Invariant Parameters Research Articles

LINGUISTIC ECOCENTRISM AS META-DISCOURSIVE PRINCIPLE OF FUNCTIONAL PARADIGM

Introduction. The paper focuses on the principle of linguistic ecocentrism specified both as a discursive mechanism of the human consciousness ecologization, which determines the invariant parameters of various institutional discourses, and as a new vector of a functionalparadigm associated with overcoming anthropocentrism.Purpose. The research aims at defining the anthropocentrism as a meta-discursive principle of various institutional discourses in view of the invariance and correlation of their cognitive, semiotic, pragmatic parameters embodied with the notion of ecocentric meta-discourse. To achieve the purpose, we have applied the methodology composing of the inquiry subject’s characterizations including its observations in scientific studies and subsequent definition, as well as hypotheses and predictions about the inquiry subject’s structure, manifestations andconstituents.The major results refer to the interpretation of the linguistic ecocentrism as the principle of harmonious coexistence of the human with the environment, reflected by various institutional discourses. This principle both influences the ecologization of linguistics of the XXI century and changes its research priorities, principles and instruments providing further integration of cognitive-discursive, cognitive semiotic, sociocognitive, cognitive-pragmatic studies in order to comprehend the strategic, behavioral, cognitive, verbal and non-verbal human practices as the noospheric factors in the development of civilization. The article introduces the concept and outlines the prospects for studying the ecocentric meta-discourse as a prototype cognitive-semiotic model of various institutional discourses with explicit or implicit ecocentric component.Conclusion. The principle of linguistic eco-centrism is the structure-forming property of the “ecocentric metadiscourse”, which is constituted by the prototype cognitive, semiotic, pragmatic parameters while specifying and developing the knowledge and values about the harmonious coexistence of man with the surrounding world – nature, society, linguistic-culturaland transcultural communities.

A polynomial-time approximation to a minimum dominating set in a graph

A dominating set of a graph G=(V,E) is a subset of vertices S⊆V such that every vertex v∈V∖S has at least one neighbor in S. Finding a dominating set with the minimum cardinality in a connected graph G=(V,E) is known to be NP-hard. A polynomial-time approximation algorithm for this problem, described here, works in two stages. At the first stage a dominant set is generated by a greedy algorithm, and at the second stage this dominating set is purified (reduced). The reduction is achieved by the analysis of the flowchart of the algorithm of the first stage and a special kind of clustering of the dominating set generated at the first stage. The clustering of the dominating set naturally leads to a special kind of a spanning forest of graph G, which serves as a basis for the second purification stage. We expose some types of graphs for which the algorithm of the first stage already delivers an optimal solution and derive sufficient conditions when the overall algorithm constructs an optimal solution. We give three alternative approximation ratios for the algorithm of the first stage, two of which are expressed in terms of solely invariant problem instance parameters, and we also give one additional approximation ratio for the overall two-stage algorithm. The greedy algorithm of the first stage turned out to be essentially the same as the earlier known state-of-the-art algorithms for the set cover and dominating set problem Chvátal [6] and Parekh [31]. The second purification stage results in a significant reduction of the dominant set created at the first stage, in practice. The practical behavior of both stages was verified for randomly generated problem instances. The computational experiments emphasize the gap between a solution of Stage 1 and a solution of Stage 2.

Time-Variant Schwarz Based Model for DC Series Arc Fault Modeling in Photovoltaic Systems

DC series arc fault(DCSAF) in photovoltaic systems is a controversial issue owing to the challenges related to its modeling and protection. This article focuses on efficient models based on Schwarz model for DCSAF utilizing actual recorded data. The original Schwarz model has four parameters, containing two coefficients and two model orders, and all four parameters are constant. Despite the original Schwarz model that uses invariant parameters for arc modeling, the proposed models employ a Schwarz model in which the time-variant nature of the arc's parameters is taken into account. The first developed Schwarz model with two constant model orders and two time-variant coefficients is derived in the first part. While the first model provides more capability compared with the original Schwarz modeling, the second developed Schwarz model with full time-variant parameters is also presented in the second part. A novel optimization technique based on Powell's dog leg algorithm is proposed to estimate the arc coefficients in every simulation window. Therefore, the parameters in both modified Schwarz models are time-variant and change every simulation window. As a result, they are classified as a time series, which is examined using the autoregressive moving average process. By actual recorded data, the performance of the proposed models is assessed. Analyses show that the proposed models are more efficient to model DCSAF in comparison with the original Schwarz model.

