This study explores the recently proposed and challenging multi-view Anomaly Detection (AD) task. Single-view tasks will encounter blind spots from other perspectives, resulting in inaccuracies in sample-level prediction. Therefore, we introduce theMulti-ViewAnomalyDetection (MVAD) approach, which learns and integrates features from multi-views. Specifically, we propose aMulti-ViewAdaptiveSelection (MVAS) algorithm for feature learning and fusion across multiple views. The feature maps are divided into neighbourhood attention windows to calculate a semantic correlation matrix between single-view windows and all other views, which is an attention mechanism conducted for each single-view window and the top-$k$most correlated multi-view windows. Adjusting the window sizes and top-$k$can minimise the complexity to$O((hw)^\frac{4}{3})$. Extensive experiments on the Real-IAD dataset under the multi-class setting validate the effectiveness of our approach, achieving state-of-the-art performance with an average improvement of+2.5$\uparrow$across10 metricsat the sample/image/pixel levels, using only18Mparameters and requiring fewer FLOPs and training time. The codes are available athttps://github.com/lewandofskee/MVAD.

Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. CNNs, with their local receptive fields, struggle to capture long-range dependencies, while Transformers, despite their global modeling capabilities, are limited by quadratic computational complexity in high-resolution scenarios. Recently, state-space models have gained popularity in the visual domain due to their linear computational complexity. Despite their low FLOPs, current lightweight Mamba-based models exhibit suboptimal throughput. In this work, we propose the MobileMamba framework, which balances efficiency and performance. We design a three-stage network to enhance inference speed significantly. At a fine-grained level, we introduce the Multi-Receptive Field Feature Interaction (MRFFI) module, comprising the Long-Range Wavelet Transform-Enhanced Mamba (WTE-Mamba), Efficient Multi-Kernel Depthwise Convolution (MK-DeConv), and Eliminate Redundant Identity components. This module integrates multi-receptive field information and enhances high-frequency detail extraction. Additionally, we employ training and testing strategies to further improve performance and efficiency. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods which is maximum ×21↑ faster than LocalVim on GPU. Extensive experiments on high-resolution downstream tasks demonstrate that MobileMamba surpasses current efficient models, achieving an optimal balance between speed and accuracy.

Autonomous racing presents a complex environment requiring robust controllers capable of making rapid decisions under dynamic conditions. While traditional controllers based on tire models are reliable, they often demand extensive tuning or system identification. Reinforcement Learning (RL) methods offer significant potential due to their ability to learn directly from interaction, yet they typically suffer from the Sim-to-Real gap, where policies trained in simulation fail to perform effectively in the real world. In this paper, we propose RLPP, a residual RL framework that enhances a Pure Pursuit (PP) controller with an RL-based residual. This hybrid approach leverages the reliability and interpretability of PP while using RL to fine-tune the controller's performance in real-world scenarios. Extensive testing on the F1TENTH platform demonstrates that RLPP improves lap times of the baseline controllers by up to 6.37 %, closing the gap to the State-of-the-Art (SotA) methods by more than 52 % and providing reliable performance in zero-shot real-world deployment, overcoming key challenges associated with the Sim-to-Real transfer and reducing the performance gap from simulation to reality by more than 8 -fold when compared to the baseline RL controller. The RLPP framework is made available as an open-source tool, encouraging further exploration and advancement in autonomous racing research. The code is available at: www.github.com/forzaeth/rlpp.

The development of autonomous driving has boosted the research on autonomous racing. However, existing local trajectory planning methods have difficulty planning trajectories with optimal velocity profiles at racetracks with sharp corners, thus weakening the performance of autonomous racing. To address this problem, we propose a local trajectory planning method that integrates Velocity Prediction based on Model Predictive Contouring Control (VPMPCC). The optimal parameters of VPMPCC are learned through Bayesian Optimization (BO) based on a proposed novel Objective Function adapted to Racing (OFR). Specifically, VPMPCC achieves velocity prediction by encoding the racetrack as a reference velocity profile and incorporating it into the optimization problem. This method optimizes the velocity profile of local trajectories, especially at corners with significant curvature. The proposed OFR balances racing performance with vehicle safety, ensuring safe and efficient BO training. In the simulation, the number of training iterations for OFR-based BO is reduced by 42.86 % compared to the state-of-the-art method. The optimal simulation-trained parameters are then applied to a real-world F1TENTH vehicle without retraining. During prolonged racing on a custom-built racetrack featuring significant sharp corners, the mean projected velocity of VPMPCC reaches$\mathbf{9 3. 1 8 \%}$of the vehicle's handling limits. The released code is available at https://github.com/zhouhengli/VPMPCC.

