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In d - Scattered Set we are given an (edge-weighted) graph and are asked to select at least k vertices, so that the distance between any pair is at least d , thus generalizing Independent Set . We provide upper and lower bounds on the complexity of this problem with respect to various standard graph parameters. In particular, we show the following: • For any d ≥ 2 , an O ∗ ( d tw ) -time algorithm, where tw is the treewidth of the input graph and a tight SETH-based lower bound matching this algorithm’s performance. These generalize known results for Independent Set . • d - Scattered Set is W[1]-hard parameterized by vertex cover (for edge-weighted graphs), or feedback vertex set (for unweighted graphs), even if k is an additional parameter. • A single-exponential algorithm parameterized by vertex cover for unweighted graphs, complementing the above-mentioned hardness. • A 2 O ( td 2 ) -time algorithm parameterized by tree-depth ( td ), as well as a matching ETH-based lower bound, both for unweighted graphs. We complement these mostly negative results by providing an FPT approximation scheme parameterized by treewidth. In particular, we give an algorithm which, for any error parameter ϵ > 0 , runs in time O ∗ ( ( tw ∕ ϵ ) O ( tw ) ) and returns a d ∕ ( 1 + ϵ ) -scattered set of size k , if a d -scattered set of the same size exists.

An adaptive state-feedback control system for a class of linear systems with piecewise-constant unknown parameters is proposed. The solution ensures a global exponential stability of a closed-loop system under condition that a regressor is finitely exciting after each parameters switch, and does not require neither any knowledge of a plant input matrix, nor the switching time instants. The obtained theoretical results are corroborated by numerical simulations., Automation and Remote Control, Выпуск 3 2023

A generalization of the dynamic regressor extension and mixing procedure is proposed, which, unlike the original procedure, first, guarantees a reduction of the unknown parameter identification error if the requirement of regressor semi-finite excitation is met, and second, it ensures exponential convergence of the regression function (regressand) tracking error to zero when the regressor is semi-persistently exciting with a rank one or higher., Automation and Remote Control, Выпуск 1 2023, Pages 16-47

The critical power (CP) and maximal lactate steady state (MLSS) are operational surrogates of the maximal metabolic steady state (MMSS). However, their concordance and their agreement with MMSS remains variable likely due to methodological factors. PURPOSE To compare the concordance between CP and MLSS estimated by various models and criteria and their agreement with MMSS. METHODS After a ramp-test, ten recreationally active males performed four-to-five severe-intensity constant-power output (PO) trials to estimate CP, and three-to-four constant-PO trials to determine MLSS and identify MMSS. CP was computed using the 3-parameter hyperbolic (CP3-hyp), 2-parameter hyperbolic (CP2-hyp), linear (CPlin), and inverse of time (CP1/Tlim) models. In addition, the model with lowest combined parameter error identified the "best-fit" CP (CPbest-fit). MLSS was determined as an increase in blood lactate concentration ≤ 1 mM during constant-PO cycling from the 5th (MLSS10-30), 10th (MLSS10-30), 15th (MLSS15-30), 20th (MLSS20-30), or 25th (MLSS25-30) to 30th minute. MMSS was identified as the greatest PO associated with the highest submaximal steady state V[Combining Dot Above]O2 (MV[Combining Dot Above]O2ss). RESULTS Concordance between the various CP and MLSS estimates was greatest when MLSS was identified as MLSS15-30, MLSS20-30, and MLSS25-30. The PO at MV[Combining Dot Above]O2ss was 243 ± 43 W. Of the various CP models and MLSS criteria, CP2-hyp (244 ± 46 W) and CPlin (248 ± 46 W) and MLSS15-30 and MLSS20-30 (both 245 ± 46 W), respectively displayed, on average, the greatest agreement with MV[Combining Dot Above]O2ss. Nevertheless, all CP models and MLSS criteria demonstrated some degree of inaccuracies with respect to MV[Combining Dot Above]O2ss. CONCLUSIONS Differences between CP and MLSS can be reconciled with optimal methods of determination. When estimating MMSS, from CP the error margin of the model-estimate should be considered. For MLSS, MLSS15-30 and MLSS20-30 demonstrated the highest degree of accuracy.