Kinetic parameters of thermal destruction of the copolymer of polyethylene glycol fumarate with acrylic acid in inert medium

Thermal decomposition of the copolymer of polyethylene glycol fumarate with acrylic acid (p-EGF:AA) of two different compositions synthesized earlier was studied in the present work. TG and DTG curves prove that decomposition takes place in several stages. According to thermogravimetric curves it has been found out that for the copolymer with higher content of acrylic acid the decomposition of the copolymer’s sample is started at higher temperatures. It has been shown the shift of the temperature of decomposition’s start to the higher area with the increase of heating rate which is necessary for the detorsion of macromolecular coil. Experimental data processed using graphical methods of Kissinger–Akahira–Sunose and Friedman allowed us to calculate the activation energy of the thermal decomposition process. It has been established that the copolymer with the composition of 21.03:78.97 mass.% has lower meaning of activation energy than the one with the composition of 68.96:31.04 mass.%. As a result of calculation one can see that the meanings found out using these methods depend slightly on conversion. Using Achar-Brindley-Sharp method and the method of invariant kinetic parameters the kinetic triplet of the decomposition process has been found which was used to build the calculated curve. The dependences of g(α) on α using these parameters showed a satisfactory agreement of calculated curves with the experimental ones. One can conclude that the decomposition process of the copolymer of polyethylene glycol fumarate with acrylic acid is well described with of D3 (three-dimensional diffusion) model.

Energy-Saving Applications Based on Train Mass Online Learning Using Time-Varying Train Model

The current train model of the train control system is unable to accurately reflect the influence of nonlinear running resistance, line conditions, the mutative train mass value, and external environment changes on the model in train dynamics, resulting in a defect of poor train model performance. The train basic model and additional resistances are discussed in this paper, a novel neural network online learning method of the time-varying dynamic train model is proposed, combined with the characteristics of rail transit lines, and a neural network learning algorithm is designed by categories and steps. This method can identify the train mass value that changes continuously with passengers during running. The energy savings resulting from using the actual varying train mass in the train control system are calculated. The results show that, when compared to the traditional model’s invariant approximate empirical parameters, the time-varying parameter model can follow changes in the train and line environment and obtain quantitative expressions of curve resistance and tunnel resistance with speed. The time-varying train model was used to conduct engineering tests on the Beijing Capital Airport Line; the online learning deviation of train mass was controlled within a margin of 3.08%, and at the same time, energy consumption decreased by 6.13%.

Spectral invariants in ultra-high spatial resolution hyperspectral images

Current operational optical satellite-based Earth observation methods targeting at vegetation are optimised for the high- and medium-resolution satellites with pixels sizes starting at ten meters. This resolution is coarser than the typical size of a vegetation structural element, such as a tree crown, and much coarser that of scattering elements, such as leaves. For this reason, vegetation can be treated as a continuous medium and the variation in the local illumination conditions on individual leaves or tree crowns can be ignored. This does not hold anymore for very and ultra-high resolution imagery, obtained from new satellite systems or unmanned aerial vehicles, where individual tree crowns or even leaves can be discerned. We tested the applicability of the spectral invariant theory to this type of imagery for characterising the local illumination conditions on plant leaves using Monte Carlo ray tracing simulations. The simulations corroborated the direct link between the spectral invariant parameter ρ and the sunlit fraction of visible leaves, earlier alleged for hyperspectral remote sensing data based on mathematical considerations. The approach allowed us to separate the direct beam and multiple scattering irradiance components and provided intuitive interpretations of the recollision probability and canopy scattering coefficient computed for each image pixel.

