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Intent-Based Security for Functional Safety in Cyber-Physical Systems

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2024年第2期12卷 615-630页

作者： Tomur, Emrah Bilgin, Zeki Gulen, Utku Soykan, Elif Ustundag Karacay, Leyli Karakoc, Ferhat Ericsson Res TR-34398 Istanbul Turkiye Arcelik TR-34425 Istanbul Turkiye

We propose a novel intent-based method to prevent security attacks on the safety functions in cyber-physical systems used in smart manufacturing. Context information about a cyber-physical system is collected from various sensors including non-safety sensors measuring device temperature, motor rotation speed, or instantaneous power consumption of machines. Such contextual information along with operational and business intent of the system under consideration are then used to check whether the current situation is indeed an emergency situation or a normal situation. Unlike the conventional safety systems that only rely on raw sensor data and safety protocol status packets from safety sensors, which might be spoofed and/or modified, decision on the safety situation in our method is intelligently made by comparing aggregated sensor information from the cyber-physical system and its environment for compliance with pre-configured operational intents that define the normal safe and secure operation of the system. We also show how to integrate Machine Learning (ML) and Artificial Intelligence (AI) into the proposed method for efficient and automated analysis of both intents and aggregated context information to make more intelligent decisions in execution of functional safety protocols. Our proposed AI/ML integration approach also enables the prediction of safety critical situations before they occur. The proposed method aims to prevent unnecessary switching to fail-safe mode causing insecure system states such as emergency doors opening or system halting in normal situations. In addition, it prevents sticking in non-safe states and not switching to fail-safe mode in real emergencies which could cause hazard on device and/or people.

关键词： Safety sensors Security Cyber-physical systems Automation sensor systems Protocols functional safety industrial automation intent-based security industrial internet of things machine learning smart manufacturing

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Fiber-optic differential pressure sensor for deep-sea large pressure background conditions

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MEASUREMENT 2025年 253卷

作者： Li, Xujin Yang, Yiguang Liu, Haodong Cao, Xuan Zhang, Weihong Yuan, Yibo Hu, Haofeng Xu, Degang Liu, Tiegen Li, Hongzhi Liu, Yan Yao, Jianquan Laoshan Lab Qingdao 266237 Peoples R China MNR Key Lab Ocean Observat Technol Tianjin 300112 Peoples R China Qilu Univ Technol Inst Oceanog Instrumentat Shandong Acad Sci Qingdao 266061 Peoples R China Qingdao Ocean Sci & Technol Ctr Qingdao 266237 Peoples R China Tianjin Univ Tianjin 300074 Peoples R China Shandong Univ Sci & Technol Qingdao 266590 Peoples R China

In this paper, a fiber-optic differential pressure sensor for marine sediment measurements is designed with a spring-diaphragm-spring multi-elastic structure (SDSMS), which enables high-precision differential pressure measurements under deep-sea large-pressure background conditions, and effectively solves the problems of overrange damage and large-pressure measurements of sensors. We performed a 10Mpa pressure test, a micropressure test and a temperature sensitivity test for the sensor respectively. The sensitivity of the sensor measurements over the +/- 500kpa differential pressure range was 6.6095 pm/kpa and 6.575 pm/kPa, with linearity of 99.986 % and 99.821 %, respectively. This is our first attempt at micro-pressure measurement with fiber-optic differential pressure sensors in the context of large pressures, which achieves differential pressure measurements at the kpa level.

关键词： Fiber optic differential pressure sensor Deep-sea large background pressure Micro pressure detection Ultrahigh-precision Over range protection

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Enhanced Connectivity for Non-Critical In-Vehicle Systems Using EnRF24L01

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IEEE sensors JOURNAL 2025年第7期25卷 11955-11962页

作者： Kumar, J. N. V. R. Swarup Venkateswararao, Kuna Ghugar, Umashankar Bhoi, Sourav Kumar Sahoo, Kshira Sagar GITAM Deemed Univ GITAM Sch Technol Dept CSE Visakhapatnam 530045 Andhra Pradesh India Chandigarh Univ Univ Inst Engn UIE Dept CSE Mohali 140413 Punjab India Parala Maharaja Engn Coll Dept CSE Berhampur 761003 Odisha India SRM Univ AP Dept CSE Amaravati 522240 India

