Data Acquisition Process Research Articles

Geospatial aspects of building information modelling (BIM) based utilities management

The concept of Building Information Modelling (BIM) which was originally used for construction design has developed with increasing BIM dimensions from 3D, 4D, 5D, 6D, 7D to 10D and with the addition of semantics to 3D objects so that BIM allows it to be developed in broader management such as utility and assets management. In a utility management system, object data acquisition process will face some problem in varying object’s dimensions and location require the merging of several 3D data which is collected from different equipment with different accuracy and precision which for this research using the High Definition Surveying (HDS) method from Terrestrial Laser Scanning (TLS) and Handheld Laser Scanning (HLS) instruments. The problem that arises is in point cloud data merging process with different point densities, in this paper merging data will use Helmert 3D transformation method. Another problem is in the objects georeferencing into the Earth coordinate system. A georeferenced system is needed to integrate all of utility object into a utility management system in a building. This process is necessary if the development of utility database management will be linked with the creation of 3D models in the bigger scopes such as smart cities or narrower scopes such as smart buildings. The end result is a georeferenced BIM-based utility database management system, which allows for spatial analysis or automation using coordinate systems embedded in 3D objects.

Research on Body Wave Extraction Technology of Mixed Active Source Ambient Noise

In complex surface environments, active source seismic exploration methods are difficult to stimulate and costly, while ambient noise detection technology is fast and convenient, with a little environmental damage and low cost. Therefore, it has aroused extensive research by scholars at home and abroad. At present, the use of passive source surface wave method for subsurface medium imaging has become mature. However, due to the low content and weak energy of the body wave signal in the actual ambient noise, it is particularly difficult to extract body waves using seismic interferometry. At the same time, body wave exploration has larger exploration depth and higher resolution. In this paper, we propose an ambient noise detection technology of mixed active sources, namely, imposing artificial seismic sources in the continuous ambient noise data acquisition process. We refer to the ambient noise of mixed active sources as "mixed ambient noise". It is found that the virtual refraction wave obtained by the ambient noise detection method of mixed active sources has high signal-to-noise ratio, strong anti-interference ability, and can obtain the real velocity information of the deeper subsurface medium, but cannot reflect the accurate time information. Supplementing the active source signal in the far field can obtain virtual refractions with higher signal-to-noise ratio, while effectively reducing the mixing phenomenon of surface wave and surface interference waves in the virtual common-shot record.

Spectral estimation model for linear displacement and vibration monitoring with GBSAR system

In recent years, there has been a growing interest in the development of ground-based Synthetic Aperture Radars (GBSAR) for the purpose of monitoring structural displacements. GBSAR offers high-resolution monitoring over a wide area and can capture data every few minutes. However, compact high-frequency multiple input multiple output (MIMO) radars have emerged as an alternative for monitoring sub-second displacements, such as structural vibrations. MIMO radar has sub-second acquisition interval. However, it has limited cross-range resolution compared to GBSAR, and interference between antennas and presence of multiple scatterers in the scene can cause strong sidelobes in the processed data. On the other hand, GBSAR utilizes a long synthetic aperture to achieve high cross-range resolution. However, due to its longer data acquisition time compared to MIMO radar, conventional methods are insufficient for detecting scatterers’ sub-second displacements that occur during the data acquisition process. This study proposes a method to effectively monitor sub-second or sub-minute displacements using GBSAR signals. The proposed method enhances the conventional radar interferometric processes by employing spectral estimation, allowing for multi-dimensional detection of targets’ azimuth angle, linear displacement, and vibrational characteristics. Consequently, this method improves both the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals. The paper presents the theoretical details and mathematical formulations of the proposed method for both MIMO radar and GBSAR imaging modes. To evaluate the effectiveness of the proposed method, numerical simulations and real experiments are conducted. The experimental results validate the capability of the proposed method in both GBSAR and MIMO configuration modes for high-resolution monitoring of fast linear displacements and vibrations. The results exhibit promising signal-to-noise ratio (SNR) and peak-to-sidelobe ratio (PSLR) values.

