In this letter, the novel asymmetrict-GARCH(1,1) (ATGARCH(1,1)) model is proposed. By calculating the tail exponent (TE), it is discovered that the TE of the ATGARCH(1,1) distribution is smaller than the degree of freedom (dof) of its corresponding driving Student-t distribution. Actually, we get that the TE of the ATGARCH(1,1) distribution is jointly determined by the corresponding dof and its model parameters. Since the closed-form density functions of the ATGARCH(1,1) noises are unknown, the corresponding filtering problems are analyzed by the heuristic method. In the ATGARCH(1,1) noise cases, the simulation results show that our heuristic algorithm is superior to the standard Kalman filter (KF), the particle filter, and those robust KFs designed by the Student-t distribution models.

Accurate hand-eye and robot-world calibration remains crucial for vision-guided robotic applications. Existing methods predominantly focus on deriving closed-form or nonlinear optimization solutions for hand-eye equationAX=XBor hand-eye-robot-world calibration equationAX=YBunder various rotation parameterizations, while the critical role of input data refinement on calibration accuracy has been largely overlooked. This paper addresses this gap by proposing a pose graph optimization model that strategically enhances the precision of key pose chains in robotic vision systems, particularly under significant discrepancies between robotic positioning and visual measurement accuracies. Building upon the optimized pose chains associated with the hand-eye and robot-world parameters, a pair of dual equations forXandYis then constructed and solved via our proposed Kronecker product-based least squares algorithm, allowing simultaneous and accurate estimation on rotation matrices and translation components ofXandY. Simulation and real-world experimental results have validated the superiority of the proposed pose graph optimization model, under significant robot-vision precision disparities. Moreover, even when the error magnitudes between the robot and visual measurement system are comparable, the proposed method achieves superior accuracy for both the hand-eye and robot-world parameters compared to nine state-of-the-art approaches.

Existing full-reference image quality assessment (FR-IQA) methods often fail to capture the complex causal mechanisms that underlie human perceptual responses to image distortions, limiting their ability to generalize across diverse scenarios. In this paper, we propose an FR-IQA method based on abductive counterfactual inference to investigate the causal relationships between deep network features and perceptual distortions. First, we explore the causal effects of deep features on perception and integrate causal reasoning with feature comparison, constructing a model that effectively handles complex distortion types across different IQA scenarios. Second, the analysis of the perceptual causal correlations of our proposed method is independent of the backbone architecture and thus can be applied to a variety of deep networks. Through abductive counterfactual experiments, we validate the proposed causal relationships, confirming the model’s superior perceptual relevance and interpretability of quality scores. The experimental results demonstrate the robustness and effectiveness of the method, providing competitive quality predictions across multiple benchmarks. The source code is available at https://anonymous.4open.science/r/DeepCausalQuality-25BC.

This study investigates the problem of large-scale swarm robots shape self-assembly problem under conditions of information asymmetry. Existing methods assume complete sharing of global information; however, this assumption has significant limitations in terms of resource consumption and swarm emergence. On the other hand, strategies that rely entirely on local information struggling to achieve the self-assembly of complex shapes, especially disconnected shapes. To address these challenges, this study proposes a novel bio-inspired distributed self-assembly strategy specifically designed for information asymmetric swarm. The strategy draws inspiration from the task specialization mechanism between scout ants and worker ants in social insects, guiding individuals to efficiently complete shape-assembly under information asymmetry through local perception, neighborhood interactions, and dynamic rule adjustments. Experimental results show that the proposed strategy successfully achieves the self-assembly of various shapes, including simple shapes (e.g., circle, rectangle), complex shapes (e.g., human, flower, and letter "A"), and disconnected shapes (e.g., letter "IO"). This demonstrates the strategy’s adaptability to shape complexity. Furthermore, experiments with varying swarm sizes validate the strategy’s robustness and scalability across different scales. During the experiments, we unexpectedly observed emergent behaviors within the swarm, further confirming that the proposed strategy not only significantly enhances task flexibility but also strengthens swarm emergence. These results indicate that the proposed method provides an efficient, scalable, and innovative solution for swarm robots self-assembly under information asymmetry.

