Search

In this paper, an adaptive neural network (NN) optimized control strategy is presented to improve the inherent tradeoff between ride comfort of passengers and the suspension travel for full vehicle active suspension system with state constraints. To the best of our knowledge, the automotive suspension system control using hydraulic actuators is a highly complex nonlinear control task, involving external disturbances and uncertainties. To address it, this paper develops the virtual and actual optimal controllers based on backstepping technique and the identifier-actor-critic structure. Meanwhile, the Barrier Lyapunov functions are developed to prevent the systems from the state constraints and the systems states are limited in the preselected compact sets. It is particularly worth mentioning that the proposed optimal control strategy ensures that all the closed-loop signals remain bounded, while the power of the control input as well as the amplitude of the vertical displacement has been minimized. The simulation results show how the full-car system can be controlled optimally and satisfactorily, and confirm the superiority of proposed method.

Scientific literature retrieval provides convenience for researchers to find scientific literature related to the query. It is an important part of scientific research to search related papers given a paper title as query. However, for scientific literature retrieval tasks, most of the existing retrieval methods do not consider sentence-level semantic matching so that the retrieval performance is limited. With the success of neural networks, neural information retrieval methods have been widely studied and achieved good retrieval results. In this paper, we propose a semi-supervised semantic-enhanced scientific literature retrieval framework. The framework is composed of two networks: a self-attention convolutional encoder-decoder network and a sentence-level attention scientific literature retrieval network. By joint training of the two networks, the proposed semi-supervised semantic-enhanced scientific literature retrieval model can fully capture the rich semantic information of scientific text data and leverages human labeled scientific text data to improve the discriminativeness of the learned semantic representation. The retrieval results on two scientific literature datasets demonstrate that the proposed method significantly and consistently outperforms the other baseline methods.

This paper investigates the problem of adaptive tracking control for multi-agent systems subject to output constraint and unknown hysteresis. Different from most existing results that use the barrier Lyapunov function and integral barrier Lyapunov function to solve the output constraint problem, the transformation technique is introduced in this paper, which can convert the original system to a constrained or completely unconstrained system. The difficulty caused by unknown Bouc-Wen hysteresis is solved via the Nussbaum gain technique. The continuous unknown function is estimated by using the radial basis function neural networks. The tracking differentiator is utilized to overcome the difficulty of “explosion of complexity”. By using the Lyapunov stability theory, it is indicated that all signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Eventually, the feasibility of the designed approach is demonstrated by some simulation results.

Person re-identification is to recognize a pedestrian who has been observed at different places in monitoring system, which is done by computer automatically. Important practical significance can be found in this paper on improving the intelligence of monitoring system. There are two challenging problems needed to be solved for person re-identification, which is feature representation and matching. Designing a suitable feature representation method is both challenging and crucial. Ideally, the extracted features should be robust enough to cope with the various situations of the monitoring environment, such as posture difference, illumination changing, and shooting angle difference, etc. There are a lot of challenges for feature matching too, such as small sample size, inter-class confusion, and intra-class variation, etc. In order to meet these challenges, a new person re-identification scheme was put forward in this paper. The following three aspects would be used to describe it. First, an improved BOF feature extraction algorithm based on SURF was put forwarded, SURF algorithm extracts the preliminary feature descriptor and generates visual dictionary, so the influence factors such as illumination and scale invariants can be deal with. And then, covariance descriptor was adopted by the algorithm. It has high robustness, and matching accuracy can be improved too for the case of small sample. Finally, an effective classifier was designed by LIBSVM based on improved BOF algorithm, so the efficiency of the person re-identification algorithm can be improved. The proposed method was compared with the current mainstream algorithm through experiment, and it can be found through the experimental results that it is effective to solve the difficult problems for person re-identification.

Many methods achieve the visual object tracking task with deep learning technologies. As the deep features of different levels contain various semantic information and functions, this paper presents a multi-hierarchy feature aggregation approach to tackle the specific issues in the tracking task, which consists of two aspects. On one hand, this paper integrates the features captured by the offline and online classifiers at the score level, which constructs complementary roles of these classifiers to enhance the stability of classification. Besides, the proposed offline classifier is continuously optimized with different levels of features to reinforce classification constraints. On the other hand, we design a butterfly attention module to promote the capacity of multi-hierarchy feature aggregation in the regression network, which aims to fuse and strengthen the multi-scale features by attending to their spatial information. It can capture more spatial contexts by utilizing the self-attention mechanism during the fusion procedure, and preserve the hierarchy of the features during the strengthening process. Extensive experiments on four public datasets, i.e., VOT2018, OTB100, NFS and LaSOT datasets, demonstrate the effectiveness of the proposed methods.

Context and rationale Intrusion Detection, the ability to detect malware and other attacks, is a crucial aspect to ensure cybersecurity. So is the ability to identify this myriad of attacks. Objective Artificial Neural Networks (as well as other machine learning bio-inspired approaches) are an established and proven method of accurate classification. ANNs are extremely versatile – a wide range of setups can achieve significantly different classification results. The main objective and contribution of this paper is the evaluation of the way the hyperparameters can influence the final classification result. Method and results In this paper, a wide range of ANN setups is put to comparison. We have performed our experiments on two benchmark datasets, namely NSL-KDD and CICIDS2017. Conclusions The most effective arrangement achieves the multi-class classification accuracy of 99.909% on an established benchmark dataset.

