Transformation Function Research Articles

Practical fixed‐time composite‐learning control for full‐state constraint strict‐feedback non‐linear systems: A dynamic regressor extension and mixing based approach

AbstractA practical fixed‐time composite learning control scheme, by combining dynamic regressor extension and mixing (DREM) parameter identification algorithm and adaptive dynamic surface control (DSC) technique, is proposed for a class of strict‐feedback non‐linear systems subjected to linear‐in‐parameters uncertainties and full‐state constraint. To address the problem of state constraint, a non‐linear transformation function is introduced to convert the originally constrained non‐linear system into an unconstrained one. Meanwhile, the hyperbolic tangent function is employed to avoid singularity issues that often appeared in the traditional fixed‐time (FXT) control designs. In order to relax the requirement of persistency of excitation condition, a modified FXT‐DREM parameter identification approach with an interval excitation condition is constructed by introducing a three‐layer transformation technique derived from the classical DREM algorithm. Then, the modified FXT‐DREM parameter identification algorithm is seamlessly integrated into the adaptive DSC framework, resulting in a composite‐learning control scheme. By employing Lyapunov stability analysis, the fixed‐time convergence of both the parameter estimation error and the trajectory tracking error is proved. Finally, the effectiveness of the proposed design is demonstrated through simulation test.

An Ensemble Learning Method for Detection of Head and Neck Squamous Cell Carcinoma Using Polarized Hyperspectral Microscopic Imaging.

Head and neck squamous cell carcinoma (HNSCC) has a high mortality rate. In this study, we developed a Stokes-vector-derived polarized hyperspectral imaging (PHSI) system for H&E-stained pathological slides with HNSCC and built a dataset to develop a deep learning classification method based on convolutional neural networks (CNN). We use our polarized hyperspectral microscope to collect the four Stokes parameter hypercubes (S0, S1, S2, and S3) from 56 patients and synthesize pseudo-RGB images using a transformation function that approximates the human eye's spectral response to visual stimuli. Each image is divided into patches. Data augmentation is applied using rotations and flipping. We create a four-branch model architecture where each branch is trained on one Stokes parameter individually, then we freeze the branches and fine-tune the top layers of our model to generate final predictions. Our results show high accuracy, sensitivity, and specificity, indicating that our model performed well on our dataset. Future works can improve upon these results by training on more varied data, classifying tumors based on their grade, and introducing more recent architectural techniques.

Constraint-following adaptive robust control for multi-vehicle system formation with collision avoidance

For the formation on unstructured road with complex road conditions, the rationality of constraint design and uncertainty compensation seriously affect the connected and autonomous vehicle (CAV) system performance. The system constraints are studied for safe and efficient formation driving, and an adaptive robust formation control scheme is proposed. First, for the desired configuration and global collision avoidance, a series of equality and unilateral inequality constraints are designed for inter-vehicle spacing. Second, a constraint handling scheme is proposed based on the diffeomorphism method, adjustable transformation function, and bounded constraint. Third, (possibly fast) time-varying but bounded multi-source uncertainty is considered, and then a self-adjusting leakage-type adaptive law is proposed to estimate the integrated uncertainty bound online. Fourth, the performance measure β is defined to evaluate the constraint-following error, and an adaptive robust control is proposed. The effectiveness of the proposed control scheme is verified by simulation. The results show that the proposed control renders the error to be uniformly bounded and uniformly ultimately bounded. The CAV system approximately follows the constraints, regardless of the uncertainty.

Adaptive sensor fault tolerant control with prescribed performance for unmanned autonomous helicopter based on neural networks

PurposeThis paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method.Design/methodology/approachTo ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme.FindingsThe developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances.Originality/valueThe tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them.

Research on Dynamic Quaternion Ship Domain Model in Open Water Based on AIS Data and Navigator State

During the process of establishing the analytical quaternion ship domain model, the impact of ship traffic conditions and navigator state was not taken into consideration. However, the significance of these factors in the ship domain cannot be ignored. To create a more realistic representation of changes in the ship domain in real navigation environments, this study further considers the influence of ship encounter course, waterway traffic intensity, relative ship velocity, and the navigator state based on the quaternion ship domain model. As a result, a new dynamic quaternion ship domain model is proposed. To assess the changes in the size and shape of the ship domain under various navigation environments, ship domain scaling and shape transformation functions are introduced. Specifically, this study focuses on analyzing the ship traffic near the Lao Tie Shan Waterway, simulating the size and shape changes of the ship domain during the navigation process in this area. The findings indicate that the size of the ship domain dynamically adjusts to the traffic conditions. Additionally, when the navigator state is excellent, the ship domain takes on an irregular diamond shape with the smallest area, whereas when the navigator state is poor, the shape approximates a rectangle with the largest area. Furthermore, the dynamic quaternion ship domain model proposed in this study is compared to the ship domain models put forth by Goodwin, Davis, and co-authors. The results demonstrate that the dynamic quaternion ship domain model is more compatible and suitable for open waters compared to the static quaternion ship domain model.

