Model-based Approach Research Articles

Neural network–based transfer learning to improve stiffness modeling of industrial robots with small experimental data sets

AbstractStiffness modeling is an essential subject for the composition of robot control. Accurate stiffness modeling is helpful for improving the control accuracy of industrial robots, particularly under dynamic load circumstances. The classic virtual joint modeling (VJM) method is challenging in predicting the deformation of the end-effector throughout the full workspace due to the nonlinear deformation of the robot joint and its serial articulated structure. This paper proposes a full-space stiffness modeling method for robots based on the integration of a multi-layer perceptual (MLP) model and VJM. To provide enough training data for the MLP model, VJM is used to build a stiffness model with a small set of experimental data to generate 106,400 training data. A model-based transfer learning approach is proposed to improve the model’s accuracy and generalization regarding the difference between generated training data and actual experimental data. The VJM stiffness model is compared with the MLP stiffness model and the existing CNN-based transfer learning model based on the same experimental data. Considering the deformation prediction in the three directions in Cartesian space, the mean absolute error, standard deviation, and maximum error of the MLP model are decreased by at least 24.90%, 14.20%, and 8.50%, respectively, than the VJM. These prediction results demonstrate that the proposed modeling technique can significantly increase the accuracy of robot stiffness modeling, which is essential for position compensation in precise motion control of robots under dynamic load.

QPSK circuit optimization Based on Model-Based Design

In order to address the difficult problem of hardware implementation of complex communication algorithm, this paper takes digital phase modulation as an example, and uses model-based design approach to quickly complete the algorithm modeling, simulation and automatic generation of HDL code. Model-based design is a fast and effective design process for algorithm modeling, simulation, testing and automatic code generation. The simulation results show that the model-based design method cannot only quickly accomplish the modeling and testing of the algorithm, but also improve the design accuracy.

A model-based approach for the preliminary design of the SAR upstream element for the Italian IRIDE EO constellation based on users’ demand

A model-based approach for the preliminary design of the SAR upstream element for the Italian IRIDE EO constellation based on users’ demand

SME representation in chambers of commerce bodies – a model perspective

Background: The research analyzes the formal representation of micro, small, and medium-sized enterprises (SMEs) in the bodies of chambers of commerce in a model-based approach. Research purpose: Chambers of commerce are managed by their bodies (board, general assembly, etc.). Their composition is most often chosen by members in elections. It should reflect the structure of the associated enterprises – at least in terms of industry and size classes. The division of mandates affects the operational activity of the organization – including the content of positions developed in consultation processes and services provided to entrepreneurs. The aim of the study is to verify whether in the countries selected for analysis (representing different models of functioning of chambers of commerce) there are regulations securing the formal representation of SMEs in the bodies of chambers. And if they do exist, what form do they take? In addition to determining the actual situation, the advisability and possible way of regulating the SME parity in several model scenarios is analyzed. Methods: Using the deductive method and critical analysis, the study analyzes the statutes of chambers of commerce in selected countries and reviews sociological, economic and legal literature. Conclusions: The findings indicate that there are only a few examples of regulations protecting the representation of SMEs in chambers’ bodies. It was also found that the rules for representing different sized enterprises in the bodies and task groups of chambers of commerce are closely linked to the model of these organizations. In the Anglo-Saxon private law model, business owners primarily participate voluntarily in selected organizations. However, in countries with a single chamber per region, free choice of organization does not in fact exist. In the continental public law model, where membership is universal by operation of law, statutes focus primarily on mapping the structure of dominant industries in a region and usually omit other criteria. The sectoral model offers the greatest potential for balancing the representation of every size of industry and enterprise.

Adaptive Random Early Detection Using Deep Reinforcement Learning for Enhanced Congestion Control in Dynamic TCP/IP Networks

