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IEEE Symposium on On-Line Testing (IOLTS)

作者： Nicola Scarano Luca Mannella Alessandro Savino Stefano Di Carlo Politecnico Di Torino Department of Control Computer Engineering Turin Italy

ISBN: (数字)9798350370553

ISBN: (纸本)9798350370560

abstract-The increasing adoption of connectivity and electronic components in vehicles makes these systems valuable targets for attackers. While automotive vendors prioritize safety, there remains a critical need for comprehensive assessment and analysis of cyber risks. In this context, this paper proposes a Social Media Automotive Threat Intelligence (SOCMATI) framework, specifically designed for the emerging field of automotive cybersecurity. The framework leverages advanced intelligence techniques and machine learning models to extract valuable insights from social media. Four use cases illustrate The framework’s potential by demonstrating how it can significantly enhance threat assessment procedures within the automotive industry.

关键词： Industries Social networking (online) Shape Machine learning Threat assessment Safety System analysis and design

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Optimizing the LSTM Model for Electricity Price Forecasting Using the MSADBO Algorithm

Optimizing the LSTM Model for Electricity Price Forecasting ...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Ahmadvand, Fatemeh Imani, Mahmood Hosseini Andani, Hamid Taheri Andani, Mansour Taheri Politecnico di Torino Department of Control and Computer Engineering Torino Italy Torino Italy Isfahan University of Technology Department of Electronic Engineering Isfahan Iran School of Manufacturing Systems and Networks Arizona State University TempeAZ United States

ISBN: (纸本)9798331541125

Achieving accurate electricity price prediction is essential for market participants aiming to maximize their profits. In this respect, forecasting wholesale electricity market price plays a pivotal role. The advanced nonlinear modeling capabilities of deep learning networks have shown notable success in forecasting electricity prices. However, designing such networks, particularly selecting their optimal hyperparameters, still remains a big challenge. This paper presents a Modified Sine Algorithm Dung Beetle Optimization algorithm (MSADBO) as an improved Dung Beetle Optimization (DBO) algorithm to optimize the hyperparameters of the Long Short-Term Memory (LSTM) neural network to handle the electricity price forecasting in the Italian wholesale market. After optimizing the three most important hyperparameters of the LSTM using MSADBO, the efficiency of the model has shown a drastic improvement. Comparisons with conventional and machine learning-based approaches show that the LSTM-MSADBO model is highly precise in predictions with increased robustness and effectively captures volatility in the electricity market price. © 2025 IEEE.

关键词： Long short-term memory

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Self-Supervised Learning with Min-Max Distance Approach for Few-Shot Steel Surface Defects Recognition 43

Self-Supervised Learning with Min-Max Distance Approach for ...

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43rd Chinese control Conference, CCC 2024

作者： Wang, Tianlei Chen, Xinyao Li, Zeliang Zhou, Zhencai Zhou, Zhiheng Wuyi University School of Electronic and Information Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Mechanical and Automation Engineering Jiangmen China Guangdong Province Kejie Machinery Automation Company Technical Department Jiangmen China

ISBN: (纸本)9789887581581

Surface defects in steel are crucial for quality inspection in the manufacturing of industrial metal products. While deep learning methods have been extensively studied in this field, the lack of large-scale annotated defect samples imposes significant labeling pressure, making training fully supervised deep models challenging. In response to this issue, we employ self-supervised learning for few-shot recognition, effectively learning representations from limited unlabeled data and achieving defect recognition with a small amount of labeled samples. In particular, we construct Other encoder with multi-view information and Base encoder with single view information based on the distillation self-supervised learning framework through the multi-crops mechanism, and the Other encoders and Base encoders perform information share. Our main contribution is that, based on this framework, we propose a new regularization method to solve the problem of instances deviating from the center of self-supervised clustering by minimizing the maximum instance distance (MMD). In testing, fine-tuning the MMD pre-trained model with a small number of labeled data achieves superior recognition performance on the steel surface defect dataset NEU, significantly outperforming other testing methods. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Self-supervised learning

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Model predictive control of hemodynamics during intravenous general anesthesia 27

Model predictive control of hemodynamics during intravenous ...