Single-object tracking is regarded as a challenging task in computer vision, especially in complex spatio-temporal contexts. The changes in the environment and object deformation make it difficult to track. In the last 10 years, the application of correlation filters and deep learning enhances the performance of trackers to a large extent. This paper summarizes single-object tracking algorithms based on correlation filters and deep learning. Firstly, we explain the definition of single-object tracking and analyze the components of general object tracking algorithms. Secondly, the single-object tracking algorithms proposed in the past decade are summarized according to different categories. Finally, this paper summarizes the achievements and problems of existing algorithms by analyzing experimental results and discusses the development trends.

In the process of Human-robot skill transfer, we require the robot to reproduce the trajectory of teacher and expect that the robot can generalize the learned trajectory. For the trajectory after generalization, we expect that the robot arm can accurately track. However, because the model of the robot can not be accurately obtained, some researchers have proposed using a neural network to approximate the unknown term. The parameters of the traditional RBF neural network are usually selected through the empirical and trial-and-error method, which maybe biased and inefficient. In addition, due to the end-effector of the mechanical arm trajectory will be constantly changing according to the needs of the task, when the neural network of compact set cannot contain the whole input vector, the neural network cannot achieve the ideal approximation effect. In this paper, the broad neural network is used to approximate the unknown terms of the robot. This method can reuse the motion controller that has been learned and complete other motions in the robot operating space without relearning its weight parameters. In this paper, the effectiveness of the proposed method is proved by the ultrasound scanning task.

The problem of pursuing the data items of a large database whose distances to a query item are the least is known as Similarity Search (Nearest Neighbor Search) problem. There exist various algorithms to address this problem. Some of the well known algorithms are i) exact algorithms ii) approximation algorithms and iii) randomized algorithms. This paper has made study only on exact and approximation algorithms because randomized algorithm produces approximate results with some probability. Recently, there are several approximation algorithms are proposed by the researchers because this type of algorithms minimizes the problem of Curse of Dimensionality. This paper mainly has two major sections. In first section, various methods under exact and approximation algorithms are discussed with regard to storage, preprocessing and query time. In the second section, efficient algorithms for similarity search suitable for certain physiological characteristics based biometric systems are considered. Biometric system has five main steps viz acquisition of Image, pre-processing, extraction of features, matching and making final decision. In this paper, indexing algorithms for similarity search suitable for iris trait based on different features are discussed in detail. Since the nature of features are distinct and different in biometric traits, there does not exist a universal (one unique) solution which can apply to all traits of biometric systems. Various performance measures like Penetration Rate and Hit Rate are used to determine the correct recognition rate with top best match (rank-1 accuracy).

In this work, we propose a Fractional-order Residual Convolutional Neural Network (FRCNN) for Low-Dose CT (LDCT) denoising. As increasing the dose of radiation is harmful to the patient, how to trade off between reducing the radiation dose and improving the quality of the CT image has become a challenging problem. To this end, this paper proposes a new approach for LDCT image denoising using Convolutional Neural Network (CNN) with Fractional-order Total Variation (FTV) loss, as well as residual learning. Firstly, this paper introduced the FTV loss function for structural details enhancement. Secondly, skip connections were added to optimize the network. Thirdly, extensive experimental analysis was used to evaluate the capacity of this method in suppressing noise and preserving detailed information. The FTV loss can retain essential structural details while suppressing noise, generating high-quality CT images ready for interpretation by radiologists. Compared with state-of-the-art methods, our method obtained better results visually and numerically, especially in structural details preservation. These promising results will significantly improve the usability of LDCT images.

This paper proposes a hybrid architecture based on neural networks, fuzzy systems, and n-uninorms for solving pattern classification problems, termed as ENFS-Uni0 (short for evolving neuro-fuzzy system based on uni-nullneurons). The model can produce knowledge in an on-line (single-pass) and evolving learning context in a particular form of neuro-fuzzy rules representing the dependencies among input features through IF-THEN type relations. The rules antecedents are thereby realized through uni-nullneurons, which are constructed from n-uninorms, leading to the possibility to express both, AND- and OR-connections (and a mixture of these) among the single antecedent parts of a rule (and thus achieving an advanced interpretability aspect of the rules). The neurons’ evolution is done through an extended version of an autonomous data partition method (ADPA). On-line interpretation of the timely evolution of rules is addressed by (i) a concept for tracking the degree of changes of the rules over data stream samples, which may indicate experts/operators how much dynamics is in the process and may be used as a structural active learning component to request operator’s feedback in the case of significant changes and (ii) a concept for updating feature weights incrementally. These weights express the (possibly changing) impact degrees of features on the classification problem: features with low weights can be seen as unimportant and masked out when showing rules to an expert ( → rule length reduction). The rules’ consequents are represented by certainty vectors and are recursively updated by an indicator-based recursive weighted least squares (I-RWLS) approach (one RWLS estimator per class) where the weights are given through the neuron activation levels in order to gain stable local learning. The model proposed in this paper was successfully compared to related hybrid and evolving approaches in the literature for classifying binary and multi-class patterns. The results obtained by the proposed model show an outperformance of the related works in terms of higher accuracy trend lines over time, while offering a high degree of interpretability through coherent neuro-fuzzy rules to solve the classification problems.

