hand in hand will provide completely new ways for 'car to diagnostics back end' communication in the future without using a scan tool device and its physical in-vehicle interface. © 2023 the Authors.

关键词： Cost benefit analysis

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Interfacing Digital and Analog models for Fast Simulation and Virtual Prototyping 9th

Interfacing Digital and Analog Models for Fast Simulation an...

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9th international conference on Model-Driven engineering and software Development (modelsWARD)

作者： Genius, Daniela Apvrille, Ludovic Sorbonne Univ CNRS LIP6 UMR 7606 Paris France Univ Paris Saclay Telecom LTCI Paris France

ISBN: (纸本)9789897584879

the paper presents an enhancement for the virtual simulation of analog/mixed-signal systems from high-level SysML models. Embedded systems, e.g. in robotic systems, feature both digital and analog circuits such as sensors and actuators. Simulation of these system requires to handle both domains (digital, analog);our aim is thus to make the interactions between these two domains explicit. For this, the paper first defines new send and receive procedures, then explains how to check semantic aspects of models to ensure a correct simulation. A running example illustrates the basic concepts of our approach. A proof of concept based on an existing rover is also presented.

关键词： System-level Design Virtual Prototyping Cyber-physical Systems Co-simulation

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Enhancing Heart Disease Prediction with GANs in Clinical Decision Support Systems

Enhancing Heart Disease Prediction with GANs in Clinical Dec...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： A.Periya Nayaki S.V. VishwaShree N. Mithra C. Priyadharshini M.S. thanabal K. Penyameen Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Heart disease remains a leading global cause of mortality, emphasizing the need for innovative diagnostic solutions. Traditional Clinical Decision Support Systems (CDSS) often struggle with limited datasets and imbalances, impacting prediction accuracy. this study proposes a novel approach by integrating Conditional Generative Adversarial Networks (CGANs) into a CDSS framework, enhancing its ability to generate high-quality synthetic data. the augmented dataset is utilized to train a Gradient Boosting model capable of identifying subtle patterns in patient data. this methodology significantly improves prediction precision and addresses limitations of traditional techniques. the results demonstrate the model's superiority inaccurately identifying heart disease risks, making it a valuable tool for early diagnosis and clinical decision-making. By combining advanced machine learning techniques with domain-specific innovations, this research sets a new benchmark in the application of AI for healthcare.

关键词： Heart Decision support systems Accuracy Medical services predictive models Generative adversarial networks Boosting Data models Diseases Synthetic data

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Comprehensive Analysis of Stress Levels Using Ensemble Learning and Neural Networks for Insightful Understanding

Comprehensive Analysis of Stress Levels Using Ensemble Learn...

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international conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Md Shohanur Rahman Akteruzzaman Ashik Md. Sadik Rahman Department of CSE Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

It is vital to comprehend and predict stress levels as it has a significant impact on both the health of the individual and the community. A wide range of machine learning models, ensemble techniques, and sequential neural networks are used in this study to provide a thorough investigation of stress level prediction. Our analysis explores the complex variables affecting stress by utilizing a carefully selected dataset from Kaggle that includes 2838 instances with 7 features each. Each model was examined in detail, displaying a range of performance measures. these models included Naive Bayes, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and XGBoost. the Voting Classifier served as an example of how well the ensemble approach combined many models, resulting in an improved total accuracy of 73%. By accurately identifying intricate non-linear correlations in the stress dataset with a 76% accuracy rate, a sequential neural network enhanced the study. the study of the results provides a basis for further research by revealing the advantages and disadvantages of each model. this work advances our knowledge of stress and its management by contributing to the creation of increasingly precise and complex prediction models as part of the continuing conversation on stress analysis.

关键词： Support vector machines Knowledge engineering Analytical models Neural networks Medical treatment Oral communication predictive models

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the Role of Stories in software Development and Business-Process Modeling 1

引用

proceedings of the 14th international conference on Subject-Oriented Business Process Management, S-BPM ONE 2023

作者： Forbrig, Peter Umlauft, Alexandru Kühn, Mathias Dittmar, Anke University of Rostock Chair of Software Engineering Albert-Einstein-Str. 22 Rostock18055 Germany

ISBN: (数字)9783031402135

ISBN: (纸本)9783031402128

Scenarios and stories are widely used across different professional domains. the paper presents a case study on their use in a multidisciplinary project aimed at supporting therapies for patients after stroke. Existing training programs with patients and therapists served as starting point for exploring usage scenarios with the humanoid robot Pepper. Based on a set of envisaged scenarios, implementations for the robot’s behavior in predefined "therapy workflows" were provided. Additionally, the domain-specific language TaskDSL4Pepper was developed that allows therapists to specify their own executable interaction models between the patient and the robot. the case study also reveals the need for involving various domain experts to create richer sets of stories with more alternatives. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Anthropomorphic robots

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Equivalent Wind Farm Model Considering Different Wind Speeds and Wake Effects

Equivalent Wind Farm Model Considering Different Wind Speeds...

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Electrical engineering and Green Energy (CEEGE), international conference on

作者： Xiaohe Wang Lei Chen Hongke Li Wei Chen Feng Jiang Min Chen Key Laboratory of Far-Shore Wind Power Technology of Zhejiang Province Power China Huadong Engineering Corporation Limited Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China

the traditional detailed model of wind farms is unsuitable for large wind farms due to the complexity, long simulation time and high hardware requirements. this paper presents an equivalent model for large wind farms, and the effects of different wind speeds and wake effects are considered. Wind turbine generators (WTGs) with the same wind speed were classified and equivalent models of each part of WTG was established. An equivalent model for WTGs was established for different wind speeds and wake effects based on multimachine equivalence method. the simulation results show that under the same hardware configuration and software step size, the required simulation time of the equivalent wind farm model is reduced significantly with good accuracy.

