Value Of Vector Research Articles

Machine-Made Synthesis of Stabilization System by Modified Cartesian Genetic Programming

A numerical solution of the problem of the general synthesis of a stabilization system by a symbolic regression method is considered. The goal is to automatically find a feedback control function using a computer so that the control object can reach a given terminal position from anywhere in a given region of the initial conditions with an optimal value of the quality criterion. Usually, the control synthesis problem is solved analytically or technically taking into account the specific properties of the mathematical model. We suppose that modern numerical approaches of symbolic regression can be applied to find a solution without reference to specific model equations. It is proposed to use the numerical method of Cartesian genetic programming (CGP). It was developed for automatic writing of programs but has never been used to solve the synthesis problem. In the present work, the method was modified with the principle of small variations in order to reduce the search area and increase the rate of convergence. To apply the general principle of small variations to CGP, we developed special types of variations and coding. The modified CGP searches for the mathematical expression of the feedback control function in the form of a code and, at the same time, the optimal value of the parametric vector which is also a new feature-simultaneous tuning of the parameters inside the search process. This approach enables working with objects and functions of any type, which is not always possible with analytical methods. The need to use the received solution on the onboard processor of the control object imposes certain restrictions on the used basic set of elementary functions. This article proposes the theoretical foundations of the study of these functions, and the concept of the space of machine-made functions is introduced. The capabilities of the approach are demonstrated on the numerical solution of the control system synthesis problems for a mobile robot and a Duffing model.

A Spatial Biarc Method for Inverse Kinematics and Configuration Planning of Concentric Cable-Driven Manipulators

Superior dexterity and extreme flexibility are typical advantages for concentric cable-driven manipulators working in confined spaces. However, its inverse kinematics and configuration planning are very complicated. In this article, we propose a spatial biarc method for the above problem. The distinguishing feature of this method is that input parameters are two positions and two direction vectors in three-dimensional (3-D) space, and the output is a reasonable spatial biarc for controlling a concentric cable-driven manipulator in 3-D space. This method has the following three advantages. First, the positions and direction vectors of the base and inner distal tip are considered simultaneously. In addition, the length and ratio of the overlapped section and separated section can be adjusted by changing the length of the direction vectors. Furthermore, by judging the angular value of the direction vectors, one can predetermine whether the spatial configuration of the entire arm is C- or S-shaped. The proposed method realizes the parameterization of a concentric cable-driven manipulator, which makes it convenient to intuitively control the manipulator to achieve interference-free motion trajectory planning in confined spaces. Finally, trajectory tracking inspections are simulated and experimentally executed. It can be seen from results that the proposed spatial biarc method can provide reasonable solutions for concentric cable-driven manipulators. The method is especially favorable in terms of 3-D-pose-determination problem and trajectory-planning problem. It can also be applied to other manipulators with similar configurations. Without loss of generality, when the given points and direction vectors are coplanar, the proposed spatial biarc method can be transformed to a planar biarc method.

Transient Storage Model Parameter Optimization Using the Simulated Annealing Method

AbstractHyporheic exchange in streams is critical to ecosystem functions such as nutrient cycling along river corridors, especially for slowly moving or small stream systems. The transient storage model (TSM) has been widely used for modeling of hyporheic exchange. TSM calibration, for hyporheic exchange, is typically used to estimate four parameters, including the mass exchange rate coefficient, the dispersion coefficient, stream cross‐sectional area, and hyporheic zone cross‐sectional area. Prior studies have raised concerns regarding the non‐uniqueness of the inverse problem for the TSM, that is, the occurrence of different parameter vectors resulting in TSM solution that reproduces the observed in‐stream tracer break through curve (BTC) with the same error. This leads to practical non‐identifiability in determining the unknown parameter vector values even when global‐optimal values exist, and the parameter optimization becomes practically non‐unique. To address this problem, we applied the simulated annealing method to calibrate the TSM to BTCs, because it is less susceptible to local minima‐induced non‐identifiability. A hypothetical (or synthetic) tracer test data set with known parameters was developed to demonstrate the capability of the simulated annealing method to find the global minimum parameter vector, and it identified the “hypothetically‐true” global minimum parameter vector even with input data that were modified with up to 10% noise without increasing the number of iterations required for convergence. The simulated annealing TSM was then calibrated using two in‐stream tracer tests conducted in East Fork Poplar Creek, Tennessee. Simulated annealing was determined to be appropriate for quantifying the TSM parameter vector because of its search capability for the global minimum parameter vector.

