Monitoring Infrastructure Research Articles

Retracing-efficient IoT model for identifying the skin-related tags using automatic lumen detection

The number of patients with skin diseases reported a dramatic increase which is a major concern and should be addressed. The evaluation of skin is crucial to the correct diagnosis during the follow-up. Through technological advances and partnership, skin disorders can be identified and predicted. PROBLEM: The manual detection of skin diseases may sometimes lead to misclassification due to the same intensity and color levels, which is crucial to the correct diagnosis. SOLUTION: An automated system to identify these skin diseases is applied. An IoT-based skin monitoring infrastructure is imposed that links the entire system. METHOD: In this study, a Retracing-efficient IoT model for identifying the moles, skin tags, and warts using Automatic lumen detection with the help of IoT-based Variation regularity is proposed with the technique imposed IoMT, Automatic lumen detection, Variation regularity, and trigonometric algorithm. RESULTS: The intensity and edge width based on moles, skin tags, and warts edge width heightened intensity accuracy is 56.2% on the image group with image count is 500 to 10000, and the enhanced low-level total sample accuracy is 95.9%. The pixel analysis for intensity with wavelength and intensity with time wavelength is improved from 4.2% to 54.6%, and accuracy is 70.9% formulated. Periodic classification on image count and classification accuracy image count is 87% against the 500 to 10000 image. Correlation performance analysis of lumen detection resolution image pixel and enhanced correlation performance accuracy is 23.50% on the 480 × 640 to 2336 × 3504 pixel images. CONCLUSION: The approach is tested for varying datasets, and comparative analysis is performed that reflects the effectiveness of the proposed system with high accuracy, thus contributing to the development of a perfect platform for skincare to the early detection and diagnosis of skin conditions.

Detection of cracks in concrete using near-IR fluorescence imaging

Structural health monitoring of civil infrastructure is a crucial component of assuring the serviceability and integrity of the built environment. A primary material used in the construction of civil infrastructure is concrete, a material that is susceptible to cracking due to a variety of causes, such as shrinkage, creep, overloading, and temperature change. Cracking reduces the durability of concrete structures, as it allows deleterious environmental agents to penetrate the surface, causing such damage as corrosion of steel reinforcement and delamination of the concrete itself. Conventional crack detection techniques are limited in scope due to issues relating to pre-planning, accessibility, and the need for close proximity to the test surface. Contactless optical image monitoring techniques offer the opportunity to overcome these limitations and have the potential to detect cracks at a distance. Concrete has been reported to have a near-infrared (Near-IR) fluorescence line at a wavelength of 1140 nm when excited with red light. This work investigates the use of fluorescence imaging for the detection of cracks in cementitious surfaces using shallow angle incidence excitation red light. Light oriented at a shallow angle does not excite interior surfaces of cracks, which appear as darker features in images of fluorescing concrete. Artificial cracks with widths of 0.2–1.5 mm were readily imaged using a near-IR camera at distances of 0.5 and 1.3 m. An additional concrete sample with a 0.08 mm wide crack was produced using a flexure apparatus and was also imaged. It is worth noting that the 0.08 mm crack was detected despite its width being below the 0.1 mm pixel resolution of the camera, with the aid of digital image enhancement algorithms.

Enhanced mine road monitoring using unmanned aerial vehicles and deep-learning approach

Mine roads are highly vulnerable to deterioration as a result of substantial vehicular traffic and excessive precipitation. Therefore, it is critical to incorporate safety monitoring of the mining road infrastructure into mining operations. This study aims to extract mining road objects and classify road conditions using the geometric standards of mining road safety. The study area comprised one of the pits of Perseroan Terbatas (PT) Berau Coal, Indonesia. This study developed a novel approach utilizing geospatial artificial intelligence that integrates drones, deep learning, and spatial geometry to monitor road conditions in terms of geometrical aspects such as slope, road width, turning angle, and cross-fall. The detection results of the mining road resulted in 30 parameter combination schemes, with the optimal scheme consisting of a combination of an 87.5% model overlap, a 512 × 512 tile size, and a 0.3 m pixel resolution. This scheme achieved an accuracy of 89%. Furthermore, the spatial geometric analysis results revealed that 64% of the grade standards, 90% of the turning angles, 61% of the cross-falls, and 51% of the road widths in the mining road area conformed to the internal mine road standards of PT Berau Coal. The method developed in this study can also be applied to other mining areas. The mining road monitoring method developed in this study can improve the mining road standard and may be applied in other mining operations to improve efficiency and safety.

