Automatic Adaptation Research Articles

Formula omitted]-SGP: Scaled Gradient Projection with [formula omitted]-divergence for astronomical image restoration

Image restoration in astronomy has been considered a vital step in many ground-based observational programs that often suffer from sub-optimal seeing due to atmospheric turbulence, distortion of stellar shapes due to instrumental aberrations, trailing, and other issues. It holds importance for various tasks: improved astrometry, deblending of overlapping sources, faint source detection, and identification of point sources near bright extended objects, such as galaxies, to name a few. We conduct an empirical study by applying the Scaled Gradient Projection (SGP) iterative image deconvolution algorithm to restore distorted stellar shapes in our observed data. We investigate using a more flexible divergence measure, the β-divergence, which contains the commonly-used Kullback–Leibler (KL) divergence as a special case and allows automatic adaptation of the parameter β to the data. An extensive set of experiments comparing the performance of SGP and its β-divergence variant (β-SGP) is carried out on extracted star stamps and on images containing multiple stars (both crowded and relatively sparser fields). We show a consistent enhancement in the flux conservation across all considered scenarios using β-SGP compared to SGP. Using a few quantifiable metrics such as the Full-Width-at-Half-Maximum (FWHM) and ellipticity of stars, we observe that β-SGP improves restoration quality, compared to the SGP, in many cases and still preserves restoration quality in others. We conclude that generalized versions of image restoration algorithms are more robust due to their enhanced flexibility and could be a promising modification for astronomical image restoration.

Battery Management System for Analysing Accurate Real Time Battery Condition using Machine Learning

The energy storage system is one of the essential components of Electric Vehicles (EV) that is anticipated to penetrate the current transport market because of the constant increase in environmental pollution and prices. Most EVs use lithium-ion batteries as their source of power. Lithium-ion batteries are rechargeable batteries that are typically used to power portable devices and EVs as well as Hybrid Electric Vehicles (HEVs) (powered both by fuel and electricity). The battery performance degrades progressively with time leading to some potential disasters. Current approaches for data-driven fault prediction provide good results on the exact processes they were trained on but here the batteries often lack the ability to adapt to flexibly change. To overcome the problem, here use Sequential learning which promises flexibility, allowing for an automatic adaption of previously learned knowledge to new tasks. Thus, it provides the State of Charge (SoC) of the battery and predicts its condition, which gives highly accurate results.

Toward the use of LES for industrial complex geometries. Part I: automatic mesh definition

With the constant increase of computational power for the past years, Computational Fluid Dynamics (CFD) has become an essential part of the design in complex industrial processes. In this context, among the scale resolving numerical methods, Large-Eddy Simulation (LES) has become a valuable tool for the simulation of complex unsteady flows. To generalise the industrial use of LES, two main limitations are identified. First, the generation of a proper mesh can be a difficult task, which often relies on user-experience. Secondly, the ‘time-to-solution’ associated with the LES approach can be prohibitive in an industrial context. In this work, these two challenges are addressed in two parts. In this Part I, an automatic procedure for mesh definition is proposed, whereas the Part II is devoted to numerical technique to reduce the LES ‘time-to-solution’. The main goal of these works is then to develop an accurate LES strategy at an optimised computational cost. Concerning the mesh definition, because LES is based on separation between resolved and modelled subgrid-scales, the quality of the computed solution is then directly linked to the quality of the mesh. However, the definition of an adequate mesh is still an issue when LES is used to predict the flow in an industrial complex geometry without a priori knowledge of the flow dynamics. This first part presents a user-independent approach for both the generation of an initial mesh and the convergence of the mesh in the LES framework. An automatic mesh convergence strategy is proposed to ensure LES accuracy. This strategy is built to guarantee a mesh-independent mean field kinetic energy budget. The mean field kinetic energy is indeed expected to be mesh independent since only turbulent scales should be unresolved in LES. The approach is validated on canonical cases, a turbulent round jet and a turbulent pipe flow. Finally, the PRECCINSTA swirl burner is considered as a representative case of complex geometry. First, an algorithm for the generation of an unstructured mesh from a STL file is proposed to generate a coarse initial mesh, before applying the mesh convergence procedure. The overall strategy including automatic first mesh generation and its automatic adaptation paves the way to use LES approach as a decision support tool for various applications, provided that the ‘time-to-solution’ is compatible with the applications constraint. A second paper, referred as Part II, is devoted to the reduction of this time.

