Accuracy Of Data Acquisition Research Articles

Dokładność modelu BIM z chmury punktów dla obiektu wykonanego w technologii szklanej

Mapping glass objects in 3D space has long raised doubts as to the possibility of obtaining data, and as to the accuracy of that data. The basics of terrestrial laser scanning technology and the principles of the physics of light propagation in the environment of transparent and reflective surfaces, as a rule, contradict the technological possibility of a faithful mapping thereof. Although Building Information Modelling (BIM) of existing objects based on data from terrestrial laser scanning is an increasingly common practice, it is recognized, nevertheless, that the accuracy of the model is primarily reflected in the accuracy of the point cloud obtained as a result of scanning. The article discusses the possibilities of developing a BIM model of an object made in glass technology, based on data obtained with the method of terrestrial laser scanning. The subject of the study was the glazed façade of the complex of buildings belonging to the University of Agriculture in Krakow. The study on the fidelity of mapping glazed surfaces included the acquisition and processing of the point cloud, 3D modelling of the object using the Revit software, and the analysis of the accuracy of mapping the existing status in comparison with architectural design and construction documentation. Based on the research, the possibility of using the BIM process was assessed using TLS data in the process of recreating the geometry of an object made in glass technology. The results of the study showed a significant convergence of the façade model geometry with the actual course of the structure, which, however, can be attributed to the development methodology, i.e. the accuracy of 3D data acquisition, the registration process, the filtration procedure, the method of parametric modelling of the façade structure itself, and ultimately fitting three-layer glazing into the model of that structure.

Improving communication between can-sized satellite and ground control station for accuracy of data acquisition

Improving communication between can-sized satellite and ground control station for accuracy of data acquisition

Physics-informed and data-driven hybrid method for transmission accuracy design optimization of planetary roller screw mechanism

The planetary roller screw mechanism (PRSM) faces an ever-increasing precision transmission demand in current advanced equipment. The relationship between machining errors and transmission accuracy remains elusive due to the over-simplified physical models and small-sample experimental datasets. This work proposes a physics-informed and data-driven hybrid strategy for PRSM transmission accuracy evaluation and tolerance optimization. In the physical model, a PRSM transmission accuracy model is developed to calculate transmission error that considers 16 machining errors in eccentric, nominal diameter, pitch, flank angle, and roller consistency. In the dataset establishment, thread profile measurements and dynamic leadscrew inspections are conducted for the machining error and transmission accuracy data acquisition. A data augmentation approach combining the physical model with the generative adversarial network is utilized to predict travel deviation, variations, and axial backlash and estimate machining error contribution with the small-sample experimental dataset. It is firstly found that the roller consistency of nominal diameter significantly affects PRSM travel variation V2π with a 17.3 % importance value. With the developed framework, the key tolerances for screw, roller, nut, and roller consistency are optimized toward a typical precision transmission requirement using the non-dominated sorting genetic algorithm. It also provides a tolerance grade recommendation table with PRSM transmission accuracy level in engineering practice.

Measuring shrinkage of expansive soils using a novel automated high‐frequency setup

AbstractSoil shrinkage characteristic curves are used to describe the shrinkage behavior and hydraulic properties of unsaturated soils. To construct soil shrinkage characteristic curves, a high‐data‐density measurement method is needed that relates water content to soil volume changes. We present a fully automated soil shrinkage measurement setup, based on the simplified evaporation method, to characterize the shrinkage behavior of undisturbed natural expansive clay soils. The high data density creates the opportunity to produce soil shrinkage characteristic curves without the need for a mathematical model. The technique allows for resaturation of the samples after drying, enabling differentiation between reversible and irreversible shrinkage. The setup consists of the commercialized HYPROP2 apparatus combined with optical distance sensors to measure the horizontal and vertical dimensions of the samples, yielding data on the sample volume, weight, and soil water suction. The measurement frequency is once per 10 min, and the measurement period is up to 4 weeks, providing a detailed time series of the drying and shrinkage characteristics. The setup can capture the different shrinkage phases and offers the opportunity to relate the soil shrinkage characteristic curve to soil water suction. The measurement data acquisition rate and accuracy enable detailed interpretation of soil water retention curves for nonrigid soils and are shown to be essential for understanding and quantifying the shrinkage potential of several types of deposits.

