Value Of Vector Research Articles

Regularity of interval max-plus matrices

Max-plus algebra is an algebraic structure in which classical addition and multiplication are replaced by maximum and addition, respectively. We say that the columns of a real matrix A are strongly independent if the max-plus linear system A⊗x=b has a unique solution for at least one real vector b. A square matrix A with strongly independent columns is called strongly regular. The investigation of the properties of regularity is important for applications. The values of vector or matrix inputs in practice are usually not exact numbers and they can be rather considered as values in some intervals. The present paper studies three versions of the regularity of matrices and interval matrices, namely, strong regularity, von Neumann regularity and Gondran-Minoux regularity. For each concept of regularity we will present equivalent conditions which can be verified in polynomial time.

Inefficiency of Random Serial Dictatorship under incomplete information

We study the problem of allocating n objects to n agents without monetary transfers in a setting where each agent's preference over objects is private. We assume that each agent's value vector (values for n objects) is independently drawn from an exchangeable distribution and show that the celebrated Random Serial Dictatorship mechanism is welfare inferior to another allocation method, the Random Boston mechanism, when the number of agents and objects is large. Specifically, every type of every agent has a strictly higher interim expected utility under the Random Boston mechanism than under the Random Serial Dictatorship mechanism. Moreover, this strict dominance holds even at the limit.

Nonlinear optimal control for permanent magnet synchronous spherical motors

Nonlinear optimal control for permanent magnet synchronous spherical motors

Large deformation of hyperelastic modified Timoshenko–Ehrenfest beams under different types of loads

In this paper, a nonlinear finite element formulation for a hyperelastic, modified Timoshenko–Ehrenfest beam with geometrical and material nonlinearities is developed for the first time. A new five-parameter beam element is introduced. The parameters contain displacement, values of difference vector and a through-the-thickness scalar value. Moreover, a new procedure is employed to apply the moment to the beam. The constitutive formulation is derived for Saint Venant–Kirchhoff (SVK) as well as the compressible neo-Hookean (n-H) hyperelastic model. The beam is subjected to different loads such as dead load, point and distributed loads, and follower pressure. To demonstrate the applicability of the formulations, several examples are solved. The results reveal that this formulation can capture the previous results reported in the literature. Furthermore, a comparative study is done between two hyperelastic models. It is demonstrated that in the case of large rotation of the beam, both Saint Venant–Kirchhoff and neo-Hookean models show the same behavior. However, in case of large deformation with large strains of the beam, the Saint Venant–Kirchhoff model behaves stiffer than the neo-Hookean hyperelastic model.

Knowledge in the discovery of market opportunities

An opportunity is a relationship between a company's objectives, the resources at its disposal, and the situation in the environment, such that it favours the achievement of the objectives with the available resources. Opportunities arise in a changing environment. The dynamics of change causes the life cycle of an opportunity to shorten and companies that discover the opportunity and can exploit it gain a competitive advantage. Opportunity discovery means that opportunities exist but the company does not know about them. Opportunity discovery is the subject of several scientific disciplines. They are dealt with by strategic management using the methods of strategic analysis. They are also of interest to agility theory, which focuses on reacting quickly to market opportunities. In addition, entrepreneurship theory deals with them, which addresses the issue of cognitive traits in their discovery. New possibilities in discovering opportunities are created by artificial intelligence. The article aims to present the opportunity discovery model understood as the relationship between three vectors: the company's goals, its resources, and the external environment in which it operates. The discovery of the opportunity consists in finding such a value of the environment vector at which the desired value of the vector of goals is achieved at a given value of the resource vector. The article answers the question of how the knowledge necessary to find such a relationship between these vectors must be structured so that this relationship reflects the opportunity. It was hypothesized that this structure can be modelled using a network of cause-and-effect relationships leading to the determination of the relationship between demand and supply. This hypothesis was verified using artificial intelligence with the Monte Carlo algorithm, Markov chain, and the Metropolis-Hastings algorithm. The results obtained confirmed the validity of the model structuring the knowledge. The article presents the result of a pilot study. The results concerning knowledge structuring are useful for opportunity discovery both in the case of traditional approaches i.e. using the achievements of strategic management theory, corporate agility and entrepreneurship theory, and in the case of using artificial intelligence. In the first case, the target audience for these results is business executives while in the second case, the research community working on this problem and IT companies.

