Joint Input Research Articles

Bayesian finite element model inversion of offshore wind turbine structures for joint parameter-load estimation

Operating in harsh and unsteady marine environment, offshore wind turbine (OWT) structures are exposed to unpredictable wind and wave loads. Identifying the structural loads and their effects on the OWTs allow for predicting the remaining fatigue life of these structures and improving the structural design procedure. In this paper, a finite element (FE) model inversion method is presented to estimate the unknown loads and model parameters of OWTs using sparse measurement data. A realistic FE model of an OWT structure with jacket substructure is created in the open-source simulation platform, OpenSees. A Bayesian inference framework is presented to integrate the measured data with the FE model to estimate unknown wind loads and mass of rotor-nacelle assembly. To evaluate the performance of this data assimilation framework, the effect of sensor type, number of sensors, and modeling errors on the estimation accuracy of wind loads and model parameters are investigated through different case studies where synthetic data are used as measurements. The results of this study are important to guide instrumentation of new OWT structures, and to understand the potential limitations and sources of error in the real-world application of this data assimilation framework for joint model parameter and input load estimation.

Piloting a community health and well-being worker model in Cornwall: a guide for implementation and spread

BackgroundThis paper evaluates the introduction of ten Community Health and Well-being Workers (CHWW) in four pilot sites across Cornwall. The period evaluated was from the initial start in June 2022 until June 2023, covering the project setup and implementation across a range of Primary Care Networks (PCNs) and Voluntary sector partners (VSCO).MethodsAll ten CHWWs and their managers at each site were interviewed (n = 16) to understand the barriers and enablers to implementation and wider learning that could be captured around the project setup. Qualitative methods were used for data collection, including semi-structured interviews and focus groups. Transcripts were thematically analysed for cross-cutting themes, as well as site-specific effects.ResultsIn terms of learning, we cover the following key areas, which were of most importance to the successful implementation of the pilot: The CHWWs were introduced into an already established, successful social prescribing (SP) system by the time the CHWW project began. CHWWs can access some of the same training and office space as SPs, with overlapping meeting schedules allowing them joint input on some topics. It seemed that all the pre-work in terms of relationships and learning about a similar role helped a rapid implementation. Each site’s CHWW management structure uses the same line management as the SPs. Roles were clustered together to remove duplication, maximise coverage and triaging of residents. The largest barrier to overcome was integrating VSCO staff into NHS systems. Conversely, hosting CHWWs within an NHS organisation has pros and cons, namely better access to NHS data and staff, but longer lead-in time for registration on systems, and more bureaucracy for procurement/spend.ConclusionsLooking to the future, the pilot’s success has spread the programme to other integrated care areas in the country, with ongoing plans for further rollout and evaluation in the coming years.

Fixed-time event-triggered adaptive control of manipulator system with input deadzone and model uncertainty

There exist universal model uncertainty and input deadzone in mechatronic motion plant due to unknown model parameters and actuator physical feature, which will degrade the motion performance and stability. In this study, a fixed-time event-triggered adaptive control is proposed in n-DOF manipulator system to address these problems. Firstly, the dynamic model of n-DOF manipulator with joint friction and input deadzone is constructed by Lagrange technique. Then a radial basis function neural network (RBFNN) is designed to approximate uncertain dynamics. Meanwhile, an event-triggered mechanism (ETM) is used to reduce the control update frequency and to save the communication resource. Furthermore, an adaptive estimation law is adopted to compensate unknown input deadzone parameters. Based on backstepping iteration technique, a fixed-time convergent controller is presented to guarantee the system state errors convergence to zero neighborhood in finite time irrelevant to arbitrary initial state. Finally, the effectiveness of the proposed control scheme is verified by sufficient comparative simulation and experimental results.