Identifying freshness of various chilled pork cuts using rapid imaging analysis.

Determining the freshness of chilled pork is of paramount importance to consumers worldwide. Established freshness indicators such as total viable count, total volatile basic nitrogen and pH are destructive and time-consuming. Color change in chilled pork is also associated with freshness. However, traditional detection methods using handheld colorimeters are expensive, inconvenient and prone to limitations in accuracy. Substantial progress has been made in methods for pork preservation and freshness evaluation. However, traditional methods often necessitate expensive equipment or specialized expertise, restricting their accessibility to general consumers and small-scale traders. Therefore, developing a user-friendly, rapid and economical method is of particular importance. This study conducted image analysis of photographs captured by smartphone cameras of chilled pork stored at 4 °C for 7 days. The analysis tracked color changes, which were then used to develop predictive models for freshness indicators. Compared to handheld colorimeters, smartphone image analysis demonstrated superior stability and accuracy in color data acquisition. Machine learning regression models, particularly the random forest and decision tree models, achieved prediction accuracies of more than 80% and 90%, respectively. Our study provides a feasible and practical non-destructive approach to determining the freshness of chilled pork. © 2024 Society of Chemical Industry.

Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition

In soil science, machine learning algorithms are preferred for pedotransfer functions due to their rapid data acquisition and high prediction accuracy. The current study aims to evaluate the prediction of soil quality in agricultural lands dominated by the humid Black Sea climate using various algorithms. Both classification and regression-based algorithms (Random Forest-RF, Light Gradient Boosting-LGB, Extreme Gradient Boosting-XGBoost, k-nearest neighbors-kNN, Logistic Regression, multilayer perceptron-MLP, Linear Regression-LR and Bayesian Ridge- BR) were used in the method. The comparison of soil maps is also included. Furthermore, the present study evaluates the Grid Search optimization method with K-Fold Cross Validation (K = 5) for both classification and regression-based algorithms. The prediction of soil quality was performed using class-based and regression-based algorithms. As a result of the study, the RF and XGBoost algorithms achieved an approximate accuracy rate of 92 % in the class-based prediction. In regression-based predictions, the most successful algorithms were BR and LR, with an R2 Score of 0.84. The Grid Search optimization method was used to improve the R2 Score, resulting in an increase to 0.90 and 0.88 for BR and LR, respectively. The optimized hyperparameters showed improved performance in predicting the soil quality index. The present study found that Gaussian and Spherical models had the lowest prediction errors in spatial distribution maps. Tree-based algorithms were found to be suitable for class-based prediction of soil quality, while the linear regression method was appropriate for regression predictions. This study is characterized by a rainy climate resulting in acidic soils with high organic matter content. Planning of new studies in different climates and soil properties is recommended.

Synchronization of Neurophysiological and Biomechanical Data in a Real-Time Virtual Gait Analysis System (GRAIL): A Proof-of-Principle Study.

The investigation of gait and its neuronal correlates under more ecologically valid conditions as well as real-time feedback visualization is becoming increasingly important in neuro-motor rehabilitation research. The Gait Real-time Analysis Interactive Lab (GRAIL) offers advanced opportunities for gait and gait-related research by creating more naturalistic yet controlled environments through immersive virtual reality. Investigating the neuronal aspects of gait requires parallel recording of brain activity, such as through mobile electroencephalography (EEG) and/or mobile functional near-infrared spectroscopy (fNIRS), which must be synchronized with the kinetic and /or kinematic data recorded while walking. This proof-of-concept study outlines the required setup by use of the lab streaming layer (LSL) ecosystem for real-time, simultaneous data collection of two independently operating multi-channel EEG and fNIRS measurement devices and gait kinetics. In this context, a customized approach using a photodiode to synchronize the systems is described. This study demonstrates the achievable temporal accuracy of synchronous data acquisition of neurophysiological and kinematic and kinetic data collection in the GRAIL. By using event-related cerebral hemodynamic activity and visually evoked potentials during a start-to-go task and a checkerboard test, we were able to confirm that our measurement system can replicate known physiological phenomena with latencies in the millisecond range and relate neurophysiological and kinetic data to each other with sufficient accuracy.

