Reference Vector Research Articles

Attitude Estimation From Vector Measurements: Necessary and Sufficient Conditions and Convergent Observer Design

The paper addresses the problem of attitude estimation for rigid bodies using (possibly time-varying) vector measurements, for which we provide a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">necessary and sufficient</i> condition of distinguishability. Such a condition is shown to be strictly weaker than those previously used for attitude observer design. Thereafter, we show that even for the single vector case the resulting condition is sufficient to design almost globally convergent attitude observers, and two explicit designs are obtained. To overcome the weak excitation issue, the first design employs to make full use of historical information, whereas the second scheme dynamically generates a virtual reference vector, which remains non-collinear to the given vector measurement. Simulation results illustrate the accurate estimation despite noisy measurements.

Magnetic resonance imaging for the differential diagnosis of primary extra-axial brain tumors: a review of radiomic studies

BACKGROUND: The analysis of magnetic resonance imaging data is considered the main method for the preoperative differential diagnosis of primary extra-axial tumors. However, the exact distinction of different primary extra-axial tumors based only on visual rating can be challenging. Radiomics is a quantitative method of analyzing medical image data, which allows us to understand and observe the connection between visual data and phenotypic and genotypic features of tumors. Earlier, several publications presented generalized results of research aimed at the differential diagnosis of primary extra-axial tumors based on the principles of radiomics. Fast accumulation of new clinical cases and increasing of the amounts of research on these cases demonstrate the need for their further analysis and systematization, which has led to this review. AIM: To conduct a systematic analysis of existing data on radiomics potential for the differential diagnosis of primary extra-axial tumors. MATERIALS AND METHODS: The search for publications over the past 5 years in Russian and English was conducted in PubMed/Medline, Google Scholar, and еLibrary databases. The final analysis included 19 papers on the differential diagnosis of extra-axial tumors. The included publications provided radiomic features used for the differential diagnosis of neoplasms. RESULTS: All studies demonstrated the existence of a connection between radiomic parameters (textural and histogram) and tumor type. The effectiveness of tumor differential diagnostics with radiomic models exceeded the neoplasm classification made by radiologists. The most frequently used algorithms for creating mathematical models of tumor classification based on radiomic parameters were the reference vector method, logistic regression, and random forest. CONCLUSION: The use of the radiomic concept shows promising results in the differential diagnosis of primary extra-axial tumors. Further development in this area demands the standardization of both the segmentation method and the set of features and an effective method of mathematics modeling.

A solution potential-based adaptation reference vector evolutionary algorithm for many-objective optimization

Decomposition-based multiobjective evolutionary algorithms are widely utilized for solving multiobjective optimization problems (MOPs). These algorithms have a higher degree of diversity due to a uniform distribution of the reference vectors in the objective space. However, these algorithms may struggle to select a suitable candidate set for solving many-objective optimization problems (MaOPs) with irregular Pareto fronts (PFs). To solve this problem, a solution potential-based adaptation reference vector evolutionary algorithm (SPARVEA) is proposed in this paper. In the algorithm, a concept called solution potential is presented to assess whether the direction of convergence of the solution to the ideal solution has potential. The solution potential is obtained with an evaluation function to calculate the potentials of the corresponding solutions. A solution potential-based adaptation strategy is then designed for adapting the reference vector to better guide the direction of convergence of the solution when solving MaOPs with irregular PFs. A modified angle penalty distance (mAPD) is adjusted to improve the convergence rate of the solution set in many-objective optimization problems. The proposed algorithm SPARVEA is compared with state-of-the-art many-objective evolutionary algorithms on four test sets with irregular PFs and four practical problems. Experimental results demonstrate the superior performance of SPARVEA on many-objective optimization problems. This contribution helps to advance the development of efficient algorithms for solving many-objective optimization problems.