A Novel Framework of Hybrid Optimization Techniques for Contrast-Enhancement in Cardiac MRI Medical Images

Medical image processing plays a crucial role in diagnosing and treating various diseases, with Magnetic Resonance Imaging (MRI) being a widely used modality. A previous study introduced an algorithm aimed at eliminating noise from MRI images through a filtering process, effectively suppressing mixed noise while preserving important structures like edges. However, limitations in optimizing parameters for image enhancement and occasional edge preservation failures were observed. Addressing the challenge of inadequate contrast enhancement in medical image processing, this study proposes a solution utilizing the hybrid cetacean optimization algorithm and Sand Cat Swarm Optimization (COA-SCSO) to enhance contrast in MRI images. The proposed approach focuses on improving the contrast nature in MRI images to mitigate image degradation during acquisition. Cardiac MRI images from SCMR consensus and AMRG Atlas databases are analyzed, emphasizing the consideration of spatial information for enhancement. The enhancement process involves integrating a cost function with a contrast measure based on multiple metrics to create a novel transformation function. Soft-computing approaches are employed to reduce time complexity, while expanding the search pattern through a transformation function in the spatial domain. This method enhances pixel intensity, contributing to increased image resolution. Performance evaluation of the proposed algorithm incorporates various parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Normalized Absolute Error (NAE), and Structural Similarity Index (SSIM). Results from analysis of ten cardiac MRI images indicate that the proposed algorithm achieves the highest PSNR (98) and SSIM (0.99) values, as well as the lowest NAE (-0.17) and MSE (0.16) values, compared to the particle swarm optimized texture-based histogram equalization (PSOTHE) method and modified sunflower optimization (MSFO) method. Overall, the COA-SCSO approach demonstrates promising results in enhancing contrast in MRI images, offering potential benefits for medical diagnosis and treatment planning. Further research could explore additional optimization techniques and validation on larger datasets to further validate its effectiveness in clinical practice.

Group decision support system based on multi-granular fractional orthotriple fuzzy 2-tuple linguistic information model

The aim of this work is to put forward the concept of a novel decision support model for multi-criteria group decision making problems with multi-granular fractional orthotriple fuzzy 2-tuple linguistic (FOF2TL) information. The idea of fractional orthotriple fuzzy 2-tuple linguistic (2TL) model is put forward by integrating the advantages of fractional orthotriple fuzzy set and 2-tuple linguistic method. We define a transformation function to deal the consistency of multi-granular FOF2TL information. Secondly, the Archimedean coupla as well as the co-coupla are used to define operational laws for fractional orthotriple fuzzy 2-tuple linguistic numbers (FOF2TLNs). We take the relationship between attribute and experts, and defined fractional orthotriple fuzzy 2-tuple linguistic Banzhaf Choquet-Copula aggregation (FOF2TLBCCA) operators. Third, a method explains how to determine the fuzzy measure of expert and attribute sets. Next, the suggested operators are used to develop associated decision-making approaches. Finally, a numerical case is offered to demonstrate the concept's usefulness, along with pertinent comments; the merits of the suggested methodology are also evaluated by comparing it to several existing decision approaches.