DyCause: Crowdsourcing to Diagnose Microservice Kernel Failure

Today many web applications in the cloud (apps) are built based on microservices. However, as the anomaly propagates in a highly dynamic and complex way, troubleshooting them becomes full of challenges. Existing diagnostic methods are mostly designed based on monitoring metrics retrieved from the microservice system kernel. Therefore, application owners and even site reliability engineers (SREs) cannot effectively resort to those methods when the microservice systems lack such a comprehensive monitoring infrastructure. In this paper, we develop DyCause, a crowdsourcing solution to the asymmetric diagnostic information problem. Our solution collects the operational status of kernel services collaboratively from the user space and initiates diagnosis on demand. Without the requirement of any architectural or functional infrastructure, it is both fast and lightweight to deploy DyCause in a microservice system. In order to discover the fine-grained dynamic causalities between services during the anomaly, we also design an efficient algorithm based on statistical analysis. Based on this algorithm, we can also analyze the anomaly propagation paths within the microservice system and generate a better interpretable diagnosis. In our evaluation, we test DyCause in a controlled simulation environment and a real-world cloud system. Our results have shown that DyCause has the best accuracy and efficiency among several state-of-the-art methods and is more robust in terms of parameters.

Health Monitoring and Diagnosis for Geo-Distributed Edge Ecosystem in Smart City

With the increasing number of Internet of things (IoT) devices being deployed and used in daily life, the load on computational devices has grown exponentially. This situation is more prevalent in smart cities where such devices are used for autonomous control and monitoring. Smart cities have different kinds of applications that are aided through IoT devices that collect data, send it to computational processing and storage devices, and get back decisions or actuate the actions based on the input data. There has been a stringent requirement to reduce the end-to-end delay in this process owing to the remote deployment of cloud data centres. This eventually led to the revolution of edge computing, wherein nano-micro-processing devices can be deployed closer to the premises of the smart application and process the data generated with a lower turnaround time. However, due to the limited computational power and storage, controlling the workload diverted to the edge devices has been challenging. The workload scheduling policies and task allocation schemes often fail to consider the run time health of the edge devices due to a lack of proper monitoring infrastructure. Thus, in this paper, we proposed a health monitoring and diagnosis framework for geo-distributed edge clusters processing big data generated by smart city applications. This framework is built over the Map-Reduce approach for distributed processing of big data on edge clusters deployed across the smart city. Within this framework, SmartMonit (a monitoring agent) is deployed that collects the health statistics of edge devices and predicts the potential failures using an artificial neural network-based self-organising maps approach. The proposed framework is deployed over different clusters to test the efficacy concerning failure detection.

Ohio Coronavirus Wastewater Monitoring Network: Implementation of Statewide Monitoring for Protecting Public Health.

Prior to the COVID-19 pandemic, wastewater influent monitoring for tracking disease burden in sewered communities was not performed in Ohio, and this field was only on the periphery of the state academic research community. Because of the urgency of the pandemic and extensive state-level support for this new technology to detect levels of community infection to aid in public health response, the Ohio Water Resources Center established relationships and support of various stakeholders. This enabled Ohio to develop a statewide wastewater SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) monitoring network in 2 months starting in July 2020. The current Ohio Coronavirus Wastewater Monitoring Network (OCWMN) monitors more than 70 unique locations twice per week, and publicly available data are updated weekly on the public dashboard. This article describes the process and decisions that were made during network initiation, the network progression, and data applications, which can inform ongoing and future pandemic response and wastewater monitoring. Overall, the OCWMN established wastewater monitoring infrastructure and provided a useful tool for public health professionals responding to the pandemic.

Leveraging Django: A Web Framework for Efficient Infrastructure Monitoring Tools

Abstract: Infrastructure monitoring is crucial for ensuring the reliability and performance of modern software systems. As these systems grow in complexity and scale, the demand for efficient and scalable monitoring tools becomes increasingly important. This review paper examines the utilization of Django, a powerful web framework, for constructing infrastructure monitoring tools and provides an in-depth review of Django's capabilities, focusing on its robustness, scalability, and extensibility, as well as its compatibility with various databases and integration with other technologies. It explores the different components and modules within Django that can be leveraged to develop efficient infrastructure monitoring tools. The review delves into the Django ORM (Object-Relational Mapping) system, highlighting its role in effective database management for monitoring data. It also evaluates Django's authentication and authorization system, emphasizing its contribution to secure access control in the monitoring environment. The review further examines the Django admin interface, which simplifies monitoring configuration management and provide a comprehensive understanding of the benefits and considerations of utilizing Django for developing efficient and scalable infrastructure monitoring tools. It serves as a valuable resource for researchers and practitioners looking to construct robust monitoring systems using Django and contributes to the ongoing discourse on effective infrastructure monitoring in modern software ecosystems. In conclusion, the paper summarizes the advantages and challenges associated with leveraging Django for infrastructure monitoring tools. It highlights Django's versatility, productivity, and strong community support, while also addressing potential areas for improvement and future research directions.

