Search

Orthonormality constraints are common in reduced rank models. They imply that matrix-variate parameters are given as orthonormal column vectors. However, these orthonormality restrictions do not provide identification for all parameters. For this setup, we show how the remaining identification issue can be handled in a Bayesian analysis via post-processing the sampling output according to an appropriately specified loss function. This extends the possibilities for Bayesian inference in reduced rank regression models with a part of the parameter space restricted to the Stiefel manifold. Besides inference, we also discuss model selection in terms of posterior predictive assessment. We illustrate the proposed approach with a simulation study and an empirical application. [ABSTRACT FROM AUTHOR]

Response surface methodology (RSM) has broader applicability, where numerous input variables may impact a given performance metric or quality attribute of the final product or process. It involves designing experiments, collecting data, and developing models to optimize the response. Rotatable designs have the property to generate information about the response surface equally in all locations, even when no or little prior knowledge is available about the nature of the response surface. These designs are constructed by imposing certain restrictions on the moment matrix of the design to achieve constancy in the variance of predicted response at all points equidistant from the design center and is invariant to rotation of axis with respect to any angle. Most rotatable response surface designs are symmetric in nature, although factors with mixed-level have more practical utility as it can explore more regions in the design space. In this article, we have proposed a procedure for creating asymmetric third-order rotatable designs (ATORDs) as well as a strategy for creating them with fewer design points when time and resources are the main limitations. Two classes of orthogonal transformation-based ATORDs viz., ATORD-I and ATORD-II have been obtained. ATORD-II does not completely satisfy the moment matrix constraints, although both ATORD-I and ATORD-II have constant prediction variance. ATORD-II is more cost-efficient for experimentation when resource and financial constraints are the primary factors to be taken into account. A comparison of the designs developed is also made using efficiency criterion and dispersion graphs. [ABSTRACT FROM AUTHOR]

Word embedding is a technique for converting a word into a vector. These are known as word vectors. Despite the fact that word embedding offers multiple powerful approaches, these existing methods can yet be improved. Normalizing word vectors and establishing orthogonal transformation algorithms are two of the method developments. The advancement of this technology has also resulted in improved results in future studies such as word similarity and word translation assignments. With the presence of these method developments, it is possible that the produced methods will be further improved into better word embedding methods in the future

This study focuses on high-temporal-resolution Vertical Total Electron Content (VTEC) estimation over Nigeria, which is crucial for enhancing satellite-based applications. Utilizing RINEX, IONEX, and SP3 data from 2011 across 10 stations, the research integrates a novel VTEC model (LIMS) based on orthogonal transformation, achieving an unprecedented 10-minute temporal resolution sampling. The model incorporates multi-Global Navigation Satellite Systems (GNSS) constellations. Geomagnetic and solar activity impact assessments involve the Ap index, sunspot number, and DSt index. Specifically, the DSt index for March 16–18, 2015, analyzes the geomagnetic storm of St Patrick’s Day. Validation compares LIMS with International GNSS Service (IGS), Center for Orbit Determination in Europe (CODE), and International Reference Ionosphere (IRI-2020) estimates, showing strong correlations during various conditions. Daily VTEC patterns reveal the lowest values in the early morning, a midday peak, occasional double peaks, secondary maximum, and post-sunset enhancements, especially during equinoxes. Seasonal analysis highlights the highest mean VTEC in September Equinox and December Solstice, and the lowest during June Solstice. Spectral analysis identifies prominent diurnal, semi-diurnal, and sub-diurnal frequency components. This research significantly advances the understanding of VTEC in Nigeria, offering a valuable tool for precise positioning, satellite communication, and space weather forecasting. Notably, 9 stations processed 2011 data, while one station from this group and an additional station were used for a 3-day storm analysis in 2015 due to data availability.