Adaptive controller design and disturbance attenuation for minimum phase multiple‐input multiple‐output linear systems with noisy output measurements and with measured disturbances

SummaryIn this paper, we present a systematic procedure for robust adaptive control design for minimum phase uncertain multiple‐input multiple‐output linear systems that are right invertible and can be dynamically extended to a linear system with vector relative degree using a dynamic compensator that is known. For this class of systems, it is always possible to dynamically extend them, and/or integrate a select set of output channels, and/or padding dummy state variables to arrive at a system model that admits uniform vector relative degree and uniform observability indices that is further minimum phase according to Reference 13. We assume that the uniform vector relative degree is known and an upper bound for the uniform observability indices is known. We also assume that the unknown parameter vector lies in a convex compact set such that the high frequency gain matrix remains invertible for any parameter vector value in the set. These are the assumptions that allow for a successful design of a robust adaptive controller. A numerical example is included to fully illustrate the controller design and the effectiveness of the controller.

Study of the Effect of Inverse Magnetostriction in Ferromagnet/Ferroelectric Heterostructures Using Ab Initio Calculations

Fe/BaTiO3, Fe/SrTiO3, Co/BaTiO3, and Co/SrTiO3 heterostructures, which exhibit magnetoelectric effect, have been investigated. It is shown that the magnetic properties of thin ferromagnetic films can be controlled using an external electric field. The structural, electronic, and magnetic properties of the heterostructures have been investigated applying ab initio calculation methods. It is shown that, using the inverse piezoelectric effect, one can reduce the absolute value of the ferromagnet magnetization vector. This approach may be a basis for controlling the properties of one of the ferromagnetic layers of a superconducting spin valve and, as a consequence, the superconducting properties of the valve.

PEAT DEPOSITS AS A NEW SOURCE OF PALEOMAGNETIC RECORDS IN THE HOLOCENE AS EXEMPLIFIED BY THE ETRUSSIA EXCURSION

The paper presents the results of microprobe, paleomagnetic and rock magnetic studies on peats from the Tyapka peat section (Khabarovsk region) formed 4 thousand years. Alternating field demagnetization of peats yielded a horizon of 57–109 cm (1833–3083 cal. BP) with negative inclination values of the magnetization vector, which corresponds to the geomagnetic Etrussia excursion. Obtaining relative paleointensity estimates for peats was pioneered, which are consistent with the literature archeomagnetic data for the western part of Eurasia. It was found that the Etrussia excursion took place against the background of increased values of the Earth’s magnetic field, and its duration was ~ 1200 years.

On torsion axial vector and gravitational energy in Lewis–Papapetrou space-time in the theory of teleparallel gravity

The teleparallel version of Lewis–Papapetrou space-time is investigated. For this space-time, the true discussion of the geometric and physical properties is given. We show that the value of the space-like torsion-axial vector does not depend on the choice of tetrad field. Consequently, the spin procession of the Dirac particle and the corresponding Hamiltonian have physical meaning. The energy and momentum densities for such space-time are calculated. We show that when choosing two sets of tetrad fields, the dependence and independence of the aforementioned quantities on the tetrad field depend on the Lorentz factor, which links the two sets of tetrad fields.

Bulk plasmon-limited mobility in semiconductors: from bulk to nanowires

Analytical expressions are obtained for the low-field mobility in semiconductors for scattering of three-dimensional (3D), two-dimensional (2D), and one-dimensional (1D) charged carriers by bulk plasmons. The consideration is based on the quantum kinetic equation and model distribution function in form of a shifted Fermi distribution and includes calculations of the dielectric function of 3D, 2D and 1D carriers in the random phase approximation. The resulting analytical expressions give dependences of the plasmon limited mobility on the dimensionality of charge carrier system, their density, effective mass, temperature and confining dimensions. The plasmon limited mobility decreases as the dimensionality of the electron gas D decreases. The physical reason for this is an increase in the absolute value of the cutoff vector with a decrease in D. Comparison of our calculations with known experimental data shows that relative contribution of the electron–plasmon scattering to total mobility reaches a maximum in the temperature range 10–100 K and can be a few percent in bulk crystals, ten of percent in quantum wells, and is close to the experimental values in nanowires. A noticeable effect of the scattering 3D, 2D and 1D electrons by bulk plasmons on mobility is expected in semiconductors with a sufficiently high mobility of more than 105 cm2 V−1 s.

