Backward Transformation Research Articles

Evolution of magnetization of Bi1-ySmyFe1-xTixO3 ceramics at the morphotropic phase boundary attested by multistep magnetization measurements, time aging and electric field

Bi1-ySmyFe1-xTixO3 ceramics with compositions at the rhombohedral- orthorhombic morphotropic phase boundary (y = 0.1, 0.12, 0 ≤ x ≤ 0.1) were prepared by solid state reaction method. Analysis of the crystal structure of the compounds based on the results of laboratory and synchrotron X-ray diffraction measurements as well as Raman spectroscopy experiments revealed a coexistence of the rhombohedral phase (SG R3c) and the anti-polar orthorhombic phase (SG Pbam) in the compounds Bi0.88Sm0.12Fe1-xTixO3 with 0 ≤ x ≤ 0.1; the compounds Bi0.9Sm0.10Fe1-xTixO3 are characterized by the mixed structural state for the concentration range 0 ≤ x ≤ 0.06 and by the single phase rhombohedral state within 0.06 < x ≤ 0.1. Room temperature magnetization measurements demonstrate a gradual increase of remanent magnetization with Ti content in the compounds of both systems up to the maximal value of MR ∼0.28 emu/g for compounds with x = 0.06 which is followed by a decrease in magnetization with further titanium doping. Temperature decrease leads to a reduction of remanent magnetization which is mainly caused by a partial recovery of spatially modulated spin structure occurred in the compounds of both systems. Time aging of the compounds for about ∼1 year leads to a drastic increase of MR, which is caused by a change in the structural phase ratio, homogeneity of the structural state and a contribution of unbounded spins demonstrated by low field measurements. Subjecting of the annealed compounds to external electric field of E ∼40 kV/cm leads to a decrease of magnetization associated with backward structural transformations and redistribution of the local structural defects in the compounds.

Stała Współpraca Strukturalna (PESCO) jako przykład dyferencjalizmu eksperymentalnego w Unii Europejskiej

POLAND IN THE EUROPEAN UNION TOWARDS EUROSCEPTIC SELF-ISOLATION By joining the European Union on 1 May 2004, Poland symbolically finalized the transformation process from the position of a nation state (NS) towards a member state (MS). The European Union Member States, in addition to being the main actors of the integration process, are also affected by various natural centrifugal forces in this atypical international organization. It results from selfishness and particularism of the states as well as from natural national selfinterests, on which stable national preferences are clearly defined and exposed by member states. From the perspective of the transformation process of Poland as the largest and most important EU member state located in Central and Eastern Europe, we can distinguish two periods: 1. 2004-2015. At that time, Poland strongly supported the European Commission (EC) and the Community method, which gives the EC a central role in the decision-making process. This period can be defined by the process of transition from a nation state to a Member State. 2. From 2015, when Poland was ruled by anti-liberal and more Eurosceptic government. This period, in turn, can be defined by the hybrid accumulation of features of a national state and a Member State, which makes us deal with a national member state (NMS). However, the tendency that can be observed runs in the direction of a backward transformation from a Member State to a nation-state. The subject of this study is only the second period in the European policy of Poland expressed by the accumulation of the features of NS and MS with a tendency in the reverse direction of transformation from a Member State to a nation- state. The main research thesis focuses on the statement that after 2015 Poland in the European Union drifts towards Eurosceptic self-isolation.

The Temporal and Spatial Characteristics of Wind–Photovoltaic–Hydro Hybrid Power Output Based on a Cloud Model and Copula Function

In a high proportion of wind–photovoltaic–hydro hybrid power systems, fluctuation and dispersion make it difficult to accurately quantify the output characteristics. Therefore, in this study, a cloud model and copula correlation coefficient matrix were constructed for a hybrid power generation system based on the output data. Multiple backward cloud transformation based on the sampling-with-replacement method was proposed to calculate the improved entropy and hyperentropy to analyze the fluctuation range and dispersion degree quantitatively. A similarity index was proposed to evaluate the similarity between wind power, PV power, and hydropower. A suitable copula function was selected, and the Kendall and Spearman coefficients show the correlation relationships of the hybrid systems. The temporal and spatial characteristics of the hybrid systems were analyzed based on the two models. A typical example in Qinghai proved the effectiveness and applicability of the method. The results show that the correlation between photovoltaic power and hydropower is better and that, in summer, hydropower can be used to adjust the output of renewable energy.