This study focuses on high-temporal-resolution Vertical Total Electron Content (VTEC) estimation over Nigeria, which is crucial for enhancing satellite-based applications. Utilizing RINEX, IONEX, and SP3 data from 2011 across 10 stations, the research integrates a novel VTEC model (LIMS) based on orthogonal transformation, achieving an unprecedented 10-minute temporal resolution sampling. The model incorporates multi-Global Navigation Satellite Systems (GNSS) constellations. Geomagnetic and solar activity impact assessments involve the Ap index, sunspot number, and DSt index. Specifically, the DSt index for March 16–18, 2015, analyzes the geomagnetic storm of St Patrick's Day. Validation compares LIMS with International GNSS Service (IGS), Center for Orbit Determination in Europe (CODE), and International Reference Ionosphere (IRI-2020) estimates, showing strong correlations during various conditions. Daily VTEC patterns reveal the lowest values in the early morning, a midday peak, occasional double peaks, secondary maximum, and post-sunset enhancements, especially during equinoxes. Seasonal analysis highlights the highest mean VTEC in September Equinox and December Solstice, and the lowest during June Solstice. Spectral analysis identifies prominent diurnal, semi-diurnal, and sub-diurnal frequency components. This research significantly advances the understanding of VTEC in Nigeria, offering a valuable tool for precise positioning, satellite communication, and space weather forecasting. Notably, 9 stations processed 2011 data, while one station from this group and an additional station were used for a 3-day storm analysis in 2015 due to data availability. [ABSTRACT FROM AUTHOR]

Image and video classification are distinct tasks in computer vision. Three-dimensional convolutional neural networks (3D CNNs) are commonly employed for video classification, while two-dimensional convolutional neural networks (2D CNNs) are more suitable for image classification. To enable image and video recognition to adopt the same network-3D CNNs, we propose a transform domain learning approach for image recognition utilizing the video recognition model 3D CNNs. The transform domain learning not only permits the use of RGB images as input for 3D CNNs but also allows for the input of orthogonally transformed image sequences into the networks. Furthermore, randomly transformed images can be fed into the networks, where the random transformation is a customized arbitrary transformation. The standard 3D CNNs can be seamlessly applied to both images and videos. The experiments show that whether orthogonal transformation or random transformation is used as the input, 3D CNNs can effectively classify images. Compared to 2D CNNs, the data after orthogonal transformation does not reduce the accuracy. To unify the tasks of image and video classification, the image dataset Caltech101 and the video dataset UCF101 are mixed, and 3D CNNs are used to recognize images in the transform domain. The results illustrate that mixed and individual training produce almost the same recognition effect. Additionally, it can be observed that directly transferring the video pre-training model to the image classification task can significantly enhance the performance.

In order to increase a nonlinear system's state estimate precision, an iterated orthogonal simplex cubature Kalman filter (IOSCKF) is presented in this study for target tracking. The Gaussian-weighted integral is decomposed into a spherical integral and a radial integral, which are approximated using the spherical simplex-radial rule and second-order Gauss–Laguerre quadrature rule, respectively, and result in the novel simplex cubature rule. To decrease the high-order error terms, cubature points with appropriate weights are taken from the cubature rule and processed using the provided orthogonal matrix. The structure supporting the nonlinear Kalman filter incorporates the altered points and weights and the calculation steps; from this, the updated time and measurement can be inferred. The Gauss–Newton iteration is employed repeatedly to adjust the measurement update until the termination condition is met and the IOSCKF is attained. The proposed algorithms are applied in target tracking, including CV target tracking and spacecraft orbit tracking, and the simulation results reveal that the IOSCKF can achieve higher accuracy compared to the CKF, SCKF, and OSCKF. In spacecraft orbit tracking simulation, compared with the SCKF, the position tracking accuracy and velocity tracking accuracy of the OSCKF are increased by 2.21% and 1.94%, respectively, which indicates that the orthogonal transformation can improve the tracking accuracy. Furthermore, compared with the OSCKF, the position tracking accuracy and velocity tracking accuracy of the IOSCKF are increased by 2.71% and 2.97%, respectively, which indicates that the tracking accuracy can be effectively improved by introducing iterative calculation into the measurement equation, thus verifying the effectiveness of the method presented in this paper. [ABSTRACT FROM AUTHOR]

Two methods for constructing damping matrix of a lightly damped linear system are proposed. In the first method, a matrix polynomial is employed to generalize Rayleigh damping so that damping of as many modes as desired can be matched. The classical Rayleigh damping is a special case two-term expansion of the generalized Rayleigh damping. In the second method, a closed-form formula of the damping matrix, using modal frequencies, modal damping ratios, and modal matrix, is derived based on the equation of motion, which avoids the presupposition of a form for the damping matrix. It is proved that for a system with only flexible modes, a unique closed-form damping matrix exists. Two numerical examples are presented to demonstrate the simplicity and efficiency of the proposed methods. Applications of damping matrices in systems with all flexible modes and with both flexible and rigid modes are discussed. [ABSTRACT FROM AUTHOR]