Comparative Analysis and Empirical Study of Prefabrication Rate Calculation Methods for Prefabricated Buildings in Various Provinces and Cities in China

As an important part of the prefabricated building standard system, the evaluation standards for prefabricated buildings play an important guiding role in the construction and development of prefabricated buildings. However, at present, the national evaluation standards for prefabricated buildings are highly extensive and general, and some of their contents are not well considered, while local standards based on the national standard have been formulated in accordance with the actual situation in their respective regions, which makes the evaluation of prefabricated buildings in various regions more confusing, which also causes differences when calculating the prefabrication rate, restricting the development of prefabricated buildings. In order to promote the construction of a complete evaluation standard system for prefabricated buildings in China, this paper combs through the methods used for the calculation of the prefabrication rate of prefabricated buildings in 28 provincial-level administrative regions in China, conducts a comparative analysis and empirical research on the calculation of the prefabrication rate under different standards in four dimensions, and applies t-tests and simulated annealing algorithms to optimize the projection pursuit model to analyze the differences in the total scores of the prefabrication rate under different standards, as well as the key factors that affect them. The study shows that (1) the same building may have different prefabrication rates under different standards for prefabricated buildings, and the way of calculating the prefabrication rate and the scoring criteria of the evaluation items will result in significant differences in the calculated prefabrication rate. (2) Depending on the value of the optimal projection direction vector, it was found that among the evaluation criteria, the enclosure wall and the internal partition wall have the greatest influence on the total score value, while the main structure has the least influence on the total score value. (3) There are similarities as well as differences in the evaluation criteria of prefabricated buildings in each province. On this basis, by analyzing the reasons for these differences, corresponding suggestions are made for governments needing to formulate or revise local standards.

Monopulse Parameter Estimation for FDA-MIMO Radar under Mainlobe Deception Jamming

Multiple input multiple output with frequency diversity array (FDA-MIMO) radar has unique advantages in mainlobe deception jamming suppression and target location. However, if the training sample contains the target signal, it will lead to poor jamming suppression performance and large target measurement error. To deal with the problem, a method of coarse target location in the time domain is proposed based on the cumulative sampling analysis. Taking full advantages of the strongest correlation characteristic between the expected steering vector and the true target, the feature vector and feature value corresponding to the true target are found after feature decomposition. The time domain location of the target is roughly estimated during the cumulative sampling analysis from near to far. Then, a pure jamming training sample can be obtained by avoiding the location. Noise subspace projection algorithm is used to measure the angle and range of the target while suppressing mainlobe jamming. The simulation results show that the proposed method can roughly estimate the target location in the time domain when the mainlobe deception jamming completely covers the target. Compared with conventional methods, the performance of jamming suppression and target localization error are closer to the performance of ideal sampling.

Novel optimistic and pessimistic family of OWA operator with constant orness

In this paper, we present a novel representative of the existing family of ordered weighted aggregation (OWA) operators with constant orness (optimism/pessimism level). In other words, orness value of weight vectors generated using the proposed operator is independent of the total number of arguments. The proposed OWA operator is the operator that is based on the beta function. Two different types of operators have been proposed with respect to the level of optimism (“or-like”) and pessimism (“and-like”) each. In addition to that the proposed operators discussed here are bi-parametric and real-valued in nature in terms of their parameters. Hence, for a given orness value (optimism or pessimism), it provides an infinite number of weight vectors as the proposed operator's orness depends on the value of one of its parameters. These properties lead to lots of flexibility for the Decision-Maker (DM). Its comparison and advantages over other existing members of the OWA family with constant orness such as S-OWA operators have been elaborated. Also, the maximum Rényi entropy of the OWA (MRE-OWA) operator and the maximum Rényi entropy of the proposed OWA operator are compared and evaluated for a specified orness level. Several other important properties and applications of the family of the proposed operators have also been analyzed in detail.