There has been a significant paradigm shift from wired to wireless technology in in-vehicular networks. This shift is driven by the need for greater scalability, cost-effectiveness, and flexibility. In the automotive industry, traditional wired protocols such as the local interconnect network (LIN) and media-oriented systems transport (MOST) for non-critical systems (NCSs) add complexity to installation and maintenance, incur higher material costs, and offer limited scalability and mobility. NCSs, such as infotainment and weather forecast systems, do not require low latency and do not impair vehicle function when unavailable. This article presents an advanced methodology for enhancing connectivity in noncritical in-vehicular networks using Nordic Semiconductor's (nRFs) enhanced-nRF24L01 (EnRF24L01) module. The EnRF24L01 module is the nRF24L01 module that incorporated the sensor-Medium Access Control (S-MAC) algorithm for energy-efficient communication. The proposed method enables seamless communication between NCSs using a tree-based primary and secondary architecture, where the primary is the actuator and the secondary is the sensor node. To optimize energy efficiency using synchronized sleep/wake schedules, reduce power consumption, and enhance scalability, the S-MAC protocol was incorporated. Comprehensive experiments were conducted in simulated environments using optimized network engineering tool (OPNET) and Proteus Circuit Simulators, analyzing critical performance metrics: latency, jitter, throughput, packet delivery ratio (PDR), and energy efficiency. The results indicate that the proposed method supports a greater number of nodes with enhanced data transmission rates and operates at lower voltages, thereby extending the communication range and reducing overall power consumption. Additionally, hardware simulation results demonstrate the successful integration of EnRF24L01 modules with Arduino for wireless data transmission, showing significant improvements i

关键词： sensors Wireless sensor networks Wireless communication Main-secondary Communication system security Safety Scalability Automotive engineering Intelligent sensors Energy efficiency In-vehicle networks local interconnect network (LIN) primary and secondary media-oriented systems transport (MOST) non-critical systems (NCSs) sensors wireless network

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Siamese Neural Network and Multimodal Data Fusion Approach for Small-Sample Learning in Industrial Soft sensor Modeling

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IEEE sensors JOURNAL 2024年第20期24卷 33763-33777页

作者： Zhao, Yuchen Liu, Zhe Yan, Feng Yang, Chunjie Cao, Yang Bao, Yaoyao Mao, Jiayun Lou, Siwei Xiao, Hang Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Alibaba Cloud Intelligence Grp Hangzhou 310027 Peoples R China

In industrial scenarios, soft sensor modeling often faces the challenge of underfitting due to limited available data. Therefore, in the field of industrial soft sensing, it is crucial to develop solutions for regression problems based on small-sample learning. One potentially feasible approach is to pair the data used for modeling to generate a larger set of data pairs, and then utilize Siamese neural networks (SNNs) to extract additional information from the paired data, thereby addressing the underfitting issues. Moreover, the integration of data from multiple sources can also help alleviate the problem of underfitting. Based on these ideas, this article proposes an SNN-based multimodal data fusion framework (SNN-MMDFF) for tackling the challenges of small-sample learning in industrial soft sensor modeling. The SNN-MDDFF employs end-to-end multimodal feature extraction (MMFE) networks to extract multimodal features from the paired multimodal data sequences, which are then employed to predict the soft sensing target. A three-stage training strategy, which incorporates hierarchical metric learning and component-by-component training approaches, is implemented to train this framework. The SNN-MMDFF is validated in a case study on cement clinker free calcium oxide (f-CaO) content soft sensing, which demonstrates superior performance and robust modeling stability.

关键词： Feature extraction free calcium oxide (f-CaO) metric learning Siamese neural networks (SNNs) soft sensors free calcium oxide (f-CaO) metric learning Siamese neural networks (SNNs) soft sensors

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BSA/PEI/GOD modified cellulose nanocrystals for construction of hydrogel-based flexible glucose sensors for sweat detection

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JOURNAL OF MATERIALS CHEMISTRY B 2025年第8期13卷 2705-2716页

作者： Zhou, Tianjun Li, Pan Sun, Yujie Wang, Wenxiang Bai, Liangjiu Chen, Hou Yang, Huawei Yang, Lixia Wei, Donglei Ludong Univ Collaborat Innovat Ctr Shandong Prov High Performa Sch Chem & Mat Sci Key Lab High Performance & Funct Polymer Univ Shan Yantai 264025 Peoples R China

