Preliminary Transformation Research Articles

Method for identification of linear dynamic measuring system based on preliminary nonlinear transformation of the input signal

Method for identification of linear dynamic measuring system based on preliminary nonlinear transformation of the input signal

Modeling and optimization of a chilled-water cooling system with multiple chillers

In order to reduce energy consumption of the centralized chilled-water cooling system in large buildings, a dynamic control strategy was proposed for cooling plants by modelling and optimization. Combined with the chilled water flow model, this paper analyzed the parallel operation characteristics of the chillers and takes the load distribution as one of the control parameters. Based on the measured data of a typical cooling system that has undergone preliminary energy-saving transformation, the residual neural network is applied to model the relationship among energy consumption, controllable parameters and environmental parameters, and the residual neural network outperforms multi-layer perceptron and support vector regression. To minimize the total energy consumption, the gray wolf optimizer was introduced to optimize the controllable variables of the cooling system. Compared with the energy consumption before optimization, the simulation energy consumption after optimization decreased 10.45% on average, while the energy saving rate is only 7.9% with equal chilled water supply temperature of parallel chillers.

Agrobacterium tumefaciens-mediated in planta transformation strategy for development of transgenics in cotton (Gossypium hirsutum L.) with GFP as a visual marker.

Cotton (Gossypium hirsutum L.), a mercantile crop plant, is grown worldwide for fiber and seed oil. As with other economically important crops, cotton is bogged down with many biotic and abiotic stress factors. Towards this,genetic engineering offers numerous protocols to engineer plantsfor better resilience. However, recalcitrance of cotton to planttissue culture has been the major constraint for successfulin vitro regeneration. Hence,alternate methods that evade tissue culture regeneration have been envisaged. Non tissue culture-based in planta transformation strategies are in vogue due to amenability and ease in the generation of transgenic plants. In the present study, we demonstrate the utility of an in planta transformation protocol and establishment of a stringent selection agent-based screening for the identificationof transgenics. The genotype independent nature of the protocol was validated in cotton cv. Pusa 8-6 using GFP. Preliminary transformation efficiency of 28% was achieved with a screening efficiency of 20% in the presence of hygromycin. The proof of T-DNA integration by various molecular and expression analysis in T1 and T2 generations proved that this technique can be employed to generate transgenic cotton.

Characterization of UV and chlorine contributions to transformation of 2,3,4-trihydroxybenzophenone under combined UV-chlorine treatment

Combined UV-chlorine treatment is a promising disinfection technology providing synergistic effects on bacteria-killing. The interaction between UV and chlorine would affect pollutants removal and disinfection by-products formation, while little is known about how UV and chlorine respectively contribute to pollutants transformation under combined UV-chlorine treatment. In this study, UV filter 2,3,4-trihydroxybenzophenone (2,3,4-THBP) was selected as a model compound to investigate the transformation characteristics and acute toxicity variation under combined UV-chlorine treatment. Especially, separative UV and chlorination treatments were conducted to illustrate their respective contribution in combined UV-chlorine treatment. It was found that the optimal removal percentage of 2,3,4-THBP under combined UV-chlorine treatment was 85.3% within 5 min and kept stable until 3 h at 3-equivalent (equiv.) of free available chlorine (FAC) and 1 mW/cm2 of irradiation intensity. Correspondingly, acute toxicity of reaction mixture at 3 h increased twice as high as that of 2,3,4-THBP itself. Four transformation products were tentatively identified, and their formation possibly involved the reactions of chlorine substitution, oxidation, hydroxylation, and hydrolysis. FAC initiated the preliminary transformation of 2,3,4-THBP, and the synergistic effects of UV and chlorine promoted the further transformation of intermediates from chlorination treatment. Most important was that, 2,3,4-THBP could form some toxic products in the real ambient water matrix under solar irradiation, and acute toxicity of reaction mixture was 1.84 times higher than that of 2,3,4-THBP. This study would provide a better understanding on the transformation characteristics of pollutants under combined UV-chlorine treatment, and provide a reference for optimizing disinfection treatment.