This paper presents an online multi-population evolution path planning (OMEPP) algorithm to address the flexible path planning problem for mobile manipulators in complex dynamic environments. The OMEPP algorithm treats the mobile manipulator as a high-dimensional system to utilize its flexibility. The OMEPP algorithm is based on random sampling and evolutionary concepts: Optimization and passive obstacle avoidance operations are performed on the path at runtime, with superior paths replacing inferior ones within the same population. A novel path population partitioning approach is proposed to maintain diverse switchable paths, thereby improving robustness. This paper also proposes an efficient manipulator collision detection method and several other mechanisms to enhance the algorithm’s effectiveness. The experimental results demonstrate the algorithm’s ability to swiftly adapt and optimize paths in response to dynamic environmental changes. Note to Practitioners—This paper presents OMEPP, an online evolutionary algorithm for real-time path planning of mobile manipulators in dynamic environments. OMEPP employs novel techniques including path population partitioning, random sampling, and evolution to efficiently generate collision-free paths among moving obstacles. A novel path population partitioning approach is proposed to maintain diverse switchable paths, thereby improving robustness. Simulations have demonstrated that the OMEPP algorithm is effective for real-time path planning of mobile manipulators in complex dynamic environments. Future work will focus on trajectory generation respecting dynamics limits.

This paper presents a novel output-feedback direct adaptive controller with a decoupling design to completely attenuate periodic disturbances in the linear multi-input multi-output (MIMO) system subject to input delay. First, the family of strictly causal stabilizing controllers for this system is derived by using the Youla parameterization method. The stabilizing controller consists of a fixed base controller J and an adjustable block Q. Periodic disturbances can be completely attenuated by adjusting the block Q. Then, to improve the attenuation rate of the periodic disturbance, a diagonal decoupling strategy is proposed. Based on the internal model principle (IMP), the interpolation condition for the complete attenuation of periodic disturbance is established. Next, to attenuate unknown time-varying periodic disturbances, a parameter adaptive algorithm is designed to update online the Q parameters. Finally, the effectiveness of the proposed controller is validated by simulation results. Note to Practitioners—The simultaneous presence of input delays and periodic disturbances in real systems not only limits control effectiveness but also leads to instability in the closed-loop system. Therefore, it is crucial to attenuate the periodic disturbances in input delay systems. However, existing adaptive controllers cannot completely attenuate unknown time-varying periodic disturbances if the system state is unavailable. Moreover, the multi-channel cross-coupling inherent in MIMO systems further constrains the controller performance. Motivated by this challenge, this paper proposes an output-feedback direct adaptive controller with decoupling design to completely attenuate periodic disturbances in linear MIMO input delay systems. It is hoped that the proposed method can provide valuable theoretical and technical guidance for the design of controllers aimed at compensating for periodic disturbances in linear MIMO systems subject to input delay.

Low-light images often suffer from varying degrees of visual degradation. Current methods for recovering image texture details fail to rely on the self-adaptive correlation texture direction of the image itself, which leads the network to be unable to address the local texture characteristics of different images. To address this challenge, we propose a semantic-aware detail adaptive network (SDANet) that fully considers the image detail information. The network divides low-light images into high-frequency and low-frequency parts. Learning different forms of noise through a novel total variation regularization module with adaptive weights ensures that the final high-frequency part adequately integrates the texture information of the image. Simultaneously, a detail-adaptive module is incorporated to restore finer details in the resulting image. SDANet not only effectively suppresses noise in real low-light images while considering texture details but also effectively addresses the degradation of visible information, and it performs better than other state-of-the-art methods. The code is available athttps://github.com/cheer79/SDANet.

Detecting small objects in remote sensing images poses significant challenges to the field of computer vision, primarily stemming from the complexity of backgrounds, limitations in pixel resolution, and information loss during the feature fusion process. While general object detection has significantly advanced in recent years, remote sensing small object detection remains an unsolved problem, with existing frameworks struggling to achieve high performance at small scales. In this article, we propose a novel framework called the boundary-aware feature fusion network (BAFNet), which significantly enhances the model’s ability to represent and locate small objects precisely within complex remote sensing scenarios. First, a dual-stream attention fusion module captures complementary foreground and background cues through bidirectional context modeling. Jointly attending to objects and their surroundings enhances discriminative power for distinguishing small objects. Additionally, we incorporate a boundary-aware branch to better preserve crucial detailed information vital for small-scale objects. This auxiliary component supervises the fusion of contextual semantics and spatial information, aiding in retaining critical boundary details that are prone to loss during cross-layer feature fusion. We conducted experiments on the challenging AI-TOD, VisDrone, DIOR, and LEVIR-Ship datasets. The results demonstrate the superiority of our approach over other state-of-the-art (SOTA) object detection methods, particularly in terms of precisely identifying small objects within remote sensing images. The code is available athttps://github.com/ooo1128/BAFNet.