Robust Deformable Image Registration Using Cycle-Consistent Implicit Representations.

Recent works in medical image registration have proposed the use of Implicit Neural Representations, demonstrating performance that rivals state-of-the-art learning-based methods. However, these implicit representations need to be optimized for each new image pair, which is a stochastic process that may fail to converge to a global minimum. To improve robustness, we propose a deformable registration method using pairs of cycle-consistent Implicit Neural Representations: each implicit representation is linked to a second implicit representation that estimates the opposite transformation, causing each network to act as a regularizer for its paired opposite. During inference, we generate multiple deformation estimates by numerically inverting the paired backward transformation and evaluating the consensus of the optimized pair. This consensus improves registration accuracy over using a single representation and results in a robust uncertainty metric that can be used for automatic quality control. We evaluate our method with a 4D lung CT dataset. The proposed cycle-consistent optimization method reduces the optimization failure rate from 2.4% to 0.0% compared to the current state-of-the-art. The proposed inference method improves landmark accuracy by 4.5% and the proposed uncertainty metric detects all instances where the registration method fails to converge to a correct solution. We verify the generalizability of these results to other data using a centerline propagation task in abdominal 4D MRI, where our method achieves a 46% improvement in propagation consistency compared with single-INR registration and demonstrates a strong correlation between the proposed uncertainty metric and registration accuracy.

An overlap estimation guided feature metric approach for real point cloud registration

Real point cloud registration, involving homologous and cross-source 3D data, poses significant challenges such as partial overlap, high noise, density disparities, and scale variations. In this paper, we introduce a framework called OEFM to guide transform estimation on the basis of feature metrics through overlap estimation with overlap region center weighting, which estimates transform parameters by minimizing feature projection error on overlap region features. Our proposed approach utilizes an overlap filtering module with a center-weighting mechanism to filter overlap points and feeds these points into a feature metric framework with a forward–backward transformation to estimate the transformation parameters. The rationale behind our approach is that only overlapping regions are useful for point cloud registration, while non-overlapping regions are anomalous. Furthermore, our proposed approach requires no correspondence search, making it robust to partial overlaps, large noise, density differences, and scale variations. We show that our approach achieves the highest registration recall for 3DMatch and 3DCSR in extensive experiments on both homologous and cross-source datasets.

Cross Entropy in Deep Learning of Classifiers Is Unnecessary-ISBE Error Is All You Need.

In deep learning of classifiers, the cost function usually takes the form of a combination of SoftMax and CrossEntropy functions. The SoftMax unit transforms the scores predicted by the model network into assessments of the degree (probabilities) of an object's membership to a given class. On the other hand, CrossEntropy measures the divergence of this prediction from the distribution of target scores. This work introduces the ISBE functionality, justifying the thesis about the redundancy of cross-entropy computation in deep learning of classifiers. Not only can we omit the calculation of entropy, but also, during back-propagation, there is no need to direct the error to the normalization unit for its backward transformation. Instead, the error is sent directly to the model's network. Using examples of perceptron and convolutional networks as classifiers of images from the MNIST collection, it is observed for ISBE that results are not degraded with SoftMax only but also with other activation functions such as Sigmoid, Tanh, or their hard variants HardSigmoid and HardTanh. Moreover, savings in the total number of operations were observed within the forward and backward stages. The article is addressed to all deep learning enthusiasts but primarily to programmers and students interested in the design of deep models. For example, it illustrates in code snippets possible ways to implement ISBE functionality but also formally proves that the SoftMax trick only applies to the class of dilated SoftMax functions with relocations.