N-version programming for nanosatellite telemetry processing

Software is a key element that ensures the functioning of any modern complex technical system. One such system is the constellation of spacecraft and associated ground control complexes that provide reception, transmission, and processing of collected telemetry. The process of data acquisition and its subsequent processing is critical to the flight control of the spacecraft and its onboard scientific equipment. Furthermore, telemetry data processed by ground control systems involves large volumes of raw data, the processing of which is a complicated and time-consuming task. In order to solve this problem, various methods of automatic data processing are used. Improving them is a key factor in ensuring the fault tolerance of onboard software and hardware, improving its reliability. Of all the existing widely-used methods of data processing, we shall focus on N-version programming (NVP) approach. N-version programming has firmly established itself as an effective method for increasing software reliability and designing fault-tolerant systems. Since its inception in the 1970s, this approach has been deeply connected with the development of aerospace software systems, including, among others, satellite ground control stations. In light of the aforementioned, this paper discusses the application of NVP for processing telemetry data gathered from nanosatellites (CubeSats). Due to the fact that there exists a skeptical view on the NVP approach in terms of its efficiency, the author covers this issue in existing literature in terms of the approach’s applicability for processing satellite telemetry.

Exploring Data Acquisition and Real-Time Analysis Algorithms in Smart Manufacturing with a Focus on Automation and Inspection Technologies

The conducted research project is based on the process followed in the smart manufacturing method. It has been observed that there are different types of methods that have been used in the smart manufacturing process and among these methods most of the methods are directly associated with different modern technology and artificial intelligence as well. Along with the inclusion of automation systems in the smart manufacturing process, the incorporation of inspection technologies is also observed in the case of performing smart manufacturing systems. In association with these, the usage of an acquisition system and real-time analysis process is also observed as well. Independent steps are present inside the phases of production process of the smart manufacturing methods. The incorporation of automation processes significantly enhances the performance of individual phases within the smart manufacturing process, thereby raising the overall production standards. Automation minimizes the likelihood of human errors, data misplacement, and mishandling. Moreover, it proves highly advantageous in the data acquisition process, ensuring error-free data transfer through automated systems.

STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysisand visualization.

Recent technological developments in spatial transcriptomics allow researchers to measure gene expression of cells and their spatial locations at the single-cell level, generating detailed biological insight into biological processes. A comprehensive database could facilitate the sharing of spatial transcriptomic data and streamline the data acquisition process for researchers. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a database that serves as a one-stop hub for spatial transcriptomics. STOmicsDB integrates 218 manually curated datasets representing 17 species. We annotated cell types, identified spatial regions and genes, and performed cell-cell interaction analysis for these datasets. STOmicsDB features a user-friendly interface for the rapid visualization of millions of cells. To further facilitate the reusability and interoperability of spatial transcriptomic data, we developed standards for spatial transcriptomic data archiving and constructed a spatial transcriptomic data archiving system. Additionally, we offer a distinctive capability of customizing dedicated sub-databases in STOmicsDB for researchers, assisting them in visualizing their spatial transcriptomic analyses. We believe that STOmicsDB could contribute to research insights in the spatial transcriptomics field, including data archiving, sharing, visualizationand analysis. STOmicsDB is freely accessible at https://db.cngb.org/stomics/.

CADTrack: Instructions and Support for Orientation Disambiguation of Near-Symmetrical Objects

Determining the correct orientation of objects can be critical to succeed in tasks like assembly and quality assurance. In particular, near-symmetrical objects may require careful inspection of small visual features to disambiguate their orientation. We propose CADTrack, a digital assistant for providing instructions and support for tasks where the object orientation matters but may be hard to disambiguate with the naked eye. Additionally, we present a deep learning pipeline for tracking the orientation of near-symmetrical objects. In contrast to existing approaches, which require labeled datasets involving laborious data acquisition and annotation processes, CADTrack uses a digital model of the object to generate synthetic data and train a convolutional neural network. Furthermore, we extend the architecture of Mask R-CNN with a confidence prediction branch to avoid errors caused by misleading orientation guidance. We evaluate CADTrack in a user study, comparing our tracking-based instructions to other methods to confirm the benefits of our approach in terms of preference and required effort.

Variable speed rolling force prediction with theoretical and data-driven models

The variations in thickness at the head and tail significantly impact the product quality in tandem cold rolling. Improving rolling force calculation accuracy under speed-up and speed-down conditions is essential to enhance thickness control. This paper proposes a sequential coupled model combining data-driven and theoretical models to predict rolling force under speed variation conditions. To enhance accuracy and overcome the limitations of traditional velocity field, a linear cross-section dynamic velocity field taking into account the effects of roll gap changes on the deformation zone is established. Moreover, a long short-term memory (LSTM) network is constructed to capture sequential information from production data and compensate for changes in friction state caused by speed variation and roughness attenuation in the theoretical model. 12 coils, totaling 12,000 samples, are selected from the process data acquisition system (PDA) and utilized as the dataset after normalization. Mutual information (MI) theory is employed for feature selection to improve the prediction accuracy and reduce the computing consumption. Learning rate (LR) adjustment is applied to enhance the stability of initial network and the convergence speed. Compared to evaluated models, the proposed model has relative error within ±2.5 %, R2 of it is 0.9935 and the prediction time is only around 2.5 ms. The results suggest that the proposed model demonstrates the most favorable overall performance. Additionally, some key rolling parameters whose physical relationship with rolling force remains uncertain are investigated.

