High Accuracy Research Articles

METHODOLOGY FOR THE APPLICATION OF THE DIGITAL IMAGE CORRELATION (DIC) FOR INVESTIGATING RC BEAMS

The article discusses the improvement of the digital image correlation (DIC) method for analyzing deformations in RC beams, specifically focusing on the importance of zero-strain verification. This step is critical for ensuring high measurement accuracy, as it helps identify and minimize systematic and random errors. Before starting the research, system calibration is conducted, which includes the assessment of background noise and stability that influence the results. The study shows that proper sample preparation, pattern creation, and control of external factors allow for obtaining reliable data. The application of DIC enables remote monitoring of cracks and evaluation of the stress-strain state of structures. It has been established that this method is useful not only for scientific experiments but also in practical engineering, contributing to the increased reliability of structures.

Intelligent Evaluation Method for Scoliosis at Home Using Back Photos Captured by Mobile Phones

The traditional scoliosis examination based on X-ray film is not suitable for large-scale screening, and it is also not suitable for dynamic evaluation during rehabilitation. Therefore, based on computer vision technology, this paper puts forward an evaluation method of scoliosis with different photos of the back taken by mobile phones, which involves three aspects: first, based on the key point detection model of YOLOv8, an algorithm for judging the type of spinal coronal curvature is proposed; second, an algorithm for evaluating the coronal plane of the spine based on the key points of the human back is proposed, aiming at quantifying the deviation degree of the spine in the coronal plane; third, the measurement algorithm of trunk rotation (ATR angle) based on multi-scale automatic peak detection (AMPD) is proposed, aiming at quantifying the deviation degree of the spine in sagittal plane. The public dataset and clinical paired data (mobile phone photo and X-ray) are used to test. The results show that this method has high accuracy and effectiveness in distinguishing the type of spinal curvature and evaluating the degree of deviation, which is higher than other deep learning algorithms.

DMFR-YOLO: an infrared small hotspot detection algorithm based on double multi-scale feature fusion

Abstract Hotspot automatic detection is an effective strategy to realize intelligent maintenance of photovoltaic (PV) modules. However, it is challenging to detect small hotspots accurately using deep learning-based object detection methods due to the weak information and small area of the target. This study presents a double multi-scale feature reconstruction (DMFR)-YOLO to detect small hotspots in PV modules. In the proposed DMFR-YOLO, the backbone of YOLOv8n is improved by eliminating redundant deep layers and introducing a shallow detection head to enhance its perception and recognition ability for small objects. Moreover, a weighted multi-layer feature reconstruction (MLFR) module is introduced to fuse the features of different depths and a multi-receptive field feature reconstruction (MRFR) module is designed to fuse the information of different receptive fields. Finally, ablation and comparison experiments are conducted to evaluate the performance of the proposed method. The model with both MLFR and MRFR can achieve a recall rate of 88.6%, with a mean average precision (mAP@0.5) of 93.3%. Experimental results demonstrate that the proposed DMFR-YOLO is able to achieve small hotspot detection in IR images with high accuracy.

Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach

This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method’s potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications.

Configuration Design and Size Optimization of a High-Precision Novel Parallel Pointing Mechanism Based on Interference Separation

AbstractPointing mechanism is widely used in aerospace field, and its pointing accuracy and stability have high requirements. The pointing mechanism will be affected by external interference when it works. In order to eliminate the impact of interference forces on the output accuracy of the mechanism, firstly, this paper proposes a design method for high-precision pointing mechanisms based on interference separation, aiming at the high-precision pointing requirements of pointing mechanisms. Based on the screw theory, a synthesis method for inner compensation mechanisms has been proposed. And a new type of double-layer parallel mechanism has been designed to compensate for interference forces. Then, the kinematics and dynamics of the mechanism are carried out. An evaluation index for compensating external interference forces is proposed. The interference compensation analysis is conducted for the pointing mechanism. The correctness of the proposed interference force compensation coefficient is verified. Finally, in order to find the optimal solution for the workspace and interference force compensation coefficient of the pointing mechanism, multi-objective optimization design of the structural parameters of the mechanism was carried out based on the particle swarm optimization algorithm. This provides a theoretical basis for the prototype design of the subsequent double-layer parallel mechanism. This double-layer parallel mechanism combines the advantages of large load-bearing capacity, large workspace, and high output accuracy. It can be better applied in the aerospace field where high-precision pointing and force interference compensation are integrated.

