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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications VII"

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Enhancing Battery Management systems with Machine Learning and 5G Connectivity beyond Maritime Transport Software Architectures 15

Enhancing Battery Management Systems with Machine Learning a...

引用

15th International conference on Communications, COMM 2024

作者： Paraschiv, Constantin-Laurentiu Tufeanu, Larisa Mihaela Vochin, Marius-Constantin POLITEHNICA Bucharest Faculty of Electronics Department of Telecommunications Bucharest Romania

ISBN: (纸本)9798350355956

The growing advancement in battery technology has revolutionized various domains and services, notably transportation and energy storage. This research focuses on improving battery management systems (BMS) for electric vehicles (EV) and hybrid vehicles (HEV) by leveraging advanced machine learning models and 5G connectivity. This study proposes an integrated software architecture designed for both industrial and road applications, aiming to optimize battery performance and safety by analyzing collected data and predicting the anomaly events by training Random Forest and Gradient Boosting machine learning (ML) models. Throughout the research, key battery performance parameters and their normal operating ranges, such as voltage, current, temperature, charge/discharge rates, internal Resistance and State of Charge (SoC) are analyzed, according to the official regulations and utilized in Python scripting. The Python script implemented simulates battery operations by updating sensor values and logging incidents with factors such as latency, jitter, and encryption time. The script trains and compares machine learning models to determine the most accurate one for anomaly detection. The final part of the script simulates battery performance under different connectivity modes (5G Non-Standalone (NSA) and 5G Standalone (SA)) to assess the impact on data transmission and real-time monitoring. Ultimately, obtained results demonstrate not only advanced, sustainable, mobility solutions by improving the safety and performance of Battery Management systems in electric and hybrid vehicles, but also the importance of leveraging cutting-edge technologies (such as ML) and connectivity (5G). © 2024 IEEE.

关键词： Hybrid vehicles

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Directional bending sensing characteristics of in-line Mach-Zehnder interferometer based on asymmetrical twin-core fiber 12

Directional bending sensing characteristics of in-line Mach-...

引用

conference on advanced sensor systems and applications XII

作者： Yang, Huiping Yan, Binbin Luo, Yanhua Yang, Liwei Wang, Kuiru Yuan, Jinhui Sang, Xinzhu Peng, Gang-ding Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Shanghai Univ Shanghai Inst Adv Commun & Data Sci Key Lab Specialty Fiber Opt & Opt Access Networks Joint Int Res Lab Specialty Fiber Opt & Adv Commu Shanghai 200444 Peoples R China Univ New South Wales Sch Elect Engn Photon & Opt Commun Sydney NSW 2052 Australia China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9781510657090;9781510657083

We propose and experimentally demonstrate a simple directional bending sensor based on an in-line Mach-Zehnder interference structure of the single mode fiber (SMF)-multimode fiber (MMF)-asymmetrical twin-core fiber (ATCF)-MMF-SMF. Due to the asymmetric structure in twin-core fiber, this sensor can discriminate different bending directions. The interference spectrum shifts with the change of curvature. Furthermore, the sensor exhibits a linear response with a maximum bending sensitivity of 33.48 and -38.72 nm/m(-1) in the curvature range from 0 to 3.01 m(-1) for bending directions at -x (180 degrees) and +x (0 degrees) directions, respectively. The proposed fiber sensor has great potential in the future Internet of Things (IoT) due to its advantages, such as low cost, compactness, and high bending sensitivity.