HALO3D: An All-Mach Approach to Hypersonic Flows Simulation, Part II

ABSTRACT This paper is Part II of the two-paper series ‘HALO3D: An All-Mach Approach to Hypersonic Flows Simulation’ presenting advances in a monolithic flow simulation software for the design of high-speed aircraft. Part II highlights the methodology behind the continuum regime finite-rate surface chemistry, weak-ionisation, and rarefied regime Direct Simulation Monte Carlo modules. Validation and benchmark simulations of flows over a 2D cylinder, the 3D RAM-C II geometry, and the 3D Lockheed Martin X-33 geometry are provided. Within HALO3D, a Hessian-based multiphysics automatic mesh adaptation methodology yields optimal solutions that ought to provide valuable benchmarks for verifying other hypersonic flow simulation tools. Such mesh optimisation automatically generates a common truly mesh-independent solution across the continuum and rarefied regimes, taking into account various thermo-chemical models. These developments establish HALO3D as a mature, readily extendible software package for hypersonic aircraft analysis.

Research on Hydraulic Concrete Strength Prediction Model Based on ANN-RBF Neural Network

In recent years, with the continuous development of construction engineering level, the number of hydraulic buildings and dam projects has gradually increased, and the prediction of hydraulic concrete strength, as the main component of buildings, is particularly important. Based on the radial basis function (RBF) provided in the neural network (ANN), the SPSS27 platform is called, considering that the method has the characteristics of automatic adaptation to determine the network and the absence of the need to give initial weights artificially. The method is applied to the prediction of hydraulic concrete strength model, and experimental data are processed using the ANN-RBF method to obtain the results of the factors and their joint effects on the concrete strength, which is a more intuitive way to understand the interrelationship of the factors and their influence mechanism. The results show that the method is able to predict the results well and the research is of great importance for accurate prediction of the strength of hydraulic concrete.

Heating System Control with Neural Network

Building performance has significant impact on humans’ life and ecology as buildings account for 39% of total greenhouse gas emissions and consume about 40% of total global energy consumption. Smart building control is one of the key points to archive high energy efficiency. Each year, the complexity of building state control grows due to the increase in the number of controlled elements that are used to achieve better indoor climate. Therefore, in the manual analysis and implementation of the building control program, an error can easily appear due to the human factor. Artificial intelligence (AI) algorithms could be used as an alternative solution as they could evaluate building dynamics independently. One of strategies for automatic building control adaptation to its dynamic is model based predictive control where neural network is used for different control strategies evaluation. Performance of such control technique is highly dependent on control strategies evaluation accuracy. To achieve top accuracy, several hyperparameters of neural network could be tuned as well as data set for specific construction must be prepared. Preparation of data set could be a problem as random control of building for generation of dataset could be not unacceptable for building users as well as it could damage construction.In this paper authors process optimization of experimental building heating system control algorithm to achieve smaller fluctuations of temperature indoors. For dataset generation were used several data from weather station as well as heating system parameters and temperature indoors while building was controlled by thermostat with build in PID regulation. For evaluation of building dynamics were used temporal convolutional neural network. To achieve high accuracy results of control strategies evaluation, several hyperparameters of neural network were tested. Finally resulting model were tested on physical building. Results indicate that in some cases developed control model could prevent temperature fluctuations which could be caused by limits of heating system power.

Research on energy‐saving technology of loader hydraulic torque converter based on MATLAB dynamic characteristic simulation

AbstractAt present, hydraulic transmission loaders have high automatic adaptability and convenient operation, but their transmission efficiency is low, and the intelligent degree of energy consumption regulation is limited. Strengthening the research of locking device in torque converter is an important measure to effectively reduce energy consumption. Therefore, the research uses MATLAB software and torque converter parameters to build the external characteristic curves of the engine and torque converter, and builds the dynamic model based on the working principle and locking process of the lockup clutch. The research and experimental analysis are carried out from three aspects of the lockup dynamic process of the torque converter, the lock‐up and unlocking conditions, and the change of the lockup oil pressure of the clutch. The results show that the speed difference range between pump wheel and turbine (233.08–365.44 rpm) is much lower than that of other cases (409.68–616.23 rpm) when the engine and torque converter are matched. And the fuel rate is the highest when the throttle opening of the loader is 70%, about 38%. The above results show that the locking structure of the hydraulic torque converter of the loader can effectively reduce the working vibration and improve the service life and fuel rate. And its energy‐saving application effect has an important enlightening effect on the performance optimization and resource utilization of construction machinery, and provides new ideas for improvement.

