Integrity Monitoring Research Articles

ARIA: Additive ReRAM-based Integrity and Aging Monitoring for ICs

ARIA: Additive ReRAM-based Integrity and Aging Monitoring for ICs

An Integrated Time-Series Relative Soil Moisture Monitoring Method Based on a SAR Backscattering Model

An Integrated Time-Series Relative Soil Moisture Monitoring Method Based on a SAR Backscattering Model

Geodetic Altitude from Barometer and Weather Data for GNSS Integrity Monitoring in Aviation

Geodetic Altitude from Barometer and Weather Data for GNSS Integrity Monitoring in Aviation

Intraoperative cortical stimulation mapping with laryngeal electromyography for the localization of human laryngeal motor cortex.

The objectives of this study were to describe the authors' clinical methodology and outcomes for mapping the laryngeal motor cortex (LMC) and define localization of the LMC in a cohort of neurosurgical patients undergoing intraoperative brain mapping. Because of mapping variability across patients, the authors aimed to define the probabilistic distribution of cortical sites that evoke laryngeal movement, as well as adjacent cortical somatotopic representations for the face (mouth), tongue, and hand. Thirty-six patients underwent left (n = 18) or right (n = 18) craniotomy with asleep motor mapping. For each patient, electromyography (EMG) electrodes were placed in the face, tongue, and hand; a nerve integrity monitor (NIM) endotracheal tube with surface electrodes detected EMG activity from the bilateral vocal folds. After dense cortical stimulation was delivered throughout the sensorimotor cortex, motor responses were then mapped onto a three-dimensional reconstruction of the patient's cortical surfaces for location characterization of the evoked responses. Finally, stimulation sites were transformed into a two-dimensional coordinate system for probabilistic mapping of the stimulation site relative to the central sulcus and sylvian fissure. The authors found that the LMC was predominantly localized to a mid precentral gyrus region, dorsal to face representation and surrounding a transverse sulcus ventral to the hand knob. In 14 of 36 patients, the authors identified additional laryngeal responses located ventral to all orofacial representations, providing evidence for dual LMC representations. The authors determined the probabilistic distribution of the LMC. Cortical stimulation mapping with an NIM endotracheal tube is an easy and effective method for mapping the LMC and is simply integrated into the current neuromonitoring methods for brain mapping.

Comparing two cryptographic hash algorithms: SHA-512 and whirlpool- a case study on file integrity monitoring

SHA-512 and Whirlpool are two distinct hashing algorithms in the cryptography domain. Although they are different, these methods share some characteristics; such as both of them produce secure digests of the same size. In addition, being increasingly used for sustaining the integrity of confidential files. As a result, it is crucial to explore a method to contrast and compare their performance. This paper presents a comparative analysis of SHA-512 and Whirlpool, in terms of: time consumption, avalanche effect, and resistance to collision. It also investigates their suitability in the context of File Integrity Monitoring. Our results reveal that Whirlpool outperforms SHA-512 in the avalanche effect scenario, whereas a comparable resistance to the collision behavior is exhibited. Regarding time consumption, it is slower than SHA-512. However, this positively reflects its resistance to brute force attacks.

How Safe Is Particle Filtering-Based Localization for Mobile Robots? An Integrity Monitoring Approach

How Safe Is Particle Filtering-Based Localization for Mobile Robots? An Integrity Monitoring Approach

Technology Focus: Well Integrity (January 2024)

The context of last year’s Well Integrity Technology Focus was the repurposing of existing oil and gas wells for carbon storage, flow-wet material conformity, and associated integrity risks. Typically, oil and gas producing wells are abandoned toward the end of their life cycles with reservoir pressures depleted compared with the virgin pressures. Carbon capture and storage (CCS) projects, however, present different operating conditions. First, the pressurized containment sites have a higher life expectancy, say, between 50 and 100 years. Second, the containment pressure is possibly higher than the virgin pressure. The overpressure limit is dependent on several factors, especially the caprock sealing capacity. It goes without saying that CCS storage in wells requires long-term integrity (i.e., for the entire project life cycle). It is well-known that barriers such as cement and casings deteriorate over time. Deterioration of barriers may be accelerated by chemical reaction with carbon dioxide or thermal cycling. Hence, these storage sites could present unique life-cycle integrity risks perhaps never experienced by the oil and gas industry. To further explain the scenario, a possible breach of the confining strata resulting in pressure communication to the overburden means CO2 finding its way through the deteriorated well barriers of any active or shut-in well, resulting in CO2 ingress to the upper formations; groundwater contamination; or, in a worst-case scenario, appearance at the surface. Therefore, one of the important subsurface aspects of a CCS site is the presence of two seals (i.e., primary and secondary), with sealing capacity of both caprocks characterized robustly. Lack of double barriers or improper characterization of these confining strata could present a scenario wherein CO2 flows into overburden (i.e., above the caprock). The wells’ outer annuli then could become a potential path of least resistance for CO2 to migrate further up. Such situations can defy the primary objective of CCS projects and can require mitigation with proper attention to detail following the “as low as reasonably practicable,” or ALARP, principle. The scenario discussed is unique and complex and requires continuous managing and monitoring of CCS storage sites. Furthermore, the role of artificial intelligence and automation is seen as imperative during the operational stage of the project. In addition to the primary paper selections synopsized here, three SPE papers focusing on artificial intelligence and machine learning as tools for well integrity management are recommended as alternates for further reading. Recommended additional reading at OnePetro: www.onepetro.org. SPE 212479 Improve Well Integrity Using an Annular Barrier AI Tool by Eirik Time, Equinor, et al. SPE 211537 Transforming Production-Phase WIMS to a Game Changer Smart Well Integrity Monitoring System With Real-Time Automated Engineering Analytics by Mohammed Jasem, ADNOC, et al. SPE 216600 Paradigm Shift in Well Integrity Management Through the Use of Autonomous Wireless Well Intervention Robots by Julio Q.M. Guedes, Ouronova, et al.