Generating overtaking trajectories in autonomous racing is a challenging task, as the trajectory must satisfy the vehicle’s dynamics and ensure safety and real-time performance running on resource-constrained hardware. This work proposes the Fast and Safe Data-Driven Planner to address this challenge. Sparse Gaussian predictions are introduced to improve both the computational efficiency and accuracy of opponent predictions. Furthermore, the proposed approach employs a bi-level quadratic programming framework to generate an overtaking trajectory leveraging the opponent predictions. The first level uses polynomial fitting to generate a rough trajectory, from which reference states and control inputs are derived for the second level. The second level formulates a model predictive control optimization problem in the Frenet frame, generating a trajectory that satisfies both kinematic feasibility and safety. Experimental results on the F1TENTH platform show that our method outperforms the State-of-the-Art, achieving an 8.93% higher overtaking success rate, allowing the maximum opponent speed, ensuring a smoother ego trajectory, and reducing 74.04% computational time compared to the Predictive Spliner method. The code is available at: https://github.com/ZJU-DDRX/FSDP.

Autonomous drifting is a complex and crucial maneuver for safety-critical scenarios like slippery roads and emergency collision avoidance, requiring precise motion planning and control. Traditional motion planning methods often struggle with the high instability and unpredictability of drifting, particularly when operating at high speeds. Recent learning-based approaches have attempted to tackle this issue but often rely on expert knowledge or have limited exploration capabilities. Additionally, they do not effectively address safety concerns during learning and deployment. To overcome these limitations, we propose a novel Safe Reinforcement Learning (RL)-based motion planner for autonomous drifting. Our approach integrates an RL agent with model-based drift dynamics to determine desired drift motion states, while incorporating a Predictive Safety Filter (PSF) that adjusts the agent’s actions online to prevent unsafe states. This ensures safe and efficient learning, and stable drift operation. We validate the effectiveness of our method through simulations on a Matlab-Carsim platform, demonstrating significant improvements in drift performance, reduced tracking errors, and computational efficiency compared to traditional methods. This strategy promises to extend the capabilities of autonomous vehicles in safety-critical maneuvers.

Extreme cornering in racing often leads to large sideslip angles, presenting a significant challenge for vehicle control. Conventional vehicle controllers struggle to manage this scenario, necessitating the use of a drifting controller. However, the large sideslip angle in drift conditions introduces model mismatch, which in turn affects control precision. To address this issue, we propose a model correction drift controller that integrates Model Predictive Control (MPC) with Gaussian Process Regression (GPR). GPR is employed to correct vehicle model mismatches during both drift equilibrium solving and the MPC optimization process. Additionally, the variance from GPR is utilized to actively explore different cornering drifting velocities, aiming to minimize trajectory tracking errors. The proposed algorithm is validated through simulations on the Simulink-Carsim platform and experiments with a 1:10 scale RC vehicle. In the simulation, the average lateral error with GPR is reduced by 52.8% compared to the non-GPR case. Incorporating exploration further decreases this error by 27.1%. The velocity tracking Root Mean Square Error (RMSE) also decreases by 10.6% with exploration. In the RC car experiment, the average lateral error with GPR is 36.7% lower, and exploration further leads to a 29.0% reduction. Moreover, the velocity tracking RMSE decreases by 7.2% with the inclusion of exploration.

Water-fertilizer integrated regulation system aims to to improve crop yield, nutrient use efficiency (NUE), and water use efficiency (WUE). This study develops an intelligent control system with a cloud server network to centralize data management and process. The system contains of the monitoring module, the intelligent cloud platform module, and the control terminal. In the monitoring module, the sensor network and phenotypic monitoring method were utilized to collect real-time crop and environment data. In the intelligent cloud platform module, a perception-computing-control integrated computing optimization framework was constructed to achieve the localization of control tasks by analyzing perceived data, training predictive control models, and optimizing deep reinforcement learning algorithms. The control terminal contains of task scheduling, equipment control, and data collection drive, as well as a reasonable deployment pipeline in the cloud. Our developed control model has increased crop productivity nearly 9% and achieved water resource savings nearly 15.6%. Multi-modal large models will be applied to adjust model parameters in different environments.

