Real-time Protection Research Articles

Real-time monitoring and protection strategies for dense granular flow spallation target in Accelerator-Driven System

An innovative concept of a high-power, gravity-driven, dense granular spallation target has been integrated into the prototype design of the China initiative Accelerator-Driven System (CiADS), which is envisioned as a promising nuclear demonstration facility aimed at converting long-lived fission products and minor actinides into short-lived isotopes. This spallation target system, bridging the proton beam accelerator and the subcritical reactor, plays a crucial role in ensuring safety control and facilitating dynamic operational strategies. In this study, we have developed a system design for an online detector array, incorporating several neutron fission chambers and thermocouples installed in the gap between the spallation target and the subcritical reactor to monitor operations. Furthermore, we have devised real-time monitoring and protection methods to tackle the challenges of measuring beam position and the blockage condition associated with granular flow, considering the distribution of neutron flux and heat deposition under abnormal irradiation conditions. Monte Carlo simulations have demonstrated the applicability and feasibility of the control system for the dense granular spallation target in the accelerator-driven system. The numerical results confirm that the proposed control system meets the requirements for stable measurement with acceptable accuracy.

Janus GO/BTA/PMMA Microcapsules for Biobased Self-Healing Anticorrosion Coatings with Ultrahigh Performance.

The giant reduction of the barrier properties due to self-healing microcapsules and the lack of real-time protection during the healing remained the main challenges in self-healing anticorrosion coatings. Herein, a facile strategy using Janus graphene oxide (GO) as a dense and flexible shell has been proposed to synergistically solve these challenges. Benzotriazole (BTA) was used to synthesize Janus GO at the oil-water interface, and Janus GO/BTA/poly(methyl methacrylate) microcapsules were prepared. Energy-dispersive X-ray spectroscopy, Fourier infrared spectroscopy, Raman spectroscopy, and ultraviolet spectrophotometer analysis confirmed the formation of a Janus GO structure with one surface hydrophilic and the other hydrophobic. The surface morphology of J-GO-capsules with a high GO coverage rate was observed by scanning electron microscopy. The high biobased content coating containing J-GO-capsules showed a low-frequency impedance value above 1010 as assessed by electrochemical impedance spectroscopy after being immersed in 3.5 wt % NaCl solution for 60 days. In addition, the low-frequency impedance values of the coating were maintained after being scratched due to the self-healing properties of the J-GO-capsules as well as the real-time protective effect of the BTA. Biobased coatings with the best overall properties among all of the self-healing anticorrosion coatings were prepared.

Advancing micro-electrometric techniques for the detection of organophosphate and carbamate residues using cricket cholinesterase.

The widespread use of organophosphate (OP) and carbamate (CM) pesticides requires efficient and cost-effective detection methods. This study introduces a micro-electrometric method using cricket cholinesterase (ChE) to detect OP and CM residues, providing a rapid and economical alternative to conventional chromatographic techniques. The parameters of the method, including the substrate concentration, incubation temperature, and incubation time, were optimized. By leveraging the sensitivity of cricket ChE to OP and CM inhibition, this approach translates enzyme inhibition into an electrical signal to quantify pesticide levels, achieving an impressive limit of detection (LOD) from 0.036 to 0.086 parts per million (ppm). This method demonstrated reproducibility and stability, making it suitable for field applications and on-site testing across various environmental matrices. This research represents a significant advancement in pesticide residue analysis with potential applications in the development of portable biosensor devices for real-time environmental monitoring and public health protection.

A rotor open-phase imbalance protection for variable speed pumped storage unit based on rotation transformation fault component ratio

Doubly-fed variable speed pumped storage unit (VSPSU) adopts slip ring injection AC excitation mode, and its rotor side structure is complex and prone to open-phase faults. It is very important to configure specific open-phase imbalance protection to ensure its safe operation. Traditional doubly-fed motors only stay in the state monitoring stage for rotor electrical imbalance, which cannot meet the requirements of large-capacity VSPSU for fast, real-time, and high-reliability protection. Therefore, this paper proposes a VSPSU rotor open-phase imbalance protection method based on the rotation transformation fault component ratio (FCR). The fundamental frequency component and fault characteristic component of the stator current space vector are extracted by rotation transformation and FCR is calculated, and then different protection action modes are divided according to the amplitude of FCR. To realize the engineering application, a microcomputer protection algorithm suitable for the proposed protection method is further proposed, and then a complete microcomputer protection scheme is formed. Simulation and experiment results show that the proposed protection scheme has high sensitivity and fast action speed. At the same time, the proposed algorithm can be directly embedded in the existing microcomputer protection device to meet the needs of engineering applications.