Subtle facial expression recognition (SFER) aims to classify facial expressions with very low intensity into corresponding human emotions. Subtle facial expression can be regarded as a special kind of facial expression, whose facial muscle movements are more difficult to capture. In the last decade, various methods have been developed for common facial expression recognition (FER). However, most of them failed to automatically find the most discriminative parts of facial expression and the correlation of muscle movements when human makes facial expression, which makes them unsuitable for SFER. To better solve SFER problem, an attention mechanism based model focusing on salient local regions and their correlations is proposed in this paper. The proposed method: 1) utilizes multiple attention blocks to attend to distinct discriminative regions and extract corresponding local features automatically, 2) a correlation attention module is integrated in the model to extract global correlation feature over the salient regions, and finally 3) fuses the correlation feature and local features in an efficient way for the final facial expression classification. By this way, the useful but subtle local information can be utilized in more detail, and the correlation of different local regions is also extracted. Extensive experiment on the LSEMSW and CK+ datasets shows that the method proposed in this paper achieves superior results, which demonstrates its effectiveness.

The purpose of this paper is to give an overview of various well known medical image registration techniques with a special focus on registration between anatomical and functional medical images. Examples of anatomical medical images (AMI) are Computer Tomography (CT), Magnetic Resonance Images (MRI), X-ray radiographs and ultrasound etc. whereas Positron Emission Tomography (PET), Single Photon Emission Tomography (SPECT) and fMRI are examples of functional medical images (FMI). Irrespective of such types (AMI or FMI), every case can be considered as a medical imaging modality. All these modalities are widely used by clinicians to study the structure/functionality of human body parts for diagnosis and treatment. It is frequently required to combine PET/SPECT with CT/MRI to simultaneous study the metabolic and molecular information received through PET/SPECT with fine anatomical details observed by CT/MRI. This concurrent study will help in the diagnosis and localization of many diseases like cancer, blockage in coronary arteries and brain-related diseases like Parkinson, Alzheimer etc. Further, in many cases, clinicians are required to co-register one or more anatomical images with functional images, for example, ultrasound-guided biopsy fused with PET and MRI. This registration can be done either at the hardware level or at the software level. The introduction of integrated PET-CT machine increases the acceptability of hardware-based registration systems as compared to software-based methods among the medics. One possible reason for this is the lack of validation of results achieved through software-based registration methods. On the other hand, software-based registration methods also have many advantages over the hardware-based registration systems like lesser exposure to radiation and no need for new investment on hardware etc. In this paper, both the methods with their merits and demerits are discussed in detail.

Extreme-learning-machine based auto-encoder (ELM-AE) is regarded as a useful architecture with fast learning speed and general approximation ability, and stacked ELM is used to develop efficient and effective deep learning networks. However, considering features learned from conventional ELM-AEs have issues of weak nonlinear representation ability and random factors in feature projection, this paper proposes an improved ELM-AE architecture which utilize low-rank matrix factorization to learn optimal low-dimensional features. Two superiorities can be obtained compared to conventional ELM-AEs. One is the dimensionality of the hidden layer in ELM-AE could be set arbitrarily, e.g. a higher-dimension hidden layer could lower the random effect in feature learning and enhance features representation ability. The other is enhancing features nonlinear ability, since features are learned directly from the nonlinear outputs of hidden layer. Finally, comparison experiments on numerical and image datasets are implemented in this paper to verify the superior performance of the proposed ELM-AE in this paper.

Deep learning has made substantial progress in crowd density estimation, but in practical application, due to the interference factors such as uneven distribution of crowd and changes in illumination, the existing methods still have large errors in counting. To solve the above problems, a crowd density estimation method based on multichannel dense grouping network is proposed. To solve the problem that it is difficult to extract the multiscale information of the crowd due to the uneven distribution of the crowd, multichannel dense grouping module (McDGM) is designed. In the module, Improved grouping convolution block (IGCB) is dense connected with other layers to obtain different levels of crowd characteristics, so as to avoid the loss of multi-scale information. In addition, the parameters of IGCB are reduced by group convolution. Then, to overcome the change of illumination, a crowd image enhancement algorithm is designed, which makes the image clear by the average pixel value and adjusting the contrast. Finally, to enhance the sensitivity of the network to crowd counting, a new loss function is proposed, which adds counting loss to the previous pixel space loss to improve the accuracy of crowd counting. The algorithm in this paper has been tested on ShanghaiTech and UCF_CC_50 datasets. The test results show that the algorithm in this paper has better statistical accuracy.

