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This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system's state via indirect observations, alongside an efficient stochastic maximum principle-type optimal control solver. By integrating weak convergence techniques for the particle filter with convergence analysis for the stochastic maximum principle control solver, we derive a weak convergence result for the optimization procedure in search of optimal data-driven feedback control. Numerical experiments are performed to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles. [ABSTRACT FROM AUTHOR]

With the development of 3D technology, the construction of 3D motion model has become an important research issue in many fields, such as virtual reality, animation and robot vision. In this paper, a 3D motion model construction method based on simulation algorithm is proposed. Firstly, the gravity simulation algorithm is used to simulate the trajectory of the object, and the position data of multiple moments with numerical accuracy of 10cm are obtained. Then, the interpolation algorithm is used to interpolate these position data into continuous curves, and the object trajectory with 1cm accuracy is obtained. Finally, we integrate the obtained trajectory data with 3D modeling software to generate a 3D motion model with real motion effect. The experimental results show that this method has high accuracy and reliability in constructing 3D motion model, and is suitable for many fields. [ABSTRACT FROM AUTHOR]

The main gearbox is very important for the operation safety of helicopters, and the oil temperature reflects the health degree of the gearbox; therefore establishing an accurate oil temperature forecasting model is an important step for reliable fault detection. Firstly, in order to achieve accurate gearbox oil temperature forecasting, an improved deep deterministic policy gradient algorithm with a CNN–LSTM basic learner is proposed, which can excavate the complex relationship between oil temperature and working condition. Secondly, a reward incentive function is designed to accelerate the training time costs and to stabilize the model. Further, a variable variance exploration strategy is proposed to enable the agents of the model to fully explore the state space in the early training stage and to gradually converge in the training later stage. Thirdly, a multi-critics network structure is adopted to solve the problem of inaccurate Q-value estimation, which is the key to improving the prediction accuracy of the model. Finally, KDE is introduced to determine the fault threshold to judge whether the residual error is abnormal after EWMA processing. The experimental results show that the proposed model achieves higher prediction accuracy and shorter fault detection time costs. [ABSTRACT FROM AUTHOR]

The multi-scale quantum harmonic oscillator optimization algorithm(MQHOA)is a natural calculation algorithm based on quantum physics proposed in recent years. Aiming at the problem that the algorithm fails to make full use of the historical information in the iteration, this paper proposes a historical information-driven multi-scale quantum harmonic oscillator optimization algorithm(HI-MQHOA). In the two-step iterative process, HI-MQHOA introduces historical data as a driver to form the parameters of the next generation individual distribution and dynamically adjust the scale of the algorithm. The next generation individual distribution parameters can effectively guide the development and exploration of the algorithm, and dynamic scaling can avoid premature stagnation. Verified by several classical test functions, the algorithm is superior to MQHOA, improved MQHOA and other natural computing algorithms in solution quality, accuracy and scalability. [ABSTRACT FROM AUTHOR]

A novel damage identification approach based on a model-driven and a data-driven combined algorithm is developed. By using this approach with only boundary strains, the existence, location, classification as well as the extent of the damage in a plate can be predicted at once with high accuracy and efficiency. To accumulate the data, the boundary element method (BEM) is applied as a model-driven in modeling plates with one or multiple damages in the forms of circular or elliptical holes or cracks and for solving the boundary strains of the defective plates. The dimensionality reduction and semi-analytical characteristics of BEM not only can compress the feature data also can improve the accuracy of the database for the data-driven algorithm. The boundary strains are obtained directly from BEM models, which are also easily collected through the use of strain gauges mounted on the surfaces of structures being monitored in real applications. A series connection neural network algorithm is established to accomplish the novel damage identification assignments with deep learning. The number of the existing damages is firstly detected by a classification neural network model, then the extracted features are transmitted to the corresponding regression neural network model to prognosis the location, classification as well as the extent of each flaw. A high accuracy of about 99.86 % is achieved by the present combined neural network algorithm, which is promising in applications of actual structural health monitoring. • A novel damage identification approach is developed based on a model-driven and data-driven combined algorithm. • With only boundary strains, the existence, location, classification and extent of damage in a plate are exactly predicted. • The dimensionality reduction and semi-analytical characteristics of BEM compress the feature data and improve the accuracy. • A series connection neural network model is developed to construct the exact mapping relationship. • A high accuracy of about 99.86% makes the algorithm promising for actual structural health monitoring applications [ABSTRACT FROM AUTHOR]

The demand for high performance of vision-based control systems is ever increasing. This article, thus, proposes, for the first time, a real-time performance recovery framework for the vision-based control of highly dynamic systems. The framework enables functionalized nominal controller and performance recovery, balanced image processing, and performance recovery, as well as enhanced real-time efficiency, which is based on the observer-based realization of the Youla parameterization of all stabilizing controllers. An online learning algorithm for real-time performance recovery against the unexpected performance degradation is developed as well. The verification of the proposed framework and algorithm promises their applications to classes of vision-based highly dynamic control systems. [ABSTRACT FROM AUTHOR]