An integrated coastal ecosystem monitoring strategy: Pilot case in Naf-Saint Martin Peninsula, Bangladesh

Rapid population growth creating an excessive pressure on the marine environment and thus monitoring of marine ecosystem is essential. However, due to high technical and financial involvement, monitoring of coastal ecosystem is always challenging in developing countries. This study aims to develop an integrated coastal ecosystem monitoring system that combines scientific sampling, numerical model simulation and citizen science observations to monitor the coastal ecosystem of Bangladesh. This concept of integrated monitoring approach was piloted from January 2022 to April 2023 at the South East coastal zone of Bangladesh. Scientific sampling and numerical model simulations were performed for temperature and salinity data collection. Citizen science approach was employed to collect data on environmental conditions, fisheries, plankton, other marine resources, and plastic pollution. Numerical model simulations and citizen scientists observations of temperature and salinity showed good agreement with the scientifically collected data. In addition, citizen scientists observations on fisheries, plankton, other marine resources and plastic pollution were also in line with the existing database and previous studies. The proposed integrated monitoring approach presents a viable technique, creating a new avenue for coastal and marine ecosystem monitoring where infrastructural facilities are limited.

Two-Level Integrity-Monitoring Method for Multi-Source Information Fusion Navigation

To address the issue of integrity monitoring for a multi-source information fusion navigation system, a theoretical framework of two-level integrity monitoring is proposed. Firstly, at the system level, a system-integrity-monitoring method based on the Kalman filter weighted least-squares form is established to detect and isolate faulty navigation sources. Secondly, at the sensor level, considering the redundancy of the faulty navigation sources, this paper presents the design of a multi-mode comprehensive fault-detection method for non-redundant navigation sources. Additionally, an extended-dimension matrix optimized sensor-fault detection and verification method for redundant navigation sources is proposed. Finally, integrity risk allocation criteria are established based on the effectiveness of navigation sources to calculate the system protection level and trigger integrity alarms. The two-level integrity-monitoring method was tested on a multi-source information fusion navigation system integrated with an inertial navigation system (INS), global positioning system (GPS), BeiDou satellite navigation system (BDS), Doppler velocity log (DVL), barometric altimeter (BA), and terrain-aided navigation (TAN). Test results demonstrate that the proposed method can effectively isolate the faulty navigation source within 10 s. Furthermore, it can detect the faulty sensors within the faulty navigation sources, thereby enhancing the reliability and robustness of the multi-source information fusion navigation system.

Just Transition in Biofuel Development towards Low-Carbon Economy: Multi-Actor Perspectives on Policies and Practices in Indonesia

Justice and sustainability are the foundation of fair and equitable biofuel development. Policies and practices are consequently necessary to support a just transition towards a low-carbon economy. Therefore, this study aimed to understand multi-actor perspectives on policies and practices relevant to the just transition in biofuel development. Applying a socio-legal approach based on the JUST framework, this study focused on the Indonesian biofuel industry, which is primarily driven by crude palm oil (CPO) production. This study also added comparative perspectives from Thailand’s biofuel industry. The primary data were gathered from in-depth interviews and focus group discussions (FGDs), while the secondary data were from research, policy, and other government archives. Based on the results, oil palm plantations in Indonesia and Thailand are crucial for macroeconomic development. However, smallholder farmers in Indonesia, especially independent ones that do not join farmer groups, remain marginalised by the current regulations. In fact, many benefits from biofuel practices and policies accumulate disproportionately towards large corporations, while marginalised groups bear the costs. The Indonesian Sustainable Palm Oil (ISPO) certification that should induce market accessibility and competitiveness has not fully reached most rural farmers. Furthermore, various overlapping regulations and perplexing data differences exist from multiple ministries and administrative levels. Thus, palm oil stakeholders in Indonesia demand a dedicated institution for integrated regulation and monitoring of the industry, similar to the National Palm Oil Policy Committee in Thailand. The myriad of legal, political, socio-economic, and justice issues thus necessitates all hands on deck to ensure a just transition for all stakeholders of the Indonesian biofuel industry.