Interferometric image reconstruction using closure invariants and machine learning

Abstract Interferometric closure invariants encode calibration-independent details of an object’s morphology. Excepting simple cases, a direct backward transformation from closure invariants to morphologies is not well established. We demonstrate using simple Machine Learning models that closure invariants can aid in morphological classification and parameter estimation. We consider six phenomenologically parametrized morphologies: point-like, uniform circular disc, crescent, dual disc, crescent with elliptical accretion disc, and crescent with double jet lobes. Using logistic regression (LR), multilayer perceptron (MLP), and random forest models on closure invariants obtained from a sparsely covered aperture, we find that all methods except LR can classify morphologies with $\gtrsim$80 per cent accuracy, which improves with greater aperture coverage. Separately from the classification problem, given an independently confirmed class, we estimate parameters of uniform circular disc, crescent, and dual disc morphologies using simple MLP models, and parametrically reconstruct images. The estimated parameters and images correspond well with inputs, but the accuracy worsens when degeneracies between parameters are present. This independent approach to interferometric imaging under challenging observing conditions such as that faced by the Event Horizon Telescope and Very Long Baseline Interferometry, in general, can complement other methods in robustly constraining an object’s morphology.

Exploring the contributors to the digital economy: Insights from Vietnam with comparisons to Thailand

This paper introduces a practical framework to quantify the digital economy and evaluates its growth in Vietnam, utilizing available Input-Output (I–O) tables from the OECD statistics database. Additionally, it conducts a direct comparison between Vietnam and Thailand, shedding light on their relative standings.The primary contribution of this paper lies in its devised approach for measuring the digital economy. This methodology generates meaningful estimations of the digital economy's size and drivers within a specific country. Moreover, these estimated results are suitable for cross-country comparisons. Concurrently, applying this approach to analyze Vietnam and comparing it with Thailand reveals valuable insights.Firstly, Vietnam's digital economy, amounting to US$17,458 and comprising 7.9% of GDP in 2018, exhibited consistent expansion during the 2012–2018 period. Secondly, the ICT sector, particularly foreign-invested ICT hardware manufacturing, contributes roughly 50% to Vietnam's digital economy, while contributions from backward linkages and digital transformation each account for 20–30%. Thirdly, the non-ICT sectors' contribution to the digital economy is quantifiable through three avenues: purchasing digital products and services, supplying the ICT sector, and generating value-added from within-sector digital transformation. Lastly, compared to Thailand, Vietnam lags in key digital economy metrics. Notably, the Thailand/Vietnam ratio for value-added in 2018 was 3.9 for the total digital economy, 3.2 for the ICT sector, 3.1 for contributions from backward linkages, and 6.4 for non-ICT sector digital transformation, while the ratio for GDP stood at 2.1. This insight underscores that while investing in digital infrastructure, promoting internet adoption, and attracting FDI to ICT hardware manufacturing are essential, they alone are insufficient for the rapid progress needed in advancing Vietnam's digital economy.

Stabilization of the coupled ODE-linearized KdV system

This paper considers the exponential stabilization of coupled ordinary differential equation (ODE)-linearized Korteweg-de Vries (KdV) equation system coupled at right boundary point with left boundary control. Firstly, we transfer the original system into an exponentially stable target system by backstepping transformation. Secondly, we show the existence of the kernels in forward and backward transformation. Finally, we prove the exponential stability of the closed-loop system.

Affective Neural Responses Sonified through Labeled Correlation Alignment

Sound synthesis refers to the creation of original acoustic signals with broad applications in artistic innovation, such as music creation for games and videos. Nonetheless, machine learning architectures face numerous challenges when learning musical structures from arbitrary corpora. This issue involves adapting patterns borrowed from other contexts to a concrete composition objective. Using Labeled Correlation Alignment (LCA), we propose an approach to sonify neural responses to affective music-listening data, identifying the brain features that are most congruent with the simultaneously extracted auditory features. For dealing with inter/intra-subject variability, a combination of Phase Locking Value and Gaussian Functional Connectivity is employed. The proposed two-step LCA approach embraces a separate coupling stage of input features to a set of emotion label sets using Centered Kernel Alignment. This step is followed by canonical correlation analysis to select multimodal representations with higher relationships. LCA enables physiological explanation by adding a backward transformation to estimate the matching contribution of each extracted brain neural feature set. Correlation estimates and partition quality represent performance measures. The evaluation uses a Vector Quantized Variational AutoEncoder to create an acoustic envelope from the tested Affective Music-Listening database. Validation results demonstrate the ability of the developed LCA approach to generate low-level music based on neural activity elicited by emotions while maintaining the ability to distinguish between the acoustic outputs.