Effect of friction stir welding with different heat input on microstructure evolution, mechanical properties and deformation behavior of twin-induced plasticity steel

The effect of friction stir welding (FSW) with different heat input on microstructure evolution, mechanical properties and deformation behavior of twin-induced plasticity steel was investigated in this paper. The results show that compared to the basal material (BM), the grain size in the stir zone (SZ) of FSW joint with low heat input was refined, and a large amount of deformation twins was formed. The grain was refined and numerous dislocations were formed in the stir zone-heat affected zone (SZ-HAZ) interface. In the FSW joint with high heat input, the grain size in SZ increased, while no deformation twins were observed. A large number of fine equiaxed grains were formed in the SZ-HAZ interface, while the grains in HAZ coarsened. Compared to the BM, the tensile strength and yield strength of low heat input joint increased to 1108 MPa and 597 MPa, respectively, while the elongation decreased to 50.4%, resulting in a strength-elongation product of 92% of BM. The joint with high heat input experienced a decrease in tensile strength, yield strength, and elongation to 806 MPa, 465 MPa, and 22.8%, respectively, with a strength-elongation product of only 30% of BM. During the tensile deformation process, the plastic deformation in the low heat input joint was mainly concentrated in BM, followed by HAZ, while SZ exhibited minimum plastic deformation, and the tensile fracture occurred at zone between SZ and HAZ. In the high heat input joint, the maximum plastic deformation occurred in the SZ, with lower plastic deformation in BM and HAZ, and finally fractured at the SZ. The constrain effect of different micro-zones and hindrance effect of dislocations and fine grains were main responsible for the deformation behavior of low and high heat input joints.

Single-network based adaptive dynamic programming optimal control for robotic manipulator with joint limitation under input saturation

This paper proposes an optimal control method based on adaptive dynamic programming (ADP) for manipulators with joint limitation under input saturation. The asymmetric input saturation problem is solved by introducing a smooth function, by which the saturation and nonsmooth issues of the input caused by the actuators can be handled. Joint limitation of the manipulator is transformed through a transfer function, which is incorporated into the cost function of the whole robotic system, to guarantee the joint limitation will not be violated. Meanwhile, due to the difficulty in solving Hamilton-Jacobi-Bellman (HJB) equation, the critic network is constructed utilising radial basis function neural networks (RBFNNs) to approximate the cost function. This approach effectively overcomes the curse of dimensionality problem associated with dynamic programming. Subsequently, an ADP-based optimal controller is designed for the robotic system. Lyapunov synthesis is used in the stability analysis to prove all the signals are convergent and ultimately uniformly bounded (UUB). Simulations are conducted on a planar 3-DOF manipulator to validate the effectiveness of the proposed method. The results demonstrate the improved performance and feasibility of the developed ADP-based optimal control strategy in handling joint limitations and input saturation challenges in robotic systems.

Joint input–output identification of unstable systems with kernel regularization

This paper discusses closed-loop identification of unstable systems. In particular, we first apply the joint input–output identification method and then convert the identification problem of unstable systems into that of stable systems, which can be tackled by using kernel-based regularization methods. We propose to identify two transfer functions by kernel regularization, the one from the reference signal to the input, and the one from the reference signal to the output. Since these transfer functions are stable, kernel regularization methods can construct their accurate models. Then the model of unstable system is constructed by ratio of these functions. The effectiveness of the proposed method is demonstrated by a numerical example and a practical experiment with a DC motor.

Detection and diagnosis of concrete void defect using percussion-based method combined with convolutional neural network

As a fundamental construction material, concrete commonly encounter void defects, which could significantly impair the integrity of concrete members and seriously weaken their load-bearing capacity. Worse still, void defects typically exist inside concrete members, making it difficult to detect them in real time. Therefore, conducting regular inspections for internal void defects of concrete structures is of great importance to maintain structural health. This paper investigates an integrated method to detect concrete voids based on percussion approach and a convolutional neural network (CNN). Firstly, percussion-induced sound signals were collected from 18 concrete specimens with different precast void defects. Each specimen was tapped 100 times, for a total of 1,800 percussion-induced acoustic signals. Subsequently, continuous wavelet transform (CWT) and Mel frequency cepstral coefficient (MFCC) were introduced to convert the collected acoustic signals into two types of images. The converted images were divided into testing and training datasets according to the ratio of 1:3, and 600 images were input to the CNN as the test dataset and 1200 images as the training dataset. The CNN was then trained and verified by the joint input of CWT and MFCC images to perform concrete voids classification. The result shows that the proposed method presents a satisfying classification accuracy of 100 %. Next, percussion tests under 5 levels of noisy environments were conducted, and the results prove the remarkable anti-noise performance of the methodology by achieving 88.12 % accuracy even at the strongest signal-to-noise ratio of 0 dB. Finally, the superiority of the proposed method was further demonstrated through the accuracy comparison with machine learning (ML) methods, with the accuracy consistently outperforming over 25 %. In conclusion, the proposed method has a great potential for future in-situ applications in concrete void detection.