Unlocking information technology infrastructure for promoting climate resilience and environmental quality

We examine the role of Information Technological Infrastructure (ITI) in promoting climate resilience and environmental quality in OECD countries. Our empirical strategy is framed in the advanced econometrics methodology. Our analysis begins with standard specification, which controls for unobserved factors in the panel data. We then explore the variables' long and short-run relationships using the Generalised Method of Moment dynamic family specifications. We also evaluate the heterogeneity drivers of environmental quality using Quantile via Moment. The analysis is also extended using alternative and complementary statistical procedures by Hausman–Taylor and Feasible Generalised Least Squares as robustness checks. Our findings indicate that ITI and renewable energy significantly mitigates carbon emissions and can be helpful in achieving a net-zero target. In contrast, the empirical evidence reveals that economic growth and non-renewable energy usage are harmful to the environment. The finding also suggests a significant degree of heterogeneity exists in the covariates on the conditional distribution of environmental quality and its driven factors. While the findings reaffirm the significance of ITI in ensuring careful planning and monitoring of critical infrastructure, they also show that ITI can be used to balance the entire system by creating resilience. We strongly suggest that policymakers should use ITI to spur innovation and drive better solutions for energy transition and environmental improvement.

Power grid monitoring based on Machine Learning and Deep Learning techniques

<p><strong>Background:</strong> In this work, some application examples of machine learning and deep learning techniques in the monitoring of electricity distribution services and infrastructures are proposed. Three different fields of application are considered to highlight the use of techniques based on neural networks: detection and classification of power quality disturbances, monitoring of underground cables in medium voltage lines and diagnosis of joints in high voltage overhead power lines. <strong>Methods:</strong> In the field of power grid monitoring, this work proposes a classification method based on a complex valued neural network to assess working conditions of junction regions in high-voltage overhead lines and insulating materials in medium voltage underground networks. The purpose of this method is to prevent the rupture of joint structures and the abnormal degradation of underground cables via frequency response measurements. This approach allows the direct processing of complex measurements and to reduce the computational effort compared to other methods available in the literature. <strong>Results:</strong> The results obtained in the monitoring of underground cables and joints of high voltage lines guarantee an overall classification rate higher than 90%. In the field of power quality, several deep learning and machine learning methods are proposed to detect the most common voltage disturbances. <strong>Conclusions:</strong> In this paper, an innovative use of widespread algorithms such as convolutional neural networks is proposed with excellent results. Furthermore, the use of a complex-valued neural network in electrical infrastructure monitoring is presented, introducing a minimally invasive classification method that could be instrumental in the transition from corrective to predictive maintenance in the near future.</p>

Graph neural networks for dynamic modeling of roller bearings

Machine learning has paved the way for the real-time monitoring of complex infrastructure and industrial systems. However, purely data-driven methods have not been able to learn the underlying dynamics and generalize them to operating conditions that have not been covered by the training datasets. Therefore, they have not been able to predict the long-term evolution of the system state of physical systems. Physics-informed neural networks (PINNs) have recently shown promising results in predicting the system state evolution over extended periods of time, owing to the loss terms derived from the underlying partial differential equations governing the dynamics of the systems. However, PINNs have limited generalization ability, i.e., a model trained on one type of boundary condition cannot generalize to other conditions. Moreover, the governing equations used for describing the dynamics of physical systems are an approximation of reality, which can lead to differences between the predictions and the actual roll-out of the trajectory. Recently, graph neural networks (GNNs) have been applied to predict the evolution of system dynamics. Due to the encoded inductive bias, they generalize well to systems with varying configurations and boundary conditions. Message-passing GNN comprises two parts that learn the interaction between nodes: an edge network that takes the translational invariant features between two nodes (for e.g., the distance vector) and generates a message, and a node network that takes the aggregated messages from all the neighboring nodes and produces a new node state. This process is repeated several times until the final node state is decoded as a required output. 
 In the presented work, we propose to apply the framework of GNNs for predicting the dynamics of a rolling element bearing. The computational efficiency and generalizability of such a method enable the scalable use of a real-time digital twin to monitor the health state of a rotating machine. To this end, a GNN is used to mimic a dynamic spring-mass-damper model. Bearings consist of different interacting parts like the inner race, outer race, and multiple rolling elements. This interconnected and interacting architecture of a typical bearing is suitable to be modeled as a graph with nodes representing different components.
 We use the dynamic spring-mass-damper model to generate the training data for the GNN, where bearing components such as rolling elements, and inner and outer raceway are modeled as discrete masses. A Hertzian contact model is used to calculate the forces between these components. We evaluate the learning and generalization capabilities of the proposed GNN framework by testing bearing configurations different from the training configurations and comparing the performance to that of the spring-mass model.

