Improve Measurement Accuracy Research Articles

BLOOD PRESSURE MEASURING TECHNIQUES AMONG HEALTHCARE PROFESSIONALS IN KSA: A SYSTEMATIC REVIEW

Background: Accurate blood pressure (BP) measurement is critical for the diagnosis and management of hypertension, a leading cause of global morbidity and mortality. However, challenges in measurement techniques and knowledge gaps among healthcare professionals can lead to inaccurate readings, compromising patient care. This systematic review aimed to evaluate the difficulties healthcare professionals face in BP measurement and explore the variability in measurement practices. Methods: A comprehensive search of PubMed, Embase, Cochrane Library, and CINAHL databases was conducted to identify relevant studies. A total of 627 articles were initially identified. After removing 524 irrelevant articles, 103 studies were screened for eligibility. Of these, 60 full-text articles were reviewed, and two studies met the inclusion criteria. Data were synthesized to highlight common challenges and discrepancies in BP measurement practices among healthcare professionals. Results: The two included studies revealed significant challenges in BP measurement. One study identified that 60% of hospital staff in a teaching hospital lacked sufficient knowledge of fundamental BP measurement techniques. Key issues included improper understanding of cuff size, arm positioning, and deflation rates, which led to inconsistent and inaccurate readings. The other one examined BP measurement devices and methods in primary healthcare settings across Saudi Arabia. The study found that mercury sphygmomanometers were most commonly used (69.8%), followed by electronic devices (16.5%) and aneroid manometers (11.8%). However, electronic and aneroid devices were prone to measurement variations, especially in rural areas, emphasizing the need for standardized devices to reduce bias. Conclusion: This review highlights critical gaps in knowledge and variability in BP measurement methods among healthcare professionals, which can adversely impact hypertension management. Addressing these challenges requires targeted educational programs and the adoption of reliable and standardized devices to improve measurement accuracy. Enhancing BP measurement practices is essential for achieving better patient outcomes and reducing the burden of hypertension-related complications.

Neural network-based method for structured light phase-shifted fringe denoising and phase-to-height calibrations

Abstract The monocular structured light measurement system is widely applied across various fields due to its simple structure, low cost, and extensive measurement range. However, measurement accuracy can be affected by noise, non-linear intensity variations, variations in object surface reflectance, and calibration precision, leading to unstable or inaccurate results. Traditional filtering methods are limited in effectively addressing complex noise and non-linear issues, and phase-to-height calibration often depends on high-precision motion platforms, which increases system costs, complexity, and calibration uncertainty. To address these challenges, this paper proposes a neural network-based approach for monocular structured light measurement. An iterative denoising algorithm based on a denoising autoencoder (DAE) is developed for phase-shift fringe images. By optimizing the iteration count, denoised phase-shift fringes are regenerated to de noise to reduce image noise and enhance phase computation accuracy effectively. Additionally, leveraging the high-precision absolute phase calculated from the denoised phase-shift fringe images, a multilayer feedforward neural network (FNN) algorithm is proposed for absolute phase-to-height calibration. This method directly maps the phase-to-height relationship, integrating both intrinsic and extrinsic camera parameters to achieve high-precision calibration without requiring a high-precision motion platform. Consequently, it significantly mitigates errors related to the motion platform and reduces operational errors during calibration. The DAE enhances the quality of phase images, providing more precise input for the FNN calibration and further improving measurement accuracy. Experimental results demonstrate that the proposed method achieves effective 3D reconstruction from low-quality phase-shift fringes and maintains robust performance when measuring objects with varying reflectances.

Mechanisms to improve measurement accuracy through differential signaling in homodyne interferometers

Mechanisms to improve measurement accuracy through differential signaling in homodyne interferometers

Improving Racing Drones Flight Analysis: A Data-Driven Approach Using Motion Capture Systems

The publication of the previous study, titled “Experimental Study on the Dynamic Behaviour of Drones Designed for Racing Competitions”, highlighted the increasing interest in employing scientific methods for their design and analysis. That study examined the flight data of 15 racing drones within a large flight area, using Doppler-type sensors for data collection. Building on these findings and seeking to enhance them, the current work introduces an upgraded data acquisition system utilising optical sensors, thereby improving measurement accuracy. These enhanced flight data facilitate the development of updated quality indices and conclusions, offering a more precise and definitive analysis than was previously possible.