To address the challenges encountered by dictionary learning-based monitoring, this article presents a novel pattern discovery scheme for detection and isolation of incipient faults that involves structured sparse coding and sequential dictionary augmentations. Through learning a basic dictionary for normal pattern and augmenting the low-dimensional sparse dictionaries for analyzing different fault patterns, the process signals can be decomposed into fault-free and fault-related components. To guarantee the in-statistical-control status of the fault-free part and improve detection sensitivity, a$\ell _{2}$-penalty is imposed on the sum of coefficient vectors to ensure that the monitoring statistic related to the fault-free part will not exceed the control limit. In addition, two Frobenius norm penalties are imposed on the zero centered coefficient matrix and atom matrix to improve the robustness of signal decomposition. Instead of imposing$\ell _{1}$-sparsity constraint on the atoms, a hard sparsity constraint is used to correctly select fault-related feature variables, so that fault patterns can be better revealed. The informative dictionaries are then incorporated into the moving window-based monitoring strategy, yielding a fault detection and isolation scheme suitable for incipient faults. The superior performance of our proposed approach is validated by application studies involving a numerical example and two practical industrial processes.

The state and input constraints of nonlinear systems could greatly impede the realization of their optimal control when using reinforcement learning (RL)-based approaches since the commonly used quadratic utility functions cannot meet the requirements of solving constrained optimization problems. This article develops a novel optimal control approach for constrained discrete-time (DT) nonlinear systems based on safe RL. Specifically, a barrier function (BF) is introduced and incorporated with the value function to help transform a constrained optimization problem into an unconstrained one. Meanwhile, the minimum of such an optimization problem can be guaranteed to occur at the origin. Then a constrained policy iteration (PI) algorithm is developed to realize the optimal control of the nonlinear system and to enable the state and input constraints to be satisfied. The constrained optimal control policy and its corresponding value function are derived through the implementation of two neural networks (NNs). Performance analysis shows that the proposed control approach still retains the convergence and optimality properties of the traditional PI algorithm. Simulation results of three examples reveal its effectiveness.

Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have sparked research efforts to utilize them for enhanced reconstruction of anomalous images. Nonetheless, these methods might face challenges related to the preservation of image categories and pixel-wise structural integrity in the more practical multi-class setting. To solve the above problems, we propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection, which consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion’s denoising network, and a feature-space pre-trained feature extractor. Firstly, The SG network is proposed for reconstructing anomalous regions while preserving the original image’s semantic information. Secondly, we introduce Spatial-aware Feature Fusion (SFF) block to maximize reconstruction accuracy when dealing with extensively reconstructed areas. Thirdly, the input and reconstructed images are processed by a pre-trained feature extractor to generate anomaly maps based on features extracted at different scales. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach which surpasses the state-of-the-art methods, e.g., achieving 96.8/52.6 and 97.2/99.0 (AUROC/AP) for localization and detection respectively on multi-class MVTec-AD dataset. Code will be available at https://lewandofskee.github.io/projects/diad.

Vehicle slipping may cause an accident while driving. However, professional drivers usually perform high side-slip angle maneuvers, such as drifting to minimize lap time or avoid obstacles. Tracking the desired trajectory while maintaining drift is a challenging task due to the complexity of the vehicle model. In this paper, we first solve a series of minimum-time cornering problems under different initial conditions. The results show that an aggressive cornering can be divided into three segments, including the entry corner stage, the drifting stage, and the exiting stage. We then propose a complete trajectory planning and motion control framework to conduct the drift cornering maneuver. The trajectory planner calculates the speed profile and then updates the initial path by optimizing curvature. A switch-mode control system is proposed for the above three stages to track the reference trajectory, which is based on pure pursuit control and Model Predictive Control (MPC). Finally, we validate the cornering framework by simulation on the Simulink-Carsim software and experiments on a 1/10 scale RC vehicle.

Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches. However, CNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Mamba-based models, with their superior long-range modeling and linear efficiency, have garnered substantial attention. This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD, which consists of a pre-trained encoder and a Mamba decoder featuring (Locality-Enhanced State Space) LSS modules at multi-scales. The proposed LSS module, integrating parallel cascaded (Hybrid State Space) HSS blocks and multi-kernel convolutions operations, effectively captures both long-range and local information. The HSS block, utilizing (Hybrid Scanning) HS encoders, encodes feature maps into five scanning methods and eight directions, thereby strengthening global connections through the (State Space Model) SSM. The use of Hilbert scanning and eight directions significantly improves feature sequence modeling. Comprehensive experiments on six diverse anomaly detection datasets and seven metrics demonstrate state-of-the-art performance, substantiating the method's effectiveness. The code and models are available at https://lewandofskee.github.io/projects/MambaAD.

Automatic detection of traffic signs is crucial for Advanced Driving Assistance Systems (ADAS). Current two-stage approaches consist of a preliminary object detection step, where the traffic signs are categorized within broader families (e.g., speed limits), and then sub-classes (e.g., speed limit 40). However, these cascading methods fail to achieve satisfying performance, especially in more realistic driving scenarios where images are acquired under more challenging conditions. Under such conditions, the first-stage detection step is likely to provide inaccurate predictions, making the subsequent classification step useless. In this paper, we propose a simple yet effective two-stage fusion framework for traffic sign detection. Different from the previous cascading method, our framework directly predicts categories in the first-stage detection and fuse the two-stage category predictions to improves overall robustness. Besides, in order to filter the false detection boxes under low-resolution inputs, we also propose an effective post-processing method called Surrounding-Aware Non-Maximum Suppression (SA-NMS) as an alternative technique for the first-stage detection. After combining the above proposed methods, our framework obtains good detection performance. Experimental results on the widely used Tsinghua-Tencent 100K (TT100K) traffic sign dataset, which contains images of traffic signs collected under a variety of challenging conditions, show that the proposed framework outperforms current approaches in both accuracy and inference speed, achieving 89.7 mAP and 65 FPS for${608\times608}$low resolution images.

The organic Rankine cycle (ORC) is an effective application for converting low-grade heat sources into power and is crucial for environmentally friendly production and energy recovery. However, the inherent complexity of the mechanism, its strong and unidentified nonlinearity, and the presence of control constraints severely impair the design of its optimal controller. To solve these issues, this study provides a novel event-triggered (ET) constrained optimal control approach for the ORC systems based on a safe reinforcement learning technique to find the optimal control law. Instead of employing the usual non-quadratic integral form to solve the control-limited optimal control problems, a constraint handling strategy based on a relaxed weighted barrier function (BF) technique is proposed. By adding the BF terms to the original value function, a modified value iteration algorithm is developed to make the control input solutions that tend to violate the constraints be pushed back and maintained in their safe sets. In addition, the ET mechanism proposed in this article is critically required for the ORC systems, and it can significantly reduce the computational load. The combination of these two techniques allows the ORC systems to achieve set-point tracking control and satisfy the control restrictions. The proposed approach is conducted based on a heuristic dynamic programming framework with three neural networks (NNs) involved. The safety and convergence of the proposed approach and the stability of the closed-loop system are analyzed. Simulation results and comparisons are presented to demonstrate its effectiveness.

It is well common for industrial processes to employ a hierarchical control structure involving a basic loop process and an operation loop process with two timescales. However, the control system suffers from another multirate challenge where control and sampling rates may differ even within a single loop. Additionally, the underlying complex mechanism of the operation loop further complicates the accurate modeling of its dynamics, especially in the presence of external unknown disturbances. This gives rise to the difficulty in obtaining desired control performance. To overcome these problems, this article develops a novel operational optimal tracking control method for a class of multirate systems subject to unknown disturbances. To this end, a lifting technique is integrated with a general model predictive controller for the basic loop process, aimed at handling the asynchronism phenomenon and achieving loop setpoint tracking control. Furthermore, a nonlinear disturbance observer is used for estimating the unknown external disturbance of the operation loop process. In this way, offset-free tracking control of the system, along with loop setpoints optimization, can be achieved using the policy iteration reinforcement learning algorithm. The convergence of the proposed method is analyzed and tangible improvements are verified by simulations.

