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Low-light stereo image enhancement (LLSIE) is a task that improves the visual quality of stereo images captured in low-light environment. The core idea of LLSIE is to exploit the cross-view cues for enhancing the stereo image pairs. However, LLSIE is still a challenging task due to the perspective difference and insufficient information interaction between left and right views. In this paper, we propose a novel Multi-Scale Interaction Network (MSINet) for LLSIE. To be specific, we present a new multi-scale stereo Interaction module (MIM), which can promote the multi-scale cross-view information flow. Firstly, MIM extracts multi-scale features from both the left and right views. Then, the features from two views are fused in different scales, so that the information from stereo image pairs can be fully exchanged. Compared to the existing parallax attention module (PAM), our MIM explores the cross-view interaction in multiple scales with less computational cost more sufficiently. Besides, we also develop a new Spatial Gating Block (SGB) for refining intra-view features. By incorporating the long residual and spatial gating mechanism, SGB is capable of completing dynamic spatial modeling and obtaining strong feature representations. Extensive experiments on the Flickr1024, KITTI 2015 and Middlebury datasets show that the proposed MSINet reaches SOTA performance and obtains better visual results than other compared methods.

Here we demonstrate that the large language model GPT-4 can accurately annotate cell types using marker gene information in single-cell RNA sequencing analysis. When evaluated across hundreds of tissue and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations. This capability can considerably reduce the effort and expertise required for cell type annotation. Additionally, we have developed an R software package GPTCelltype for GPT-4’s automated cell type annotation.

This article is devoted to studying the bipartite synchronization issue of multicoupled complex dynamic networks with mismatched parameters. A generalized processing analysis method for dealing with the network with derivative coupling is first presented. To acquire suitable input intervals, a novel memory-based self-triggered impulsive controller is elaborately designed. Accordingly, the triggering moments are precisely determined, which take the average error states of a small fraction of monitoring moments into account. Sufficient conditions for the bipartite synchronization are eventually obtained by utilizing the Lyapunov stability theorem in conjunction with the parameter variation approach and definition of average impulsive interval. Since time-varying impulsive effects are considered, the definition of average impulsive gain is introduced in order to estimate the convergence rates and error bounds, respectively. The capability of derived mathematical deductions is ultimately demonstrated by numerical examples. Further, comparative experiments are given to show the superiority of performance within three event-based mechanisms.

Kernel partial least squares (KPLS) has poor robustness and cannot achieve effective monitoring for key performance indicators (KPI). This study investigates a new KPI-oriented robust KPLS approach to mitigate these drawbacks. In this methodology, the robust KPLS inspired by the gradient boosting principle incorporates a weighting matrix into the KPLS to mitigate the influence of outliers. Simultaneously, the associated coefficient matrix is derived in detail. Then, a decomposition approach is used to separate the process variable space into two orthogonal parts. Two strategies are discussed to obtain the unknown projection matrices based on the kernel principal component analysis and the elastic network frameworks. Finally, the performance of the proposed methods in terms of prediction, monitoring, and robustness to outliers is evaluated by the Tennessee Eastman process and the three-phase flow facility. The results show that the proposed methods have good prediction accuracy and monitoring performance in the presence of outliers, demonstrating the effectiveness and advantages of the proposed approaches.

Synchronization of complex networks with nonlinear couplings and distributed time-varying delays is investigated in this article. Since the mismatched parameters of individual systems, a kind of leader-following quasisynchronization issues is analyzed via impulsive control. To acquire appropriate impulsive intervals, the dynamic self-triggered impulsive controller is devoted to predicting the available instants of impulsive inputs. The proposed controller ensures the control effects while reducing the control costs. In addition, the updating laws of the dynamic parameter is settled in consideration of error bounds to adapt to the quasisynchronization. With the utilization of the Lyapunov stability theorem, comparison method, and the definition of average impulsive interval, sufficient conditions for realizing the synchronization within a specific bound are derived. Moreover, with the definition of average impulsive gain, the parameter variation scheme is extended from the fixed impulsive effects case to the time-varying impulsive effects case. Finally, three numerical examples are given to show the effectiveness and the superiority of proposed mathematical deduction.