With the miniaturization, integration and intelligence of sweat electrochemical sensor technology, hydrogel flexible sensors have demonstrated immense potential in the field of real-time and non-invasive personal health monitoring. However, it remains a challenge to integrate excellent mechanical properties, self-healing properties, and electrochemical sensing capabilities into the preparation of hydrogel-based flexible sensors. The utilization of CBPG (cellulose nanocrystals (CNCs)@bovine serum albumin (BSA)@polyethyleneimine (PEI) glucose oxidase (GOD) nanomaterial) as both an enhancing phase and sensor probe within a hydrogel matrix, with poly(vinyl alcohol) (PVA) serving as the primary network constituent, has been proposed as a non-invasive technique for monitoring trace glucose levels in sweat. In this study, BSA was initially attached to CNCs through electrostatic interactions. To further boost the surface active sites of the nanomaterial (CNCs@BSA), PEI was grafted onto the nanomaterial surface. The resulting CNC@BSA@PEI nanomaterials were then used as carriers for GOD. The prepared hydrogel exhibited good self-healing properties (87.5%) and excellent breaking strength (0.8 MPa), effectively converting glucose stimulation in human sweat into electrical output. The sensor had a detection range of 1.0-100.0 mu M and a detection limit as low as 0.9 mu M. Due to its ability to specifically recognize trace glucose levels in sweat, the CBPG-PVA sensor can perform highly selective, flexible, and reliable real-time monitoring of human sweat, offering significant potential for wearable biofluid monitoring in personalized health applications.

关键词： Glucose oxidase

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Laser Thermochemical Synthesis of MXene/Graphene Heterostructure for a Highly Sensitive Flexible Pressure sensor

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ACS APPLIED ELECTRONIC MATERIALS 2024年第7期6卷 5117-5125页

作者： Li, Yunfan Yang, Zezhou Chen, Xiao Zhang, Shizhuo Xu, Shuqi Li, Peilong Yi, Longju Liu, Feng Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Hubei Peoples R China Wuhan Univ Inst Technol Sci Wuhan 430072 Hubei Peoples R China

Laser-induced graphene (LIG) shows broad application prospects in flexible pressure sensors due to its adjustable electrical properties, good economy, and roll-to-roll compatibility. Incorporating an appropriate nanomaterial into LIG is an effective method to significantly improve its pressure-sensitive properties. In this study, we report an MXene nanoengineered LIG for highly sensitive flexible piezoresistive sensors. The photochemically synthesized MXene-derived nanosheets are anchored in the porous network of LIG to form a MXene/graphene heterostructure (LIMG) by in situ coconversion of MXene/Polyamide acid (PAA) composite under laser irradiation. Benefiting from the conductive paths created by MXene nanosheets in LIG matrix and the stable chemical bonding of MXene-LIG interfaces, the LIMG sensor exhibits a sensitivity of 20 kPa(-1), which is 567% higher than the LIG sensor. Meanwhile, the sensor has a wide range of 80 kPa, fast response/recovery time of 42/28 ms, and excellent stability over 4000 cycles. In practical applications, the LIMG sensor effectively monitors human physiological signals, such as voice, pulse, and respiration, proving its broad prospects in wearable health monitoring. Furthermore, the preparation of two-dimensional/three-dimensional (2D/3D) heterostructures by one-step laser coconversion is expected to promote the development of nanomaterial synthesis technology.

关键词： high sensitivity flexiblepiezoresistive sensor MXene laser-induced graphene heterostructure

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Framework for a practical and cost-effective IoT-enhanced structural health monitoring and damage diagnostics system with digital twinning

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2025年 1-26页

作者： Huang, Jack Broekman, Andre Markou, George Chen, Hua-Peng Univ Pretoria Dept Civil Engn Pretoria South Africa Cyprus Univ Technol Dept Civil Engn & Geomat CY-3036 Limassol Cyprus East China Jiaotong Univ Sch Transportat Engn Nanchang 330013 Jiangxi Peoples R China

Structural Health Monitoring (SHM) has emerged as a viable alternative to traditional visual and non-destructive assessment methods for civil infrastructure. The integration of sensor systems, the Internet of Things (IoT), and advanced data processing has further digitised SHM, leading to the development of Digital Twin (DT) technology, enabling dynamic, real-time simulations for proactive risk prediction and asset management. However, many existing DT-based SHM systems are costly, complex, and resource-intensive, limiting their practicality for small-scale implementations. This study investigates the feasibility of a practical and cost-effective SHM framework enhanced by DT technology for civil infrastructure. The hardware system explored two low-cost displacement sensors: a potentiometer contact sensor and an infrared non-contact sensor. During the static load testing, the potentiometer demonstrated high accuracy and stability, whilst the infrared sensor, despite higher noise, was effective for submillimetre measurements. These sensors were integrated with an IoT-enabled Arduino Nano 33 microcontroller for remote access via the cloud platform. The software system, "ReConTwin", developed using open-source resources, provides near real-time updates, analysis, and damage diagnosis through an automated post-processing system. The calibrated DT replicated the force-displacement response, accurately estimated the applied load, and closely predicted mid-span strain and crack formations of a Reinforced Concrete (RC) beam specimen subjected to short-term static three-point bending loads in a controlled laboratory setting. The user-friendly design and compatibility with standard commercial computers enhance the accessibility and feasibility of the proposed DT-SHM framework, making it a promising scalable solution for widespread adoption in real-world civil infrastructure applications.