Pilot study to evaluate automated laboratory data transfer into Medidata Rave for NCI-supported pediatric cooperative group trials in the Pediatric Early Phase Clinical Trials Network (PEP-CTN) and Pediatric Brain Tumor Consortium (PBTC).

e23017 Background: Electronic data capture (EDC) has the potential to improve data accuracy and efficiency. As an initial step toward improvement in the accuracy and completeness of data acquisition for clinical trials, PEP-CTN and PBTC conducted a pilot project to assess the feasibility of automated transfer of laboratory data from participating institutions’ electronic health records to the NCI Medidata Rave EDC that is housed within each consortium’s Uniform Resource Locator (URL). The pilot aimed to assess feasibility of automated laboratory data extraction and transfer and to evaluate discrepancies between laboratory data obtained retrospectively through automated extraction and data manually ascertained during the original conduct of the study. Methods: Data from patients treated on 7 completed PEP-CTN or PBTC trials at 7 sites were included. Each consortium created a Rave EDC study to receive data. Sites used the ExtractEHR R package or local methods to map and extract laboratory data for designated patients. Post-extraction data were uploaded via a file transfer protocol (FTP) periodically polled by the Rave Batch Uploader (BU) that uploaded data into Rave EDC. After successful upload, extracted data (EHR) were compared to laboratory data that had been manually reported when patients were enrolled on the trials (MANUAL) and discrepancies were identified. Discrepancy types were categorized and 2 sites reviewed discrepancies at their sites to identify reasons. Results: Preparation and support for automated extraction and upload required significant central operations and local site effort. All sites successfully extracted specified laboratory data and had at least one complete upload via BU to Rave EDC. There were more laboratory data in the EHR datasets than MANUAL datasets across most sites. These results were due to multiple EHR results on a given day or from additional days in the study window than specified in MANUAL. Sites verified that laboratory data in MANUAL and not EHR were stored in un-extractable formats (e.g. PDFs) or outside laboratories. Most discrepant results in both EHR and MANUAL were mismatched values or units. Conclusions: This pilot demonstrated the feasibility of implementing automated laboratory data extraction and transfer in the cooperative group setting. Automated data transfer requires significant central operational support as well as detailed protocols, data specifications, and case report forms and engagement of site technical teams to be successful, but also permits collection of more comprehensive data with reduced manual data entry. Clinical trial information: NCT05020951 .

Design of Lumbar Rehabilitation Training System Based on Virtual Reality

A virtual reality-based lumbar rehabilitation training system is designed to address the increasing number of patients with low back pain (LBP) year by year. Attitude sensors are used to track lower back movement. In order to improve the effect of rehabilitation training, several virtual rehabilitation training games and assessment scenes are designed based on the Unity3D engine to complete different tasks from simple to complex. The goal is to increase patients’ interest in rehabilitation training. The experimental results verify the accuracy of rehabilitation data acquisition, real-time interactive communication, and the smooth operation of rehabilitation scenes.

Research on Motion Capture System of Motor Skill Based on Computer Vision Technology

Compared with the existing motion capture system, the inertial conduction based motion capture method proposed in this project can effectively overcome the shortcomings of the current motion capture, such as limited communication distance, low accuracy of data acquisition, low visualization degree of model display software, narrow application range, etc., and improve the practical application value of the method. A method of motion capture and motion control based on wireless network is proposed. Since most of the skeleton of the human body is rigid, the complete expression of the human movement is realized through the study of 17 important joints, and all the movement information of the athlete can be obtained by placing sensors on the corresponding nodes. Through wireless network transmission technology, the player's action information is transmitted to the 3D modeling software to achieve real-time control of the action. When measuring each sensor, strapdown inertial navigation technology is used to improve the accuracy of acquisition. The 3D modeling software is based on a professional motion capture software, and through the forward and inverse kinematics of its motion, the 3D modeling is highly consistent with the wearable device.