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

The use of in-hole imaging to investigate geological structure characteristics is one of the crucial methods for the study of rock mass stability and rock engineering design. The in-hole images are usually influenced by the lighting and imaging characteristics, resulting in the presence of interference noise regions in the images and consequently impacting the classification accuracy. To enhance the analytical efficacy of in-hole images, this paper employs the proposed optimal non-concentric ring segmentation method to establish a new database. This method establishes the transformation function based on the Ansel Adams Zone System and the fluctuation values of the grayscale mean, adjusting the gray-level distribution of images to extract two visual blind spots of different scales. Thus, the inner and outer circles are located with these blind spots to achieve the adaptive acquisition of the optimal ring. Finally, we use the optimal non-concentric ring segmentation method to traverse all original images to obtain the borehole image classification database. To validate the effectiveness of this method, we conduct experiments using various segmentation and classification evaluation metrics. The results show that the Jaccard and Dice of the optimal non-concentric ring segmentation approach are 88.43% and 98.55%, respectively, indicating superior segmentation performance compared to other methods. Furthermore, after employing four commonly used classification models to validate the performance of the new classification database, the results demonstrate a significant improvement in accuracy and macro-average compared to the original database, with the highest increase in accuracy reaching 4.2%. These results fully demonstrate the effectiveness of the proposed optimal non-concentric ring segmentation method.

Holistic Environmental Risk Index for Oil and Gas Industry in Colombia

Risk management for technological hazards mainly focuses on the consequences for human lives. Although technological risk analysis evaluates environmental vulnerability, it does not reflect the consequences of environmentally exposed elements. This paper’s objective is to propose a conceptual framework and create a multidisciplinary evaluation model for environmental risk analysis in the oil and gas industry. A holistic assessment was carried out based on probabilistic risk analysis methodologies to obtain a holistic environmental risk index, HERi. Moncho’s Equation was adapted by combining ecological risk, ER, and an aggravating coefficient, F. Transformation functions were utilized to represent the risk probability distributions. The results from the holistic environment risk index were standardized in a sigmoidal function using the ALARP criteria. Finally, the methodology was applied in two case studies in Colombia, comparing the results with an alternative model. This study found that Colombian armed conflict is a key factor that increases environmental risk in oil and gas projects. The proposed methodology takes a holistic approach by integrating socioeconomic factors and resilience considerations into the risk assessment process. This approach provides a more comprehensive understanding of the environmental risks associated with oil and gas projects in Colombia and promotes more effective sustainable management actions.

Optimizing Mental Health Detection in Indian Armed Forces Personnel through Feature Engineering Driven Dataset Reduction, Addressing Suicide, Depression, and Stress

Within the realm of machine learning, the construction of high-quality datasets stands as a crucial factor profoundly influencing model performance. This research aims to furnish a comprehensive guide for enhancing the accuracy and efficiency of dataset construction. It achieves this by integrating multi-variate reduction techniques and innovative feature engineering strategies, implemented within the Python programming ecosystem. As the landscape of datasets becomes increasingly diverse and complex, the imperative to optimize precision grows more critical. This study explores the judicious application of dimensionality reduction methods, such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), alongside various feature selection approaches to strategically streamline datasets while preserving vital information. In conjunction with these reduction techniques, the research introduces novel feature engineering methods to amplify the discriminative power of remaining features, thereby enriching the dataset's representational capacity. The exploration spans a spectrum of multi-variate reduction techniques and delves into feature engineering methodologies, including polynomial feature creation, interaction term generation, and domain-specific transformation functions. Practical implementations of these techniques are demonstrated through Python, showcasing their applicability across diverse domains. Empirical evaluations on real-world datasets underscore the efficacy of the proposed methodology, revealing superior accuracy and efficiency compared to conventional dataset construction approaches. The insights derived from this research contribute significantly to the broader discourse in machine learning, presenting a generic yet potent framework for enhancing precision in datasets. Beyond deepening our understanding of multi-variate reduction and feature engineering, the findings offer a practical guide for researchers and practitioners seeking to optimize precision in various machine learning applications.

Bipartite containment control of nonlinear multi‐agent systems with unknown inputs and state constraints

SummaryThis article addresses the issue of bipartite containment tracking for nonlinear strict feedback multi‐agent systems (MASs) with disturbances and measurement uncertainties. To provides a better performance against strong disturbance, a coordination transformation is applied such that all the nonlinear functions, controller disturbances and measurement uncertainties are lumped into a single disturbance signal in the last state equation. Then, the lumped disturbance is compensated through a novel disturbance rebuilding method. Moreover, in order to get better control performance, state constraints are also considered through a nonlinear transformation function (NTF). Different from traditional methods dealing with the state constraint problems, the NTF method can avoid using any discontinuous Lyapunov function. Finally, an adaptive finite‐time distributed controller is established via the disturbance estimates, backstepping technique and fuzzy logic system (FLS). The performance of the controller is illustrated by a simulation example in which the bipartite containment formation of followers is realized in a practical finite time.

