Transformation Function Research Articles

A neural network-based prediction model for cultivating skilled personnel in higher vocational education

Abstract In this paper, the neurons in the BP neural network are used to represent the skill-based talent cultivation feature vectors, and the feature vectors are trained on the network to obtain the vector transformation function. On the basis of the vector transformation function, after constructing the technical talent cultivation prediction model by using the backpropagation algorithm, the data indicators are determined according to the requirements of skill-based talent cultivation, and the initial data are normalized by overfitting for the technical talent cultivation prediction results. The empirical research on the synthesis of technically skilled talents in higher vocational education is designed by means of questionnaires, and the data analysis software is used to analyze the examples of skilled talent cultivation in vocational education under the background of big data technology. The results show that from 2015 to 2020, the predicted values of the number of technically skilled talents demanded in a province are 92,130, 105,396, 160,946, 225,045, 232,313 and 216,150 respectively, and the relative error values are less than 0.05 compared with the actual demand values of technically skilled talents in the same period, indicating that based on the BP neural network-based technical talent cultivation prediction model outputs a good fit between the extrapolation test prediction value and the real value. This study guides the cultivation of skilled talents in higher vocational education.

Log-exponential transformation function for interpreting NMR relaxation measurements of hydrocarbon in organic porous media for enhancing absolute adsorption estimation

Assessing unconventional petroleum resources, particularly shale gas reserve estimation, is challenging due to the adsorption phenomenon. This study presents a new method combining experimental and mathematical procedures to determine the absolute adsorption amount in shale samples. In the experimental section of this study, a new behavior was observed in the NMR decay time series of hydrocarbons in organic porous media. The fast section has a linear relationship with time rather than an exponential one. As a result, a Log-Exponential fit to these decay curves was proposed. Then the logarithmic portion was then used to estimate the absolute adsorbed amount. The method was validated against literature data and conventional methods.Based on this newly developed method, absolute adsorption isotherms were obtained for a Duvernay shale sample and activated carbon packs at pressures ranging from 0.77 MPa to 7 MPa. The model proved to be applicable and consistent with pressure variations. Methane in Duvernay shale exhibited adsorbed isotherm type V, with a methane adsorption capacity of 0.08 g per gram of Total Organic Content (TOC) at 6.24 MPa.In addition to absolute adsorption, the competitive adsorption of methane and CO2 was explored, which is critical for evaluating CO2-enhanced shale gas recovery and storage techniques. The injected CO2 resulted in the displacement of methane from smaller shale pores to larger ones.Adsorption and desorption experiments were conducted to investigate hysteresis. The new method was able to better describe the physical behavior of gaseous hydrocarbons in organic porous media. It was able to capture phase transition and critical pressure specific to organic porous media, which differ from non-organic porous media. This innovative approach enables the accurate characterization of shale reservoirs.

A Simplified Optimization Model for Hydrokinetic Blades with Diffuser and Swept Rotor

The use of a diffuser in hydrokinetic turbines can improve the power coefficient. However, the risk of cavitation in the rotor blades increases. Studies suggest that backward-curved blades can reduce the axial load on the rotor and therefore prevent cavitation. Therefore, this work develops an optimization procedure applied to backward-curved blades in hydrokinetic turbines with diffusers based on the Blade Element Momentum Theory. The main contribution is to consider both the sweep effect and the presence of a diffuser in the optimization in an innovative way. We use a radial transformation function that adjusts the radial position considering the curvature of the blade during optimization under the effect of the diffuser. The results showed that the increase in blade curvature resulted in greater chord distributions and twist angles, especially at the blade tips. The Prandtl’s loss factor was not sensitive to sweep, but the linked circulation increased at the blade tips, suggesting an increased risk of cavitation. Depending on the sweep angle, the optimized blades were able to mitigate or avoid cavitation. In particular, a sweep angle of 30∘ eliminated cavitation. This study indicated that the proposed optimization can effectively prevent cavitation, showing satisfactory results.

Formula omitted]-TFA based multiscale analysis of failure in elasto-plastic composites

This paper describes a novel homogenization methodology for analyzing the failure of elastoplastic composite materials based on elastic and eigen influence tensors-driven transformation field analysis (E2-TFA). The proposed technique considers the microscopic eigenstrain field accounting for intra-phase damage and inelastic strains. This results in realistic computations by alleviating the post-damage stiffness response, which is a drawback of TFA-based methods. We attain computational efficiency by identifying the preprocessing data solely from the elastic and eigen transformation functions and adopting a reduced order modeling technique with a piecewise constant eigenstrain field throughout the subdomains. The performance of the model is assessed by simulating the response for (a) the representative volume element (RVE) as a homogenized continuum and (b) the various composites under complex load histories with intricate macroscale morphologies. Furthermore, the nonlinear shear stress–strain response of a glass fiber composite is calculated and compared to experimentally measured fracture initiation parameters, failure plane orientation, and strain histories. Finally, we show that E2-TFA can accurately and efficiently capture damage and inelastic deformations in order to estimate the mechanical response of composite materials in a better way.