Significance of three-dimensional turbo spin-echo magnetic resonance imaging with semiquantitative assessment of knee osteoarthritis: correlation to two-dimensional routine magnetic resonance imaging

Abstract Background As one of the most prevalent chronic degenerative diseases, knee osteoarthritis (KOA) is associated with the progressive degradation of articular cartilage, meniscus, synovium, ligaments, bone, muscles, and tendons. Knee osteoarthritis affects the quality of life and can cause chronic disability worldwide. Magnetic resonance imaging (MRI) is a crucial imaging modality for the morphological assessment of cartilage and all other joint tissues involved in osteoarthritis. The objective of this study was to evaluate the effectiveness of the three-dimensional (3D) turbo spin-echo (TSE) MRI and to compare its relevance to two-dimensional (2D) routine MRI in adding additional information and in the early detection of KOA. Results Total agreement between the two techniques ranged between 82.7% (cartilage degeneration), 91.4% (osteophytes), 98.3% for bone marrow edema and periarticular cyst up to 100% of other several features regarding meniscal injury and ligamentous tear. Conclusions When compared to standard 2D TSE MRI, 3D TSE MRI demonstrates substantial to almost complete agreement and high accuracy for semiquantitative assessment of knee osteoarthritis (OA). 3D TSE MRI also takes less time, which is important for large OA studies and can be used for the detection of early knee joint changes.

Artificial Intelligence and Image Analysis-Assisted Diagnosis for Fibrosis Stage of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Ultrasonography: A Pilot Study

Purpose: Elastography increased the diagnostic accuracy of liver fibrosis. However, several challenges persist, including the widespread utilization of equipment, difficulties in measuring certain cases, and the influence of viscosity factors. A rough surface and a blunted hepatic margin have long been acknowledged as valuable characteristics indicative of hepatic fibrosis. The objective of this study was to conduct an image analysis and quantitative assessment of the contour of the sagittal section of the left lobe of the liver. Methods: Between February and October 2020, 486 consecutive outpatients underwent ultrasound examinations at our hospital. A total of 214 images were manually annotated by delineating the liver contour to create annotation images. U-Net was employed for liver segmentation, with the dataset divided into training (n = 128), testing (n = 42), and validation (n = 44) subsets. Additionally, 43 Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) cases with pathology data from between 2015 and 2020 were included. Segmentation was performed using the program developed in the first step. Subsequently, shape analysis was conducted using ImageJ. Results: Liver segmentation exhibited high accuracy, as indicated by Dice loss of 0.044, Intersection over Union of 0.935, and an F score of 0.966. The accuracy of the classification of the liver surface as smooth or rough via ResNet 50 was 84.6%. Image analysis showed MinFeret and Minor correlated with liver fibrosis stage (p = 0.046, 0.036, respectively). Sensitivity, specificity, and AUROC of Minor for ≥F3 were 0.571, 0.862, and 0.722, respectively, and F4 were 1, 0.600, and 0.825, respectively. Conclusion: Deep learning segmentation of the sagittal cross-sectional contour of the left lobe of the liver demonstrated commendable accuracy. The roughness of the liver surface was correctly judged by artificial intelligence. Image analysis showed the thickness of the left lobe inversely correlated with liver fibrosis stage.

Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions.

