Error Constraints Research Articles

Separable Coupled Dictionary Learning for Large-Scene Precise Classification of Multispectral Images

Large-scene precise classification of multispectral images (MSIs) has become one of the hot topics in remote sensing field. MSIs usually have wide swath and a meter or even submeter level of spatial resolution, which make large-scene observation possible. However, the limited number of spectral bands leads to the confusion of land covers in classification, especially for the large-scene conditions with abundant land cover types. Therefore, overlapped hyperspectral images (HSIs) can be used to improve the precision degree of classification. To achieve this purpose, coupled dictionary learning has been proposed as a major means. Aiming at separating the class-specific characteristics and mutual patterns among different land covers, this paper proposed a separable coupled dictionary learning (SCDL) method, which converts the separation of mutual features into the construction of separable coupled dictionaries and learns both class-specific coupled dictionaries and mutual coupled dictionaries simultaneously with the aid of label information. More specifically, the proposed method uses the labels of training samples to construct class-specific reconstruction error constraint, class-specificity constraint and separable dictionary incoherence constraint as regularization terms, to make sure that the learned coupled dictionaries to be both compact and discriminative. The learned separable coupled dictionaries facilitate pixels belong to the same category to be represented by the mutual dictionary and the class-specific sub-dictionary of corresponding class. The experiments compared with several state-of-the-art methods on three pairs of HSI and MSI have shown better classification performance.

Information-Theoretic Optimal Radar Waveform Selection With Multi-Sensor Cooperation for LPI Purpose

For the complex battlefield electromagnetic environment, low probability of interception (LPI) performance has become an indispensable ability for modern radars. Waveform selection is one of the most fundamental and effective technical approaches to achieve the LPI performance of radar. However, the existing LPI waveforms are often optimized by some incomplete or weak performance representation metrics of radar or passive intercept devices (PIDs), which leads to a poor LPI performance of the designed waveform. From the perspective of information flow, this paper reformulated the processes of radar target tracking and the interception and identification of PIDs. For simplifing the LPI waveform selection optimization model, the interception and identification performance optimization criterions of PIDs are well-integrated into a comprehensive metric, which is measured by Kullback-Leibler (KL) divergence. Combining it with the radar tracking performance metric which is measured by the mutual information, an LPI waveform selection optimization model is established, which gives full consideration to both radar and PIDs performance. And, a two-round selection method is proposed to solve the optimization model. In addition, a multi-sensor cooperative tracking mechanism based on the accumulated tracking error constraint is designed for radar radiation control. The optimal waveform selection in the framework of multi-sensor cooperative tracking can improve the LPI performance of radar in both the waveform domain and the energy domain. Simulation results validate the effectiveness of the performance optimization metrics of radar and PIDs, and the superiority and feasibility of the designed waveform selection method in the multi-sensor cooperative target tracking performance and LPI performance.

High Precision Motion Control of Electro-Mechanical Launching Platform with Modeling Uncertainties: A New Integrated Error Constraint Asymptotic Design

For the demands of a high precision motion control of an uncertain electro-mechanical launching platform, a novel integrated error constraint asymptotic control in the presence of parametric uncertainties and uncertain disturbance is proposed, of which the barrier function method and a continuous asymptotic control design are integrated for the first time. The former technique can effectively avoid excessive tracking errors at the transient phase, which is caused by the disturbance and the large uncertain system parameters’ deviation between the initial estimated value and the actual value, by selecting a proper barrier threshold, while the latter technique can handle the uncertain disturbance to achieve asymptotic tracking. A rigorous stability analysis is given to illustrate the theoretical performance. In addition, as a supplementary measure, repetitive control is employed to estimate and compensate the possible periodic-like disturbance under certain conditions. Two experimental cases on a prototype of a launching platform demonstrate the effectiveness of the proposed controller.

Knapsack-Based Sensor Selection for Target Localization Under Energy and Error Constraints

Knapsack-Based Sensor Selection for Target Localization Under Energy and Error Constraints

Resilient Cooperative Control for High-Speed Trains Under Denial-of-Service Attacks

The denial-of-service (DoS) attacks injected in the communication network of high-speed trains can block the transmission of information between trains by preventing trains from sending data and interfering with communication network. In order to solve this problem, this paper investigates the cooperative control strategy for high-speed trains subject to DoS attacks through a resilient control scheme. A secure control framework is proposed to restore the communication network of the multi-train system caused by DoS attacks to the initial communication network by proposing an acknowledgment-based attack detection strategy and a recovery mechanism. Then, distributed controllers with considering input saturation are proposed under this framework by applying the Lyapunov theory and low-gain feedback method. In addition, the speed error and position error constraints can be guaranteed during the operating process of trains. Finally, the effectiveness of the proposed resilient control scheme is evaluated by the simulation example.

