Parameter Update Research Articles

Co-Frequency Interference Suppression of Integrated Detection and Jamming System Based on 2D Sparse Recovery

The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, to achieve effective interference, the integrated waveform must overlap with the adversary radar signal within the frequency band. Consequently, the detection echoes are susceptible to the strong co-frequency direct wave generated by the adversary signals. This paper proposes a co-frequency direct wave interference suppression algorithm based on 2D generalized smoothed-l0 norm sparse recovery. The algorithm exploits a joint dictionary comprising our integrated signals and adversary signals, along with the sparsity of 2D range-Doppler maps. The direct solution of the sparse decomposition optimization problem, formulated for the entire echo matrix, enhances the target detection performance for integrated signals even in the presence of robust co-frequency direct wave interference. Furthermore, the proposed method achieves robustness to interference of varying intensities through the adaptive updating and adjustment of relevant parameters. The effectiveness of the proposed method is validated through simulation and experimental results.

Probabilistic fatigue life prediction using multi-layer perceptron with maximum entropy algorithm

In the structural fatigue fields, such as aerospace, maritime, and civil engineering, the accurate construction of the Probability-Stress-Life (P-S-N) curve is crucial for preventing unexpected structural failures and is fundamental to the engineering design process. Conventional methods usually assume that the fatigue life distribution follows a normal or weibull distribution, which overlooks the differences of life distribution at different stress levels and introduces subjective errors. To address this issue, this paper proposes a new approach based a multi-layer perceptron model with maximum entropy algorithm (MPME) for probabilistic fatigue life prediction. During the derivation of life probability distribution function in this approach, the type not require any pre-assumption, thus effectively avoiding the introduction of subjective errors. Specifically, the dispersed information of fatigue life is quantified as various life distribution statistical features such as mean, variance, skewness, and kurtosis. Next, learn the continuous functional relationship between stress and these features using a multi-layer perceptron (MLP). Finally, the target probability distribution is derived using the maximum entropy algorithm, with the predicted distribution features obtained from the MLP serving as constraints for it. In addition, to ensure the consistency between the prediction of MLP and physical laws, we encode physical knowledge into the network loss to guide the updating of network parameters. Experimental results of two different material fatigue data demonstrate that, compared to traditional deterministic model-based methods and machine learning methods, the MPME in this paper can more accurately predict the probabilistic fatigue life.

MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation

Accurate, real-time, and efficient traffic data, crucial for intelligent transportation systems, which is often disrupted in the real world due to the influence of weather, interference, and equipment failures. Generative Adversarial Networks (GANs), have achieved significant results in image restoration, providing insights for traffic data imputation. Recent research has focused on developing more effective and reasonable model by learning transferable common knowledge from different cities or different scenarios, which is also an excellent idea for improving the data imputation performance. In light of, we contrive to design a GANs based transferable traffic data imputation model, namely Multi Domain Generative Adversarial Transfer Learning Network (MDTGAN). Firstly, the model consists of two stages. In pre-training stage, the model utilizes the source domain datasets (Similar cities road network datasets) to update parameters and transferred them. Then, the model parameters are optimized using the target domain dataset (Target city road network dataset) in fine-tuning stage to avoid the issue of insufficient samples caused by data missing. Secondly, to adapt the model to different dataset topologies, MDTGAN models the node-level data by taking node time series as input, enabling the model to autonomously learn the prevalent spatiotemporal correlations inherent in nodes. Additionally, we introduce a domain discriminator module that guides the spatial encoder in learning domain-invariant features of network node spatial information, enhancing the model generalization ability. The experiments conducted on three publicly available datasets, in which one dataset is regarded as the target dataset, while the others serve as source datasets. The experimental results demonstrate that the MDTGAN model consistently outperforms the baseline models. Specifically, the MDTGAN model can transfer valuable knowledge from the source domain datasets to improve data imputation performance on the target domain dataset, providing practical significance for cities lacking historical traffic data.