CSADF: ingesting cuckoo search optimization algorithm enabled with fitness function for effective model transformation pertaining to ADF

CSADF: ingesting cuckoo search optimization algorithm enabled with fitness function for effective model transformation pertaining to ADF

The biotic effects of lignite on humic acid components conversion during chicken manure composting

Targeted regulation of composting to convert organic matter into humic acid (HA) holds significant importance in compost quality. Owing to its low carbon content, chicken manure compost often requires carbon supplements to promote the humification progress. The addition of lignite can increase HA content through biotic pathways, however, its structure was not explored. The Parallel factor analysis revealed that lignite can significantly increase the complexity of highly humified components. The lignite addition improved phenol oxidase activity, particularly laccase, during the thermophilic and cooling phases. The abundance and transformation functions of core bacteria also indicated that lignite addition can influence the activity of microbial transformation of HA components. The structural equation model further confirmed that lignite addition had a direct and indirect impact on enhancing the complexity of HA components through core bacteria and phenol oxidase. Therefore, lignite addition can improve HA structure complexity during composting through biotic pathways.

Anatomical organization of the cerebrum of the praying mantis Hierodula membranacea.

Many predatory animals, such as the praying mantis, use vision for prey detection and capture. Mantises are known in particular for their capability to estimate distances to prey by stereoscopic vision. While the initial visual processing centers have been extensively documented, we lack knowledge on the architecture of central brain regions, pivotal for sensory motor transformation and higher brain functions. To close this gap, we provide a three-dimensional (3D) reconstruction of the central brain of the Asian mantis, Hierodula membranacea. The atlas facilitates in-depth analysis of neuron ramification regions and aides in elucidating potential neuronal pathways. We integrated seven 3D-reconstructed visual interneurons into the atlas. In total, 42 distinct neuropils of the cerebrum were reconstructed based on synapsin-immunolabeled whole-mount brains. Backfills from the antenna and maxillary palps, as well as immunolabeling of γ-aminobutyric acid (GABA) and tyrosine hydroxylase (TH), further substantiate the identification and boundaries of brain areas. The composition and internal organization of the neuropils were compared to the anatomical organization of the brain of the fruit fly (Drosophila melanogaster) and the two available brain atlases of Polyneoptera-the desert locust (Schistocerca gregaria) and the Madeira cockroach (Rhyparobia maderae). This study paves the way for detailed analyses of neuronal circuitry and promotes cross-species brain comparisons. We discuss differences in brain organization between holometabolous and polyneopteran insects. Identification of ramification sites of the visual neurons integrated into the atlas supports previous claims about homologous structures in the optic lobes of flies and mantises.

Adaptive fuzzy tracking control for a class of time-varying output constrained nonlinear systems with non-affine nonlinear faults

This paper investigates a tracking control issue for a class of time-varying output constrained nonlinear systems subject to non-affine nonlinear faults and virtual control coefficients. In the controller design process, the nonlinear function is approximated by fuzzy logic systems. Utilizing a feasible function to convert the system, a new transformation strategy is proposed to handle the system with time-varying output constraints or without output constraints. The mean value theorem is applied to split non-affine faults, and the Nussbaum-type function is used to eliminate the effect of the unknown directional affine variable gain of input resulted from non-affine faults. Combined with adaptive fuzzy backstepping technology and the error transformation function, a new control strategy is presented, which not only deals with the time-varying output constraint but also handles non-affine faults, effectively. Finally, all closed-loop system signals are bounded, and the tracking error can converge to a small preset set. Two simulations are performed to confirm the validity of the presented strategy.

Determining features in the application of redundancy for the thermistor cubic transformation function using computer simulation

The object of research is the process of temperature measurement with a platinum thermistor. We have conducted studies on the cubic transformation function of the thermistor when using redundancy that yielded the equation of redundant measurements of the desired temperature. Owing to this, it became possible to directly apply the resulting equation without additional measures to linearize the function of the thermistor transformation. In addition, the obtained value of the desired temperature does not depend on the values of the parameters of the cubic transformation function and their deviations from the nominal values. Experimental studies have proven that the value of the normalized temperature T0 has a greater influence on the result of redundant measurements and the value of the normalized temperature DT on the entire range of measured temperatures Tx is almost unaffected. The best accuracy results (value of relative error δ=0.02 %) were obtained at T0 values lower than –60 °C. When the error of reproduction of normalized temperatures increased from ±0.02 °C to ±0.1 °C, the best accuracy results (value of relative error δ=0.06 %) were obtained at values of normalized temperature T0 below –130 °C. Analysis of results of the absolute error DT revealed that with an error of reproduction of normalized temperatures of ±0.02 °C and at T0=–180 °C, its value does not exceed 0.02 °C, that is, it is within the error of reproduction of normalized temperatures. This allows us to state that it is recommended to use sources of standardized temperatures of high accuracy during measurement control. Thus, there are reasons to assert the prospect for redundant measurements when directly measuring temperature with a thermistor with a cubic transformation function with high accuracy