A novel feedrate scheduling method based on Sigmoid function with chord error and kinematic constraints

A novel feedrate scheduling method based on Sigmoid function with chord error and kinematic constraints

Structure–Adaptive Sequential Testing for Online False Discovery Rate Control

Consider the online testing of a stream of hypotheses where a real-time decision must be made before the next data point arrives. The error rate is required to be controlled at all decision points. Conventional simultaneous testing rules are no longer applicable due to the more stringent error constraints and absence of future data. Moreover, the online decision-making process may come to a halt when the total error budget, or alpha-wealth, is exhausted. This work develops a new class of structure-adaptive sequential testing (SAST) rules for online false discovery rate (FDR) control. A key element in our proposal is a new alpha-investing algorithm that precisely characterizes the gains and losses in sequential decision making. SAST captures time varying structures of the data stream, learns the optimal threshold adaptively in an ongoing manner and optimizes the alpha-wealth allocation across different time periods. We present theory and numerical results to show that SAST is asymptotically valid for online FDR control and achieves substantial power gain over existing online testing rules.

Error Constraint Enhanced Particle Filter Using Quantum Particle Swarm Optimization

The position is one of the foremost imperative properties of an object. The Inertial Measurement Unit (IMU) development has enabled the system to continuously decrease in size, reduce power consumption, and become more and more universal. Although IMU meets the real-time needs of the localization system, it faces accumulative error and drifting problems. This paper presents an error constraint enhanced particle filter algorithm using quantum particle swarm optimization (EC-QPF), which achieves high-precision position estimation of target tracking with IMU. First, we proposed a quantum particle swarm optimization-based resampling method taking the place of the traditional weight-based resampling method in the particle filter, which avoids the particle impoverishment problem and keeps the particles’ diversity. The theoretical foundation of error constraint was raised applied to enhance the performance of the proposed particle filter. The error constraint is established utilizing the known estimated center and confidence scale to achieve particle screening based on the geometrical position of particles. Numerical experimental results show that the proposed EC-QPF has a 67% improvement instead of other modified particle filters and more efficiently eliminates the cumulative error. Furthermore, higher accuracy and stability are obtained under the exact condition, demonstrating better performance than traditional particle filter methods.

Optimization and Estimation Algorithm of Vehicle Spatial Form Based on Monocular Traffic Camera

Accurate and rapid acquisition of vehicle spatial form is of great importance in the fields of intelligent transportation and autonomous driving. However, given the limitations of projective geometry, it is difficult to obtain the 3-D structure of vehicles using monocular cameras. The purpose of this paper is, therefore, to estimate the vehicle spatial form using monocular traffic cameras. Firstly, we establish the camera calibration model of the road scene, and jointly construct the geometric constraint model of the vehicle spatial form by vanishing points. Secondly, the contour and edge constraints of the vehicle are obtained based on Mask R-CNN. Then, based on these constraints, the error constraint function is constructed to calculate the projection error of the vehicle spatial form. Finally, a particle swarm optimization algorithm is used to iteratively optimize the parameters in the constraint space to obtain accurate vehicle spatial form information. Experiments are carried on the BrnoCompSpeed data set and the home-made data set. The experimental results show that the processing time of a single frame is less than 0.5 s and the average accuracy is higher than 94%. Moreover, the proposed algorithm has good robustness to the issue of vehicle occlusion and queuing in the scene, which outperforms existing methods.

MISS: finding optimal sample sizes for approximate analytics

Nowadays, sampling-based Approximate Query Processing (AQP) is widely regarded as a promising way to achieve interactivity in big data analytics. To build such an AQP system, finding the minimal sample size for a query regarding given error constraints in general, called Sample Size Optimization (SSO), is an essential yet unsolved problem. Ideally, the goal of solving the SSO problem is to achieve statistical accuracy, computational efficiency and broad applicability all at the same time. Existing approaches either make idealistic assumptions on the statistical properties of the query, or completely disregard them. This may result in overemphasizing only one of the three goals while neglect the others. To overcome these limitations, we first examine carefully the statistical properties shared by common analytical queries. Then, based on the properties, we propose a linear model describing the relationship between sample sizes and the approximation errors of a query, which is called the error model. Then, we propose a Model-guided Iterative Sample Selection (MISS) framework to solve the SSO problem generally. Afterwards, based on the MISS framework, we propose a concrete algorithm, called $$L^{2}\textsc{Miss}$$ , to find optimal sample sizes under the $$L^{2}$$ norm error metric. Moreover, we extend the $$L^{2}\textsc{Miss}$$ algorithm to handle other error metrics. Finally, we show theoretically and empirically that the $$L^{2}\textsc{Miss}$$ algorithm and its extensions achieve satisfactory accuracy and efficiency for a considerably wide range of analytical queries.