Метод дихотомической кластеризации и оптимальный портфель

The problem of optimal portfolio finding is considered in the paper. Many papers are devoted to the solving of this problem in various its formulations, that is why the problem is relevant. For the solving of the problem the methods of robust optimization and machine learning are used, namely, the splitting of the sample of the random asset returns into clusters and subsequent construction of an ellipsoid in each cluster. The method of maximal likelihood is used for dividing on two clusters, the method of dichotomous clustering is used for dividing on several clusters. The sample average and the sample covariance matrix are used for the constructing of the ellipsoid; the radius is calculated based on the assumption that the sample elements have a normal distribution. The example of calculating the optimal portfolio is given. It uses the real values of the return vectors. In this case part of the sample is used to calculate the sample means and the sample covariance matrices of the clusters, the rest part of the sample is used for verification of the portfolio. The tables show the dependence of the optimal portfolio return on the model parameter and on the number of clusters (ellipsoids). The comparing of results is considered; there are cases in which there is an increase in the income of the investor.

Gradient field divergence-based small target detection in infrared images

Infrared small target detection in complex cloud backgrounds has long been a research challenge. A novel robust target detection method based on the divergence of gradient field is proposed to enhance the target and suppress the complex background synchronously. The negative gradient field of the target intensity (NIG field) matches with characteristics of the positive source. The cloud cluster, on the other hand, lacks this feature. First, the NIG field is calculated based on the target’s property from the original image. The divergence values of NIG field are then calculated to produce a defined divergence map (D map), which highlights the target regions while suppressing the clutter regions. Meanwhile, a local vectors angle measure (LVAM) operator of the NIG field is designed to measure the angle distribution of 8-neighbour vectors and eliminate false target areas. Then, the defined local angle map (LA map) is obtained by measuring the local angle value of 8-neighbour vectors for each patch of NIG field. In addition, the divergence-local angle map (D-LA map) is obtained as the Hadamard product of the D map and LA map. Finally, we can easily obtain the target via a constant false alarm ratio based on the D-LA map. The performance evaluation results of real image sequences show that the proposed method is satisfactory for clutter suppression and target detection. Moreover, the results from comparative experiments show that the proposed method outperforms conventional methods in terms of detection accuracy and false alarm rate.

Substantiation of the main design parameters of the separation chamber of the pneumatic separator using various methods for calculating particle trajectories in the pneumoseparating channel

The article presents a study of the influence of methods for calculating particle trajectories in the pneumatic separation channel (PSC) on the design parameters of the separation chamber (length and height of the exit window), completed in 2013-2021. The field of air flow velocities and trajectories of spring wheat seed material components in the separation chamber of fractioning pneumatic seed separator was constructed. The initial data for the calculation were the information obtained earlier when calculating the trajectories of particles in the PSC by various methods. The location at the outlet of the channel, the value and direction of the particle velocity vectors were determined by computer modelling using the Solidworks Flow Simulation software package and two experimental and theoretical methods. They took into account the real velocity field of the air flow with and without grain load. The obtained values of the design parameters are compared with the results of experimental studies of the separation chamber. It should be noted that the methods of calculating the trajectories of grain impurity particles have a significant impact on the length of the separation chamber and the height of the exit window. The maximum length of the separation chamber of 0.75 m will be required for computer modelling of the velocity field in the PSC. Much smaller values of 0.50 m and 0.45 m will be required when calculating the trajectories of particles in the PSC using the velocity field obtained by experimental and theoretical methods. During the experimental study of the separation chamber during the cleaning of wheat seeds, the optimal length of the separation chamber was determined to be 0.55 m with an exit window height of 0.30 m. Thus, the values of the design parameters of the separation chamber obtained by experimental and theoretical methods are the closest to experimental data, which makes it possible to recommend them for use at the design stage of pneumatic separators.