Natural Enfumafungin Analogues from Hormonema carpetanum and Their Antifungal Activities.

Ibrexafungerp, an inhibitor of fungal β-(1,3)-d-glucan synthase, represents the first new class of antifungals to be approved in the last 20 years. Ibrexafungerp is a semisynthetic derivative of the naturally occurring triterpene glycoside enfumafungin. In order to search for new analogues of enfumafungin and to probe its biosynthesis, we undertook a reinvestigation of Hormonema carpetanum, which led to the isolation of two new analogues, enfumafungins B and C, together with enfumafungin. Due to the presence of a hemiacetal moiety in the structure, the enfumafungins appear as a mixture of two interconverting epimers during both the purification process and NMR data acquisition. The structure elucidation, including the differentiation of 25S* and 25R* epimers, was completed by combined analyses of NMR and MS spectroscopic data. The discovery of enfumafungins B and C may have implications for enfumafungin biosynthesis. The antifungal activity of enfumafungins B and C was significantly lower than that of enfumafungin, suggesting that the C-2 substituents and the C-19 carboxy acid are important for activity. Molecular docking simulations revealed significant hydrogen bond interactions between enfumafungins and β-(1,3)-d-glucan synthase, which may be useful for developing new antifungal agents.

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

On‐site inspection of invisible subsurface defects in multiscale structural materials by conventional nondestructive testing (NDT) methods, such as X‐ray and ultrasound, requires complex sample preparation and data acquisition processes. Moreover, the inspected area is very small. Herein, a simple, inexpensive, and ultrasensitive NDT method for identifying and classifying the geometries of subsurface defects using commercial cameras, digital image correlation software, and object detection (OD) algorithms is developed. Three OD algorithms—Faster region‐based convolutional neural network (Faster R‐CNN), Mask R‐CNN, and you‐only‐look‐once (YOLO)v3—are evaluated for their ability to locate defects and identify defect geometries. Specifically, bounding boxes of two sizes (large and small) are applied to the regions of defect‐induced perturbations in strain tensors, which serve as virtual representatives of invisible subsurface defects. The performance of the proposed approach is validated on test datasets of known and unknown defect types. The experimental results confirm that the proposed approach can effectively utilize the surface deformation field information to accurately and reliably locate and identify subsurface defects. The method is nondestructive and low cost, enables real‐time detection, is robust against noise‐dominated deformation fields, and can be applied to various structural deformations. The method is therefore suitable for multiscale structural health monitoring and characterization of internal defects in materials.

Early detection and identification of white spot syndrome in shrimp using an improved deep convolutional neural network

White Spot Syndrome Virus (WSSV) is a major virus found in shrimp that causes huge economic loss in shrimp farms. A selective diagnostic approach for WSSV is required for the early diagnosis and protection of farms. This work proposes a novel recognition method based on improved Convolutional Neural Network (CNN) namely Dense Inception Convolutional Neural Network (DICNN) for diagnoses of WSSV disease. Initially, the process of data acquisition and data augmentation is carried out. The Inception structure is then used to improve the performance of multi-dimensional feature extraction. As a result, the proposed work has the highest accuracy of 97.22% when compared to other traditional models. The proposed work is targeted to Litopenaeus Vannamei (LV), and Penaeus Monodon (PM) diversities for major threats detection of White Spot Syndrome (WSS). Performance metrics related to accuracy have been compared with other traditional models, which demonstrate that our model will efficiently recognize shrimp WSSV disease.

Application of a mathematical model to clarify the statistical characteristics of a pan-tissue DNA methylation clock.

DNA methylation clocks estimate biological age based on DNA methylation profiles. This study developed a mathematical model to describe DNA methylation aging and the establishment of a pan-tissue DNA methylation clock. The model simulates the aging dynamics of DNA methylation profiles based on passive demethylation as well as the process of cross-sectional bulk data acquisition. As a result, this study identified two conditions under which the pan-tissue DNA methylation clock can successfully predict biological age: one condition is that the target tissues are sufficiently well represented in the training dataset, and the other condition is that the target sample contains cell subsets that are common among different tissues. When either of these conditions is met, the clock performs well. It is also suggested that the epigenetic age of all samples in the target tissue tends to be either over or underestimated when biological age prediction fails. The model can reveal the statistical characteristics of DNA methylation clocks.