Quantum-chemical fragmentation methods offer an attractive approach for the accurate calculation of protein-ligand interaction energies. While the molecular fractionation with conjugate caps (MFCC) scheme offers a rather straightforward approach for this purpose, its accuracy is often not sufficient. Here, we upgrade the MFCC scheme for the calculation of protein-ligand interactions by including many-body contributions. The resulting fragmentation scheme is an extension of our previously developed MFCC-MBE(2) scheme [J. Comput. Chem. 2023, 44, 1634-1644]. For a diverse test set of protein-ligand complexes, we demonstrate that by upgrading the MFCC scheme with many-body contributions, the error in protein-ligand interaction energies can be reduced significantly, and one generally achieves errors below 20 kJ/mol. Our scheme allows for systematically reducing these errors by including higher-order many-body contributions. As it combines the use of single amino acid fragments with high accuracy, our scheme provides an ideal starting point for the parametrization of accurate machine learning potentials for proteins and protein-ligand interactions.

Methods of Calculation of Daily Flooded Areas in the Volga Delta During the Flood Periods Based on the Remote Sensing Data

We developed methods for calculating the daily flooded areas in the Volga delta (VD) for the entire flood period using satellite data. The methodology is based on the construction of dependences of the flooding areas of the hydrographic network and interchannel spaces of VD (Ftotal) on the means of daily water levels in the channels of watercourses (НР). Ftotal for individual flood dates were determined using satellite images. Data on НР were taken at hydrological stations (h/s) on the same dates from the State Water Cadastre. These dependencies were used to calculate the daily flooding areas of DV with high accuracy for flood periods of different water contents (high-water, low-water and medium-water contents). This method was developed in two modifications: 1 – modification for the case of the sufficient number of satellite images to cover all the main changes in the course of each phase of the flood, 2 – modification for the case of the insufficient number of satellite images to cover all the main changes in the course of each phase of the flood. We conducted a comparative analysis of daily Ftotal obtained with high accuracy using modification 1 for floods of different water contents (high-water, low-water, medium-water contents). We revealed how floods of different water contents differ in characteristics including timing of passage, amplitude, area of maximum flooding area, duration of the rise phase, decline phase and flood plateau phase. Such calculations have never been conducted before. The results of calculations by this method allow us to identify the spatial-temporal patterns of the VD flooding under different types of water contents. Our method enables to predict the dynamics of floods and to calculate the water balance of the Volga delta.

Response Surface Methodology Approach for the Prediction and Optimization of the Mechanical Properties of Sustainable Laterized Concrete Incorporating Eco-Friendly Calcium Carbide Waste

This study investigated the combined effects of calcium carbide waste (CCW) and lateritic soil (LS) on sustainable concrete’s fresh and mechanical properties as a construction material for infrastructure development. The study will explore the possibility of using easily accessible materials, such as lateritic soils and calcium carbide waste. Therefore, laterite soil was used to replace some portions of fine aggregate at 0% to 40% (interval of 10%) by weight, while CCW substituted the cement content at 0%, 5%, 10%, 15%, and 20% by weight. A response surface methodology/central composite design (RSM/CCD) tool was applied to design and develop statistical models for predicting and optimizing the properties of the sustainable concrete. The LS and CCW were input variables, and compressive strength and splitting tensile properties are response variables. The results indicated that the combined effects of CCW and LS improve workability by 18.2% compared to the control mixture. Regarding the mechanical properties, the synergic effects of CCW as a cementitious material and LS as a fine aggregate have improved the concrete’s compressive and splitting tensile strengths. The contribution of LS is more pronounced than that of CCW. The established models have successfully predicted the mechanical behavior and fresh properties of sustainable concrete utilizing LS and CCW as the independent variables with high accuracy. The optimized responses can be achieved with 15% CCW and 10% lateritic soil as a substitute for fine aggregate weight. These optimization outcomes produced the most robust possible results, with a desirability of 81.3%.