Evaluation of the effectiveness of strategic and tactical controlling based on the analysis of the company’s financial reports

The implementation of controlling is a rational necessity to ensure an effective management system in a company, so it is essential to evaluate its effectiveness in terms of tactical and strategic components. However, there is no general system of indicators reflecting the effectiveness of strategic and tactical controlling, therefore, the purpose of the study was to develop and improve a methodological approach to evaluate the effectiveness of such controlling based on the analysis of the company’s financial reports as a tool for the validity of management decisions. Based on the method of comparative analysis, the differences between tactical and strategic controlling were identified and characterized, which made it possible to specify the criteria for evaluating their effectiveness. It was found that the monitoring basis of controlling mechanisms is comprehensive and covers the entire spectrum of operational and management processes in the company. A system of indicators was formed to evaluate the effectiveness of tactical and strategic controlling. A methodological approach to evaluating the effectiveness of controlling components based on the analysis of the company’s financial reports is proposed. It was tested on the example of PJSC Trust Zhytlobud-1 by calculating generalizing integral indicators of the effectiveness of controlling components based on data from the company’s financial reports. Using the algorithm of the taxonomic analysis method, a matrix of observations was formed and standardized, a reference vector was constructed and integral indicators for evaluating the effectiveness of the company’s tactical and strategic controlling were calculated. The application of the proposed method made it possible to reveal the problematic aspects of distribution processes in the company and in the context of managerial decision-making. This allowed to evaluate the effectiveness of the controlling system in the company and to determine the reserves for improving the effectiveness of controlling processes. The practical value of the study lies in the proposed methodological approach, which may be useful in the process of diagnosing the effectiveness of the quality of managerial decision-making in companies both in the current activity and in the long run

Two-Phase Model Predictive Direct Duty-Cycle Control for PMSM With Off-Line Lookup Table

Finite control set model predictive control has poor steady-state performance due to limited voltage vectors. To improve the steady-state performance, multiple vectors are used to synthesize more accurate voltage vectors, but the selection of optimal vectors is always complex. In this paper, a two-phase model predictive direct duty-cycle control based on off-line lookup table (OLT) is constructed. The proposed prediction model converts three-phase duty cycles to two-phase duty cycles, which reduces the number of duty cycles calculations. In addition, to optimize the reference voltage vectors space, a new flux linkage deviation mechanism is proposed where the virtual reference vectors can be determined with low complexity by using OLT. Then, due to further improve the steady-state accuracy and ensure low complexity, a new flux linkage constraint is proposed, which restricts the flux linkage while reducing the torque ripple. More importantly, no weight factor is needed to balance the torque and stator flux. Finally, the process of the multi-stage and multi-duty-cycle calculation is converted into the process of a one-stage and small range single-variable enumeration selection, which significantly reduces calculation cost while improving the steady-state performance. The feasibility and effectiveness ofthe proposed method isverified bytheoryand experiments.

A Knowledge Graph-Based Many-Objective Model for Explainable Social Recommendation

As an application area of social computing, social recommendation aims to exploit the richness of social relationships to improve recommendation accuracy. Current research mainly considers social information that can be directly observed, with little attention to indirect social relationships between users or the explainability of social recommendation results. To address this challenge, this article proposes a knowledge graph-based many-objective model for explainable social recommendation (KGMESR) by considering the explainability, accuracy, novelty, and content quality of social recommendation results. The model takes advantage of social behavior information to calculate user similarity and quantifies the explainability of social recommendation results using entity vectors and embedding vectors. To ensure model efficiency, a many-objective recommendation algorithm based on the partition deletion strategy is designed. It employs the association of individuals with the nearest reference vector to render the diversity of the population and then obtains the final solution by deleting poorly converged individuals in each partition. Experimental results show that many-objective optimization recommendation algorithm based on partition deletion strategy (MaOEA-PDS) allows for preferable recommendation results for users in real datasets. The explainability of the proposed model is demonstrated by two case studies.