Thermal management is a critical task of battery control to ensure the safe, efficient, and enduring performance of the battery system, which can be considered as an interconnected thermal network of cells. The basis of thermal management is the estimation of temperature and its gradient across the battery system, which has received extensive attention in the literature. However, existing works neglect two important constraints in practical battery systems: 1) limited number of available sensors and 2) presence of system uncertainty such as parameter error. This paper is the first to investigate robust battery system temperature estimation under sparse sensing and system uncertainty. We first propose a framework consisting of optimization problems at three different levels: 1) evaluation of the worst case estimation performance (error) under uncertainty; 2) robust observer design to minimize the worst case error; and 3) optimization of sensor locations. Two robust estimation methods are then used to solve the problem. The system uncertainty considered in this paper is the unknown resistance variability among battery cells, but the methodology can be applied to address other types of uncertainty. It is shown that the designed observers could guarantee and improve the robustness and reliability of estimation by significantly reducing the worst case estimation errors induced by uncertainty.

The aim of the study is research of the influence of the sampling interval on the precision of the parameter estimations of a dual-mass DC electromechanical system (DMDCEMS) via Symlet wavelet. Four options are discussed in which the same wavelet function and time of observation are used and only the sampling period is different. The system parameters are evaluated. The relative output error and the summary relative parameter error are also obtained. The results are compared and analyzed.

Leakage diagnosis model of water supply network was established by SVM. ACO is used to search and optimize the penalty coefficient c of SVM and the important parameter error E of kernel function in order to improve the speed and accuracy of SVM. The water leakage characteristic data of the water supply network with single leakage point was measured by simulation test. The sample data was verified based on ACO- SVM model, which can effectively locate the leakage point and accurately predict the leakage rate.

International audience; In this paper an online identification method based on a model of a Wound Rotor Synchronous Machine (WRSM) is proposed. With this method, all the values of the electrical and electromagnetic parameters are determined with no need for any prior knowledge of the machine parameters. In this method, two new parameters are proposed which represent the whole parameter error. An Extended Kalman Filter (EKF) is used to estimate these two new parameters. So that, by operating the machine at three distinct operating points, all the parameter errors can be calculated. To validate the proposed method, in addition to simulation, an experimental test is also performed on a WRSM.

Convolutional Neural Networks (CNNs) are widely used in image classification tasks. To fit the application of CNNs on resource-limited embedded systems, pruning is a popular technique to reduce the complexity of the network. In this paper, the robustness of the pruned network against errors on the network parameters is examined with VGG16 as a case study. The effects of errors on the weights, bias, and batch normalization (BN) parameters are evaluated for the network with different pruning rates based on error injection experiments. The results show that in general networks with more weights pruned are more robust for a given error rate. The effect of multiple errors on bias or BN parameters is almost the same for the networks with different pruning rates that are lower than 90%. Further experiments are performed to explain the bimodal phenomenon of the network performance with errors on the parameters, to find that only errors on 6% of the parameter bits will cause large degradation of the neural network performance.

Our main contribution is a general framework to design efficient polynomial time approximation schemes (EPTAS) for fundamental classes of stochastic combinatorial optimization problems. Given an error parameter $\epsilon>0$, such algorithmic schemes attain a $(1+\epsilon)$-approximation in only $t(\epsilon)\cdot poly(n)$ time, where $t(\cdot)$ is some function that depends only on $\epsilon$. Technically speaking, our approach relies on presenting tailor-made reductions to a newly-introduced multi-dimensional extension of the Santa Claus problem [Bansal-Sviridenko, STOC'06]. Even though the single-dimensional problem is already known to be APX-Hard, we prove that an EPTAS can be designed under certain structural assumptions, which hold for our applications. To demonstrate the versatility of our framework, we obtain an EPTAS for the adaptive ProbeMax problem as well as for its non-adaptive counterpart; in both cases, state-of-the-art approximability results have been inefficient polynomial time approximation schemes (PTAS) [Chen et al., NIPS'16; Fu et al., ICALP'18]. Turning our attention to selection-stopping settings, we further derive an EPTAS for the Free-Order Prophets problem [Agrawal et al., EC'20] and for its cost-driven generalization, Pandora's Box with Commitment [Fu et al., ICALP'18]. These results improve on known PTASes for their adaptive variants, and constitute the first non-trivial approximations in the non-adaptive setting., Comment: 33 pages