Few-shot online anomaly detection and segmentation

Detecting anomaly patterns from images is a crucial artificial intelligence technique in industrial applications. Recent research in this domain has emphasized the necessity of a large volume of training data, overlooking the practical scenario where, post-deployment of the model, unlabeled data containing both normal and abnormal samples can be utilized to enhance the performance. Consequently, this paper focuses on addressing the challenging yet practical few-shot online anomaly detection and segmentation (FOADS) task. Under the FOADS framework, models are trained on a few-shot normal dataset, followed by inspection and improvement of their capabilities by leveraging unlabeled streaming data containing both normal and abnormal samples simultaneously. Since the data stream has no ground truth labels, the model needs to filter the anomalous data by detection to avoid contaminating its parameters, which presents a significant challenge when initial samples are insufficient. To tackle this issue, we propose modeling the feature distribution of normal images using a Neural Gas network, which offers the flexibility to adapt the topology structure to identify outliers in the data flow. In order to achieve improved performance with limited training samples, we employ multi-scale feature embedding extracted from a CNN pre-trained on ImageNet to obtain a robust representation. Furthermore, we introduce an algorithm that can incrementally update parameters without the need to store previous samples. Comprehensive experimental results demonstrate that our method can achieve substantial performance under the FOADS setting, while ensuring that the time complexity remains within an acceptable range on MVTec AD and BTAD datasets.

Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement.

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations τi, weighted nuclear norm, and the L2,1 norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm (Lw,∗) to assign weights to singular values to each retinal images and utilize the L2,1 norm to eliminate correlated samples and outliers in the retinal images. Moreover, τi is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including τi, by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method's superiority over existing state-of-the-art methods across various datasets.

Bayesian structural model updating with multimodal variational autoencoder

A novel framework for Bayesian structural model updating is presented in this study. The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE). The method facilitates an approximation of the likelihood when dealing with a small number of observations. It is particularly suitable for high-dimensional correlated simultaneous observations applicable to various dynamic analysis models. The proposed approach was benchmarked using a numerical model of a single-story frame building with acceleration and dynamic strain measurements. Additionally, an example involving a Bayesian update of nonlinear model parameters for a three-degree-of-freedom lumped mass model demonstrates computational efficiency when compared to using the original VAE, while maintaining adequate accuracy for practical applications.

Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States

AbstractAgricultural soils containing nitrogen‐rich fertilizers are a substantial source of reactive nitrogen to the atmosphere with potential to impact air quality. One form of reactive nitrogen, nitrogen oxides (NOx = NO + NO2), are a harmful air pollutant and form secondary pollutants, including particulate matter (PM) and ozone (O3). Soil nitrogen oxide emissions (SNOx) are heavily influenced by environmental conditions, however the understanding of the influence of environmental drivers on the behavior of SNOx is limited. Here, we implement a modified soil moisture‐dependent SNOx parameterization into the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) and investigate the impact on regional air quality in the central U.S. Evaluating against TROPOspheric Monitoring Instrument (TROPOMI) column NO2 observations, WRF‐Chem columns better capture the TROPOMI column magnitudes earlier in the growing season when using the updated SNOx parametrization, with modeled column bias improved to −1.1% over the most heavily fertilized regions. Evaluating against Environmental Protection Agency (EPA) surface NO2 observations, the relationship between surface NO2 and soil moisture is better represented in agriculturally‐dominant regions when using the updated parameterization, with greatest surface NO2 concentrations at moderate soil moisture and lower concentrations at wetter or drier soil conditions. In simulations, these SNOx lead to increased O3 in select urban regions, with more than double the occurrences of O3 exceeding the EPA 8‐hr O3 standard of 70 ppb.

A resilience control method for mitigating the sudden change in online group opinion based on Q-Learning and PSO

In today's era, individuals can speak and communicate online anytime and anywhere. The Online Group Opinion (OGO), formed by the online statements of netizens, is regarded as a complex system. OGO is easy to get rapid and unpredictable sudden changes under external stimuli such as social emergencies, which may further lead to online violence, catalyze spread of emergencies, posing significant threats to social organization and cybersecurity. This paper proposes an innovative method to mitigate sudden changes in OGO using Q-learning from Reinforcement Learning and the Particle Swarm Optimization (PSO) algorithm, aiming to mitigate these sudden changes by controlling the resilience of the OGO. First, an OGO resilience index based on catastrophe theory and resilience theory is established to portray OGO sudden changes phenomena. Next, a resilience control algorithm integrated with Q-learning to optimize particles’ parameter update process is proposed, called QLPSOND. QLPSOND is used to control the system’s resilience to improve its ability to resist external stimuli, thus mitigating the sudden changes crisis. Through comparisons with other three algorithms on two online forums datasets, our proposed QLPSOND resilience control method demonstrates significant advantages in addressing complex social control issues, with average fitness improvement of 37.59 % and 7.98 %, and best fitness improvement of 31.66 % and 10.28 %, respectively, compared to most competitive state-of-the-art baseline. Additionally, this paper further discusses the practical significance of resilience control strategies and management implications. These efforts can extend the opinion dynamics approaches in online environments and assist enterprises and governments in monitoring and managing online communities.