Vibration isolators are essential for sensitive and precise industrial applications working on instrumentation and control systems since they isolate microvibration, prevent error propagation, and enhance processing/image quality. This article proposes a novel gravity compensator (GC) for vibration isolation in satellite applications based on permanent magnets. Owing to the new magnetic circuit topology, the proposed system features a lower natural frequency and higher force density, enabling superior low-frequency isolation performance and bearing capacity compared with traditional isolators. Owing to its low natural frequency, the proposed system is superior to conventional GCs in isolating low-frequency disturbances. Moreover, the high force density enables the GC to exploit a heavier payload with less magnetic material, which is economical and space-saving. In addition, well-designed electromagnets are utilized to adjust the static levitation force, which can improve the ability to satisfy the requirements of different loads without drastically changing the stiffness of the entire system. The results suggest that the proposed GC could be utilized for satellite applications with heavy loads and limited mounting space.

Multispectral object detection has shown great promise in security and industrial applications. RGB images offer rich texture but are limited by lighting, whereas IR images excel in low light but lack texture. Current methods face challenges in accurately capturing information differences and achieving effective feature fusion across modalities. To address these issues, we propose a graph aggregation alignment network (GAANet) for multispectral object detection. GAANet consists of two key modules: the graph interaction fusion module (GIFM) and the information alignment module (IAM). GIFM uses graph representation learning to effectively process single-modality features, and the direct connection information flow mechanism guides and references low-level multimodal features, ensuring the global and comprehensive fusion of node information in the graph space. The results are then refined through the IAM for secondary calibration and alignment of corresponding local regions, ensuring accurate fusion. We also introduce an information reconstruction path (IRP) and reconstruction loss to prevent the loss of single-modality information due to multiple IAM calculations. GAANet achieves excellent fusion detection capability and significantly reduces the number of parameters, reducing the model size by 61.2% compared with that of representative baselines such as CALNet. GAANet achieves state-of-the-art results on the DroneVehicle, LLVIP, and FLIR datasets, with superior object detection accuracy. It also performs well on the unaligned DVTOD dataset, effectively capturing feature offsets across modalities through global graph perception.

Infrared small target detection has been extensively studied due to its wide range of applications. Most studies treat infrared small target detection as an independent task, either as a detection-based or a segmentation-based, failing to fully leverage the supervisory information from different annotation forms. To address this issue, we propose a multi-task mutual learning network (MTMLNet) specifically designed for infrared small targets, aiming to enhance both detection and segmentation performance by effectively utilizing various forms of supervisory information. Specifically, we design a multi-stage feature aggregation (MFA) module capable of capturing features with varying gradients and receptive fields simultaneously. Additionally, a hybrid pooling down-sampling (HPDown) module is proposed to mitigate information loss during the down-sampling process of infrared small targets. Finally, the hierarchical feature fusion (HFF) module is designed to adaptively select and fuse features from different semantic layers, learning the optimal way to fuse features across semantic layers. The results on IRSTD-1k and SIRST-V2 datasets show that our proposed MTMLNet achieves state-of-the-art (SOTA) performance in both detection-based and segmentation-based methods. The codes are available at https://github.com/YangBo0411/MTMLNet.

Vehicle trajectory prediction is gaining significant attention from academia and industry because of its vital role in autonomous driving. However, current theories face two challenges. First, they generally underperform when faced with longer historical trajectory inputs, especially in large-scale scenarios. Second, they usually ignore the temporal continuity of the target vehicle itself. To address these issues, our study proposed a novel extended temporal sequence fusion attention (ETSFA) network. This network can fully capture the information from the historical trajectory and the dynamic influences of adjacent agents. In addition, the novel dual-channel decoupled model can precisely characterize the intricate spatiotemporal interplay of on-road vehicles. Specifically, the proposed network consists of two main parts. For temporal analysis, the linear inference network (LIN) is reparameterized into complex diagonal forms at the state–space model (SSM) layer to express the linear recurrence capability, thus effectively mining the long-term historical trajectory temporal features of the target vehicle. For spatial analysis, an advanced spatial perception module (SPM) based on graph attention networks (GATs) is proposed to aggregate vehicle and intervehicle interaction features. In addition, a spatial inference module (SIM) based on a convolutional linear inference unit (CONVLIN) is customized for spatiotemporal graph features. Finally, the proposed ETSFA is trained and validated across diverse public datasets, including HighD and NGSIM, demonstrating a marked improvement in the prediction accuracy of the proposed ETSFA over existing methods.

