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Mid-term load forecasting (MTLF) is of great significance for power system planning, operation, and power trading. However, the mid-term electrical load is affected by the coupling of multiple factors and demonstrates complex characteristics, which leads to low prediction accuracy in MTLF. Furthermore, MTLF is faced with the ``curse of dimensionality'' problem due to a large number of variables. This article proposes an MTLF method based on manifold learning, which can extract the underlying factors of load variations to help improve the accuracy of MTLF and significantly reduce the calculation. Unlike linear dimensionality reduction methods, manifold learning has better nonlinear feature extraction ability and is more suitable for load data with nonlinear characteristics. Furthermore, long short-term memory (LSTM) neural networks are used to establish forecasting models in the low-dimensional space obtained by manifold learning. The proposed MTLF method is tested on independent system operator (ISO) New England datasets, and load forecasting in 24, 168, and 720 h ahead is carried out. The numerical results validate that the proposed method has higher prediction accuracy than many mature methods in the mid-term time scale.

With the recent advancement in baseband integrated circuits and radio frequency (RF) technology, there is a strong demand to minimize the size of array-based microwave systems. However, array aperture miniaturization often leads to strong mutual coupling in the arrays. To date, mitigating and/or compensating the mutual coupling (nonlinear) effect remains a technical challenge. For instance, realizing microwave orientations in the presence of strong mutual coupling is a challenge. In this paper, we propose a unified electromagnetic machine learning (EMML) technique for overcoming this challenge. Antenna current’s Green function (ACGF) is employed for characterization of the EM response of elements in form of digital signal processing (DSP) module with the inclusion of mutual coupling effect among the elements. An EMML framework is then formulated and combined with the DSP module based on ACGF, to consequently come up with a nonlinear mutual coupling mitigation framework that provides better decoupling capability than the previous linear techniques. EMML is verified by application to a multiple signal classification (MUSIC)-based direction-of-arrival estimation scenario, and compared with the conventional methods. The results obtained show the validity, effectiveness of the EMML method. This method can be further used in coupled array-based beam forming, steering, and nulling applications.

This paper presents a practical optimum design solution of prismatic simply supported prestressed concrete girders that are subjected to uniformly distributed static loads. While assuming linear elastic behaviour and employing working stresses method, the optimum values of two design variables, namely the cross-sectional dimensions and the prestressing force, are formulated as a nonlinear optimization model. Optimum values of the design variables are computed to satisfy either a minimum weight or a minimum cost objective function. To solve the nonlinear programming models, a program named Non-Linear Programming Prestressed Concrete Optimizer (NLPRECO) was coded in MATLAB® programming environment. Some illustrative design problems were solved using NLPRECO and their results are demonstrated herein. Sensitivity analysis of the optimized design variables, to variations in design parameters, was also investigated and the results are presented. Results show substantial improvement over the regular un-optimized solutions and moderate improvement over a linear programming optimization solutions.

Musculoskeletal simulations are useful in biomechanics to investigate the causes of movement disorder, to estimate non-measurable physiological quantities or to study the optimality of human movement. We introduce Bioptim, an easy-to-use Python framework for biomechanical optimal control, handling musculoskeletal models. Relying on algorithmic differentiation and the multiple shooting formulation, Bioptim interfaces nonlinear solvers to quickly provide dynamically consistent optimal solutions. The software is both computationally efficient (C++ core) and easily customizable, thanks to its Python interface. It allows to quickly define a variety of biomechanical problems such as motion tracking/prediction, muscle-driven simulations, parameters optimization, multiphase problems, etc. It is also intended for real-time applications such as moving horizon estimation and model predictive control. Six contrasting examples are presented, comprising various models, dynamics, objective functions and constraints. They include data-driven simulations (i.e., a multiphase muscle driven gait cycle and an upper-limb real-time moving horizon estimation of muscle forces) and predictive simulations (i.e., a muscle-driven pointing task, a twisting somersault with a quaternion-based model, a position controller using external forces, and a multiphase torque-driven maximum-height jump motion).

Inferential modeling has been of significance for modern manufacturing in estimating the quality-related process variables. As an effective inferential model, probabilistic slow feature analysis (PSFA) has gained attention in regression tasks to interpret dynamic properties with a slowness preference. However, PSFA is often challenged by the nonlinear sequential data due to its linear state-space structure. In this study, a new nonlinear extension of PSFA is proposed under the deep learning framework to enhance the dynamic feature extraction with limited labels, incorporating variational inference and Monte Carlo inference to derive the objective function. The proposed model considers the relevance of inputs with outputs as the input weights to upgrade prediction performance. The proposed model is verified through an industrial hydrocracking process to predict diesel yield with missing labels ranged from 0% to 50%, and the root mean squared error is reduced by at least 8.78% compared to PSFA.

Stress-strain and moment-curvature behavior of the reinforced concrete (RC) square columns have been analytically investigated according to different confined concrete models. The effect of transverse reinforcement diameter, transverse reinforcement spacing and concrete grade on the behavior of RC column models were investigated. For different confined concrete models, the confinement effectiveness coefficient, effective lateral confining stress, confined concrete compressive strength, strain at maximum concrete stress and ultimate concrete compressive strain values were calculated. In the second part, a parametric investigation was carried out for examining the effects of different design parameters on the moment-curvature relationships. Analytical moment-curvature relationships were obtained for RC cross-sections by using the TBEC (2018), Mander model (1988), Saatcioglu and Ravzi (1992) confined concrete models. The effects of the design parameters on the RC square column behavior were evaluated in terms of moment capacity and the curvature of the cross-section. In RC column models, stress-strain and moment-curvature relationships are obtained and compared according to different parameters. Confined concrete strength and the ultimate moment values obtained from the Mander model were higher than the Saatcioglu and Ravzi model when the transverse reinforcement close to the minimum spacing values. The results obtained from the Mander model and TBEC (2018) are close to each other.