Abstract—Point cloud registration is a central problem in many computer vision problems. However, ensuring global consistency of the results of pairwise registration of point clouds is still a challenge when there are multiple clouds because different scans should be converted to a common coordinate system. This paper describes a global refinement algorithm that first estimates rotations and then estimates parallel translations. For global refinement of rotations, a closed-form algorithm based on matrices is used. For global refinement of parallel translations, a closed-form algorithm is also used. The proposed algorithm is compared with other global refinement algorithms. [ABSTRACT FROM AUTHOR]

The dimensional statistics approach of principal component analysis is utilized in this paper to transform a set of variable observations using orthogonal transformation to convert the data to a new coordinate. The original observations data are summed linearly by selecting appropriate values through cross-validation and calculating the spatial data of works with literary elements. Determine a sufficient number of basis functions selected mainly based on the fluctuation of time variables. The number of students who believe that reading classic works enhances scientific literacy was 92.56%, with the highest contribution of linguistic and literary elements found to be 92.56%. The uniqueness and richness of children's literature can enhance character-building and overall core literacy of students, as indicated by this.

Significant and composite indices for infectious disease can have implications for developing interventions and public health. This paper presents an investment for developing access to further analysis of the incidence of individual and multiple diseases. This research mainly comprises two steps: first, an automatic and reproducible procedure based on functional data analysis techniques was proposed for analyzing the dynamic properties of each disease; second, orthogonal transformation was adopted for the development of composite indices. Between 2000 and 2019, nineteen class B notifiable diseases in China were collected for this study from the National Bureau of Statistics of China. The study facilitates the probing of underlying information about the dynamics from discrete incidence rates of each disease through the procedure, and it is also possible to obtain similarities and differences about diseases in detail by combining the derivative features. There has been great success in intervening in the majority of notifiable diseases in China, like bacterial or amebic dysentery and epidemic cerebrospinal meningitis, while more efforts are required for some diseases, like AIDS and virus hepatitis. The composite indices were able to reflect a more complex concept by combining individual incidences into a single value, providing a simultaneous reflection for multiple objects, and facilitating disease comparisons accordingly. For the notifiable diseases included in this study, there was superior management of gastro-intestinal infectious diseases and respiratory infectious diseases from the perspective of composite indices. This study developed a methodology for exploring the prevalent properties of infectious diseases. The development of effective and reliable analytical methods provides special insight into infectious diseases' common dynamics and properties and has implications for the effective intervention of infectious diseases. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The statistical shape analysis called Procrustes analysis minimizes the Frobenius distance between matrices by similarity transformations. The method returns a set of optimal orthogonal matrices, which project each matrix into a common space. This manuscript presents two types of distances derived from Procrustes analysis for exploring between-matrices similarity. The first one focuses on the residuals from the Procrustes analysis, i.e., the residual-based distance metric. In contrast, the second one exploits the fitted orthogonal matrices, i.e., the rotational-based distance metric. Thanks to these distances, similarity-based techniques such as the multidimensional scaling method can be applied to visualize and explore patterns and similarities among observations. The proposed distances result in being helpful in functional magnetic resonance imaging (fMRI) data analysis. The brain activation measured over space and time can be represented by a matrix. The proposed distances applied to a sample of subjects—i.e., matrices—revealed groups of individuals sharing patterns of neural brain activation. Finally, the proposed method is useful in several contexts when the aim is to analyze the similarity between high-dimensional matrices affected by functional misalignment. [ABSTRACT FROM AUTHOR]

The development of measures to ensure the quality of electric energy is possible only after assessing the actual state of the quality of electric energy in all nodes of the electric network. Therefore, the basis of the system of ensuring the necessary quality of electric energy should be the system of its monitoring. An approach to the construction of a system for monitoring the quality of electrical energy in real time is presented by developing a generalized identifier for the presence of distortion of the quality of electrical energy, regardless of its type, time of appearance and duration, based on the construction of the spatial-temporal distribution of the information signal and subsequent orthogonal analysis of the frequency-temporal changes of its spectral components. This makes it possible to create a system for monitoring the quality of electric energy in real time, in contrast to existing methods, which use sequential processing of the measurement signal to determine individual indicators of the quality of electric energy, which makes it impossible to conduct it in real time. Ref. 3, fig. 4.