A Comprehensive IoT Cloud-based Wind Station Ready for Real-time Measurements and Artificial Intelligence Integration

Accurate and efficient wind measurement and monitoring are crucial for various applications, including renewable energy, meteorology, public safety, and environmental research. This paper presents an integrated approach for a cloud-computing Internet of Things-based Automated Weather Station, with edge devices, designed to provide real-time wind measurements in diverse environments. The presented system comprises a dedicated station frame, a self-sufficient solar power setup, an ultrasonic wind sensor, a data transmission node, cloud computing capabilities, and an end-user visualization application. Calibration procedures reduced the maximum mean error of the ultrasonic wind sensors from 0.7 m/s to 0.2 m/s, achieving a 71% improvement in measurement accuracy. Using this method, all station data are processed every 3 seconds and stored ready for Artificial Intelligence usage with an approximate latency of 150–300 milliseconds, representing up to an 85% reduction in processing delay compared to similar solutions with over 1-second latencies. The lightweight and resistant frame design facilitates easy deployment, while the power setup with sub-watt efficiency ensures a reliable energy source. The ultrasonic wind sensor was subjected to calibration procedures and design improvements to increase its performance under specific rain conditions. Internet of Things communication via Hypertext Transfer Protocol enables efficient data transmission between the Automated Weather Station and the cloud server, which processes and stores the data for future analysis. The responsive and auto-adaptive web application allows user-friendly data visualization, making the wind data easily accessible and interpretable. The proposed cloud-computing Internet of Things-based Automated Weather Station framework demonstrates significant potential for accurate and efficient wind measurement and monitoring, paving the way for future advancements in high temporal resolution wind monitoring systems capable of producing big data prepared for subsequent machine learning model approaches.

In Situ Ultrasonic Characterization of Hydrogen Damage Evolution in X80 Pipeline Steel.

A nondestructive evaluation of the hydrogen damage of materials in a hydrogen environment is important for monitoring the running conditions of various pieces of equipment. In this work, a new thermostatic electrolytic hydrogenation in situ ultrasonic test system (In Situ TEH-UT) was developed. The system operates by combining cross-correlation delay estimation and frequency domain amplitude estimation and hence improves measurement accuracy with respect to ultrasonic propagation time and amplitude, allowing in situ ultrasonic evaluation of the hydrogen-charging process in X80 pipeline steel. The experimental results show that under a 30 mA/cm2 hydrogen-charging current, the hydrogen saturation time of X80 pipeline steel is 800 min. Between 0 and 800 min, the attenuation coefficient and amplitude attenuation both demonstrate a strong linear relationship with the hydrogen-charging time. After 800 min, the attenuation coefficient and amplitude attenuation do not change further, while the attenuation coefficient fluctuates greatly. Through the characterization of the microstructures of the materials analyzed, it was found that hydrogen-induced cracks (HICs) constituted the main reason for the change in the ultrasonic parameters, and the mechanism behind the hydrogen-induced damage layer (HIDL) was determined. This study provides reference significance for clarifying the change mechanism of ultrasonic parameters under hydrogen damage conditions and determining the extent of hydrogen damage using an ultrasonic technique.

Generative Adversarial Networks (GANs) 2D imaging optimization in image–J for estimation of endodontic filling material measurement: A review