Long-term Coastline Monitoring in the Thanh Hoa Province (Vietnam) Using Landsat 5 and Landsat 8 Data

In recent years, extensive human activities have had a profound impact on the estuaries and coastal areas of Vietnam, most notably in coastal erosion and accretion. This paper used the Landsat multi-temporal data for the period 1988–2022 to assess coastline change in Thanh Hoa province (North Central Vietnam). Water indices calculated from Landsat imagery data, including NDWI, ANDWI, MNDWI, AWEInsh, AWEIsh, and BandWet, are used to extract surface water areas and then vectorize and overlay to estimate shoreline variability. The Otsu thresholding method is used to classify “water surface” and “land objects” and then evaluate the accuracy using the Kappa coefficient. The obtained results show that the ANDWI index has the highest accuracy in extracting the water body of the study area, in which the value of the Kappa coefficient reaches 0.95 compared to 0.91, 0.92, 0.93, 0.92 and 0.92 at using NDWI, MNDWI, AWEInsh, AWEIsh and BandWet indicies. Boundary vectorization and vector image overlays were performed to assess shoreline variability and map shoreline dynamics. The results obtained show that in the northern part of the coastal zone of Thanh Hoa province there is active accretion (increment) of the coastline. The average accretion rate was 150 m/year, the maximum rate was 457 m/year. In contrast, on the southern coast of Thanh Hoa province, coastline erosion predominates with a maximum rate of 38 m/year and an average rate of about 10 m/year.

Comparative Assessment of Different Architectures of Convolutional Neural Network for Semantic Segmentation of Forest Disturbances from Multi-Temporal Satellite Images

Algorithms based on convolutional neural networks are the most efficient for semantic segmentation of images, including segmentation of forest cover disturbances from satellite images. In this study, we consider the applicability of various modifications of the U-net architecture of convolutional neural network for recognizing logged, burnt and windthrow areas in forests from multi-temporal and multi-seasonal Sentinel-2 satellite images. The assessment was carried out on three test sites that differ significantly in the characteristics of forest stands and forest management. The highest accuracy (average F-measure of 0.59) was obtained from the U-net model, while the models that showed the best results during training (Attention U-Net and MobilNetv2 U-Net) did not improve segmentation of independent data. The resulting accuracy estimates are close to those previously published for forests with a substantial proportion of selective logged areas. Characteristics of logged areas and windthrows, namely their area and type are the main factor determining the accuracy of semantic segmentation. Substantial differences were also revealed between the images taken in different seasons of the year, with the maximum segmentation accuracy based on winter pairs of images. According to summertime and different-season pairs of images, the area of forest disturbances is substantially underestimated. Forest species composition has a less significant effect, although for two of the three test sites, the maximum accuracy was observed in dark coniferous forests, and the minimum in deciduous forests. There was no statistically significant effect of slope lighting factor calculated from digital elevation model on segmentation accuracy based for winter pairs of images. The accuracy of segmentation of burnt areas, which was assessed using the example of 14 large forest fires in 2021-2022, is unsatisfactory, which is probably due to the varying degrees of damage to the forest cover in the burnt areas.

Alterations of gray matter asymmetry in internet gaming disorder.

Structural asymmetry is a subtle but pervasive property of the human brain, which has been found altered in various psychiatric and neurocognitive disorders. However, little is known regarding potential alterations of structural asymmetry underlying internet gaming disorder (IGD). Therefore, this study aimed to investigate the structural features of gray matter asymmetry in IGD. High-resolution structural magnetic resonance imaging data were collected from 104 individuals with IGD and 104 recreational game users (RGUs). We applied a whole-brain voxel-based asymmetry (VBA) approach to determine the asymmetrical aberrations of gray matter in relation to IGD. Furthermore, the local abnormalities of structural asymmetry were employed as features to examine the effect of classification using a support vector machine (SVM). The results indicated that individuals with IGD as compared to RGUs showed asymmetrical alterations of gray matter in the medial prefrontal cortex (mPFC), orbitofrontal cortex, precuneus, middle temporal gyrus, superior parietal lobule and inferior temporal gyrus, regions implicated in hedonic motivation, self-reflection, information integration and visuospatial attention processing. Moreover, these atypical asymmetrical features can distinguish IGD subjects from RGUs with high accuracy. These results suggested that disrupted structural asymmetry of motivational reward, visuospatial and default mode circuits might be potential biomarkers for identifying pathological gaming dependence. These findings extended our understanding of structural underpinnings of IGD and provided new insights for developing effective interventions to alleviate compulsive gaming usage.

FlowDiff: a simple, flow cytometry-based approach for performing a leukocyte differential count.