The microgrid with the high proportion of renewable sources has become the trend of the future. However, the negative features, such as renewable energy perturbation, nonlinear counterpart, and so on, are prone to causing the low-power quality of the ac microgrid. To deal with these problems, this article proposes an event-triggered consensus control approach. First, the nonlinear state-space function regarding the ac microgrid is built, which is further transformed into the standard linear multiagent model by using the singular perturbation method. It provides indispensable preprocessing for the direct application of advanced linear control approaches. Then, based on this standard linear multiagent model, the secondary consensus approach with the leader is designed to compensate for the output voltage deviation and achieve accurate power sharing. In order to decrease the communication among various distributed generators, the event-triggered communication method is further proposed. Meanwhile, the Zeno behavior is avoided through the theoretical proof. Finally, simulation results are presented to demonstrate the effectiveness of the proposed approach.

In this article, a rapid sensor fault diagnosis (SFD) method is presented for a class of nonlinear systems. First, by exploiting the linear adaptive observer technology and the deterministic learning method (DLM), an adaptive neural network (NN) observer is constructed to capture the information of the unknown sensor fault function. Second, when the NN input orbit is a period or recurrent one, the partial persistent excitation (PE) condition of the NNs can be guaranteed through the DLM. Based on the partial PE condition and the uniformly completely observable property of a linear time-varying system, the accurate state estimation and the sensor fault identification can be achieved by properly choosing the observer gain. Third, a bank of dynamical observers utilizing the experiential knowledge is constructed to achieve rapid SFD and data recovery. The attractions of the proposed approach are that accurate approximations of sensor faults can be achieved through the DLM, and the data that are destroyed by the sensor faults can be recovered by using the learning results. Simulation studies of a robot system are utilized to show the effectiveness of the proposed method.

This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. Using the backstepping technique, a boundary control scheme is designed to suppress the vibration and regulate the angle of the spacecraft. A modified asymmetric barrier Lyapunov function is utilized to ensure that the output constraint is never transgressed. Considering the system robustness, neural networks are used to handle the system parameter uncertainties and compensate for the effect of input nonlinearity. With the proposed adaptive neural network control law, the stability of the closed-loop system is proved based on the Lyapunov analysis, and numerical simulations are carried out to show the validity of the developed control scheme.

In this research, the fuzzy adaptive event-triggered control (FAETC) issue is addressed for uncertain nonlinear networked control systems with network-induced delays (NIDs) and external disturbance. In order to effectively capture parameter uncertainties, the interval type-2 (IT-2) Takagi-Sugeno (T-S) fuzzy model is utilized to represent such a system. Considering the fact that the controller is fuzzy and the threshold can promptly update its state according to the current and latest sampled signals (SSs), it becomes quite challenging to solve the dissipative stabilization problem (DSP) with the existing schemes. Then, a novel FAETC protocol is put forward to reduce the utilization of communication resources while maintaining the desired control performance. By employing the fuzzy-logic technique and the looped Lyapunov functional (LLF) approach, sufficient conditions related to the relationship between the stabilization and desired dissipative performance for the resulting system are formulated. A numerical example is used to validate the feasibility of our attained results.

Many vision-based systems suffer from poor levels of contrast and brightness, mainly because of inadequate and improper illumination during the image acquisition process. As a result, the required specified information from the acquired image is not available for the particular application. In general, it is hard to achieve a balance between the improvement of contrast and brightness in image enhancement. By introducing nature-inspired optimization in image enhancement, the best features of the image are utilized, and the complexity related to the nonlinearity of images can be solved with various constraints, like a balance between contrast and brightness. In this work, a novel automatic method for image enhancement to find a balance between contrast and brightness is developed by using Cuckoo Search-optimized image fusion. First, the Cuckoo Search-based optimization algorithm generates two sets of optimized parameters. These parameter sets are used to generate a pair of enhanced images, one with a high degree of sharpness and contrast, the other is bright and has been improved without losing the level of detail. Furthermore, the two enhanced images are fused by the fusion process to obtain an output image where the contrast and brightness are in balance. The effectiveness of the proposed method is verified by applying it to standard images (CVG-UGR image database) and lathe tool images. Experimental results demonstrated that the proposed method performs better with regard to both the quality of contrast and brightness, moreover, yields enhanced quality evaluation metrics compared to the other conventional techniques.

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset contains meshes with the same vertex connectivity, which is common for deformation analysis. To help localize deformations, we introduce sparse regularization in this framework, along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability of the extracted deformation components. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. As an important application of localized deformation components and a novel approach on its own, we further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework, and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing.

This article studies the tracking control problem with a large-scale group of agents. Unlike traditional control techniques used in multiagent systems (MASs), a new type of intelligent design is needed to handle the intractable ``Curse of Dimensionality" caused by the extremely large number of agents. To address this challenge, the mean field game (MFG) theory has been embedded into reinforcement learning to advance intelligent tracking control with large-scale MAS. Specifically, MFG-based control can calculate the optimal strategy based on one unified fix-dimension probability density function (pdf) instead of high-dimensional large-scale MAS information collected from individual agents. Moreover, the approximate dynamic programming technique is adopted to generate a new type of MFG-based algorithm. Each agent has three neural networks (NNs) to approximate the solution of the mean field type control. In addition to the algorithm development, the performance of the NNs is also analyzed using the Lyapunov method. Finally, the linear and nonlinear tracking control simulations are given to evaluate the algorithm's performance.