Background: Root canal treatment (RCT) remains the most common clinical intervention in dentistry, treating a wide range of pulp-related conditions and structural tooth injuries. RCT begins by partially removing the crown to access and remove the infected pulp through a meticulous process of cleaning, reshaping, and irrigating the canal. This prepared canal is filled with gutta-percha as the endodontic filling material (EFM), and adhesive cement is used to seal the canal before final restoration with a crown. The success of RCT, however, hinges on accurately measuring and applying the EFM volume within the canal. Current measurement practices using 2D radiographic imaging fall short in accuracy due to operator limitations and the inherent lack of depth and clarity in 2D images, which contribute to high variability in EFM application. CBCT has emerged as a viable alternative, offering comprehensive 3D imaging for a more accurate EFM volume assessment, yet its high radiation levels, cost, and accessibility issues pose challenges. Thus, with increasing demand for safer and cost-effective alternatives, AI-driven enhancements in 2D imaging are being explored to achieve CBCT-level accuracy without its drawbacks, marking a significant step forward in RCT reliability and success rates. Purpose: To explore the potential of GANs and Image–J integration on 2D images for estimation of EFM measurement. Method: A literature review of studies published within the past five years from sources like ResearchGate, ScienceDirect, PubMed, and Google Scholar. Result: This study explores the integration of artificial intelligence, specifically convolutional neural networks (CNN) and generative adversarial networks (GANs), to enhance the accuracy of endodontic filling material (EFM) measurements in root canal treatments (RCT). CNN's structure, comprising multilayered networks with pooling layers, has shown potential in medical imaging by segmenting anatomical details for EFM measurement. However, challenges remain due to CNN's reliance on extensive datasets and its susceptibility to misinterpretation when faced with complex anatomical variations. GANs, combining a generator and discriminator, address these limitations by enabling unsupervised learning, allowing the generation of photorealistic data samples for more precise EFM measurement. This bidirectional learning system refines measurement accuracy through continuous feedback between the generator and discriminator, achieving superior outcomes across parameters like radiopacity, dimensional consistency, classification performance, and sealing ability. Furthermore, the AI model is enhanced by integrating Image-J, a Java-based image analysis software with precise 2D measurement capabilities. Despite its effectiveness in analyzing length and volume, Image-J has limitations due to its dependence on the operator’s interpretation, especially with low-quality 2D images. The combined use of GANs with Image-J allows for automated, detailed border detection of the pulp anatomy, improving measurement accuracy even with complex root canal forms. The results suggest that this integrated AI approach can advance clinical practices by providing precise, rapid, and consistent EFM measurements, potentially overcoming the limitations of traditional 2D radiographic methods and addressing CBCT’s drawbacks in radiation exposure and accessibility. Conclusion: The integration of generative adversarial networks (GANs) with Image-J offers a promising advancement in the measurement of endodontic filling materials (EFM) for root canal treatments based on 2D imaging. GANs enhance Image-J’s measurement precision and accuracy by utilizing bidirectional deep learning, which enables continuous learning and refinement without requiring extensive external inputs. This approach not only improves EFM estimation but also presents a viable alternative to cone-beam computed tomography (CBCT), reducing potential health risks and economic burdens associated with CBCT. The combined GANs and Image-J system thus supports a more accessible, efficient, and precise methodology for EFM assessment in clinical dentistry.

Phase-shifting profilometry for 3D shape measurement of moving objects on production lines

It is challenging for phase-shifting profilometry to measure objects in motion on production lines due to two main issues. Firstly, the movement of the object causes a mismatch in its position across different fringe patterns. Additionally, there is a violation of the ideal phase shift between these fringe patterns. To address these problems, we propose a method that takes advantage of the known moving direction of objects on production lines. By aligning the isophase direction of fringe patterns with the known direction of the moving objects, our method essentially solves the well-known phase error problem caused by object motion. Furthermore, we have developed a projection and imaging technique to track the measured object that is covered with different fringe patterns. Both of the previously listed issues have been resolved in this manner. The proposed method is validated by comparisons with state-of-the-art methods, proving its simplicity, broader applicability, and improved measurement accuracy.

A Coloring-Based Packet Loss Rate Measurement Scheme on Network Nodes

Network measurement is an efficient way to understand network behavior. Traditional measurement techniques focus on internet protocol (IP) networks, where the processing capacity of network nodes is limited and primarily dedicated to packet forwarding. As a result, these techniques typically rely on end hosts or external systems to analyze traffic and evaluate network performance. This reliance introduces several challenges, such as increased measurement latency and scalability limitations, particularly in large-scale networks. With the emergence of next-generation internet architectures, especially information-centric networking (ICN), network nodes have gained enhanced capabilities, enabling measurement tasks to be performed directly at these nodes. This paper proposes a distributed measurement scheme where network nodes collaborate to monitor the packet loss rate on the intermediate link. By setting an unused bit in the packet header, the upstream node “colors” the packets into different color blocks. The minimum duration of each block is determined by the degree of reordering on the link, and the number of packets in each block must be a power of two. The downstream node recognizes blocks, assigns packets to the right block, and deduces the original number of packets for each block to calculate packet loss. Moreover, the upstream node adjusts the number of packets in each block based on the packet transmission rate on the link, aiming to balance measurement accuracy and frequency. A P4-based implementation on a BMv2 software switch is presented to demonstrate the feasibility of the proposed scheme. Simulations show that this scheme improves measurement accuracy and is more robust against packet reordering. Additionally, the proposed scheme maintains relatively low network overhead and, at higher measurement frequencies, exhibits the lowest overhead compared to existing methods.