With the rapid development of manufacturing, how to model machining process and optimize process parameters becomes the key to sustainable development. This paper aims at investigating the effect of the cutting parameters (cutting speed, feed rate, and depth of cut) on the response performance indicators (cutting force, surface roughness and energy consumption) of ZG55 cast steel cutting process. Firstly, the variance analysis was employed to quantify the effects of cutting speed, feed rate, and depth of cut on the output parameters. Research results show that cutting speed is the most significant parameter on energy consumption with a contribution of 59.41%. Additionally, the feed rate significantly influences surface roughness and cutting force, with contributions of 55.94% and 97.94%, respectively. Then, the high-order response surface methodology, regression tree and the support vector regression approaches were applied and compared for output parameters modeling. Simulation shows that the high-order response function was more suitable for cutting force, surface roughness and energy consumption modeling, which have higher precision compared with other methods. Finally, utilizing the desirability function approach, three objectives were considered: minimum surface roughness, minimum energy consumption, and compromise among surface roughness, energy consumption and cutting force. The high-order response surface methodology and particle swarm optimization were employed to solve the optimization of cutting parameters, and the optimal processing combination process parameters were obtained. The established model and optimization method provide theoretical support for parameter optimization in cutting process.

PD-1 blockade unleashes CD8 T cells1, including those specific for mutation-associated neoantigens (MANA), but factors in the tumour microenvironment can inhibit these T cell responses. Single-cell transcriptomics have revealed global T cell dysfunction programs in tumour-infiltrating lymphocytes (TIL). However, the majority of TIL do not recognize tumour antigens2, and little is known about transcriptional programs of MANA-specific TIL. Here, we identify MANA-specific T cell clones using the MANA functional expansion of specific T cells assay3in neoadjuvant anti-PD-1-treated non-small cell lung cancers (NSCLC). We use their T cell receptors as a ‘barcode’ to track and analyse their transcriptional programs in the tumour microenvironment using coupled single-cell RNA sequencing and T cell receptor sequencing. We find both MANA- and virus-specific clones in TIL, regardless of response, and MANA-, influenza- and Epstein–Barr virus-specific TIL each have unique transcriptional programs. Despite exposure to cognate antigen, MANA-specific TIL express an incompletely activated cytolytic program. MANA-specific CD8 T cells have hallmark transcriptional programs of tissue-resident memory (TRM) cells, but low levels of interleukin-7 receptor (IL-7R) and are functionally less responsive to interleukin-7 (IL-7) compared with influenza-specific TRM cells. Compared with those from responding tumours, MANA-specific clones from non-responding tumours express T cell receptors with markedly lower ligand-dependent signalling, are largely confined to HOBIThighTRM subsets, and coordinately upregulate checkpoints, killer inhibitory receptors and inhibitors of T cell activation. These findings provide important insights for overcoming resistance to PD-1 blockade.

With the development of the manufacturing industry, energy consumption is growing rapidly, which makes the energy crisis and environmental problems become more and more serious. CNC machine tools play an essential role and are the primary energy consumption devices in the manufacturing industry. The accurate prediction of machine tool energy consumption can provide support for energy production plans and reduce energy waste. This paper proposes a novel energy consumption prediction model based on support vector regression (SVR) optimized by an improved artificial hummingbird algorithm (IAHA). Firstly, as the artificial hummingbird algorithm (AHA) may easily get trapped in a local optimum, an improved AHA based on chaotic mapping and local backtracking exploitation strategy is proposed. The chaotic mapping is used to initialize individual positions, which is good for maintaining population diversity. The local backtracking exploitation strategy is employed to improve the local optimization ability. The effectiveness and feasibility of the IAHA algorithm have been verified through 12 benchmark functions. Then, the IAHA algorithm is employed to optimize the parameters of the SVR, and the IAHA-SVR energy consumption prediction model is established. Finally, the effectiveness and feasibility of the IAHA-SVR model are verified through a case study.