关键词： Asymmetric twin-core fiber Optical fiber sensor Mach-Zehnder interferometer Directional bending sensing

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UKF-Based Multi-sensor Data Processing Algorithm for SLAM 9

UKF-Based Multi-sensor Data Processing Algorithm for SLAM

引用

9th International conference on Control Science and systems Engineering, ICCSSE 2023

作者： Xu, Kun Zhang, Boheng Li, Jiangang Wang, Guizhong Sun, Mingjian School of Information Science and Engineering Harbin Institute of Technology Weihai China Harbin Institute of Technology Department of Control Science and Engineering Harbin China Co. Ltd. Shenzhen China Hebei Key Laboratory of Grid Advanced Measurement Monitor Jinyanhagong Industrial Technology Research Institute Zhangjiakou China

ISBN: (纸本)9798350339055

Simultaneous localization and mapping (SLAM) algorithm is widely used in unmanned and robotic applications. SLAM usually uses sensor data, which are from the LiDAR, the inertial measurement unit (IMU) and the wheeled odometry, to build maps of the surrounding environment. However, the motion distortion of LiDAR data and the error accumulation of wheeled odometry can affect the accuracy of the front-end odometry of the SLAM algorithm, which can lead to serious errors in the maps created. Therefore, in order to solve the problem of poor map construction results of SLAM algorithm due to various types of sensor errors. we propose the traceless Kalman filtering based (UKF-based) multi-sensor data processing algorithm. Firstly, the proposed algorithm uses the UKF to fuse IMU and wheeled odometry data to reduce the cumulative error of wheeled odometry. And then the algorithm uses IMU and corrected wheeled odometry data and combines with the motion states of the robot to solve the motion distortion problem of LiDAR data. Finally, we verified the effectiveness of the proposed algorithm for wheeled odometry data correction in Gazebo simulation environment and real environment experiments, and compared the mapping effectiveness of SLAM by using the original data and the data processed by our algorithm. The experimental results show that the map construction effect of SLAM algorithm is significantly improved when using the sensor data processed by the algorithm. Experimental validation on the robot shows that the proposed algorithm has universal applicability and good compatibility. © 2023 IEEE.

关键词： sensor data fusion

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Deterministic Multi-sensor Measurement-adaptive Birth using Labeled Random Finite Sets 26

Deterministic Multi-sensor Measurement-adaptive Birth using ...

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26th International conference on Information Fusion, FUSION 2023

作者： Bondarchuk, Jennifer Trezza, Anthony Bucci, Donald J. Advanced Technology Laboratories Lockheed Martin Cherry HillNJ United States

ISBN: (纸本)9798890344854

Measurement-adaptive track initiation remains a critical design requirement of many practical multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birth density using measurements from multiple sensors. A truncation procedure has also been provided that leverages a stochastic Gibbs sampler to truncate the birth density for scalability. In this work, we introduce a deterministic herded Gibbs sampling truncation solution for efficient multi-sensor adaptive track initialization. Removing the stochastic behavior of the track initialization procedure without impacting average tracking performance enables a more robust tracking solution more suitable for safety-critical applications. Simulation results for linear sensing scenarios are provided to verify performance. © 2023 International Society of Information Fusion.

关键词： Target tracking

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Tactile Image Processing and Shape Detection Method of Array Tactile sensor for Surgical Robot Based on Deep Learning 17th

Tactile Image Processing and Shape Detection Method of Array...

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17th International conference on Intelligent Robotics and applications, ICIRA 2024

作者： Niu, Xinyao Ju, Feng Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789819607761

The rapid evolution of modern medical technology has propelled surgical robots to the forefront of surgical innovation. These robots offer unprecedented accuracy and safety in procedures, empowering surgeons with advanced technical capabilities to tackle complex medical challenges. In tumor treatment, surgical robots excel in precise tumor tissue resection, minimizing damage to healthy tissue and reducing surgical risks. However, accurately identifying tumor boundaries and depth remains challenging due to limitations of traditional palpation methods. This study aims to develop a deep learning model for real-time tumor boundary detection within surgical robot systems. By processing array tactile sensor image data, this model will enable precise boundary and depth detection, enhancing surgical robot recognition and operational capabilities. This advancement promises improved surgical success rates, patient survival rates, and reduced risks of complications, ultimately enhancing patient treatment outcomes and quality of life. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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A Simplified Open-loop Control Strategy for Integrated Shading and Lighting systems Using Machine Learning 17

A Simplified Open-loop Control Strategy for Integrated Shadi...