To overcome the challenges of a manual leukocyte differential count, we have developed FlowDiff, an 8-colour, single tube flow cytometry panel, and investigated whether it could potentially replace the manual differential in our laboratory. The instrument was set up in accordance with the EuroFlow settings, and the protocol comprised a stain-lyse no wash process, taking approximately 30 min of working time, without the addition of a toxic lysis reagent. We found a very good correlation for all leukocyte populations between FlowDiff and the Sysmex XN analyzer in 80 normal, non-flagged samples. In addition, FlowDiff showed a very good correlation with manual differential in 168 abnormal samples, as well as a high diagnostic accuracy. FlowDiff correctly identified all samples with acute leukemia (N = 13) and differentiated all B-lymphomas (N = 49) in samples with lymphocytosis. Moreover, FlowDiff detected an additional five samples with B-lymphocytosis without any prior hematological malignancy, which turned out to be a B-lymphoma. Our data suggest that FlowDiff, our 8-colour flow cytometry-based differential, is comparable to, and can successfully substitute the manual differential.

Robust estimation of lithium-ion battery state of health based on electro-thermal features and machine learning

Accurately estimating the State of Health (SOH) of lithium-ion (Li-ion) batteries is crucial for preventing overcharging and over-discharging, thereby extending battery lifespan. This paper presents a novel method for SOH estimation in lithium-ion batteries by leveraging electro-thermal features, a backpropagation neural network (BPNN), and a particle swarm optimization (PSO) algorithm. First, three health-related features—constant current charging time (CCCT), relative constant current charging time (RCCCT), and maximum temperature during discharge (MTT)—are extracted as indicators of SOH. The correlation between these features and the SOH is validated using Grey Relational Analysis (GRA). Next, a BP neural network is utilized to model the nonlinear relationship between the extracted features and SOH. The PSO algorithm is then employed to optimize the parameters of the BP neural network, enhancing the accuracy of the SOH estimation. The proposed method, which combines electro-thermal features with the optimized BP neural network, is validated through three independent lithium-ion battery aging experiments. Experimental results demonstrate that the proposed approach achieves high estimation accuracy and exhibits strong generalization performance for SOH prediction.

Untargeted metabolomics to detect and identify plasma metabolic signatures associated with intracranial aneurysm and its rupture.

The biological basis for metabolic differences between unruptured and ruptured intracranial aneurysm (UIA and RIA) populations and their potential role in triggering IA rupture remain unclear. The aim of this study was to analyze the plasma metabolic profiles of patients with UIA and RIA using an untargeted metabolomic approach and to develop a model for early rupture classification. Plasma samples were analyzed using an ultra-high-performance liquid chromatography high-resolution tandem mass spectrometry-based platform. Least absolute shrinkage and selection operator regression and random forest machine learning methods were employed for metabolite feature selection and predictive model construction. Among 49 differential plasma metabolites identified, 31 were increased and 18 were decreased in the plasma of RIA patients. Five key metabolites-canrenone, piperine, 1-methyladenosine, betaine, and trigonelline-were identified as having strong potential to discriminate between UIA and RIA patients. This combination of metabolites demonstrated high diagnostic accuracy, with an area under the curve exceeding 0.95 in both the training and validation datasets. Our finding highlights the significance of plasma metabolites as potential biomarkers for early detection of IA rupture risk, offering new insights for clinical practice and future research on IA management.

Research on information leakage in time series prediction based on empirical mode decomposition.