When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels. We show that GeneSegNet significantly improves cell segmentation performances over existing methods that either ignore gene expression information or underutilize imaging information.

This paper focuses on flexible manufacturing systems, which is a typical discrete event system. Different from traditional approaches for the system, this paper provides a multidimensional, ordinal, temporal topology structure to depict the dynamic production process from the perspective of complex networks. Firstly, the factors of real manufacturing environment are mapped to the multilayer networks and the frameworks of the model are given. The proposed temporal multilayer networks model includes intra-layers and inter-layers, involving two aspects of task and resource nodes and time-depending graphs respectively. Secondly, the generation rules to construct the networks are presented. A novel approach for evaluating bottleneck resources in time blocks is presented to evolve the networks model. Finally, the temporal multilayer networks model is generated based on theory of constraints. The proposed model based on operation sequence is superior in measurement of time-dependent performance of the system. Moreover, an application case for energy consumption evaluation is confirmed that the proposed model can support exploratory analysis of the time-related performance criterion in discrete manufacturing.

This article considers the issues of robust H∞ guaranteed cost control and memory state feedback stabilization for a class of uncertain switched descriptor delayed systems with nonlinear disturbance. Sufficient conditions for the existences of robust H∞ guaranteed cost controller in terms of linear matrix inequalities and switching strategy are proposed based on generalized Lyapunov function and convex optimization approach, and the memory state feedback controller ensures that the closed-loop systems satisfy the guaranteed cost function with a prescribed H∞γ-disturbance attenuation. Moreover, a convex optimization problem with linear matrix inequality constraints is formulated and solved by mean of the linear matrix inequality toolbox, and the optimal H∞ guaranteed cost memory state feedback controller with an upper bound of cost function is obtained. Finally, a numerical example illustrates the effectiveness of the proposed method.

Visualizing low-dimensional representations with scatterplots is a crucial step in analyzing single-cell genomic data. However, this visualization has significant biases. The first bias arises when visualizing the gene expression levels or the cell identities. The scatterplot only shows a subset of cells plotted last, and the cells plotted earlier are masked and unseen. The second bias arises when comparing the cell-type compositions across samples. The scatterplot is biased by the unbalanced total number of cells across samples. We developed SCUBI, an unbiased method that visualizes the aggregated information of cells within non-overlapping squares to address the first bias and visualizes the differences of cell proportions across samples to address the second bias. We show that SCUBI presents a more faithful visual representation of the information in a real single-cell RNA sequencing (RNA-seq) dataset and has the potential to change how low-dimensional representations are visualized in single-cell genomic data.

Efforts to understand genetic variability involved in an individual's susceptibility to chronic pain support a role for upstream regulation by epigenetic mechanisms. To examine the transcriptomic and epigenetic basis of chronic pain that resides in the peripheral nervous system, we used RNA-seq and ATAC-seq of the rat dorsal root ganglion (DRG) to identify novel molecular pathways associated with pain hypersensitivity in two well-studied persistent pain models induced by Chronic Constriction Injury (CCI) of the sciatic nerve and intra-plantar injection of Complete Freund's Adjuvant (CFA) in rats. Our RNA-seq studies identify a variety of biological process related to synapse organization, membrane potential, transmembrane transport, and ion binding. Interestingly, genes that encode transcriptional regulators were disproportionately downregulated in both models. Our ATAC-seq data provide a comprehensive map of changes in chromatin accessibility in the DRG. A total of 1123 regions showed changes in chromatin accessibility in one or both models when compared to the naïve and 31 shared differentially accessible regions (DAR)s. Functional annotation of the DARs identified disparate molecular functions enriched for each pain model which suggests that chromatin structure may be altered differently following sciatic nerve injury and hind paw inflammation. Motif analysis identified 17 DNA sequences known to bind transcription factors in the CCI DARs and 33 in the CFA DARs. Two motifs were significantly enriched in both models. Our improved understanding of the changes in chromatin accessibility that occur in chronic pain states may identify regulatory that play a central role in changing gene expression in the DRG. F32NR015728 (KES), KL2 TR003108 (KES), NS110598 (YG), NS117761 (YG), Johns Hopkins Blaustein Pain Research Fund (SDT).

Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscapes in single cells. Single-cell ATAC-seq data are sparse and noisy, and analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We demonstrate that SCATE can be used to better reconstruct the regulatory landscape of a heterogeneous sample.

Background: The rapid development of single-cell RNA-sequencing (scRNA-seq) technologies has led to the emergence of many methods for removing systematic technical noises, including imputation methods, which aim to address the increased sparsity observed in single-cell data. Although many imputation methods have been developed, there is no consensus on how methods compare to each other. Results: Here, we perform a systematic evaluation of 18 scRNA-seq imputation methods to assess their accuracy and usability. We benchmark these methods in terms of the similarity between imputed cell profiles and bulk samples and whether these methods recover relevant biological signals or introduce spurious noise in downstream differential expression, unsupervised clustering, and pseudotemporal trajectory analyses, as well as their computational run time, memory usage, and scalability. Methods are evaluated using data from both cell lines and tissues and from both plate- and droplet-based single-cell platforms. Conclusions: We found that the majority of scRNA-seq imputation methods outperformed no imputation in recovering gene expression observed in bulk RNA-seq. However, the majority of the methods did not improve performance in downstream analyses compared to no imputation, in particular for clustering and trajectory analysis, and thus should be used with caution. In addition, we found substantial variability in the performance of the methods within each evaluation aspect. Overall, MAGIC, kNN-smoothing, and SAVER were found to outperform the other methods most consistently.

IMPORTANCE: Previous reports have linked maternal prepregnancy obesity with low folate concentrations and child overweight or obesity (OWO) in separate studies. To our knowledge, the role of maternal folate concentrations, alone or in combination with maternal OWO, in child metabolic health has not been examined in a prospective birth cohort. OBJECTIVE: To test the hypotheses that maternal folate concentrations can significantly affect child metabolic health and that sufficient maternal folate concentrations can mitigate prepregnancy obesity-induced child metabolic risk. DESIGN, SETTING, AND PARTICIPANTS: This prospective birth cohort study was conducted at the Boston Medical Center, Boston, Massachusetts. It included 1517 mother-child dyads recruited at birth from 1998 to 2012 and followed up prospectively up to 9 years from 2003 to 2014. MAIN OUTCOMES AND MEASURES: Child body mass index z score calculated according to US reference data, OWO defined as a body mass index in the 85th percentile or greater for age and sex, and metabolic biomarkers (leptin, insulin, and adiponectin). RESULTS: The mean (SD) age was 28.6 (6.5) years for mothers and 6.2 (2.4) years for the children. An L-shaped association between maternal folate concentrations and child OWO was observed: the risk for OWO was higher among those in the lowest quartile (Q1) as compared with those in Q2 through Q4, with an odds ratio of 1.45 (95% CI, 1.13-1.87). The highest risk for child OWO was found among children of obese mothers with low folate concentrations (odds ratio, 3.05; 95% CI, 1.91-4.86) compared with children of normal-weight mothers with folate concentrations in Q2 through Q4 after accounting for multiple covariables. Among children of obese mothers, their risk for OWO was associated with a 43% reduction (odds ratio, 0.57; 95% CI, 0.34-0.95) if their mothers had folate concentrations in Q2 through Q4 compared with Q1. Similar patterns were observed for child metabolic biomarkers. CONCLUSIONS AND RELEVANCE: In this urban low-income prospective birth cohort, we demonstrated an L-shaped association between maternal plasma folate concentrations and child OWO and the benefit of sufficient folate concentrations, especially among obese mothers. The threshold concentration identified in this study exceeded the clinical definition of folate deficiency, which was primarily based on the hematological effect of folate. Our findings underscore the need to establish optimal rather than minimal folate concentrations for preventing adverse metabolic outcomes in the offspring.