An enhanced multifunctional chitosan-based active packaging film reinforced with cinnamon essential oil encapsulating onion peel anthocyanins

The fabrication of bio-hybrid composites utilizing polymers, nanomaterials, essential oils, and other substances is gaining popularity due to its multi-functionality and application across various fields. Many industries today effectively employ innovative composite and multilayer materials by considering their benefits in several areas. In particular, the food industry uses innovative, multi-functional packaging materials based on biodegradability to accomplish multiple tasks. Many researchers have recently shown interest in an innovative and intelligent food packaging film with indicators for real-time food safety and protection. It is preferable to use anthocyanins, as an indicator in the active packaging film to track the freshness of food by observing color changes in the packaged film upon a shift in pH. In the current investigation, ethanol was used as a solvent to obtain anthocyanin-rich onion peel extracts (AROPE), using conventional and ultrasound-assisted extraction techniques, which were then used as an indicator in the packaging films. To develop antioxidant and pH-sensing films, anthocyanin-rich onion peel extract was blended into the chitosan and cinnamon essential oil matrix. Films displayed significant color variations in acidic and alkaline environments over short periods making them easy to perceive with the naked eye. For real-world applications, the fabricated films mechanical, structural, and physical characteristics were further explored. Because of their sensory qualities, colors, and antimicrobial benefits, these novel chitosan-based films could be ideal for food packaging to enhance food security.

An efficient and low cost realization of LoRa based real-time forest protection system

The forest is a natural habitat for a variety of fauna and flora, and helps to maintain the ecosystem equilibrium. However, wildfire incidents and deforestation lead to forest degradation. Moreover, most of the existing methods, to preserve the forest resources, are ineffective due to their large establishment cost, more power consumption, and poor coverage. This paper brings out a sustainable solution by developing a forest protection system (FPS) that uses internet of things (IoT) technology together with long range (LoRa) communication. The work focuses on the development of an IoT framework for the detection of any intrusion into the forest as well as the detection of fire incidents in the vicinity of the equipment. Powering the equipment through solar energy makes the system cost-effective. The system is examined in terms of acquisition of data from sensor nodes pertaining to forest protection, relaying the same to the cloud using LoRa wide area network (LoRaWAN) technology and analyzing using cloud based visualization tools. The developed system has been deployed at Eturnagaram Wildlife Sanctuary, Mulugu district, Telangana, India for validation in the forest environment. The obtained results have shown that the system has an accuracy of 97.14% for intrusion detection and 100% for fire detection.

Efficient Task-driven Video Data Privacy Protection for Smart Camera Surveillance System

As one of the most commonly used AIoT sensors, smart cameras and their supporting services, namely cloud video surveillance (CVS) systems, have brought great convenience to people’s lives. Recent CVS providers use different machine learning techniques to improve their services (regarded as tasks) based on the uploaded video. However, uploading data to the CVS providers may cause severe privacy issues. Existing works that remove privacy information could not achieve a high tradeoff between data usability and privacy, because the importance of information varies with the task. In addition, it is challenging to design a real-time privacy protection mechanism, especially in resource-constrained smart cameras. In this work, we design a task-driven and efficient video privacy protection mechanism for a better tradeoff between privacy and data usability. We use Class Activation Mapping to protect privacy while preserving data usability. To improve the efficiency, we utilize the motion vector and residual matrix produced during video codec. Our work outperforms the region of interest–based methods in data protection while preserving data usability. The attack accuracy drops 70%, while the task accuracy is comparable to those without protection (within ± 4%). The average protection frame rate of the High Definition video can exceed 16 fps+ even on a CPU.