Image processing systems are currently used to solve many applied problems. The article is devoted to the identification of negative factors affecting the growth of grain in different periods of harvesting, using a program implemented in the MATLAB software environment, based on aerial photographs. The program is based on the Law's textural mask method and successive clustering. This paper presents the algorithm of the program and shows the results of image processing by highlighting the uniformity of the image. To solve the problem, the spectral luminance coefficient (SBC), normalized difference vegetation index (NDVI), Law's textural mask method, and clustering are used. This approach is general and has great potential for identifying objects and territories with different boundary properties on controlled aerial photographs using groups of images of the same surface taken at different vegetation periods. That is, the applicability of sets of Laws texture masks with original image enhancement for the analysis of experimental data on the identification of pest outbreaks is being investigated. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the wireless communication technology, the data volume of 5G mobile network continues to grow, which leads to the continuous reduction of signaling analysis and processing efficiency. To overcome the problem, the intelligent communication will be one of the mainstream directions of mobile communication development which combines with 5G and AI. In this paper, we introduce a method of using machine learning classification and signaling analysis technology. The proposed method in this paper is an improved signaling analysis algorithm based on naive Bayesian classification, which improves the signaling classification accuracy of the algorithm. In the signaling analysis process of the algorithm, the key messages of signaling data are selected as user characteristics before the association and synthesis of user signaling processes. Then, the supervised signaling feature classification model is trained according to the user ID in the signaling data, and the model is used for signaling classification. Subsequently, aiming at the zero probability problem in the algorithm, we use Laplace smoothing to correct it. Then, the algorithm continues to use orthogonal matrix to make orthogonal transformation on continuous attributes and discrete attributes after numerical marking, so as to enhance the independence between attributes. It closes to the assumption of naive Bayes, which improves the classification accuracy of the algorithm. The experimental results show that the improved algorithm model has high comprehensive performance index and good classification performance. The F1 score of this algorithm reaches 67.86%, and achieves the expected effect. The improved signaling analysis method proposed in this study is expected to be useful in 5G network optimization and testing. [ABSTRACT FROM AUTHOR]

In order to increase a nonlinear system’s state estimate precision, an iterated orthogonal simplex cubature Kalman filter (IOSCKF) is presented in this study for target tracking. The Gaussian-weighted integral is decomposed into a spherical integral and a radial integral, which are approximated using the spherical simplex-radial rule and second-order Gauss–Laguerre quadrature rule, respectively, and result in the novel simplex cubature rule. To decrease the high-order error terms, cubature points with appropriate weights are taken from the cubature rule and processed using the provided orthogonal matrix. The structure supporting the nonlinear Kalman filter incorporates the altered points and weights and the calculation steps; from this, the updated time and measurement can be inferred. The Gauss–Newton iteration is employed repeatedly to adjust the measurement update until the termination condition is met and the IOSCKF is attained. The proposed algorithms are applied in target tracking, including CV target tracking and spacecraft orbit tracking, and the simulation results reveal that the IOSCKF can achieve higher accuracy compared to the CKF, SCKF, and OSCKF. In spacecraft orbit tracking simulation, compared with the SCKF, the position tracking accuracy and velocity tracking accuracy of the OSCKF are increased by 2.21% and 1.94%, respectively, which indicates that the orthogonal transformation can improve the tracking accuracy. Furthermore, compared with the OSCKF, the position tracking accuracy and velocity tracking accuracy of the IOSCKF are increased by 2.71% and 2.97%, respectively, which indicates that the tracking accuracy can be effectively improved by introducing iterative calculation into the measurement equation, thus verifying the effectiveness of the method presented in this paper.

Point cloud collection forming a 3D scene typically uses information from multiple data scans. The common approach is to register the point cloud pairs consequentially using a variant of the iterative closest point (ICP) algorithm, but most versions of the ICP algorithm only work correctly for a small movement between two point clouds. This makes it difficult to accumulate multiple scans. Global registration algorithms are also known, which theoretically process point clouds at arbitrary initial positions. Recently, a multiparameter variational functional was described and used in the ICP variant to register point clouds at arbitrary initial positions. The disadvantage of this algorithm was the need for manual selection of parameters. In this paper, a modified version of the algorithm with automatic selection of the model parameters is proposed. The proposed algorithm is a fusion of the ICP and RANSAC concepts. Moreover, the algorithm can be parallelized. The performance of the proposed algorithm is compared with that of known global registration algorithms. [ABSTRACT FROM AUTHOR]