The impact of laboratory automation on efficiency and accuracy in healthcare settings

Automated sample processing systems, such as handlers, have played a role in expediting specimen handling, especially during emergencies. Additionally, automated analyzers have contributed to increased testing efficiency by enabling high throughput screening and quicker access to information. This article explores how the use of automated technology in laboratories has greatly improved efficiency and accuracy in healthcare settings. By examining the integration of automated systems for processing samples and conducting tests this review highlights the impact automation has had on outcomes. One notable benefit is reduced turnaround times, streamlined workflows, and enhanced precision in diagnostic testing. The incorporation of laboratory information management systems (LIMS) has further improved efficiency through data integration and real-time monitoring. Accuracy is an aspect of processes, and automated systems meticulously adhere to predefined protocols, resulting in reduced error rates and consistently reliable results. The introduction of intelligence (AI) has enhanced accuracy, particularly in image analysis within the pathology and radiology fields. Effective clinical management of laboratory automation entails technology selection planning for implementation and ongoing monitoring. Interoperability between systems, continuous education on advancements, and efficient workforce management are all crucial components for successful implementation. Despite challenges faced along the way, adopting laboratory automation is essential for optimizing laboratories' workflows while delivering timely information. The review consistently affirms laboratory automation's valid influence in improving efficiency and accuracy within healthcare environments.

Design and Development of an Agricultural Parameter Monitoring System for Hydroponics

The global surge in population, surpassing environmental carrying capacity, poses challenges in ensuring food security, especially in densely populated urban areas. As the demand for food is projected to increase by 60% by 2050, innovative solutions are sought to address constraints like limited land, time, and motivation for traditional agriculture. The study explores hydroponics, a soilless farming method, as a viable solution to enhance food production in confined spaces. Hydroponics involves cultivating plants directly in nutrient-rich water, presenting an efficient and space-saving alternative. The paper details various hydroponic techniques, including Nutrient Film Technique (NFT), Deepwater Culture, and Aeroponics, illustrating their structures and advantages. However, implementing hydroponics in urban settings faces challenges related to time constraints and limited farming knowledge among residents. To address these challenges, the study introduces an innovative PLC-controlled system for hydroponic farming with automated features. The system precisely regulates environmental conditions such as temperature, humidity, water pH, and light intensity through algorithms. Leveraging cloud technology, users can remotely monitor and control the system, ensuring optimal plant growth. An integrated alarm system provides timely alerts, reducing the need for constant physical inspections. The methodology outlines the development stages of the monitoring system, emphasizing the selection of sensors, PLC programming, and integration with the cloud platform. Results indicate successful implementation, with the hydroponic system demonstrating effective communication with the cloud and optimal growth conditions for lettuce. The study concludes with future perspectives, suggesting improvements such as renewable power integration and advanced monitoring features. The proposed automated hydroponic system aims to make urban farming more accessible and manageable, contributing to sustainable and efficient food production.

Scientometric Analysis of the Dynamics of Artificial Intelligence Applications in NonDestructive Testing and Structural Integrity Monitoring

This study aims to present a scientometric analysis of Artificial Intelligence (AI) applications in NonDestructive Testing and Evaluation (NDT&E) and Structural Integrity Monitoring. The data is collected from the research papers contained in the Web of Science Core Collection, covering studies on AI applications in various NDT methods employed across different industry sectors. The collected 654 research articles-dataset published between April 1989 and January 2023 was processed using CiteSpace software, which classified the dataset into twelve clusters. Moreover, a subset of the data pertaining to the relevant research in the nuclear industry was considered for analysis. Various metrics were used to analyse the consistency of the clusters and publications' influence and quality. The results, which quantify the dynamics and quality of research in AI for NDT&E, are given in visualised mappings, and tabulated data and the content of the major clusters was discussed to reveal the used AI and NDT&E methods and the sectors or industries where they have been used.