Exploring Enhanced Conjugate Gradient Methods: A Novel Family of Techniques for Efficient Unconstrained Minimization

Given that the conjugate gradient method (CGM) is computationally efficient and user-friendly, it is often used to address large-scale, unconstrained minimization issues. Numerous researchers have created new conjugate gradient (CG) update parameters by modifying the initial set, also referred to as classical CGMs. This has resulted in the development of several hybrid approaches. This work's major goal is to create a new family of techniques that can be used to create even more new methods. Consequently, Hestenes-Stiefel's update parameter and a new family involving Polak-Ribiere-Polyak and Liu-Storey CGMs are considered. By changing the parameters of this CGM family, a novel approach that possesses sufficient descent characteristics is obtained. A numerical experiment including many unconstrained minimization problems (UMP) is carried out to assess the novel method's efficacy compared to existing approaches. The result reveals that the new CG approach performs better than the current ones.

Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models

Hidden Markov models (HMMs) are popular models to identify a finite number of latent states from sequential data. However, fitting them to large datasets can be computationally demanding because most likelihood maximization techniques require iterating through the entire underlying dataset for every parameter update. We propose a novel optimization algorithm that updates the parameters of an HMM without iterating through the entire dataset. Namely, we combine a partial E step with variance-reduced stochastic optimization within the M step. We prove the algorithm converges under certain regularity conditions. We test our algorithm empirically using a simulation study as well as a case study of kinematic data collected using suction-cup attached biologgers from eight northern resident killer whales (Orcinus orca) off the western coast of Canada. In both, our algorithm converges in fewer epochs, with less computation time, and to regions of higher likelihood compared to standard numerical optimization techniques. Our algorithm allows practitioners to fit complicated HMMs to large time-series datasets more efficiently than existing baselines. Supplemental materials are available online.

New-Style Logic Operation and Neuromorphic Computing Enabled by Optoelectronic Artificial Synapses in an MXene/Y:HfO2 Ferroelectric Memristor.

Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.

SHM approach with indirect load measurements and Kalman update of state and model parameters

SHM approach with indirect load measurements and Kalman update of state and model parameters

Channel Adaptive and Sparsity Personalized Federated Learning for Privacy Protection in Smart Healthcare Systems.

With the booming development of Smart Healthcare Systems (SHSs), employing federated learning (FL) in SHS devices has become a research hotspot. FL, as a distributed learning framework, can train models without sharing the original data among users, and then protect the user privacy. Existing research has proposed many methods to improve the security and efficiency of FL, which may not fully consider the characteristics of SHSs. Specifically, the requirements of privacy protection and efficiency pose significant challenges to FL. Current studies have struggled to balance privacy security and efficiency, and the degradation of model training efficiency in SHSs can be critical to patient health. Therefore, to improve the privacy protection of healthcare data and ensure communication efficiency, this work proposes a novel personalized FL framework based on Communication quality and Adaptive Sparsification (pFedCAS). In order to achieve privacy protection, a control unit is proposed and introduced to adjust the sparsity of the local model adaptively. To further improve the training efficiency, a selection unit is added during global model aggregation to select suitable clients for parameter updates. Finally, we validate the proposed method operated on the HAM10000 dataset. Simulation results validate that pFedCAS can not only improve privacy protection, but also gain an improvement of 15% in training accuracy and a reduction of 30% in training costs based on communication quality. The simulation results also validate the excellent robustness of pFedCAS to non-iid data.