The geometric errors parameters are important for machining accuracy of five-axis machine tool. However, most of the error identification methods only based on the detection instruments to achieve identification of single error parameter, without considering the coupling relationship of these errors between five axes. This paper measures and identifies the position-dependent error parameters and verticality errors of non-orthogonal five-axis machine tools. First, position-dependent error parameters related to the translational axis are measured by the laser interferometer. Next, based on its identification method, the rotary axis is measured in the axial, radial and tangential measurement modes by using a double ball bar. With the measured result, 18 position-dependent errors which related to the motion of linear axis, 6 position-dependent error parameters and 2 verticality errors of axis C and 4 position-dependent error parameters and 1 verticality error of axis B are identified. Finally, the identification results are verified by the compensation of an actual process on the sample of S-shaped piece, whose precise results show that the machining precision increased by 84.2% on average after compensation. Consequently, the measurement and identification method as well as the effectiveness of the compensation method of this paper are verified.

For GSTAR models, the least squares estimation method is commonly used since errors are assumed be uncorrelated. However, this method is not appropriate when errors are correlated, either in time or spatially. For these cases, the generalized least squares (GLS) method can be applied. GLS is more powerful since it has an error parameter that can act as a controller of the model to produce an efficient estimator. In this study, R Software was used to estimate GSTAR parameters. The resulted model was applied to real data, i.e. the monthly tea production of five plantations in West Java, Indonesia. The best model for forecasting was the GSTAR(1;1) model with temporally correlated error assumption.

This paper discusses the influence of geometric error parameters and measuring error parameters on the positioning accuracy of an industrial robot. Firstly, the kinematic model of a 6-DOF industrial robot based on the modified Denavit-Hartenberg (D-H) parameter method was established. Secondly, the influence of the geometric error parameters on the positioning accuracy of the robot’s end-effector is analyzed by simulation experiments in detail, including the link length errors, link twist errors, link offset errors and joint angle errors. And then the influence of measuring error parameters is also discussed. The results show that under individual effect of the linear errors, the length of positioning error of the robot’s end-effector is equal to the value of the corresponding error parameter. However, the angle errors have remarkable amplification effects on the accuracy of the robot. Moreover, it should be noted that the influence of measuring error parameters on the positioning accuracy of the robot is on the same order of magnitude as that of the geometric error parameters. Therefore, the measuring errors parameters should be taken into account during kinematic calibration.

For target location behind wall, the non-line-of-sight (NLOS) propagation caused by wall effect is the main factor that influences the localization performance. Based on the signal transmitting model, the NLOS is only determined by wall thickness and dielectric constant. In this paper, we use generalized Cramer-Rao lower bound (G-CRLB) as a basic tool to analyze target location performance. We deduced the G-CRLB of target localization behind wall. The Fisher information matrix (FIM) is divided into two parts: the observation matrix and prior information matrix, and the former corresponding to the measuring error, while the latter corresponding to wall parameter error. Simulation results evaluated the effect of wall parameters and measuring error on G-CRLB qualitatively.

