Search Results (14)

Self-Organizing Map (SOM) neural networks can project complex, high-dimensional data onto a two-dimensional plane for data visualization, enabling an intuitive understanding of the distribution and symmetric structures of such data, thereby facilitating the clustering and anomaly detection of complex high-dimensional data. However, this algorithm is sensitive to the initial weight matrix and suffers from insufficient feature extraction. To address these issues, this paper proposes an improved SOM based on virtual winning neurons (virtual-winner SOMs, vwSOMs). In this method, the principal component analysis (PCA) is utilized to generate the initial weight matrix, allowing the weights to better capture the main features of the data and thereby enhance clustering performance. Subsequently, when new input sample data are mapped to the output layer, multiple neurons with a high similarity in the weight matrix are selected to calculate a virtual winning neuron, which is then used to update the weight matrix to comprehensively represent the input data features within a minimal error range, thus improving the algorithm’s robustness. Multiple datasets were used to analyze the clustering performance of vwSOM. On the Iris dataset, the S is 0.5262, the F1 value is 0.93, the ACC value is 0.9412, and the VA is 0.0012, and the experimental result with the Wine dataset shows that the S is 0.5255, the F1 value is 0.93, the ACC value is 0.9401, and the VA is 0.0014. Finally, to further demonstrate the performance of the algorithm, we use the more complex Waveform dataset; the S is 0.5101, the F1 value is 0.88, the ACC value is 0.8931, and the VA is 0.0033. All the experimental results show that the proposed algorithm can significantly improve clustering accuracy and have better stability, and its algorithm complexity can meet the requirements for real-time data processing. Full article

Urban development of clusters situated along rivers significantly affects the health of the river ecosystems, the quality of urban environments, and the overall well-being of local communities. Ecosystem service supply value (ESSV) measures the delivery of ecosystem goods and services within a specific timeframe in a particular area. Using the lower Yellow River urban agglomeration (Shandong section) as a case, we comprehensively applied land use structure and intensity change analysis, quantitative calculation of ESS, and geographical probe methods to unveil ESS and its mechanism of response to the spatio-temporal evolution of the intensity of land use in urban agglomeration along the river. The key results were as follows: (1) Over the past two decades, farmland and construction land areas have continued to decrease and increase, respectively, with the intensity of land use change being highest from 2005 to 2010. (2) ESS has continued to rise over the past 20 years, with the income in 2020 being 11.142 billion yuan, an increase of 31.13%. The “low-value areas” are mainly concentrated in Liaocheng City, Dezhou City, and Tai’an City, which are characterized by predominantly flat terrains where farmland constitutes the principal land use type. Conversely, “high-value areas” are largely in the counties bordering the Yellow River, including the upper estuary in the north and the rugged, southeastern terrains. (3) Areas with concentrated ESSV were primarily localized in the northern estuary area and along the Yellow River in a scattered point-like pattern. The spatial distribution of hotspots has become increasingly concentrated, transitioning from points to planes. Conversely, cold spots initially increased in number before subsequently decreasing. Waterbody was the most sensitive ESSV-determining factor. (4) The spatial heterogeneity of ESSV emerges as a consequence of the interaction of multiple factors, and among these interactions, those involving NDVI and POP contain the greatest explanatory power. Our findings are expected to offer a scientific foundation for optimizing land spatial patterns and enhancing ecological management in the lower Yellow River region. Full article

The canopy of perennial evergreen fruit trees in southern China has a unique Bidirectional Reflectance Factor (BRF) due to its complex multi-branch structure and density changes. This study aimed to address the lack of clarity regarding the changes in BRF of evergreen fruit trees in southern China. Litchi, a typical fruit tree in this region, was chosen as the subject for establishing a three-dimensional (3D) real structure model. The canopy BRF of litchi was simulated under different leaf components, illumination geometry, observed geometry, and leaf area index (LAI) using a 3D radiation transfer model. The corresponding changes in characteristics were subsequently analyzed. The findings indicate that the chlorophyll content and equivalent water thickness of leaves exert significant influences on canopy BRF, whereas the protein content exhibit relatively weak effects. Variation in illumination and observation geometry results in the displacement of hotspots, with the solar zenith angle and view zenith angle exerting significant influence on the BRF. As the LAI of the litchi orchard increases, the distribution of hotspots becomes more concentrated, and the differences in angle information are relatively smaller when observed from multiple angles. With the increase in LAI in litchi orchards, the BRF on the principal plane would be saturated, but observation at hotspots could alleviate this phenomenon. The above analysis provides a reference for quantitative inversion of vegetation parameters using remote sensing monitoring information of typical perennial evergreen fruit trees. Full article