AQM is used to meet targets for congestion control and low delay, high throughput in TCP/IP networks. But methods developed for AQM like Random Early Detection (RED) work strictly on the control parameters pre-set on them and thus may not respond very well to changing network conditions. Thus, this paper presents a composite AQM model using DRL based on DQN, and the ability to learn independently regarding the AQM weight parameter of the queue. The fact that DRL is adaptive means the effectiveness of the proposed system is also assured. As it is observed in implementing traditional model-based approach, the stability and performance degrade when tested in different network conditions; however, DRL’s ability to learn independently enables ML to solve network congestion as it happens. Consequently, more stability is achieved, the delay is lesser, more bandwidth is used and the overall packet drop rate is low; the proposed DRL-RED model is ideal for dynamic network environments. The proposed DRL-RED model is compared with the regular RED algorithm for both low density and high-density networks. This proposed model has achieved sustained throughput of up to 49.9 Mbps with 0.949 % reduction on delay and very low PLR of 8.38043%. From the comparative analysis, it is clear that the employment of DRL with RED improves the stock of network throughput, the reduction on packet drop frequency, and the general delay in network situations. There are two main disadvantages of this approach: first, it cannot reach the heavy-traffic, the problem partly solved by model-based approach; second, the values of the congestion-control parameters cannot be reset, an issue eradicated by DRL as a result of its inherent adaptiveness. Consequently, new stability and increased performance can be achieved under various network conditions.

Model-Based Clustering of Categorical Data Based on the Hamming Distance

A model-based approach is developed for clustering categorical data with no natural ordering. The proposed method exploits the Hamming distance to define a family of probability mass functions to model the data. The elements of this family are then considered as kernels of a finite mixture model with an unknown number of components. Conjugate Bayesian inference has been derived for the parameters of the Hamming distribution model. The mixture is framed in a Bayesian nonparametric setting, and a transdimensional blocked Gibbs sampler is developed to provide full Bayesian inference on the number of clusters, their structure, and the group-specific parameters, facilitating the computation with respect to customary reversible jump algorithms. The proposed model encompasses a parsimonious latent class model as a special case when the number of components is fixed. Model performances are assessed via a simulation study and reference datasets, showing improvements in clustering recovery over existing approaches. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Data and model synergy-driven rolling bearings remaining useful life prediction approach based on deep neural network and Wiener process

Abstract Various Remaining Useful Life (RUL) prediction methods, encompassing model-based, data-driven, and hybrid methods, have been developed and successfully applied to prognostics and health management for diverse rolling bearing. Hybrid methods that integrate the advantages of model-based and data-driven approaches have garnered significant attention. However, the effective integration of the two methods to address the randomness in rolling bearing full lifecycle processes remains a significant challenge. To overcome the challenge, this paper proposes a data and model synergy-driven RUL prediction framework that includes two data and model synergy approaches. Firstly, a convolutional stacked bidirectional long and short-term memory network with temporal attention mechanism is established to construct health index. The RUL prediction is achieved based on HI and polynomial model. Secondly, a three-phase prediction model based on the Wiener process is established by considering the evolution law of different degradation phases. Then, two synergy methods are designed. Strategy 1: HI is adopted as the observation value for online updating of physics degradation model parameters under Bayesian framework, and the RUL prediction results are obtained from the physics degradation model. Strategy 2: The RUL prediction results from the data-driven and physics-based model are weighted linearly combined to improve the overall prediction accuracy. The effectiveness of the proposed model is verified using two bearing full lifecycle datasets. The results indicate that the proposed approach can accommodate both short-term and long-term RUL predictions, outperforming state-of-the-art single models.

Expected First Occurrence Time of Uncertain Future Events in One-Dimensional Linear Systems

The rapid advancement of machine learning algorithms has significantly enhanced tools for monitoring system health, making data-driven approaches predominant in Prognostics and Health Management (PHM). In contrast, model-based approaches have seen modest progress, as they are often constrained by the need for prior knowledge of specific governing equations, limiting their applicability to a wide range of problems. Recently, rigorous theoretical foundations have been established to extend dynamical systems theory by incorporating prognosis of uncertain events. This article leverages this formal framework to introduce and demonstrate a fundamental mathematical result for one-dimensional linear dynamical systems. The presented theorem offers an exact expression for calculating the expected time at which an event will first occur in the future. Unlike typical thresholds, this event is triggered by a hazard zone, defined as an uncertain event likelihood function over the system's state space. Applications of this theorem can be found in implementing real-time prognostic frameworks, where it is crucial to quickly estimate the magnitude of impending failures. Emphasis is placed on minimizing computational burden to facilitate prognostic decision-making.