A worldwide data science investigation of rainfall

Abstract We perform a worldwide analysis of the rain drop size distribution using 166 dis-drometer datasets from 76 distinct sites for a total of 1,527,963 1-minute drop counts, 428,410 2-minute drop counts, and a total of 988,922,720 drops. Following data science tenets, we adopt a functional-agnostic description of the rain drop size distribution. In this way, we uncover the presence of an invariant structure of statistical relationship among the distribution parameters, not depending on location, synoptic origin, or type of disdrometer. The features of this structure are: 1) count-shape independence: there is no dependence between the drop count N and the shape of rain drop spectra. 2) mean-skewness prominence: the variability of the shape of rain drop spectra can be fully captured by its mean µ and skewness γ. 3) mean-skewness invariant parametrization: we derive empirical invariant functional forms expressing all other shape describing parameters in terms of the free parameters (µ, γ). The presented analysis reveals the global and local properties of the rain drops size distribution offering a coherent and universally applicable methodology to describe the rain drop size distribution.

Simple adaptive V-stack piezoelectric based airfoil flutter suppression system

The suppression of aeroelastic vibrations due to flutter using a trailing edge flap is analyzed to increase the speed operability range of wings. The aeroelastic model of a 3DOF airfoil in incompressible flow is presented and an augmented state-space representation is used for the time domain analysis. A finite element model of a V-stack piezoelectric actuator, used to move the trailing edge flap, is introduced to model the actuator dynamics. The use of the simple adaptive controller is investigated to realize an adaptive flutter suppression system. A parallel feedforward compensator is tuned to make the plant almost strictly passive and a metaheuristic algorithm is used to determine the invariant parameters. Numerical simulations are carried out to verify the performance of the simple adaptive flutter suppression system in presence of speed variations and disturbances. Numerical results proved that the SAC flutter suppression system improves the closed loop system flutter boundary with respect to a gain scheduled PID controller.

Multiscale Modeling for Texture and Grain Topology of Polycrystalline Microstructures Under Uncertainty

The present work addresses multiscale modeling for grain topology of polycrystalline microstructures under the effects of the microstructural uncertainties. The special focus is on the titanium-7wt%-aluminum alloy (Ti-7Al), which is a candidate material for many aerospace systems owing to its outstanding mechanical performance in elevated temperatures. The electron backscatter diffraction samples of Ti-7Al in small-scales are used to reconstruct the alloy microstructures in larger domains. The reconstruction provides a statistically equivalent synthetic representation to the small-scale test samples while introducing epistemic uncertainty on microstructural features. Here, the grain topology of the polycrystalline microstructures is quantified using shape moment invariants. These invariant parameters can capture the shapes of physical objects with a numerical representation, and they are invariant to shape transformations. To quantify the effects of the uncertainties on the homogenized properties of the microstructures, a surrogate model is developed as a function of shape moment invariants with Gaussian process regression by utilizing the experiments and crystal plasticity simulations for training data.

Accurately targeting an arbitrary probability of response in staircase procedures

Staircase procedures are commonly used to identify the stimulus level corresponding to a specific probability of response. The primary benefit of staircase procedures is that they require only that the probability of response changes monotonically with the stimulus level. The probability of response targeted by the procedure is typically estimated from the mean of reversals. However, the mean of reversals can be asymptotically biased away from the targeted probability. Existing methods to address the bias add assumptions about task performance, thereby limiting their use to well understood tasks and listener groups. We evaluated the hypothesis that the bias could be mitigated without adding assumptions by accounting for the effect of invariant procedural parameters on the estimate of the target probability. The approach was successful for staircases with different ascending and descending steps, for which the bias was found to depend on step size only. For other staircases, predicting bias required adding assumptions comparable to existing solutions. Results suggest that the bias is caused by the factthat reversals are influenced by parameters that do not affect the target point. A method is proposed to accurately estimate an arbitrary probability of response without adding assumptions about the task or listeners. [R01DC015051.]

Study of Thermal Decomposition of the Copolymer Based on Polyethylene Glycol Fumarate with Acrylic Acid

Thermal decomposition of the copolymer based on polyethylene glycol fumarate with acrylic acid (p-EGF:AA) at four different heating rates in a nitrogen atmosphere has been investigated by the dynamic thermogravimetry (TGA) method. The TGA curves show that the decomposition process occurs in several stages. The experimental data processed by the Kissinger–Akahira–Sunose and Friedman isoconversion methods made it possible to calculate the activation energy of the main decomposition stage. According to the calculations results, one can see that the values found by these methods do not strongly depend on the conversion. The Achar–Brindley–Sharp method has been applied to determine the compensation parameters. Then, using the compensation effect, the invariant kinetic parameters of the decomposition processes have been found. An experimental curve has been plotted to find the reaction model in comparison with theoretical ones.