Cloud-Cluster: An uncertainty clustering algorithm based on cloud model

As a cornerstone of the world, uncertainty embodies the nature of data and knowledge. Existing uncertainty theory-based clustering algorithms learn fuzziness, i.e., the uncertainty of clustering objects belonging to different clusters. However, these algorithms do not refer to the fuzziness of objects themselves, i.e., the randomness of data. Here, we propose a clustering algorithm named Cloud-Cluster, which simultaneously characterizes the fuzziness and randomness of objects to reserve uncertain information, and to describe clusters into concepts. It embeds random uncertainty of concepts to extend the data distribution range for better data partitions and gradually constructs accurate concepts by an improved backward cloud transformation algorithm (MBCT-SR-Ex). Moreover, to ensure that the concept clustering process gradually converges, Cloud-Cluster introduces the Cluster Concept Drift Degree to evaluate the uncertainty of concepts during the clustering process. Experiments on UCI and OpenML clustering datasets show that Cloud-Cluster improves the average clustering accuracy by over 14% compared to K-Means and uncertainty theory-based clustering algorithms. Extensive experimental results on the evaluation of uncertainty show that Cloud-Cluster can handle the uncertainty of datasets in the clustering process well, in addition to exhibiting robustness with unclear clusters.

Sigmund Freud, 19 Berggasse, Vienna. André Breton, 42 Rue Fontaine, Paris

Sigmund Freud, 19 Berggasse, Vienna. André Breton, 42 Rue Fontaine, Paris

Component calibration and configuration planning in assembly automation with a parallel manipulator

Parallel manipulator (PM) is widely used as posture alignment mechanism in heavy component machining and assembly process. To achieve precise kinematics backward transformation, the relative spatial relationship between the component and PM’s spherical joints need be calibrated, which suffers from the pain-point that the spherical joint center is inside the mechanical socket and its position can hardly be directly recovered. We refer this problem as component calibration. In this research, we present an alternate solution by formulating kinematic chain in form of AX = XB based on designedly established PM reference configuration and base coordinate system, which does not rely on the measurement of spherical joint center position. To improve the calibration accuracy, accuracy-influencing factors, PM’s dexterity distribution over workspace and properties of angle-axis space are jointly analyzed then 4 centrosymmetric angle-axis vector pairs on the boundary of maximum feasible angle-axis space at zero manipulator translation are set as configuration pairs to establish the kinematic chain. Configuration planning in angle-axis space facilitates the visualization and control of the critical factors in calibration process and tends to set these factors to optimum simultaneously. Experiments are carried out finally and the results show that the calibration accuracy could be notably improved with such method compared with no configuration planning, especially for translation accuracy. • A component calibration method in assembly automation with parallel manipulator. • Dexterity analysis and calibration configuration planning in angle-axis space. • A fast calibration implementation using fiducial tag and monocular vision.

An integrated risk assessment methodology based on fuzzy TOPSIS and cloud inference for urban polyethylene gas pipelines