Adaptive Mode Filter for Lamb Wavefield in the Wavenumber-Time Domain Based on Wavenumber Response Function

Aerospace thin-walled structures are susceptible to various forms of damage and they can be effectively inspected using Lamb wavefields. However, Lamb wavefields contain at least two dispersive modes which interfere with the generation of a clear image for damage visualization. Conventional mode filters produce inconsistent results due to the need for the ad hoc or manual adjustment of the processing parameters by experienced users. An automatic, adaptive mode filter is proposed to remove human subjectivity, thereby improving the consistency of the results and making it more practical to utilize. It converts the wavefield from the space-time domain to the wavenumber-time domain and then consolidates the data in the time and angular axes into a wavenumber response function (WRF) where the modes can be automatically isolated. The single-mode data were converted back into space-time domain for result visualization. Its effectiveness was experimentally proven by keeping 78.2–122.0% of energy for a wanted mode and suppressing the energy of an unwanted mode to 0.1–4.5%. Its automatic adaptability was demonstrated through the improved visibility of a blind hole, corrosion, water-ingress in honeycomb panel, and impact damage in a complex composite wing. Consistent results can be generated in a highly efficient manner while significantly reducing the computational workload and hardware requirements.

Computational Complexity-based Fractional-Order Neural Network Models for the Diagnostic Treatments and Predictive Transdifferentiability of Heterogeneous Cancer Cell Propensity

Neural networks and fractional order calculus are powerful tools for system identification through which there exists the capability of approximating nonlinear functions owing to the use of nonlinear activation functions and of processing diverse inputs and outputs as well as the automatic adaptation of synaptic elements through a specified learning algorithm. Fractional-order calculus, concerning the differentiation and integration of non-integer orders, is reliant on fractional-order thinking which allows better understanding of complex and dynamic systems, enhancing the processing and control of complex, chaotic and heterogeneous elements. One of the most characteristic features of biological systems is their different levels of complexity; thus, chaos theory seems to be one of the most
 applicable areas of life sciences along with nonlinear dynamic and complex systems of living and non-living environment. Biocomplexity, with multiple scales ranging from molecules to cells and organisms, addresses complex structures and behaviors which emerge from nonlinear interactions of active biological agents. This sort of emergent complexity is concerned with the organization of molecules
 into cellular machinery by that of cells into tissues as well as that of individuals to communities. Healthy systems sustain complexity in their lifetime and are chaotic, so complexity loss or chaos loss results in diseases. Within the mathematics-informed frameworks, fractional-order calculus based Artificial Neural Networks (ANNs) can be employed for accurate understanding of complex biological
 processes. This approach aims at achieving optimized solutions through the maximization of the model’s accuracy and minimization of computational burden and exhaustive methods. Relying on a transdifferentiable mathematics-informed framework and multifarious integrative methods concerning computational complexity, this study aims at establishing an accurate and robust model based upon
 integration of fractional-order derivative and ANN for the diagnosis and prediction purposes for cancer cell whose propensity exhibits various transient and dynamic biological properties. The other aim is concerned with showing the significance of computational complexity for obtaining the fractional-order derivative with the least complexity in order that optimized solution could be achieved. The multifarious
 scheme of the study, by applying fractional-order calculus to optimization methods, the advantageous aspect concerning model accuracy maximization has been demonstrated through the proposed method’s applicability and predictability aspect in various domains manifested by dynamic and nonlinear nature displaying different levels of chaos and complexity.

Lagrangian and Eulerian algorithms for water droplets in in-flight ice accretion

A particle tracking framework for the computation of the collection efficiency for in-flight ice accretion is presented. Algorithms for the computation of the collection efficiency are presented for both Lagrangian and Eulerian descriptions of the droplets equations. Strengths and weaknesses of each method are analyzed and possible solutions are described and implemented. For the Lagrangian solver, a method for automatic resolution adaptation is introduced, and a strategy for exploiting parallelism is described. In the Eulerian frame a scheme is presented for the solution of the equations on unstructured 3D grids using a relaxation approach. An hybrid Lagrangian Eulerian approach for the computation of the collection efficiency is also introduced which is relevant for Super-cooled Large Droplet condition. All presented algorithms are thoroughly verified, and a comprehensive validation is performed.

Replication of Computational Results Report for “Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns”

In this article, a reproducibility study is presented, with reference to the computational results reported in the article “Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns,” by P. Wilsdorf, A. Wolpers, J. Hilton, F. Haack, and A. M. Uhrmacher. Based on the achieved results, the Artifacts Available badge is assigned.

Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns

Simulation experiments are typically conducted repeatedly during the model development process, for example, to revalidate if a behavioral property still holds after several model changes. Approaches for automatically reusing and generating simulation experiments can support modelers in conducting simulation studies in a more systematic and effective manner. They rely on explicit experiment specifications and, so far, on user interaction for initiating the reuse. Thereby, they are constrained to support the reuse of simulation experiments in a specific setting. Our approach now goes one step further by automatically identifying and adapting the experiments to be reused for a variety of scenarios. To achieve this, we exploit provenance graphs of simulation studies, which provide valuable information about the previous modeling and experimenting activities, and contain meta-information about the different entities that were used or produced during the simulation study. We define provenance patterns and associate them with a semantics, which allows us to interpret the different activities and construct transformation rules for provenance graphs. Our approach is implemented in a Reuse and Adapt framework for Simulation Experiments (RASE), which can interface with various modeling and simulation tools. In the case studies, we demonstrate the utility of our framework for (1) the repeated sensitivity analysis of an agent-based model of migration routes and (2) the cross-validation of two models of a cell signaling pathway.

Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration.

A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies. Through automatic adaptation and recalibration, adaptive soft sensor concepts have the potential to generalize soft sensor principles and make them applicable across bioprocesses. In this study, a new generalized adaptation algorithm for soft sensors is developed to provide phase-dependent recalibration of soft sensors based on multiway principal component analysis, a similarity analysis, and robust, generalist phase detection in multiphase bioprocesses. This generalist soft sensor concept was evaluated in two multiphase bioprocesses with various target values, media, and microorganisms. Consequently, the soft sensor concept was tested for biomass prediction in a Pichia pastoris process, and biomass and protein prediction in a Bacillus subtilis process, where the process characteristics (cultivation media and cultivation strategy) were varied. High prediction performance was demonstrated for P. pastoris processes (relative error = 6.9%) as well as B. subtilis processes in two different media during batch and fed-batch phases (relative errors in optimized high-performance medium: biomass prediction = 12.2%, protein prediction = 7.2%; relative errors in standard medium: biomass prediction = 12.8%, protein prediction = 8.8%).

Laboratory Radiometric Calibration Technique of an Imaging System with Pixel-Level Adaptive Gain.

In a routine optical remote sensor, there is a contradiction between the two requirements of high radiation sensitivity and high dynamic range. Such a problem can be solved by adopting pixel-level adaptive-gain technology, which is carried out by integrating multilevel integrating capacitors into photodetector pixels and multiple nondestructive read-outs of the target charge with a single exposure. There are four gains for any one pixel: high gain (HG), medium gain (MG), low gain (LG), and ultralow gain (ULG). This study analyzes the requirements for laboratory radiometric calibration, and we designed a laboratory calibration scheme for the distinctive imaging method of pixel-level adaptive gain. We obtained calibration coefficients for general application using one gain output, and the switching points of dynamic range and the proportional conversion relationship between adjacent gains as the adaptive-gain output. With these results, on-orbit quantification applications of spectrometers adopting pixel-level automatic gain adaptation technology are guaranteed.

Motion Compensation for Long Integration Times and DoA Processing in Passive Radars

In this work, a multistage target motion compensation solution for long integration times and the direction of arrival processing in geostationary-satellite-based passive radars is presented. Long integration processing intervals are considered to compensate for the associated propagation loss, but during this time target dynamics can extend the backscattering in more than one range or Doppler cell. To control the gain-processing reduction, a combination of detection, tracking, feature extraction, and filtering techniques is designed to provide automatic adaptation to each unknown target dynamic in the area of interest. The proposed methodology is validated with real data acquired by the passive radar demonstrator developed by the University of Alcalá (IDEPAR), and the results confirm that target monitoring exploiting digital video broadcasting-satellite (DVB-S) signals is clearly improved.

Automated process planning in milling of hybrid components

Hybrid material composites can meet the increasing demands for high strength and low weight due to their different workpiece properties. Usually, hybrid components require post-machining after their fabrication. Due to the different material properties, new challenges arise in the machining process. It is essential to recognize the course of the material boundary in order to adapt the process planning accordingly and to enable a uniform material transition during machining. This paper presents a method for automated material recognition and automatic adaptation of the process parameters considering a uniform force level during the milling of hybrid materials. This way, the load on the milling tool in the material transition area can be reduced by up to 71%, which prevents premature tool failure. An optical laser line scanner is used to localize of material transitions within hybrid components. This enables a digital mapping of the material distribution in the discretized workpiece model. In combination with an empirical force model, it is possible to predict the cutting forces of the different materials and determine the material transition area for adapting them to specified target values.