Vibration Suppression for Coupled Wave PDEs in Deep-Sea Construction

A deep-sea construction vessel (DCV) is used to install underwater parts of an offshore oil drilling platform at the designated locations on the seafloor. By using extended Hamilton's principle, a nonlinear partial differential equation (PDE) system governing the lateral-longitudinal coupled vibration dynamics of the DCV consisting of a time-varying-length cable with an attached item is derived, and it is linearized at the steady state generating a linear PDE model, which is extended to a more general system including two coupled wave PDEs connected with two interacting ordinary differential equations (ODEs) at the uncontrolled boundaries. Through a preliminary transformation, an equivalent reformulated plant is generated as a 4×4 coupled heterodirectional hyperbolic PDE-ODE system characterized by spatially varying coefficients on a time-varying domain. To stabilize such a system, an observer-based output-feedback control design is proposed, where the measurements are only placed at the actuated boundary of the PDE, namely, at the platform at the sea surface. The exponential stability of the closed-loop system, boundedness and exponential convergence of the control inputs, are proved via Lyapunov analysis. The obtained theoretical result is tested on a nonlinear model with ocean disturbances, even though the design is developed in the absence of such real-world effects.

Метод формалізації даних при вирішенні задач розпізнавання повітряних об'єктів

В роботі розроблена методика формалізації структур даних ознак повітряних об’єктів яка враховує характеристики точності і достовірності джерел інформації з нестохастичною невизначеністю і рекурентними правилами узагальнення оцінок значень ознак при багаторазовому спостереженні повітряних об’єктів. В основу розробленої методики були покладені методи формалізації процесу розпізнавання, оцінки ступеня істинності різнорідних ознак, об'єднання незалежних результатів розпізнавання, пошуку рішень про класи розпізнаваних повітряних об'єктів з урахуванням неповноти і надмірності даних про ознаки. Розроблений метод формалізації процесу розпізнавання повітряних об’єктів на основі описів класів різнорідними ознаками, відрізняється від відомих використанням ієрархічної функціональної мережі. Удосконалення методу об'єднання незалежних результатів розпізнавання на основі правила Демпстера полягає в попередньому перетворенні одного з неузгоджених розподілів можливостей класів алфавіту в розподіл ймовірностей. Це дозволяє проводити коректне об'єднання неузгоджених розподілів можливостей класів алфавіту з використанням правила Демпстера. Проведено дослідження застосовності розробленого правила при об'єднанні неузгоджених розподілів ймовірностей класів алфавіту. Розроблений метод пошуку рішень про класи розпізнаваних об'єктів на графі функціональної мережі відрізняється від відомих розробленим способом отримання інтервальної оцінки ступеня істинності класів в умовах відсутності даних щодо окремих ознак повітряних об’єктів і запропонованим евристичним правилом, що дозволяє скоротити розмірність безлічі вершин мережі, які перебираються при пошуку рішення про клас повітряних об’єктів. Метод дозволяє проводити пошук рішень про класи повітряних об’єктів в умовах неповноти і надмірності даних про ознаки.

The matching of images based on de Rham current formation

The application of the de Rham current theory for matching forms of images is considered. The determination the measure of differences between simplicial complexes of forms does not imply a point bijective correspondence between the structures of objects. The RKHS method for obtaining object shape metrics is proposed. The study of the forms of objects is proposed to be carried out by forming test vector fields. Simplicial complexes of objects can be supplemented by the functions of the elements representing the signals. To improve the accuracy of matching the forms of the source and terminal images, the original image is subjected to preliminary affine transformation. The minimization of the functional of the squared distance between the image forms is carried out on the basis of the QPSO algorithm.

The Determination of Distances between Images by de Rham Currents Method

The goal of the paper is to develop an algorithm for matching the shapes of images of objects based on the geometric method of de Rham currents and preliminary affine transformation of the source image shape. In the formation of the matching algorithm, the problems of ensuring invariance to geometric image transformations and ensuring the absence of a bijective correspondence requirement between images segments were solved. The algorithm of shapes matching based on the current method is resistant to changes of the topology of object shapes and reparametrization. When analyzing the data structures of an object, not only the geometric form is important, but also the signals associated with this form by functional dependence. To take these signals into account, it is proposed to expand de Rham currents with an additional component corresponding to the signal structure. To improve the accuracy of shapes matching of the source and terminal images we determine the functional on the basis of the formation of a squared distance between the shapes of the source and terminal images modeled by de Rham currents. The original image is subjected to preliminary affine transformation to minimize the squared distance between the deformed and terminal images.