引用

17th conference of International-Building-Performance-Simulation-Association (IBPSA)

作者： Xie, Jiarong Sawyer, Azadeh Omidfar Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781775052029

Conventional integrated shading and lighting systems are usually sensor-dependent, which could entail excessive cost and labor associated with sensor installation, calibration, and maintenance. advanced systems use daylight modeling to eliminate the use of physical sensors. However, real-time daylight simulation can be computation-heavy, leading to a slow response of the system. This paper proposed a data-driven method for integrated shading and lighting control, employing machine learning models developed from pre-simulated data to predict real-time daylighting and control the blind and lighting accordingly. Verification using climate-based daylight simulation with a case study showed that the method prevented 94.7% of annual glare and reduced lighting use by 64%. The study will contribute to the development of effective daylight-linked control systems for industrial applications.

关键词： Machine learning

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DL-Based Secure Wearable Scheme for Remote Patient Data Monitoring in Healthcare 4.0 4

DL-Based Secure Wearable Scheme for Remote Patient Data Moni...

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

作者： Ramoliya, Fenil Darji, Krisha Gupta, Rajesh Kakkar, Riya Tanwar, Sudeep Kumari, Aparna Inst. of Technology Nirma University Dept. of Computer Sci. and Engg. Ahmedabad382481 India

ISBN: (纸本)9798350306118

The paper articulates an exploration into the integration of wearable devices for remote patient data monitoring, which marks a transformative shift in healthcare practices. This transition is underscored by user-centric interfaces and applications, enabling convenient remote monitoring. However, the escalating concern of patient data vulnerability to network breaches presents a substantial impediment. In response, we introduce the proposed scheme that harnesses Machine Learning (ML) and Deep Learning (DL) techniques. The main objective of the proposed scheme is to meet the exacting requirements of healthcare systems, aiming to fortify data integrity and confidentiality. Within this context, the proposed scheme unveils a sophisticated DL-driven detection model, adeptly tailored for discerning and countering data manipulation endeavors through a nuanced multi-label classification paradigm. Notably, the emphasis lies on safeguarding patient data privacy and security, which is paramount in today's information-rich landscape. The narrative seamlessly interweaves elements of wearable technology, sensor networks, advanced DL capabilities, and robust security measures, resulting in an innovative scheme. Finally, the proposed scheme is evaluated against various metrics such as accuracy and loss curve and F1, recall, and precision comparison. © 2023 IEEE.

关键词： sensor networks

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A Robust Framework to Secure IoT sensor Data using Elliptic Curve Cryptography based CP-ABE and Secret Sharing Scheme 2

A Robust Framework to Secure IoT Sensor Data using Elliptic ...

引用

2nd International conference on Information and Communication Technology, ICICT 2024

作者： Bushra, Anika Paul, Mahit Kumar Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (纸本)9798331508227

The growth of the Internet of Things (IoT) sector has ushered in an unprecedented era of connectedness, enabling a profusion of applications ranging from smart homes to industrial automation. Researchers predict that by 2030, there will be about 50 billion IoT devices and gadgets globally, completely changing how we interact with our environment. However, because these interconnected systems are vulnerable to many cyber attacks, the rise of IoT devices has also raised worries about data security and privacy. This paper investigates the potential of cryptographic approaches to mitigate IoT data security issues. It proposes an all-encompassing framework to protect IoT data from various threats and vulnerabilities. The framework leverages the robustness of the advanced Encryption Standard (AES) as the primary data encryption technology. Additionally, it integrates ciphertext-policy attribute-based encryption (CP-ABE) in conjunction with Elliptic Curve Cryptography to enable fine-grained access control, ensuring that only authorized entities can access specific IoT data. Furthermore, to fortify the encryption process, the framework implements a secret sharing method for the encryption key, distributing the key across multiple entities, reducing the risk of a single point of failure. The suggested framework seeks to improve the security posture of IoT systems by integrating these cryptographic algorithms and guaranteeing the confidentiality, integrity, and accessibility of data in IoT contexts. ©2024 IEEE.