Hourly near-ground NO2 concentration retrieval from geostationary satellite observations

Abstract. Nitrogen dioxide (NO2) is an important contributor to the formation of acid rain, photochemical smog and aerosol particles, which seriously endangers public health. At present, remote sensing of polar-orbiting satellites is a conventional means to obtain large-scale NO2 distribution, but it cannot capture the rapid change because of long revisit periods. The Advanced Himawari Imager (AHI) on the Himawari-8 geostationary satellite has the advantage of high time resolution, which makes it possible to realize near-real-time atmospheric monitoring. Here, based on the absorption characteristics of NO2 in infrared radiation, hourly near-surface NO2 concentrations are retrieved based on the brightness temperature from AHI and auxiliary information such as meteorology and aerosol. The results of 10-fold cross-validation show that NO2 estimations from satellite are in good agreement with in-situ measurements, and their determination coefficient (R2) can reach 0.79. Due to different emission and atmospheric diffusion conditions at different time, the model performance presents a diurnal variation of high accuracy in the noon and afternoon but low accuracy in the morning. Based on the retrieval dataset, it is found that high NO2 concentrations are mainly concentrated in the densely populated and industrial areas such as the North China area. In addition, NO2 pollution mainly occurs in autumn and winter, and the average NO2 concentration in winter is about 1.63 times that in summer in 2021. This study provides a new insight for satellite retrieval of NO2, which is of great significance for real-time pollution monitoring and public health protection.

Optimization of Network Security Intelligent Early Warning System Based on Image Matching Technology of Partial Differential Equation

In order to effectively avoid network information leakage and computer network paralysis, and improve the ability of computer network security early warning, it is necessary to design a computer network security intelligent early warning system based on network behavior. Security warning is considered as the second defense mechanism behind the firewall. It can monitor and warn the network without affecting the network performance, so as to provide real-time protection for external attacks, internal attacks and misoperations, and improve the network security. This paper designs an intelligent early warning system for network security based on partial differential equation image matching technology. The system adopts B/S development mode, and the server and browser are located in the campus network. Data fusion technology is used to assess network security, predict potential threats, and add new intrusion features to the feature database for subsequent use. In this paper, a target matching algorithm based on partial differential equation is proposed by using partial differential equation. The algorithm calculates the phase difference through the algebraic combination of orthogonal filter outputs. For scenes with continuous disparity changes, the error matching rate of this algorithm is lower than that of Michael Bleyer algorithm and vertical constraint algorithm. In general, the disparity map generated by this algorithm has high matching accuracy. The results show that the distribution of alarm information is reasonable and in line with the actual situation.

Real-Time Rotor Ground Fault Protection Based on the Convolution Integral for Synchronous Motors With Static Excitation

The detection of motor failures is a very active research topic within the literature. In particular, ground faults in wound rotors of synchronous motors have a relatively high failure rate since the rotor winding is exposed concomitantly to electrical, mechanical, and thermal stress cycles. Its detection depends on the grounding type of the electrical power system, and, in its early stages, it may present significant resistance. This manuscript provides a new rotor ground fault protection method based on the convolution integral of the neutral voltage that is measured by a single extra sensor. In addition, it is able to fill out the gaps of the current state-of-the-art, such as: provides a larger detection frontier for incipient faults; has low sensitivity for dynamic variances of the firing angle, allowing its utilization in cyclical applications; and can be implemented in power electronic controller systems with limited bandwidth. In this sense, mathematical modeling, simulations and experimental data are presented to support to the proposed method. Furthermore, it was implemented in the controller of static excitation converters for real-time protection and successfully tested with synchronous motors (2.4MW/3.3kV) of a cold rolling mill plant owned by ArcelorMittal Group.