Due to the superior performance of polyethylene (PE) gas pipes, an increasing number of PE pipelines are being used in urban areas. However, PE gas pipelines are basically located in densely populated areas with high consequences. Once a leak occurs, combustion and explosion accidents can easily happen causing a major threat to human life and property. Furthermore, the assessment results of the common risk assessment methods often have great uncertainty and deviation. It is difficult to accurately identify the main indicators that need priority management. To address these problems, this paper proposes a novel methodology by integrating a fuzzy TOPSIS model and cloud inference for conducting risk assessments of urban PE gas pipelines. The developed methodology is composed of two branches. In the first branch, a rapid risk assessment model for PE pipelines is established to determine whether there are some special emergency situations, which can be directly identified as high risk. In the second branch, a detailed risk assessment process is adopted to obtain the final result, which mainly includes risk factor analysis, risk status assessment and risk classification. Among them, risk factor analysis is used to establish a risk index system and develop a risk index management strategy by means of mathematical statistics and the fuzzy TOPSIS analysis method. The risk status assessment is used to determine the final risk value based on the virtual cloud and fault tree analysis, which is mainly realized by the triangular fuzzy number, structural entropy weight method and backward cloud transformation algorithm. Finally, the risk assessment results of PE gas pipelines are clearly presented in the form of cloud inference, and the final risk classification is determined. The case study shows that the pipeline in this section is at the medium risk level, and the management of time-related indicators and third-party damage is given priority. The result is consistent with the actual situation, which verifies the effectiveness and practicability of the methodology.

Iterative structure transformation and conditional random field based method for unsupervised multimodal change detection

Change detection between heterogeneous images has become an increasingly interesting research topic in remote sensing. The different appearances and statistics of heterogeneous images bring great challenges to this task. In this paper, we propose an unsupervised iterative structure transformation and conditional random field (IST-CRF) based multimodal change detection (MCD) method, combining an imaging modality-invariant based structure transformation method with a random filed framework specifically designed for MCD, to acquire an optimal change map within a global probabilistic model. IST-CRF first constructs graphs to represent the structures of the images, and transforms the heterogeneous images to the same differential domain by using graph based forward and backward structure transformations. Then, the change vectors are calculated to distinguish the changed and unchanged areas. Finally, in order to classify the change vectors and compute the binary change map, a CRF model is designed to fully explore the spectral-spatial information, which incorporates the change information, local spatially-adjacent neighbor information, and global spectrally-similar neighbor information with a random field framework. As the changed samples will influence the structure transformation and reduce the quality of change vectors, we use an iterative framework to propagate the CRF segmentation results back to the structure transformation process that removes the changed samples, and thus improve the accuracy of change detection. Experiments conducted on different real data sets show the effectiveness of IST-CRF. Source code of the proposed method will be made available at https://github.com/yulisun/IST-CRF.

Joint Angle and Range Estimation in Monostatic FDA-MIMO Radar via Compressed Unitary PARAFAC

In this paper, we study the joint range and angle estimation problem based in monostatic frequency diverse-array multiple-input multiple-output (FDA-MIMO) radar, and propose a method for range and angle estimation base on compressed unitary parallel factor (PARAFAC). First, the received complex signal matrix is stacked into a third-order complex signal tensor. Then, we can transform the obtained third-order complex signal tensor into a third-order real-valued signal tensor by employing forward–backward and unitary transformation techniques. Next, a smaller third-order real-valued signal tensor is composed by using compressing the third-order real-valued signal tensor. After that, PARAFAC decomposition is applied to obtain the direction matrix. Lastly, the angle and range are estimated by employing the least square (LS) fitting. The estimation error of the proposed method is about 10% lower than that of the traditional PARAFAC method under the low number of snapshots. When the number of snapshots is high, the performance of the two methods is close. Moreover, the computational complexity of the proposed method is nearly 96% less than those of the traditional PARAFAC methods in the case of low snapshots, while the gap is larger in the case of high snapshots. The superiority and effectiveness of the method are proved by complexity analysis and simulation experiments.

Probe distance error analysis for phased array calibration based on BTM

AbstractBased on backward transformation method (BTM), the probe distance error analysis in z‐axis for phased array calibration is described. When BTM is employed in phased array calibration to obtain the electric field over the radiation aperture of antenna, the Taylor expansion of probe distance error is used for analyzing the accuracy of reconstructed aperture field. It is found that probe distance error less than 1/35 of a wavelength is recommended. To verify this conclusion, the measured results demonstrate the validation of this analysis, which is helpful in the near‐field calibration for phased array.