Adaptive fast finite‐time control for nonlinear systems subject to output hysteresis by fuzzy approach

SummaryThis article investigates an adaptive fast finite‐time tracking control problem for a class of uncertain nonlinear systems with output hysteresis. The idea of output hysteresis compensation is skillfully extended to adaptive design by employing a hysteresis inverse transformation and barrier Lyapunov function. The backstepping technique is adopted to establish the fast finite‐time control strategy, which can realize finite‐time convergence of the closed‐loop system. A rigorous theoretical analysis is performed to illustrate that the signals in the closed‐loop systems are bounded in finite time, and the tracking error can converge into a compact set with sufficient accuracy. Finally, a numerical simulation is given to confirm the effectiveness of the presented strategy.

On a mathematical theory for the crystallography of shear transformations in metals: Unification of mechanisms

A theory for shear transformations in crystalline materials linking their Crystallography, atomic-scale mechanisms and Thermodynamics is introduced. The lattice and transformation function are treated as vector space and functions respectively to overcome shortcomings of classical matrix-based theories for martensite crystallography. The extremum principle is used to obtain the atomic paths minimising and maximising the energy from all possible solutions, therefore explaining which sequences are more Thermodynamically feasible. The face-centred cubic (FCC)→body-centred cubic (BCC) transformation in Fe is used as case study. The theory predicts that the average values of the extrema correspond to the crystallography, shear magnitude, interface defects and habit planes of main transformations in Fe, i.e. Widmanstätten Ferrite, Bainite, lath martensite, plate and lenticular martensite. Bainite represents the macroscopic average minimum energy configuration, whereas Widmanstätten Ferrite corresponds to a local and discrete minimum energy configuration. Lath martensite corresponds to the average shear configurations minimising atomic displacements when there is no other driving force, e.g. diffusion, whereas the maximum energy configurations correspond to plate and lenticular martensite forming twin pairs. Targeted results for the FCC→hexagonal close-packed lattice transformation are presented to demonstrate the robustness of the theory. The predictions are combined with elasticity theory to compute the Driving Force and predict the start temperatures – therefore connecting for the first time – local and macroscopic crystallographic features with the Thermodynamics of shear-controlled structures in Fe. This work presents new results towards addressing longstanding challenges in Materials Science such as theoretically demonstrating that Bainite is a shear transformation.

Bias Correction of IMERG Data in the Mountainous Areas of Sumatra Based on A Single Gauge Observation

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra’s mountainous region to improve performance. HIGHLIGHTS The LS method shows the best improvement in the accuracy of IMERG data in the mountainous area of Sumatera compared to the LOCI, PT, and CDF methods, as indicated by the largest decrease in RMSE and RB values CSI values prove that LOCI, PT, and CDF methods successfully improve the detection capability of IMERG hourly and daily data in the mountainous region of Sumatra The CDF method shows the best quality in improving extreme rainfall observations in the mountainous region of Sumatra GRAPHICAL ABSTRACT

H[formula omitted] finite-time prescribed performance trajectory tracking control of MSVs with self-adjusting neural networks

The paper proposes a robust adaptive finite-time prescribed performance trajectory tracking control scheme for marine surface vessels (MSVs) under unknown external disturbance and model uncertainty, based on H∞ control and self-adjusting neural networks (SANN). Firstly, to tackle the challenge of unknown velocity, a high-gain observer is introduced. Secondly, SANN is designed to tackle model uncertainty, achieving a balance between the optimal number of neurons and the best expected performance, thus saving network resources. Simultaneously, finite-time (FT) auxiliary functions, error transformation functions, and H functions are introduced, ensuring a bounded expected decay level with L2 norm within FT. Finally, by combining backstepping, SANN, prescribed performance control and FTH∞ control methods, the system ensures the achievement of practically finite-time stable using fewer network resources under prescribed performance conditions. Simulation results validate the effectiveness of the proposed controller.