LDVoxelMorph: A precise loss function and cascaded architecture for unsupervised diffeomorphic large displacement registration.

The traditional learning-based non-rigid registration methods for medical images are trained by an invariant smoothness regularization parameter, which cannot satisfy the registration accuracy and diffeomorphic property simultaneously. The diffeomorphic property reflects the credibility of the registration results. To improve the diffeomorphic property in 3D medical image registration, we propose a diffeomorphic cascaded network based on the compressed loss (CL), named LDVoxelMorph. The proposed network has several constituent U-Nets and is trained with deep supervision, which uses a different spatial smoothness regularization parameter in each constituent U-Nets for training. This cascade-variant smoothness regularization parameter can maintain the diffeomorphic property in top cascades with large displacement and achieve precise registration in bottom cascades. Besides, we develop the CL as a penalty for the velocity field, which can accurately limit the velocity field that causes the deformation field overlap after the velocity field integration. In our registration experiments, the dice scores of our method were 0.892 ± 0.040 on liver CT datasets SLIVER37 , 0.848 ± 0.044 on liver CT datasets LiTS38 , 0.689 ± 0.014 on brain MRI datasets LPBA38 , and the number of overlapping voxels of deformation field were 325, 159, and 0, respectively. Ablation study shows that the CL improves the diffeomorphic property more effectively than increases. Experiment results show that our method can achieve higher registration accuracy assessed by dice scores and overlapping voxels while maintaining the diffeomorphic property for large deformation.

Standardization and implementation of fluorescence molecular endoscopy in the clinic.

.Significance: Near-infrared fluorescence molecular endoscopy (NIR-FME) is an innovative technique allowing for in vivo visualization of molecular processes in hollow organs. Despite its potential for clinical translation, NIR-FME still faces challenges, for example, the lack of consensus in performing quality control and standardization of procedures and systems. This may hamper the clinical approval of the technology by authorities and its acceptance by endoscopists. Until now, several clinical trials using NIR-FME have been performed. However, most of these trials had different study designs, making comparison difficult.Aim: We describe the need for standardization in NIR-FME, provide a pathway for setting up a standardized clinical study, and describe future perspectives for NIR-FME.Body: Standardization is challenging due to many parameters. Invariable parameters refer to the hardware specifications. Variable parameters refer to movement or tissue optical properties. Phantoms can be of aid when defining the influence of these variables or when standardizing a procedure.Conclusion: There is a need for standardization in NIR-FME and hurdles still need to be overcome before a widespread clinical implementation of NIR-FME can be realized. When these hurdles are overcome, clinical outcomes can be compared and systems can be benchmarked, enabling clinical implementation.

Semantics of “Shaking One’s Head” Gesture as a Unit of Russian Communicative System

The study was carried out by the method of semantic communicative analysis, which the author expands to Russian kinesic means (pose, mimic, gesture). These means are regarded as units of communicative level which reflects the interconnection between the position of the speaker, the listener and situation. This system includes such elements as the purport, variative sets of constructions as well as the units these constructions are made of. These means are not only heterogeneous (in particular, they include both verbal and kinesic units), they are characterized by extramentality of meanings and require special methods of defining their semantic parameters. In the paper with the help of scientific instruments of the school of semantic communicative analysis, the invariant parameter of Russian “shaking one’s head” gesture is identified. The obtained results can form the basis of the vocabulary description of this gesture in the dictionaries of a new type. The article also covers the question of the functioning of “shaking one’s head” gesture within the artistic text, where it aquires an aesthetic charge.

Potential Defects and Improvements of Equivalent Uniform Dose Prediction Model Based on the Analysis of Radiation-Induced Brain Injury.