• Multiple deep autoencoders are established to learn the more discriminative feature space for the multi-view data. • A-block diagonal regularizer is imposed on the weight of the self-expression module, seeking the block diagonal representation matrix directly. • A diverse representation is introduced into this subspace clustering network to reduce the redundancy, obtaining more diverse representation matrices. • Experimental results obtained on the UCI Digit, Caltech101-20, COIL100 and Caltech101-7 datasets have demonstrated the superior performance. Learning multi-view data, and especially multi-view data clustering, is a popular area in computer vision and pattern recognition. The multi-view subspace clustering has achieved a better clustering quality than the single-view subspace clustering, mainly because of the complementarity of multi-view information. First, for not directly pursuing a block diagonal representation matrix of previous ℓ 1 or ℓ 2 regularizers in a deep subspace clustering network, a k -block diagonal regularizer is proposed to replace traditional regularizers. This block diagonal representation module is integrated into this multi-view subspace clustering network, and it can improve the clustering quality. Secondly, there exists some redundancy among the representation matrices, and a diverse representation module can be introduced into this network. This can boost the diversity of representation matrices, and make learned representation matrices more discriminative and help improve the clustering performance. In this paper, based on the deep subspace clustering network, we integrate the block diagonal and diverse representation into the network, and a multi-view subspace clustering network with the block diagonal and the diverse representation is proposed. The experimental results on the UCI Digit, Caltech101-20, COIL100 and Caltech101-7 datasets have demonstrated the superior performance of the proposed algorithm over other popular multi-view algorithms. [ABSTRACT FROM AUTHOR]

This study considers state and fault estimation for a switched system with a dual noise term. A zonotopic and Gaussian Kalman filter for state estimation is designed to obtain state estimation interval in the presence of both stochastic and unknown but bounded (UBB) uncertainties. The switching state and fault state of the system are distinguished by detecting whether the system measurement date is within the bounds of its predicted output. Once the switched time is detected in the system, the filter zonotopic and Gaussian Kalman functions are initialized. Once the fault time is detected, a zonotopic and Gaussian Kalman filter-based fault estimator is constructed to estimate the corresponding faults. Finally, a numerical simulation is presented to demonstrate the accuracy and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the identification problem of Hammerstein–Wiener errors-in-variable systems where the measurement errors of the system input and output are either temporally white or have relatively short memory size compared to the data length, but the corresponding variances are unknown. A two-stage algorithm is developed to estimate the unknown parameters with the first stage employing a modified bias-eliminating least squares algorithm, followed by a singular value decomposition in the second stage. Our proposed estimator is shown to be asymptotically unbiased. The simulation result shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this letter, we demonstrate coherent beam combination of laser diode array using the external Talbot cavity with double feedback. The double feedback elements consist of grating and high-reflection plane mirror. Compared with single high-reflection plane mirror feedback, the external Talbot cavity with double feedback reduce the number of interference strips in the far-field pattern and narrow spectral line-width of the laser diode array. The results indicate that the application of the external Talbot cavity with double feedback produces a clear far-field interference pattern. In addition, line-width is reduced to 0.15 nm full-width at half-maximum (FWHM).