Anti-deadzone adaptive fuzzy dynamic surface control for planar space robot with elastic base and flexible links

In order to combat the impact of the dead zone and reduce vibration of the space robot's elastic base and flexible links, the trajectory tracking and vibration suppression of a multi-flexible-link free-floating space robot system are addressed. First, the elastic connection between the base and the link is considered as a linear spring. Then the assumed mode approach is used to derive the dynamic model of the flexible system. Secondly, a slow subsystem characterizing the rigid motion and a fast subsystem relating to vibration of the elastic base and multiple flexible links are generated utilizing two-time scale hypotheses of singular perturbation. For the slow subsystem with a dead zone in joint input torque, a dynamic surface control method with adaptive fuzzy approximator is designed. Dynamic surface control scheme is adopted to avoid calculation expansion and to simplify calculation. The fuzzy logic function is applied to approximate uncertain terms of the dynamic equation including the dead zone errors. For the fast subsystem, an optimal linear quadratic regulator controller is used to suppress the vibration of the multiple flexible links and elastic base, ensuring the stability and tracking accuracy of the system. Lastly, the simulation results verify the effectiveness of the proposed control strategy.

An unscented Kalman filter under unknown input without direct feedthrough for joint input and system identification of structural systems

Given the difficulty of accurately measuring external inputs acting on structures, the research on joint input-system identification has gained significant attention in recent years. Among them, several unscented Kalman filters under unknown input (UKF-UI) based methods have been proposed for joint input and system identification. However, the existing UKF-UI methods require unknown inputs to be presented in the observation equations, which is not always available in practical engineering. To solve this problem, an unscented Kalman filter under unknown input without direct feedthrough (UKF-UI-WDF) is proposed in this paper for real-time joint input and system identification of structural systems without direct feedthrough using limited response measurements. The proposed UKF-UI-WDF is derived for recursive identification of the structural states, parameters, and unknown inputs by integrating unknown input estimation into the conventional UKF framework. In each identification step, the unknown inputs are first identified by minimizing posterior residuals of measurements, and then the structural states and parameters are identified by a traditional unscented Kalman filtering using the identified unknown inputs. According to whether the uncertainty of the identified unknown inputs can be quantitatively described or not, two implementation algorithms are provided in this paper. In algorithm 1, only the mean values of unknown inputs can be identified. In algorithm 2, the unknown inputs are identified as a set of sigma points, so not only the mean values but also the variance of the unknown inputs can be estimated. The effectiveness of the proposed method is examined by the joint input and system identification of various case studies of linear and nonlinear hysteretic shear structures subjected to unknown external or base excitations, respectively. The results show that the proposed UKF-UI-WDF method can effectively perform real-time joint input-system identification of structural systems without direct feedthrough using limited response measurements.