Hydrogeological characterization of the Silala River catchment

AbstractThis article reviews hydrogeological studies carried out between 2016 and 2018 in the Silala River basin, a catchment shared by Chile and Bolivia. These were conducted in the context of the Case Concerning the Status and Use of the Waters of the Silala River, submitted to the International Court of Justice in 2016, and contributed to multidisciplinary science to demonstrate that this system is an international watercourse. In 2016, the hydrogeological understanding of the Silala River basin was poor. The studies reviewed here filled many knowledge gaps, providing a solid hydrogeological baseline, and establishing new monitoring infrastructure to collect relevant aquifer data. The most important hydrogeological units were identified as the fluvial deposits, alluvial deposits, and the Cabana ignimbrite. The latter is highly heterogeneous, weathered and fractured, and exhibits a high permeability. It is the most important unit in terms of productivity, and is the major regional aquifer providing spring flows to Bolivian wetlands and groundwater flow across the international border. The studies provided a preliminary understanding of the main aquifers in Chile and their properties, which underpinned the development of a robust hydrogeological conceptual model of the system, reviewed elsewhere in this special issue. Subsequent refinements are also summarized. This work confirmed that both surface water and groundwater flows from Bolivia to Chile, and thus confirms the status of the Silala River as an international watercourse and provided the basis for a basin‐scale groundwater numerical model, used to investigate the impact of wetland channelization on surface water/groundwater partitioning.This article is categorized under: Science of Water > Hydrological Processes

Advancing civil infrastructure assessment through robotic fleets

Modern civil engineering structures, instrumented with Internet-of-Things-enabled smart sensors and actuators, are considered cyber-physical systems that integrate physical processes with computational and communication elements. This short communication aims to portray a milestone in the field of monitoring and inspection of civil infrastructure, collaboratively conducted by autonomous, robotic devices orchestrated in robotic fleets. It is expected that robot-based civil infrastructure assessment will revolutionize structural maintenance of the deteriorating building stock, which is increasingly exacerbated by the effects of climate change and develops into a major societal challenge.

EVALUATING A NADIR AND AN OBLIQUE CAMERA FOR 3D INFRASTRUCTURE (CITY) MODEL GENERATION

Abstract. The analysis of Earth’s surface is strongly associated with the creation of three dimensional representations. In light of this, researchers involved in any realm of research as, geological, hydrological, ecological planning, city modelling, civil infrastructure monitoring, disaster management and emergency response, require 3D information of high fidelity and accuracy. For many decades, aerial photos or satellite data and photogrammetry provided the necessary information. In recent years, high-resolution imagery acquired by Unmanned Aerial Vehicles (UAV) has become a cost-efficient and quite accurate solution. In this framework, an infrastructure-monitoring project, named called “PROION”, focuses among others on the generation of very fine and highly accurate 3D infrastructure (city) model. The specific study evaluates a high-resolution nadir camera and an oblique camera for the creation of a 3D representation of the Patras University Campus. During the project, two identical flights over a part of the campus were conducted. The flights were performed with a vertical take-off and landing (Vtol) fixed wind UAV equipped with PPK receiver on-board. Based on the conducted flights, many data sets have been evaluated regarding the accuracy and fidelity. It was proved that both nadir and oblique cameras produced very accurate 3D representations of the University campus buildings. The RMSE error of the nadir imagery is almost two times higher than the respective error of the oblique imagery reaching 30cm.

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.