Time series analysis predicts the future based on existing historical data and has a wide range of applications in finance, economics, meteorology, biology, engineering, and other fields. Although the combination of decomposition techniques and machine learning algorithms can effectively solve the problem of predicting nonstationary sequences, this kind of decomposition-integration-prediction strategy of the prediction method has serious defects. After the decomposition of the division of the training set and the test set, the information of the test set in the process of decomposition of the information leakage ultimately shows a high accuracy of the prediction of the illusionary. This paper proposes three improvement strategies for this type of "information leakage" problem: sliding window decomposition (SW-EMD), single training and multiple decomposition (STMP-EMD), and multiple training and multiple decomposition (MTMP-EMD). They are combined with a bidirectional multiscale temporal convolutional network (MSBTCN), bidirectional long- and short-term memory network (BiLSTM), and attention mechanism (DMAttention), which introduces a dependency matrix based on cosine similarity to be applied to water quality prediction. The experimental results show that the model achieves good performance in the prediction of three water quality indicators (pH, DO and KMnO4), and the accuracies of the three models proposed in this paper are improved by 1.958% and 0.853% in terms of the RMSE and MAPE, respectively, compared with those of the mainstream LSTM models. The key contributions of this study include the following: (1) three methods are proposed to improve the class EMD decomposition, which can effectively solve the problem of "information leakage" that exists in the current models via class EMD decomposition; (2) the CEEMDAN-MSBTCN-BiLSTM-DMAttention model structure is innovated by combining improved class EMD decomposition methods; and (3) the three improved decomposition methods proposed in this paper can effectively solve the problem of "information leakage" and optimize the prediction model at the same time. This study provides an effective experimental method for water quality prediction and can effectively address the problem of "overfitting" models via class EMD decompositions during model training and testing.

Enhanced Tomato Disease Classification Using Hybrid Deep Learning Approach

Tomato disease classification is important for agricultural productivity and food security, yet achieving high accuracy stays challenging due to the complexity of disease symptoms. This study proposed a model classification utilizing DenseNet121 combined with an Autoencoder for feature extraction and Simple neural network (SNN) for classifier. The dataset contains 10 classes of tomato diseases. Experimental results shows that the proposed approach achieved a high accuracy of 99%, outperforming the performance of several approaches. The integration of the Autoencoder enhances feature extraction and dimensionality reduction, important for improved classification outcomes. This study highlights the efficacy of the proposed work in achieving state-of-the-art accuracy in tomato disease detection. Future work may focus on refining the model using advanced augmentation techniques and expanding the dataset to enhance its applicability across diverse agricultural conditions.

Density interface prediction using neural networks: Incorporating fluctuation features from observed gravity data

Predicting density interfaces in the subsurface using neural networks is a promising research area in deep learning-based gravity exploration. Constructing a comprehensive training dataset that captures the fluctuation characteristics of the density interface is vital for predictions. However, large datasets often introduce extraneous features and significantly increase the computational time. To address these challenges, we have developed a method that enhances the accuracy of the prediction by integrating fluctuation features of observed gravity data, specifically the z-component gravity data, into the training dataset. This is achieved by perturbing and deforming the approximate spectrum of the density interface derived from a physics-driven algorithm, thereby generating training samples that closely match the prediction target. Such an approach enables the neural network to concentrate on learning relevant fluctuation features associated with the target. Experimental results underscore the efficacy of our method in accurately discerning density interface features and its robustness against noise interference. Furthermore, our method maintains high prediction accuracy with smaller datasets, outperforming the conventional approaches. This advantage enhances the efficiency and practicality of neural networks in addressing density interface problems by reducing the computational costs for predictions with high accuracy.

A Triangular Structure Constraint for Pedestrian Positioning with Inertial Sensors Mounted on Foot and Shank

To suppress pedestrian positioning drift, a velocity constraint commonly known as zero-velocity update (ZUPT) is widely used. However, it cannot correct the error in the non-zero velocity interval (non-ZVI) or observe heading errors. In addition, the positioning accuracy will be further affected when a velocity error occurs in the ZVI (e.g., foot tremble). In this study, the foot, ankle, and shank were regarded as a triangular structure. Consequently, an angle constraint was established by utilizing the sum of the internal angles. Moreover, in contrast to the traditional ZUPT algorithm, a velocity constraint method combined with Coriolis theorem was constructed. Magnetometer measurements were used to correct heading. Three groups of experiments with different trajectories were carried out. The ZUPT method of the single inertial measurement unit (IMU) and the distance constraint method of dual IMUs were employed for comparisons. The experimental results showed that the proposed method had high accuracy in positioning. Furthermore, the constraints built by the lower limb structure were applied to the whole gait cycle (ZVI and non-ZVI).