Choice of method imputation missing values for obstetrics clinical data

Clinical decision support systems use patients’ various medical records: medical history, laboratory diagnostics, doctor examination, etc. However, the data may have missing values, which may affect the diagnosis. The purpose of this article was to compare eight missing values imputation algorithms for obstetric data, which will further be used in the work of CDSS. The smallest error was shown by Iterative Imputer and fast-KNN algorithms. Preliminary transformation such as dividing data into several groups by gestational age helped to improve the results but not for all data parameters.

Synthesis of smart sensors based on the introduction of minimal structural redundancy

The problems of operation of measuring systems and changes in their parameters due the influence of various kinds of destabilizing factors, including time, are considered, and the primary elements of the measuring path – sensors and converting equipment – are most susceptible to such an influence. The deviation of the values of the parameters of the measurers from the nominal values leads to significant errors in the assessment of the unknown input signal, which necessitates the current (during operation) identification of the measurer. The problem of the current identification of the measurer parameters under unknown input influences is solved. The identification procedure is carried out by introducing an additional channel for transforming the measured value in the spatial domain. In this case, the transformation of the measured value in the additional channel of the structurally redundant measurer has the form of a preliminary functional (nonlinear) transformation of the m-th power over the input signal. The solution to the current identification problem is considered for the linear static characteristic of the main channel of the measuring transducer. An additional equation in solving the problem of current identification and conducting metrological self-control in an intelligent sensor is presented in the form of a regression relationship between the output signals of the additional and main channels of the structurally redundant measurer. The unknown parameters of the regression equation are determined from the results of processing the time samples of the output signals of the additional and main channels using the least squares method. The dependences of the rms measurement error of the input quantity on the rms value of the measurement noise in the output signals of the structurally redundant measurer, the dynamics of the input signal change and the power of nonlinearity m of the preliminary functional transformation in the additional measuring channel are presented. In the course of research, it was revealed that the shape and spectrum of the input signal do not significantly affect the measurement accuracy. It is shown that the highest measurement accuracy is provided by the preliminary quadratic conversion of the input signal in the additional channel of the minimum-redundant sensor. The research results can be used for metrological selfcontrol in smart sensors or smart measuring systems.

Алгоритм кумулятивных сумм для обнаружения изменений ковариационной матрицы многомерных временных рядов

The problem of detecting spontaneous changes in the probabilistic characteristics (mismatching) of vector time series using the well-known cumulative sum algorithm (CUSUM) is considered. The case in which it is in the form of a stepwise change in the entries of the time series covariance matrix is studied. To solve this problem, it is proposed to use a preliminary linear transformation of the values of the observed time series, which ensures simultaneous transformation of the covariance matrix to the unity form before the change, and to the diagonal form after the change. Basic provisions that describe the corresponding detection algorithm and the relationships using which the above- mentioned linear transformation is implemented are given. It is shown that its application can significantly simplify the solution of matters concerned with synthesizing a control algorithm with the specified properties. This is due to the fact that the time series resulting from such transformation has uncorrelated components, and the changes boil down to deviation of the variances of these components from the original unity value. For the two-dimensional case, the procedure for synthesizing the control algorithm is considered in detail. For its practical implementation, the calculation relations obtained using the simulation method are presented that make it possible to find the threshold of the control algorithm from the given value of the interval between false alarms and to estimate the average delay time in detecting a mismatch. An example of implementing such synthesis is presented, which illustrates the entire process of constructing a controlling procedure.

Application of Fuzzy Decision Tree for Signal Classification

A typical algorithm for signal classification consists of two steps: signal preliminary transformation and classification itself. The procedures of preliminary transformation are used to extract specific features of the initial signal and reduce its dimension for effective classification. The result of this transformation is information loss of initial signal, which implies uncertainty of data used in classification. This uncertainty can be taken into account by the application of fuzzy classifiers. In this paper, a new algorithm with application of fuzzy classifier is proposed for signal classification. A new procedure of fuzzification is added into the preliminary transformation and fuzzy decision tree is used for classification. The efficiency of this algorithm is examined based on the problem of detection of defective blades of an aircraft engine gas turbine. The experiments showed that the accuracy of the classification for the considered example is 0.989. This is the best result in comparison with other classification methods used to solve this problem.