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Farbakhsh, Hamed Yumuk, Erhan Othman, Ghada Ben De Keyser, Robin Copot, Dana Birs, Isabela Ionescu, Clara M. Ghent University Department of Electromechanics Systems and Metal Engineering Research Group on Dynamical Systems and Control Tech Lane Science Park 125 Zwijnaarde9052 Belgium Istanbul Technical University Department of Control and Automation Engineering Istanbul Turkey Technical University of Cluj-Napoca Department of Automation Memorandumului no. 28 Cluj-Napoca400114 Romania

ISBN: (纸本)9798350337983

In the operating rooms and the intensive care unit, it is crucial to manage the patient's hemodynamic status, which includes factors like cardiac output and mean arterial pressure. Anesthesiologists confront a difficult task while monitoring high-risk patients. Cardiac output optimization has been found to enhance the result of high-risk patients in terms of hospital stay, mortality rate, post-operative problems, etc. The application of standard control approaches is restricted because the mean arterial pressure response of a patient using vasoactive medicines is modeled by a first-order dynamical system with time-varying parameters and a time-varying delay in the control input. In order to circumvent implementation challenges, this work develops an approximation technique that describes the system using a higher-order model. Predictive control is therefore used to comprehend the practical application of higher-order hemodynamic systems. The effectiveness of this strategy is demonstrated by the simulations and outcomes that are given. © 2023 IEEE.

关键词： Model predictive control

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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Fault Diagnosis Based on the Updating Strategy of Interval-Valued Belief Structures

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Chinese Journal of Electronics 2025年第4期23卷 753-760页

作者： Xiaobin Xu Ping Liu Yanbo Sun Chenglin Wen Institute of Systems Science and Control Engineering School of Automation Hangzhou Dianzi University Hangzhou China Department of Control Science and Engineering School of Astronautics Harbin Institute of Technology Harbin China

This paper presents the dynamic method for fault diagnosis based on the updating of Interval-valued belief structures (IBSs). The classical Jeffrey's updating rule and the linear updating rule are extended to the framework of IBSs. Both of them are recursively used to generate global diagnosis evidence with the form of Interval basic belief assignment (IBBA) by updating the previous evidence with the incoming evidence. The diagnosis decision can be made by global diagnosis evidence. In the process of evidence updating, the similarity factors of evidence are used to determine switching between the extended Jeffrey's and linear updating rules, and to calculate the linear combination weights. The diagnosis examples of machine rotor show that the proposed method can provide more reliable and accurate results than the diagnosis methods based on Dempster-Shafer evidence theory.

关键词： Fault diagnosis Accuracy Evidence theory Rotors Switches Reliability theory Real-time systems

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Automatic Cataract and Cholesterol Detection System Based on Recurrent Neural Network (RNN): A Comparison with Convolutional Neural Network (CNN) and Siamese Neural Network (SNN) 1

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19th International Conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Lie, Zener S. Widodo, Jatmiko A. F. Hanesi, Isabel K. P. Moningka, Matthew R. N. Mariana, Y. Astuti, Winda Akmeliawati, Rini Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Product Design program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Robotics and Automation Research Group School of Electrical and Mechanical Engineering The University of Adelaide North Terrace Campus Adelaide Australia

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

In Indonesia, the majority of blindness cases, approximately 81%, are attributed to cataract. Additionally, 35% of the population has higher cholesterol levels than the standard normal range. The lack of specialized medical equipment, such as the slit-lamp (Ruparelia in Dalhousie Med J 49(1), [1]) bio-microscope, and trained professionals, especially in rural areas, make it difficult to detect these conditions through eye examinations. To address this issue, a supervised machine learning method, the Recurrent Neural Network (RNN), has been utilized to create an iris scanner system that can identify cataract and high cholesterol in a patient. This system will classify eyes into three categories, i.e., cataract, high cholesterol, and normal iris. The computer simulations indicate that this technique is more accurate and requires less training time compared to the existing Convolutional Neural Network (ANN) and Siamese Neural Network (SNN)-based method, while achieving 100% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Vector-Logical In-Memory Simulation of Faults as Truth Table Addresses

Vector-Logical In-Memory Simulation of Faults as Truth Table...