Significant and composite indices for infectious disease can have implications for developing interventions and public health. This paper presents an investment for developing access to further analysis of the incidence of individual and multiple diseases. This research mainly comprises two steps: first, an automatic and reproducible procedure based on functional data analysis techniques was proposed for analyzing the dynamic properties of each disease; second, orthogonal transformation was adopted for the development of composite indices. Between 2000 and 2019, nineteen class B notifiable diseases in China were collected for this study from the National Bureau of Statistics of China. The study facilitates the probing of underlying information about the dynamics from discrete incidence rates of each disease through the procedure, and it is also possible to obtain similarities and differences about diseases in detail by combining the derivative features. There has been great success in intervening in the majority of notifiable diseases in China, like bacterial or amebic dysentery and epidemic cerebrospinal meningitis, while more efforts are required for some diseases, like AIDS and virus hepatitis. The composite indices were able to reflect a more complex concept by combining individual incidences into a single value, providing a simultaneous reflection for multiple objects, and facilitating disease comparisons accordingly. For the notifiable diseases included in this study, there was superior management of gastro-intestinal infectious diseases and respiratory infectious diseases from the perspective of composite indices. This study developed a methodology for exploring the prevalent properties of infectious diseases. The development of effective and reliable analytical methods provides special insight into infectious diseases’ common dynamics and properties and has implications for the effective intervention of infectious diseases.

Computer simulations using the combination of finite element method and modal analysis are now common methods for vibration prediction. However, when it comes to detailed models with large degrees-of-freedom, it is time consuming to evaluate and treat each characteristic mode one by one. Therefore, an efficient vibration analysis method is strongly desired. Macroscopically, characteristic modes of vehicle bodies, which mainly consist of frame structures and attached panels, are regarded as combinations of global and local deformations. Global deformation is attributable to frame structure which is defined as main component of the model, and local deformation is attributable to each panel component defined as sub component. As these deformations differ in priorities and countermeasures in design phase, it is essential to clarify the relation of global and local deformations, especially when they combine and compose coupled vibration. The purpose of this study is to present an analytical method to efficiently evaluate characteristic modes based on global and local deformations. In this paper, component mode decomposition is proposed to automatically decompose global and local deformation from characteristic modes of a frame-panel structure model. In this method, characteristic mode is first transformed into volume data and then three-dimensional wavelet transform is applied to separate global and local deformations. Then, from these separated deformations, component mode which consists of each single global and local deformation is derived using orthogonal transformation. By means of this orthogonal transformation, characteristic modes are effortlessly analyzed how much they contain each global and local deformation. In this paper, the proposed method is demonstrated on a simple frame-panel structure model.

Three-dimensional point cloud registration algorithms calculate an orthogonal transformation that maximizes the consistent overlap of two point clouds. The most common registration method that uses only geometric characteristics is the iterative closest point (ICP) algorithm. The disadvantage of classical ICP implementations is their dependence on the initial location of point clouds. Coarse registration algorithms are used to find a suitable initial registration of two clouds. A new algorithm for determining common parts and coarse registration of point clouds is proposed. [ABSTRACT FROM AUTHOR]

The Sky View Factor (SVF) is a critical parameter for studying the thermal environment of buildings theoretically. However, there are two main definitions of SVF, one based on the visible area ratio and the other based on the view factor. When utilizing fisheye images for SVF calculation, various issues often arise, such as conceptual confusion and algorithmic errors, significantly impeding the accurate scientific analysis of thermal environmental problems. Therefore, in this study, we employed methods such as theoretical model deduction, field analysis, and software simulation and validation to investigate the calculation methods for fisheye images with equidistant projection, equisolid angle projection, and stereographic projection. We proposed the Image Distance Ratio Transformation Method (IDRTM), a correction method for calculating the SVF of fisheye images with the three projection types and corresponding calculation formulas. Finally, two cases demonstrate the computational advantages of simplicity and high accuracy associated with the orthogonal transformation formulas applied to the three types of fisheye images. In this study, the error rates of SVF calculation using three non-orthographic fisheye images directly are all higher than 29 %. After applying IDRTM, the average error rates of the transformed IDRs of three types of fisheye images are all less than 0.58 %, and the error rates of the corrected SVF of three types of fisheye images are all less than 1 %. This study elucidates the scientific definition and algorithm of SVF and provides a scientific basis for the accurate selection of SVF calculation methods corresponding to fisheye image types. [Display omitted] • A selection method for SVF applicable to thermal environment analysis is proposed. • A correction method suitable to all types of fisheye images is provided for SVF. • The accuracy of the correction method for SVF is verified by two cases. [ABSTRACT FROM AUTHOR]