Electrical conductance lab: a low-cost, simple and useful project with Arduino

This paper introduces a project carried out with an advanced physics class in a secondary school, where Arduino-based classroom measurements were used to strengthen the students’ knowledge in the topic of direct current. The aim of the project was to engage students in the classroom work and provide them with modern and applicable knowledge. The project involved students working in small groups, where they used Arduino to measure the conductance of various fluids, including tap water, and saltwater. Students relied on digital technology for data acquisition and data processing and they evaluated the measurement data digitally, too. Graphical representation and function plotting—qualitative and quantitative explanation of the measurements support students to deepen their knowledge. The project demonstrated the potential of applying Arduino-based classroom measurements to engage students in learning physics and provide them with hands-on experience with modern technology. In this paper, we introduce a method we designed for conducting Arduino-supported classroom measurements effectively, while developing competences that are essential for getting by in the 21st century.

Comparative Study of Cloud Forensic Investigation Using ADAM And NIST 800-86 Methods in Private Cloud Computing

As information technology advances, associated risks also increase, particularly in the field of private cloud computing services. These services are subject to potential internal abuse risks, either due to system vulnerabilities or other factors. However, the investigation of these incidents in private cloud computing varies greatly due to the different frameworks and unique characteristics of each cloud service. The lack of a standardized approach to analyzing and assessing investigative processes in cloud computing services has been a persistent problem. This lack of consensus affects the accuracy, efficiency, and data acquisition process when dealing with digital evidence in each method, causing concern among researchers. To overcome this, a comparative study was carried out with a focus on the ADAM (The Advanced Data Acquisition Model) method and the NIST (National Institute of Standards and Technology) method. The goal is to identify the most effective investigative process to deal with cyber attack incidents on both the server and client side of cloud computing services. By testing these methods in a network that is built on private cloud computing services, then the results from this research include the weaknesses and strengths of the ADAM and NIST methods are found when applied to cloud computing case studies and these have not been identified in previous research, then produce recommendations for investigators when conducting investigations on case studies on cloud computing, and in this study managed to find a bug in the ownCloud application version 10.9.1. Then this study also aims to provide researchers with valuable references to carry out analysis and assessment in the investigative process, where standardization is still an unresolved issue.

A Clinician's Roadmap to the Research and Data Acquisition Process Within the Veterans Health System.

Objective Identify and address potential obstacles to initiation of older population-related research within the NF/SG VHS through the provision of a concise flowchart. Setting North Florida/South Georgia Veterans Health System (NF/SG VHS). Practice Description The Department of Veterans Affairs (VA) Research Service with connection to the University of Florida facilitates research that contributes to improving the delivery of inpatient/outpatient care to veterans and their families. Practice Innovation A compendium of a senior care pharmacist's experiences will be compiled and reviewed by other specialists within the field while attempting to submit research protocols for publication within the VHS through the use of data platforms such as VINCI (VA Informatics and Computing Infrastructure) and the Corporate Data Warehouse. Main Outcome Measurements Impact of navigating research websites affiliated with and directly pertaining to the NF/SG VHS upon clinicians attempting to begin research processes within the institution. Results It was determined that for the most expeditious publication experience to result the following six steps had to be pursued sequentially: VA Institutional Review Board (IRB) Net documentation/ submission, local myIRB submission, Data Access Request Tracker/VINCI access request, setup of VINCI workspace, drafting of VINCI cohort specification, data analysis and tracking, statistical methods, abstract, and manuscript production. Conclusion This study outlines a consistent/clear method from first-hand experiences on how to navigate, more efficiently, complex research processes to produce successful future impacts on patient care.

Test Method of Secure and Controllable FPGA Chip Used in Flexible DC Transmission Control and Protection System

Flexible DC transmission control and protection system is a high-performance distributed computing system, which has the characteristics of high-speed data acquisition and fast processing, and uses a large number of high-performance FPGA (Field Programmable Logic Array) chips. FPGA has the characteristics of high integration and complex manufacturing process, and its security and controllability is faced with many challenges. The reliability of FPGA is an important standard to measure its performance, so reliability test fault screening is very important to ensure its normal operation. Although the automatic test equipment (ATE) can test the fault of FPGA, it is not convenient for field testing due to the price, test environment and other factors. In this paper, a design scheme of built-in self-test (BIST) for fault detection and fault diagnosis is proposed, which can detect faults of look-up table (LUT) and interconnection resources (wires and programmable switches). At the same time, considering the effect of rising temperature on fault probability, a controlled self-heating element (SHE) is constructed by using the internal resources of FPGA to provide the required temperature for BIST testing. Finally, an integrated layout scheme of BIST and SHE is proposed. An experiment of self-heating of FPGA chip is completed by TSINGHUA PGL22G, which verifies the correctness and feasibility of the theory.