Beyond Numbers: Challenges in Measuring SDG4 Targets—Serbia’s Perspective

Achieving the targets and indicators outlined in Sustainable Development Goal 4 (SDG 4) presents significant global challenges, particularly in countries like Serbia, where diverse socio-economic contexts and policy planning shape educational outcomes. SDG4 focuses on providing inclusive and fair access to quality education for everyone and encourages lifelong learning opportunities for people of all ages. It comprises ten specific targets. This paper focuses on the complexities and challenges associated with measuring five of these targets, which were not analyzed in Serbia’s official report on SDGs. By using Serbia as a case study, the paper systematically examines national policies, educational data, and assessment frameworks, identifying persistent barriers that impede progress toward the SDG4 objectives. A comparative analysis was conducted to better understand Serbia’s progress in achieving these targets. This analysis compares Serbia’s performance with that of neighboring countries in the region as well as with more developed nations. Methodological limitations in data collection and monitoring further complicate efforts to accurately assess SDG 4 indicators. Through specific examples of non-measurable indicators, the paper illustrates these challenges and explores potential strategies for improving measurement accuracy and policy effectiveness. By evaluating Serbia’s experience, this study offers insights that are valuable for finding ways to start measuring targets that currently lack data, considering the close deadline for achieving the goals.

Esophageal pressure as estimation of pleural pressure: a study in a model of eviscerated chest

BackgroundTranspulmonary pressure is the effective pressure across the lung parenchyma and has been proposed as a guide for mechanical ventilation. The pleural pressure is challenging to directly measure in clinical setting and esophageal manometry using esophageal balloon catheters was suggested for estimation. However, the accuracy of using esophageal pressure to estimate pleural pressure is debated due to variability in the mechanical properties of respiratory system, esophagus and esophageal catheter. Furthermore, while a vertical pleural pressure gradient exists across lung regions, esophageal pressure balloon provides a single value, representing, at most, the pressure surrounding the esophagus.MethodsIn a swine model with a preserved esophagus and a single homogenous, easily measurable intrathoracic pressure, we evaluated esophageal pressure’s agreement with intrathoracic pressure at different positive end-expiratory pressure (PEEP) levels (0, 5, 10, 15 cmH2O). We assessed the improvement of measurement accuracy by correcting absolute esophageal values using a previously described technique, that accounts for the pressure generated by the esophageal wall in response to esophageal balloon inflation.The study involved five swine, wherein two different esophageal catheters were used alongside the four distinct PEEP levels. Swings, uncorrected and corrected absolute esophageal pressures (end-inspiratory, end-expiratory) were compared with their respective intrathoracic pressures. The effect of correction technique was assessed with manual incremental step inflation procedure.ResultsWe found that both catheters significantly overestimated absolute esophageal pressure compared to intrathoracic pressure (5.01 ± 3.32 and 6.06 ± 5.62 cmH2O at end-expiration and end-inspiration, respectively), with error increasing at higher positive end-expiratory pressure levels (end-expiration: 2.36 ± 2.03, 3.77 ± 1.37, 6.24 ± 2.51 and 7.69 ± 4.02 for each PEEP level, P < 0.0001; end-inspiration: 1.71 ± 2.10, 3.70 ± 1.73, 7.67 ± 3.62 and 11.14 ± 7.60 for each PEEP level, P = 0.0004).Applying the correction technique significantly improved agreement for absolute values (0.82 ± 1.62 and 1.86 ± 3.94 cmH2O at end-expiration and end-inspiration, respectively). Esophageal pressure swings accurately estimated intrathoracic pressure swings at low-medium intrathoracic pressures (-0.64 ± 0.62, -0.07 ± 0.53, 1.43 ± 1.51, and 3.45 ± 3.94 at PEEP 0, 5, 10 and 15 cmH2O, respectively; P = 0.0197).ConclusionsThe correction technique, based on the mechanical response of esophageal wall to the balloon inflation, is fundamental for obtaining reliable estimations of absolute intrathoracic pressure values, and for ensuring its correct application in clinical setting.

Improving methods for sample preparation of biological fluids by inductively coupled plasma mass spectrometry

This study presents the results of analyzing biological fluids using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) after applying various sample preparation methods. Optimal conditions for preparing biological fluids have been identified, which help minimize matrix effects and improve measurement accuracy. The most effective methods were selected considering the specifics of matrices and their interactions with analytical equipment. The study evaluated the contributions of spectral interferences to the analytical signal when using ICP-MS. It was found that the physical properties of acid solutions, such as viscosity, surface tension, density, and volatility, significantly affect the analysis results, introducing additional interferences and data distortions. The use of internal standards to eliminate spectral interferences was investigated. Criteria for choosing internal standards were established based on proximity to ionization potentials and mass characteristics, which allows for more accurate result correction and minimizes interference effects. Additionally, it was discovered that polyatomic ions play a significant role in enhancing analyte signals. This influence is due not to the background levels of elements in the samples or reagents, but to ions formed from matrix components in the samples. The study demonstrated that isobaric interferences caused by polyatomic ions significantly impact analytical accuracy. These findings contribute to improving sample preparation and analysis methods for biological fluids using ICP-MS, leading to more reliable and accurate measurements of element concentrations.