Asymptotic Properties for Cumulative Probability Models for Continuous Outcomes.

Regression models for continuous outcomes frequently require a transformation of the outcome, which is often specified a priori or estimated from a parametric family. Cumulative probability models (CPMs) nonparametrically estimate the transformation by treating the continuous outcome as if it is ordered categorically. They thus represent a flexible analysis approach for continuous outcomes. However, it is difficult to establish asymptotic properties for CPMs due to the potentially unbounded range of the transformation. Here we show asymptotic properties for CPMs when applied to slightly modified data where bounds, one lower and one upper, are chosen and the outcomes outside the bounds are set as two ordinal categories. We prove the uniform consistency of the estimated regression coefficients and of the estimated transformation function between the bounds. We also describe their joint asymptotic distribution, and show that the estimated regression coefficients attain the semiparametric efficiency bound. We show with simulations that results from this approach and those from using the CPM on the original data are very similar when a small fraction of the data are modified. We reanalyze a dataset of HIV-positive patients with CPMs to illustrate and compare the approaches.

Prescribed performance sliding mode control for the PAMs elbow exoskeleton in the tracking trajectory task

PurposeThis study discusses the tracking trajectory issue of the exoskeleton under the bounded disturbance and designs an useful tracking trajectory control method to solve it. By using the proposed control method, the tracking error can be successfully convergence to the assigned boundary. Meanwhile, the chattering effect caused by the actuators is already reduced, and the tracking performance of the pneumatic artificial muscles (PAMs) elbow exoskeleton is improved effectively.Design/methodology/approachA prescribed performance sliding mode control method was developed in this study to fulfill the joint position tracking trajectory task on the elbow exoskeleton driven by two PAMs. In terms of the control structure, a dynamic model was built by conforming to the adaptive law to compensate for the time variety and uncertainty exhibited by the system. Subsequently, a super-twisting algorithm-based second-order sliding mode control method was subjected to the exoskeleton under the boundedness of external disturbance. Moreover, the prescribed performance control method exhibits a smooth prescribed function with an error transformation function to ensure the tracking error can be finally convergent to the pre-designed requirement.FindingsFrom the theoretical perspective, the stability of the control method was verified through Lyapunov synthesis. On that basis, the tracking performance of the proposed control method was confirmed through the simulation and the manikin model experiment.Originality/valueAs revealed by the results of this study, the proposed control method sufficiently applies to the PAMs elbow exoskeleton for tracking trajectory, which means it has potential application in the actual robot-assisted passive rehabilitation tasks.

Reverse design of involute helical gear pairs considering machining errors based on IMAGEWARE and HYPERMESH

Gears inevitably contain errors during the manufacturing and installation process, which can increase the vibration and noise of the gear system. The finite element model with error can accurately solve the variation of vibration and noise and meshing characteristics of such gear systems. This paper develops a combined analytical and simulation method for involute helical gears when machining errors are included. First, the tooth profile accurate point clouds of the involute gear are derived by cutting the tooth blank with the rack and the coordinate transformation function. Subsequently, the gear is reconstructed by the IMAGEWARE and UG. With HYPERMESH's powerful node editing capabilities, sinusoidal profile errors can be assigned to gears. Also, the intersection of the error gear with the tooth blank is used to find the apex, which has an essential effect on noise. Finally, the difference in static transmission errors of gears with and without errors is analyzed by ABAQUS. The results show that gears containing errors increase the peak-to-peak values of transmission errors, i.e., increasing noise. The method proposed in this paper can provide a basis for modeling and analysis of meshing characteristics of special gears that cannot be directly parameterized or gears with various errors.

Reinforcement learning-based optimal adaptive fuzzy control for nonlinear multi-agent systems with prescribed performance

In this paper, the problem of optimal adaptive consensus tracking control for nonlinear multi-agent systems with prescribed performance is investigated. To address the issue of satisfying the initial value conditions in existing results, an improved performance function is employed as the prescribed performance boundary, effectively resolving this problem. Then, by employing the error transformation function, the constrained system is converted into an unconstrained one. Furthermore, fuzzy logic systems are employed to identify unknown system parts. By applying the dynamic surface technique, the problem of "differential explosion", which often occurs in backstepping, is solved. Moreover, a distributed optimal adaptive fuzzy control protocol based on the reinforcement learning actor-critic algorithm is proposed. Under the proposed control scheme, it is proved that all the signals within the closed-loop system are bounded, and the consensus tracking errors have remained within the predefined bounds. Finally, the numerical simulation results demonstrate the effectiveness of the proposed scheme.