From the perspective of large-scale system, a directed complex dynamic network (DCDN) may be considered as a coupling system of the node subsystem (NS) and the link subsystem (LS). In this paper, by using the outgoing link vector and incoming link vector for DCDN, the dynamics of LS is described by employing the vector differential equation instead of the matrix differential equation. Since the outgoing and incoming link vectors have the stronger geometric intuition, the results in this paper show that this kind model of links can not only reflect the direction of links but also find the dynamic tracking goal of links more easily when the state synchronization of NS emerges. Furthermore, by employing the simple mathematical conditions, the nonlinear controller of NS and the coupling term of LS are proposed to ensure achieving the asymptotical state synchronization for DCDN. Finally, the numerical simulations are given to demonstrate the validity of the results in this paper.

In this paper, a neural synchronization of the optimal structure-based group of neural networks is proposed. Asymmetric cryptography is widely used to generate a key amongst two parties and to exchange the key through an insecure channel. However, since the methods that used this strategy, like RSA, have been compromised, new methods for producing a key that can offer security must be discovered. A new group of cryptography known as neural cryptography was created to solve this issue. The primary aim of neural cryptography is to produce a secret key over an unreliable medium. The optimal neural network architecture for creating and defining a secret key between the two authorized individuals is examined in this article. Furthermore, studies into the coordination of a group of neural networks are uncommon. For the design of the public key exchange protocol, synchronization of a cluster of neural networks with Three Layer Tree Parity Machine (TLTPM) is proposed. To calculate the synchronization time, steps taken, and the number of times the attacking neural network could replicate the actions of the two accepted networks, more than 15 million simulations were run. Various parametric experiments have been conducted on the proposed methodology. Simulations of the approach show that it is correct, according to the results of the paper.

This paper tackles the event-triggering filtering problem for a class of nonlinear networked control systems subject to the prescribed disturbance attenuation level. To alleviate the data transmission burden of communication channels, an event-triggering mechanism is adopted, where the data can be transmitted only if certain condition is fulfilled. The T-S fuzzy model approach is utilized to approximate the nonlinear dynamics. It is the purpose of this paper to design a filter for the addressed nonlinear system ensuring that the filtering error system is asymptotically stable whereas the prespecified H ∞ criterion is guaranteed. By using Lyapunov approach in combination with matrix analysis method, the sufficient conditions are established for the existence of desired filtering parameters in terms of the solvability of certain linear matrix inequalities. Finally, a numerical simulation example is put forward to demonstrate the validity and effectiveness of the developed theoretical results.

This paper investigates the motion control of a Stewart parallel robot with uncertain load. The external disturbances caused by load can trigger dynamic coupling among six degree-of-freedoms and have a significant impact on control precision. In practice, the dynamic parameters of load is difficult to be precedent identified and the robot with load is a multi-input multi-output(MIMO) system in physical space, which makes the dynamic coupling problem difficult to solve effectively and the high-performance control is hard to achieve. In this paper, the dynamic effect of the load is first analyzed. Then a novel motion control method, modal space neural network compensation control, is designed with the aim of reducing the effect of the load disturbances. This controller implements the neural network to compensate for the load disturbances. In addition, the modal space control theory is introduced to realize the independent control of each control channel. The feasibility of the proposed controller is evaluated in numerical simulations. Results reveal that the proposed control method gives exceptional tracking performance and dynamic coupling suppression capability.

With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. In this article, we concentrate on this topic and provide a systematic review of the relevant literature. Specifically, the contributions of this paper are twofold. First, the research progress of related methods is categorized according to the learning paradigm, including transfer learning, active learning and few-shot learning. Second, a number of experiments with various state-of-the-art approaches has been carried out, and the results are summarized to reveal the potential research directions. More importantly, it is notable that although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model. For reproducibility, the source codes of the methods assessed in the paper can be found at https://github.com/ShuGuoJ/HSI-Classification.git.

This paper considers the leader-following consensus problem for delayed neural networks with multi-layer signed digraph topologies. Both structurally balanced and unbalanced topologies are considered. By using the structural balance theory and the pinning control strategy, some sufficient conditions are given to guarantee that the bipartite leader-following consensus can be reached in structurally balanced multi-layer signed neural networks, which depend on the node dynamics and the topology of the multi-layer signed network. In addition, a scheme is designed for selecting the pinning nodes and gains of each layer to achieve bipartite leader-following consensus. When the multi-layer signed network is structurally unbalanced, the asymptotic stability of the neural network is investigated by decomposing the structurally unbalanced network into a structurally balanced subgraph and some special edges. The conditions proposed in this paper are also suitable for addressing the consensus problem of traditional single layer signed neural networks. Numerical examples are given to illustrate the correctness and effectiveness of the proposed theorems and algorithms.

This paper considers the stochastic consensus problem of high-order linear discrete-time multiagent systems (MASs) with uncertain topologies and external disturbances. The randomly switching interaction signed topologies are governed by a time-homogenous Markov chain with incomplete knowledge of the transition probabilities. Based on Lyapunov function theory and adaptive control strategies, we employ a homogeneous parameter-dependent Lyapunov function to propose sufficient conditions under a set of linear matrix inequality (LMI) constraints. A free-weighting matrix method is introduced to give the proposed LMIs extra freedom to search feasible solutions. Furthermore, the adaptive controllers applied in this paper are independent of topologies and fully distributed; i.e., the system achieves robust consensus without continuously updating the controller and dealing with global data. Additionally, we extend the theoretical framework to analyze the disturbance rejection performance of MASs under the stochastic process. Two numerical examples are provided to show the effectiveness of the algorithms.