Fractal adaptive weight synthesized-local directional pattern-based image classification using enhanced tree seed algorithm.

Coral reefs are one of the most prominent marine ecosystems on the Earth; they are threatened due to various factors, including growing anthropogenic impacts and the effects of global change. Hence, the automatic classification of coral species is significant for tracking and detecting threatened and susceptible coral species. This paper proposes a new feature descriptor known as fractal adaptive weight synthesized-local directional pattern (FAWS-LDP) method to classify coral images using enhanced tree seed algorithm with extreme learning machine (ETSA-ELM) technique. The proposed feature descriptor (FAWS-LDP) inherits the advantage of both fractal pixel intensity information and local directional characteristics by indexing both feature vector values. Finally, the extracted features are imported to the extreme learning machine (ELM) network for classification. The ELM classifier is a single hidden layer feed-forward neural network with a faster learning speed and produces good generalization performance. The random selection of input weight and biases of the ELM classifier produces non-optimal or unnecessary input biases and weights to the network. Hence, to fine-tune the parameters of the ELM classifier, an enhanced tree seed algorithm (ETSA) is proposed. The proposed ETSA is a new learning technique to overcome the drawbacks like local optima and a lower coverage rate. The classification performance of ELM employing the ETSA optimizer is compared to the original TSA and other well-known metaheuristic algorithm (MHA) trainers, such as genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC), utilizing model performance measures like specificity, sensitivity, and classification accuracy. The evaluation of coral classification datasets reveals that the proposed ETSA-ELM produces consistently superior performance to existing methods. Finally, the proposed feature descriptor technique is statistically analyzed using a non-parametric Friedman test to demonstrate the efficiency.

Contextualizer: Connecting the Dots of Context with Second-Order Attention

Composing the representation of a sentence from the tokens that it comprises is difficult, because such a representation needs to account for how the words present relate to each other. The Transformer architecture does this by iteratively changing token representations with respect to one another. This has the drawback of requiring computation that grows quadratically with respect to the number of tokens. Furthermore, the scalar attention mechanism used by Transformers requires multiple sets of parameters to operate over different features. The present paper proposes a lighter algorithm for sentence representation with complexity linear in sequence length. This algorithm begins with a presumably erroneous value of a context vector and adjusts this value with respect to the tokens at hand. In order to achieve this, representations of words are built combining their symbolic embedding with a positional encoding into single vectors. The algorithm then iteratively weighs and aggregates these vectors using a second-order attention mechanism, which allows different feature pairs to interact with each other separately. Our models report strong results in several well-known text classification tasks.

A Residual LSTM and Seq2Seq Neural Network Based on GPT for Chinese Rice-Related Question and Answer System

Rice has a wide planting area as one of the essential food crops in China. The problem of diseases and pests in rice production has always been one of the main factors affecting its quality and yield. It is essential to provide treatment methods and means for rice diseases and pests quickly and accurately in the production process. Therefore, we used the rice question-and-answer (Q&A) community as an example. This paper aimed at the critical technical problems faced by the agricultural Q&A community: the accuracy of the existing agricultural Q&A model is low, which is challenging to meet users’ requirements to obtain answers in real-time in the production process. A network based on Attention-ResLSTM-seq2seq was used to realize the construction of the rice question and answer model. Firstly, the text presentation of rice question-and-answer pairs was obtained using the GPT pre-training model based on a 12-layer transformer. Then, ResLSTM(Residual Long Short-Term Memory) was used to extract text features in the encoder and decoder, and the output project matrix and output gate of LSTM were used to control the spatial information flow. When the network contacts the optimal state, the network only retains the constant mapping value of the input vector, which effectually reduces the network parameters and increases the network performance. Next, the attention mechanism was connected between the encoder and the decoder, which can effectually strengthen the weight of the keyword feature information of the question. The results showed that the BLEU and ROUGE of the Attention-ResLSTM-Seq2seq model reached the highest scores, 35.3% and 37.8%, compared with the other six rice-related generative question answering models.