Lithium battery model parameter identification based on the GA-LM algorithm

ABSTRACT The accuracy of lithium battery model parameters is the key to lithium battery state estimation. The offline parameter identification method for lithium batteries requires the nonlinear fitting of the voltage rebound curve of the hybrid pulse discharge experiment. The genetic algorithm has a strong global search ability, but it is easy to fall into local solutions. The Levenberg-Marquardt algorithm has a strong local optimization ability, but the algorithm cannot converge when the prior value is unknown. Given the above problems, this paper proposes a parameter identification method based on the Genetic-Levenberg-Marquardt (GA-LM) algorithm, which takes the sum of the squared model voltage errors as the objective function, and predicts the initial value of the parameter vector through the GA, providing the LM algorithm with prior value. In the case of unknown prior values, the GA-LM algorithm can achieve high-precision nonlinear optimization. Finally, the simulation test under the conditions of constant current discharge and hybrid pulse power discharge. The mean absolute error, mean relative error, and root mean square error of the model voltage in the two working conditions are 7.23 mV, 0.20%, 9.61 mV, and 13.37 mV, 0.37%, 15.44 mV, which shows that the algorithm has high accuracy.

Emergent information dynamics in many-body interconnected systems.

The information implicitly represented in the state of physical systems allows for their analysis using analytical techniques from statistical mechanics and information theory. This approach has been successfully applied to complex networks, including biophysical systems such as virus-host protein-protein interactions and whole-brain models in health and disease, drawing inspiration from quantum statistical physics. Here we propose a general mathematical framework for modeling information dynamics on complex networks, where the internal node states are vector valued, allowing each node to carry multiple types of information. This setup is relevant for various biophysical and sociotechnological models of complex systems, ranging from viral dynamics on networks to models of opinion dynamics and social contagion. Instead of focusing on node-node interactions, we shift our attention to the flow of information between network configurations. We uncover fundamental differences between widely used spin models on networks, such as voter and kinetic dynamics, which cannot be detected through classical node-based analysis. We illustrate the mathematical framework further through an exemplary application to epidemic spreading on a low-dimensional network. Our model provides an opportunity to adapt powerful analytical methods from quantum many-body systems to study the interplay between structure and dynamics in interconnected systems.

Streaming Algorithms for Non-Submodular Functions Maximization with d-Knapsack Constraint on the Integer Lattice

We study the problem of maximizing a monotone non-submodular function under a [Formula: see text]-knapsack constraint on the integer lattice. We propose three streaming algorithms to approach this problem. We first design a two-pass [Formula: see text]-approximate algorithm with total memory complexity [Formula: see text], and total query complexity for each element [Formula: see text]. The algorithm relies on a binary search technique to determine the amount of the current elements to be added into the output solution. It also requires to have a good estimate of the optimal value, we use the maximum value of the unit standard vector which can be obtained by reading a round of data to construct a guess set of the optimal value. Then, we modify our algorithm to avoid a repetitive reading of data by dynamically update the maximum value of the unit vector along with the coming elements, and obtain a one-pass streaming algorithm with same approximate ratio. Moreover, we design an improved StreamingKnapsack algorithm to reduce the memory complexity to [Formula: see text].

Normal calculation of artillery cradle before the robot arm assisted ultrasonic phased array automatic detection

This study is interested in the normal calculation of artillery cradle. When the beam axis of ultrasonic phased array probe cannot detect the workpiece surface vertically, the detection result is usually inaccurate. Thus, in order to determine the normal of the surface to be detected, a method based on visual point cloud to calculate the normal of artillery cradle is proposed in this paper. The proposed method solves two key problems. One is to sparse the point cloud while preserving the tiny information on the surface. A mesh division method along the uv direction is proposed. The disordered point cloud data in 3D space are connected into regular polygon meshes. While establishing the local relationship of point cloud data, it also maintains the overall integrity of point cloud data. The other is to calculate the normal vector of the point cloud. The local plane fitting method based on the least square method calculates the normal at a point of the point cloud. At this time, the calculated normal are nondirective. When there is no abrupt curvature change near the calculated point cloud, the direction of the normal is determined based on the same slope at two adjacent points. The calculated point cloud normal vector is compared with the actual normal vector of the mathematical model. The maximum deviation between the theoretical value and the actual value of the normal vector is 0.465°, and the average deviation is 0.302°. The experimental results show that the proposed method can be used to detect the actual normal of artillery cradle.