Aimed at improving the nonlinear integrated navigation solution performance of multiple low-cost sensors fusion, this paper presents a multilayer loosely-coupled, local-global, and step-optimized MF5DCKF (Multisensor Federated fifth-degree Cubature Kalman filter) state estimation algorithm for the small unmanned aerial vehicle (UAV). This method establishes a multilayer nonlinear integrated navigation model composed of the nonlinear attitude and heading reference system (AHRS) error model, strapdown inertial navigation system/global positioning system (SINS/GPS) error model, and strapdown inertial navigation system/barometer (SINS/BARO) error model to enhance the robustness and richness of the navigation module. Further, based on the above navigation models, a loosely-coupled error state fusion frame is designed to obtain the local convergent state vector. Simultaneously, a three-layer fifth-degree Cubature Kalman filter is proposed to improve the local state estimation accuracy. Subsequently, to optimize the estimated local state, this paper presents a novel distributed MF5DCKF scheme fusing the local state vector to calculate the global optimal state parameters in a step-optimized process. The experimental flight test results show that the proposed algorithm achieves a higher state solution accuracy and a better convergent performance compared with some conventional multisensor fusion algorithms. The new algorithm framework can provide applicability and reliability for the small UAV during the flight.

Adaptive output-feedback consensus with funnel performance is studied for nonlinear uncertain multi-agent systems (MASs). The nonlinear MASs contain unknown dynamics and only an output variable can be measured. The other states are not measured directly and are reconstructed via fuzzy state observers. The presence of output feedback makes the proposed design significantly different from the existing literature on control with funnel performance. In particular, appropriate adaptive laws must be defined to handle the presence of uncertain dynamics and local output information from a few neighboring nodes. Interestingly, with a suitable error transformation, it is shown that the proposed control law has a simpler structure than barrier function methods proposed in the literature to handle funnel-like performance. With respect to this point, simulation studies illustrate that the proposed method can dramatically reduce the control effort while satisfying transient and steady-state performance imposed by the funnel.

Most studies about deep learning are based on neural network models, where many layers of parameterized nonlinear differentiable modules are trained by backpropagation. Recently, it has been shown that deep learning can also be realized by non-differentiable modules without backpropagation training called deep forest. We identify that deep forest has high time costs and memory requirements---this has inhibited its use on large-scale datasets. In this paper, we propose a simple and effective approach with three main strategies for efficient learning of deep forest. First, it substantially reduces the number of instances that needs to be processed through redirecting instances having high predictive confidence straight to the final level for prediction, by-passing all the intermediate levels. Second, many non-informative features are screened out, and only the informative ones are used for learning at each level. Third, an unsupervised feature transformation procedure is proposed to replace the supervised multi-grained scanning procedure. Our theoretical analysis supports the proposed approach in varying the model complexity from low to high as the number of levels increases in deep forest. Experiments show that our approach achieves highly competitive predictive performance with reduced time cost and memory requirement by one to two orders of magnitude.

A radial nonlinear multi-spring-damper tire (RNMST) model is proposed in this paper for dynamics behavior analysis of the cooperative bridge-vehicle (CBV) system by considering different road surface roughness and separation. First, both the finite element (FE) and force analysis of the RNMST model were analyzed and studied under different velocity and different class of road surfaces. The three types of tire models, nonlinear single-spring tire (NSST) model, nonlinear multi-spring tire model and RNMST model, were introduced as the vehicle tire devices for a quarter vehicle models into nonlinear cooperative bridge-vehicle system. The cooperative dynamics problem, alongside separation and the responses of road surface in terms of its uneven nature, is a typical dynamics problem with the nonlinearity of material which can be solved using the Newmark-β method. The comparison of the results demonstrated that the analysis of the dynamics of the cooperative bridge-vehicle system under the RNMST model and NMST model are generally similar. However, the radial nonlinear multi-spring-damper tire model is more accurate and closer to the stress state of the real tire model. Also, to further illustrate the comprehensive performance of the RNMST, the responses under different pavement excitations were analyzed by using the amplitude-frequency spectrum method. Finally, to research the influence dynamics of tire force on the CBV system, the horizontal component of tire force was analyzed. This is conducive to numerical research of the dynamic responses of both the bridge and the vehicle.

Seismic isolation has been used to improve the seismic performance of buildings for the last decades. However, it has been shown that the code-based displacement capacity of isolation members does not provide adequate collapse probabilities for different Risk Categories of ASCE 7–16. Seismic design codes stipulate to determine maximum isolator displacement at the Risk-Targeted Maximum Considered Earthquake (MCER) level. Although the code-based isolator displacement capacity is checked with the mean displacement of the MCER level nonlinear time history analysis results, extreme event effects and incorporating uncertainties into fragility curves create additional displacement demands. Therefore, there is a need to amplify the code-based displacement capacity of isolators to obtain sufficient collapse performance. A previous study, assumed elastic behavior and rigid mass model for the superstructure, proposes an iterative probabilistic approach. In the present study, seven different seismically isolated buildings are modeled, and the nonlinear behavior of isolators and superstructures is considered in the incremental dynamic analyses achieved for all models to obtain fragility curves. Although the seismic isolation considerably decreases the spectral accelerations of the superstructure, it is shown that the R-μ-T relations for diaphragm level accelerations, transferred accelerations from base to superstructure, become more sensitive to nonlinear behavior at the superstructure mode periods. The fragility curves of five case study buildings are obtained using the FEMA P695 procedure, and it is figured out that the collapse probability of isolation units is highly correlated with the required isolator displacement to code-based maximum isolator displacement (D/DM) ratio. Then, two simple equations with high correlation coefficients are acquired to estimate the required displacement capacity for prescribed risk-target levels. Further, the obtained equations are verified for two different isolated building models, and their results are compared with the Incremental Dynamic Analysis (IDA) results. The verification results show that the given equations can be used in the preliminary design phase of seismically isolated mid-rise reinforced concrete buildings.