This paper proposes an improved neuroendocrine–proportional–integral–derivative controller for nonlinear multi-input–multi-output crane systems using a sigmoid-based secretion rate of the hormone regulation. The main advantage of the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative is that the hormone secretion rate of neuroendocrine–proportional–integral–derivative can be varied according to the change of error. As a result, it can provide high accuracy control performance, especially in nonlinear multi-input–multi-output crane systems. In particular, the hormone secretion rate is designed to adapt with the changes of error using a sigmoid function, thus contributing to enhanced control accuracy. The parameters of the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller are tuned using the safe experimentation dynamics algorithm. The performance of the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller-based safe experimentation dynamics algorithm is evaluated by tracking the error and the control input. In addition, the performances of proportional–integral–derivative and neuroendocrine–proportional–integral–derivative controllers are compared with the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative performance. From the simulation work, it is discovered that the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative design provides better control performances in terms of the objective function, the total norm of error and the total norm of input compared to proportional–integral–derivative and neuroendocrine–proportional–integral–derivative controllers. In particular, it is shown the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller contributes 5.12% of control accuracy improvement by changing the fixed hormone secretion rate into a variable hormone secretion rate based on the change of error. [ABSTRACT FROM AUTHOR]

This article addresses the online reinforcement $Q$ -learning algorithms to design $H_{\infty }$ tracking controller for unknown discrete-time linear systems. An augmented system composed of the original system and the command generator is constructed, and a discounted performance function is introduced to establish a discounted game algebraic Riccati equation (GARE). The existence conditions of a solution to the GARE are proposed and a lower bound is found for the discount factor to assure the stability of the $H_{\infty }$ tracking control solution. The $Q$ -function Bellman equation is then derived, based on which the reinforcement $Q$ -learning algorithm is developed to learn the solution to $H_{\infty }$ tracking control problem without knowing the system dynamics. Both state-data-driven and output-data-driven reinforcement $Q$ -learning algorithms toward finding the control policies are proposed. Unlike the value function approximation (VFA)-based approach, it is proved that the $Q$ -learning scheme brings out no bias of solution to the $Q$ -function Bellman equation under the probing noise satisfying the persistent excitation (PE) condition, and therefore, converges to the nominal discounted GARE solution. Moreover, the proposed output-data-driven method is more powerful than the state-data-driven method as it may not be available to completely measure the full system states in practical applications. A simulation example with a single-phase voltage-source UPS inverter is used to verify the effectiveness of the proposed $Q$ -learning algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the data-based optimal control for a class of networked industrial processes with a double-layer architecture. Without knowing the dynamics of subsystems at the device layer, the index prediction function is constructed via the input/output signals, and radial basis function neural networks. The tuning laws for the index prediction function are obtained through the optimal control strategy. Then, by treating the network-induced phenomenon as random round-trip time delay and introducing the predictive algorithm, the compensation scheme is designed at the operation layer to dynamically decompose the setpoints. Finally, two simulation examples are given to further illustrate the effectiveness of the proposed compensation strategy. [ABSTRACT FROM AUTHOR]

In this paper, a new multi-scale image fusion algorithm for multi-sensor images is proposed based on Empirical Wavelet Transform (EWT). Different from traditional wavelet transform, the wavelets of EWT are not fixed, but the ones generated according to the processed signals themselves, which ensures that these wavelets are optimal for processed signals. In order to make EWT can be used in image fusion, Simultaneous Empirical Wavelet Transform (SEWT) for 1D and 2D signals are proposed, by which different signals can be projected into the same wavelet set generated according to all the signals. The fusion algorithm constructed on the 2D SEWT contains three steps: source images are decomposed into a coarse layer and a detail layer first; then, the algorithm fuses detail layers using maximum absolute values, and fuses coarse layers using the maximum global contrast selection; finally, coefficients in all the fused layers are combined to obtain the final fused image using 2D inverse SEWT. Experiments on various images are conducted to examine the performance of the proposed algorithm. The experimental results have shown that the fused images obtained by the proposed algorithm achieve satisfying visual perception; meanwhile, the algorithm is superior to other traditional algorithms in terms of objective measures. [ABSTRACT FROM AUTHOR]

We propose a data-driven method to realize high-quality detailed hair animations in interactive applications like games. By devising an error metric method to evaluate hair animation similarities, we take hair features into consideration as much as possible. We also propose a novel database construction algorithm based on Secondary Motion Graph. Our algorithm can improve the efficiency of such graphs to reduce redundant data and also achieves visually smooth connection of two animation clips while taking into consideration their future motions. The costs for the run-time process using our Secondary Motion Graph are relatively low, allowing real-time interactive operations. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we introduce visual attention mechanism from the human vision system to detect fabric defects, and propose the integrated computational model of top-down and bottom-up visual attention. Firstly, utilizing data driven of bottom-up visual attention generates the overall saliency map to pop out fabric defects. Secondly, using target feature driven (task driven) of top-down visual attention forms region of interest (ROI) of fabric defects. Finally, the fabric defects are segmented from ROI using the threshold. Experimental results show that compared with the traditional detection methods, the proposed algorithm can segment accurately common defects from fabric images and enhance detection rate, and it has strong universality for different fabric texture, which can provide the possibility for the realization of automatic fabric defect detection. [ABSTRACT FROM AUTHOR]