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Using Multiple Code Representations to Prioritize Static Analysis Warnings 14

Using Multiple Code Representations to Prioritize Static Ana...

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14th international conference on Knowledge and Systems engineering, KSE 2022

作者： Vu, thanh Trong Vo, Hieu Dinh Vnu University of Engineering and Technology Faculty of Electronics and Telecommunications Hanoi Viet Nam Vnu University of Engineering and Technology Faculty of Information Technology Hanoi Viet Nam

ISBN: (数字)9781665452816

ISBN: (纸本)9781665452809

In order to ensure the quality of software and prevent attacks from hackers on critical systems, static analysis tools are frequently utilized to detect vulnerabilities in the early development phase. However, these tools often report a large number of warnings with a high false-positive rate, which causes many difficulties for developers. In this paper, we introduce VULRG, a novel approach to address this problem. Specifically, VuLRG predicts and ranks the warnings based on their likelihoods to be true positives. To predict these likelihoods, VuLRG combines two deep learning models CNN and BiGRU to capture the context of each warning in terms of program syntax, control flow, and program dependence. Our experimental results on a real-world dataset of 6,620 warnings show that VuLRG's Recall at Top-50% is 90%. this means that using VuLRG, 90% of the vulnerabilities can be found by examining only 50% of the warnings. Moreover, at Top-5%, VULRG can improve the state-of-the-art approach by +30% in both Precision and Recall. © 2022 IEEE.

关键词： Static analysis

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Load Characterization and Fatigue Reliability Prediction of Large Aerospace Planetary Gear

Load Characterization and Fatigue Reliability Prediction of ...

引用

Data-driven Optimization of Complex Systems (DOCS), international conference on

作者： Ming Li Wanyu Jiang Bo Fan Liyang Xie School of Mechatronics Engineering Shenyang Aerospace University Shenyang China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

the reliability level of large aviation planetary mechanisms, serving as the cornerstone and essential component for various heavy aircraft transmission systems, significantly impacts aircraft affordability and service safety. Before predicting the fatigue performance of the gear transmission system, it is necessary to develop a comprehensive simulation model of the gear system and calculate the stress values for failure modes such as tooth contact fatigue and tooth root bending fatigue. In addition, By using hierarchical finite element technology, the coupling effect of the global elastic behavior of the system on the fatigue load history of the gear teeth is analyzed in depth, and accurate calculations are carried out based on this to fit the probability fatigue strength of the gear teeth. this provides both economical and efficient load and strength input variables for the construction of the system reliability prediction model, and innovatively establishes a new series system reliability model. Finally, the computational findings from the new model is contrasted with the traditional one to show the superiority of the proposed model.

关键词： Gears Computational modeling Interference predictive models Fatigue Finite element analysis software reliability Reliability Stress Load modeling

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Combining TPE-PSO and BiGRU for High-Precision Short-Term Wind Power Forecasting

Combining TPE-PSO and BiGRU for High-Precision Short-Term Wi...

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Energy Systems and Electrical Power (ICESEP), international conference on

作者： Yunlong Li Yong Shao Xin Guan School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

Given the significant volatility and unpredictability of wind power generation, accurately forecasting its future power output has become a key element in ensuring the stability and reliability of energy systems. this paper proposes a network model based on TPE-PSO hyperparameter optimization, aimed at maximizing the search for optimal parameters globally and locally to achieve the highest precision. the predictive performance of the model was evaluated using two sets of wind power data with a resolution of 1 hour, employing four commonly used evaluation metrics in the field. the results show that all power forecasting results within 24 hours have R2 decision values consistently above 0.973. Under the same environmental conditions, our model significantly outperforms other models in predicting wind power generation, as evidenced by its superior MAE, RMSE, and NMAE. this significant research outcome provides strong support and precise guidance for the safe and stable operation of power grids in practical applications.

关键词： Accuracy Uncertainty Weather forecasting Wind power generation predictive models Power system stability Stability analysis

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BOAformer: Object Detection Network Based on Background-Object Attention Transformer

BOAformer: Object Detection Network Based on Background-Obje...

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international conference on Communications, Information System and Computer engineering (CISCE)

作者： Yong Yang Chenbin Liang Shuying Huang Xiaozheng Wang School of Computer Science and Technology Tiangong University Tianjin China School of Electronics and Information Engineering Tiangong University Tianjin China School of Software Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

At present, deep learning has achieved great success in the field of object detection. To ensure that positive samples in the image are not missed, most deep-learning object detection methods set many prediction boxes and use the same prediction operation on them. Although these methods can obtain higher predictive values for high certainty objects, they may identify objects with lower certainty, such as small or fuzzy objects, as negative samples or miss them. To address this issue, this paper presents an object detection network based on background-object attention transformer (BOAformer), which improves the accuracy of object detection by establishing the relationship between background and object features. Firstly, the existing classification model is employed to obtain scores of object prediction boxes. On this basis, a scoring mechanism is designed to filter out high scoring object prediction boxes to reduce the computational complexity of the later network. then, to improve the accuracy of object detection, BOAformer is constructed, which obtains auxiliary classification values of objects by learning the relationship between background and object features, and adds them to the initial detection results. Finally, many experiments are conducted to verify the effectiveness of BOAformer. the experimental results show that the designed network improves the accuracy of object detection and can detect small or lost objects in the background.

关键词： Training Visualization Accuracy Computational modeling Semantic segmentation Object detection predictive models

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