Aiming at a class of systems with multiple-input multiple-output (MIMO) output-error, this paper proposes a novel bias compensation based partially coupled recursive least squares (RLS) algorithm with forgetting factors, based on the coupled identification concept and the bias compensation technique. The proposed algorithm can not only give the unbiased estimates of the system parameters in the presence of colored noises, but also improve the tracking capability of the time-varying parameters. Additionally, complex matrix inversion is avoided in the proposed algorithm, which is required in the multivariable RLS algorithm to identify MIMO systems. The analysis indicates that the proposed algorithm requires less computational burden than the traditional multivariable RLS algorithm. Finally, the proposed algorithm is tested on a surface permanent-magnet synchronous motor example, the efficiency of which is demonstrated by the experimental results. [ABSTRACT FROM AUTHOR]

Boolean network inference is essential for gaining insights into gene regulatory networks through multivariate gene expression time series. However, most existing algorithms cannot accurately reconstruct large-scale Boolean networks due to the complex and diverse relationships among genes and the overfitting problem. To address these problems, a novel inference algorithm using a mutual information-based fuzzy genetic programming approach (MIFuGP) is proposed to infer large-scale Boolean networks accurately. To represent complex regulatory relationships in Boolean networks, MIFuGP encodes Boolean functions as syntax tree programs. Taking the dependency between genes into account, MIFuGP fully extracts the mutual information from the syntax trees to alleviate the bloat problem. MIFuGP also provides a novel fitness function to make full use of state-transitions and topology information, together with a fuzzy logic control strategy to reduce the overfitting problem. Extensive experiments validate that MIFuGP significantly outperforms state-of-the-art algorithms on both real-world gene regulatory networks and artificial Boolean networks. • A novel inference approach MIFuGP is proposed to reconstruct large-scale gene regulatory networks accurately. • A novel mutual information metric is presented to fully excavate the dependency relationship among genes. • Different other existing algorithms, MIFuGP imposes no limit on the in-degree to allow the flexibility of solutions. • A fuzzy logic control strategy is introduced for the first time to reduce the over-fit problem in Boolean network inference. [ABSTRACT FROM AUTHOR]

This paper presents a data filtering based stochastic gradient algorithm for estimating the parameters of multivariable Hammerstein FIR-MA-like systems. By filtering the input and output data, the FIR-MA-like model is transformed into a controlled autoregressive model. The examples confirm that the proposed algorithm is more accurate and has a better performance than the stochastic gradient algorithm. [ABSTRACT FROM AUTHOR]

A novel interval observer filtering-based fault diagnosis method for linear discrete-time systems with dual uncertainties is proposed to detect actuator faults. The idea of minimization is adopted to design a fault-free state estimator by merging unknown but bounded and Gaussian disturbances and noises according to the signal average power principle. Using a fault-free state interval and measurement residual of the system, a fault detection indicator is designed based on the residual probability ratio, to achieve dynamic fault detection, isolation and identification. Finally, various simulation examples are provided to demonstrate the accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Considering that most popular models solving aspect-level sentiment classification problems mainly focus on designing complicated neural networks to scale the importance of each word in the sentence, this paper addresses this problem from the view of semantic space. Motivated by the fact that the senses of a word can be sophisticatedly embedded into the semantic space using a distributed representation, this paper hypothesizes that each sense of a word can be represented by one or more specific dimensions, and thus the target of aspect-level sentiment classification can be simplified into searching the related dimensions for the aspects and sentiments concerned. Particularly, an Attention Vector (ATV) based on attention mechanism is designed for each aspect in terms of a specific task, which involves two sub-vectors, i.e., a Dimension Attention Vector (DATV) and a Sentiment Attention Vector (SATV). The DATV determines the significances of different dimensions based on their correlations with an aspect ; and the SATV allocates weights for the attributes of words, which are decided by sentiment polarities and part-of-speech (PoS) tagging. Given a sub-dataset related to an aspect , the ATV will be optimized by an Artificial Bee Colony (ABC) algorithm with a Support Vector Machine (SVM) classifier, the objective of which is to maximize classification accuracy. Intrinsically, the DATV can reduce the ambiguity existing in polysemy, meanwhile, the SATV is an auxiliary means for the optimization of the DATV , which can help eliminate the misunderstandings caused by antonyms. Then, the optimized DATV will be applied on a Convolutional Neural Network (CNN) model via simply scaling the pretrained word embeddings as inputs (named as ATV-CNN model). Experimental results show that the ATV-CNN model can have substantial advantages when compared with state-of-the-art models. [ABSTRACT FROM AUTHOR]