Assessing and improving radiation safety in cardiac catheterization: a study from Cairo University Hospital

BackgroundCatheter laboratories are high-radiation exposure environments, especially during X-ray procedures like percutaneous transluminal coronary angioplasty and electrophysiological studies. Radiation exposure poses risks of stochastic (e.g., cancer) and deterministic (e.g., skin changes) effects. This study assessed radiation safety and health practices in a cardiac catheterization unit to optimize radiation safety. A cross-sectional study in Cairo University Hospital (March–September 2019) evaluated 700 patients and healthcare workers. Real-time radiation measurements, educational lectures, and radiation protection measures were implemented in three phases. Data on radiation exposure, procedures, and compliance were collected and analyzed.ResultsThe total procedure time and fluoroscopy time per cardiologist did not significantly differ between phases, but there was a statistically significant reduction in the mean total cumulative radiation doses between Phase I and Phase III for cardiologists (P = 0.013). Among nurses and technicians, there was no significant difference in radiation doses between the two phases. Significant correlations were found between operators' radiation doses, procedure time, and fluoroscopy time. Patients' radiation doses decreased significantly from Phase I to Phase III, with correlations between dose, procedure time, and gender. Compliance with radiation protection measures was suboptimal.ConclusionsCompliance with radiation safety standards in the cardiac catheterization unit at the Cairo University Hospital needs improvement. The study highlights the importance of adhering to radiation safety principles and optimizing protective measures to reduce radiation exposure for both patients and healthcare personnel. Despite low compliance, significant reductions in radiation doses were achieved with increased awareness and adherence to specific protection measures. Future efforts should focus on enhancing radiation safety protocols and organ-specific radiation impact assessments.

Assessing NI FPGA-based platform with MXIe interface for use in ITER hard real-time investment protection applications

Assessing NI FPGA-based platform with MXIe interface for use in ITER hard real-time investment protection applications

Adversarial Perturbation Prediction for Real-Time Protection of Speech Privacy

Adversarial Perturbation Prediction for Real-Time Protection of Speech Privacy

Wearable real-time multi-health parameter monitoring system

PurposeThe purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.Design/methodology/approachA multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.FindingsThe smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.Originality/valueThe smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

A Blockchain-Enabled Approach for Enhancing Synchrophasor Measurement in Smart Grid 3.0

Smart Grid 3.0 is the latest evolution of the smart grid and incorporates advanced computing and communication technologies. The synchrophasor communication system plays a critical role in wide-area measurement systems (WAMS) for real-time protection and control of power systems, supporting the objectives of Smart Grid 3.0. This system relies on synchrophasor communication technologies, where Phasor Measurement Units (PMUs) transmit synchrophasor data to Phasor Data Concentrators (PDCs) over the synchrophasor communication network. The communication infrastructure of this network is based on the TCP/IP protocol stack, which, unfortunately, is susceptible to cyberattacks, posing security threats such as data tampering and false data injection. These vulnerabilities undermine the intended benefits of synchrophasor applications in terms of situational awareness, observability, grid reliability, resiliency, and synchronized monitoring and control in the smart grid. To address these challenges, it is crucial to enhance the security, integrity, and confidentiality of synchrophasor data within the communication system. This paper proposes a blockchain-based synchrophasor communication system that preserves the security and integrity of synchrophasor data. In this paper, an architecture is proposed for a synchrophasor communication system based on blockchain technology. The proposed architecture aims to enhance the security and integrity of synchrophasor measurements. Furthermore, the architecture is developed as a peer-to-peer distributed blockchain network, leveraging the robustness of a distributed, decentralized, hierarchical PDC architecture. To evaluate the efficacy of the proposed architecture, two case studies, one using the IEEE 9 bus and the other using IEEE 14 bus systems are considered. Moreover, various challenges with potential solutions are also recommended. The proposed work is envisioned to contribute to the advancement of Smart Grid 3.0 by adopting blockchain technology for synchrophasor applications.

A Triple Real-Time Trajectory Privacy Protection Mechanism Based on Edge Computing and Blockchain in Mobile Crowdsourcing