A Space Vector Modulation Strategy for PMSMs Operating at Low Switching-to-Fundamental Frequency Ratio

A novel space vector modulation (SVM) strategy for a permanent magnet synchronous machine drive, operating at low switching-to-fundamental frequency ratio, is presented in this paper. The proposed strategy is based on applying more than two active vectors in a switching period, aiming to consider the expected rotation and possible movement from one sector to another of the voltage reference vector within that period. The method is simple as the calculation of the duty cycles is still based on the expressions for a conventional SVM. Current distortion and efficiency calculations are also provided, comparing the proposed SVM with conventional SVM and six-step modulation. The results show that the proposed method generates a current distortion similar to the conventional SVM at high frequency ratios but much lower at low frequency ratios. Moreover, comparing with six-step modulation, the current distortion generated by the proposed SVM is always lower, independent of the frequency ratio. On the other hand, the efficiency of the inverter obtained with the proposed SVM is higher than the one obtained with conventional SVM. The effectiveness of the proposed strategy is validated via simulations and experiments.

An information entropy-driven evolutionary algorithm based on reinforcement learning for many-objective optimization

Many-objective optimization problems (MaOPs) are challenging tasks involving optimizing many conflicting objectives simultaneously. Decomposition-based many-objective evolutionary algorithms have effectively maintained a balance between convergence and diversity in recent years. However, these algorithms face challenges in accurately approximating the complex geometric structure of irregular Pareto fronts (PFs). In this paper, an information entropy-driven evolutionary algorithm based on reinforcement learning (RL-RVEA) for many-objective optimization with irregular Pareto fronts is proposed. The proposed algorithm leverages reinforcement learning to guide the evolution process by interacting with the environment to learn the shape and features of PF, which adaptively adjusts the distribution of reference vectors to cover the PFs structure effectively. Moreover, an information entropy-driven adaptive scalarization approach is designed in this paper to reflect the diversity of nondominated solutions, which facilitates the algorithm to balance multiple competing objectives adaptively and select solutions efficiently while maintaining individual diversity. To verify the effectiveness of the proposed algorithm, the RL-RVEA compared with seven state-of-the-art algorithms on the DTLZ, MaF, and WFG test suites and four real-world MaOPs. The results of the experiments demonstrate that the suggested algorithm provides a novel and practical method for addressing MaOPs with irregular PFs.

Subspace segmentation based co-evolutionary algorithm for balancing convergence and diversity in many-objective optimization

With the increase of the objective dimension of optimization problems, the effect of comparing individuals through Pareto dominance relation drops sharply. While some algorithms enhance Pareto dominance via diversity preservation strategies, performance indicators, and reference vectors, many of them encounter difficulties in balancing the convergence and diversity of populations. Therefore, this paper proposes a subspace segmentation based co-evolutionary algorithm for balancing convergence and diversity in many-objective optimization. First, the decision space is divided into a convergence subspace and a diversity subspace, which are searched in the early and late stages to improve the population convergence and diversity, respectively. Second, a capacity adaptively adjusted archive is used to retain elite individuals with better convergence in the population, which is further used to mate with the population. Moreover, an indicator with penalty factor is proposed to retain the boundary individuals so as to maintain the population diversity. Comparing with 6 advanced many-objective evolutionary algorithms on 63 benchmark cases, the proposed algorithm obtains smallest IGD on 36 benchmark cases, the experimental results show that the proposed algorithm can balance convergence and diversity well and has exhibit competitiveness.

Three-Dimensional Space Vector Modulation for Four-Leg Current-Source Inverters

Unbalanced loads are general in uninterruptible power supply (UPS), standalone power generation applications and the failure mode of three-phase inverters. Three-phase four-leg inverters are a well-known solution to handle neutral currents caused by unbalanced loads. In four-leg inverters, three-dimensional space vector modulation (3DSVM) is widely used. However, the 3DSVM of four-leg current source inverter (CSI) has not been systematically studied so far. To fill this gap, this paper proposes a 3DSVM for CSI. The definition of the current vector, the identification of the position of reference vector and the sequence of switches are introduced. Under unbalance loads, the symmetrical load voltage can be realized, which complies with IEEE Std. 1159-2019. The output current can comply with IEEE Std. 519-2014. The proposed 3DSVM has the potential to extend the advanced modulation strategies that have been implemented/ proposed on three-leg CSI.