In this paper, the optimum linear/nonlinear spring and linear/nonlinear damper force versus displacement and force versus velocity characteristic functions, respectively, are determined using simple lumped parameter models of a quarter car front independent suspension and a half car rear solid axle suspension of a light commercial vehicle. The complexity of a nonlinear function optimisation problem is reduced by determining the shape a priori based on typical shapes supplied by the car manufacturer and then scaling it up or down in the optimisation process. The vehicle ride and handling responses are investigated considering models of increased complexity. The linear and nonlinear optimised spring characteristics are first obtained using lower complexity lumped parameter models. The commercial vehicle dynamics software Carmaker is then used in the optimisation as the higher complexity, more realistic model. The performance of the optimised suspension units are also verified using this more realistic Carmaker model.

Two dimensional (2D) Photonic crystals (PhCs) based cross waveguide structure (CWs) consisting of resonant cavity is investigated in this paper. Investigations are done using Two Dimensional Finite Difference Time Domain (2DFDTD) method. Transmission characteristics have been obtained for different radii of central rod placed at crossing point of two waveguides and number of regular rods surrounding central rod. Nonlinear behavior is also studied for different operating wavelengths.

Strategic maintenance is essential for sustainable road infrastructure development. Accurate estimation of road maintenance effects can support the assessment of maintenance strategies and reasonable allocation of budgets and resources. Road deterioration is affected by sophisticated factors, but accurate investigation of the integrated deterioration factors is limited. This study developed a dynamic trade-off model (DTOM), a hybrid nonlinear and machine learning method, for quantifying temporally varied impacts of factors and examining maintenance effects at the network level. Pavement deterioration factors are classified into three categories: (i) historical observations of roughness, (ii) pavement age, and (iii) traffic, climate and environment factors. Their respective impacts on pavements are estimated using a non-linear least square regression, a joinpoint regression and a random forest model, respectively. Vehicle-based laser scanner monitored high-resolution deterioration data was collected for a large spatial scale road network in Western Australia from 2007 to 2018. Results show that the resurfacing and rehabilitation are essential for strategic reduction of deterioration. Twelve-year maintenance activities reduced the distress of roughness by 7.5% and increased road performance (the percentage of roads with roughness lower than 2.085 IRI) by 14.5% for the whole road network. The DTOM has great potentials in accurately assessing infrastructure maintenance effects and predicting deterioration scenarios.

This paper focuses on the design of fuzzy secure control for a class of coupled systems, which are modeled by a nonlinear $N$ -dimensional (N-D) parabolic partial differential equation (PDE) subsystem and an ordinary differential equation (ODE) subsystem. Under stochastic deception attacks, a fuzzy secure control scheme is designed, which is effective to tolerate the attacks and ensure the desired performance for the considered systems. A new fuzzy-dependent Poincare-Wirtinger's inequality (PWI) is proposed. Compared with the traditional Poincare's inequality, the fuzzy-dependent PWI is more flexible and less conservative. Meanwhile, an augmented Lyapunov-Krasovskii functional (LKF) is newly constructed, which strengthens the correlations of the PDE subsystem and ODE subsystem. Then, on the ground of the fuzzy-dependent PWI and the augmented LKF, new exponential stabilization criteria are set up for the PDE-ODE coupled systems. Finally, a hypersonic rocket car is presented to verify the effectiveness and less conservatism of the obtained results.

This paper studies the coverage performance of unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) networks under the nonlinear and linear energy harvesting (EH) models in the rich scattering scenarios including smart farming and smart ranching, where the none-line-of-sight (NLoS) links are the dominate component of the wireless channel. Multiple UAVs are equipped with directional antennas to transfer information and energy to ground users (GUs). Power splitting (PS) or time switching (TS) architecture is employed at GUs. In order to evaluate the system performance in fading channels, the information and energy coverage probabilities of the system are discussed, and by using the stochastic geometry approach and the approximate scaling method, the general and lower bound explicit expressions of the coverage probabilities are derived. Numerical results show that the coverage performance of PS-based systems is superior to that of TS-based systems. Moreover, although the linear EH model yields better results than the nonlinear one, as the linear EH model is too ideal, its yielded results may mismatch practical EH circuits, and the ones yielded by the nonlinear EH model is much closer to practice, as the nonlinear EH model is based on real data measurement. Additionally, the nonlinear EH model has a relatively small effect on the harvested energy coverage probability, and the linear EH model introduces greater bias for TS-based system than that for PS-based one.

Fuzzy neural network (FNN) is an effective learning system that combines neural network and fuzzy logic, which has achieved great success in nonlinear system modeling. However, when the input is practical-complex data with external disturbance, the existing FNN cannot extract effective input-features sufficiently, leading to unsatisfactory performances in learning speed and accuracy. It also fails to achieve a better generalization capability because of its fixed structure size (the number of rule neurons). In this work, an efficient self-organizing FNN (SOFNN) with incremental deep pre-training (IDPT), called IDPT-SOFNN, is developed to overcome these shortcomings. First, IDPT is designed to extract effective features and consider them as the input of SOFNN. Different from the existing pre-training, the self-growing structure of IDPT improves pre-training efficiency with a more compact structure. Second, SOFNN can dynamically add and delete neurons according to the current error and error-reduction rate. In this case, it can obtain better modeling performance with a more compact structure as well. Third, as a novel hybrid model with the cascade dual-self-organizing algorithm, IDPT-SOFNN combines the advantage of IDPT and SOFNN. Moreover, the convergence and stability are analyzed. Finally, simulation studies and comparisons demonstrate that the proposed IDPT-SOFNN has better performances than its peers in learning speed, accuracy and generalization capability.