To realize the error parameter estimation of strap-down inertial navigation system (SINS) and improve the navigation accuracy for aircraft, a hybrid improved restricted Boltzmann machine BP neural network (IRBM-BPNN) approach, which combines restricted Boltzmann machine (RBM) and BP neural network (BPNN), is proposed to forecast the inertial measurement unit (IMU) instrument errors and initial alignment errors of SINS. Firstly, the error generation mechanism of SINS is analyzed, and initial alignment error model and IMU instrument error model are established. Secondly, an unsupervised RBM method is introduced to initialize BPNN to improve the forecast performance of the neural network. The RBM-BPNN model is constructed through the information fusion of SINS/GPS/CNS integrated navigation system by using the sum of position deviation, the sum of velocity deviation and the sum of attitude deviation as the inputs and by using the error parameters of SINS as the outputs. The RBM-BPNN structure is improved to enhance its forecast accuracy, and the pulse signal is increased as the input of the neural network. Finally, we conduct simulation experiments to forecast and compensate the error parameters of the proposed IRBM-BPNN method. Simulation results show that the artificial neural network method is feasible and effective in forecasting SINS error parameters, and the forecast accuracy of SINS error parameters can be effectively improved by combining RBM and BPNN methods and improving the neural network structure. The proposed IRBM-BPNN method has the optimal forecast accuracy of SINS error parameters and navigation accuracy of aircraft compared with the radial basis function neural network method and BPNN method.

This paper presents an optimized predictive current control (PCC) for a Halbach array-linear linear vernier permanent magnet machine(H-LVPMM). The stability and robustness of the PCC are affected by parameter variation. Toward this, a modified proportional integral disturbance observer (PIDO) is proposed to estimate the disturbance voltage value caused by parameter error and inject the correspondent observed component into the control system. Experimental results show the validity of the introduced control scheme.

Synthetic aperture radar (SAR) has a good ability to detect the microwave scattering characteristics of the target and has a good capability of slant range Doppler positioning. Using multi-view SAR images in combination with image matching and positioning equations, the 3D position of the target can be obtained. In the past, multi-view 3D reconstruction mainly used side-looking images with parallel trajectories but different incident angles. That method is not universal for different configurations and lacks analysis of the relationship between solution and parameter error. This study aims at the problem of multi-view SAR 3D reconstruction. The authors establish general 3D reconstruction equations that can be used for non-parallel track and non-side-looking and the solution method is deduced. Based on this, an analysis method of error sensitivity is proposed and the dependence relationship between the reconstruction error and configuration, velocity, position, and slant range is analysed. The correctness of this method is verified by simulation experiments, which provides guidance for selection of configuration and sensor accuracy when applying 3D reconstruction.

In recent years, researchers around the world have been researching and improving the technique of 1D calibration of cameras. The previous work has been primarily focused on reducing the motion constraints of one-dimensional calibration objects, however the accuracy of the existing methods still needs to be improved when random noise is introduces. In order to improve the accuracy of the one-dimensional calibration of the camera, in this paper, we propose a new calibration method by combining a weighted similar invariant linear algorithm and an improved nonlinear optimization algorithm. Specifically, we use the weighted similar invariant linear algorithm to obtain the camera parameters as the initial calibration parameters, and then optimize the parameters by using improved nonlinear algorithm. Finally, in the case of introducing random noise, the results of computer simulations and laboratory experiments show that when the noise level reaches 2 pixels, the parameter error of this method is mostly reduced to 0.2% compared with other methods, which verifies the feasibility of our proposed method.

For critical engine parts, such as crankshafts, fatigue strength is one of the most important parameters in the design and manufacturing stage. In previous work, fatigue limit loads of the crankshafts with different structural features were predicted based on different theories of critical distance (TCDs). The results showed that the definition of the TCD had an obvious impact on the accuracy of such predictions. This paper, first the critical distance was determined based on the limit stress distribution of a given crankshaft and the definition of the approach. Then, the fatigue limit load of another crankshaft was predicted based on the parameters obtained in the previous step. Finally, corresponding experimental verification and model parameter error influence analysis were conducted to evaluate the accuracies of the predictions. The results showed that for the modified indirect-defined TCD (ITCD), the predictions based on the line and point methods were approximately equal, and the parameter errors had an obvious impact on the predictions. However, for the direct-defined TCD (DTCD), the critical line approach had much better accuracy than the critical point approach, and the DTCD was much less sensitive to the model parameter errors than the ITCD approach, therefore, the DTCD approach is much more suitable for actual engineering applications.