DYNAMICS OF CULTURAL SYMBOLIZATION: FROM PLACES OF MEMORY TO PLACES OF TRANSITION (ON THE EXAMPLE OF FRENCH-LANGUAGE SCIENTIFIC DISCOURSES)

The article discusses the terminology of cultural memory research - the designations of reference points and/or vectors of culture that are important for the identification of a person and a collective. It is shown that the symbolization of culture can occur in different forms: the author examines the nominations that are used in the French-language scientific literature when developing the concepts of places (lieux): places of memory, places of knowledge, not places, places of transition. Most of the names with the place component are still close to metaphors and are not well known to the Russian-speaking audience. The article aims to trace the evolution of a number of designations proposed by the French-speaking authors of the second half of the XX-XXI centuries within the framework of theories of places of culture that can find wider application in domestic science. Based on the material of France and Quebec, it is established that in the French-speaking scientific discourse, the reference points of culture can be associated with the symbolism of places and reflect two sides of the existence of culture itself: static and dynamic. These sides are mutually reversible: in the process of the development of culture and language, new references arise, while “old” references are stored in memory, forgotten and resurface, revised and processed. The interchange and reinterpretation of old and new reference points, places of memory and places of transition are a sign and condition for the development of society, culture and discourses about society and culture at different stages of history.

Adaptive stochastic fractal search algorithm for multi-objective optimization

Stochastic fractal search algorithm, a relatively efficient algorithm has certain advantages in solving multi-objective optimization problems. Decomposition-based strategy is effective for solving multi-objective optimization, which guarantees the diversity of solutions. On the other hand, a challenge for decomposition-based strategy is to enhance the algorithm’s convergence while maintaining the algorithm’s distribution. This paper proposes a reference vector-guided stochastic fractal search algorithm for multi-objective optimization(RVSFS). In the proposed algorithm, the leader solution of each subspace is selected by reference vectors to assist the generation of new solutions and ensure the convergence of the algorithm. A new selection criterion length–angle distance (LAD) is proposed. Furthermore, the Gaussian walk and Levy flight are conducted in different searching stages to balance the exploration and exploitation ability. To speed up convergence and maintain the diversity of solutions, an adaptive dynamic proportion mechanism is designed when using the random fractals and Gaussian walks to generate offspring. Since information is shared and exchanged among different solutions in each iteration, the searching process is accelerated and the exploration capability can be enhanced contemporary. On top of this, a vector-guided enhanced selection operator is designed in the elite selection step to provide desirable distribution. Our experimental results on 45 benchmarks from UF and WFG test suits show prominent competitiveness compared with five state-of-the-art algorithms. RVSFS is very competitive on all 9 UF benchmarks in terms of the metric inverted generational distance and also performs well on 14 of 36 benchmarks from WFG in terms of the metric inverted generational distance and the metric hypervolume.

Sliding Mode Controller Based on Genetic Algorithm and Simulated Annealing for Assured Crew Reentry Vehicle

A sliding mode (SM) re-entry control system is proposed for an assured crew re-entry vehicle (ACRV). The vehicle has a low lift/drag ratio (0.3). The trajectory controller drives the ACRV in a predefined track. The reference vectors for the attitude controller are generated by the trajectory controller and navigation system. The feedback SM controller is designed to accommodate the structural parametric uncertainties and environmental disturbances. The SM controller is optimized by both the genetic algorithm (GA) and the simulated annealing (SA) methods. A comparative study is done between both optimization methods and the efficiency of the SA method is proved. The system is subjected to a sensitivity study in terms of various thrust torque profiles. A robustness check is done by changing the system parameters. A pulse width pulse frequency (PWPF) modulator modulates the output torque dictated by the attitude controller. The simulation results demonstrate the proposed method’s effectiveness in achieving the various required parameters.