Photovoltaics intelligent edge terminals (PV IETs) are new devices for data acquisition and management of PV power station. However, due to the massive, scattered, and disordered nature of PV stations and the expensive price of PV IET, installing a PV IET at each PV station has prohibited for large-scale adoption. Therefore, to reduce the life cycle cost of PV IET in a region, this paper proposes an optimal configuration method for PV IET. We propose a mathematical model for the optimal configuration of PV IETs. For a given regional grid, the model aims to obtain the optimal number and location of PV IETs and the connection mode between PV IETs and PV power stations. In addition, an improved Harris hawk optimization (IHHO) is proposed to solve the nonlinear mathematical model. A case study is carried out under different problem sizes, boundary and devices parameters. The simulation results show that under different case, the PV IET configuration method in this paper can obtain lower life cycle cost than the conventional method, and IHHO has higher solution accuracy than other algorithms. The optimal configuration method in this article can effectively solve the problem of the optimal configuration of PV IET.

This article offers an islanding detection approach based on dual-tree complex discrete wavelet transform (DTCDWT) and extreme gradient boosting method (XGBoost). Initially, the DTCDWT is used to extract the feature vector from collected negative sequence voltage and current signals at the respective distributed energy resource ends. Afterward, the nonlinear synthetic minority oversampling technique (SMOTE)-based XGBoost is used to classify the islanding (I) and nonislanding (NI) events with the following characteristics: high accuracy, good sensitivity, and faster speed. Here, the SMOTE is used as an offline prefeature processing approach to reduce the unbalancing/uncertainty of “I” and “NI” classes and helps the intelligent classifier to impart a fair classification result. This scheme is found to be effective for the detection of I and NI conditions, satisfying the IEEE islanding detection standard. Additionally, the obtained simulation results are compared with other existing works to show its usefulness.

In this article, we present a multiagent framework for real-time large-scale 3-D reconstruction applications. In SLAM, researchers usually build and update a 3-D map after applying nonlinear pose graph optimization techniques. Moreover, many multiagent systems are prevalently using odometry information from additional sensors. These methods generally involve extensive computer vision algorithms and are tightly coupled with various sensors. We develop a generic method for the key challenging scenarios in multiagent 3-D mapping based on different camera systems. The proposed framework performs actively in terms of localizing each agent after the first loop closure between them. It is shown that the proposed system only uses monocular cameras to yield real-time multiagent large-scale localization and 3-D global mapping. Based on the initial matching, our system can calculate the optimal scale difference between multiple 3-D maps and then estimate an accurate relative pose transformation for large-scale global mapping.

This paper presents a novel compliant geo-structural systems bio-inspired by awns on grass seeds for increasing anchor capacity while minimizing material usage. A compliant deployable structure is here defined as a system that reacts to global displacements by continued elastic shape change and awns are slender flexible structures rigidly connected to the exterior of an anchor. When the anchor is loaded in tension, the awns react off the soil mass and deploy outwards from the pile shaft, enabling space-saving measures for transportation. This paper creates a structural pushover model to establish awn deformations and stress values, a scale model of the compliant system fabricated using additive manufacturing, geo-plasticity numerical models of soil awn interaction, and a finite element model of an example application. This research elucidates the soil displacement mechanisms around the awns, the structural deformation of individual awns, and the enhancement of overall anchor capacity due to awn deployment.

Online transfer learning (OTL) has attracted much attention in recent years. It is designed to handle the transfer learning tasks, where the data of the target domain isn’t available in advance but may arrive in an online manner, which may be a more realistic scenario in practice. However, there typically are two limitations of existing OTL algorithms. 1) Existing OTL algorithms are based on shallow online learning models (SOLMs), e.g., linear or kernel models. Due to this limitation of SOLMs they cannot effectively learn complex nonlinear functions in complicated application and the OTL algorithms based on SOLMs cannot either. 2) Existing algorithms only utilize the latest arrived instance to adjust the model. In this way, the previously arrived instances are not utilized. It may be better to utilize the previously arrived instances as well. In this paper, to overcome the abovementioned two limitations, a buffered online transfer learning (BOTL) algorithm is proposed. In the proposed BOTL algorithm, the learner is designed as a deep learning model, referred to as Online Hedge Neural Network (OHNN). In order to enable the OHNN to be effectively learned in an online manner, we propose a buffered online learning framework that utilizes several previously arrived instances to assist learning. Further, to enhance the performance of the OHNN, a model learned in the source domain is transferred to the target domain. The regret bound of the proposed BOTL algorithm is analyzed theoretically. Experimental results on realistic datasets illustrate that the proposed BOTL algorithm can achieve lower mistake rate than the algorithms compared.

This paper addresses the event-triggered output-feedback funnel tracking control problem for a class of switched nonlinear systems. A novel switched-observer-based event-triggered adaptive fuzzy funnel control approach for solving the problem is proposed by exploiting the average dwell time (ADT) method and backstepping. To estimate unmeasurable system states, a switched fuzzy observer and switched update laws for individual subsystems are designed. Compared with the existing funnel controllers designed in the framework of continuous-time feedback control, the proposed approach constructs event-triggered funnel controllers of subsystems and a switching event-triggered mechanism (ETM), which greatly alleviates the unnecessary waste of communication and computation resources, and, without any known limitation on maximum asynchronous time, handles the issue of asynchronous switching between the candidate controllers of subsystems and subsystems. Also, a strictly positive lower bound on inter-event time is ensured by introducing a piecewise constant variable into the ETM, which overcomes the difficulty of switched measurement error being discontinuous. It is guaranteed that the tracking error evolves within a pre-specified performance funnel. Finally, two examples including a mass-spring-damper system are presented for illustrating the validity of the developed approach.