Texture Memory in Hexagonal Metals and Its Mechanism

Texture memory is a phenomenon in which retention of initial textures occurs after a complete cycle of forward and backward transformations, and it occurs in various phase-transforming materials including cubic and hexagonal metals such as steels and Ti and Zr alloys. Texture memory is known to be caused by the phenomena called variant selection, in which some of the allowed child orientations in an orientation relationship between the parent and child phases are preferentially selected. Without such variant selection, the phase transformations would randomize preferred orientations. In this article, the methods of prediction of texture memory and mechanisms of variant selections in hexagonal metals are explored. The prediction method using harmonic expansion of orientation distribution functions with the variant selection in which the Burgers orientation relationship, {110}β//{0001}α-hex &lt;11¯1&gt;β//21¯1¯0α-hex, is held with two or more adjacent parent grains at the same time, called “double Burgers orientation relation (DBOR)”, is introduced. This method is shown to be a powerful tool by which to analyze texture memory and ultimately provide predictive capabilities for texture changes during phase transformations. Variation in nucleation and growth rates on special boundaries and an extensive growth of selected variants are also described. Analysis of textures of commercially pure Ti observed in situ by pulsed neutron diffraction reveals that the texture memory in CP-Ti is indeed quite well predicted by consideration of the mechanism of DBOR. The analysis also suggests that the nucleation and growth rates on the special boundary of 90° rotation about 21¯1¯0α-hex should be about three times larger than those of the other special boundaries, and the selected variants should grow extensively into not only one parent grain but also other grains in α-hex(hexagonal)→β(bcc) transformation. The model calculations of texture development during two consecutive cycles of α-hex→β→α-hex transformation in CP-Ti and Zr are also shown.

Experimental study of bubble size distribution in bubbly and bubbly-to-slug transition flow

The current work experimentally investigates the evolving characteristics of bubble size distribution (BSD) in bubbly and bubbly-to-slug transition flow in a 25.4 mm inner diameter pipe. Four-sensor conductivity probes used in present research enables accurate acquisition of bubble chord length in the flows with void fraction varying from 0.1 to 0.6. The flow regime transition is implemented by increasing superficial gas velocity while keeping superficial liquid velocity constant. Six right-skewed probability distribution functions (PDFs) commonly used for predicting bubble chord length distribution (CLD) are selected and used to fit the chord length data. The goodness of fit for all PDFs is evaluated by R-square, root-mean-square error (RMSE) and corrected Akaike information criterion. The results show that the PDF which reasonably fits the CLD in bubbly flow fails to fit the CLD in bubbly-to-slug transition flow. The evolving characteristics of CLD in flow regime transition process are qualitatively and quantitatively discussed based on bubble coalescence and break-up mechanisms. It is found that the invalidation of uniform morphology assumption and different mechanisms of bubble coalescence and break-up may result in the failed fitting. To properly predict real bubble size distribution (BSD) in bubbly-to-slug transition flow from CLD, an analytical backward transformation method is proposed to relate the CLD to BSD based on the bubble geometrical characteristics in the transition flow. The predicted BSD can be applied to calculate the interfacial area concentration (IAC) in the transition flow. The calculated IAC shows good agreements with experimental data which demonstrates the validity of predicted BSD in the transition flow.

CMYK-CIELAB Color Space Transformation Using Machine Learning Techniques

In this work four different machine learning approaches have been implemented to perform the color space transformation between CMYK and CIELAB color spaces. We have explored the performance of Support-Vector Regression (SVR), Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Radial Basis Function (RBF) models to achieve this color space transformation, both AToB and BToA direction. The data set used for this work was FOGRA53 which is composed of 1617 color samples represented both in CMYK and CIELAB color space values. The accuracy of the transformation models was measured in terms of ΔE* color difference. Moreover, the proposed models were compared, in practical terms, with the performance of the standard ICC profile for this color space transformation. The results showed that, for the forward transformation (CMYK to CIELAB), the highest accuracy was obtained using RBF. While, for the backward transformation (CIELAB to CMYK), the highest accuracy was obtained with DNN.