A Statistical Interpretation of Spectral Embedding: The Generalised Random Dot Product Graph

AbstractSpectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network model known as the random dot product graph, to allow interpretation of those vector representations as latent position estimates. The generalisation is needed to model heterophilic connectivity (e.g. ‘opposites attract’) and to cope with negative eigenvalues more generally. We show that, whether the adjacency or normalised Laplacian matrix is used, spectral embedding produces uniformly consistent latent position estimates with asymptotically Gaussian error (up to identifiability). The standard and mixed membership stochastic block models are special cases in which the latent positions take only K distinct vector values, representing communities, or live in the (K − 1)-simplex with those vertices respectively. Under the stochastic block model, our theory suggests spectral clustering using a Gaussian mixture model (rather than K-means) and, under mixed membership, fitting the minimum volume enclosing simplex, existing recommendations previously only supported under non-negative-definite assumptions. Empirical improvements in link prediction (over the random dot product graph), and the potential to uncover richer latent structure (than posited under the standard or mixed membership stochastic block models) are demonstrated in a cyber-security example.

Molecular Dynamics Investigation of Spreading Performance of Physiological Saline on Surface.

Physiological saline is an indispensable element for maintaining the functions of life. The spreading performance of physiological saline droplets on the surface of graphene under different NaCl concentrations and electric field intensities was studied in the present work. The results show that the increase in NaCl concentration reduces the displacement vector value of molecules in droplets. In addition, NaCl is easy to aggregate on the surface of graphene. The increase in NaCl concentration makes it more difficult for droplets to penetrate the surface of graphene, and the penetration angle of droplets increases with the rise in NaCl concentration. With the increase in electric field intensity, the wetting effect of droplets is more obvious. The greater the electric field intensity is, the smaller the penetration angle is, which is mainly due to the polarity of water molecules. This study has reference significance for the study of body fluid volatilization on the human surface.

Risk Warning for Aircraft Bleed Air System with Multivariate State Estimation Technique

The engine bleed air system (BAS) is one of the important systems for civil aircraft, and its operating state directly affects the operational safety of the aircraft. The effective risk warning of the BAS is critical to improving aircraft safety and operators’ profits; thus, a multivariate state estimation technique with the dynamic process memory matrix is proposed to warn the failure risk of the bleed air system. First, to obtain the optimal estimation value of the observation vector, the memory matrix is formed by searching for the first vectors that are similar to each input observation vector from the healthy data pool that can cover the common working space. Then, the similarity function is defined to quantitatively measure the deviation between the observed vector and the estimated vector, and the amount of risk information contained in each variable is quantified by the analytic hierarchy process. Finally, the dynamic threshold different from the traditional engineering experience threshold is designed, based on the idea of interval estimation. The developed approach is validated on an Airbus A320-series aircraft with quick access recorder data for one year. The results show that the proposed strategy can provide an effective risk warning for the abnormal state of the BAS before a failure occurs.

Hand Image Based Skin Disease Identification Using Machine Learning and Deep Learning Algorithms

The skin disease is most commonly affected to all peoples. It is a big challenging process in skin image identification because of the combined texture shape with the color variations. This paper presented to identify a skin using texture features (Local Binary Pattern and Gray Level Co-occurance Matrix) and color features with various learning models. The proposed work technique for identification of skin disease has two stages: either extracted feature values and identification. During the first stage, texture features and color feature vector values are taken from the images in a dataset of hand skin database. During the second stage, the extracted feature vectors are trained by the various kinds of Machine Learning models. This hand skin image dataset also applied into Deep Learning model for identification. The performance measures of the system is evaluated using accuracy measure. The Convolutional Neural Network (CNN) model gives accuracy of 87.5% when compared to Machine Learning models.