The revolution of Industry 4.0 and Internet of Things facilitate large and cheap data acquisition from complex systems. However, the online processing and analytics of such multi-stream data still suffer difficulties. The key reason is that data from different sensors (multi-stream data) contain nonlinear within-and-cross sensor correlations, which poses challenges in the modeling and analysis of such data, especially for the online scenarios. To jointly model multi-stream data, multi-output Gaussian Process (MGP) has become popular and achieved great success. However, existing methods assume different outputs are linearly correlated, which significantly limits their applications. In this paper, we propose a nonlinear online MGP framework to simultaneously realize nonlinear correlation modeling and online processing of multi-stream data. Specifically, a tailored co-variance structure is developed based on the convolution process, which can capture nonlinear self-and-cross correlations. To realize online processing, Bayesian analysis and marginalized particle filter are applied to estimate function values and model parameters. Comparing to existing methods, the proposed method features superiorities in i) the non-linear characterization of self-and-cross correlations, and ii) the efficient modeling and prediction capability. These features make the proposed method especially appropriate for industrial informatics with complex relationship and large-scale data sets. The proposed method is validated with simulation studies and a real-case study of biophysical signals.

A novel method for close-proximity formation flying under differential atmospheric drag using Coulomb forces is investigated for applications in Earth sensing, space-situational awareness (SSA), and aeronomy. Objects in LEO are supersonic with respect to the ambient environment, creating a thinned out wake region behind the craft as it travels through the ionosphere. Objects within this wake experience little drag acceleration and are able to attain voltages much greater than in the ambient ionospheric plasma, creating implications for the design and control of close-proximity leader–follower spacecraft pairs. The proposed system consists of a leader craft with a set of affixed, conducting spheres and a charged follower craft located in the wake of the leader. The differential drag acceleration between the leader and follower craft is countered by a controlled Coulomb repulsion to maintain precise separation. The charged structure on the rear of the leader craft is designed such that the charged follower craft sits in an electrostatic potential well which opposes off-axis perturbations. A conceptual method for controlling such a pair without the use of propellant using a set of charged spheres is investigated, with nonlinear models of the system’s relative motion derived and discussed. Linearized models are used to demonstrate the local controllability of the system to demonstrate the proposed system’s merit. This linear analysis is used to derive conditions on controllability and control performance under different charge geometries and environmental assumptions.

This paper presents a novel control strategy for a Dual Active Bridge Series Resonant Converter (DAB-SRC). The strategy seeks to ensure the stability of the converter over its entire dynamic range while enhances the transient response. Both properties allow the use of the converter in new applications where fast dynamics are required, surpassing the performance of existing feedback loops. Starting from the generalized averaged model of the converter, we propose a nonlinear control strategy by means of Lyapunov's stability theory. After that, we derive a series of modifications in order to implement the strategy in a microcontroller or a DSP, including a sensorless method to tackle the lack of measurements of certain variables and an adaptive control law to deal with uncertain parameters in the model. The strategy is evaluated in simulations and experiments, employing a commercial converter and comparing the results with other control policies.

This study proposes an exact model for timetable recovery after disturbances in the context of high-frequency public transport services. The objective of our model is the minimization of the deviation between the actual headway and the respective planned value. The resulting mathematical program for the rescheduling problem is nonlinear and non-smooth; thus, it cannot be solved to optimality. To rectify this, we reformulate the model using slack variables. The reformulated model can be solved to global optimality in real-time with quadratic programming. We apply the model to real data from the red metro line in Washington D.C. in a series of experiments. In our experiments, we investigate how many upstream trips should be rescheduled to respond to a service disturbance. Our findings demonstrate an improvement potential of service regularity of up to 30% if we reschedule the five upstream trips of a disturbed train.

In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron is of great significance for maintaining stable furnace conditions, improving hot metal quality, and reducing energy consumption. However, most of the current research works employ linear correlation coefficient methods to select input features in modeling, which may not fully take the nonlinear and coupling relationships between features into account. Therefore, this article considers the input feature selection issue of silicon content prediction model from a new perspective and proposes a multiobjective evolutionary nonlinear ensemble learning model with evolutionary feature selection mechanism (MOENE-EFS), in which extreme learning machine is adopted as the base learner. MOENE-EFS takes the input feature scheme of each base learner as well as their network structure and parameters as decision variables and proposes a modified nondominated sorting differential evolution algorithm to optimize two conflicting objectives, i.e., accuracy and diversity of base learners, simultaneously. Through the optimization, a set of Pareto optimal base learners with high accuracy and strong diversity can be obtained. Moreover, different from the linear ensemble methods commonly used in classical evolutionary ensemble learning, this article proposes a nonlinear ensemble method to combine the obtained base learners based on differential evolution. Experimental results indicate that the two proposed strategies, i.e., evolutionary feature selection and nonlinear ensemble, are very effective in improving the accuracy and stability of the prediction model. MOENE-EFS also outperforms the other prediction models in both benchmark data and practical industrial data. Furthermore, analysis on the input features of all Pareto optimal base learners shows that the evolutionary feature selection is capable of selecting essential features and is consistent with human experience, which indicates it is a promising method to deal with the input feature selection issue in silicon content prediction.

Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein, to cope with these urgent healthcare demands, the state authorities seek ways to develop policies based on the estimated future casualties. These policies are mainly non-pharmacological policies including the restrictions, curfews, closures, and lockdowns. In this paper, we construct three model structures of the SpInItIbD-N (suspicious Sp, infected In, intensive care It, intubated Ib, and dead D together with the non-pharmacological policies N) holding two key targets. The first one is to predict the future COVID-19 casualties including the intensive care and intubated ones, which directly determine the need for urgent healthcare facilities, and the second one is to analyse the linear and non-linear dynamics of the COVID-19 pandemic under the non-pharmacological policies. In this respect, we have modified the non-pharmacological policies and incorporated them within the models whose parameters are learned from the available data. The trained models with the data released by the Turkish Health Ministry confirmed that the linear SpInItIbD-N model yields more accurate results under the imposed non-pharmacological policies. It is important to note that the non-pharmacological policies have a damping effect on the pandemic casualties and this can dominate the non-linear dynamics. Herein, a model without pharmacological or non-pharmacological policies might have more dominant non-linear dynamics. In addition, the paper considers two machine learning approaches to optimize the unknown parameters of the constructed models. The results show that the recursive neural network has superior performance for learning nonlinear dynamics. However, the batch least squares outperforms in the presence of linear dynamics and stochastic data. The estimated future pandemic casualties with the linear SpInItIbD-N model confirm that the suspicious, infected, and dead casualties converge to zero from 200000, 1400, 200 casualties, respectively. The convergences occur in 120 days under the current conditions.

The high-precision parameters in distribution networks are dif-ficult to obtain, which brings difficulties to the model-based methods and analysis. With the widespread deployment of high-precision measurement units, data-driven methods have greater advantages in practice. In addition, with massive integra-tion of distributed renewable generation and fast electric vehicle chargers, the fluctuations of net load increase significantly. The data-driven power flow calculation method based on the linear model becomes difficult to obtain accurate results for the low nonlinear adaptability. To improve the data-driven power flow calculation accuracy under high penetration of renewable distri-bution generation, this paper proposed an approach with high adaptability to the nonlinearity of power flow. Based on the thought of Koopman operator theory, the nonlinear relationship in power flow calculation is converted into a linear mapping in a higher dimension state space, which can significantly improve the calculation accuracy. Case studies on different IEEE cases have demonstrated that the proposed method can realize higher accu-racy in power flow calculation with large-scale power fluctuations, compared to the existing data-driven method, in both mesh and radial networks. Finally, measurement data of a practical 10kV distribution network has been further used to verify the effec-tiveness of the proposed method in practical applications.

Pollutant emissions into the atmosphere are recognized as a significant problem in fossil fuel combustion. The pollution emissions measurement in industrial boilers is difficult and expensive but fundamental for monitoring and controlling. Frequently continuous emissions monitoring (CEM) system is out of service or useless due to obsolescence, high maintenance cost, etc., or simply is not installed. When a system for measuring pollutant emissions is not available, an alternative method must be employed to get the pollutant emissions value. According to the black-box model approach, this article describes the nonlinear modeling of NOx emissions from a utility boiler. Bayesian-Gaussian (BG), Multilayer perceptron (MLP), and Volterra polynomial basis functions (VPBF) neural networks are developed for model benchmarking. Experimental data from a utility boiler was acquired in order to model definition and evaluation. The models process two boiler variables, oxygen excess and flue gas recirculation gates for NOx emissions estimation. Models with BG show better performance than models with MLP and VPBF for NOx prediction. Implications StatementThe technology to control NOx emissions generated by combustion operates under strict regulations. In order to reduce NOx emissions, theoretical models of NOx generation have been studied extensively, including nitrogen chemistry and the dynamic flow of gas particles which is very complex. The new technology trends would require the continuous measurement of high precision NOx emissions to achieve further reductions in NOx emissions. Currently, NOx emissions are measured by a Continuous Emission Monitoring system, which turns out to be extremely expensive and difficult to maintain, so alternative low-cost solutions are desirable. Our contribution shows how algorithms based on different artificial intelligence techniques are viable and quality alternatives for the measurement of continuous NOx emissions. The NOx emissions models based on IA algorithms are viable alternatives that have versatility and self-tuning capacity due to the fact that they are based on boiler operation parameters which have valuable information few explored nowadays.

The dynamic impact load after a single insulator of a tension composite multi-strings of insulator (MSI) set has failed is a key factor that should be considered in design. In this paper, an experimental method to obtain the dynamic tensile impact factor (DTIF) is established by considering a typical double-strings set, which is commonly used in practical engineering. A nonlinear numerical simulation method based on the finite element model, which is convenient for cases that are more complex than the double-strings set, is proposed to calculate the DTIF and demonstrated through the experimental results. The values of the DTIF on the unbroken insulator string obtained by the experimental models fall between 2.6 and 3.3, which should be a very valuable reference for the design of composite MSI sets. The size of connection fittings is a key factor that influences the DTIF, and the yoke height between the insulator strings and the conductors is the most sensitive parameter. The application of the proposed method to calculate the DTIF can improve the existing design approach to ensure the safety of the tension composite MSI sets, especially for high voltage transmission lines.