Rotatable designs that are available for process/ product optimization trials are mostly symmetric in nature. In many practical situations, response surface designs (RSDs) with mixed factor (unequal) levels are more suitable as these designs explore more regions in the design space but it is hard to get rotatable designs with a given level of asymmetry. When experimenting with unequal factor levels via asymmetric second order rotatable design (ASORDs), the lack of fit of the model may become significant which ultimately leads to the estimation of parameters based on a higher (or third) order model. Experimenting with a new third order rotatable design (TORD) in such a situation would be expensive as the responses observed from the first stage runs would be kept underutilized. In this paper, we propose a method of constructing asymmetric TORD by sequentially augmenting some additional points to the ASORDs without discarding the runs in the first stage. The proposed designs will be more economical to obtain the optimum response as the design in the first stage can be used to fit the second order model and with some additional runs, third order model can be fitted without discarding the initial design. [ABSTRACT FROM AUTHOR]

The most popular algorithm for registering clouds of points in 3D space is the iterative closest point (ICP) algorithm. The point-to-point variational problem for orthogonal transformations is mathematically equivalent to the absolute orientation problem in photogrammetry. In this paper, we briefly overview closed-form solutions to the point-to-point variational problem. The well-known Horn algorithm solves the problem for the O(3) group. We propose a modified Horn algorithm that makes it possible to solve the problem for the SO(3) group. Computer simulation illustrates the registration accuracy of the considered methods for various geometric transformations. [ABSTRACT FROM AUTHOR]

An efficient algorithm for registration of two non-rigid objects based on geometrical transformation of the template object to target object is proposed. The transformation is considered as warping of the template onto the target. To choose the most suitable transformation from all possible warps, a registration algorithm should satisfy deformation constraints referred to as regularization of non-rigid objects. In this work, we use variational functionals for affine transformations. With the help of computer simulation, the proposed method for searching the optimal geometrical transformation is compared with that of common algorithms.

Wildfires have significant environmental and socio-economic impacts, affecting ecosystems and people worldwide. Over the coming decades, it is expected that the intensity and impact of wildfires will grow depending on the variability of climate parameters. Although Bulgaria is not situated within the geographical borders of the Mediterranean region, which is one of the most vulnerable regions to the impacts of temperature extremes, the climate is strongly influenced by it. Forests are amongst the most vulnerable ecosystems affected by wildfires. They are insufficiently adapted to fire, and the monitoring of fire impacts and post-fire recovery processes is of utmost importance for suggesting actions to mitigate the risk and impact of that catastrophic event. This paper investigated the forest vegetation recovery process after a wildfire in the Ardino region, southeast Bulgaria from the period between 2016 and 2021. The study aimed to present a monitoring approach for the estimation of the post-fire vegetation state with an emphasis on fire-affected territory mapping, evaluation of vegetation damage, fire and burn severity estimation, and assessment of their influence on vegetation recovery. The study used satellite remotely sensed imagery and respective indices of greenness, moisture, and fire severity from Sentinel-2. It utilized the potential of the landscape approach in monitoring processes occurring in fire-affected forest ecosystems. Ancillary data about pre-fire vegetation state and slope inclinations were used to supplement our analysis for a better understanding of the fire regime and post-fire vegetation damages. Slope aspects were used to estimate and compare their impact on the ecosystems’ post-fire recovery capacity. Soil data were involved in the interpretation of the results.

For aerial earth observation system, the accuracy of motion parameters provided by airborne distributed position and orientation system (POS) is crucial to the observation load imaging quality. According to the working principle, slave systems of distributed POS rely on the master node information to realize error estimation and correction, and then to achieve multiple node high accuracy measurement. Due to the nonlinearity of the error model, nonlinear filtering algorithm is used to the estimation process. Then in order to achieve higher estimation performance, a transformed cubature quadrature information filtering algorithm is developed for distributed POS estimation. In this method, the contributions are located that: (1) spherical radial cubature rule and Gauss-Laguerre quadrature rule are used to generate sigma points; (2) orthogonal transformation is conducted on the sampling points to deal with nonlocal sampling; (3) information filtering framework is adopted to simplify filtering update process, then the algorithm is applied in distributed POS nonlinear transfer alignment estimation. By the flight test validation, the outcomes are found that the developed algorithm can enhance the distributed POS estimation accuracy effectively, including attitude, velocity, position accuracy, then can be used to loads imaging quality improvement. [ABSTRACT FROM AUTHOR]

Orthogonal transformation of digital images can be represented as a decomposition over basis matrices or basis images. Grayscale and color basis images are introduced. For particular case of DWT (Discrete Wavelet Transform) obtained basis wavelet images have a block structure similar to frequency bands of the the DWT coefficients. A steganographic scheme for frequency domain watermarking based on this representation is considered. Presented example of detection algorithm illustrates how this representation can be used for frequency embedding techniques.