To address the limitations of wireless sensing in static gesture recognition and the issues of Computer Vision’s dependence on lighting conditions, we propose a method that utilizes millimeter-wave near-field SAR (Synthetic Aperture Radar) imaging for static gesture recognition. First, a millimeter-wave near-field SAR imaging system is used to scan the defined static gestures to obtain data. Then, based on the distance plane, the three-dimensional gesture is divided into multiple two-dimensional planes, constructing an imaging dataset. Finally, an HOG (Histogram of Oriented Gradients) is used to extract features from the imaging results, PCA (Principal Component Analysis) is applied for feature dimensionality reduction, and RF (Random Forest) performs classification. Experimental verification shows that the proposed method achieves an average recognition precision of 97% in unobstructed situations and 93% in obstructed situations, providing an effective means for wireless-sensing-based static gesture recognition. Full article

Aiming at some stress-induced failure phenomena in surrounding rock that occur during the construction of super-long and deep-buried tunnels, a method for estimating the in situ stress in the tunnels based on multivariate information integration is proposed, which uses a small amount of in situ stress measurement, stereographic projection technology, and a numerical simulation method. Firstly, by conducting a macroscopic analysis of the regional geological structure, topography, and pre-excavated small tunnels (such as exploration of adits and pilot tunnels), the strength of the tectonic stress field and the orientation of the principal stresses in the tunnel sections are preliminarily determined. Secondly, the reliability of the in situ stress measurement data were analyzed using full-space stereographic projection and the plane stress projection method. Then, some representative measurement points that reflected the distribution characteristics of in situ stress in the project area, on the whole, were determined. Thirdly, the finite difference (FDM) and multiple regression analysis (MRA) methods were used to inverse the in situ stress field in the project area. The proposed method was applied to a super-long and deep-buried tunnel project in Qinling, and the in situ stress distribution characteristics of the tunnel sections at different mileages were obtained. The results show that both the calculated principal stress values and the azimuth angle of the maximum horizontal principal stress are in good agreement with the measured ones, indicating that the method used in this study is reasonable. Finally, the typical surrounding rock failure phenomena encountered during the excavation of the project were investigated, and targeted treatment measures were proposed. The research results can provide references for support design and disaster management of surrounding rock in deep-buried long tunnels. Full article

The treatment of chronic lymphocytic leukemia (CLL) currently relies on the use of chemo-immunotherapy, Bruton’s tyrosine kinase inhibitors, or BCL2 inhibitors alone or combined with an anti-CD20 monoclonal antibody. However, the availability of multiple choices for the first-line setting and a lack of direct head-to-head comparisons pose a challenge for treatment selection. To overcome these limitations, we performed a systematic review and a network meta-analysis on published randomized clinical trials performed in the first-line treatment setting of CLL. For each study, we retrieved data on progression-free survival (according to del17/P53 and IGHV status), overall response rate, complete response, and incidence of most frequent grade 3–4 adverse event. We identified nine clinical trials encompassing 11 different treatments, with a total of 5288 CLL patients evaluated. We systematically performed separated network meta-analyses (NMA) to evaluate the efficacy/safety of each regimen in the conditions previously described to obtain the surface under the cumulative ranking curve (SUCRA) score, which was subsequently used to build separated ranking charts. Interestingly, the combination of obinutuzumab with acalabrutinib reached the top of the chart in each sub-analysis performed, with the exception of the del17/P53mut setting, where it was almost on par with the aCD20 mAbs/ibrutinib combination (SUCRA aCD20-ibrutinib and O-acala: 93.5% and 91%, respectively) and of the safety evaluation, where monotherapies (acalabrutinib in particular) gave better results. Finally, considering that NMA and SUCRA work for single endpoints only, we performed a principal component analysis to recapitulate in a cartesian plane the SUCRA profiles of each schedule according to the results obtained in each sub-analysis, confirming again the superiority of aCD20/BTKi or BCL2i combinations in a first-line setting. Overall, here we demonstrated that: (1) a chemotherapy-free regimen, such as the combination of aCD20 with a BTKi or BCL2i, should be the preferred treatment choice despite biological/molecular characteristics (preferred regimen O-acala); (2) there is less and less room for chemotherapy in the first line treatment of CLL. Full article