Copyright © 2021 The Author(s) and Tongji University. Recent earthquakes have shown that tunnels are prone to damage, posing a major threat to safety and having major cascading and socioeconomic impacts. Therefore, reliable models are needed for the seismic fragility assessment of underground structures and the quantitative evaluation of expected losses. This paper builds on previous research and presents a probabilistic framework based on an artificial neural network (ANN), aiming at the development of fragility curves for circular tunnels in soft soils. Initially, a two-dimensional incremental dynamic analysis of the nonlinear soil-tunnel system is performed to estimate the response of the tunnel under ground shaking. The effects of soil-structure-interaction and ground motion characteristics on the seismic response and fragility of tunnels are adequately considered within the proposed framework. An ANN is employed to develop a probabilistic seismic demand model, while its results are compared with the traditional linear regression models. Fragility curves are generated for various damage states accounting for the associated uncertainties. The results indicated that the proposed ANN-based probabilistic framework results to reliable fragility models, having similar capabilities as the traditional approaches, while lower computational cost is required. The proposed fragility models can be adopted for the risk analysis of typical circular tunnel in soft soils subjected to seismic loading, and they are expected to facilitate decision-making and risk management toward more resilient transport infrastructure. National Natural Science Foundation of China (Grant Nos. 52108381, 52090082, 41772295, 51978517); Innovation Program of Shanghai Municipal Education Commission (Grant No. 2019-01-07-00-07-456 E00051); and key innovation team program of innovation talents promotion plan by MOST of China (No. 2016RA4059).

Heat-integrated water network synthesis (HIWNS) has received considerable attention for the advantages of reducing water and energy consumptions. HIWNS is effective in water and energy sustainability. Mixed integer non-linear programming (MINLP) is usually applied in HIWNS. In this work, a novel nonlinear programming (NLP) was proposed for HIWNS by considering wastewater reuse and wastewater regeneration reuse. Integer variables are changed to non-linear equation by the methods for identifying stream roles and denoting the existence of process matches. The model is tested by examples with single and multiple regeneration unit problems. The testing results showed that the NLP is an alternative method for HIWNS with wastewater reuse and regeneration reuse.

This paper investigates the seismic behavior of an electrical cabinet considering the influence of equipment-anchor-interaction (EAI) that is generally not taken into consideration in a decoupled analysis. The hysteresis behavior of an anchor bolt in concrete was thereby considered to highlight this interaction effect. To this end, the experimental behavior of an anchor bolt under reversed cyclic loading was taken from the recently developed literature, and a numerical model for the anchor hysteresis was developed using the component approach. The hysteresis properties were then used to calibrate the multi-linear link element that is implemented as a boundary condition for the cabinet incorporating the EAI. To highlight this EAI further, the nonlinear time history analysis was performed for a cabinet considering the hysteresis behavior comparative to a fixed boundary condition. Additionally, the influence on the seismic fragility was evaluated for the operational and structural condition of the cabinet. The numerical analysis considering the anchor hysteresis manifests that the in-cabinet response spectra (ICRS) are significantly amplified with the corresponding reduction in the seismic capacity of 25% and 15% for an operational and structural safety condition under the selected protocols. Considering the fixed boundary condition over a realistic hysteresis behavior of the anchor bolt is more likely to overestimate the seismic capacity of the cabinet in a seismic qualification procedure.

This note considers the design and analysis of a distributed sensor fault accommodation scheme for a class of nonlinear dynamical systems, modelled as interconnected subsystems. Each subsystem is subject to possible local sensor faults or may be impacted by a sensor fault in a neighboring subsystem due to the exchange of information between neighboring controllers. The proposed adaptive approximation-based distributed fault accommodation scheme is triggered in the event that a sensor fault is detected. The stability of the closed-loop system in the presence of sensor faults is rigorously analyzed both for the period prior to the detection of the fault, as well as after the detection of the fault when the fault accommodation scheme is activated. The effectiveness of the proposed scheme is illustrated by a simulation example.

Semiconductor gas sensors are important candidates with small size, low power consumption, and low cost characteristics for gas sensing in the internet of things, but the nonlinear response, unstable baseline, and the variability of the responses towards temperature and humidity fluctuation harm its performance. Here, we proposed a new technique called virtual alternating current (AC) measurements to enable semiconductor gas sensors to achieve high linear response (R2 > 0.99), stable baseline, and easily calibrated temperature and humidity fluctuation. The most outstanding advantage of our technique is to achieve excellent performance simply by measuring resistance. The universality and feasibility of our technique were verified by a range of commercial and homemade sensing elements and a range of analyte. We also successfully generalized our technique to non-Debye relaxation systems, which further expands the scope of semiconductor gas sensor types available in our technique. We believe that our technique will enable gas sensing nodes built with semiconductor gas sensors in the internet of things to achieve markedly superior performance.

Surface Electromyography (sEMG) plays a key role in many applications such as control of Human-Machine Interfaces (HMI) and neuromusculoskeletal modeling. It has strongly nonlinear relations to joint kinematics and reflects the subjects’ intention in moving their limbs. Such relations have been traditionally examined by either integrated biomechanics and multi-body dynamics or gesture-based classification approaches. However, these methods have drawbacks that limit their usability. Different from them, joint kinematics can be continuously reconstructed from sEMG via estimation approaches, for instance, the Artificial Neural Networks (ANNs). The Comparison of different ANNs used in different studies is difficult, and in many cases, impossible. The current study focuses on fairly evaluating four types of ANN over the same dataset and conditions in proportional and simultaneous estimation of 15 hand joint angles from 10 sEMG signals. The presented ANNs are Feedforward, Cascade-Forward, Radial Basis Function (RBFNN), and Generalized Regression (GRNN). Each ANN is applied to its special parametric study. All the methods efficiently solved the regression problem of the complex multi-input multi-output bio-system. The RBFNN has the best performance over the others with a 79.80% mean correlation coefficient over all joints, and its accuracy reaches as high as 92.67% in some joints. Interestingly, the highest accuracy over individual joints is 93.46%, which is achieved via the GRNN. The good accuracy suggests that the proposed approaches can be used as alternatives to the previously adopted ones and can be employed effectively to synchronously control multi-degrees of freedom HMI and for general multi-joint kinematics estimation purposes.