Vector Of Variables Research Articles

Vector Control and Modeling of Modular Multilevel High Voltage Converter for Rail Vehicle Traction

Rail transit plays an important role in the social and economic life of China and even all countries in the world, especially some populous countries or regions. The traction drive system of rail vehicle provides three-phase AC with adjustable voltage and frequency for the traction motor, controls the speed and torque of the traction motor, and then controls the operation of the vehicle. The modular multilevel converter has the advantages of low harmonic, good power quality of output waveform, high reliability, no input filtering and power compensation, and is suitable in the field of frequency conversion. In this work, the open-loop scalar control and vector closed-loop control of modular multi-level high-voltage inverter were adopted. It was found that driven by modular multi-level variable frequency vector control system, asynchronous motor not only has less harmonic content of voltage and current waveform, but also its speed regulation characteristics have been improved.

An integrated approach for incorporation of voltage and transient stabilities into optimal power flow study

Today's power systems are very likely to be stricken by the interference amongst different types of stability and associated control actions owing to their growing complexity resulted from expansion and interconnection. Taking control action into account with the aim of improving one stability phenomenon may deteriorate the others while their mutual interactions are omitted from a comprehensive study. In this paper, an integrated approach for incorporation of voltage and first-swing stabilities into optimal power flow study is proposed so as to not only preclude any potential inconsistency between such stability phenomena but also bring these two stabilities in harmony aiming to simultaneous improvement. During the exploration of an optimal operating point, the setpoint of voltage and power of generating units and other available VAr supporting sources like SVCs are adaptively adjusted whereby critical clearing time of fault (CCT) and network loadability limit (NLL) are maximized. Active power output and terminal voltage of generators along with injecting/absorbing reactive power of SVC constitute a vector of decision variables for solving the optimization problem through a powerful multi-objective evolutionary algorithm (MOEA), namely ISPEA2. The best compromise solution lying on Pareto front by which fulfilling all objective functions is determined by TOPSIS as a decision-making approach. The effectiveness of the proposed method is verified by its implementation in IEEE 39-bus test system using MATLAB software and the results demonstrate its applicability.

A Poverty Measurement Method Incorporating Spatial Correlation: A Case Study in Yangtze River Economic Belt, China

The UN 2030 Agenda sets poverty eradication as the primary goal of sustainable development. An accurate measurement of poverty is a critical input to the quality and efficiency of poverty alleviation in rural areas. However, poverty, as a geographical phenomenon, inevitably has a spatial correlation. Neglecting the spatial correlation between areas in poverty measurements will hamper efforts to improve the accuracy of poverty identification and to design policies in truly poor areas. To capture this spatial correlation, this paper proposes a new poverty measurement model based on a neural network, namely, the spatial vector deep neural network (SVDNN), which combines the spatial vector neural network model (SVNN) and the deep neural network (DNN). The SVNN was applied to measure spatial correlation, while the DNN used the SVNN output vector and explanatory variables dataset to measure the multidimensional poverty index (MPI). To determine the optimal spatial correlation structure of SVDNN, this paper compares the model performance of the spatial distance matrix, spatial adjacent matrix and spatial weighted adjacent matrix, selecting the optimal performing spatial distance matrix as the input data set of SVNN. Then, the SVDNN model was used for the MPI measurement of the Yangtze River Economic Belt, after which the results were compared with three baseline models of DNN, the back propagation neural network (BPNN), and artificial neural network (ANN). Experiments demonstrate that the SVDNN model can obtain spatial correlation from the spatial distance dataset between counties and its poverty identification accuracy is better than other baseline models. The spatio-temporal characteristics of MPI measured by SVDNN were also highly consistent with the distribution of urban aggregations and national-level poverty counties in the Yangtze River Economic Belt. The SVDNN model proposed in this paper could effectively improve the accuracy of poverty identification, thus reducing the misallocation of resources in tracking and targeting poverty in developing countries.

A Subspace Projection Approach for Clutter Mitigation in Holographic Subsurface Imaging

The holographic subsurface radar (HSR) is recognized as an effective remote sensing modality for the detection of shallowly buried objects with a high-resolution image in plain view. However, subsurface detection with HSR is prone to be impaired by clutter contamination, which often obscures the target response. In this letter, a novel clutter mitigation method combining singular value decomposition (SVD) and response cross correlation analysis is presented. The proposed method first applies SVD to decompose the radar data matrix to a number of singular components. Furthermore, the signal cross correlation characteristics are analyzed to demonstrate that the variance of left singular vectors is directly proportional to the target proportion in radar data. Then, target and clutter subspaces can be identified by maximizing the defined weighted target-to-clutter ratio (WTCR). Results of numerical simulation and laboratory experiments corroborate the effectiveness of the proposed method in reducing clutter while preserving the target image.

CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints.

The nature of high-throughput screening (HTS) puts certain limits on optimal test conditions for each particular sample; therefore, on top of usual data normalization, additional parsing is often needed to account for incomplete read outs or various artifacts that arise from signal interferences.CurveP is a heuristic, user-tunable curve-cleaning algorithm that attempts to find a minimum set of corrections, which would give a monotonic dose-response curve. After applying the corrections, the algorithm proceeds to calculate a set of numeric features, which can be used as a fingerprint characterizing the sample, or as a vector of independent variables (e.g., molecular descriptors in case of chemical substances testing). The resulting output can be a part of HTS data analysis or can be used as input for a broad spectrum of computational applications, such as quantitative structure-activity relationship (QSAR ) modeling, computational toxicology, bioinformatics, and cheminformatics.

ЭКСПЕРИМЕНТАЛЬНОЕ ИССЛЕДОВАНИЕ РАБОТОСПОСОБНОСТИ МЕТОДИКИ АДАПТИВНОЙ ИДЕНТИФИКАЦИИ ЭЛЕКТРИЧЕСКИХ ПАРАМЕТРОВ АСИНХРОННОЙ МАШИНЫ С РАЗОМКНУТОЙ ОБМОТКОЙ РОТОРА В УСТАНОВИВШЕМСЯ РЕЖИМЕ НА ОСНОВЕ БАЛАНСА МОЩНОСТЕЙ

Relevance For the operation of a closed-loop sensorless electric drive as part of an electrical complex, it is necessary to ensure adequate restoration of the vector of state variables of an induction motor using a customizable mathematical model in the control system. The accuracy of restoring the state variable vector depends on two factors. The first factor is the mathematical description of the substitution circuit of an asynchronous motor by a system of differential equations with certain assumptions that reliably reflects the physical processes occurring in an asynchronous motor. Used in the electric drive control system, T- and L-shaped equivaent circuits of an asynchronous motor, described by a system of differential equations in a customizable mathematical model, are universal and allow simulating the behavior of an asynchronous motor in all operating modes: «start», «load surge», «load shedding», «reverse», «stop», which is a sufficient condition for their use without modifications within the framework of this article. The second factor — the coefficients in the system of differential equations with some error correspond to the electrical parameters of the equivalent circuit of an asynchronous motor. In turn, the electrical parameters of the equivalent circuit of an asynchronous motor are unique for each motor and cannot be used in a system of differential equations without their refinement, i.e. no identification process. There are several types of identification of the electrical parameters of an induction motor: by the experience of idling and short circuit, by catalog data, by induction motor attenuation with a locked rotor of an induction motor, and others. The described methods for identifying parameters in most cases require the decommissioning of an asynchronous motor and conducting separate experimental studies, after which the electrical parameters in the equivalent circuit are considered constant and unchanged during operation. It is known that when an asynchronous motor is heated during operation, the active resistances of the stator and rotor circuits change. The inductance of the main magnetization circuit, stator and rotor circuits vary depending on the shape of the supply voltage and the operating modes of the asynchronous motor. Changing these parameters means that the restoration of the state variable vector will occur with a larger error. Given the above, the development of a methodology for adaptive identification of the electrical parameters of the equivalent circuit of an asynchronous motor during operation, without decommissioning and shutting down the entire electrical complex, is an urgent scientific and practically significant task. Aim of research Development and experimental study of a method for determining the electrical parameters of the stator winding in steady-state operation modes without decommissioning and stopping the electrical complex. Research methods Systems of nonlinear equations, iterative procedures, Newton's method, numerical methods, state space method, optimization methods. Results A method of adaptive identification of the electrical parameters of the stator winding replacement circuit in steady-state operation based on the power balance has developed and experimentally tested, which allows for accurate estimation of the parameters of the stator winding replacement circuit during induction motor operation. The developed technique in the future, with the expansion of the mathematical apparatus, will allow determining the parameters of both the stator and rotor windings of the induction motor when the electric drive is operating as part of an electrical complex. The practical application of open woundrotor induction motor is not possible, therefore this article is of a research nature and is intended only for experimental confirmation of the operation of the technique.