Representation learning of road networks is essential for various downstream traffic-related tasks, as road network contain multi-modal data with rich information, and the learned embeddings can be directly used in machine learning models. However, due to the dynamic changes in road networks with respect to topology and associated data, as well as the local and long-range dependency caused by complex mobility semantics, learning robust and effective representations remains challenging. To this end, we exploit the properties of the road network and the mobility semantics embedded in trajectories, and propose a novel S emantic- E nhanced G raph C ontrastive L earning (SE-GCL) framework, for learning general-purpose embeddings of road networks. Specifically, in this framework, we propose (1) a multi-modal feature embedding module to capture both the attribute and visual information of road segments, (2) a semantic-enhanced graph augmentation strategy to simulate topological changes and data missing in the road network, and (3) a semantic-enhanced contrastive optimization module that leverages geo-locality and mobility semantics to guide representation learning. Extensive experiments are conducted on two real-world road networks with three representative downstream tasks. The result demonstrate that SE-GCL yields more robust and effective representations, outperforming the state-of-the-art baselines. The source code is available at https://github.com/csjiezhao/SE-GCL .

Region representations (also called embeddings) are useful for various urban computing tasks. While graph-based region representation learning methods have shown outstanding performance, they encounter two major challenges: 1) the pervasive data noise and missing data can affect the quality of the constructed region graphs; and 2) high-order relationships (i.e., group-wise relationships) among regions are often insufficiently modeled and sometimes entirely overlooked. To this end, we proposeHyperRegion, an unsupervised region representation learning framework that integrates graph and hypergraph contrastive learning to learn comprehensive region embeddings from multi-modal data. Built upon a region hybrid graph network, this framework models both pair-wise and group-wise dependencies involving POI semantics, mobility patterns, geographic neighbors, and visual semantics. To mitigate the impact of data noise and missing data, graph and hypergraph contrastive learning are performed in parallel, and a cross-module contrast is further introduced to facilitate information exchange and collaboration. Extensive experiments on real-world datasets across three downstream tasks demonstrate thatHyperRegionoutperforms all baselines, particularly improving check-in prediction by reducing MAE and RMSE by approximately 8.5% and 8.2%, respectively, and increasing$R^{2}$by about 7%.

Multispectral object detection, which combines RGB visible light and thermal infrared spectral information, has broad applications in complex environments and varying illumination conditions. However, existing methods face challenges in processing multispectral data, such as inconspicuous object features in spectral images and significant discrepancies between input modality spaces and output detection spaces. To address these issues, we propose an innovative multispectral object detection method that combines contrastive learning and a new cross-modal feature fusion module. We introduce a mask feature contrastive loss that maximizes the similarity between the box-level mask features and modal features while suppressing background responses, enabling effective representative alignment between the input and output spaces. Additionally, we propose a mask-guided attention fusion module that uses a predicted pseudo mask to guide the fusion of different modal features, enhancing object responses and reducing background noise interference. Our extensive experiments on several challenging multispectral datasets demonstrate that our proposed COFNet achieves state-of-the-art performance.

Underwater images usually exhibit severe color cast, hazy appearance, and/or dark regions because of the complex lighting absorption and scattering in water. How to increase the quality of these degraded underwater images has emerged as a key issue for various underwater application tasks. Recent efforts have been made to deal with single type degradation, however, it is still challenging to deal with multiple degradations that usually coexist in an underwater image with a general network. The degradations in underwater images can be divided into medium-agnostic (hazy or low-light which also encountered in in-air images) and medium-specific (color distortion caused by the specific light attenuation property in water) ones. According to this observation, this article proposes a cascaded multimodule underwater image enhancement (UIE) framework to address the coexisted multiple degradations. In the proposed framework, an in-air image enhancement module and a novel proposed adaptive color channel compensation network (AC3Net) are cascaded, in which the former focuses primarily on solving medium-agnostic degradations and the latter is for handling the medium-specific degradation. This framework has good flexibility by cascading different types of in-air image enhancement networks with AC3Net to achieve various UIE. The effectiveness of the proposed framework has been extensively validated on various degraded underwater images as well as different underwater visual perception tasks.