A novel nonlinear fault diagnosis method for detecting various slowly varying faults is proposed. The idea of linear programming binding constraints is adopted and the orthotopic spaces are recursively calculated to approximate the exact feasible parameter set, and a parameter global expansion filtering fault diagnosis algorithm is derived. Moreover, to reduce the amount of calculation and improve the efficiency of fault diagnosis, a parameter directional expansion filtering fault diagnosis algorithm is proposed based on the spatial dimension reduction. Finally, various simulation examples are provided to demonstrate the accuracy and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A multi-objective linear-programming-based four-judgment modeling algorithm is proposed for an unknown but bounded noise system. Because there is no prior knowledge about the bounded noise term, during each recursive step, the noise signal is warped in a strip and the hyperplanes can be obtained by samples of input and output signals. The feasible parameter set of a linear discrete-time system with bounded noise, viewed as a convex polytope, is transformed into a polyhedral cone with increasing parameter dimension. One of the vertices of the polyhedral cone is the origin, and the polyhedral vertices can be calculated when the polyhedral cone edge vectors are determined. Moreover, by adopting the multi-objective linear programming idea, a four-judgment modeling algorithm is proposed for linear discrete-time systems. The given simulations illustrate the feasibility and effectiveness of the given algorithm. [ABSTRACT FROM AUTHOR]

The Artificial Bee Colony (ABC) algorithm has shown competitive performance for handling various optimization problems. However, despite its strong global search ability, it suffers from a poor convergence rate and it loses the balance between exploitation and exploration. To compensate for this weakness, our paper proposes a cellular structured neighborhood, with Gaussian-based search equation and local attractor, and a redefined probability calculation for the ABC algorithm after an empirical analysis. The proposed algorithm is named as CGABC-Cellular neighborhood with Gaussian distribution ABC. The cellular automata (CA) model can keep individuals interact with specific neighbors while maintaining the population diversity. The Gaussian-based search equation combined with the local attractor can help exploit locally the search space, and the modified probability calculation based on rank sorting can make the selection of onlooker bees more robust and appropriate. Theoretical analysis are made to prove the global convergence of the CGABC algorithm based on the theory of probability metric spaces, and the results show that CGABC will converge to the global optimum. The proposed algorithm is tested on a set of benchmark functions and three real-world problems (the “Lennard Jones potential problem”, the “frequency-modulated sound wave synthesis problem” and the “feature selection problem”), and the results demonstrate that our proposed strategies help ABC achieve higher accuracy and faster convergence when compared with other ABC variants and swarm-based evolutionary algorithms (EAs). [ABSTRACT FROM AUTHOR]

A hierarchical recursive least squares algorithm and a hierarchical least squares iterative algorithm are presented for Wiener feedback finite impulse response moving average model. By combining the least squares idea and hierarchical principle, the finite impulse response moving average model can be decomposed into three subsystems. The simulation results illustrate the effectiveness of the proposed two algorithms. [ABSTRACT FROM AUTHOR]

This paper is devoted to discussing the exponential synchronization of a class of nonlinearly coupled complex networks (NCCNs) with time-varying delays and stochastic disturbance. In consideration of the complex networks would subject to certain impulse disturbances, a kind of distributed controller combining with pinning control and impulsive control schemes is designed. Based on the concept of average impulsive interval and comparison principles, sufficient conditions for achieving the exponential synchronization are derived. In addition, the exponential convergence velocities are obtained, respectively, with considering different functions of the impulsive inputs. Finally, a numerical simulation is presented to demonstrate the feasibility of the theoretical deduction. [ABSTRACT FROM AUTHOR]