This paper presents a novel nonlinear adaptive sensor fusion method for integrated navigation systems with varying noise parameters. The innovation is utilizing deep neural networks to mine the noise-related patterns of specific sensors and combining it with conventional nonlinear filters. This hybrid approach improves the feasibility and robustness of adaptive filtering by achieving an effective estimation of the originally weakly observable noise parameters. The specific sensors are defined as α -type sensors whose errors are entirely generated by themselves. The mathematical model for analyzing α -type sensors output sequence and the deep neural network for mining the patterns of interest are established. All adaptive filtering systems using α -type sensors can benefit from this paper. Specifically, it is applied to inertial and satellite integrated navigation system. The numerical experiments indicate that the proposed filter achieves promising accuracy and robustness improvement as compared to conventional nonlinear filters. And the comparisons between different nonlinear approximation algorithms indicate that the first-order approximation is accurate enough for our application.

This paper presents the neural dynamical network to compute the generalized and restricted singular value decompositions (GSVD/RSVD) in the regularization methods for ill-posed problems. The neural network model is defined by ordinary differential equations (ODE) which can be solved by many state-of-the-art techniques. The main purpose of the paper is to develop two neural network models for finding approximations of the GSVD and the RSVD. The globally asymptotic stability analyses are provided and numerical experiments illustrate our theory and methods. For small scale problems, the estimation can be as accurate as O ( 10 - 15 ) . For ill-posed problems, the truncation regularization method implementing the GSVD/RSVD algorithms also produces accurate results.

Time-varying linear complementarity problem (TLCP) has received a great deal of attention due to its broad variety of scientific and engineering applications. Several efficient Zhang neural networks are introduced for solving TLCP in this paper. Theoretical analysis shows that the related error function of the model proposed in this paper eventually tends to zero. The state convergence time periods of those Zhang neural networks with three types of activation functions are proved to be finite and can be quantitatively estimated by using some given parameters. Further, it is shown that the proposed neural network is of noise-tolerance, which means the neural network is more appropriate for a wider application. Moreover, in order to implement neural network numerically, a related discrete-time version is also studied. Finally, numerical simulations confirm the analysis of the proposed models concretely.

Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor systems as well as the continued use of unstructured manual data recording mechanisms. Irregular data and the resulting missing values severely limit the data's ability to be analysed and modelled for classification and forecasting tasks. Often, conventional methods used for handling time series data introduce bias and make strong assumptions on the underlying data generation process, which can lead to poor model predictions. Traditional machine learning and deep learning methods, although at the forefront of data modelling, are at best compromised by irregular time series data sets and fail to model the temporal irregularity of incomplete time series. Gated recurrent neural networks (RNN), such as LSTM and GRU, have had outstanding success in sequential modelling, and have been applied in many application fields, including natural language processing. These models have become an obvious choice for time series modelling and a promising tool for handling irregular time series data. RNNs have a unique ability to be adapted to make effective use of missing value patterns, time intervals and complex temporal dependencies in irregular univariate and multivariate time series data. In this paper, we provide a systematic review of recent studies in which gated recurrent neural networks have been successfully applied to irregular time series data for prediction tasks within several fields, including medical, human activity recognition, traffic monitoring and environmental monitoring. The review highlights the two common approaches for handling irregular time series data: missing value imputation at the data pre-processing stage and modification of algorithms to directly handle missing values in the learning process. Reviewed models are confined to those that can address issues with irregular time series data and does not cover the broader range of models that deal more generally with sequences and regular time series. This paper aims to present the most effective techniques emerging within this branch of research as well as to identify remaining challenges, so that researchers may build upon this platform of work towards further novel techniques for handling irregular time series data.

This paper solves the fault-tolerant control (FTC) problem of uncertain nonlinear systems in nonstrict-feedback form. The controlled plants considered in this paper are more complicated than existing ones, which is composed of the unknown nonlinearities, unmeasured states, and sensor faults and unknown dead zone input nonlinearity. Fuzzy logic systems and a fault-estimation-based state observer are used for identifying the unknown nonlinearities and obtaining the unmeasured states, respectively. By designing the sensor faults compensation and the dead zone inverse compensation methods, an observer-based fault-tolerant compensation control scheme is developed under backstepping recursive design frame. And it is testified that the closed-loop signals are bounded, and the tracking performance is satisfied even when the controlled systems are not free of sensor faults and unknown dead zone input nonlinearity. An electromechanical system is used to test the effectiveness of the developed control strategy.

This paper proposes a solution to translating the visible images into infrared images, which is challenging in computer vision. Our solution belongs to unsupervised learning, which has recently become popular in image-to-image translation. However, existing methods do not produce satisfactory results because (1) most existing methods are mainly used in entertainment scenarios with single scenes and low complexity. The problem solved by this article is more diverse and more complicated. (2) The infrared response of objects depends not only on itself but also on the current environment, and existing methods cannot correlate long-range dependent objects. In this paper, We propose Global Spatial Attention (GSA), which enhances dependence between long-range objects and improves the synthesized image quality. Compared with other methods, GSA can save more space and time. Moreover, we introduce the idea of subspace learning into the neural network to make training more stable. Our method takes unpaired visible images and infrared images for training, which are easy to collect. Experimental results show that our method can generate high-quality infrared images from visible images and outperforms state-of-the-art methods.