Urban planning, noise propagation modelling, viewshed analysis, etc., require determination of routes or supply lines for propagation. A point-to-point routing algorithm is required to determine the best routes for the propagation of noise levels from source to destination. Various optimization algorithms are present in the literature to determine the shortest route, e.g., Dijkstra, Ant-Colony algorithms, etc. However, these algorithms primarily work over 2D maps and multiple routes. The shortest route determination in 3D from unlabeled data (e.g., precise LiDAR terrain point cloud) is very challenging. The prediction of noise data for a place necessitates extraction of all possible principal routes between every source of noise and its destination, e.g., direct route, the route over the top of the building (or obstruction), routes around the sides of the building, and the reflected routes. It is thus required to develop an algorithm that will determine all the possible routes for propagation, using LiDAR data. The algorithm uses the novel cutting plane technique customized to work with LiDAR data to extract all the principal routes between every pair of noise source and destination. Terrain parameters are determined from routes for modeling. The terrain parameters, and noise data when integrated with a sophisticated noise model give an accurate prediction of noise for a place. The novel point-to-point routing algorithm is developed using LiDAR data of the RGIPT campus. All the shortest routes were tested for their spatial accuracy and efficacy to predict the noise levels accurately. Various routes are found to be accurate within ±9 cm, while predicted noise levels are found to be accurate within ±6 dBA at an instantaneous scale. The novel accurate 3D routing algorithm can improve the other urban applications too. Full article

The magnetic resonance Diffusion Tensor Imaging (DTI) is a powerful extension of Diffusion Weighted Imaging (DWI) utilizing multiple bipolar gradients, allowing for the evaluation of the microstructural environment of the highly anisotropic tissues. DTI was predominantly used for the assessment of the central nervous system (CNS), but with the advancement in magnetic resonance (MR) hardware and software, it has now become possible to image the peripheral nerves which were difficult to evaluate previously because of their small caliber. This study focuses on the assessment of the human median peripheral nerve ex vivo by DTI microscopy at 9.4 T magnetic field which allowed the evaluation of diffusion eigenvalues, the mean diffusivity and the fractional anisotropy at 35 μm in-plane resolution. The resolution was sufficient for clear depiction of all nerve anatomical structures and therefore further image analysis allowed the obtaining of average values for DT parameters in nerve fascicles (intrafascicular region and perineurium) as well as in the surrounding epineurium. The results confirmed the highest fractional anisotropy of 0.33 and principal diffusion eigenvalue of 1.0 × 10⁻⁹ m²/s in the intrafascicular region, somewhat lower values of 0.27 and 0.95 × 10⁻⁹ m²/s in the perineurium region and close to isotropic with very slow diffusion (0.15 and 0.05 × 10⁻⁹ m²/s) in the epineurium region. Full article

We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground plane, which is regarded as the new Cartesian plane. Second, to reduce the complexity of data processing, the data are projected into two dimensions and transformed into a binary image via the operation of an improved radius outlier removal (ROR) filter. Third, a traditional thinning algorithm is adopted to extract the image skeleton. Then, we propose a method for calculating slope through the nearest neighbor point. Moreover, the line is represented with the slopes to obtain information pertaining to the interior planes. Finally, the outline of the line is restored to a three-dimensional structure. The proposed method is evaluated in multiple scenarios, and the results show that the method is accurate (the maximum error of 0.03 m was in three scenarios) in indoor environments. Full article

This paper presents a model-based approach for 3D pose estimation of a single RGB image to keep the 3D scene model up-to-date using a low-cost camera. A prelearned image model of the target scene is first reconstructed using a training RGB-D video. Next, the model is analyzed using the proposed multiple principal analysis to label the viewpoint class of each training RGB image and construct a training dataset for training a deep learning viewpoint classification neural network (DVCNN). For all training images in a viewpoint class, the DVCNN estimates their membership probabilities and defines the template of the class as the one of the highest probability. To achieve the goal of scene reconstruction in a 3D space using a camera, using the information of templates, a pose estimation algorithm follows to estimate the pose parameters and depth map of a single RGB image captured by navigating the camera to a specific viewpoint. Obviously, the pose estimation algorithm is the key to success for updating the status of the 3D scene. To compare with conventional pose estimation algorithms which use sparse features for pose estimation, our approach enhances the quality of reconstructing the 3D scene point cloud using the template-to-frame registration. Finally, we verify the ability of the established reconstruction system on publicly available benchmark datasets and compare it with the state-of-the-art pose estimation algorithms. The results indicate that our approach outperforms the compared methods in terms of the accuracy of pose estimation. Full article