Experiment and prediction of enhanced gas storage capacity in depleted gas reservoirs for clean energy applications

Utilizing depleted gas reservoirs for gas storage is the most efficient method. However, the impact of multi-cycle injection-production and water invasion on rock properties and gas-water seepage dynamics remains unclear. This study investigates such reservoirs' multi-cycle stress sensitivity and gas-water displacement behavior. Results show that multi-cycle injection-production and water invasion increase the stress sensitivity of the gas and damage gas storage space and seepage channels. This study also proposed a new unsteady gas-water relative permeability testing method and a prediction model based on in-situ X-ray CT scanning, improving measurement accuracy by 30 %. The results found that the gas's relative permeability is directly proportional to the injection rate and inversely proportional to the initial gas saturation. A gas-water relative permeability model was established, demonstrating that gas-water interference intensifies as the number of cycles increases. In a gas-water transition zone in depleted gas reservoirs, gas relative permeability damage reaches 81 %, reducing the movable pore space and impacting injection-production rates and capacity. This study offers valuable recommendations for optimizing production and dynamic prediction in depleted gas reservoirs, contributing to clean energy applications.

In-Device Ballistic-Electron-Emission Spectroscopy for Accurately In Situ Mapping Energy Level Alignment at Metal-Organic Semiconductors Interface.

Energy level alignment at metal/organic semiconductors (OSCs) interface governs electronic processes in organic electronics devices, making its precise determination essential for understanding carrier transport behaviors and optimizing device performance. However, it is proven that accurately characterizing the energy barrier at metal/OSC interface under operational conditions remains challenging due to the technical limitations of traditional methods. Herein, through integrating highly-improved device constructions with an ingenious derivative-assisted data processing method, this study demonstrates an in-device ballistic-electron-emission spectroscopy using hot-electron transistors to accurately characterize the energy barrier at metal/OSC interface under in-operando conditions. This technique is found that a remarkable improvement in measurement accuracy, reaching up to ±0.03eV, can be achieved-surpassing previous techniques (±0.1-0.2eV). The high accuracy allows us to monitor subtle changes in energy barriers at metal/OSC interface caused by variations in the aggregation state of OSCs, a phenomenon that is theoretically possible but failed to be directly demonstrated through conventional methods. Moreover, this study makes demonstration that this technology is universally applicable to various metal/OSC interfaces consisting of electron-transporting, hole-transporting, and ambipolar OSCs. These findings manifest the great potential of this method to advance both theoretical exploration and technical applications in organic electronics.

Drift compensation system of an autocollimator based on reciprocal roll angles and a feedback combination target

To address the issues of limited field of view and beam drift in traditional autocollimation measurements, a novel, to our knowledge, autocollimation measurement method is proposed. This method is based on the principle of full-circle continuous measurement using reciprocal roll angles of a compound biaxial system, combined with a feedback and compensation mechanism for beam drift using both calibration and compensation targets. A self-collimating small-angle measurement system model, coupled with a compound biaxial turntable and combined target, is established using ray tracing and perspective projection transformation techniques. Additionally, a closed-loop beam drift compensation system is developed by applying a BP neural network and a PID control algorithm. Experimental tests on the compensation system demonstrate a significant improvement in measurement accuracy and stability. The results show that this method satisfies the requirements for small-angle autocollimation measurement, and the method also enhances the overall stability of the autocollimation system.

Scene Measurement Method Based on Fusion of Image Sequence and Improved LiDAR SLAM

To address the issue that sparse point cloud maps constructed by SLAM cannot provide detailed information about measured objects, and image sequence-based measurement methods have problems with large data volume and cumulative errors, this paper proposes a scene measurement method that integrates image sequences with an improved LiDAR SLAM. By introducing plane features, the positioning accuracy of LiDAR SLAM is enhanced, and real-time odometry poses are generated. Simultaneously, the system captures image sequences of the measured object using synchronized cameras, and NeRF is used for 3D reconstruction. Time synchronization and data registration between the LiDAR and camera data frames with identical timestamps are achieved. Finally, the least squares method and ICP algorithm are employed to compute the scale factor s and transformation matrices R and t between different point clouds from LiDAR and NeRF reconstruction. Then, the precise measurement of the objects could be implemented. Experimental results demonstrate that this method significantly improves measurement accuracy, with an average error within 10 mm and 1°, providing a robust and reliable solution for scene measurement.