Road landmark inventory is becoming an important data product for the maintenance of transport infrastructures. Several commercial sensors are available which include synchronized optical cameras that allowto build 360° panoramic images of the surroundings of the vehicle used for road inspection. This paper is devoted to the analysis of such panorama images,specifically the area that contains themost relevant information. Road lane landmark detection is posed as a two class classification problem that may be solved bymachine learningapproaches, such as Random Forest (RF) and ensembles of Extreme Learning Machines (V-ELM). Besides model parameter selection, a central problem is the construction of a labeled training and validation datasetto cope with the highly uncontrolled conditions of image capture. Besides, human labor cost makes image data labeling a very expensive process. This paper proposes an open ended Active Learning (AL) approach involving a human oraclein the loop who provides the data labeling and can trigger the AL process when detection quality is degraded by the change in imaging conditions. The paper reports encouraging results over a collection of sample images selected from an industrial road landmark inventory operation. As an additional contribution, this paper assesses the ability of AL to overcomesome of the issues raised by highly class imbalanced datasets.

Multi-view clustering has gained soar attention in the field of data mining in recent years. Most multi-view clustering methods are considered to be complete for each view. However, in many applications, some views may contain missing instances, resulting in the incomplete multi-view learning problem. Incomplete views cannot be directly dealt with traditional multi-view clustering methods. In this paper, we propose a One-step multi-view subspace clustering with incomplete views (OMVSC-IV) method based on low-rank matrix factorization to overcome this problem. Specifically, the proposed algorithm uses low-rank matrix factorization to learn a consensus representation matrix, and then combined with the objective function of non-negative embedding and spectral embedding subspace clustering proposed in this paper. The whole process is established into the same objective function for joint optimization and requires no post-processing (e.g., K-means), thus avoiding the defect of being sensitive to initial values. Furthermore, the accuracy, parameter, sensitivity and convergence of the algorithm are studied by experiments. Experimental results on nine data sets show that the proposed algorithm performs superior to some of the latest algorithms.

Depth estimation is a classic task in computer vision, which is of great significance for many applications such as augmented reality, target tracking and autonomous driving. Traditional monocular depth estimation methods are based on depth cues for depth prediction with strict requirements, e.g. shape-from-focus/ defocus methods require low depth of field on the scenes and images. Recently, a large body of deep learning methods have been proposed and has shown great promise in handling the traditional ill-posed problem. This paper aims to review the state-of-the-art development in deep learning-based monocular depth estimation. We give an overview of published papers between 2014 and 2020 in terms of training manners and task types. We firstly summarize the deep learning models for monocular depth estimation. Secondly, we categorize various deep learning-based methods in monocular depth estimation. Thirdly, we introduce the publicly available dataset and the evaluation metrics. And we also analysis the properties of these methods and compare their performance. Finally, we highlight the challenges in order to inform the future research directions.

With the advent of smart phones, the popularity of free Android applications has risen rapidly. This has led to malicious Android apps being involuntarily installed, which violate the user privacy or conduct attack. Malware detection on Android platforms therefore is a growing concern because of the undesirable similarity between malicious behavior and benign behavior, which can lead to slow detection, and allow compromises to persist for comparatively long periods of time in infected phones. The contributions of this paper are first a multiple dimensional, kernel feature-based framework and feature weight-based detection (WBD) designed to categorize and comprehend the characteristics of Android malware and benign apps. Furthermore, our software agent is orchestrated and implemented for the data collection and storage to scan thousands of benign and malicious apps automatically. We examine 112 kernel attributes of executing the task data structure in the Android system and evaluate the detection accuracy with a number of datasets of various dimensions. We find that memory- and signal-related features contribute to more precise classification than schedule-related and other descriptors of task states listed in our paper. Particularly, memory-related features provide fine-grain classification policies for preserving higher classification precision than the signal-related and others. Furthermore, we study and evaluate 80 newly infected attributes of the Android kernel task structure, prioritizing the 70 features of most significance based on dimensional reduction to optimize the efficiency of high-dimensional classification. Our second contribution is that our experiments demonstrate that, as compared to existing techniques with a short list of task structure features (16 or 32 features), our method can achieve 94%-98% accuracy and 2%–7% false positive rate, while detecting malware apps with reduced-dimensional features that adequately abbreviate online malware detections and advance offline malware inspections.

Recently, due to its widespread applications in public safety, anomaly detection in crowd scenes has become a hot topic. Some deep-learning-based methods attain significant achievements in this field. Nevertheless, most of them suffer from over-fitting to some extent because of scarce data, which are usually abrupt and low-frequency in the real world. To remedy the above problem, this paper firstly develops a synthetic anomaly event generating system, which could simulate typical specific abnormal events. By utilizing this system, a large synthetic, diverse anomaly event dataset is built, which contains 2,149 video sequences. After getting the dataset, a 3D CNN is designed to detect the abnormal types at the video level. However, we find that there are obvious domain differences (also named as “domain gap/shifts”) between synthetic videos and real-world data, which results in performance degradation when applying the model to the real world. Thus, this paper further proposes a cyclic 3D GAN for domain adaption to reduce the domain gap, which translates the synthetic data to the photorealistic video sequences. Then the detection model is trained on the translated data and it can perform well in the real data. Experimental results illustrate that the proposed method outperforms these baselines for the domain adaptation anomaly detection.