It is important to efficiently and accurately locate the fault root cause to maintain the control performance, when the industrial control system fails. However, this task is very challenging because the industrial control system is large in scale and complex in connection. This paper proposes a novel neural causality analysis network with directed acyclic graph to locate the root cause for complex industrial systems. This network fits the temporal nonlinearity and intervariable non-linearity to mine the causal graph. The proposed method is data-driven, which acts without process knowledge. Compared with the state-of-the-art, this method can effectively output accurate root cause from nonlinear and highly coupled data. The effectiveness and advantages are demonstrated by industrial cases.

This article presents a novel singular value decomposition (SVD)-based robust distributed model predictive control (SVD-RDMPC) strategy for linear systems with additive uncertainties. The system is globally constrained and consists of multiple interrelated subsystems with bounded disturbances, each of whom has local constraints on states and inputs. First, we integrate the steady-state target optimizer into the MPC problem through the offset cost function to formulate a modified single optimization problem for tracking changing targets from real-time optimization. Then, the concept of constraint tightening is utilized to enhance the robustness and ensure robust constraint satisfaction in the presence of interferences. On this basis, the SVD method is introduced to decompose the new optimization problem into several independent subsystems on the orthogonal projection space, and a distributed dual gradient algorithm with convergence proved is implemented to obtain the control of each nominal subsystem. The recursive feasibility is then ensured and the tracking ability of the strategy is analyzed. It is verified that for a target, the system can be steered to a neighborhood of the closest possible steady setpoint. At last, the effectiveness of the raised SVD-RDMPC strategy is established in two simulations on building temperature control and load frequency control.

Industrial oscillation recordings are often corrupted by underlying nonstationary trend and noisy artifacts, which can occlude features of interest and complicate subsequent oscillation detection and diagnosis. However, there are considerably fewer techniques available in the literature for systematically removing both trend and noise terms from the oscillation measurements. To cater for the general detrending and denoising needs of oscillatory signals, this article proposes an integrated framework featuring the following steps: 1) The ensemble empirical mode decomposition is first adopted to decompose the industrial single-loop data into several intrinsic mode functions (IMFs). 2) To eliminate the trend term, a surrogate-based nonstationarity testing algorithm is implemented to automatically identify and remove the requisite IMFs. 3) By applying canonical correlation analysis on the remained IMFs, the noise-dependent components can be further isolated, which finally yields the detrended and denoised oscillation data. We conducted performance comparison study through extensive simulations and industrial examples. The results demonstrate that the proposed work is a promising tool for industrial oscillation data preprocessing.

To improve the monitoring performance of dictionary learning-based methods, this article proposes a row-column overcomplete structured dictionary learning (RCOSDL) method for fault detection and isolation of industrial processes. Unlike conventional dictionary learning approaches which are overcomplete column-wise, the proposed method involves a dictionary that is overcomplete both row- and column-wise. The introduction of row-column overcomplete dictionary results in monitoring statistics that are more sensitive to incipient faults. In order to incorporate structured information, two graph Laplacian regularization terms, namely, manifold graph Laplacian and full graph Laplacian are considered. While the inherent local geometric structure in the data is preserved in the sparse representation by the manifold graph Laplacian term, the correlation structure between process variables is preserved by using the full graph Laplacian term. Hence, violation in the geometric or correlation structure will be promptly detected. To pinpoint faulty variables, a fault isolation method is developed by imposing the$l_{1}$/$l_{2,1}$-norm constraint on the sparse coefficients. In addition, theoretical property involving the condition for guaranteed fault isolation is presented in Theorem 1. The contributions of this article include the introduction of monitoring statistics based on RCOSDL that are suitable for incipient faults, a new fault isolation scheme as well as theoretical analysis on the condition of guaranteed fault isolation. The better performance of the proposed method is illustrated by applications to numerical studies and practical industrial cases.