Adaptive fuzzy prescribed settling time consensus control for multi-agent systems with input hysteresis via dynamic event-triggered mechanism

An adaptive fuzzy prescribed settling time consensus control is proposed based on the dynamic event-triggered mechanism for a class of uncertain nonlinear multi-agent systems (UNMASs) subject to input hysteresis. In practical systems, the plant inevitably suffers from input hysteresis and unknown nonlinear characteristics, which dramatically deteriorate its performance. Therefore, to eliminate the effects of those constraints, an adaptive fuzzy prescribed settling time control scheme is constructed by applying backstepping technique and first-order filter. Wherein each agent needs to be equipped with only one adaptive law for fuzzy-logic-system approximation, reducing the computational burden. Simultaneously, the synchronization error is mapped using a transformation function to achieve prescribed settling time consensus control. Moreover, a dynamic event-triggered mechanism is employed to reduce unnecessary communication transmission and save energy. With the presented method, all agents synchronize to the virtual leader within a prescribed settling time. Theoretical analysis and simulation examples verify the presented method.

Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction

Machine learning (ML) algorithms have produced remarkable advances in streamflow prediction, exceeding the performance of calibrated conceptual and physics-based hydrological models that have been developed over many decades. ML algorithms seem to overcome the issue of errors known to be present in rainfall and streamflow estimates that have hindered the performance of hydrological models for decades. In this paper, we propose a methodology for testing and benchmarking ML algorithms using artificial data generated by physically-based hydrological models. Our approach makes it possible to design controlled numerical experiments that can improve our understanding of this new generation of black-box models. We conducted a diagnostics study to demonstrate our methodology in which we attempted to determine if ML algorithms can identify a function relating streamflow and rainfall. This exercise combined the implementation of the distributed hillslope-link hydrological model (HLM) on a 4,385 km2 basin driven by precipitation fields created using the stochastic storm transposition (SST) framework, and an advanced deep learning algorithm based on gated recurrent unit (GRU)-Attention neural networks. The data generated allowed us to create prediction scenarios that are equivalent to the hindcast and real-time forecast problems. We proposed a set of scale-independent performance metrics to evaluate the results of our experiment and found that the GRU can correctly identify a predictive function for all analyzed locations in the river network. We concluded that under the circumstances tested in this study, deep learning can identify the transformation function when trained in Hindcast Mode, making it a powerful tool to determine the streamflow response of a basin to predetermined rainfall scenarios. However, it fails to significantly outperform the predictions of temporal persistence when tested in Forecast Mode.

Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security.

In the current digital era, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are evolving, transforming human experiences by creating an interconnected environment. However, ensuring the security of WSN-IoT networks remains a significant hurdle, as existing security models are plagued with issues like prolonged training durations and complex classification processes. In this study, a robust cyber-physical system based on the Emphatic Farmland Fertility Integrated Deep Perceptron Network (EFDPN) is proposed to enhance the security of WSN-IoT. This initiative introduces the Farmland Fertility Feature Selection (F3S) technique to alleviate the computational complexity of identifying and classifying attacks. Additionally, this research leverages the Deep Perceptron Network (DPN) classification algorithm for accurate intrusion classification, achieving impressive performance metrics. In the classification phase, the Tunicate Swarm Optimization (TSO) model is employed to improve the sigmoid transformation function, thereby enhancing prediction accuracy. This study demonstrates the development of an EFDPN-based system designed to safeguard WSN-IoT networks. It showcases how the DPN classification technique, in conjunction with the TSO model, significantly improves classification performance. In this research, we employed well-known cyber-attack datasets to validate its effectiveness, revealing its superiority over traditional intrusion detection methods, particularly in achieving higher F1-score values. The incorporation of the F3S algorithm plays a pivotal role in this framework by eliminating irrelevant features, leading to enhanced prediction accuracy for the classifier, marking a substantial stride in fortifying WSN-IoT network security. This research presents a promising approach to enhancing the security and resilience of interconnected cyber-physical systems in the evolving landscape of WSN-IoT networks.