This paper discusses the containment control (CC) problem of general linear multi-agent systems (MAS)s by means of two kinds of distributed event-triggered mechanisms. Two types of event-triggered control protocols, namely, the state feedback control law and the dynamic output feedback control protocol, are designed for each follower. Under the proposed control protocols and triggering mechanisms, the containment control problem can be solved by proving that the containment error converges to zero based on the assumption and algorithms. At last, we verify the rationality of the theoretical results. Through two numerical simulation, we can see that the trajectory of each follower converges to the convex hull formed by all leaders. In addition, in order to verify the advantages of the obtained results, we give a simulation example and compare the methods designed in this paper with the methods in other literature.

While meta learning approaches have achieved remarkable success, obtaining a stable and unbiased meta-learner remains a significant challenge, since the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks. In order to avoid a biased meta-learner and improve its generalizability, this paper proposes a generic meta learning method that aims to learn an unbiased meta-learner towards a variety of tasks before its initial model is adapted to unseen tasks. Specifically, this paper presents a meta weight learning method for minimizing the inequality of performance across different training tasks. An end-to-end training approach is introduced for the proposed algorithm that allows for effectively learning weight and initializing the network model. Alternatively, a variety of measurement methods of weight is also designed to test the effectiveness of different weight learning methods on the improvement of model-agnostic meta-learning algorithm. The simulation results show that the proposed meta weight learning method not only outperforms state-of-the-art meta learning algorithms, but also is superior to other manually designed measurement methods of weight on discrete and continuous control problems.

Human intention prediction is of great significance in many applications, such as human-robot interaction, intelligent rehabilitation robots. This paper studies the problem of short-term next-active-object prediction in egocentric images. The short-term next-active-object refers to the object that a human is going to interact with in the short-term future, which is an embodiment of human intention. Most current methods usually use object-centered cues, such as the deviation of object appearance change and the unique shape of the egocentric object trajectory, to predict the next-active-object. In this paper, inspired by the fact that human intention is also revealed by human-centered cues, we propose a deep neural network model that integrates the cues from visual attention and hand positions to predict the next-active-object. Firstly, the probability maps of visual attention and hand positions are constructed, and then the probability distribution of next-active-object is generated. We experimentally compare our method with several baseline methods using two datasets and confirm its effectiveness. In addition, ablation experiments are conducted, and crucial points concerning the next-active-object are discussed.

Peer reviews form an essential part of scientific communications. Research papers and proposals are reviewed by several peers before they are finally accepted or rejected for publication and funding, respectively. With the steady increase in the number of research domains, scholarly venues (journal and/or conference), researchers, and papers, managing the peer review process is becoming a daunting task. Application of recommender systems to assist peer reviewing is, therefore, being explored and becoming an emerging research area. In this paper, we present a deep learning network based Meta-Review Generation considering peer review prediction of the scholarly article (MRGen). MRGen is able to provide solutions for: (i) Peer review prediction (Task 1) and (ii) Meta-review generation (Task 2). First, the system takes the peer reviews as input and produces a draft meta-review. Then it employs an integrated framework of convolution layer, long short-term memory (LSTM) model, Bi-LSTM model, and attention mechanism to predict the final decision (accept/reject) of the scholarly article. Based on the final decision, the proposed model MRGen incorporates Pointer Generator Network-based abstractive summarization to generate the final meta-review. The focus of our approach is to give a concise meta-review that maximizes information coverage, coherence, readability and also reduces redundancy. Extensive experiments conducted on the PeerRead dataset demonstrate good consistency between the recommended decisions and original decisions. We also compare the performance of MRGen with some of the existing state-ofthe- art multi-document summarization methods. The system also outperforms a few existing models based on accuracy, Rouge scores, readability, non-redundancy, and cohesion.

The problem of distributed fixed-time leader-following consensus tracking control for nonholonomic chained-form dynamic systems with nonlinear disturbances is considered in this paper. Compared with the existing works, this paper considers a class of more general nonholonomic multi-agent systems where both the leader and the followers have nonlinear uncertainties. For undirected graphs, a distributed observer for each follower is investigated such that both the leader state and input can be estimated by the followers in a fixed time. Based on the distributed observers, a consensus tracking controller is proposed to ensure that the tracking errors convergence to zero within a fixed time. Lyapunov analysis proves the stability of the closed-loop system. A simulation example of wheeled mobile robots is given to demonstrate the effectiveness of the proposed controllers.