Non-destructive multi-sensor core logging allows for rapid imaging and estimation of frozen bulk density and volumetric ice content in permafrost cores

Abstract. Permafrost cores provide physical samples that can be used to measure the characteristics of frozen ground. Measurements of core physical properties, however, are typically destructive and time intensive. In this study, multi-sensor core logging (MSCL) is used to provide a rapid (∼2–3 cm core depth per minute), high-resolution, non-destructive method to image and collect the physical properties of permafrost cores, allowing for the visualization of cryostructures and estimation of frozen bulk density, magnetic susceptibility, and volumetric ice content. Six permafrost cores with differing properties were analyzed using MSCL and compared with established destructive analyses to assess the potential of this instrument both in terms of accuracy and relative rate of data acquisition. A calibration procedure is presented for gamma ray attenuation from a 137Cs source that is specific to frozen-core materials. This accurately estimates frozen bulk density over the wide range of material densities found in permafrost. MSCL frozen bulk density data show agreement with destructive analyses based on discrete-sample measurements, with an RMSE of 0.067 g cm−3. Frozen bulk density data from the gamma attenuation, along with soil dry bulk density measurements for different sediment types, are used to estimate volumetric ice content. This approach does require an estimation of the soil dry bulk density and assumption of air content. However, the averaged results for this method show good agreement with an RMSE of 6.7 %, illustrating MSCL can provide non-destructive estimates of volumetric ice contents and a digital archive of permafrost cores for future applications.

Design of industrial equipment data acquisition system based on ZYNQ

Abstract In this paper, an industrial equipment data acquisition system based on a ZYNQ chip is constructed using an AD7607 data acquisition module containing a symmetric layout. Next, four modules of signal output, data writing, caching and communication are designed. Focusing on the design of the signal processing system after analog-to-digital conversion, the D-S evidence processing algorithm is utilized to judge the obtained data independently and fuse the data based on its D-S combination rule. Finally, the industrial equipment acquisition system design is tested, and the test includes system performance, accuracy, and stability. The frequency peaks obtained by the system in this paper are all 1000.000Hz, as shown by the results. The signal peak value of 95.18mV, the NI acquisition card signal peak value of 98.33mV, and the error is only 0.89%. The signal-to-noise ratio of the sampled signal is 50.12dB, and the effective number of bits reaches 10.40 bits. The performance of the system and the accuracy and stability of data acquisition on industrial equipment are verified.

SCAN-TO-BIM method and analysis of measurement accuracy on the example of a historic church

Building information modeling (BIM) data for existing buildings based on scans and point clouds acquired from terrestrial laser scanning (TLS) is the basis of the Scan-to-BIM methodology and is becoming common practice. However, work on the accuracy of the resulting model is still desired. The article discusses the possibility of developing a BIM model of a historical building, based on data obtained by terrestrial laser scanning. The subject of the study was the church in Posada Rybotycka (Poland). The mapping reliability studies included PointCab and ReCap point cloud processing, 3D modeling of the object using Revit software, and analysis of the accuracy of distance measurements made by TLS with data obtained from measurements made with traditional methods: total station and laser rangefinder. Based on the conducted research, the possibility of using the BIM with TLS data in the process of reconstructing the geometry of a historic building was evaluated. The results of the study showed that the convergence of the 3D model geometry with the actual course of the structure depends on the development methodology, i.e. the accuracy of 3D data acquisition, the registration process, the filtering procedure, or the parametric structural modeling method used.

Optical Electric Current Transformer for Monitoring Its Performance in the Substation

The optical electric current transformer (OECT) is an important measuring and protection equipment in the power system. Its accuracy and reliability are vital to the power system’s safe, reliable, and economical operation. Because the energy supply of a high-voltage side data acquisition system is the biggest obstacle in developing this kind of transformer, a low-power data acquisition system of OECT is designed from cost, reliability, accuracy, and practicability. In the design process, the low-power microcontroller MSP430 produced by TI company is used for control. Its rich on-chip and off-chip function modules and ultra-low power performance meet the system’s requirements. The 16-bit low-power high-speed chip ADS8325 is used as an A/D converter to ensure the instantaneity and accuracy of data acquisition and the low-power characteristics of the system. The optical fiber digital transmission system is utilized to achieve high/low voltage electrical isolation, and the low-power optical fiber transmission drive circuit is devised. After the main hardware design is completed, the software is adjusted and used in the electric energy monitoring of the substation. The test shows that the error of three-phase current and three-phase voltage is less than 2%. After setting the voltage amplitude, current amplitude, and power factor, the error of its active and reactive power is less than 2% after electric energy monitoring. After multiple harmonic tests, the measurement errors of voltage harmonics and current harmonics are smaller than 5%, that is, the measurement results of each index achieve high measurement accuracy.