关键词： Structural health monitoring Digital twin IoT sensors Low-cost alternative Damage diagnostics

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The Application of Wearable sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

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sensors 2023年第18期23卷 7667页

作者： Wei, Suyao Wu, Zhihui Nanjing Forestry Univ Coll Furnishings & Ind Design Nanjing 210037 Peoples R China Nanjing Forestry Univ Coinnovat Ctr Efficient Proc & Utilizat Forest Res Nanjing 210037 Peoples R China

The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

关键词： wearable sensor machine learning disease rehabilitation rehabilitation training

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Highly sensitive microdisk laser sensor for refractive index sensing via periodic meta-hole patterning

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NANOPHOTONICS 2025年第8期14卷 1193-1202页

作者： Jeong, Haerin Park, Nu-Ri Park, Byoung Jun Kim, Moohyuk Kim, Jin Tae Kim, Myung-Ki Korea Univ KU KIST Grad Sch Converging Sci & Technol Seoul 02841 South Korea Elect & Telecommun Res Inst ETRI Quantum Technol Res Dept Daejeon 34129 South Korea Korea Inst Sci & Technol KIST Ctr Quantum Informat Seoul 02792 South Korea

Microdisk lasers have emerged as compact on-chip optical sensors due to their small size, simple structure, and efficient lasing capabilities. However, conventional microdisk laser sensors face challenges in enhancing interactions with external analytes, as their energy remains predominantly confined within the laser material. In this study, we present a novel microdisk laser sensor incorporating periodic meta-hole patterning, designed to enhance external interaction while maintaining the integrity of the whispering gallery mode (WGM). Numerical simulations show that in an InGaAsP microdisk laser (5 mu m diameter, 250 nm thickness), the WGM remains stable with periodic meta-holes (period a = 340 nm, diameter d < 0.4a), achieving a resonant wavelength near 1,500 nm. The inclusion of meta-holes led to a substantial improvement in sensitivity, reaching up to 100.8 nm/RIU - a 2.26-fold increase over nonpatterned microdisks. Experimental validation confirmed lasing in structures with a d/a ratio of 0.32, achieving a maximum sensitivity of 74.5 nm/RIU, which represents a 2.02-fold enhancement compared to nonpatterned designs. This advancement in microdisk laser design not only opens new possibilities for high-performance, miniaturized optical sensors but also holds significant potential for integration into next-generation on-chip sensing technologies.

关键词： microdisk laser sensor meta-hole patterning metasurface whispering gallery mode (WGM) on-chip optical sensor

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Ultrasensitive Flexible Strain sensor Made with Carboxymethyl-Cellulose-Anchored Carbon Nanotubes/MXene for Machine-Learning-Assisted Handwriting Recognition

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ACS APPLIED MATERIALS & INTERFACES 2024年第38期16卷 51447-51458页

作者： Cao, Junming Yuan, Xueguang Zhang, Yangan Wang, Qi He, Qi Guo, Shaohua Ren, Xiaomin Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

The combination of wearable sensors with machine learning enables intelligent perception in human-machine interaction and healthcare, but achieving high sensitivity and a wide working range in flexible strain sensors for signal acquisition and accurate recognition remains challenging. Herein, we introduced carboxymethyl cellulose (CMC) into a carbon nanotubes (CNTs)/MXene hybrid network, forming tight anchoring among the conductive materials and, thus, bringing enhanced interaction. The silicone-rubber-encapsulated CMC-anchored CNTs/MXene (CCM) strain sensor exhibits an excellent sensitivity (maximum gauge factor up to 71 294), wide working range (200%), ultralow detection limit (0.05%), and outstanding durability (over 10 000 cycles), which is superior to most of the recently reported counterparts also based on a conductive composite film. Moreover, the sensor achieves seamless integration with human skin with the help of a poly(acrylic acid) adhesive layer, successfully obtaining stable and clear waveforms with meaningful profiles from the human body. On this basis, we proposed and realized a novel in-air handwriting recognition method via extracting multiple features of high-quality strain signals assisted by deep neural networks, achieving a high classification accuracy of 98.00 and 94.85% for Arabic numerals and letters, respectively. Our work provides an effective approach for significantly improving strain sensing performance, thereby facilitating innovative applications of flexible sensors.

关键词： carboxymethyl cellulose carbon nanotube MXene strain sensor handwriting recognition deepneural network

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