Research on the Detection Principle of Coal Ash by X-Ray Transmission Based on FLUKA

This study addresses the timely and accurate measurement of coal ash content by proposing a detection model based on nuclear science technology, which is validated using FLUKA 4-4.0 simulation software. The background provided highlights the fact that coal ash content is a critical sales indicator, and its precise measurement is essential for adjusting production parameters in coal preparation plants. In terms of methodology, this study employs the widely used FLUKA4-4.0 software in the field of nuclear physics to simulate X-ray transmission through coal, investigating the impact of changes in coal type on the accuracy of ash measurements. The results indicate that, when the proportions of high-atomic-number elements in coal remain constant, the ash measurement results are accurate and reliable. However, significant fluctuations occur when these proportions change. The conclusion emphasizes the fact that variations in coal type are the primary cause of inaccuracies in ash measurement, particularly when the ratios of high-atomic-number elements are altered. This research provides a new perspective on the online measurement of coal ash content and offers theoretical support for improving measurement accuracy.

Identifying Potential Landslides in Low-Coherence Areas Using SBAS-InSAR: A Case Study of Ninghai County, China

The southeastern coastal regions of China are characterized by typical hilly terrain with abundant rainfall throughout the year, leading to frequent geological hazards. To investigate the measurement accuracy of surface deformation and the effectiveness of error correction methods using the small baselines subset–interferometry synthetic aperture radar (SBAS-InSAR) method in identifying potential geological hazards in such areas, this study processes and analyzes 129 SAR images covering Ninghai County, China. By processing coherence coefficients using the Stacking technique, errors introduced by low-coherence images during phase unwrapping are mitigated. Subsequently, interferograms with high coherence are selected for time-series deformation analysis based on the statistical parameters of coherence coefficients. The results indicate that, after mitigating errors from low-coherence images, applying the SBAS-InSAR method to only high-coherence SAR datasets provides reliable surface deformation results. Additionally, when combined with field geological survey data, this method successfully identified landslide boundaries and potential landslides not accurately detected in previous geological surveys. This study demonstrates that using the SBAS-InSAR method and selecting high-coherence SAR images based on interferogram coherence statistical parameters significantly improves measurement accuracy and effectively identifies potential geological hazards.

Optimizing Ship Draft Observation with Wave Energy Attenuation and PaddlePaddle-OCR in an Anti-Fluctuation Device

With the development and application of artificial intelligence (AI) in the shipping industry, using AI to replace traditional draft survey methods in bulk carriers can significantly reduce manpower, lower the risks associated with visual observations, improve measurement accuracy, and minimize the impact of human subjective factors. Ultimately, the integration of software and hardware technologies will replace human visual observations with automated draft measurement calculations. A similar anti-fluctuation device described in this article has been used in ship draft observation based on AI-assisted proving, which can ease the fluctuation of the wave inside the pipe. Observers can directly read the water surface inside the pipe and compare it to the ship’s draft mark to obtain the final draft, effectively improving draft observation accuracy. However, some surveyors refuse to accept the readings obtained from this device, citing a lack of theoretical basis or the absence of accreditation from relevant technical authorities, leading to the rejection of results. To address these issues, this paper integrates wave energy attenuation theory with PaddlePaddle-OCR recognition to further validate the anti-fluctuation device for accurate ship draft observation. The experimental results are as follows: first, the pipe effectively suppresses the amplitude of external water surface fluctuations by 75%, explaining the fundamental theory that wave heights within the anti-fluctuation device are consistent with external swell heights. When taking a draft measurement, the system dynamically adjusts the position of the main tube in response to the ship’s movements, maintaining the stability of the measurement section and significantly reducing the difficulty of observations. Due to the reduction in fluctuation amplitude, there is a noticeable improvement in observation accuracy.

An Optimal SVD Filtering Method for Measurement Accuracy Improvement against Harmonic Disturbance in Grid-Connected Inverters

An Optimal SVD Filtering Method for Measurement Accuracy Improvement against Harmonic Disturbance in Grid-Connected Inverters