An autoencoder-based confederated clustering leveraging a robust model fusion strategy for federated unsupervised learning

Concerns related to data privacy, security, and ethical considerations become more prominent as data volumes continue to grow. In contrast to centralized setups, where all data is accessible at a single location, model-based clustering approaches can be successfully employed in federated settings. However, this approach to clustering in federated settings is still relatively unexplored and requires further attention. As federated clustering deals with remote data and requires privacy and security to be maintained, it poses particular challenges as well as possibilities. While model-based clustering offers promise in federated environments, a robust model aggregation method is essential for clustering rather than the generic model aggregation method like Federated Averaging (FedAvg). In this research, we proposed an autoencoder-based clustering method by introducing a novel model aggregation method FednadamN, which is a fusion of Adam and Nadam optimization approaches in a federated learning setting. Therefore, the proposed FednadamN adopted the adaptive learning rates based on the first and second moments of gradients from Adam which offered fast convergence and robustness to noisy data. Furthermore, FednadamN also incorporated the Nesterov-accelerated gradients from Nadam to further enhance the convergence speed and stability. We have studied the performance of the proposed Autoencoder-based clustering methods on benchmark datasets and using the novel FednadamN model aggregation strategy. It shows remarkable performance gain in federated clustering in comparison to the state-of-the-art.

Distributed Control Under Transmission Delays: A Model-Based Hybrid System Approach

Distributed Control Under Transmission Delays: A Model-Based Hybrid System Approach

A Framework for Multimodal Medical Image Interaction.

Medical doctors rely on images of the human anatomy, such as magnetic resonance imaging (MRI), to localize regions of interest in the patient during diagnosis and treatment. Despite advances in medical imaging technology, the information conveyance remains unimodal. This visual representation fails to capture the complexity of the real, multisensory interaction with human tissue. However, perceiving multimodal information about the patient's anatomy and disease in real-time is critical for the success of medical procedures and patient outcome. We introduce a Multimodal Medical Image Interaction (MMII) framework to allow medical experts a dynamic, audiovisual interaction with human tissue in three-dimensional space. In a virtual reality environment, the user receives physically informed audiovisual feedback to improve the spatial perception of anatomical structures. MMII uses a model-based sonification approach to generate sounds derived from the geometry and physical properties of tissue, thereby eliminating the need for hand-crafted sound design. Two user studies involving 34 general and nine clinical experts were conducted to evaluate the proposed interaction framework's learnability, usability, and accuracy. Our results showed excellent learnability of audiovisual correspondence as the rate of correct associations significantly improved (p < 0.001) over the course of the study. MMII resulted in superior brain tumor localization accuracy (p < 0.05) compared to conventional medical image interaction. Our findings substantiate the potential of this novel framework to enhance interaction with medical images, for example, during surgical procedures where immediate and precise feedback is needed.

ISTA Award 2023: Toward functional reconstruction of the pre-diseased state in total knee arthroplasty.

The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee's functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population. We used an active appearance model-based approach to segment the preoperative CT of 21 osteoarthritic patients, which identified the osteophyte-free surfaces and estimated cartilage from the segmented bones; these geometries were used to construct patient-specific musculoskeletal models of the pre-diseased knee. Subsequently, implantations were simulated using the MA method, and a previously developed optimization technique was employed to find the optimal implant position that minimized the root mean square deviation between pre-diseased and postoperative ligament strains and kinematics. There were evident biomechanical differences between the simulated patient models, but also trends that appeared reproducible at the population level. Optimizing the implant position significantly reduced the maximum observed strain root mean square deviations within the cohort from 36.5% to below 5.3% for all but the anterolateral ligament; and concomitantly reduced the kinematic deviations from 3.8 mm (SD 1.7) and 4.7° (SD 1.9°) with MA to 2.7 mm (SD 1.4) and 3.7° (SD 1.9°) relative to the pre-diseased state. To achieve this, the femoral component consistently required translational adjustments in the anterior, lateral, and proximal directions, while the tibial component required a more posterior slope and varus rotation in most cases. These findings confirm that MA-induced biomechanical alterations relative to the pre-diseased state can be reduced by optimizing the implant position, and may have implications to further advance pre-planning in robotic-assisted surgery in order to restore pre-diseased knee function.