Fully connected (FC) layers play a significant role in deep neural networks (DNNs) models. Owing to the complexity of its parameters, an FC layer has sufficient capacity to manage high-dimensional tasks, so a large amount of memory and powerful computing capabilities become essential requirements. However, the large number of parameters in an FC layer greatly limits the practical application of this model. To address this problem, we apply matrix optimization to an FC layer. First, an added penalty term properly maintains the sparsity of the imposed weights. Second, a rank constraint is applied to the two components of the factorized weight matrix. Our compression algorithm can effectively reduce the number of required network parameters, which not only reduces the computational complexity of the network but also results in better generalizability on a test dataset. Finally, the effectiveness of the proposed method is verified in two different computer vision task domains. Experiments show that our sparse reduced-rank method achieves a better compression ratio with a lower accuracy loss relative to the competing approaches. The code is available at https://github.com/cheer79/Compress_FC .

Graphs offer a flexible structure for vision tasks, with CNNs and Transformers conditioned as two specific cases of graph structures. In CNNs, the input images are treated as graphs where only neighboring patches are connected, whereas Transformers view images as fully connected graphs. To leverage the potential of graphs in video representation learning, effective graph generation and training methods are crucial. To this end, we propose VideoGNN, which represents the video as a discrete time dynamic graph and learns the dynamic graph efficiently. Given the multitude of frames in videos, we introduce an efficient graph generation module characterized by low complexity and high quality, facilitating the transformation of videos into dynamic graphs. Additionally, we introduce a dual-view graph neural network to capture spatial and temporal information from the generated dynamic graphs. Then, a sequential model is applied to capture the long-term temporal information and generate the final frame embeddings. Experiments demonstrate that VideoGNN can achieve competitive results in terms of graph quality assessment and video downstream tasks. The codes are available at https://github.com/Dodo-D-Caster/VideoGNN .

Methods based on transfer learning have achieved rich research results in the field of intelligent diagnosis of mechanical devices. However, current transfer learning methods typically require the source and target domains to be known in advance, heavily relying on historical data, which fails to meet the requirements of practical applications. Therefore, this study proposes a multisource online transfer learning with joint adaptive distribution selection (MSOTL-JADS) algorithm for real-time diagnosis of online samples. First, during the offline phase, a metric function is designed to extract sub-domain feature information in the multisource domain scenario, facilitating accurate knowledge transfer. Second, the multisource weight distribution parameters obtained based on the distribution distance are dynamically selected for the multisource domains to achieve differentiated transfer in the domain. Third, in the online phase, the pretrained offline model is integrated using online input target samples to construct an online diagnostic model and fine-tuning the weight parameters to match the model accuracy. Finally, several online transfer diagnostic tasks were constructed using two public datasets and one self-constructed dataset. The experimental results demonstrate that the proposed MSOTL-JADS outperforms the comparison methods in terms of performance.

Visual Simultaneous Localization and Mapping (SLAM) is crucial to many applications such as self-driving vehicles and robot tasks. However, it is still challenging for existing visual SLAM approaches to achieve good performance in low-texture or illumination-changing scenes. In recent years, some researchers have turned to edge-based SLAM approaches to deal with the challenging scenes, which are more robust than feature-based and direct SLAM methods. Nevertheless, existing edge-based methods are computationally expensive and inferior than other visual SLAM systems in terms of accuracy. In this study, we propose EdgeSLAM, a novel RGB-D edge-based SLAM approach to deal with challenging scenarios that is efficient, accurate, and robust. EdgeSLAM is built on two innovative modules: efficient edge selection and adaptive robust motion estimation. The edge selection module can efficiently select a small set of edge pixels, which significantly improves the computational efficiency without sacrificing the accuracy. The motion estimation module improves the system's accuracy and robustness by adaptively handling outliers in motion estimation. Extensive experiments were conducted on TUM RGBD, ICL-NUIM and ETH3D datasets, and experimental results show that EdgeSLAM significantly outperforms five state-of-the-art (SOTA) methods in terms of efficiency, accuracy, and robustness, which achieves 29.17% accuracy improvements with a high processing speed of up to 120 FPS and a high positioning success rate of 97.06%.