Two‐stage diffusion model deriving FDG‐PET from T1 Weighted Magnetic Resonance Images for diagnosis of Alzheimer’s disease

AbstractBackgroundAlzheimer’s Disease (AD) is a neurodegenerative disease which is characterized by cognitive and functional impairment, and defined by triad of Amyloid status, Tau pathology, and Neuroimaging marker of neurodegeneration or neuronal injury. An early diagnosis of AD can be challenging and it can be detected earlier in brain functional imaging than brain structural imaging.MethodWe develop a pipeline for synthesizing FDG‐PET/MRI from source MRI‐T1‐Weighted (MRI‐T1W) images based on denoising diffusion mechanism [1]. The pipeline includes a forward pass which encodes the source MRI‐T1WI images to gaussian noise space, and a backward pass which generates the corresponding FDG‐PET from the derived noises with repeated denoising steps. Furthermore, to validate the effectiveness of the synthesized FDG‐PET images, we also develop multiple types of classifiers and the experiments for both single input of MRI‐T1WI and joint input of MRI‐T1WI and synthesized FDG‐PET.ResultThe experiments are performed on 1036 of FDG‐PET/MRI pairs (286,474, 276 for Cognitive Normal, Mild Cognitive Impaiement and Alzheimer’s Disease each) are obtained from ADNI [2] datasets and display the promising results with low Mean Absolute Error (MAE) and high Structural Similarity Index Measure (SSIM) for the two stage diffusion denoising based FDG‐PET synthesis pipeline. The results for the additional classification experiments also show that in most cases the joint input enhances the classifiers' performance in accuracy and F1‐score. The results also indicate that the synthesized FDG‐PET images are not only visually similar to the ground truth images, but also improve the performance of machine learning based AD diagnosis.ConclusionThis work provides support for the notion that machine learning‐derived image analysis may be a useful approach to improving the diagnosis of AD.Reference:[1] Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840‐6851.[2] Petersen, R., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., … & Toga, A. W. (2010). Alzheimer’s disease neuroimaging initiative (ADNI). Neurology, 74(3), 201‐209.

Back-Analysis of Structurally Controlled Failure in an Open-Pit Mine with Machine Learning Tools

Over the past decades, numerical modelling has become a powerful tool for rock mechanics applications. However, the accurate estimation of rock mass input parameters remains a significant challenge. Machine learning (ML) tools have recently been integrated to enhance and accelerate numerical modelling processes. In this paper, we demonstrate the novel use of ML tools for calibrating a state-of-the-art three-dimensional (3D) finite-element (FE) model of a kinematic structurally controlled failure event in an open-pit mine. The failure event involves the detachment of a large wedge, thus allowing for the accurate identification of the geometry of the rock joints. FE models are automatically generated according to estimated ranges of joint input parameters. Subsequently, ML tools are used to analyze the synthetic data and calibrate the strength parameters of the rock joints. Our findings reveal that a relatively small number of models are needed for this purpose, rendering ML a highly useful tool even for computationally demanding FE models.

DAEM: A Data- and Application-Aware Error Analysis Methodology for Approximate Adders

Approximate adders are some of the fundamental arithmetic operators that are being employed in error-resilient applications, to achieve performance/energy/area gains. This improvement usually comes at the cost of some accuracy and, therefore, requires prior error analysis, to select an approximate adder variant that provides acceptable accuracy. Most of the state-of-the-art error analysis techniques for approximate adders assume input bits and operands to be independent of one another, while some also assume the operands to be uniformly distributed. In this paper, we analyze the impact of these assumptions on the accuracy of error estimation techniques, and we highlight the need to address these assumptions, to achieve better and more realistic quality estimates. Based on our analysis, we propose DAEM, a data- and application-aware error analysis methodology for approximate adders. Unlike existing error analysis models, we neither assume the adder operands to be uniformly distributed nor assume them to be independent. Specifically, we use 2D joint input probability mass functions (PMFs), populated using sample data, in order to incorporate the data and application knowledge in the analysis. These 2D joint input PMFs, along with 2D error maps of approximate adders, are used to estimate the error PMF of an adder network. The error PMF is then utilized to compute different error measures, such as the mean squared error (MSE) and mean error distance (MED). We evaluate the proposed error analysis methodology on audio and video processing applications, and we demonstrate that our methodology provides error estimates having a better correlation with the simulation results, as compared to the state-of-the-art techniques.