Model management to support systems engineering workflows using ontology-based knowledge graphs

System engineering has been shifting from document-centric to model-based approaches, where assets are becoming more and more digital. Although digitisation conveys several benefits, it also brings several concerns (e.g., storage and access) and opportunities. In the context of Cyber-Physical Systems (CPS), we have experts from various domains executing complex workflows and manipulating models in a plethora of different formalisms, each with their own methods, techniques and tools. Storing knowledge on these workflows can reduce considerable effort during system development not only to allow their repeatability and replicability but also to access and reason on data generated by their execution. In this work, we propose a framework to manage modelling artefacts generated from workflow executions. The basic workflow concepts, related formalisms and artefacts are formally defined in an ontology specified in OML (Ontology Modelling Language). This ontology enables the construction of a knowledge graph that contains system engineering data to which we can apply reasoning. We also developed several tools to support system engineering during the design of workflows, their enactment, and artefact storage, considering versioning, querying and reasoning on the stored data. These tools also hide the complexity of manipulating the knowledge graph directly. Finally, we have applied our proposed framework in a real-world system development scenario of a drivetrain smart sensor system. Results show that our proposal not only helped the system engineer with fundamental difficulties like storage and versioning but also reduced the time needed to access relevant information and new knowledge that can be inferred from the knowledge graph.

Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods

Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood.

ACMamba: A State Space Model-Based Approach for Multi-Weather Degraded Image Restoration

In computer vision, eliminating the effects of adverse weather conditions such as rain, snow, and fog on images is a key research challenge. Existing studies primarily focus on image restoration for single weather types, while methods addressing image restoration under multiple combined weather conditions remain relatively scarce. Furthermore, current mainstream restoration networks, mostly based on Transformer and CNN architectures, struggle to achieve an effective balance between global receptive field and computational efficiency, limiting their performance in practical applications. This study proposes ACMamba, an end-to-end lightweight network based on selective state space models, aimed at achieving image restoration under multiple weather conditions using a unified set of parameters. Specifically, we design a novel Visual State Space Module (VSSM) and a Spatially Aware Feed-Forward Network (SAFN), which organically combine the local feature extraction capabilities of convolutions with the long-range dependency modeling capabilities of selective state space models (SSMs). This combination significantly improves computational efficiency while maintaining a global receptive field, enabling effective application of the Mamba architecture to multi-weather image restoration tasks. Comprehensive experiments demonstrate that our proposed approach significantly outperforms existing methods for both specific and multi-weather tasks across multiple benchmark datasets, showcasing its efficient long-range modeling potential in multi-weather image restoration tasks.

BCI-based real-time processing for implementing deep learning frameworks using motor imagery paradigms

As a cognitive process, motor imagery (MI) includes mentally simulating motor actions in the absence of physical movement. It has a variety of uses, including assistive technologies, medical diagnostics, and rehabilitation. MI paradigms are utilized in conjunction with brain-computer interfaces (BCI), which use electroencephalographic recordings (EEG) because of their high temporal resolution, cheap cost, portability, and non-invasiveness. BCIs apply MI paradigms by directly connecting the human brain to a computer. However, because scalp readings are non-stationary and non-linear, real-time processing of EEG signals is challenging. Furthermore, in order to minimize the impact of outside noise and artifacts, clinical MI methods must be implemented under carefully monitored laboratory conditions. A deep learning model-based approach is shown for analyzing EEG data and giving real-time feedback to a brain-computer interface. Generally, the system's design is portable and low-cost, allowing the MI paradigm to perform under poorly regulated sampling conditions.

A model-based microfluidic feedback control system for replicating blood pressure and wall shear stress on the inner surface of common carotid artery induced by external counterpulsation

Replicating the hemodynamic signal of endothelial cells (ECs) induced by an external counterpulsation (ECP) using a microfluidic system is crucial for further studying EC mechanobiology associated with ECP theraphy. However, there is currently no suitable method to construct a microfluidic system that can accurately reproduce the hemodynamic signals including blood pressure (BP) and wall shear stress (WSS) on the inner surface of the common carotid artery following ECP intervention. In this study, we propose a model-based approach to build a microfluidic feedback control system (MFCS), which consists of a microfluidic chip and a pump system (PS) to resolve the above issue. An integrated transfer function (TF) model formed by the expression of the input impedance and the proportion integration differentiation (PID) controller is used to design the microfluidic chip and PS. The performance of the proposed MFCS for replicating the hemodynamic signals is evaluated by flow-field analysis, and calcium ion (Ca2+) response experiments are conducted to validate the functionality of the proposed MFCS for investigating mechanobiological mechanisms.