Adaptive fixed-time inverse optimal consensus of multi-agent systems with limited-time interval state constraints

This article presents a study on fixed-time adaptive fuzzy inverse optimal consensus for multi-agent systems (MASs) subject to limited-time interval state constraints. To handle the constraints that occur within a limited-time interval during the system's operating cycle, we introduce a shifting function to shift the boundary and a state-dependent transformation function to transform the constrained MASs into non-constrained ones. We then propose an adaptive fixed-time inverse optimal consensus controller that achieves the convergence of consensus error within a fixed-time and inverse optimality without directly learning the solution of the Hamilton-Jacobi-Bellman (HJB) equations. To establish the criteria for fixed-time stabilization, we design an auxiliary controller instead of the auxiliary system and introduce two Lyapunov functions to prove the inverse optimality and fixed-time stabilization. The developed consensus scheme ensures that the synchronization error asymptotically converges to a user-predefined region within a fixed settling time. We validate the effectiveness of the developed scheme through a simulation example.

An RGB Pseudo-Colorization Method for Filtering of Multi-Source Graphical Data

Artificial colorization (pseudo-colorization) is a commonly used method to improve the readability of images obtained from sources (sensors) that do not reflect the original color of the object of observation (e.g., X-ray). It is designed to draw the observer’s attention to the important details of the analyzed image (e.g., disease changes in medical imaging). Analogous needs occur in the process of assessing the emission security (EMSEC) of imaging devices used to process classified information, which is made on the basis of the analysis of images reproduced from compromising emanations related to the operation of these devices. The presence of many graphic elements in an image may reduce the level of perception of the information contained in it. Such images may be very noisy or contain overlapping graphic symbols, the source of which is devices processing graphic information operating in close proximity to each other. The use of various types of measures enabling data filtration at various stages of their processing, e.g., the use of a directional antenna, frequency filtering, point filtering or contextual contrast modification, does not always prove effective. The solution to the filtration problem is the pseudo-colorization of the image. However, the image colorization used based on the typical “Hot”, “Radar” or “Cold” color palettes does not meet the requirements for filtering graphic data from many sources. It is necessary to use a filter that will allow the sharp cut-off of graphic data at the border between the background and the graphic symbol. For the pseudo-colorization process itself, the exponential function as a function of transforming the amplitudes of image pixels from the gray color space to the RGB color space is sufficient. However, the smooth transition of the function shape from zero values to values greater than zero results in a low efficiency of filtering graphic data from noise. In this article, a method of filtering an image based on the pseudo-colorization of its content, i.e., reproduction of a compromising emanation signal level in the RGB value of image pixel color components, was proposed. A quadratic function was proposed as the transformation function. The higher effectiveness of the method based on the use of a square function (compared to the exponential function) was shown by conducting tests on many images, some of which are presented in this article. The proposed solution is a universal approach and can be used in various fields related to image analysis and the need for their filtration. Its universality is related to the possibility of changing function parameters affecting its position on the value axis from 0 to 255, its width, its minimum and its maximum value for each RGB channel.

Construction of a cross-domain integration teaching model for physical education oriented to the characteristics of disciplines

Abstract In this paper, we first use SemiSDAE and matrix decomposition to model the source and target domains according to the subject features and convert the encoder feature space to the matrix decomposition feature space through the feature transformation function to deeply integrate the features extracted from both. Combining the implicit feedback information of users for cross-domain recommendation and retaining the semantic information, the features of various disciplines are integrated into the online and offline cross-domain teaching based on the SPOC platform. The method in this paper has a high recall rate of 0.99 in 60% of training data, and 0.96 in 80% of training data, and the results of various sports programs are excellent, which can organically combine sports with other disciplines and promote the development and innovation of sports teaching.

A multiattribute financial time series forecast model based on double hierarchy fuzzy linguistic term set

Based on the double hierarchy linguistic term sets (DHLTS), a novel forecasting model is proposed considering both the internal fluctuation rules and the external correlation of different time series. The innovative aspects of this model consist of: (i) It can expresses more internal fluctuation and external correlation information, providing guarantees for improving the predictive performance of the model. (ii) The equivalent transformation function of DHLTS reduces the fuzzy granularity and improves the prediction accuracy. (iii) The application of similarity measures can extract the closest rules from historical states based on the distance operators of DHLTS. In addition, experiments on TAIEX considering the impact of the U.S. stock market and other data show that the model has good predictive performance.

Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method

Failure mode and effects analysis (FMEA) is an important risk analysis tool that has been widely used in diverse areas to manage risk factors. However, how to manage the uncertainty in FMEA assessments is still an open issue. In this paper, a novel FMEA model based on the improved pignistic probability transformation function in Dempster–Shafer evidence theory (DST) and grey relational projection method (GRPM) is proposed to improve the accuracy and reliability in risk analysis with FMEA. The basic probability assignment (BPA) function in DST is used to model the assessments of experts with respect to each risk factor. Dempster’s rule of combination is adopted for fusion of assessment information from different experts. The improved pignistic probability function is proposed and used to transform the fusion result of BPA into probability function for getting more accurate decision-making result in risk analysis with FMEA. GRPM is adopted to determine the risk priority order of all the failure modes to overcome the shortcoming in traditional risk priority number in FMEA. Applications in aircraft turbine rotor blades and steel production process are presented to show the rationality and generality of the proposed method.