This paper investigates the problem of neural network (NN) prescribed performance tracking control for a class of switched stochastic nonlinear pure-feedback systems which contain unknown nonlinear functions, unmeasured state variables, and unknown hysteresis input. It provides an adaptive NN controller which is not restricted to a particular type of hysteresis input. A general mathematical model is introduced to describe two kinds of hysteresis nonlinearities and to utilize in the control design producer. Other focus of this paper is on the performance constraint problem to avoid performance degradation and system damage in practical control systems. Prescribed performance control (PPC) and backstepping technique are thus synthesized to develop an adaptive NN output feedback tracking control scheme under deterministic switching signal. Regarding this concern, to cope with the cause of non-differentiable difficulties and complex deductions in traditional PPC, a new asymmetry error transformation is employed. Moreover, radial basis function NNs (RBFNNs) are applied to approximate the unknown nonlinear functions and to construct a NN nonlinear observer to estimate the immeasurable state variables. Based on Lyapunov stability theory, it is demonstrated that the proposed controller can guarantee that all signals in the closed-loop system are semiglobally uniformly ultimately bounded in probability and the tracking error converges to a small neighborhood of the origin with the prescribed performance bounds. Finally, two simulation examples are provided to confirm the advantages of the presented control design approach.

With the booming development of smart phones and location-based services, spatial keyword queries of semantic trajectories can take location information and multiple keywords as parameters and return semantic trajectories related to these parameters in space and text. With the development of the Location Based Social Network (LBSN) in recent years, social data can be used to improve query results. Generally, the semantic trajectory of having more supporters (users with positive comments about the location) who are more closely related to the query user is more likely to be the recommended result. Considering social information in the query can recommend more satisfactory results for users, this paper studies a new problem of semantic trajectory space keyword query: Social space Keyword Query based on semantic Trajectory (SKQT). This paper proposes a hybrid index structure that introduces social factors called Social Inverted Linear Quadtree (SIL-Quadtree) to improve the efficiency of SKQT query. The experiments use two datasets, Gowalla and Brightkite. The results show that the algorithm and index structure proposed in this paper perform better on running time and I/O.

The choice of activation functions in Convolutional Neural Networks (CNNs) is very important. Rectified Linear Unit (ReLU) has been widely-used in most CNNs. Recently, a series of non-monotonic activation functions gradually become the new standard to enhance performance of CNNs. Inspired by them, this paper firstly proposes a novel non-monotonic activation function called Power Function Linear Unit (PFLU). The negative part of PFLU is non-monotonic and closer to zero with the negative input decreasing, which can maintain sparsity of the negative part while introducing negative activation values and non-zero derivative values for the negative part. The positive part of PFLU does not use identity mapping but is closer to identity mapping with the positive input increasing, which can bring non-linearity property for the positive part. Next, this paper proposes faster PFLU (FPFLU). A wide range of classification experiments show that PFLU tends to work better than current state-of-the-art non-monotonic activation functions, and FPFLU can run faster than most non-monotonic activation functions.

Bayesian networks have many applications. Learning the optimal structure of a Bayesian network has always been important in this respect. In this paper, a bidirectional heuristic search algorithm is proposed for the order graph space commonly used in a Bayesian network. At the same time, heuristic functions that are admissible and consistent in terms of both forward and backward search are proposed to ensure convergence of the algorithm to the optimal solution. The experimental results show that, compared with traditional unidirectional heuristic search, in most cases, the bidirectional heuristic search proposed in this paper needs to expand fewer states, the convergence efficiency is higher, and less running time is needed.

Local Differential Privacy (LDP) can provide each user with strong privacy guarantees under untrusted data curators while ensuring accurate statistics derived from privatized data. Due to its powerfulness, LDP has been widely adopted to protect privacy in various tasks (e.g., heavy hitters discovery, probability estimation) and systems (e.g., Google Chrome, Apple iOS). In particular, ( ∊ , δ ) -LDP has been studied in related statistical tasks like private learning and hypothesis testing, but is mainly achieved by using Gaussian mechanism, leading to the limited data utility. In this paper, we investigate several novel mechanisms that achieve ( ∊ , δ ) -LDP with higher data utility in collecting and analyzing users’ data. Specifically, we first design two ( ∊ , δ ) -LDP algorithms for mean estimations on multi-dimensional numeric data, which can ensure higher accuracy than the optimal Gaussian mechanism. Then, we investigate different local protocols for frequency estimations on categorical attributes under ( ∊ , δ ) -LDP. Based on the proposed mechanisms, we further study on ( ∊ , δ ) -LDP-compliant stochastic gradient descent algorithms for machine learning models. Besides, the theoretical analysis of the error bound and the variance of the proposed algorithms are also presented in the paper. We have conducted extensive experiments on both real-world and synthetic datasets and demonstrated the high data utility of our proposed algorithms in the perspectives of simple data statistics tasks and complex machine learning tasks. The experimental results have shown that our proposed algorithms can effectively improve the data utility in different tasks while alleviating the privacy concerns of each individual.

This paper investigates the formation containment control under sampling and with time delays for multi-agent systems (MASs). First, a novel sampling control protocol for the MASs to achieve formation containment is proposed, where the communication among agents is only occurred at the sampling instance. Then the communication load and the waste of energy can be decreased significantly. In addition, time delays are considered in this paper. Second, by establishing an appropriate Lyapunov function, sufficient conditions for MASs with sampled data and time delays reaching formation containment are obtained by handling the established Lyapunov function. Finally, the obtained results are demonstrated by a simulation example.