Cross Hardening of a Shape Memory Alloy during Compression

Abstract—The influence of the preliminary direct martensitic transformation in titanium nickelide under a constant compressive stress on the diagrams of subsequent loading under martensitic inelasticity at a monotonically increasing compressive stress is experimentally studied. These diagrams are found to reach the martensitic inelasticity diagram corresponding to the compression of initially fully twinned martensite when the stress increases. The cross hardening on compression is shown to be more intense than that on tension. The phase-structural strain deviator can be considered as a state parameter for a shape memory alloy.

Non-destructive diagnostic of aircraft engine blades by Fuzzy Decision Tree

A new algorithm for non-destructive diagnosis based on signal classification is proposed. This algorithm permits classifying objects with different properties depending on signals characterizing the objects. In this paper, the algorithm is applied to diagnose blades of aircraft engine gas turbine according to vibration signal after a non-destructive shock excitation and to classify them as defective and faultless. Just like other signal classification algorithms, this algorithm consists of two steps that are signal preliminary transformation and classification. However, these steps are modified in the proposed algorithm. Unlike other algorithms, the fuzzy classifier is used in the classification step. The change of the classifier type causes modification of the preliminary transformation step because the attributes for the classification must be fuzzy. Therefore, fuzzification procedure is added into the step of preliminary transformation. The fuzzy classifier for the problem of aircraft engine blades diagnosis is ordered Fuzzy Decision Tree (oFDT) that is inducted by estimation of Cumulative Mutual Information. This induction has good efficiency for a small set of initial data. The accuracy of the classification of defective and faultless blades for the proposed algorithm is 98.5%, and oFDT for this classification is inducted based on 32 signals only. The comparison with other classification algorithms shows that oFDT based algorithm considered in this paper gives the best result for this problem.

Neural Network Classification of Difficult-to-Distinguish Types of Vegetation on the Basis of Hyperspectral Features

It is experimentally demonstrated that the classification of fragments of a hyperspectral image with preliminary transformation of the spectral features of the image into the principal components and with the use of the Hilbert-Huang spectral transform is fairly effective in the case of vegetation types that are difficult-to-distinguish on the basis of hyperspectra. This classification is compared with traditional methods, where hyperspectral features transformed to the principal components without using spatial information are used. RBF neural networks are used in all methods at the final stage of the classification.

Reducibility for a fast-driven linear Klein–Gordon equation

We prove a reducibility result for a linear Klein-Gordon equation with a quasi-periodic driving on a compact interval with Dirichlet boundary conditions. No assumptions are made on the size of the driving, however we require it to be fast oscillating. In particular, provided that the external frequency is sufficiently large and chosen from a Cantor set of large measure, the original equation is conjugated to a time independent, diagonal one. We achieve this result in two steps. First, we perform a preliminary transformation, adapted to fast oscillating systems, which puts the original equation in a perturbative setting. Then we show that this new equation can be put to constant coefficients by applying a KAM reducibility scheme, whose convergence requires a new type of Melnikov conditions.

A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI

Abstract. A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI Timely and accurate earth observation with short revisit interval is usually necessary, especially for emergency response. Currently, several new generation sensors provided with similar channel characteristics have been operated onboard different satellite platforms, including Sentinel-2 and Landsat 8. Joint use of the observations by different sensors offers an opportunity to meet the demands for emergency requirements. For example, through the combination of Landsat and Sentinel-2 data, the land can be observed every 2–3 days at medium spatial resolution. However, differences are expected in radiometric values (e.g., channel reflectance) of the corresponding channels between two sensors. Spectral response function (SRF) is taken as an important aspect of sensor settings. Accordingly, between-sensor differences due to SRFs variation need to be quantified and compensated. The comparison of SRFs shows difference (more or less) in channel settings between Sentinel-2 Multi-Spectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI). Effect of the difference in SRF on corresponding values between MSI and OLI was investigated, mainly in terms of channel reflectance and several derived spectral indices. Spectra samples from ASTER Spectral Library Version 2.0 and Hyperion data archives were used in obtaining channel reflectance simulation of MSI and OLI. Preliminary results show that MSI and OLI are well comparable in several channels with small relative discrepancy (< 5 %), including the Costal Aerosol channel, a NIR (855–875 nm) channel, the SWIR channels, and the Cirrus channel. Meanwhile, for channels covering Blue, Green, Red, and NIR (785–900 nm), the between-sensor differences are significantly presented. Compared with the difference in reflectance of each individual channel, the difference in derived spectral index is more significant. In addition, effectiveness of linear transformation model is not ensured when the target belongs to another spectra collection. If an improper transformation model is selected, the between-sensor discrepancy will even largely increase. In conclusion, improvement in between-sensor consistency is possibly a challenge, through linear transformation based on model(s) generated from other spectra collections.