Temporal Analysis of Land Use and Land Cover Changes in Vizianagaram District, Andhra Pradesh, India using Remote Sensing and GIS Techniques

Land use and land cover change (LULCC) has become a significant global concern due to its wide-ranging environmental, social and economic impacts. This literature review aims to provide a comprehensive overview of the key ideas, drivers, consequences and approaches to studying LULCC. By synthesizing various research articles, this review offers insights into the causes and impacts of LULCC, as well as the methods used to analyze and monitor these changes. The review also highlights the importance of understanding LULCC dynamics for sustainable land management and policy making. Between 2017 and 2022, the LULC categories underwent several changes. Data acquisition process for satellite imagery combining Sentinel-2 digital remote sensing data digital remote sensing data through the Copernicus Open Access Hub. The spectral resolution is 10, 20, and 30 meters respectively, while the spatial resolution is 10 meters which was used for the LULC analysis of the study area. This analysis underscores the importance of LULCC monitoring to inform sustainable land management practices and conservation efforts. The trends identified provide a basis for further investigation into the underlying drivers of these changes and their potential impacts on ecosystems, water resources and human well-being. Continued monitoring and proactive measures are essential to mitigate adverse impacts and promote sustainable land use in the future.

Critical Assessment of the Chemical Space Covered by LC-HRMS Non-Targeted Analysis.

Non-targeted analysis (NTA) has emerged as a valuable approach for the comprehensive monitoring of chemicals of emerging concern (CECs) in the exposome. The NTA approach can theoretically identify compounds with diverse physicochemical properties and sources. Even though they are generic and have a wide scope, non-targeted analysis methods have been shown to have limitations in terms of their coverage of the chemical space, as the number of identified chemicals in each sample is very low (e.g., ≤5%). Investigating the chemical space that is covered by each NTA assay is crucial for understanding the limitations and challenges associated with the workflow, from the experimental methods to the data acquisition and data processing techniques. In this review, we examined recent NTA studies published between 2017 and 2023 that employed liquid chromatography-high-resolution mass spectrometry. The parameters used in each study were documented, and the reported chemicals at confidence levels 1 and 2 were retrieved. The chosen experimental setups and the quality of the reporting were critically evaluated and discussed. Our findings reveal that only around 2% of the estimated chemical space was covered by the NTA studies investigated for this review. Little to no trend was found between the experimental setup and the observed coverage due to the generic and wide scope of the NTA studies. The limited coverage of the chemical space by the reviewed NTA studies highlights the necessity for a more comprehensive approach in the experimental and data processing setups in order to enable the exploration of a broader range of chemical space, with the ultimate goal of protecting human and environmental health. Recommendations for further exploring a wider range of the chemical space are given.

Few-shot learning for image-based bridge damage detection

Autonomous bridge visual inspection is a real-world challenge due to various materials, surface coatings, and changing light and weather conditions. Traditional supervised learning relies on massive annotated data to establish a robust model, which requires a time-consuming data acquisition process. This work proposes a few-shot learning (FSL) approach based on improved ProtoNet for damage detection with just a few labeled examples. Feature embedding is achieved through cross-domain transfer learning from ImageNet instead of episodic training. The ProtoNet is improved with embedding normalization to enhance transduction performance based on Euclidean distance and a linear classifier for classification. The approach is explored on a public dataset through different ablation experiments and achieves over 94% mean accuracy for 2-way 5-shot classification via the pre-trained GoogleNet after fine-tuning. Moreover, the proposed fine-tuning methods based on a fully connected layer (FCN) and Hadamard product are demonstrated with better performance than the previous method. Finally, the approach is validated using real bridge inspection images, demonstrating its capability of fast implementation for practical damage inspection with weakly supervised information.

Applications of Active Learning in Predictive Maintenance

Nowadays, the common choice in maintenance strategies is predictive maintenance (PdM), deprecating the corrective and preventive kinds. Even with various machine learning techniques to get advanced predictive models to achieve PdM, difficulties remain in the data acquisition process. While there is a plethora of unlabeled data from sensors, most of those available techniques can only process labeled data, i.e, supervised learning. To combat the fact that the availability of the labeleddata is limited, this paper proposes the use of Active Learning to label and annotate the informative instances while minimizing overall processing time. This approach maintains high performance and decreases the number of labeled instances, with support from experimental results and a discussion of the applicability of this method.