State of charge estimation for lithium batteries based on multi-timescale FFRLS-UKF

Abstract State of charge (SOC) can represent the remaining battery capacity, and accurately estimating SOC is very useful for the drivers of electric vehicles. This study proposes a lithium battery SOC estimation technique based on multi-timescale forgetting factor recursive least squares as well as an unscented Kalman filter (MTS-FFRLS-UKF). The MTS-FFRLS-UKF uses a circuit with a 2-RC network as the battery model, whose parameters are continuously updated at a timescale that is different from SOC estimation. The experimental results under a dynamic test prove the availability and accuracy of MTS-FFRLS-UKF. Continuous parameter update enhances the accuracy of battery modelling and furthermore the SOC estimation accuracy. The mean absolute error (MAE) and root-mean square error (RMSE) of SOC estimation decrease to 0.58% and 0.62%, respectively.

Anaphylaxis in Practice: A Guide to the 2023 Practice Parameter Update

This review summarizes new research developments and clinical practice recommendations for the diagnosis and management of anaphylaxis presented in the Joint Task Force on Practice Parameters 2023 Anaphylaxis practice parameter Update. It is intended to serve as a high-level summary of the 2023 practice parameter, which makes clinically impactful recommendations based on evidence that has emerged since the 2015 practice parameter. We invite clinicians to explore the full 2023 practice parameter to understand the research methods and underlying evidence that have informed the recommendations summarized here. There are new and evolving diagnostic criteria for anaphylaxis, rules for defining elevated tryptase levels, and recognition of signs and symptoms particular to infants and toddlers. The administration of epinephrine should not be used as a surrogate to diagnose anaphylaxis. Risk factors for anaphylaxis should be assessed on a case-by-case basis. Patient counseling and shared decision-making are essential to support patients' treatment decisions and capacity to manage the risk of anaphylaxis at home and in other community settings. Activation of emergency medical services after home epinephrine administration may not be required in all cases, and patients should be engaged in shared decision-making to determine when home management may be appropriate.

An Adaptive Estimation Approach for Integrating Real-World Operation Dynamics in Engine-Out NOx Emission Modeling of a Wheel Loader

Accurately predicting engine-out nitrogen oxides (NOx) emissions on-board is crucial for effective emission control in heavy-duty engines. Real-world engine operating conditions, especially in non-road applications with frequent dynamic changes, can significantly affect NOx emission characteristics. However, these engine emission characteristics are conventionally measured on steady-state or regulated driving cycles, which may not fully reflect the emission levels under real-world operational dynamics. This highlights the necessity of integrating engine performance during transient operation into the NOx prediction model to enhance the accuracy of on-board predictions. This paper introduces a novel data-driven model to predict engine-out NOx emissions during the construction activities of a wheel loader. This paper begins by addressing discrepancies between steady-state map predictions and on-board NOx measurements. To bridge these gaps, the model identifies engine transient operating conditions by analyzing the time derivatives of engine speed and torque. The model structure integrates steady-state and transient emission maps, with the transient map being iteratively refined using the Kalman filter principle, thereby improving its accuracy and robustness in response to engine dynamics. The proposed method maintains a model structure that is easily implemented and similar to conventional steady-state emission maps, while also enabling online self-learning for model parameter updates. Model validation shows that the model has high prediction accuracy and the ability to differentiate between steady-state and transient engine working conditions during construction activities.

Neural adaptive dynamic surface control for position tracking of electrohydraulic servo systems based on extended state observer

High-performance motion tracking of electro-hydraulic servo systems plays an important role in developing industrial technology. However, the system itself has parameter uncertainty, uncertain nonlinearity, and measurement noise, significantly decreasing the performance of conventional controllers designed based on nominal models. To improve the tracking performance of electro-hydraulic servo systems, an adaptive dynamic surface control method combining extended state observer and radial basis function neural network is proposed. Firstly, an extended state observer combining neural network output and parameter adaptation technology is designed without the need for speed and pressure feedback to observe the system’s unknown state variables and nonlinear matched disturbance. The design of neural networks is used to approximate mismatched disturbance with significant nonlinearity online. Then, the proposed extended state observer is introduced into the neural adaptive backstepping design, effectively compensating for simultaneous matched and mismatched disturbances, thereby suppressing most of the uncertain nonlinearity of the system. Meanwhile, online update of uncertain parameters further improves the tracking performance of the controller. In addition, the introduction of dynamic surface overcomes the “explosion of complexity” issue in backstepping design. The rigorous stability of the closed-loop system is proved by the Lyapunov stability theory. Finally, the effectiveness of the proposed controller is validated through comparative experiments on valve-controlled hydraulic cylinder.