ИССЛЕДОВАНИЕ ИНДИКАТОРОВ КОМПРОМЕТАЦИИ ДЛЯ СРЕДСТВ ЗАЩИТЫ ИНФОРМАЦИОННЫХ И КИБЕРФИЗИЧЕСКИХ СИСТЕМ

Purpose of work: study of existing standards of compromise indicators and methods of their exchange for enrichment of protection systems of information and cyber-physical systems. Research method: systematic analysis of open sources of data on indicators of compromise, standards of their description and methods of exchange in the organization of cyberintelligence. The result obtained: the actual problems of proactive search of threats are formulated on the example of the application of open sources of indicators of compromise in the processing of event flows in security event management systems. The classification of indicators derived from internal sources is proposed. The main problems of processing dynamic threat data streams under changing attack vectors are formulated. It was found that the threat intelligence industry currently lacks a unified solution in terms of standardization of information exchange between different platforms, but there are a number of dominant standards and formats of such data exchange. In the course of preparing the review of existing, the tasks of identifying previously unknown attack methods based on the use of open sources of indicators of compromise in data processing in security incident management systems were considered and structured, and methods for their solution were proposed. Scientific novelty: the presented article is one of the first domestic works, devoted to the analysis of research in recent years in the field of organization of work with threat intelligence data sources. Reviewed and systematized the sources of indicators of compromise and proposed their classification. Formulated the main problems of processing dynamic threat data streams under conditions of variable attack vectors.

Cybersecurity Risk Assessment of Industrial Control Systems Based on Order-α Divergence Measures Under an Interval-Valued Intuitionistic Fuzzy Environment

With the increasing deployment of network technologies in industrial control systems (ICSs), cybersecurity has become a challenge in ICSs. Cybersecurity risk assessment (CRA) plays an important role in cybersecurity protection of ICSs. However, the weights of risk indices are constants in traditional CRA methods, and they do not fully consider the requirements of risk identification. In this paper, we define a novel order- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> divergence measure for interval-valued intuitionistic fuzzy numbers (IVIFNs) and further develop a novel CRA approach for ICSs based on the proposed divergence measure under an interval-valued intuitionistic fuzzy environment to contribute to the research gap. First, an order- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> divergence measure for IVIFNs is defined considering flexibility and robustness of divergence measures with the parameter. Next, a variable weight-based CRA approach for ICSs is developed. In this approach, IVIFNs are adopted to describe evaluation values of risk indices. The weights of risk indices are variable weight vectors and they are determined by the relative divergence closeness. Integration approaches of each node and each attack path in attack-defense trees (ADTs) are proposed based on the operations of IVIFNs, and risk scores of each attack path are calculated by using the score function. Finally, we apply the proposed method to the CRA of a civil aviation fuel supply automatic control system and verify its effectiveness and advantages by comparing it with other methods. This method can dynamically adjust the weights of risk indices considering the relationship between each risk index and the highest risk, and therefore, it can more effectively recognize the highest risk of ICSs than the traditional CRA method. In addition, it can also match the risk attitude of decision-makers by adjusting the parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> .

Control Strategy of Three-Level NPC Inverter Based on Variable Coefficient Virtual Vector Model Predictive Control

The Conventional model predictive control (C-MPC) uses only a single voltage vector in each control period, resulting in poor control performance. In addition, the computational burden in discrete space state generated by three-level inverter cannot be ignored. To improve the control performance, this paper proposes a variable coefficient virtual vector MPC (VC-VV-MPC) method, a five-level virtual space voltage vector is constructed to improve the control performance, and a scaling coefficient is used to further improve the control performance. In addition, a voltage vector pre-selection method is used to reduce the computational burden. The simulation and experiment results shown that, compare with C-MPC and adjacent vector MPC (AV-MPC), the proposed VC-VV-MPC can achieve the improvement of control performance under lower computational burden.

Lagrangian approach for optimizing fiber bundle placements of CFRP shell structures

We present a non-parametric shape and topology optimization method for fiber bundle placement and orientation design aimed at controlling the rigidity of CFRP shell structures. Since the fiber bundles are modeled by the beam element, the placement and the orientation of the fiber bundles can be explicitly obtained while moving the positions. In order to achieve the target displacement at an arbitrarily specified position, we considered the problem of minimizing the squared error under the length constraint of the fiber bundles, and formulated it as a distributed-parameter optimization problem. Using the derived shape gradient function and the density gradient function, the optimum placement and the orientation of the fiber bundles are determined simultaneously by the H1 gradient method for vector and scalar variables. The effectiveness of the proposed method is confirmed by numerical examples.