Tree Architecture & Blockchain Integration: An off-the-shelf Experimental Approach

Temporally sensitive tree modeling and urban park spatially explicit simulation offer advantages to large-scale landscape planning and design, especially in the context of smart applications for virtual parks and forests, while Blockchain technology provides collaborative engineering, data integrity, and information confidence. A proof-of-concept 2.5D tree architecture and Blockchain integration technique (distributed Internet-of-Trees images, “IoTr-images”) was presented as a low-cost metaverse case study that affects the forest monitoring and digital landscape architecture design infrastructures. At the core of the proposed feature-based parametric modeling methodology is a 2.5D tree CAD model composed of two perpendicular 2D tree frames on which recorded tree texture has been assigned. A “Batch command-line programming” technique has been implemented, as a user-defined routine at the top of a commercial CAD platform, to describe the proposed off-the-self method and to create tangible tree-image NFT tokens (Internet-of-Trees-images Blockchain). As important findings were recorded, the add-in planning intelligence, the superior data integrity, and confidence, the offline relaxed error-free CAD design, and the superiority in terms of time and cost compared to traditional 3D tree modeling methods (laser scanning, close-range photogrammetry, etc.); as well as the satisfactory tree modeling accuracy for smart forest monitoring and landscape architecture applications. The proposed 2.5D parametric tree model added new value to the CAD-Blockchain integration industry because a plain “Blockchain/Merkle hash tree” tracks tree geometry growth and texture change temporarily with simple parametric transactions (i.e. controlled hash tree magnification/scaling). So, metaverse functionality (decentralized, autonomous, coordinated, and parallel design; same-data sharing; data validation), modification and redesign ability, and planning intelligence are effectively supported by the proposed technique. Main contributions are regarded as the ability for smart forest distributed surveillance and collaborative parallel landscape architecture design, open-source Web-based educational simulations, as well as the potential for off-the-shelf contractual collaborative frameworks (smart contracts between designers and clients). Stratification based on forest types improved above-ground biomass (AGB) estimation, especially when AGB was greater than 500 Mg/ha, using the proposed “IoTr-images” technique. So, this research provides new insight into AGB modeling and monitoring. Finally, the proposed method’s robustness has been validated by performance evaluation testing.

Radar Interferometry for Urban Infrastructure Stability Monitoring: From Techniques to Applications

Urban infrastructure is an important part of supporting the daily operation of a city. The stability of infrastructure is subject to various deformations related to disasters, engineering activities, and loadings. Regular monitoring of such deformations is critical to identify potential risks to infrastructure and take timely remedial actions. Among the advanced geodetic technologies available, radar interferometry has been widely used for infrastructure stability monitoring due to its extensive coverage, high spatial resolution, and accurate deformation measurements. Specifically, spaceborne InSAR and ground-based radar interferometry have become increasingly utilized in this field. This paper presents a comprehensive review of both technologies for monitoring urban infrastructures. The review begins by introducing the principles and their technical development. Then, a bibliometric analysis and the major advancements and applications of urban infrastructure monitoring are introduced. Finally, the paper identifies several challenges associated with those two radar interferometry technologies for monitoring urban infrastructure. These challenges include the inconsistent in the distribution of selected measurements from different methods, obstacles arising from rapid urbanization and geometric distortion, specialized monitoring techniques for distinct urban features, long-term deformation monitoring, and accurate interpretation of deformation. It is important to carry out further research to tackle these challenges effectively.

MANAJEMEN SARANA DAN PRASARANA DI TK ANANDA KOTA SUKABUMI

One effort to improve the quality of preschool education is to ensure that existing facilities and infrastructure at preschool education facilities are adequate, of good quality and appropriate for children's development, as well as ready-to-use facilities and infrastructure. . Therefore, to improve the quality of preschool education, good management of facilities and infrastructure is needed. The focus of research at Ananda Kindergarten includes the process of managing facilities and infrastructure, including planning needs for facilities and infrastructure, procurement of facilities and infrastructure, inventory of facilities and infrastructure, preservation of facilities and infrastructure, relocation of facilities and infrastructure. and monitoring facilities and infrastructure. This research uses a qualitative method with a case study design. Data collection techniques used interview, observation and recording techniques related to the management of Ananda Kindergarten facilities and infrastructure. The data obtained during the research process was analyzed through stages, namely data reduction, data presentation and drawing conclusions. Meanwhile, checking the validity of the data uses the source triangle, technical triangle and time triangle. Based on the data analysis that has been carried out; (1) Planning is carried out using a needs analysis method that takes into account all proposals from educators, teaching staff and student guardians and is carried out in meetings every two years. (2) Facilities and infrastructure can be obtained through the disbursement of BOP (Education Operational Support) funds for early childhood education. (3) Inventory is recorded for each entry of facilities and infrastructure, however, in the recording process, little attention is paid to the procedure for recording it in the inventory book. (4). There is no list of names of warehouses and facilities, but Ananda Kindergarten's facilities and infrastructure meet PAUD specifications and equipment. (5). Maintenance is carried out by educators and teaching staff. (6). Installation and dismantling of infrastructure carried out at PAUD Ananda consisted of filling the swimming pool and removing the rotating bowl. (7). Supervision of Ananda Kindergarten facilities and infrastructure is carried out by all parties such as educators, teaching staff and parents.