Reduction of Spurious Switching in Direct Vector Quantized Space Vector Pulse Density Modulation Scheme for Multilevel Inverters

A generalized online switching scheme for multi-level inverter based on Direct Vector Quantized Space Vector Pulse Density Modulation (DVQSVPDM) suitable for adjustable speed drive is proposed in this article. The existing DVQSVPDM schemes have spurious switching as the voronoi region is determined from integrated error vector of Sigma Delta Modulator (SDM). This results in high Total Harmonic Distortion (THD) of Common Mode Voltage (CMV) at lower modulation index. In the proposed DVQSVPDM the nearest three space vectors to the reference vector forms the triangular voronoi region. The integrated error vector of SDM is quantized to its nearest space vector in the voronoi region to generate the switching vectors. This reduces spurious switching associated with DVQSVPDM schemes and improves the performance at lower modulation index by reducing THD of CMV. The proposed scheme works for all n-level inverter topologies and the computations required remain same irrespective of level of inverter. Redundant states of switching vectors are not considered for switching which results in the discontinuous nature of proposed scheme. The performance of proposed DVQSVPDM is compared with space vector based modulation schemes and third harmonic injected sine PWM scheme and the results are presented. The proposed scheme is experimentally validated for a 3hp open end winding induction motor fed with three-level inverters on both sides.

Quasi Z-Source Neutral-Point-Clamped Inverter Using SVM Technique to Eliminate Common Mode Voltage

In recent year, common mode voltage (CMV) elimination methods are considered to improve the reliability of three-level quasi-switched boost T-type inverter (TL-qSBT2I). The space-vector modulation method (SVM) can control the inverter operation to eliminate the CMV. The proposed method applies medium vectors and zero vector to synthesize the reference vector to eliminate CMV while the output voltage is unchanged compared to SinPWM technique. In order to prevent the active vectors and the output voltage from being affected, the shoot-through vector is inserted inside the zero vector. Accordingly, the proposed method not only maintains the CMV elimination advantage of the SVM technique for TL-qSBT2I but also reduces the inductor current ripple and enhances the voltage gain without any topology modification or adding electrical components. Results from simulations and experiments are used to evaluate the efficiency of the suggested strategy. Additionally, by comparing the proposed scheme with the traditional pulse-width modulation technique, the superiority of the proposed scheme is shown.

Analysis and optimization of junction between cylindrical part and end dome of filament wound pressure vessels using data driven evolutionary algorithms

The junction between cylindrical part and end dome is the most critical place of the filament wound composite pressure vessels because the shear forces and bending moments occur in this area. The strength analyses of junction between cylindrical part and different types of known end domes based on influence coefficients method were performed for filament wound composite pressure vessels manufactured using glass-epoxy and carbon-epoxy composites. Four known end domes were considered that is, a spherical shell, a geodesic-isotensoid shell, a shell based on minimizing of the Tsai-Hill’s strength criterion and shell designed in authors’ previous work (Hoffman 1 shell). Moreover, the study is also dealing with design of optimal end dome shape which takes account to the junction area. Finding of optimal end dome shape was based on data-driven evolutionary algorithms. Using input dataset from the analytical simulation surrogate models were created using Evolutionary Deep Neural Nets (EvoDN2) algorithm. The multicriterial optimization procedure was performed using Constrained Reference Vector Algorithm (cRVEA) optimization algorithm. Results indicates that the optimal end dome shape highly depend on material properties.