A dataset for plain language adaptation of biomedical abstracts

Though exponentially growing health-related literature has been made available to a broad audience online, the language of scientific articles can be difficult for the general public to understand. Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature. Deep Learning algorithms for automatic adaptation are a possible solution; however, gold standard datasets are needed for proper evaluation. Proposed datasets thus far consist of either pairs of comparable professional- and general public-facing documents or pairs of semantically similar sentences mined from such documents. This leads to a trade-off between imperfect alignments and small test sets. To address this issue, we created the Plain Language Adaptation of Biomedical s dataset. This dataset is the first manually adapted dataset that is both document- and sentence-aligned. The dataset contains 750 adapted abstracts, totaling 7643 sentence pairs. Along with describing the dataset, we benchmark automatic adaptation on the dataset with state-of-the-art Deep Learning approaches, setting baselines for future research.

A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Serdes Receiver

This paper presents a dedicated digital signal process (DSP) for four pulse amplitude modulation (PAM4) SerDes receivers. It is targeted to implement data recovery and adaptive equalization under ultra-high-speed and large channel attenuation with a small area and high power efficiency. The DSP consists of a clock data recovery (CDR), a 16-tap feed forward equalizer (FFE), a 1-tap decision feedback equalizer (DFE), and an automatic adaptation engine. An adaptive least mean square (LMS) algorithm is utilized to make the system more intelligent in calculating tap coefficients of the FFE and DFE. To address the timing limitation associated with traditional digital DFE that cannot handle large amounts of parallel data at a high speed, speculative techniques and a customized 4-to-1 multiplexer (MUX) unit are employed to remove the summation time and reduce the selection time, respectively. A first-order sigma-delta modulator is used to replace the traditional moving average to calculate average voltages, which could prominently save the hardware resources and power consumption. Additionally, the influence of input quantization resolution on the equalization ability is analyzed. Implemented in a 28-nm CMOS, the DSP could compensate for up to 33-dB loss at 100 Gb/s with a power consumption of 7.22 pJ/bit.

Trend filtering for functional data

Despite increasing accessibility to function data, effective methods for flexibly estimating underlying functional trend are still scarce. We thereby develop a functional version of trend filtering for estimating trend of functional data indexed by time or on general graph by extending the conventional trend filtering, a powerful nonparametric trend estimation technique for scalar data. We formulate the new trend filtering by introducing penalty terms based on ‐norm of the differences of adjacent trend functions. We develop an efficient iteration algorithm for optimizing the objective function obtained by orthonormal basis expansion. Furthermore, we introduce additional penalty terms to eliminate redundant basis functions, which leads to automatic adaptation of the number of basis functions. The tuning parameter in the proposed method is selected via cross validation. We demonstrate the proposed method is locally adaptive and can identify change points through simulation studies and applications to real‐world datasets.

Automatic shape adaptation scheme planning for CAD models in direct modeling

Designing a new CAD model by adapting a retrieved candidate model offers several advantages; however, shape adaptation is often a tough job. To facilitate shape adaptation, direct modeling (technology) provides a very flexible and intuitive approach. However, based on new design requirements, the research on automatic and effective methods to determine the geometries on the candidate model that need adapting and how to adapt these geometries has rarely been reported till date. The shape adaptation based on direct modeling is often dependent on manual processing and thus it is usually a tedious process. To account for this, a novel automatic shape adaptation scheme planning approach is proposed in this study. The candidate model is usually retrieved using a more shape-simple query model that indicates the overall shape meeting the main functional requirements of a final product (i.e., new primary shape requirements); therefore, this study focuses on the automatic adaptation of the shape of the candidate model to that of the query model to accelerate its entire adaptation progress. First, to describe the shape requirements indicated by a CAD model, an attributed face-layout graph (AFLG) is presented. Further, by comparing the candidate model’s AFLG to that of the query model, the geometries (i.e., faces) on the candidate model requiring adaptation can be determined. Next, integrating with a geometric detail reuse strategy and a novel incremental adapting method on the presented graph, an improved particle swarm optimization method is developed to determine an optimized shape adaptation scheme (i.e., an optimized direct modeling operation sequence) based on the determined geometries. Finally, by applying the above-mentioned scheme, the face layout and shape parameters are smoothly transferred from the query model to the candidate model as shape requirements, and the shape of the candidate model gets automatically adapted to the query model while its geometric details are reused adaptively. Besides, a prototype system is also implemented, and the experimental results are analyzed to verify the effectiveness and characteristics of the proposed approach. • Determining geometrics with different shape requirements by comparing new descriptors. • Generating optimized operation sequence based on a novel graph incremental adapting. • Reusing geometric details adaptively based on topology change prediction.