Diffusion Hammerstein Spline Adaptive Filtering Based on Orthogonal Gradient Adaptive Algorithm

In this paper, we propose a class of nonlinear diffusion filtering based on Hammerstein function with the spline adaptive filter (HSAF) implemented by normalised version of orthogonal gradient adaptive (NOGA) algorithm over the distributed network. Diffusion adaptation algorithm approximates a variable vector with the help of a network of agents using a joint optimisation on the sum of cost function. A HSAF comprises of memoryless function during learning by interpolating polynomials with respect to the linear filter. We derive a diffusion adaptation framework on HSAF motivated from NOGA algorithm; called DHSAF-NOGA. There are two types of adaptive diffusion strategies with the combine-then-adapt (CTA) algorithm and the adapt-then-combine (ATC) algorithm that are considered and implemented by DHSAF-NOGA algorithm. The network stability and performance over mean square error networks is derived. Experiment results depict that proposed CTA-DHSAF-NOGA and ATC-DHSAF-NOGA algorithms can learn robustly underlying the nonlinear Hammerstein model compared with a non-cooperative solution and existing techniques.

Exchange Rate Pass-Through and Historical Decomposition of Inflation in Turkiye

The main purpose of this paper is to examine the effects of macroeconomic shocks in particular exchange rate shocks on Consumer Price Index (CPI) and 12 main subgroups of CPI. Our empirical model is a seven variable Vector Autoregressive (VAR) model, incorporating pricing along a distribution chain proposed by McCarthy (2007). The results of historical decomposition and impulse-response anaysis show that contribution of exchange rate shocks and domestic producer prices shocks to headline inflation is more significant in respect to other macroeconomic shocks.

Multi-Modal Variational Graph Auto-Encoder for Recommendation Systems

Graph embedding based methods have been used in recommendation systems recently, owing to their advances in modeling nodes as embeddings in a low-dimensional space. By effective neighborhood aggregation, graph convolutional networks can exploit high-order connections of neighbors such that the learned embeddings could be more informative thus improve the recommendation performance. However, user and item representations learned by graph aggregation inherently contain uncertainty due to sparsity of user-item interactions and noise of item features. To address these challenges, we propose a multi-modal variational graph auto-encoder (MVGAE) method. Specifically, we design modality-specific variational encoders that learn a Gaussian variable for each node whereas the mean vector represents semantic information and the variance vector denotes the noise level of the corresponding modality. Moreover, with the conditional independence assumption, the modality-specific Gaussian node embeddings are fused according to the product-of-experts principle, where the semantic information in each modality is weighted based on the estimated uncertainty level. Extensive experiments on three public datasets, Amazon Movies, Amazon Electronics and AliShop-7 C, demonstrate that our proposed method achieves competitive performance when compared with the state-of-the-art algorithms.

Simultaneous optimization of material orientation angle and shape/topology of CFRP shell structure

The objective of this study is to develop a method for simultaneous optimization of shape, topology and orientation angle to maximize the stiffness of CFRP shell structures under volume constraints. In the optimization of orientation angle, the material directions of orthotropic anisotropic material in finite elements are used as design variable. The optimal shape is determined by the H1 gradient method for vector variables, and the optimal solutions for topology optimization and orientation angle are determined by the H1 gradient method for scalar variables. The effectiveness of the proposed method is confirmed through a numerical example.

Characterizing Global Sea Surface Local Wind Variability From ASCAT Data

Recent advances in the sea surface wind quality control of the Advanced Scatterometer (ASCAT) show that spatial wind variability within a resolution cell of 25 km × 25 km, namely the subcell wind variability, is highly correlated with the ASCAT quality indicators, such as the wind inversion residual (maximum likelihood estimator, MLE) and the singularity exponent (SE) derived from singularity analysis. This opens up opportunities for quantifying the instantaneous spatial wind variability over the global sea surface. In this paper, it is assumed that the spatial wind variability is linearly proportional to the temporal variation of buoy sea surface winds time-series following the Taylor’s hypothesis. As such, the moored buoy winds with 10-minute sampling are used to examine the subcell wind variability. Then the sensitivity of ASCAT quality indicators to the subcell wind variability is evaluated. The results indicate that although SE is more sensitive than MLE in characterizing the wind variability, they are mainly complementary in flagging the most variable winds. Consequently, an empirical model is derived to relate the buoy wind vector variability to the ASCAT MLE and/or SE values. Although the overall procedure is based on the one-dimensional temporal analysis and such empirical model cannot fully represent the two-dimensional spatial variability, it leads for the first time to the development of an ASCAT-derived local wind variability product. The empirical method presented here is straightforward and can be applied to other scatterometer systems.

Structural and Parametric Identification of Knowm Memristors.