PurposeTo study the impact of dose distribution on volume-effect parameter and predictive ability of equivalent uniform dose (EUD) model, and to explore the improvements.Methods and MaterialsThe brains of 103 nasopharyngeal carcinoma patients treated with IMRT were segmented according to dose distribution (brain and left/right half-brain for similar distributions but different sizes; V D with different D for different distributions). Predictive ability of EUDV D (EUD of V D ) for radiation-induced brain injury was assessed by receiver operating characteristics curve (ROC) and area under the curve (AUC). The optimal volume-effect parameter a of EUD was selected when AUC was maximal (mAUC). Correlations between mAUC, a and D were analyzed by Pearson correlation analysis. Both mAUC and a in brain and half-brain were compared by using paired samples t-tests. The optimal D V and V D points were selected for a simple comparison.ResultsThe mAUC of brain/half-brain EUD was 0.819/0.821 and the optimal a value was 21.5/22. When D increased, mAUC of EUDV D increased, while a decreased. The mAUC reached the maximum value when D was 50–55 Gy, and a was always 1 when D ≥55 Gy. The difference of mAUC/a between brain and half-brain was not significant. If a was in range of 1 to 22, AUC of brain/half-brain EUDV55 Gy (0.857–0.830/0.845–0.830) was always larger than that of brain/half-brain EUD (0.681–0.819/0.691–0.821). The AUCs of optimal dose/volume points were 0.801 (brain D2.5 cc), 0.823 (brain V70 Gy), 0.818 (half-brain D1 cc), and 0.827 (half-brain V69 Gy), respectively. Mean dose (equal to EUDV D with a = 1) of high-dose volume (V50 Gy–V60 Gy) was superior to traditional EUD and dose/volume points.ConclusionVolume-effect parameter of EUD is variable and related to dose distribution. EUD with large low-dose volume may not be better than simple dose/volume points. Critical-dose-volume EUD could improve the predictive ability and has an invariant volume-effect parameter. Mean dose may be the case in which critical-dose-volume EUD has the best predictive ability.

Research on Multi-View Image Reconstruction Technology Based on Auto-Encoding Learning

Traditional three-dimensional (3D) image reconstruction method, which highly dependent on the environment and has poor reconstruction effect, is easy to lead to mismatch and poor real-time performance. The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology. To solve the problem, a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper. The algorithm first extracts the feature information of multiple two-dimensional (2D) images based on scale and rotation invariance parameters of Scale-invariant feature transform (SIFT) operator. Secondly, self-encoding learning neural network is introduced into the feature refinement process to take full advantage of its feature extraction ability. Then, Fish-Net is used to replace the U-Net structure inside the self-encoding network to improve gradient propagation between U-Net structures, and Generative Adversarial Networks (GAN) loss function is used to replace mean square error (MSE) to better express image features, discarding useless features to obtain effective image features. Finally, an incremental structure from motion (SFM) algorithm is performed to calculate rotation matrix and translation vector of the camera, and the feature points are triangulated to obtain a sparse spatial point cloud, and meshlab software is used to display the results. Simulation experiments show that compared with the traditional method, the image feature extraction method proposed in this paper can significantly improve the rendering effect of 3D point cloud, with an accuracy rate of 92.5% and a reconstruction complete rate of 83.6%.

Reinforcement Learning-Based Visible Light Positioning and Handover Scheme With Stereo-Camera

Aimed at solving the problems of frequent handover and large overheads for positioning in 6G, this paper proposes a reinforcement learning (RL)-based visible light positioning (VLP) handover scheme by means of a stereo camera system in which a lower handover rate and higher positioning accuracy were achieved simultaneously. Because of the randomness of the distribution location of indoor light sources and obstacles, even if the parameters of light sources and receivers are consistent, users cannot rely on unitary and invariable parameters to determine whether to change the access point (AP) in the process of moving, especially in some special locations. The proposed scheme, which decomposed the user’s moving track at different speeds in a VLP system to optimize selecting the AP by using RL at each step, was exhibited and tested. Experimental results show that the proposed scheme achieved millimeter-level positioning accuracy, improved the normalized reward by over 40% and reduced the handover rate by 87% and 78% compared to the immediate handover (IHO) and dwell handover (DHO) methods concurrently.