Mapping the Potential of High-Reflective Roof Coverings in Residential Buildings in Italy

The current warming trend of the earth’s climate is making it increasingly necessary to focus on solutions to improve indoor comfort while reducing energy demand for buildings during the hot season. The use of high-reflective products on the building envelope, and particularly on the roof, has been suggested as a potential solution. The overall impact of using high-reflective products on roofs largely depends on the local climatic conditions and on the characteristics of the building, making it difficult to determine whether the use of these products is beneficial without the use of detailed modelling approaches. In this context, this study aims to propose a method to provide indications for designers and policymakers to make early decisions on the potential benefits of the use of high-reflective products on residential buildings, based on simple and readily-available climatic data of the location. The approach, based on the use of state-of-the-art simulation software, allows researchers to obtain a multi-linear regression of the effect of high-reflective products on roofs as a function of the heating degree days and of the local value of the climate vector. The approach is applied to the case of the Italian territory, where a block-of-flats is selected as the reference building, with features common to a significant part of the national residential stock. The proposed approach allows for the provision of a quantitative estimate of the expected impact of high-reflective roof coverings on the energy performance of residential buildings in all Italian municipalities. The analysis shows that high-reflective products on roofs are likely to reduce the building annual energy demand for heating and cooling in 9% to 25% of Italian municipalities, depending on the building energy renovation state. The proposed approach, whose results are expressed also by means of thematic maps, can be replicated to other geographic areas and can be extended to different energy saving technologies.

The method of limitation of dynamic loads of nonlinear electromechanical systems under state vector robust control

Aim. Development of the method of limitation of dynamic loads of nonlinear electromechanical systems under state vector robust control. Methodology. Limitation of dynamic loads of nonlinear electromechanical systems is carried out using the minimum selector of choosing the minimum value of the control vector from formed with the help of local controllers and with the vector of maximum control values. Calculation of the gain coefficients of nonlinear robust controllers and observers are based on solutions of the Hamilton–Jacob–Isaacs equations. Results. The results of computer simulation of transitional processes of main roll drives of the rolling mill 950 of the Zaporozhye plant «Dnіprospetsstal» with limitation of dynamic loads are given. Originality. For the first time the method of limitation of dynamic loads of nonlinear electromechanical systems under state vector robust control based on minimum selector and nonlinear robust control of state variables which is needed limitation is developed. Practical value. Examples of transitional processes of main roll drives of the rolling mill 950 of the Zaporozhye plant «Dnіprospetsstal» with limitation of dynamic loads are given.

A New Scheme for Spreading & De-spreading in the Direct Sequence Spread Spectrum Mechanism

Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping Spread Spectrum (FHSS) techniques are widely used to implement code-division multiple access (CDMA) in wireless communication systems. Both DSSS and FHSS systems help reducing the effects of interference on the transmitted information making it robust against channel impairments. DSSS uses a signal bandwidth that is much broader than the information signal bandwidth. Traditionally, the wide band signal is generated by multiplying the narrowband information signal with a binary code, often designated as a spreading code, to generate the wideband signal that is transmitted. The original information signal can be recreated at the receiver by multiplying the received wideband signal by the same binary code (now designated as a de-spreading code) used to generate the wideband transmitted signal. To extract the original information signal, the spreading and de-spreading codes must be in synchronism at the receiver and amplitude match with each other. A new modification for the direct sequence spread spectrum is proposed in this paper. The mechanism introduced in this approach implicates generating the wideband signal by circularly shifting the spreading code (PN) by n places, where n represents the value of the current byte of information signal. The yielded signal is modulated using BPSK modulator before transmitting it. The original information signal is extracted at the receiver by correlating the received signal (which is actually the original spread sequence circularly shifted by n places) with a locally generated replica of the spreading code. The position of the maximum value of the cross-correlation vector represents the value of the information signal byte. The proposed configuration has been implemented using Simulink simulator and the obtained results show that its performance is identical with the conventional DSSS.

Quantile-Based Iterative Methods for Corrupted Systems of Linear Equations

Often in applications ranging from medical imaging and sensor networks to error correction and data science (and beyond), one needs to solve large-scale linear systems in which a fraction of the measurements have been corrupted. We consider solving such large-scale systems of linear equations $\mathbf{A}\mathbf{x}=\mathbf{b}$ that are inconsistent due to corruptions in the measurement vector $\mathbf{b}$. We develop several variants of iterative methods that converge to the solution of the uncorrupted system of equations, even in the presence of large corruptions. These methods make use of a quantile of the absolute values of the residual vector in determining the iterate update. We present both theoretical and empirical results that demonstrate the promise of these iterative approaches.