This paper proposes a novel identification method for memristive devices using Knowm memristors as an example. The suggested identification method is presented as a generalized process for a wide range of memristive elements. An experimental setup was created to obtain a set of intrinsic I–V curves for Knowm memristors. Using the acquired measurements data and proposed identification technique, we developed a new mathematical model that considers low-current effects and cycle-to-cycle variability. The process of parametric identification for the proposed model is described. The obtained memristor model represents the switching threshold as a function of the state variables vector, making it possible to account for snapforward or snapback effects, frequency properties, and switching variability. Several tools for the visual presentation of the identification results are considered, and some limitations of the proposed model are discussed.

Research on Stability of Gray Value of Excited-State Fluorescent Oil Film Based on Variable Light Vector Angle

In global skin-friction measurement of aircraft, the fluorescent oil film method can characterize the distribution of skin friction well. However, in an actual wind tunnel test, the wing of the aircraft will inevitably produce corresponding vibrations due to the influence of wind, which will change the relative position between fluorescent oil film and UV (ultraviolet) excitation light source (position fixed). This also directly affects gray value imaging of fluorescent oil films. Based on this, a mathematical model is established to judge the stability of the gray value of fluorescent oil film in this vibrational environment; then, the model can be solved to obtain the vibrational range constraint that enables the gray value of fluorescent oil film to be stabilized. In order to simplify the calculation process, the light vector angle is used to describe the constraint, which also makes the results more intuitive. Through experimental analysis and demonstration, the prediction accuracy of this model can reach 95.61%, which has certain practical engineering application significance.

Divergence in Corn Mycorrhizal Colonization Patterns Due to Organic Treatment.

Excessive application of chemical fertilizers and other agrochemicals can cause large imbalances in soils and agricultural ecosystems. In this context, mycorrhizae represent a viable solution to mitigate these negative effects. Arbuscular mycorrhizae are vital symbionts due to the multiple benefits they bring to both crops and the entire agroecosystem. The main purpose of this study was to observe whether differentiated fertilization has an influence on mycorrhizal colonization patterns in corn. Observed frequencies and intensities of colonization varied widely between phenophases and treatments, with 20% variation for frequency and 14% for intensity, which implies the constant development of both partners during the vegetation period. Arbuscules and vesicles were present in all development stages, but the overall mean was lower than 4% for arbuscules and 1% for vesicles in the analyzed root fragments. Intensity was highly correlated with frequency of colonization compared with arbuscules, where the coefficient was 0.54, and vesicles, with a coefficient of 0.16. Both PCA and NMDS provided good graphical solutions, with a high resolution due to explained variance and good spatial position of vectors. The use of mycorrhizal maps permits the full exploration of colonization patterns and fungal strategy, and the assessment of mycorrhizae-free areas. For the untreated variant, the strategy was oriented toward a longitudinal colonization followed by an irregular development of hyphae with multiple non-colonized areas. Treatment acts to stimulate the appearance of mycorrhizal spots, which further develop radially.

Identification and Control of Multidimensional Delayed Processes in Conditions of Incomplete Information

Problems of identification and control of multidimensional discrete-continuous processes with delay in conditions of incomplete information about the object are considered. In such conditions, the form of parametric equations for various channels of the object is absent due to the lack of a priori information. Moreover, multidimensional processes have stochastic dependences of the components of the vector of output variables. Under such conditions, the mathematical description of such processes leads to a system of implicit equations. Nonparametric identification and control algorithms for multidimensional systems are proposed. The main task of modeling such processes is to determine the predicted values of the output variables from the known input. Moreover, for implicit equations, it is only known that one or another output variable can depend on other input and output variables that determine the state of a multidimensional system. In this study, a nontrivial situation arises when solving a system of implicit equations under conditions when the dependences between the components of the output variables are unknown. The application of the parametric theory of identification in this case will not lead to success. One of the possible directions is the use of the theory of nonparametric systems. The main content of the work is the solution of the identification problem in the presence of dependencies of the output variables and then the solution of the control problem for such a process. Here you should pay attention to the fact that when determining the reference actions for a multidimensional system, it is first necessary to solve the system of reference actions, since it is not possible to choose arbitrarily setting influences from the range of definition of output variables. Computational eXperiments aimed at investigating the effectiveness of the proposed identification and control algorithms are presented.

Intraday variations of polarization vector in blazars: a key to the optical jet structure?

AbstractThis report presents the results of optical polarimetric observations carried out with 6-m and 1-m telescopes at SAO RAS. The study of the blazar S5 0716+714 radiation showed the presence of a period of the variability of brightness and polarization vector variations on scales of ∼1.5 hours, constant on a long time scale; multi-colour monitoring of BL Lac polarization before, during and after the flare demonstrates the difference in the patterns of polarization vector variability depending on the wavelength. Several geometrical models and physical descriptions are discussed.