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2024 IEEE East-West Design and Test Symposium, EWDTS 2024

作者： Hahanov, Vladimir Litvinova, Eugenia Hahanova, Hanna Chumachenko, Svetlana Davitadze, Zaza Hahanova, Irina Kulak, Heorhii Ponomarova, Veronika Abdullayev, Vugar Hacimahmud Kharkiv National University of Radio Electronics Design Automation Department Kharkiv Ukraine Batumi Shota Rustaveli State University Department of Computer Sciences Batumi Georgia Azerbaijan State Oil and Industry University Computer Engineering Department Baku Azerbaijan

ISBN: (纸本)9798331515768

A vector-logical mechanism to faults as addresses simulation on smart data structures that eliminate the simulation algorithm of test sets to obtain a testing map for logic functionality is proposed. Smart data structures are represented by a logical vector and its derivatives as truth tables and matrices. The testing map is a matrix whose coordinates are determined by the combinations of all logical faults detected on the binary sets of the exhaustive test. The construction of the testing map is focused on the architecture of in-memory computing based on read-write transactions, which makes the simulation mechanism economical regarding simulation time and energy consumption due to the absence of a central processor. The logic vector, as the only component of the input data, does not require synthesis into a technologically permitted structure of elements. Synthesizing smart data structures based on four matrix operations creates a fault-detected testing map for any logic. The proposed mechanism is focused on servicing SoC IP cores under the IEEE 1500 standard, which can be positively received by engineers in the EDA market. In terms of simplicity and predictability of the size of data structures and the absence of a simulation algorithm of test sets, the proposed mechanism has no analogues in design and industry tests. The introduction presents useful technological trends DAC 61 in the development of computing for the IT industry and academic science of tomorrow. The following areas are highlighted: in-memory computing, immersion computing, and AI computing, which are focused on energy saving and reducing computation time when providing services. © 2024 IEEE.

关键词： Vectors

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Use of Facial Expressions to Improve the Social Acceptance of Level 4 and 5 Automated Driving System Equipped Vehicles 30

Use of Facial Expressions to Improve the Social Acceptance o...

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30th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2022

作者： Marceddu, Antonio Costantino Sini, Jacopo Montrucchio, Bartolomeo Violante, Massimo Politecnico di Torino Department of Control and Computer Engineering Turin Italy

ISBN: (数字)9789532901177

ISBN: (纸本)9789532901177

According to the World Health Organization (WHO), more than one million people die yearly from car accidents. At the same time, between 20 and 50 million people suffer non-fatal injuries, which can also lead to permanent disabilities. Recently, vehicles equipped with SAE level 3, 4, and 5 Automated Driving System (ADS) have become one of the hottest topics in the automotive industry. In fact, their main expected benefit is that they could significantly reduce the number of road accidents. Their actual success will depend on how people react to their introduction: considering that the absence of the steering wheel and pedals is possible for levels 4 and 5, will people trust these advanced forms of driving automation? In this regard, the authors of this paper have proposed two different ideas. The first, which can be implemented during the proactive phase, consists in calibrating the driving algorithm of the vehicle based on the volunteers' reaction to simulations of common driving situations. The second, which can be implemented during the reactive phase, consists in dynamically adapting the driving style of the vehicle based on the average feeling inside the vehicle. Both of these ideas could help improve social acceptance and facilitate the transition to vehicles equipped with SAE level 4 and 5 ADS. © 2022 University of Split, FESB.

关键词： Face recognition

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Triplet losses-based matrix factorization for robust recommendations

Triplet losses-based matrix factorization for robust recomme...

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2022 International Conference on Information and Knowledge Management Workshops, CIKM-WS 2022

作者： Giobergia, Flavio Department of Control and Computer Engineering Politecnico di Torino Turin Italy

Much like other learning-based models, recommender systems can be affected by biases in the training data. While typical evaluation metrics (e.g. hit rate) are not concerned with them, some categories of final users are heavily affected by these biases. In this work, we propose using multiple triplet losses terms to extract meaningful and robust representations of users and items. We empirically evaluate the soundness of such representations through several "bias-aware" evaluation metrics, as well as in terms of stability to changes in the training set and agreement of the predictions variance w.r.t. that of each user. © 2022 Copyright for this paper by its authors.

关键词： Recommender systems

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