关键词： Anonymity

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Multi-Vehicle System Localization by Distributed Moving Horizon Estimation over a sensor Network with Sporadic Measurements

Multi-Vehicle System Localization by Distributed Moving Hori...

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IEEE conference on Control Technology and applications (CCTA)

作者： Venturino, Antonello Bertrand, Sylvain Maniu, Cristina Stoica Alamo, Teodoro Camacho, Eduardo F. Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Gif Sur Yvette France Univ Paris Saclay ONERA Traitement Informat & Syst F-91123 Palaiseau France Univ Seville Dept Ingn Sistemas & Automat Camino Descubrimientos Seville 41092 Spain

ISBN: (数字)9781665473385

ISBN: (纸本)9781665473385

This paper proposes a Distributed Moving Horizon Estimator (DMHE) for the Multi-Vehicle system localization problem using sensor Networks with sporadic measurements. Due to its capability to efficiently exploit environmental information via constraints, the proposed DHME technique is well-suited to better estimate the system state when measurements are available at time instants a priori unknown. Indeed, the use of output constraints can contribute to locally improve estimation accuracy, especially when dealing with sporadic measurements and biased sensors data. A realistic case study is proposed within the Robot Operating System framework and Gazebo to localize a Multi-Vehicle System using an inexpensive sensor Network with low-computation capabilities. A comparative campaign simulation is performed to confirm the effectiveness of the proposed DMHE algorithm in terms of accuracy, computation time, and constraints handling with respect to existing results.

关键词： Location awareness Constraint handling Operating systems Computational modeling Estimation Robot sensing systems Time measurement

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LiFi-CFI: Light-weight Fine-grained Hardware CFI Protection for RISC-V 1

LiFi-CFI: Light-weight Fine-grained Hardware CFI Protection ...

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1st IEEE International conference on Design, Test and Technology of Integrated systems, DTTIS 2023

作者： Roodsari, Mahboobe Sadeghipour Sheikhshoaei, Fatemeh Maunero, Nicolo Prinetto, Paolo Navabi, Zain University of Tehran School of Electrical and Computer Engineering Tehran Iran Imt School for Advanced Studies Lucca Italy Politecnico di Torino Department of Automation and Information Turin Italy

ISBN: (纸本)9798350316957

In today's open-source industry, RISC-V based embedded devices are used in various applications where security is critical. RISC-V processors are vulnerable to many runtime attacks, including powerful code reuse and code injection in jump/return-oriented programming (JOP/ROP), known as control flow attacks (CFA). For this, several security techniques have been proposed to protect embedded devices from CFA, and one of the most secure categories is Control Flow Integrity (CFI). Alternative techniques consider the tradeoff between runtime hardware overhead and security protection. In particular, hardware-based techniques have attracted more attention in recent years because they consume less power and have less runtime overhead, which is critical in embedded systems. Considering these constraints, we have proposed a novel fine-grained technique for jump-oriented programming, where the required memory is replaced with a short Programmable Array Logic (PAL) to reduce runtime and hardware overhead. Also, a low-overhead shadow stack is proposed for return-oriented programming to reduce the required memory capacity compared to the traditional shadow stack implementation by handling the recursive function calls. The proposed Architecture called LiFi-CFI, is a lightweight hardware monitoring solution that can be attached to any IoT-class soft processor. LiFi-CFI not only reduces power consumption and hardware overhead compared to other advanced solutions but also maintains the security guarantees of the main CFI solution. It is a compact solution as a countermeasure with less than 1% hardware resource utilization for Xilinx Artix7 and 0.5% runtime overhead. © 2023 IEEE.

关键词： Embedded systems

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