Selective online model updating in hybrid simulation of a full‐scale steel moment frame

AbstractThis study presents the implementation of an online model updating algorithm within a full‐scale hybrid simulation (HS) of a six story, four bay steel moment frame. The experimental substructure consists of a cruciform subassembly generating critical data on the nonlinear behavior of the first story column and two beams with reduced beam sections (RBSs) on each side. The updating algorithm focuses on the modeling parameters of plastic hinge elements representing the RBSs in the numerical model. A smooth plasticity model is utilized for beam plastic hinges with updating parameters identified from on‐line experimental data through a modified version of the unscented Kalman filter. The HS shows that the numerical beam hinges based on simple hysteretic model with updated parameters are able to capture the characteristic behavior observed in experiments. Due to fracture of beam flanges is observed in the experiments, a selective updating concept is proposed to allow for updating multiple numerical components accounting for asymmetric behavior and variability in the response. The selective updating method is validated through virtual HSs that are better able to identify and isolate the effects of fracture and other behavioral characteristics. The combination of results from physical and virtual tests highlights the benefits of model updating on the local and overall system‐level response.

Adaptive SARIMA modelling for continuous chamber temperature tracking in ultra-low temperature freezers

Ultra-low temperature (ULT) freezers are vital for storing perishable biological materials, requiring continuous monitoring of chamber thermal conditions to ensure sample integrity. A reliable dynamic data-driven model is key for smart proactive surveillance of the ULT freezers through real-time status tracking. However, the dynamic nature of ULT freezers, influenced by external disturbances, demands adaptive models to sustain performance. Complex models thus face challenges due to time-consuming parameter updates. To ensure model prediction performance while minimising model updating time, this study proposes a Seasonal Autoregressive Integrated Moving Average (SARIMA) model-based approach for short-term ULT freezer chamber temperature prediction. This method solely relies on historical chamber temperature data, eliminating the need for additional measurements. Moreover, two adaptive duty cycle length (DCL) selection strategies and a one-step ahead DCL estimation method are developed to address auto-correlation issues caused by recurring duty cycles. Comparative analyses with reference models demonstrate that the SARIMA model, incorporating future DCL effects, excels in one-step prediction and multi-step forecast under stable and variant DCL periods. Moreover, it outperforms the reference models in auto-correlation representation, affirming its reliability in predicting future states. The proposed modelling approach promises an effective method for freezing chamber temperature prediction, enabling continuous status tracking and early fault detection. Moreover, it holds great potential for modelling other critical temperatures in refrigeration systems and facilitating flexible energy operations.

Assessment of Nocturnal NOx Heterogeneous Reaction Mechanisms in the Community Multiscale Air Quality (CMAQ) Model

AbstractNitrogen oxides (NOx) have adverse human health impacts and play a central role in the production of ozone and PM2.5. Nighttime heterogeneous chemistry regulates the nocturnal reservoirs and sinks of NOx such as N2O5 removal and ClNO2 production. However, existing parameterizations of nocturnal NOx heterogeneous chemistry in air quality models do not capture the variability in observations. Here, we implemented for the first time in the Community Multiscale Air Quality (CMAQ) model the Gaston N2O5 uptake (γ(N2O5)) mechanism that accounts for the role of particulate organic matter in regulating N2O5 uptake and the Staudt ClNO2 yield (Φ(ClNO2)) mechanism that includes the role of reactive solutes in suppressing ClNO2 production. With the Gaston and Staudt parameterizations, the coarse mode contributed modestly to N2O5 loss (17.2%) but significantly to ClNO2 production (60.3%), highlighting the impact of coarse mode chemistry. The Gaston γ(N2O5) parameterization in the fine mode increased agreement between modeled N2O5 concentration and observations (RMSEnew = 0.37ppb) compared to the model default (RMSEdefault = 0.43ppb). The Gaston γ(N2O5) parameterization was overall biased low due to underestimates in modeled particle oxygen to carbon ratio (O:C). The Staudt Φ(ClNO2) parameterization resulted in further underestimation (NMBnew = −73.7%) compared to the model default (NMBdefault = −37.9%) because of underestimation of fine mode particle chloride concentration. We expect that the updated parameterizations may more accurately capture the mean state and variability in γ(N2O5) and Φ(ClNO2) under conditions where model particulate O:C and chloride are better represented.