Underwater image enhancement method based on golden jackal optimization

The propagation of light underwater is affected by water absorption and scattering, resulting in poor visibility of obtained underwater images, and exhibiting degradation phenomena such as color deviation and low contrast. To improve the quality and visibility of underwater images, a method of underwater image enhancement based on Golden Jackal optimization is proposed. The algorithm consists of two processing stages. The first stage is mainly processed in the RGB color space. Based on the attenuation characteristics of underwater color channels, a constructed color compensation and correction strategy is used to eliminate color deviation caused by water absorption. In second stage, it is mainly processed in the Lab color space, using the Golden Jackal optimization algorithm and fitness function to search the optimal parameters of transformation function to improve the image contrast by optimizing the grey intensity of L component and its histogram distribution. The feasibility and effectiveness of the proposed method have been verified through testing and analysis of much underwater degraded image data. In subjective and objective analysis with multiple state-of-the-art underwater image clarity methods, the proposed method performs excellent in color restoration and detail texture enhancement. Further experiments have shown that the proposed method has strong robustness and universality. It is not only suitable for enhancing various underwater scene images, but also for enhancing environmental images with fog, low lighting, and sandstorms. Meanwhile, the proposed method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection.

Uncertainty induced and preference involved aggregation in relative basic uncertain information decision environment

Uncertainty exists in numerous evaluation and decision making problems and therefore it also provides space for the subjective preferences of decision makers to affect the aggregation and evaluation results. Recently, relative basic uncertain information is proposed to further generalize basic uncertain information, but currently there is no research on how to apply this type of uncertainty in both theory and practices. There is also a paucity of decision methodology about how to build systematic preference involved decision model considering this new type of uncertainty. The relative basic uncertain information can serve as a general frame to enable the possibility for simultaneously handling heterogeneous uncertain information including interval information, basic uncertain information, and relative basic uncertain information. Different types of bipolar subjective preferences commonly should be taken into consideration in practical decision making. With the individual heterogeneous uncertain information and the involved two types of subjective preferences, namely bipolar preferences for uncertainties and bipolar optimism-pessimism preferences, the evaluation and decision making become more complex. This work proposes a systematic intersubjective decision model which can effectively and reasonably deal with the decision scenario with such complex uncertainty, in which Yager preference induced weights allocation is applied. Some novel preference conversion and transformation functions, specified techniques, and the related decision making procedures and sub-modules are proposed and analyzed. An application is also presented to showthe practicality of the proposed decision models and related conversion and transformation functions.

Adaptive state feedback control of output‐constrained stochastic nonlinear systems with stochastic integral input‐to‐state stability inverse dynamics

AbstractThis article studies the adaptive state‐feedback control problem of output‐constrained stochastic high‐order nonlinear systems with stochastic integral input‐to‐state stability (SiISS) inverse dynamics. A key nonlinear transformation function is constructed to convert the original output‐constrained stochastic nonlinear system into an equivalent form without any output constraint. By subtly using the SiISS small‐gain condition and fully extracting the characteristics of system nonlinearities, two new control design and analysis methods are developed to guarantee that the closed‐loop system has an almost surely unique solution, all the closed‐loop signals are bounded almost surely, and the equilibrium point is stable in probability without the violation of output constraint. A simulation result is provided to show the effectiveness of this control method.

Expert knowledge–based system for risk assessment of the occurrence of Amyloodinium ocellatum in semi-intensive fish farms

The implementation of a system to assess the risk of Amyloodinium ocellatum occurrence in rearing ponds in fish farms located in southern Spain is a fundamental aspect to ensure the economic viability of these facilities. For this purpose, a computer program (called Amy) for Windows PCs and an application for mobile devices (AmyAPP), based on the Android operating system, were developed integrating transformation functions and weightings associated with environmental parameters and fish behavioural factors from which it is possible to estimate the level of risk of occurrence of A. ocellatum. The weights for each of the environmental parameters and behavioural factors were estimated from the responses of a panel of experts (the fish farmers) using a Delphi methodology. The results indicate that, under operational validation, Amy/AmyAPP responses were statistically sensitive to the occurrence of A. ocellatum outbreaks in sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax) rearing ponds.