An efficient numerical algorithm in the classical 3d theory of thin lifting surface in a flow of non-viscous incompressible fluid

The work is devoted to a new semi-analytical method, to treat the classical dual integral equation of the lifting surface theory. The method operates with the initial form of the integral equation, and no preliminary transformation is needed for the applied efficient approach.

Preparation of Aluminum-Scandium Master Alloys by Aluminothermal Reduction of Scandium Fluoride Extracted from Sc2O3

Aluminium alloys containing small additions of scandium exhibit unique operating properties. Alloying material with scandium significantly improves product weldability, reduces the tendency towards hot cracks and improves welded joint mechanical properties. The aim of this work is to increase the scandium extraction into a master alloy by preliminary transformation of scandium oxide into fluoride and introduction into the composition of an aluminium powder alloying additive. Results are provided of laboratory experiments for preparation of aluminium-scandium master alloy by aluminothermic reduction of scandium fluoride using sodium fluoride and aluminium powder within the alloy composition. Scandium fluoride is prepared by treating scandium oxide with 40% hydrofluoric acid. The overall extraction of scandium from oxide into fluoride and from fluoride to the aluminium-scandium master alloy is 88.5% with an average scandium concentration in the master alloy of 1.90 wt.%. The microstructure of the master alloy obtained is represented by scandium aluminide Al3Sc crystals with a size from 10 to 25 μm uniformly distributed within the master alloy. An additional reserve for increasing the extraction of scandium into the master alloy and reducing its cost is processing of slags formed in the preparation of the ligature. The use of an aluminium powder with high specific surface area in the composition of the alloying additive increases scandium extraction into the master alloy due to better contact of the reacting phases. The resultant Al–Sc master alloy has uniform distribution of Al3Sc particles within the volume of the metal.

Box–Cox‐chord transformations for community composition data prior to beta diversity analysis

In studies of spatial or temporal beta diversity, community composition data, often containing many zeros, must be transformed in some way before they are analysed by multivariate methods of data analysis. Data are transformed to reduce the skewness of species distributions and make dissimilarities double‐zero asymmetrical. Criteria have recently been proposed to determine which dissimilarity functions (or the corresponding data transformations) can be used for beta diversity assessment. The chord transformation is often used as the preliminary transformation for frequency data. When the Euclidean distance is computed on chord‐transformed data, a chord dissimilarity matrixDis produced, which obeys the proposed criteria. The Hellinger transformation, i.e. the chord transformation applied to square‐root transformed frequencies, is also often used with community composition data prior to multivariate analyses; it leads to the Hellinger dissimilarity, which is another widely usedDfunction in beta diversity studies. Among the data transformations often used in simple or multivariate data analysis, the Box–Cox method provides a useful series of transformations to make data distributions more symmetrical, where exponent 1 is the absence of a transformation, exponent 0.5 is the square‐root, exponent 0.25 is the fourth‐root, and the log transformation is the limit of the Box–Cox function corresponding to exponent 0. Combining the two previous ideas, this paper proposes to combine any transformation of the Box–Cox family with exponent in the [0,1] range with the chord transformation. In particular, one can compute the loge(y + 1) transformation of a community composition (or other frequency) data table and follow with a chord transformation. ADmatrix can be computed from the doubly‐transformed data. The transformations andDfunctions in that family inherit the properties of the chord dissimilarity, and this ensures that they all follow the necessary criteria for beta diversity assessment that have recently been proposed.