Disturbance observer-based backstepping formation control of multiple quadrotors with asymmetric output error constraints

This paper presents a distributed formation tracking control strategy which acts on multiple quadrotor unmanned aerial vehicles (QUAVs) formation control under external disturbance and asymmetric output error constraints. An asymmetric barrier Lyapunov function (BLF) is applied to ensure the constraint of output error. Based on graph theory and backstepping control method, a distributed formation controller is designed to achieve the formation and maintenance of formations, where external disturbance is handled by disturbance observer (DO). In the framework of Lyapunov theory, the bounded stability of the closed-loop system is proved, and the output error is remained within the constraint range. The superiority and effectiveness of the designed control strategy is verified by the compared simulation.

Differentially private data aggregating with relative error constraint

Privacy preserving methods supporting for data aggregating have attracted the attention of researchers in multidisciplinary fields. Among the advanced methods, differential privacy (DP) has become an influential privacy mechanism owing to its rigorous privacy guarantee and high data utility. But DP has no limitation on the bound of noise, leading to a low-level utility. Recently, researchers investigate how to preserving rigorous privacy guarantee while limiting the relative error to a fixed bound. However, these schemes destroy the statistical properties, including the mean, variance and MSE, which are the foundational elements for data aggregating and analyzing. In this paper, we explore the optimal privacy preserving solution, including novel definitions and implementing mechanisms, to maintain the statistical properties while satisfying DP with a fixed relative error bound. Experimental evaluation demonstrates that our mechanism outperforms current schemes in terms of security and utility for large quantities of queries.

Concise leader-follower formation control of underactuated unmanned surface vehicle with output error constraints

In this paper, a concise leader-follower formation control approach is presented for a group of underactuated unmanned surface vehicle with dynamic system uncertainties and external environment disturbances, where the output errors are required to be within constraints. To settle the output error constraints, a standard barrier Lyapunov function (BLF) is incorporated into the backstepping control method. Furthermore, the “differential explosion” problem of virtual control laws is avoided by introducing the dynamic surface control. To estimate the unknown dynamic terms, an adaptive neural network is designed and a nonlinear disturbance observer is adopted to compensate for the approximation errors of neural network and ocean environment disturbances. Under the constraint of output error, the presented controller based on standard BLF has simpler structure and better control performance than depended on tan-type BLF. The presented controller can ensure that the formation errors converge to a small range around zero, while the output error constraint requirements are met. All signals in the closed-loop system are bounded, and the numerical simulation further shows the effectiveness of the presented control scheme.

PID-type fault-tolerant prescribed performance control of fixed-wing UAV

This paper introduces a fault-tolerant control (FTC) design for a faulty fixed-wing unmanned aerial vehicle (UAV). To constrain tracking errors against actuator faults, error constraint inequalities are first transformed to a new set of variables based on prescribed performance functions. Then, the commonly used and powerful proportional-integral-derivative (PID) control concept is employed to filter the transformed error variables. To handle the fault-induced nonlinear terms, a composite learning algorithm consisting of neural network and disturbance observer is incorporated for increasing flight safety. It is shown by Lyapunov stability analysis that the tracking errors are strictly constrained within the specified error bounds. Experimental results are presented to verify the feasibility of the developed FTC scheme.

On the Sensitivity of the Simulated Diurnal Cycle of Precipitation to 3-Hourly Radiosonde Assimilation: A Case Study over the Western Maritime Continent

AbstractThe diurnal cycle is the most prominent mode of rainfall variability in the tropics, governed mainly by the strong solar heating and land–sea interactions that trigger convection. Over the western Maritime Continent, complex orographic and coastal effects can also play an important role. Weather and climate models often struggle to represent these physical processes, resulting in substantial model biases in simulations over the region. For numerical weather prediction, these biases manifest themselves in the initial conditions, leading to phase and amplitude errors in the diurnal cycle of precipitation. Using a tropical convective-scale data assimilation system, we assimilate 3-hourly radiosonde data from the pilot field campaign of the Years of Maritime Continent, in addition to existing available observations, to diagnose the model biases and assess the relative impacts of the additional wind, temperature, and moisture information on the simulated diurnal cycle of precipitation over the western coast of Sumatra. We show how assimilating such high-frequency in situ observations can improve the simulated diurnal cycle, verified against satellite-derived precipitation, radar-derived precipitation, and rain gauge data. The improvements are due to a better representation of the sea breeze and increased available moisture in the lowest 4 km prior to peak convection. Assimilating wind information alone was sufficient to improve the simulations. We also highlight how during the assimilation, certain multivariate background error constraints and moisture addition in an ad hoc manner can negatively impact the simulations. Other approaches should be explored to better exploit information from such high-frequency observations over this region.