Geophysical key indicators for tailings dam physical integrity monitoring - Brazil

Geophysical key indicators for tailings dam physical integrity monitoring - Brazil

Non-Destructive Testing Methods for In Situ Crack Measurements and Morphology Analysis with a Focus on a Novel Approach to the Use of the Acoustic Emission Method

This article presents a concise review of modern non-destructive testing (NDT) methods that allow the detection, tracking, and measurement of cracks in reinforced concrete structures. Over the past decades, the range of solutions available on the market has increased. This provides excellent opportunities when choosing and designing systems for diagnosing and continuously monitoring structures. Cracking affects the mechanical properties, durability, and serviceability of a structure or its elements. Therefore, there is a need to develop methods that would allow the determination of the moment of a destructive process’s formation, i.e., a crack’s appearance. At the same time, it is crucial to be able to track the development of cracks for the entire structure, not just selected locations. This work also presents the concept of combining selected NDT methods and creating a system for the continuous monitoring of structural integrity and predicting changes in the durability of existing and future buildings.

Characterizing the fault performance of real-time precise satellite orbit and clock correction products

The a priori fault probability of the real-time precise satellite orbit and clock correction products is the critical parameter for integrity monitoring of precise point positioning (PPP). The traditional fault probability evaluation methods use the worst-case instantaneous user ranging error (IURE) as the conservative test statistic. However, the systematic biases of IURE contained in the worst-case IURE barely affect the PPP accuracy, which will undermine the statistical distribution of test statistic and reduce the sensitivity of fault detection. The fault probability will be estimated over-conservatively for the traditional methods. By clarifying the sources of the systematic biases, a new test statistic is constructed by deliberately removing the systematic biases of IURE originated from satellite orbit and clock errors. One-year Global Positioning System correction products evaluation results have demonstrated that the constructed test statistic follows the Gaussian distribution with the decreased uncertainty and the improved fault detection sensitivity. The real-world data experiments have shown that the a priori probabilities of the satellite fault and the constellation fault are at the order of 10−4 and 10−5 levels, respectively.

Mitigation of multiple outliers using consistency checking for GNSS standard point positioning in urban areas

High-precision positioning in urban environments is still a challenging task for global navigation satellite system (GNSS) as the observations are susceptible to outliers. Receiver autonomous integrity monitoring (RAIM) has been widely used in GNSS positioning to resist the outliers. However, conventional RAIM typically assumes only a single outlier, when multiple outliers are present, the method’s robustness can be adversely affected and a time-consuming iterative processing procedure is often required. To process multiple outliers more robustly and efficiently, we propose a consistency-checking approach based on the concept of random sample consensus (RANSAC). Modifications are made to enhance the RANSAC algorithm, including the use of spatial and temporal continuity of positioning geometry to accelerate RANSAC, and adaptive determination of the inlier-outlier threshold based on observation and parameter uncertainty. The effectiveness and efficiency of the proposed method are validated in GNSS single point positioning (SPP) in both a simulation and a real driving test.

RTK ramp faults detection and exclusion by the hybrid control chart

For real-time kinematic (RTK) positioning in intelligent transportation systems (ITS), the slow-growing faults detection and exclusion (FDE) in carrier phase measurements with time-correlated satellite navigation data poses a significant challenge. In response, we propose the short-baseline RTK Receiver Autonomous Integrity Monitoring (RTK-RAIM), which utilizes a hybrid control chart with a colored Kalman filter for detecting small yet persistent faults. The hybrid control chart incorporates the windowed multivariate homogeneously weighted moving average and dual multivariate cumulative sum charts to accelerate fault detection. The fault exclusion is conducted using the solution separation. Experimental validation using static data and dynamic data in suburban and urban areas demonstrates the efficacy of RTK-RAIM in detecting ramp faults with time-correlated noise. Results show that Colored KF can attenuate the detection metrics in the fault-free case when measurements are time-correlated. The hybrid control chart, operating in an open environment, has the ability to detect carrier phase slope faults with a high probability, even when the slope is as small as 0.01cycle/s. It can also detect carrier-phase ramp faults whose gradient are 0.1cycle/s in an urban area. By enhancing the detection ability of FDE in RTK, this hybrid-control-chart-based RAIM promotes safer and more dependable performance in future ITS applications.

A new method for fault identification in real-time integrity monitoring of autonomous vehicles positioning using PPP-RTK

A new method for fault identification in real-time integrity monitoring of autonomous vehicles positioning using PPP-RTK

A medium-high frequency FBG accelerometer based on a V-shaped flexible hinge

Acquiring medium- and high-frequency acceleration signals is vital for broad infrastructure health monitoring, such as bridges, railways, and tunnels. This article introduces an innovative fiber Bragg grating (FBG) accelerometer featuring a V-shaped flexure hinge structure with enhanced sensitivity. Theoretical calculation and simulation analysis software were employed to evaluate the proposed structure's parameters. Experimental results show a sensitivity of approximately 67.9 pm/G under a working bandwidth of 400–900 Hz and a resonant frequency of 1250 Hz. The influence of temperature at 8.4 pm/°C and transverse interference validate 3.5%. These findings align with simulations, affirming the sensor's performance. This innovation holds promise for enhancing structural integrity monitoring in large-scale structures by detecting medium- and high-frequency vibration measurements.