Investigation of SMFL monitoring technique for evaluating the load-bearing capacity of RC bridges

This study investigates the use of Self-Magnetic Flux Leakage (SMFL) monitoring to assess the load-bearing capacity of RC bridges under vehicular loading. Cyclic tensile experiments of HRB400 rebars were conducted and the SMFL intensity was monitored. The integrated vector value of SMFL intensity BC was introduced to analyze the characteristics of the rebar force-magnetic coupling effect. Moreover, SMFL monitoring experiments were conducted on Gaojia Garden Bridge to evaluate the engineering applicability of the method. The results show that the mechanical indexes have a positive correlation with SMFL intensity during rebar cyclic loading. The increase in the cycle number drives the stability of the force-magnetic relationship. The rebar proportional ultimate strength σpl is an important factor affecting the BC variation. When the stress exceeds σpl, the rising process of BC curve retreats. The rebar stress range can be accurately determined based on the change rate of SMFL intensity. In the testing of Gaojia Garden Bridge, vehicles loading and unloading contribute to the phased increase and monotonic decrease of BC curves, respectively. Comparing the FEM simulation results, the linear characteristic relationships between BC and |με| as well as σ are obtained, which verifies the engineering applicability and accuracy of SMFL monitoring method. This research provides a new approach for the non-destructive quantitative detection and test application of RC bridge load-bearing capacity.

Comparison between wavefront-derived refraction and auto-refraction

BackgroundThis study aimed to compare objective refractive errors and keratometry measurements obtained using the Nidek OPD-Scan II aberrometer/topographer and Topcon KR 8900 autorefractokeratometer. MethodsThe right eye medical records of 176 patients aged 18-35 years who were admitted to our clinic as refractive surgery candidates were tested for refractive status and keratometry measurements with a Nidek OPD-Scan II aberrometer/topographer and a standard table-top autorefractokeratometer (Topcon KR 8900) before and after the induction of cycloplegia. Patients who had undergone any eye surgery and had hereditary, ectatic, or acquired corneal pathology were excluded. Refractive data were compared as spheres, cylinders, spherical equivalents, and power vectors before and after the induction of cycloplegia. Flat and steep keratometry (K1-K2) readings were recorded in diopters (D) and axis degrees, respectively, for each eye. ResultsThe spherical, cylindrical, spherical equivalence, J0-J45 vector values and K1-K2 readings (D, axis) between the two devices were statistically significant before and after the induction of cycloplegia (p<0.05). Bland-Altman analysis identified mean differences (95%CI of limits of agreement) of 0.77 (-0,57 to 2,11) in sphere, 0.74 (-0,54 to 2,01) in spherical equivalent, -0,07 (-0,41 to 0,26) in J0 vector, 0,06 (-0,31 to 0,43) in J45 vector, -0,16 (-0,66 to 0,33) in K1, -0,23 (-0,79 to 0,33) in K2 values before induction of cycloplegia. ConclusionThe refractive and keratometry results of the Nidek OPD Scan II system and Topcon KR 8900 standard table-top autorefractokeratometer are not interchangeable in healthy adult population before and after induction of cycloplegia.

Identifying top influential spreaders based on the influence weight of layers in multiplex networks

Detecting influential nodes in multiplex networks is a complex task due to the presence of multiple layers. In this study, we propose a method for identifying important layers with strong spreaders based on several key parameters. These include a layer's position within a well-connected neighborhood, the number of active edges and critical nodes, the ratio of active nodes to all possible connections, and the intersection of intra-layer communication compared to other layers. To accomplish this, we have formulated a layer weighting method which takes into account these parameters, and developed an algorithm for mapping and computing the rank of nodes based on their spreading capability within multiplex networks. The resulting layer weighting is then used to map and compress centrality vector values to a scalar value, allowing us to calculate node centrality in multiplex networks via a coupled set of equations. Moreover, our method combines the important layer parameters to compute the influence of nodes from different layers. Our experimental results, conducted on both synthetic and real-world networks, demonstrate that the proposed approach significantly outperforms existing methods in detecting high influential spreaders. These findings highlight the importance of using a suitable layer weighting measure for identifying potential spreaders in multiplex networks.