A multi-physics formulation for data-driven prognosis (DDP) is developed. Unlike traditional predictive strategies that require controlled offline measurements or 'training' for determination of constitutive parameters to derive the transitional statistics, the proposed DDP algorithm relies solely on in situ measurements. It uses a deterministic mechanics framework, but the stochastic nature of the solution arises naturally from the underlying assumptions regarding the order of the conservation potential as well as the number of dimensions involved. The proposed DDP scheme is capable of predicting onset of instabilities. Because the need for offline testing (or training) is obviated, it can be easily implemented for systems where such a priori testing is difficult or even impossible to conduct. The prognosis capability is demonstrated here via a balloon burst experiment where the instability is predicted using only online visual observations. The DDP scheme never failed to predict the incipient failure, and no false-positives were issued. The DDP algorithm is applicable to other types of datasets. Time horizons of DDP predictions can be adjusted by using memory over different time windows. Thus, a big dataset can be parsed in time to make a range of predictions over varying time horizons. [ABSTRACT FROM AUTHOR]

Semi-structured processes are data-driven, human-centric, flexible processes whose execution between instances can vary dramatically. Due to their unpredictability and data-driven nature, it's becoming increasingly important to mine traces of events collected from these processes. This enables the extraction of mined process models that could help users handle new process instances. Process-mining techniques can help facilitate this goal, but it can be daunting for users new to process-aware analytics to sift through the literature and available software to determine which process-mining algorithm to use. The authors compare five process-mining algorithms and present a decision tree to help readers determine which mining algorithm to use for a specific problem. Semi-structured processes, however, present challenges that these mining techniques don't address. So, the authors also identify three key characteristics of semi-structured processes and the mining challenges they present, highlighting a selection of emerging mining approaches that can address these challenges. [ABSTRACT FROM PUBLISHER]

During recent years, the Internet has witnessed a rapid growth in deployment of data-driven (or swarming based) peer-to- peer (P2P) media streaming. In these applications, each node independently selects some other nodes as its neighbors (i.e., gossip- style overlay construction) and exchanges streaming data with the neighbors (i.e., data scheduling). To improve the performance of such protocol, many existing works focus on the gossip-style overlay construction issue. However, few of them concentrate on optimizing the streaming data scheduling to maximize the throughput of a constructed overlay. In this paper, we analytically study the scheduling problem in data-driven streaming system and model it as a classical mm-cost network flow problem. We then propose both the global optimal scheduling scheme and distributed heuristic algorithm to optimize the system throughput. Furthermore, we introduce layered video coding into data-driven protocol and extend our algorithm to deal with the end-host heterogeneity. The results of simulation with the real-world traces indicate that our distributed algorithm significantly outperforms conventional ad hoc scheduling strategies especially in stringent buffer and bandwidth constraints. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the density g of identically distributed variables Xi, from a sample Z1, ..., Zn, where Zi=Xi+σεi, i=1, ..., n and σεi is a noise independent of Xi with known density σ-1fε(·/σ). We numerically study the adaptive estimators, constructed by a model selection procedure described by Comte et al. [2006, Penalized contrast estimator for density deconvolution, Canadian Journal of Statistics, 37(3)]. We illustrate their properties in various contexts and test their robustness (misspecification of errors, dependency and so on). Comparisons are made with respect to deconvolution kernel estimators. It appears that our estimation algorithm, based on a fast procedure, performs very well in all contexts. [ABSTRACT FROM AUTHOR]

Current assessments of rock mass quality of a NATM tunnel face are important in the practice of tunnel excavation. This study establishes a multi-source database and proposes a data driven method for the assessment. Thirteen multi-source variables describing the tunnel faces are considered as inputs, and the rock mass rating (RMR) values computed by the empirical formula are the target outputs. We adopted two meta machine learning models (classification and regression tree (CART) and multiple layers perceptron (MLP)) and two ensemble learning models (gradient boosting regression tree (GBRT)) and random forest (RF)) to capture the relationships between the inputs and outputs. The tree-structured Parzen estimator (TPE) algorithm is applied to automatically determine the optimized model hyper-parameters. The experimental results suggest that the proposed hybrid ensemble learning models (TPE-RF and TPE-GBRT) perform well at assessing rock mass quality. The feature importance ranks of the input variables are determined by a sensitivity analysis, which enhances the knowledge on assessing the rock mass quality of a tunnel face. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input–output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks. [ABSTRACT FROM AUTHOR]

Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13–14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%. [ABSTRACT FROM AUTHOR]