With the rapid development of the Internet of Things (IoT) and the rapid popularization of 5G networks, the data that needs to be processed in Mobile Crowdsourcing (MCS) system is increasing every day. Traditional cloud computing can no longer meet the needs of crowdsourcing for real-time data and processing efficiency, thus, edge computing was born. Edge computing can be calculated at the edge of network so that greatly improve the efficiency and real-time performance of data processing. In addition, most of the existing privacy protection technologies are based on the trusted third parties. Therefore, in view of the semi-trustworthiness of edge servers and the transparency of blockchain, this paper proposes a triple real-time trajectory privacy protection mechanism (T-LGEB) based on edge computing and blockchain. Through combining the localized differential privacy and multiple probability extension mechanism, the T-LGEB mechanism is proposed to send the requests and data to the edge server in this paper. Then, through the spatio-temporal dynamic pseudonym mechanism proposed in the paper, the entire trajectory of task participants is divided into multiple unrelated trajectory segments with different pseudonymous identities in order to protect the trajectory privacy of task participants while ensuring high data availability and real-time data. Through a large number of experiments and comparative analysis on multiple real data sets, the proposed T-LGEB has extremely high privacy protection capabilities and data availability, and the resource consumption caused is relatively low.

Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning

Smart speaker voice assistants (VAs) such as Amazon Echo and Google Home have been widely adopted due to their seamless integration with smart home devices and the Internet of Things (IoT) technologies. These VA services raise privacy concerns, especially due to their access to our speech. This work considers one such use case: the unaccountable and unauthorized surveillance of a user's emotion via speech emotion recognition (SER). This paper presents DARE-GP, a solution that creates additive noise to mask users' emotional information while preserving the transcription-relevant portions of their speech. DARE-GP does this by using a constrained genetic programming approach to learn the spectral frequency traits that depict target users' emotional content, and then generating a universal adversarial audio perturbation that provides this privacy protection. Unlike existing works, DARE-GP provides: a) real-time protection of previously unheard utterances, b) against previously unseen black-box SER classifiers, c) while protecting speech transcription, and d) does so in a realistic, acoustic environment. Further, this evasion is robust against defenses employed by a knowledgeable adversary. The evaluations in this work culminate with acoustic evaluations against two off-the-shelf commercial smart speakers using a small-form-factor (raspberry pi) integrated with a wake-word system to evaluate the efficacy of its real-world, real-time deployment.

A Graph-Theoretic Approach for Optimal Phasor Measurement Units Placement Using Binary Firefly Algorithm

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability.

On the self-adjustment of privacy safeguards for query log streams

Internet-based services process and store numerous search queries around the globe. The use of web search engines, such as Bing and Google, as well as personal assistants (e.g., Alexa and Cortana) and task specific systems (e.g., YouTube, Netflix, Amazon) are relevant examples. The queries associated to such services may be stored and sold out for profit. Before doing so, personal and sensitive information must be sanitized, as requested by current regulations. This can be cumbersome for some organizations. We present an automated solution for anonymizing unstructured data, like the one used within query logs. Our solution uses a light-weight probabilistic k-anonymity approach, which allows verifiable real-time privacy protection. It addresses previous limitations and improves performance. We validate the feasibility of the approach, under some evaluation metrics including data utility, privacy and speed.

An Integrated Testbed for Power System Cyber-Physical Operations Training

The increased adoption of information and communication technology for smart grid applications will require innovative cyber–physical system (CPS) testbeds to support research and education in the field. Groundbreaking CPS testbeds with realistic and scalable platforms have progressively gained interest in recent years, with electric power flowing in the physical layer and information flowing in the network layer. However, CPSs are critical infrastructures and not designed for testing or direct training, as any misbehaving in an actual system operation could cause a catastrophic impact on its operation. Based on that, it is not easy to efficiently train professionals in CPSs. Aiming to support the advancement and encourage the training of industry professionals, this paper proposes and develops a complete testbed using a real-time simulator, protection and automation devices, and a supervisory control and data acquisition (SCADA) system. The testbed replicated the performance of smart grids, and the main potential cyber threats that electric grids may face. Different case scenarios include a distribution system protection study, a denial of service (DoS) attack, a jamming attack, a network packet manipulation attack, a sensor data manipulation attack, a false trip command attack, etc. The system’s performance before and after the cyberattacks are studied using packet-sniffing tools and a network packet analyzer. The impact on the grid is analyzed using metrics such as voltage oscillation, frequency deviation, and loss of active power generation. Moreover, the complex interdependencies between the cyber and physical domains are discussed in detail, providing insightful guidelines for key features and design decisions for future smart grid testbeds.