Comparative analysis of the lattice Boltzmann method and the finite difference technique of thermal convection in closed domains with heaters

PurposeThe purpose of this paper is to investigate numerically thermal convection heat transfer in closed square and cubical cavities with local energy sources of various geometric shapes.Design/methodology/approachThe analyzed regions are square and cubical cavities with two isothermally cold opposite vertical walls, whereas other walls are adiabatic. A local energy element of rectangular, trapezoidal or triangular shape is placed on the lower surface of the cabinet. The lattice Boltzmann technique has been used as the main method for the problem solution in two-dimensional (2D) and three-dimensional (3D) formulations, whereas the finite difference technique with non-primitive parameters such as stream function and vorticity has been also used.FindingsThe velocity and temperature fields for a huge range of Rayleigh number 104–106, as well as for various geometry shapes of the heater have been studied. A comparative analysis of the results obtained on the basis of two numerical techniques for 2D and 3D formulations has been performed. The dependences of the energy transfer strength in the region on the shape of energy source and Rayleigh number have been established. It has been revealed that the triangular shape of the energy source corresponds to the maximum values of the velocity vector and temperature within the cavity, and the rectangular shape corresponds to the minimum values of these mentioned variables. With the growth of the Rayleigh number, the difference in the values of these mentioned variables for rectangular and triangular shapes of heaters also increases.Originality/valueThe originality of this work is to scrutinize the lattice Boltzmann method and finite difference method for the problem of natural convection in 2D and 3D closed chambers with a local heated element.

Prediction of bone microstructures degradation during osteoporosis with fuzzy cellular automata algorithm

A novel fuzzy cellular automata is proposed to simulate bone degradation during osteoporosis. The initial three-dimensional (3D) bone microstructure is obtained from computed tomography (CT) images. Cellular automata algorithm is implemented to the 3D lattice and a Sugeno Fuzzy inference system is designed with nine sets of fuzzy rules to simulate the degradation process. A distance vector parameter is defined to describe the number of neighborhood cells that each cell can have a connection with. It is shown that by increasing the value of this distance vector, the results converge toward a quasi-constant degraded microstructure. The obtained microstructure is considered to be the final result and compared to prediction of bone degradation of the literature based on phase exchange calculated from mechanical strain energy. It is shown that the fuzzy cellular automata model predicts a more realistic bone degradation and microstructure distribution than the phase exchange method while having a model significantly simpler.

Synthesis Based on Linearization of Vector Speed Control of an Induction Motor without a Speed Sensor

The aim of the work was parametric synthesis of vector sensorless (i. e. without speed sensor) control of an electric drive with an induction motor. The structure of the system is based on the application of an adaptive model for estimating the rotor flow coupling vector and velocity. The speed is estimated by the mismatch of the real stator current and the current value calculated in the model. Stability is guaranteed in this well-known structure, obtained on the basis of Lyapunov functions, but it remains problematic to calculate the parameters of regulators and an adaptive model to ensure high-quality dynamics of the system. For a vector control system of an induction electric motor without a speed sensor with an adaptive model, a linearized structure in a synchronously rotating coordinate system was proposed. This makes it possible to calculate control parameters using the modal control method to ensure quality indicators in each of the closed circuits of the system. Such parametric synthesis is based on the assumption that the flow coupling of the rotor is maintained constant, and therefore the mutual influence of the flow coupling and torque control channels can be neglected. The calculation of the parameters of control (regulators and the adaptation channel) is based on the method of assigning the roots of characteristic contour polynomials in such a way that each internal contour has a higher speed than the external one with respect to it. The method is approximate, but it makes it possible to take into account the main cause-and-effect relationships in dynamics and obtain simple calculation expressions. The simulation of the system was carried out using a simulation model that takes into account the digital software-algorithmic method for generating a microcontroller control signal, as well as electromagnetic processes under conditions of pulse-width modulation in an electric energy converter and an electric motor, the use of the values of the rotor flow coupling vector estimated by the model in coordinate transformations of the system, the formation of a spatial vector of the converter voltage. The analysis of the synthesized speed control system by the simulation method has confirmed the effectiveness of the proposed method of parametric synthesis and the acceptable accuracy of speed estimation.