Feasible set estimation under functional uncertainty by Gaussian Process modelling

In this paper we deal with the estimation of a feasible set defined by an inequality constraint on the output of a time-consuming black-box simulator. We focus on the setting where the black-box simulator takes as inputs both a set of scalar controlled variables and a functional uncontrolled variable. We then place ourselves in a probabilistic framework, modelling the functional uncontrolled variable by a random process. The inequality constraint is formulated as the expectation of the output of the simulator conditional on the values taken by the set of controlled variables. We propose an original method to solve the above feasibility problem with a reduced number of evaluations of the costly simulator. A Gaussian Process model of the simulator is learned in the joint space of controlled and uncontrolled input variables, on the basis of a set of simulations which is enriched through a sequential procedure. This procedure aims to reduce the estimation error of the feasible set by evaluating the simulator on new points chosen sequentially in the joint input space according to specific enrichment criteria. It involves as a preliminary step the reduction of the dimension of the uncontrolled input space. A variation of this strategy is also proposed, which increases adaptively the dimension of the reduced space, leading to an improvement in terms of number of calls to the simulator. The procedure we propose is compared with other sampling procedures and another modelling approach on analytical examples. Finally our methodology is implemented on an automotive industrial application. For this application, the feasible set to be recovered is the set of values of controlled variables of a gas after-treatment device, leading to the respect of pollutant emission standards of a vehicle under driving profile uncertainties.

Neural-Network-Based Equalization and Detection for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communications: A Low-Complexity Approach

The performance of the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is often restrained by time-varying channels with large delays. The existing frequency domain equalizers do not work well because of the high complexity and difficulty of finding the real-time signal-to-noise ratio. To solve these problems, we propose a low-complexity neural network (NN)-based scheme for joint equalization and detection. A simple NN structure is built to yield the detected symbols with the joint input of the segmented channel response and received symbol. The coherence bandwidth is investigated to find the optimal hyperparameters. By being completely trained offline with real channels, the proposed detector is applied independently in both simulations and sea trials. The results show that the proposed detector outperforms the ZF and MMSE equalizers and extreme learning machine (ELM)-based detectors in both the strongly reflected channels of the pool and time-variant channels of the shallow sea. The complexity of the proposed network is lower than the MMSE and ELM-based receiver.

Time domain design of PI-C controllers

Abstract PI-C (Proportional-Integral-Compensating) controllers are observer-based compensators with integral action containing an inverse model of the plant. Originally developed for windup prevention in case of sensor saturation they are also useful in the presence of actuator saturation. They allow to achieve a nearly perfect disturbance attenuation while the loop gain satisfies the Circle Criterion. It has also been demonstrated that PI-C controllers can solve the windup problem in case of joint input and output saturation. The design of PI-C controllers has only been formulated in the frequency domain so far which, in the MIMO case, requires the manipulation of polynomial matrices. Here, the state-space design of PI-C controllers is presented.

Deep Compressed Super-Resolution Imaging with DMD Alignment Error Correction

In the field of compressed imaging, many attempts have been made to use the high-resolution digital micromirror array (DMD) in combination with low-resolution detectors to construct imaging systems by collecting low-resolution compressed data to reconstruct high-resolution images. However, the difficulty of achieving micrometer-level alignment between DMD devices and detectors has resulted in significant reconstruction errors. To address this issue, we proposed a joint input generative adversarial network with an error correction function that simulates the degradation of image quality due to alignment errors, designed an optical imaging system, and incorporated prior imaging system knowledge in the data generation process to improve the training efficiency and reconstruction performance. Our network achieved the ability to reconstruct 4× high-resolution images with different alignment errors and performed outstanding reconstruction in real-world scenes. Compared to existing algorithms, our method had a higher peak signal-to-noise ratio (PSNR) and better visualization results, which demonstrates the feasibility of our approach.

