Continuous Curve Research Articles

IMPROVING MODEL ACCURACY OF THE "LUNG AGE” ESTIMATION THROUGH MACHINE LEARNING

IMPROVING MODEL ACCURACY OF THE "LUNG AGE” ESTIMATION THROUGH MACHINE LEARNING

Trajectory Planning for UAVs Based on Interfered Fluid Dynamical System and Bézier Curves

In this paper, a 3D trajectory planner for Unmanned Aerial Vehicles (UAVs) based on Interfered Fluid Dynamical System (IFDS) and Bézier curves is introduced. The proposed strategy joints the potentialities of IFDS with the use of Bézier curves to obtain optimized trajectories with continuous curvature. While IFDS computes an initial trajectory to safely avoid fixed and dynamic obstacles, Bézier curve optimization generates a trajectory satisfying kinematic constraints. This combination is computationally efficient for online applications with limited hardware and a smoothed path is obtained, for safe and flyable trajectories. Simulations are performed for a fixed-wing UAV in a complex and dynamic environment.

UAV safe route planning based on PSO-BAS algorithm

In order to solve the current situation that unmanned aerial vehicles (UAVs) ignore safety indicators and cannot guarantee safe operation when operating in low-altitude airspace, a UAV route planning method that considers regional risk assessment is proposed. Firstly, the low-altitude airspace is discretized based on rasterization, and then the UAV operating characteristics and environmental characteristics are combined to quantify the risk value in the low-altitude airspace to obtain a 3D risk map. The path risk value is taken as the cost, the particle swarm optimization-beetle antennae search (PSO-BAS) algorithm is used to plan the spatial 3D route, and it effectively reduces the generated path redun dancy. Finally, cubic B-spline curve is used to smooth the plan ned discrete path. A flyable path with continuous curvature and pitch angle is generated. The simulation results show that the generated path can exchange for a path with a lower risk value at a lower path cost. At the same time, the path redundancy is low, and the curvature and pitch angle continuously change. It is a flyable path that meets the UAV performance constraints.

Shape Reconstruction of Soft Manipulators Using Vision and IMU Feedback

In recent times, soft manipulators have gardened immense interest given their dexterous abilities. A critical aspect for their feedback control involves the reconstruction of the manipulator shape. The research, for the first time, presents shape reconstruction of a soft manipulator through sensor fusion of information available from Inertial Measurement Units (IMUs) and visual tracking. The manipulator is modeled using multi-segment continuous curvature Pythagorean Hodograph (PH) curves. PH curves are a class of continuous curvature curves with an analytical expression for the hodograph (slope). The shape reconstruction is formulated as an optimization problem that minimizes bending energy of the curve with a length constraint and the information from IMUs and/or visual markers. The paper experimentally investigates the robustness of shape reconstruction for scenarios when position of all visual markers, or slope at all the knots (placement of sensors) are known. Occlusion of manipulator segments is frequent, hence, this scenario is simulated by fusing information of available slopes (IMUs) at all knots and position (vision) at some knots. The experiments are performed on a planar tensegrity manipulator with IMUs feedback and visual tracking. The robustness study indicates reliability of these models for real world applications. Additionally, the proposed sensor fusion algorithm provides promising results where, for most cases, the shape estimates benefit from additional position information. Finally, the low dimensionality of the optimization problem argues for extension of the approach for real-time applications.

Automated dispersion curve picking using multi-attribute convolutional-neural-network based machine learning