Due to the variety of categories and uneven distribution of available samples, automatic traffic sign detection and recognition is still a challenging task. For those categories with less training data, existing deep learning methods cannot achieve desirable performance, and the overall detection effect is not satisfactory as well. In this letter, we fully explore the relationship between different traffic signs with digital characters and transform the category objects into multi-level classes to alleviate the uneven distribution of samples. We design a lightweight two-stage object detection framework with high real-time performance. The first stage network is proposed to obtain the category groups of traffic signs, and then we construct another object detection network to identify the digital characters of the detected traffic signs. To make the prediction in the first stage more accurate, we put forward a boxes fusion algorithm in the post-processing process and a refine module to improve the recognition performance. Experimental results show that our approach possesses significantly improved performance compared with the latest object detection networks and other traffic sign detectors. Even some traffic signs that only exist in testset can also be recognized accurately by our method.

To successfully implement the platoon control of connected and automated vehicles (CAVs), the model uncertainties, external disturbances, and time delays must be addressed. In this study, we propose a distributed Model Predictive Control scheme to achieve offset-free tracking for CAVs with model uncertainties and mixed disturbances which contains both stochastic and deterministic noises. A simple Vehicle-to-Vehicle communication flow with the short-distance and low-volume information exchange is adopted to reduce the effect of the communication delay and dropout. First, the deterministic and stochastic perturbances are addressed separately in constraints handling with an integrated control law based on the mismatched prediction model. Additional constraints on coupling input and output are imposed to guarantee the assumption about forecast trajectory. Then an observer gives an unbiased estimate of the state and integrated disturbance. Based on the estimation, the target calculator eliminates the steady tracking offset by designing a new target in real-time and the Min-Max Model Predictive Control steers the uncertain systems to the new target under the worst case within the domain of disturbances. Lastly, the Optimal Control Problem is reformulated and can be efficiently solved by existing solvers. Simulation results indicate that the proposed method shows superiority in interference rejection and offset-free tracking.

In a rally competition, professional drivers usually adopt a very aggressive strategy. Agile maneuvering such as ‘drifting’ often occurs during the cornering process. In this paper, a controller framework is present for the vehicle's agile maneuver based on Model Predictive Control (MPC). We introduce the 3-state vehicle model and the brush tire model and analyze the trajectory characteristics of drift equilibrium. The proposed control system can track the variable drift state and the reference path simultaneously and is applicable for both regular driving and drift cornering. Different from the previous studies on drift stability or drift cornering, the system can not only realize lane keeping in complex scenarios but also achieve autonomous drift maneuvering during the cornering process. The effectiveness of the system is validated via simulations on the Matlab-Carsim platform.

Various algorithms based on Convolutional Neural Network (CNN) have achieved great performance in the task of face anti-spoofing (FAS). However, the issue of most approaches is that the robustness in unknown scenes is not strong due to the different quality of attack images and environmental factors. In this letter, we propose a real-time face anti-spoofing method based on stereo matching. We input the left and right views of a pair of infrared face images into our proposed lightweight stereo matching network to get a disparity map. Then, we use the disparity map as input for the classification network to obtain face living information. Experimental results show that the proposed approach has significantly improved performance compared with the latest state-of-the-art face anti-spoofing methods. Considering the real-time requirement, our method is superior to most single-image-based models in inference time and far less than those based on other large scale stereo matching networks in computational complexity. The code is available athttps://github.com/lizhishan1997/StereoMatching_FAS.

Deep neural networks are proved to be very effective to solve problems on image classification, object detection and segmentation. However, in cases where only limited hardware is acquired, it may be a problem to deploy big models with excellent performance as they are sometimes calculation consuming. To overcome the limits on power, memory and calculation, channel pruning is proposed to compress the model in channel wise and soon become a common approach to have big models compressed. Generally, pruning is a three-stage pipeline containing training, pruning and finetuning. In this work, we come up with a new pruning approach that needs no finetuning. The major idea is extracting channel saliences by squeeze and excitation block and pushing the salience to either 0 or 1 by a sin-based function. Then take the salience as criteria for pruning. As the criteria of our approach is activation rather than trainable parameter, finetuning is not necessary in our pruning strategy which make the pruning process more stable and time saving. Experiment on flowers demonstrates our new designed pruning method is effective on reducing the model scale while maintaining the overall accuracy.