Radiative effects in two-dimensional models of quantum electrodynamics in a constant magnetic field

The radiative energy shift of an electron in a constant magnetic field has been considered in the framework of the ($2+1$)-dimensional quantum electrodynamics with four-component fermions as well as in reduced ${\mathrm{QED}}_{3+1}$ in which photons propagate in a ($3+1$)-dimensional bulk and fermions are localized on a 2-brane. Analytical expressions are obtained for the energy of interaction of the electron spin with an external field and the radiative shift of the electron mass after averaging over the spin states of the electron. For ultrarelativistic energies of an electron and relatively weak magnetic fields, the total probability of a photon emission by a massive electron and the anomalous magnetic moment of an electron in a reduced ${\mathrm{QED}}_{3+1}$ are calculated. The dependence of the obtained values on the invariant dynamic parameter of synchrotron radiation is investigated. The total probabilities of synchrotron radiation of a charged massless fermion and the production of a pair of charged massless fermions by a photon in an external magnetic field are calculated in ($2+1$)-dimensional models of quantum electrodynamics.

Bayesian dynamic extreme value modeling for conflict-based real-time safety analysis

Real-time safety analysis and optimization using surrogate safety measures such as traffic conflicts and techniques such extreme value theory (EVT) models is an emerging research topic in the context of proactive traffic safety management. However, the predictive performance and temporal transferability of the existing real-time safety analysis EVT models are subject to the assumption of invariant model parameters, which do not account for the temporal variability and is not suitable for real-time traffic data analysis. This study proposes a Bayesian dynamic extreme value modeling approach for conflict-based real-time safety analysis which integrates a Bayesian dynamic linear model with the extreme value distribution. The proposed approach has several unique advantages as it: 1) allows the model parameters to be time-varying; 2) integrates the newer data with prior information to recursively update the model parameters and account for state-space changes and react to sudden trend changes; 3) accounts for temporal variability and non-stationarity in conflict extremes; and 4) quantitatively evaluates the real-time safety levels of a road facility. The proposed approach is applied for cycle-by-cycle safety analysis at four signalized intersections in the city of Surrey, British Columbia. Traffic conflicts are characterized by the modified time to collision indicator. Three traffic parameters (traffic volume, shock wave area, and platoon ratio) at the signal cycle level are considered as covariates to account for non-stationarity. Several Bayesian dynamic and static extreme value models are developed and two safety indices, namely risk of crash (RC) and return level (RL), are generated to quantitatively represent the cycle-level safety. The RC directly reflects whether a cycle is risky while the RL can evaluate the safety levels of individual cycles. The results show that the dynamic model can identify more crash-risk cycles with either a positive RC or a positive RL than the static model and is more capable of differentiating the safety levels for individual cycles in terms of RL. Overall, the dynamic model outperforms the static model in terms of the statistical fit and aggregate crash estimation accuracy.

Search for identical regions in images using invariant moments

One of the ways to describe objects on images is to identify some of their characteristic points or points of attention. Areas of neighborhoods of attention points are described by descriptors (lots of signs) in such way that they can be identified and compared. These signs are used to search for identical points in other images. The article investigates and establishes the possibility of searching for arbitrary local image regions by descriptors constructed with using invariant moments. A feature of the proposed method is that the calculation of the invariant moments of local areas is carried out with using the integral representation of the geometric moments of the image. Integral representation is a matrix with the same size as the image. The elements of the matrix is the sums of the geometric moments of individual pixels, which are located above and to the left with respect to the coordinates of this element. The number of matrices depends on the order of the geometric moments. For moments up to the second order (inclusively), there will be six such matrices. Calculation of one of six geometric moments of an arbitrary rectangular area of the image comes down up to 3 operations such as summation or subtraction of elements of the corresponding matrix located in the corners of this area. The invariant moments are calculated on base of six geometric moments. The search is performed by scanning the image coordinate grid with a window of a given size. In this case, the invariant moments and additional parameters are calculated and compared with similar parameters of the neighborhoods of the reference point of different size (taking into account the possible change in the image scale). The best option is selected according to a given condition. Almost all mass operations of the procedures for calculating the parameters of standards and searching of identical points make it possible explicitly perform parallel computations in the SIMD mode. As a result, the integral representation of geometric moments and the possibility of using parallel computations at all stages will significantly speed up the calculations and allow you to get good indicators of the search efficiency for identical points and the speed of work