Novel communication system for buried water pipe monitoring using acoustic signal propagation along the pipe

PurposeWireless sensor networks (WSN), as a solution for buried water pipe monitoring, face a new set of challenges compared to traditional application for above-ground infrastructure monitoring. One of the main challenges for underground WSN deployment is the limited range (less than 3 m) at which reliable wireless underground communication can be achieved using radio signal propagation through the soil. To overcome this challenge, the purpose of this paper is to investigate a new approach for wireless underground communication using acoustic signal propagation along a buried water pipe.Design/methodology/approachAn acoustic communication system was developed based on the requirements of low cost (tens of pounds at most), low power supply capacity (in the order of 1 W-h) and miniature (centimetre scale) size for a wireless communication node. The developed system was further tested along a buried steel pipe in poorly graded SAND and a buried medium density polyethylene (MDPE) pipe in well graded SAND.FindingsWith predicted acoustic attenuation of 1.3 dB/m and 2.1 dB/m along the buried steel and MDPE pipes, respectively, reliable acoustic communication is possible up to 17 m for the buried steel pipe and 11 m for the buried MDPE pipe.Research limitations/implicationsAlthough an important first step, more research is needed to validate the acoustic communication system along a wider water distribution pipe network.Originality/valueThis paper shows the possibility of achieving reliable wireless underground communication along a buried water pipe (especially non-metallic material ones) using low-frequency acoustic propagation along the pipe wall.

A cabled ocean acoustic array to be installed in the Gulf of Maine

It is often noted that we know more about the planets than the processes of our oceans. A network of ocean observatories is improving our knowledge; however, measurements are still relatively scarce and do not provide enough data to validate and refine models of ocean currents, soundscapes, and biological activity. A cabled acoustic array is being developed for deployment in 2024 in the coastal waters of the Gulf of Maine to complement existing oceanographic monitoring infrastructure. The system will make measurements of the nearshore (6 km) environment and shed light on the connections between coastal measurements and the overall Gulf of Maine environment. The system is designed to synoptically collect acoustic, oceanographic, and biogeochemical data, while also remaining flexible enough in design to incorporate new sensors in the future. Measurements made with this ocean asset will serve as a baseline for pattern and trend analyses of changing environmental conditions in an area of high productivity, human use, and species diversity. The data management and visualization team are developing an infrastructure to provide public access to the data and its products. Public data access will permit researchers at all levels to advance our understanding of ocean acoustics and oceanographic processes.

Anomaly Detection in Time Series Flight Parameter Data Using Machine Learning Approach

Abstract: Infrastructure monitoring is more crucial than ever, especially in the aviation industry. To attempt to overcome the difficulties brought on by the problems given by the exponential growth of connected devices and data volume, this study analyzes the application of machine learning approaches for anomaly identification in time series flight parameter data. The introduction of network telemetry, which automates data collecting, is presented as a remedy, however processing enormous data quantities in real-time still poses a challenge. With a focus on time-series data, the paper explores the role of machine learning in network telemetry anomaly detection. Statistical, proximity-based, deviation-based, and supervised classifiers are used to identify anomalies, or departures from predicted patterns, in flight parameter data. For a few examples of anomalous data, long short-term memory networks (LSTMs) are used. The objective is to provide an effective anomaly detection system that can process complex time series flight data and includes data purification, anomaly discovery, temporal reference, and value prediction. The methodology describes the univariate anomaly detection strategy, in which distinct models record particular patterns for each flight parameter. The findings offer new understanding of thresholds, repeated anomaly correction, and prediction errors. The results show that the method is accurate in separating instances of normal data from those containing anomalies, making it a useful tool for practical applications needing accurate anomaly identification.