Adaptive Acquisition and Visualization of Point Cloud Using Airborne LIDAR and Game Engine

The development of digital twin for smart city applications requires real-time monitoring and mapping of urban environments. This work develops a framework of real-time urban mapping using an airborne light detection and ranging (LIDAR) agent and game engine. In order to improve the accuracy and efficiency of data acquisition and utilization, the framework is focused on the following aspects: (1) an optimal navigation strategy using Deep Q-Network (DQN) reinforcement learning, (2) multi-streamed game engines employed in visualizing data of urban environment and training the deep-learning-enabled data acquisition platform, (3) dynamic mesh used to formulate and analyze the captured point-cloud, and (4) a quantitative error analysis for points generated with our experimental aerial mapping platform, and an accuracy analysis of post-processing. Experimental results show that the proposed DQN-enabled navigation strategy, rendering algorithm, and post-processing could enable a game engine to efficiently generate a highly accurate digital twin of an urban environment.

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.

Application of quantum key distribution in intelligent security operation and maintenance of power communication networks

With the continuous development of power communication network and the improvement of intelligent degree, network security and operation and maintenance become more and more important. Therefore, this paper proposes a solution for intelligent security operation and maintenance of power communication network based on Quantum key distribution (QKD). QKD is an encryption technology based on the principle of quantum mechanics, which can realize secure Key exchange and resist attacks of traditional encryption algorithms. We use QKD technology to generate and distribute security keys for protecting communication and data transmission in power communication networks. The results show that the efficiency and accuracy of communication data acquisition, transmission, analysis and processing of the Artificial Intelligence for IT Operations system of the power communication network using the power Internet of Things technology are far higher than that of the traditional operation and maintenance technology, which improves the operation and maintenance accuracy of the power communication network by about 25%. The intelligent operation and maintenance system of the power communication network designed by the author, it effectively improves the communication data processing capability and the reliability of operation and maintenance results, and can meet the current practical engineering application requirements. This study provides an innovative solution for the secure operation and maintenance of power communication networks, which is of great significance for protecting the security and stable operation of power communication networks.

Design of Portable High Precision Scale Hardness Tester Based on STM32

Hardness measurement is a very important index in the process of production and quality inspection. In the past, the precision of portable hardness tester is low, and the stability is poor. Combined with the internal high-speed ADC of the STM32 hardness tester can achieve a more demanding test accuracy requirements. Low power consumption, high accuracy and reliability of data acquisition is realized. And designed with the stemwin library can do more perfect function, human-computer interaction interface is more friendly.

Human Motion Pattern Recognition Based on Nano-sensor and Deep Learning

A human motion pattern recognition algorithm based on Nano-sensor and deep learning is studied to recognize human motion patterns in real time and with high accuracy. First, human motion data are collected by micro electro mechanical system, and the noise in such data is filtered by smoothing filtering method to obtain high-quality motion data. Second, key time-domain features are extracted from high-quality motion data. Finally, after fusing and processing the key time-domain features, it is input into the deep long and short-term memory (LSTM) neural network to build a deep LSTM human motion pattern recognition model and complete human motion pattern recognition. The results show that the proposed algorithm can realize the recognition of various motion patterns with high accuracy of data acquisition, the average recognition accuracy is 94.8%, the average recall reaches 89.7%, and the F1 score of the algorithm are high, and the recognition time consuming is short, which can realize accurate and efficient human motion pattern recognition and provide guarantee for effective monitoring of the target human motion health.

Advances in remote sensing for sustainable forest management: monitoring and protecting natural resources

Remote sensing has emerged as a powerful tool for the monitoring and management of forests, contributing to the sustainable utilization and protection of natural resources. This paper presents a review of recent advances in remote sensing techniques and technologies for forest management, highlighting their role in monitoring and protecting forests. The integration of remote sensing with other geospatial methods enhances the accuracy and efficiency of data acquisition, aiding in the assessment of forest cover, biomass estimation, disturbance detection, and biodiversity monitoring. Furthermore, the potential of remote sensing for supporting decision-making processes in sustainable forest management is explored, emphasizing its versatility, cost-effectiveness, and ability to provide invaluable insights at both local and global scales. The paper concludes by discussing current challenges and future opportunities in remote sensing applications for sustainable forest management.