SUMMARY Surface wave dispersion curves are useful to characterize shallow subsurface structures while accurately picking them is typically laborious. To make these approaches more efficient and practical, it is important to automate the picking process. We propose a convolutional neural network (CNN) based ML method to automatically pick multimode surface wave dispersion curves. We modify the typical U-net architecture to convert the conventional 2-D image segmentation problem into direct multimode curve fitting and subsequent picking. A variety of attributes of the data amplitude (A) in the (f, k) domain, such as frequency (F), wavenumber (K), maximum coherency (Coh) and Power weighted amplitude (Pwa), are combined to constrain the picking more accurately than a single attribute does. The effects of two different loss functions on the final picking results are compared; the one that combines conventional wavenumber residuals and curve slope residuals produces more continuous curves. Pre-training the network with synthetic data, and thus using transfer learning, improves the efficiency of the algorithm when the data set is large. To determine the frequency band of each dispersive mode (effective frequency band) in the picked curves, the CNN outputs are post-processed by using measurements such as long/short moving average ratios of squared picked wavenumbers, posterior uncertainty of picked wavenumbers and wavenumbers in the picked curves or the power attribute. We demonstrate the effectiveness of this automatic picking by applying it to a 2-D line and a 3-D subset from a field ocean bottom node data set, where the fundamental and first higher modes of Scholte waves are accurately picked.

INFLUENCES OF DATA ACQUISITION AND LAYER PARAMETERS ON FUNDAMENTAL DISPERSION CURVE IN ACTIVE MULTICHANNEL ANALYSIS OF SURFACE WAVE (A-MASW) METHOD

Aktif ÇKYDA yönteminde, sığ yeraltının güvenilir bir S-dalgası hız-derinlik profilini elde etmek için en önemli aşamalarından biri, geniş frekans aralığında sürekli ve yüksek ayrımlı bir dispersiyon eğrisinin elde edilmesidir. Bu makalede, tabaka (S- ve P-dalga hızları, yoğunluk ve kalınlık) ve veri toplama (kaynak ofseti-X0, alıcı aralığı-dx ve sayısı-N, serim boyu-L=(N-1)*dx) parametrelerinin, yüzey dalga alanının temel mod dispersiyon eğrisi üzerindeki etkileri, yapay atış verilerinin analizi ile incelenmiştir. Yapay veriler, tabaka yansıma/iletim katsayısı tekniği ile hesaplanan dispersiyon eğrisi kullanılarak, her bir alıcıda harmonik mod toplama tekniği ile oluşturulmuştur. Buna göre, düşük S-dalga hızlı ara tabakalar dispersiyon eğrisinin düşük frekans bölgesinde düşük hızlara kaymasına, yüksek hızlı ara tabakalar ise, dispersiyon eğrisinin yüksek frekanslarda sürekliliğinin bozulmasına (zig-zag etkisi) ve yüksek mod etkisi yanılgısına neden olabileceği gözlenmiştir. Kaynak ofseti yüzey dalgalarının tam dalga formlarının oluşmasında etkin iken, ilk ve son alıcı arası uzaklık olan serim uzunluğunun ise, dispersiyon eğrisinin frekans bandını ve çözünürlüğünü etkilediği görülmüştür. Ayrıca, geniş alıcı aralıklarının, dispersiyon eğrisinin düşük frekans bölgesindeki güvenirliğinin arttırmasının yanında, yüksek frekans bölgesinde dalga sayısı katlanması nedeniyle dispersiyon eğrisinin sürekliliğini bozduğu görülmüştür. Sonuç olarak, farklı dalga hızlarına sahip yer modelleri için üretilen yapay veriler üzerinde yapılan analizler N

Effect of Various Edge Configurations on the Accuracy of the Modelling Shape of Shell Structures Using Spline Functions

Spline functions are a useful tool for modelling the shape of shell structures. They have curvature continuity that allows good approximation accuracy for various objects, including hyperboloid cooling towers, spherical domes, paraboloid bowls of radio telescopes, or many other types of smooth free surfaces. Spline models can be used to determine the displacement of structures based on point clouds from laser scanning or photogrammetry. The curvature continuity of splines may, however, cause local distortions in models that have edges. Edges may appear in point clouds where surface patches are joined, on surfaces equipped with additional technical infrastructure or with cracks and shifts in the structure. Taking the properties of spline functions into account, several characteristic types of edge configurations can be distinguished, which may, to a different extent, affect the values of modelling errors. The research conducted below was aimed at identifying such configurations based on theoretical considerations and then assessing their effect on the accuracy of modelling shell structures measured by laser scanning. It turned out to be possible to distinguish between edge configurations, based on the deviation values.