A Localized High-Fidelity-Dominance-Based Many-Objective Evolutionary Algorithm

The practicality of Pareto-dominance in solving many-objective optimization problems becomes questionable due to its inability to factor the critical Human Decision-making (HDM) elements, including, the number of better objectives, the degree of betterment in objectives, and objectives&#x2019; relative preference. Relevant dominance principles are recently proposed to incorporate the first two HDM elements, often with the need for new tunable parameters. This paper proposes a high-fidelity-dominance principle, that factors all the three HDM elements, explicitly and simultaneously, and without requiring tuning of any parameter. This principle has been implemented in a reference vector based framework, leading to a computationally efficient many-objective Evolutionary Algorithm (MaOEA), namely localized high-fidelity-dominance based EA (LHFiD). Critically, LHFiD also has an inbuilt mechanism for on-the-fly determination of the timing for: (a) intermittent nadir point estimation that enables faster convergence, and (b) its self-termination that bears practically utility. This paper is based on an extensive study involving 41,912 experiments, in which the proposed LHFiD approach is compared with existing competitive MaOEAs. The paper reports statistically better performance in about 60% instances, making it practical and worthy of further investigation and application.

Level vector pulse width modulation for multilevel inverters

SummaryIn this paper, a novel method named as level vector pulse width modulation (LVPWM) is proposed for multilevel inverters. Similar to the conventional space vector pulse width modulation (SVPWM), the proposed method is a vector‐based method in the α–β frame. Unlike the SVPWM, the variables of each of the α and β axes are considered separately. Instead of space vectors, level vectors are considered for the synthesis of the reference vectors. There is no need to consider multiple switching states, vectors, and large lookup tables in the algorithm. The appropriate switching states for the synthesis of the reference vectors are determined using simple computations at each sampling time. Generality for different inverter topologies, online implementation, simplicity of the algorithm and computations, independency of the algorithm complexity from the number of switching states, and easy extension to overmodulation region are the advantages of the proposed method. Implementation of the LVPWM in different inverter topologies is studied. The validation of the proposed modulation strategy is carried out by simulation and experimental results.

A many-objective evolutionary algorithm with metric-based reference vector adjustment

To deal with the inconsistency of the distribution of reference vectors (RVs) and the shape of Pareto front in decomposition-based multiobjective evolutionary algorithms, many literatures proposed to adjust RVs in evolutionary process. However, most of the existing algorithms adjust RVs either in each generation or at a fixed frequency, not considering the evolving information of the population. To tackle this issue, we propose a many-objective evolutionary algorithm with metric-based reference vector adjustment named MBRA. To adjust RVs periodically and conditionally, we use the improvement rate of convergence degree of subproblems computed through d1 distance to reflect the whole convergence of the population. Only when the subproblems are considered convergent on the whole, are RVs allowed to be adjusted: delete the invalid RVs associated with no solutions and add new ones using the farthest solution in the subregion of the most crowded RVs for diversity. Besides, in mating selection and environmental selection, we adopt the sum of objectives and fitness-based sorting based on Iε+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$I_{\\varepsilon + }$$\\end{document} as the secondary principles to promote convergence, respectively. Validated through extensive experiments, MBRA is effective and competitive on many-objective optimization problems, especially for problems with irregular Pareto fronts.

A Flexible and Decoupled Space Vector Modulation Scheme With Carrier-Based Implementation for Multilevel Converters

This article proposes a novel space vector pulse width modulation (SVPWM) scheme for multilevel converters in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">abc</i> coordinate system, in which the reference vector is decomposed into an offset vector and a remainder vector that can be synthesized using two-level SVPWM. The switching state satisfying a specific relationship is first selected as the offset vector such that the common-mode components are eliminated and therefore the phase voltages are decoupled. A dynamic reference point mechanism establishes a one-to-one correspondence between all available vector combinations and one unique variable, i.e., the number of level shifts. This feature further facilitates a general approach to determining the optimum switching states to satisfy different control objectives, such as common-mode voltage rejection. Besides, phase decoupling enables the introduction of carrier-based modulation to simplify implementation, where the duty cycle of the zero vector can be flexibly adjusted while the non-zero vectors remain the same to generate an equivalent output. Consequently, two-level SVPWM-based method and carrier-based PWM are combined to exploit their respective strengths. The proposed scheme achieves overmodulation operation, and provides more flexibility, i.e., redundant switching states and adjustable duty cycles, to optimize switching patterns. Simulation and experimental results validate the proposed algorithm.