Adaptive Fixed-Time Control of Error-Constrained Pure-Feedback Interconnected Nonlinear Systems

This article addresses the problem of decentralized adaptive fuzzy fixed-time control for a class of pure-feedback interconnected nonlinear systems with full-state tracking error constraints. The fuzzy logic systems (FLSs) are adopted to model the unknown nonlinear functions. Combining the properties of the prescribed performance functions (PPFs) with barrier Lyapunov functions (BLFs), the predefined state tracking error dynamic performance and good tracking accuracy are guaranteed. Then, on the basis of the backstepping recursive design technique, a structurally simple adaptive fuzzy fixed-time controller is designed. Furthermore, by the fixed-time stability criterion and the Lyapunov stability theory, it is proven that the boundedness of system signals is guaranteed, and the full-state tracking errors can evolve within the prescribed performance regions within the fixed time. Finally, the effectiveness of the proposed control strategy is demonstrated by some simulation results.

A fuzzy-weighted approach for malicious web domain identification

Malicious web domains represent a serious threat to online users’ privacy and security, causing monetary loss, theft of private information, and malware attacks, among others. In recent years, machine learning methods have been widely used as prediction models to identify malicious web domains. In this study, we propose a Fuzzy-Weighted Least Squares Support Vector Machine (FW-LS-SVM) model for malicious web domain identification. In our proposed model, a fuzzy-weighted operation is applied to each data sample considering the fact that different samples may have different importance. This fuzzy-weighted operation is also able to alleviate the influence of noise data and improve the model’s robustness by assigning weights to error constraints. For comparison purposes, three commonly used single machine learning classifiers and three widely used ensemble models are included in our experiments, in order to assess the performance of our proposed FW-LS-SVM and its ensemble version. Hyperlink indicators and uniform resource locator-based features are used to train the prediction models. Experimental results show that our proposed approach is highly effective in identifying malicious web domains, outperforming the well-established single and ensemble models being compared.

Risk-Constrained Minimization of Combined Event Detection and Decision Time for Online Transient Stability Assessment

This paper addresses the problem of `quickest possible' online transient stability assessment, by minimizing the decision time of combined event detection that might lead to a system split and unstable generator group prediction, from real-time wide area power system measurements. More importantly it does so by respecting predefined probabilistic error constraints for the prediction. The statistical theory of optimal detection is applied, firstly to choose the detection threshold and secondly to select a flexible assessment time, after using probabilistic neural networks to provide a temporal representation of the data. On simulated wide area measurements from the interconnected New England test system and New York power system this approach is between two and three times faster on average than strategies based on fixed assessment times, despite having comparable error rates.

Error-constrained fixed-time trajectory tracking control for a stratospheric airship with disturbances

In this paper, a novel fixed-time trajectory tracking control method is presented for a six degrees of freedom (6-DOF) stratospheric airship with time-varying error constraints and disturbances. The tan-type barrier Lyapunov function (BLF) method is extended by the adding a power integrator technique to deal with the time-varying error constraints with the fixed-time stability guaranteed, which ensures that the convergence of a stratospheric airship to the reference trajectory can be achieved in a finite time regardless of its initial condition. Also the complex differential calculation is avoided in the backstepping design. A new fixed-time disturbance observer (DO), which counts on less prior knowledge of disturbances, is constructed to estimate the unknown disturbances. The proposed control method is developed based on the transformed error model, thus can be readily extended to a wide range of second-order mechanical systems. Finally, numerical simulations demonstrate the effectiveness of the proposed control scheme.

Imbalanced classification: A paradigm‐based review

AbstractA common issue for classification in scientific research and industry is the existence of imbalanced classes. When sample sizes of different classes are imbalanced in training data, naively implementing a classification method often leads to unsatisfactory prediction results on test data. Multiple resampling techniques have been proposed to address the class imbalance issues. Yet, there is no general guidance on when to use each technique. In this article, we provide a paradigm‐based review of the common resampling techniques for binary classification under imbalanced class sizes. The paradigms we consider include the classical paradigm that minimizes the overall classification error, the cost‐sensitive learning paradigm that minimizes a cost‐adjusted weighted type I and type II errors, and the Neyman–Pearson paradigm that minimizes the type II error subject to a type I error constraint. Under each paradigm, we investigate the combination of the resampling techniques and a few state‐of‐the‐art classification methods. For each pair of resampling techniques and classification methods, we use simulation studies and a real dataset on credit card fraud to study the performance under different evaluation metrics. From these extensive numerical experiments, we demonstrate under each classification paradigm, the complex dynamics among resampling techniques, base classification methods, evaluation metrics, and imbalance ratios. We also summarize a few takeaway messages regarding the choices of resampling techniques and base classification methods, which could be helpful for practitioners.