B-Spline Curve Fitting of Hungry Predation Optimization on Ship Line Design

The ship line often describes by the offset table of discrete data points, which leads to the problems that three view coordinates may not correspond, the fitting error is large and the fitted curve cannot be easily modified. This will seriously affect the subsequent ship performance evaluation and op-timization. To solve this problem, this paper develops a B-spline curve fitting of hunger predation optimization on ship line design (HPA), which contains knot guidance technology, hungry preda-tion optimization technology and adaptive adjustment of algorithm input parameters. HPA transforms the discrete ship line into a continuous B-spline curve description, which improves the accuracy and modifiability of the ship line design. Through the real-time feedback of the results of each round of iteration, the knot vector is adaptively adjusted towards a better fitness, and then the optimal control point set that satisfies the error threshold can be obtained. The effectiveness and superiority of HPA are verified by comparing with related research and engineering software.

High-precision blood glucose prediction and hypoglycemia warning based on the LSTM-GRU model

Objective: The performance of blood glucose prediction and hypoglycemia warning based on the LSTM-GRU (Long Short Term Memory - Gated Recurrent Unit) model was evaluated. Methods: The research objects were 100 patients with Diabetes Mellitus (DM) who were chosen from Henan Provincial People’s Hospital. Their continuous blood glucose curves of 72 hours were acquired by a Continuous Glucose Monitoring System (CGMS). The blood glucose levels were predicted based on the LSTM, GRU and LSTM-GRU models, respectively. Analyses of the best predictive model were performed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and correlation analysis between the prediction blood glucose level and the original blood glucose level acquired by CGMS and Clark Error Grid Analysis (EGA). Repeated-measures analysis of variance (ANOVA) was used to analyze whether the RMSE values of the three models were statistically significant. 60 patients who had experienced hypoglycemia among 100 cases were selected for hypoglycemia warning. The sensitivity, false-positive rate and false-negative rate were used to evaluate the hypoglycemia warning performance of the LSTM-GRU model. This paper explored the changing relationship of the hypoglycemia warning performance of the model over time. Results: The predicted blood glucose levels of the three models were strongly correlated with the blood glucose levels acquired by CGMS (p < 0.001). The correlation coefficient (R-value) of the LSTM-GRU model remained stable over time (R = 0.995), nevertheless, a reduction in the R-value of the LSTM and GRU models when the Prediction Horizon (PH) was 30 min or longer. When PH was 15min, 30min, 45min and 60min, the mean RMSE values of the LSTM-GRU model were 0.259, 0.272, 0.275 and 0.278 (mmol/l), respectively, which were lower than the LSTM and GRU models and the RMSE values were statistically significant (p < 0.001). The EGA results showed the LSTM-GRU model had the highest proportion in zones A and B, as the PH extended. When PH was 30min or longer, the sensitivity and false-negative rate of the hypoglycemia warning of the LSTM-GRU model had subtle changes and the false-positive rate remained stable over time. Conclusions: The LSTM-GRU model demonstrated good performance in blood glucose prediction and hypoglycemia warning.

Toolpath generation for ultraprecision machining: Freeform surface blending

This paper is the final part in a two-part series investigating toolpath generation for freeform optics manufacturing. Building on the surface generation techniques already presented, this paper introduces a 3D blending surface that has the form of a continuous polynomial curve and is used to smoothly fill in the space between two non-intersecting, polar boundaries and two surfaces of the form Z(X,Y). The proposed blending surface renders it possible to smoothly join two surfaces defined over different domains together, as well as to smooth over the discontinuity created where two surfaces intersect. It is shown that increasing the order of the blending polynomial increases the continuity of the overall piecewise, blended surface. As a corollary, it is possible to increase the overall continuity of a toolpath along its cutting direction. To illustrate the robustness of the proposed surface blending technique, experimental data is presented for the toolpath generation, diamond machining, and measurement of a freeform microlens array. Designed with no exploitable symmetries or simplifications and very difficult to model with traditional CAD software, this surface is comprised of an array of freeform lenslets superimposed and blended onto a freeform base surface. All lenslets are individually tilted about the X and Y-axes such that they are normal to the base surface at their respective locations, and all blends have the same form as the intersection curve between each respective lenslet and base surface. Machined in brass to optical quality, this C3 continuous surface represents a general case of the proposed surface generation and blending techniques.