The golf swing is a multidimensional movement requiring alternative data analysis methods to interpret non-linear relationships in biomechanics data related to golf shot outcomes. The purpose of this study was to use a combined principal component analysis (PCA), fuzzy coding, and multiple correspondence analysis (MCA) data analysis approach to visualise associations within key biomechanics movement patterns and impact parameters in a group of low handicap golfers. Biomechanics data was captured and analysed for 22 golfers when hitting shots with their own driver. Relationships between biomechanics variables were firstly achieved by quantifying principal components, followed by fuzzy coding and finally MCA. Clubhead velocity and ball velocity were included as supplementary data in MCA. A total of 35.9% of inertia was explained by the first factor plane of MCA. Dimension one and two, and subsequent visualisation of MCA results, showed a separation of golfers’ biomechanics (i.e., swing techniques). The MCA plot can be used to simply and quickly identify movement patterns of a group of similar handicap golfers if supported with appropriate descriptive interpretation of the data. This technique also has the potential to highlight mismatched golfer biomechanics variables which could be contributing to weaker impact parameters. Full article

Extracting horizontal planes in heavily cluttered three-dimensional (3D) scenes is an essential procedure for many robotic applications. Aiming at the limitations of general plane segmentation methods on this subject, we present HoPE, a Horizontal Plane Extractor that is able to extract multiple horizontal planes in cluttered scenes with both organized and unorganized 3D point clouds. It transforms the source point cloud in the first stage to the reference coordinate frame using the sensor orientation acquired either by pre-calibration or an inertial measurement unit, thereby leveraging the inner structure of the transformed point cloud to ease the subsequent processes that use two concise thresholds for producing the results. A revised region growing algorithm named Z clustering and a principal component analysis (PCA)-based approach are presented for point clustering and refinement, respectively. Furthermore, we provide a nearest neighbor plane matching (NNPM) strategy to preserve the identities of extracted planes across successive sequences. Qualitative and quantitative evaluations of both real and synthetic scenes demonstrate that our approach outperforms several state-of-the-art methods under challenging circumstances, in terms of robustness to clutter, accuracy, and efficiency. We make our algorithm an off-the-shelf toolbox which is publicly available. Full article

High energy 2D X-ray powder diffraction experiments are widely used for lattice strain measurement. The 2D to 1D conversion of diffraction patterns is a necessary step used to prepare the data for full pattern refinement, but is inefficient when only peak centre position information is required for lattice strain evaluation. The multi-step conversion process is likely to lead to increased errors associated with the ‘caking’ (radial binning) or fitting procedures. A new method is proposed here that relies on direct Digital Image Correlation analysis of 2D X-ray powder diffraction patterns (XRD-DIC, for short). As an example of using XRD-DIC, residual strain values along the central line in a Mg AZ31B alloy bar after 3-point bending are calculated by using both XRD-DIC and the conventional ‘caking’ with fitting procedures. Comparison of the results for strain values in different azimuthal angles demonstrates excellent agreement between the two methods. The principal strains and directions are calculated using multiple direction strain data, leading to full in-plane strain evaluation. It is therefore concluded that XRD-DIC provides a reliable and robust method for strain evaluation from 2D powder diffraction data. The XRD-DIC approach simplifies the analysis process by skipping 2D to 1D conversion, and opens new possibilities for robust 2D powder diffraction data analysis for full in-plane strain evaluation. Full article

This paper presents a density functional theory (DFT)/time-dependent DFT (TD-DFT) study on the lowest lying singlet and triplet excited states of 20 selected polybrominateddiphenyl ether (PBDE) congeners, with the solvation effect included in the calculations using the polarized continuum model (PCM). The results obtained showed that for most of the brominated diphenyl ether (BDE) congeners, the lowest singlet excited state was initiated by the electron transfer from HOMO to LUMO, involving a π–σ* excitation. In triplet excited states, structure of the BDE congeners differed notably from that of the BDE ground states with one of the specific C–Br bonds bending off the aromatic plane. In addition, the partial least squares regression (PLSR), principal component analysis-multiple linear regression analysis (PCA-MLR), and back propagation artificial neural network (BP-ANN) approaches were employed for a quantitative structure-property relationship (QSPR) study. Based on the previously reported kinetic data for the debromination by ultraviolet (UV) and sunlight, obtained QSPR models exhibited a reasonable evaluation of the photodebromination reactivity even when the BDE congeners had same degree of bromination, albeit different patterns of bromination. Full article