A Crimp Force Monitor (CFM) is equipment for detecting crimp errors by analyzing crimp signals obtained from force and strain sensors. The analysis is commonly performed by aligning a measured crimp signal with a reference signal and comparing their difference. Current analysis methods often suffer from wrong alignments that result in false negative detections. This paper presents a new crimp signal analysis method in CFM. First, a falling edge alignment is proposed that matches falling edges of the measured and the reference signals by minimizing the absolute difference summation. Second, a signal parameter error is introduced to evaluate the crimp quality difference between the measured signal and the reference. For calculating the signal parameter error, part of a signal is identified and divided into several regions to maximize the signal parameter errors. Experiments showed that the proposed method can improve the signal alignment and accurately detect bad crimps especially with the strain sensor.

To realize the effective estimation of the inertial measurement unit (IMU) error parameters of a hypersonic vehicle and satisfy the high-precision navigation requirements, a hybrid neural network (HYNN) method that consists of a quasi-Newton semi-determined weight neural network (QNSWNN) and a semi-determined weight recurrent neural network (SWRNN) is proposed. In contrast to conventional neural networks, the weights of the HYNN model, which are determined by the IMU error parameters, have physical meanings. First, the IMU error model and strap-down inertial navigation system (SINS) navigation model are established. Second, the QNSWNN model is constructed based on the SINS/celestial navigation system (CNS) integrated navigation system information. The quasi-Newton is used to adjust the weights, which include the gyroscope error. The rest weights of QNSWNN are fixed based on a SINS attitude calculation model. The gyroscope error parameters can be estimated during the QNSWNN training process. Lastly, the SWRNN model is constructed based on the SINS/GPS integrated navigation system information. The BP algorithm is used to adjust the weights, which include the accelerometer error. The rest weights of SWRNN are fixed based on the SINS navigation calculation model. The accelerometer error parameters can be estimated during the SWRNN training process. The simulation results show that the IMU error parameter can be effectively estimated by the HYNN, and the relative errors are below 25%. Moreover, when the signal of the auxiliary navigation system is interrupted, the HYNN method still has high prediction accuracy for the SINS navigation parameters.

In the field of high-risk observation, the nodes in Wireless Sensor Network (WSN) are distributed randomly. The result from sensing becomes meaningless if it is not known from where the originating node is. Therefore, a sensor node positioning scheme, known as the localization scheme, is required. The localization scheme consists of distance estimation and position computing. Thus, this resear ch used connectivity as distance estimation within range free algorithm DV-Hop and Amorphous, and then trilateral algorithm for computing the position. Besides that, distance estimation using the connectivity between nodes is not needed for the additional hardware ranging as required by a range-based localization scheme. In this research compared the localization algorithm based on range free localization, which are DV-Hop algorithm and Amorphous algorithm. The simulation result shows that the amorphous alg orithm have achieved 13.60% and 24.538% lower than dv-hop algorithm for each parameter error localization and energy consumption. On node density variations, dv-hop algorithm gained a localization error that is 26.95% lower than amorphous algorithm, but for energy consumption parameter, amorphous gained 14.227% lower than dv-hop algorithm. In the communication range variation scenario, dv-hop algorithm gained a localization error that is50.282% lower than amorphous. However, for energy consumption parameter, amorphous algorithm gained 12.35%. lower than dv-hop algorithm.

A model presented in current paper designed for dynamic classifying of real time cases received in a stream of big sensing data. The model comprises multiple remote autonomous sensing systems; each generates a classification scheme comprising a plurality of parameters. The classification engine of each sensing system is based on small data buffers, which include a limited set of “representative” cases for each class (case-buffers). Upon receiving a new case, the sensing system determines whether it may be classified into an existing class or it should evoke a change in the classification scheme. Based on a threshold of segmentation error parameter, one or more case-buffers are dynamically regrouped into a new composition of buffers, according to a criterion of segmentation quality.