Winter Wheat SPAD Value Inversion Based on Multiple Pretreatment Methods

SPAD value was measured by a portable chlorophyll instrument, which can reflect the relative chlorophyll content of vegetation well. Chlorophyll is an important organic chemical substance in plants that acquires and transmits energy during photosynthesis. The continuous spectral curve of winter wheat can be obtained rapidly in a specific band range by using hyperspectral remote sensing technology to estimate the SPAD value of winter wheat, which is of great significance to the growth monitoring and yield estimation research of winter wheat. In this study, with winter wheat as the research object, the spectral data and corresponding SPAD value in different growth stages were used as the data source, 20 kinds of data preprocessing spectra and sensitive spectral indices set the data as model input values, the partial least square regression (PLSR) model was established to estimate the SPAD value, and the model estimation results of different model input values at different growth stages were compared in detail. The results showed that the set of sensitive spectral indices selected in this study as input values can effectively improve the accuracy and stability of the PLSR model. In addition, the effects of 20 spectral data pretreatment methods on the estimation results of the SPAD value were compared and analyzed in different growth stages. It was found that the spectral data pretreated by the combination of wavelet packet denoising, first-order derivative transformation and principal component analysis can improve the accuracy and stability of PLSR model, and it is suitable for all growth stages. The results also showed that the estimation model is highly sensitive to the standard deviation of the SPAD value (STDchl) in sample sets. When the standard deviation is greater than 5.5 SPAD, the larger the STDchl is, the higher the model estimation accuracy is, and the more stable the model is. At this time, the model estimation accuracy is higher (R2V is greater than 0.5, ratio of performance to deviation is greater than 1.4), which can meet the estimation requirements of the SPAD value.

Investigation of wet particle drying process in a fluidized bed dryer by CFD simulation and experimental measurement

Wet particle drying process is complex due to the change of particle moisture, which has big effect on the gas–solid properties, flow dynamics and mass and heat transfer behavior. In this research, a two-fluid model (TFM) coupled with the mass and heat transfer was used to simulate the particle drying process in a fluidized bed dryer. Meanwhile, online electrical capacitance tomography (ECT) was used to measure the solids concentration and off-line moisture meter was employed to measure the particle moisture. Two periods, i.e., constant-rate period and falling-rate period, were captured in the experimental drying curve. The continuous drying curve was obtained by the 2D CFD simulation. The drying rate increases sharply in the pre-warming period, reaches high in the constant-rate period, and decreases markedly in the falling-rate period at the beginning and then decreases slowly. The particle temperature increases gradually until it reaches the air temperature and keeps constant. In order to capture detailed flow dynamics in the drying process, 3D CFD simulations at certain moisture contents were performed. It is found that the simulated cross-sectional particle volume fraction shows core-annular structure and is verified by the on-line ECT measurement. An internal circulation is formed along the reactor height. The mass and heat transfer mainly happens at the lower part of the dryer and at the contact region between the bubbles and the solids phase. The minimum fluidization velocity decreases, and the bubble rising velocity increases when the particle moisture level decreases.