As convolutional neural network contains many redundant parameters, a lot of methods have been developed to compress the network for accelerating inference. Among these, network pruning, which is a kind of widely used approaches, can effectively decrease the memory capacity and reduce the computation cost. Herein, we propose a competitive pruning approach based on Soft Filter Pruning (SFP) by taking account of the scaling factors y of Batch Normalization (BN) layers as the criterion of filter selection strategy. During the soft pruning procedure, in each epoch only y values of BN layers less than threshold are set to zero instead of setting the weights of selected filters in convolutional layers to zero. Compared to the existing approaches, the proposed method can obtain a highly increased accuracy on image recognition. Notably, on CIFAR-10, the proposed method reduces the same 40.8% FLOPs as SFP on ResNet-110 with even 0.87% top-1 accuracy improvement.

Abdominal electrocardiogram is an important means to obtain fetal health condition during high-risk pregnancy. In this paper, a novel method for extracting fetal heart rate from multi-channel mother abdomen electrocardiograms is proposed using fast multivariate empirical mode decomposition technique (FMEMD). Firstly, FMEMD decomposes the multichannel ECG signals into a set of modes. Two significant channels are selected according to the standard deviation of the fifth layer. Then the continuous wavelet transform technique (CWT) is applied to these two channels to denoise. The baseline is removed by zero-crossing rate. Following, the interference of the mother QRS complexes and non-overlapped fetal R-peaks can be eliminated and detected by CWT coefficient. The overlapped fetal R-peaks are obtained by combining the dynamic pattern matching program and creative algorithm. The proposed method achieves an accuracy of 99.9% on the existing data set, and the calculating time is only 1/6.39 of the MEMD-based method.

The identification of protein coding regions is one of the important applications of genome sequence analysis. Many digital signal processing (DSP) based methods, which rely on 3-base periodicity of DNA sequences, have been proposed. However, for most Fourier Transform based methods, a prior time-domain window length limits their performances. Even though several wavelet-based methods get rid of the dependence of window length, an overly wide scale range results in the loss of identification accuracy of these methods. In this paper, we propose a novel method based on Scale-limited Gabor Wavelet Transform (SLGWT) for identifying protein coding regions. This method inherits the advantage of wavelet-based methods in the independence of time-domain window length, while maintaining the consistent performance under different wavelet window lengths. More importantly, compared with other wavelet-based method, SLGWT identifies coding regions under narrower and more suitable scale range, thereby improving the identification accuracy and reducing computational load. The experimentations in the sequence and dataset levels verify the superiority of our proposed method.

This article develops a novel probabilistic monitoring framework for industrial processes with multiple operational conditions. The proposed method is based on the probabilistic linear discriminant analysis (PLDA), which relies on two sets of latent variables, i.e., the between-class and within-class latent variables. In order to deal with the large within-class variations in multi-mode industrial processes, this approach modifies the original PLDA by introducing a separate within-class loading matrix for each operational mode and designs an expectation maximization (EM) algorithm to estimate the model parameters from the training samples. Mode identification for test samples is achieved by investigating the cosine similarity in the between-class latent variables and two monitoring statistics corresponding to within-class latent variables and the residuals are considered for fault detection. To diagnose the process fault, this article further develops a sparse probabilistic generative model based on PLDA for fault isolation. The enhanced performance of the proposed method is illustrated by applications to numerical examples and industrial processes.

In order to improve the performance of fault isolation and diagnosis of principal component analysis (PCA) based methods, this article proposes a novel fault detection and isolation approach using the structured joint sparse PCA (SJSPCA). The objective function involves two regularization terms: the$l_{2,1}$norm and the graph Laplacian. By imposing the$l_{2,1}$norm, SJSPCA is able to achieve row-wise sparsity, and introducing the graph Laplacian term can incorporate structured variable correlation information. The row-sparsity property of$l_{2,1}$norm ensures that the score indices associated with normal variables approaching zero and the graph Laplacian constraint helps the isolation of correlated faulty variables. Once a fault is detected, a two-stage fault-isolation strategy is considered and a score index is calculated for each variable. It is proved that the proposed two-stage strategy is capable of isolating faulty variables. The improved fault-isolation performance of SJSPCA is illustrated by a simulation example and a gas flow fault observed in an industrial blast furnace iron-making process.