MrNISC from LWE with polynomial modulus

Introduced by Benhamouda and Lin [TCC'20], a multi-party reusable non-interactive secure computation protocol (mrNISC) consists of a commitment phase and an unbounded number of computation phases. In the commitment phase, a number of parties first commit to their input in a single broadcast round. Later in a computation phase, any subset of the parties can compute a function on their joint input by each sending a single broadcast message.Benhamouda and Lin [TCC'20] constructed the first mrNISC for all functions based on standard hardness assumptions in pairing groups. Soon after their work, two concurrent papers by Benhamouda et al. [EUROCRYPT'21] and Ananth et al. [EUROCRYPT'21] constructed mrNISC for all functions based on the hardness of LWE with super-polynomial modulus-to-noise ratio.In this work we build the first mrNISC for all functions based solely on LWE with polynomial modulus-to-noise ratio. We thus place mrNISC in the same category as public-key encryption and leveled fully homomorphic encryption in terms of the required LWE hardness assumption. We achieve our result by carefully introducing a bootstrapping step in the construction of Benhamouda et al.

Investigation into microstructures and properties of AISI 430 ferritic steel butt joints fabricated by GMAW

Heat input is extremely important for achieving joints with high mechanical properties in ferritic stainless steels. Thus, one of the objectives of this study is to show the effect of heat input on microstructural evolution and thus on the mechanical behavior. For this aim, 5 mm thick AISI 430 plates were butt welded using two heat input levels. Extensive optical microscopy and SEM investigations, microhardness measurements, mechanical tests (i.e., tensile and bending) were conducted to investigate the microstructures evolving in the weld area and to determine the mechanical properties of the joints produced. Strength overmatching was obtained in the weld region of both joints fabricated with both heat inputs employed. In addition, the HAZ region is much wider in the joint fabricated with higher heat input. Although the higher heat input joint exhibited a wider HAZ region the hardness values were similar in the HAZ regions of both joints. As a result, both joints exhibited similar tensile properties, i.e., a strength performance of about 99%. In contrast, the root bend specimen of the higher heat input joint did crack in the bending test while both surface and root bend specimens of the lower heat input joint did not fail.

A data-driven LQG method for linear time-varying systems to monitor the controller performance in the batch process

PurposeThe flexibility of batch process enables its wide application in fine-chemical, pharmaceutical and semi-conductor industries, whilst its complexity necessitates control performance monitoring to ensure high operation efficiency. This paper proposes a data-driven approach to carry out controller performance monitoring within batch based on linear quadratic Gaussian (LQG) method.Design/methodology/approachA linear time-varying LQG method is proposed to obtain the joint covariance benchmark for the stochastic part of batch process input/output. From historical golden operation batch, linear time-varying (LTV) system and noise models are identified based on generalized observer Markov parameters realization.FindingsOpen/closed loop input and output data are applied to identify the process model as well as the disturbance model, both in Markov parameter form. Then the optimal covariance of joint input and output can be obtained by the LQG method. The Hotelling's Tˆ2 control chart can be established to monitor the controller.Originality/value(1) An observer Markov parameter approach to identify the time-varying process and noise models from both open and closed loop data, (2) a linear time-varying LQG optimal control law to obtain the optimal benchmark covariance of joint input and output and (3) a joint input and output multivariate control chart based on Hotelling's T2 statistic for controller performance monitoring.

A linear Bayesian filter for input and state estimation of structural systems

AbstractThis paper proposes a linear recursive Bayesian filter for minimum variance unbiased joint input and state estimation of structural systems. Unlike the augmented Kalman filter (AKF), the proposed filter falls within the category of Bayesian filters in which unknown inputs are estimated without attributing any fictitious input model or statistics. Also, in contrast with the existing algorithms in the latter category, such as the Gillijns and De Moor Filters (GDFs), the developed filter applies to systems with and without direct feedthrough, in particular, systems with a rank‐deficient feedforward matrix. Because of the latter features, the filter is referred to as universal filter (UF) for convenience. The numerical examples show that the UF performs better than the AKF. Due to its structure, the UF does not require the tuning of the hyperparameters for inputs, and therefore the problematic instability of the AKF is not encountered in the case of a large modeling error variance of the input. For systems with direct feedthrough, the error and covariance propagation terms differ due to the distinct state space. Consequently, the UF can enhance estimations due to the well‐conditionedness of the relevant inversion problem. Moreover, the UF can deal with systems with rank‐deficient feedforward matrix where these systems are not covered by GDFs.