Stochastic optimization of a hybrid photovoltaic/fuel cell/parking lot system incorporating cloud model

The interaction and optimization of electric parking lots (EPLs) with hybrid renewable energy to exchange power and improve the system load reliability has been welcomed systems in energy industry, but it is accompanied by challenges such as uncertain parameters, and also implementing the optimal energy management. This paper proposes a novel stochastic optimization framework for a hybrid energy system including photovoltaic resources, hydrogen storage based-fuel cell integrated with an EPL (PV/FC/EPL) aiming for minimization of the total net present cost (TNPC) while ensuring the maximum probability of power shortage (PPSHmax) to meet a commercial load in Tianjin, China using the real meteorological data. A new improved kepler optimization algorithm (IKOA) utilizing the golden sine strategy is applied to optimize system component sizes and EPL charge/discharge. In a deterministic scenario, the optimal system configuration ensures cost-efficiency and reliability for load supply. Also, a new stochastic modeling framework is proposed using the Cloud Model (CM) to evaluate the effect of uncertainties in irradiance, temperature, and system demand on TNPC and PPSH. Deterministic findings reveal enhanced reliability and reduced TNPC through FC and EPL overlap during power shortages, compared to systems lacking EPL. Specifically, TNPC and PPSH are $8,632,643 and 1.45 %, respectively, for PPSHmax = 2 % over 20 years of operation. However, stochastic optimization demonstrates a 10.82 % increase in TNPC and an 8.67 % decrease in PPSH compared to the deterministic model under PPSHmax = 2 %, indicating heightened cost and reduced reliability when uncertainties are considered. This underscores the efficacy of the cloud model-based stochastic approach in addressing uncertainty in hybrid system optimization. Also, it reveals fluctuations in optimal cost and reliability values with changes in cloud droplet distribution around the expected value, emphasizing the significance of stochastic modeling for robust decision-making in hybrid energy systems. Moreover, evaluating the incorporating the penalty for the system's inability to supply the load, changing the storage investment cost, and interest rate variations on the system optimization cleared considerable effect of the system cost and reliability.

Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench

Improving the reliability of blade bearings is essential for the safe operation of wind turbines. This challenge can be met with the help of virtual testing and digital-twin driven condition monitoring. For such approaches, a precise digital representation of the blade bearing and its test bench is an essential prerequisite. However, various factors prevent the capture of all parameters of the blade bearing and the associated test bench. Parameters such as bearing preload, rolling element and raceway dimensions, and bolt preload during assembly vary with each bearing and test bench setup. As these parameters cannot be measured directly, an alternative solution is required. This article presents a methodology to efficiently estimate non-measurable parameters of the test bench using a combination of model-based and data-driven approaches, improving the detailed and accurate virtual testing of blade bearings. It must be ensured to enable the fastest possible, most computationally efficient estimation of parameters during virtual testing or condition monitoring. The developed methodology is evaluated using the example of bolt preload on the test bench. By employing a random forest model and the strain gauge measurements attached to the blade bearing, the bolt preload parameters are estimated. The results demonstrate that the accuracy of the digital model of the blade bearing test bench is improved by up to 11 % in three out of four test bench setups. The great improvement in the accuracy of the digital model highlights the effectiveness of the proposed methodology in enhancing virtual blade bearing testing and digital-twin driven condition monitoring.

Diffusion-model-based inverse problem processing for optically-measured sound field

This paper proposes a diffusion-model-based method for addressing inverse problems in optical sound-field imaging. Optical sound-field imaging, known for its high spatial resolution, measures sound by detecting small variations in the refractive index of air caused by sound but often suffers from unavoidable noise contamination. Therefore, we present a diffusion model-based approach for sound-field inverse problems, including denoising, noisy sound-field reconstruction and extrapolation. During inference, sound-field degradation is introduced into the inverse denoising process, with range-null space decomposition used as a solver to handle degradation, iteratively generating degraded sound-field information. Numerical experiments show that our method outperforms other deep-learning-based methods in denoising and reconstruction tasks, and obtains effective results in extrapolation task. The experimental results demonstrate the applicability of our model to the real world.