Abstract Background For many practical hypothesis testing (H-T) applications, the data are correlated and/or with heterogeneous variance structure. The regression t-test for weighted linear mixed-effects regression (LMER) is a legitimate choice because it accounts for complex covariance structure; however, high computational costs and occasional convergence issues make it impractical for analyzing high-throughput data. In this paper, we propose computationally efficient parametric and semiparametric tests based on a set of specialized matrix techniques dubbed as the PB-transformation. The PB-transformation has two advantages: 1. The PB-transformed data will have a scalar variance-covariance matrix. 2. The original H-T problem will be reduced to an equivalent one-sample H-T problem. The transformed problem can then be approached by either the one-sample Student’s t-test or Wilcoxon signed rank test. Results In simulation studies, the proposed methods outperform commonly used alternative methods under both normal and double exponential distributions. In particular, the PB-transformed t-test produces notably better results than the weighted LMER test, especially in the high correlation case, using only a small fraction of computational cost (3 versus 933 s). We apply these two methods to a set of RNA-seq gene expression data collected in a breast cancer study. Pathway analyses show that the PB-transformed t-test reveals more biologically relevant findings in relation to breast cancer than the weighted LMER test. Conclusions As fast and numerically stable replacements for the weighted LMER test, the PB-transformed tests are especially suitable for “messy” high-throughput data that include both independent and matched/repeated samples. By using our method, the practitioners no longer have to choose between using partial data (applying paired tests to only the matched samples) or ignoring the correlation in the data (applying two sample tests to data with some correlated samples). Our method is implemented as an R package ‘PBtest’ and is available at https://github.com/yunzhang813/PBtest-R-Package.

The on-load tap changer (OLTC) switching arc will affect the life of the contact, so it is necessary to test its working condition. Based on DQ coordinate transformation, fundamental wave component extraction, and wavelet algorithm, proposed an OLTC arc detection method based on energy conservation theory in this paper. Used the fundamental wave component extraction algorithm extracted the transformer excitation parameters, and established the OLTC equivalent circuit by combining the Habedank arc model. Compared the differences between the follow effect and frequency calculation of simulated and measured waveforms, which were caused by the five orthogonal transformation algorithms, including Transport-Delay, Hilbert Transformation, Inverse Park Transformation, SOGI and Improved SOGI. The DQ coordinate transformation based on the Transport-Delay orthogonal converter is determined to calculate the active power of each part of the circuit. According to the principle of energy conservation, obtained the data of arcing loss under nine simulated conditions. Based on the simulation circuit, built the OLTC prototype and dSPACE hardware in loop simulation platform, and measured three load conditions. The sampling signal was filtered by the wavelet algorithm. Considering the measurement and calculation errors, the simulation and measured results show that the actual arcing power has a consistent change with the calculated value between different working conditions, and the maximum difference between simulation results and measured results is thirty times and ten times. It can distinguish the difference of arcing energy of diverter switch under different working conditions and has the measurability. Meanwhile, it verifies the effectiveness of this study, hoping to provide a theoretical basis for developing OLTC on-line contact detection system.

A new distributed online learning scheme for classifying data captured from distributed data sources is proposed in this paper. The scheme consists of multiple distributed learners that independently classify different streams of data. Each local learner uses an ensemble classifier trained by shared data to make a prediction. We propose a novel form of shared data, that is, the covariance matrix and mean vector, that has small and stable network traffic when transmitted between nodes. Then, we provide a systematic online ensemble learning approach based on these shared data. In contrast to boosting and bagging, our proposed learning approach is based on orthogonal transformation, which can increase the differences between individual learners without a significant loss in accuracy. Moreover, we discuss the ensemble maintenance method based on weight to adapt the underlying data dynamics. Empirical studies demonstrate the effectiveness of our approach in comparison to existing state-of-the-art methods on several datasets.