An isogeometric finite element formulation for frictionless contact of Cosserat rods with unconstrained directors

This paper presents an isogeometric finite element formulation for nonlinear beams with impenetrability constraints, based on the kinematics of Cosserat rods with unconstrained directors. The beam cross-sectional deformation is represented by director vectors of an arbitrary order. For the frictionless lateral beam-to-beam contact, a surface-to-surface contact algorithm combined with an active set strategy and a penalty method is employed. The lateral boundary surface of the beam is parameterized by its axis and cross-sectional boundary curves with NURBS basis functions having at least C^2-continuity, which yields a continuous surface metric and curvature for the closest point projection. Three-dimensional constitutive laws of hyperelastic materials are considered. Several numerical examples verify the accuracy and efficiency of the proposed beam contact formulation in comparison to brick element solutions. The lateral contact pressure distribution of the beam formulation is in excellent agreement with the contact pressure of the brick element formulation while requiring much less degrees-of-freedom.

A Sufficient Condition for a Curve to Belong to Boundary of its Own Convex Hullin An

The paper discusses some generalizations of the convex curve and the relationship between the classes of curves they define.
 The main result concerns the curves in n-dimensional affine space An: a non-degenerate curve in An lies on the boundary of its convex hull if any hyperplane intersects it in at most n points. It is justified by the theory of convex sets. This statement generalizes an earlier (2014) result of the author concerning the curves that are compact sets in An. V. Gustin proved in 1947 a stronger theorem for a Euclidean space En of dimension n: a connected set intersected by any hyperplane at no more than n points is a simple continuous curve lying on the boundary of a convex set. Note that in a projective space of dimension n the requirement that the curve intersects all planes at no more than n points is included in the definition of a convex curve. Thus, the established fact, together with the result of V. Gustin, shows the contiguity of the concepts of convexity in all three spaces.

Types of Operational Mathematical Procedures in Dynamics Descriptions. A Short Note

Abstract Assuming that the dynamics of complex systems can be described on continuous and non-differentiable curves, a multifractal mathematical procedure was proposed to analyze the dynamics of complex systems. Thus, using the Schrodinger monofractal model and the hydrodynamic multifractal model, in the case of the dynamics of systems described by Peano-type fractal curves, fractal theories of movement can be developed.

Solving the Moment Amplification Factor of a Lateral Jet by the Unsteady Motion Experimental Method

In this paper, unsteady motion tests of a lateral jet adjusting an air vehicle’s attitude are carried out. Curves of pitch moment amplification factors (KM) for a lateral jet versus angle of attack (α) are obtained using a wind tunnel free-flight test technique with a jet and data processing method. This new method overcomes the disadvantage of previous experiments that can study only one unsteady characteristic. The free-flight test technique in the proposed method ensures that the test model can be coupled in real-time with multiple parameters (unsteady flow caused by the jet, unsteady air vehicle aerodynamic force, and unsteady air vehicle motion). This approach simulates an actual air vehicle’s complete jet test process and ensures more authentic and reliable test results. In the new data processing method, continuous data curves are fitted to discrete data points, making it easier to convert the angular displacement versus time curve into the pitch moment versus α curve to obtain KM. The results show that when the pressure of the micro high-pressure gas cylinder is 2.0 MPa, KM is below 1, indicating that the lateral jet does not significantly promote the pitching moment. When the gas cylinder pressure is 4.0 MPa and the angle of attack is 5° < |α| < 16°, KM is greater than 1, and the lateral jet promotes the pitching moment. When 16° < |α| < 20°, KM is less than 1, and the lateral jet does not significantly contribute to the pitching moment. It was further found that KM decreases slowly with increasing α. When |α| > 30°, the influence of the jet on the pitching moment nearly disappears.