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, a novel robust fault estimation scheme to ensure efficient and reliable operation of wind turbines has been presented. Wind turbines are complex systems with large flexible structures that work under very turbulent and unpredictable environmental conditions for a variable electrical grid. The proposed observer-based estimation scheme consists of a set of possible faults affecting the dynamics, sensors, and actuators of wind turbines. First, the pitch and drivetrain system faults occur simultaneously with process and sensor disturbances that are called unknown input signals. Second, through a series of coordinate transformations, the faulty subsystem is decoupled from the rest of the system. The first subsystem is affected by unknown inputs, and the second one is affected by faults. A reduced-order unknown input observer is designed to reconstruct states accurately, whereas a reduced-order sliding mode observer is designed for the second subsystem such that it is robust against unknown inputs and faults. Moreover, the reduced-order unknown input observer guarantees the asymptotic stability of the error dynamics using the Lyapunov theory method and completely removes unknown inputs; on the other hand, the reduced-order sliding mode observer is designed to reconstruct faults for the faulty subsystem accurately. Until now, authors only focused on an unknown input signal in the dynamics of the system, especially in nonlinear systems. The estimated fault will be adequate to accommodate the control loop, and sufficient conditions are developed to guarantee the stability of the state estimation error. In the next step, to figure the effectiveness of the proposed approach, a wind turbine benchmark system model is considered with faults and unknown inputs scenarios. The simulation results are used to validate the robustness of the proposed algorithms under noise conditions, and the results show that the algorithm could classify faults robustly. [ABSTRACT FROM AUTHOR]

A new parametric hypothesis test of mean interval for interval-valued data set, which can deal with massive information contained in nowadays massive data "Big data" sets, is proposed. An approach using an orthogonal transformation is introduced to obtain an equivalent hypothesis test of mean interval in terms of the mid-point and mid-range of the interval-valued variable. The new test is very efficient in small interval-valued sample scenarios. Some simulation studies are conducted for the investigation of the sample size and the power of test. The performance of the proposed test is illustrated with two real-life examples. [ABSTRACT FROM AUTHOR]

Equivalent modelling for active distribution networks (ADNs) is essential for improving the efficiency of analysing transmission networks. Current equivalent modelling methods for ADNs neglect the probabilistic characteristics of renewable energy sources (RESs) and loads. To address this issue, this study proposes a probabilistic equivalent modelling method (PEMM) for ADNs considering the uncertainty of RESs and loads. The uncertainty of the RESs and loads is transferred to the equivalent boundary bus injection using the properties of cumulant and power transfer matrices. The PEMM is extended to incorporate the correlations of RESs through an orthogonal transformation. A sampling method using the Gaussian copula function is employed to generate the correlated samples and the joint cumulants, providing the input data for the PEMM. The comparative results of the case studies on two different test systems demonstrate the effectiveness of the PEMM. The equivalent model developed in this study is a practical solution for analysing the transmission network efficiently and taking the uncertainty of RESs and loads in the ADNs into account simultaneously. [ABSTRACT FROM AUTHOR]

We prove the equivalence—under rotations of distinct terms—of different forms of a determinantal equation that appears in the studies of wave propagation in Hookean solids, in the context of the Christoffel equations. To do so, we prove a general proposition that is not limited to R 3 , nor is it limited to the elasticity tensor with its index symmetries. Furthermore, the proposition is valid for orthogonal transformations, not only for rotations. The sought equivalence is a corollary of that proposition. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a concurrent computing technique for the acceleration of digital stochastic measurement simulations. The digital stochastic measurement presents an advanced methodology based on the specific parallel hardware structure, utilized for an orthogonal transformation calculus/decomposition. Methodology is analyzed in detail, starting from the very basic idea, toward recent references, covering main research directions and trends. An oversampling nature of the evaluated digital stochastic measurement, along with demanding arithmetic requirements, implies exhausting simulation complexity. As a test case, several typical power grid signals were harmonically analyzed through a discrete Fourier transformation based on the proposed methodology. A harmonic decomposition was simulated with several levels of computing concurrency. Through all the simulated scenarios main success criterion was model accuracy, while the parameter used for selection of the optimal simulation computing technique was the overall calculus speed. Final results exposed thread pool computing technique as an optimal simulation platform. [ABSTRACT FROM AUTHOR]

The behavior of solution trajectories usually changes if we replace the classical derivative in a system with a fractional one. In this article, we throw light on the relation between two trajectories X(t) and Y(t) of such a system, where the initial point Y(0) is at some point X (t 1) of the trajectory X(t). In contrast with classical systems, these trajectories X and Y do not follow the same path. Further, we provide a Frenet apparatus for both trajectories in various cases and discuss their effect. [ABSTRACT FROM AUTHOR]

Abstract In this article, we present a QR updating procedure as a solution approach for linear least squares problem with equality constraints. We reduce the constrained problem to unconstrained linear least squares and partition it into a small subproblem. The QR factorization of the subproblem is calculated and then we apply updating techniques to its upper triangular factor R to obtain its solution. We carry out the error analysis of the proposed algorithm to show that it is backward stable. We also illustrate the implementation and accuracy of the proposed algorithm by providing some numerical experiments with particular emphasis on dense problems.