A TMPS-designed personalized mandibular scaffolds with optimized SLA parameters and mechanical properties

With the rapid development of 3D printing technology, porous titanium scaffolds have provided a new restoration method to repair bone defects. Compared with the traditional body-centered cubic (bcc) dot matrix structure with a simple arrangement and repetitive structure, the topology-driven properties of triply periodic minimal surfaces (TPMS) can offer a continuous surface and smooth curvature, an excellent platform for cell proliferation. In this study, we used reverse engineering techniques to model the mandible. Sheet and solid networks of gyroid structure, the most common type of TPMS, were selected for porous design and then molded using metal 3D printing technology. At the same time, the surface treatment parameters of sandblasted, large-grit, and acid-etched (SLA) were optimized by orthogonal experimental design. Then, the optimized SLA parameter was used to treat the gyroid with 70% porosity. The result showed that reverse engineering reconstructed the TPMS-based mandibular model had good formability. Furthermore, the best surface morphology, wettability, and roughness were obtained for 3D printed Ti6Al4V under the treatment of 80 mesh Al2O3, blasting distances of 4 cm, and a 1:1:2 acid ratio. Moreover, the mechanical properties of Sheet-Gyroid and Solid-Gyroid were significantly different at 70% porosity. The porosity of the scaffolds was close to the design porosity after SLA treatment. However, no significant changes were found in its mechanical properties, all matching the mandible’s mechanical properties to meet the implantation conditions.

Understanding Accretion Variability through TESS Observations of Taurus

Interpreting the short-timescale variability of the accreting, young, low-mass stars known as Classical T Tauri stars remains an open task. Month-long, continuous light curves from the Transiting Exoplanet Survey Satellite (TESS) have become available for hundreds of T Tauri stars. With this vast data set, identifying connections between the variability observed by TESS and short-timescale accretion variability is valuable for characterizing the accretion process. To this end, we obtained short-cadence TESS observations of 14 T Tauri stars in the Taurus star formation region along with simultaneous ground-based, UBVRI-band photometry to be used as accretion diagnostics. In addition, we combine our data set with previously published simultaneous near-UV–near-IR Hubble Space Telescope spectra for one member of the sample. We find evidence that much of the short-timescale variability observed in the TESS light curves can be attributed to changes in the accretion rate, but note significant scatter between separate nights and objects. We identify hints of time lags within our data set that increase at shorter wavelengths, which we suggest may be evidence of longitudinal density stratification of the accretion column. Our results highlight that contemporaneous, multiwavelength observations remain critical for providing context for the observed variability of these stars.

High Speed Machining for Linear Paths Blended with G3 Continuous Pythagorean-Hodograph Curves

High Speed Machining for Linear Paths Blended with G3 Continuous Pythagorean-Hodograph Curves

A smooth path planning method for mobile robot using a BES-incorporated modified QPSO algorithm

The design of path planning algorithms to obtain a smooth path that satisfies constraints such as obstacle avoidance, minimized cost, and dynamic feasibility for mobile robots in the workspace is a hot issue in the field of intelligent robotics. The metaheuristic optimization algorithm, which aims to solve path planning problems, is more efficient compared to traditional algorithms. In the current metaheuristic approaches, avoiding local minimum traps is the core goal of the scheme. In this paper, with the benefit of a high-order continuous Bezier curve, we construct an optimization problem of smooth path planning based on the length and the requirement of collision-free safety as constraints of the robot's expected path. Then, the smooth path planning problem is transformed into an optimization problem that searches the locations of control nodes of the Bezier curve. We solve the constructed path planning problem with our modified quantum particle swarm optimization algorithm (QPSO). Specifically, to overcome the shortcomings of the current PSO and QPSO-based particle swarm algorithms like prematurity, unbalanced global search, local exploitation, and local minima, we implement an improved QPSO algorithm denoted as BES-MQPSO that incorporates the search and swoop mechanisms from bald eagle search (BES) strategies. The method consists of four improved modifications: an improved local attractor, improved feature length, BES-enhanced intensive exploitation Strategy, and random variation mechanism. Our proposed BES-MQPSO with these four modifications is tested with benchmark functions and then applied to the smooth path planning problem. Finally, the superiority of BES-MQPSO is confirmed, and the ideal smooth path of the mobile robot is successfully planned. The planned path length is reduced by 4.45% and 2.82% compared to e-QPSO and SDEQPSO respectively, while the standard deviation is reduced by 67.46% and 69.82%, respectively.