Aiming at the large differences between tasks in continuous remaining useful life (RUL) prediction and the limited information capturing capability of the existing continuous learning (CL) methods, this article develops a novel multibranch horizontal augmentation network (MBHAN). First, a hierarchical self-attention (HSA) mechanism is proposed to capture the local degradation features and dependencies at different scales and enhance the representation capacity of RUL prediction model. Based on HSA and temporal convolutional network (TCN), a time-frequency fusion TCN (TFFTCN) is designed to mine the hidden degradation information from the time-domain and frequency-domain data. Then, a memory weight constraint (MWC) regularization term is built to control the update of important parameters for pervious tasks during the learning of new task. A horizontal network augmentation rule based on the task similarity and MWC is proposed, including the augmentation of a task branch network for small task difference and the augmentation of a feature extraction backbone network for large task difference. On this basis, the MBHAN is proposed to continuously predict RUL of machinery. Finally, the experimental results on the life-cycle bearing and gear datasets demonstrate that TFFTCN achieve an average accuracy of 93% across both datasets, surpassing the existing prediction methods.

In complex unstructured environments, accurate terrain traversability analysis is a fundamental requirement for the successful execution of any movements of ground robots, especially given that terrain traversability often exhibits anisotropy. However, the difficulty in obtaining multi-directional terrain labels hinders the emergence of end-to-end multi-directional traversability network. This paper introduces a framework for real-time multi-directional traversability maps (MTraMap) generation tailored for unstructured environments. It involves pre-training a uni-directional traversability classifier, termed UniTraT, through self-supervised learning using ground robot travel simulation. Furthermore, it employs Uni-directional to Multi-directional Traversability Distillation (UMTraDistill) to distill a multi-directional traversability network, termed MultiTCNN, which is capable of directly generating MTraMap. We evaluated both networks on our traversability dataset, achieving an 89% accuracy in terrain traversability classification with the UniTraT. Compared to UniTraT, the accuracy of the MultiTCNN distilled via UMTraDistill only decreases by 1.8%, and it can process 10 m × 10 m elevation map at a speed of 74 fps. Field robotics experiments were also conducted and showed that MultiTCNN can generate MTraMap of the surrounding 20 m × 20 m environment at a rate of 9.39 fps, with a slight reduction of 0.61 fps compared to the lidar data publishing rate, and the generated MTraMap can clearly delineate the multi-directional traversability of the surrounding environments.

The quality of a robot’s environmental perception determines whether it can achieve more intelligent applications, such as semantic interaction with humans. SLAM, on the other hand, is one of the crucial capabilities for a robot to perceive its environment. However, when only a monocular image is provided, dynamic objects in the environment significantly impact the accuracy of map construction by the robot, leading to erroneous perception results. To address this issue, we propose a Visual SLAM framework based on BEV perception results, named BE-SLAM. With this framework, we can handle dynamic objects, occlusions, and incompletely observed objects. It can construct a stable static map by strengthening trust in static objects. Considering that object-level semantic maps can enhance a robot’s perception abilities, we also reconstruct static objects in the map and use them to optimize the pose. Through experiments on existing publicly available dataset, we compare BE-SLAM with several existing methods that have shown good performance. The experimental results demonstrate that BE-SLAM performs exceptionally well on high-dynamic sequences and achieves comparable results on static or low-dynamic sequences.

Versatile video coding (VVC) aims to achieve high compression but also issues like varying content/network conditions. Existing rate control (RC) methods struggle to achieve optimal quality under these complex scenarios. This paper proposes a novel RC scheme for VVC based on a linear model. The Lagrange minimization multiplier is introduced under bit budget constraints, allowing optimized bit allocation. RC optimization is formulated as a convex solution, and is derived into the optimal quantization parameter (QP) for RC. Experimental analysis demonstrates the proposed linear model-based RC algorithm performances are better compared to other state-of-the-art methods due to their use of a linear model and optimal QP determination.

With the widespread adoption of virtual reality and 360-degree video, there is a pressing need for objective metrics to assess quality in this immersive panoramic format reliably. However, existing image quality assessment models developed for traditional fixed-viewpoint content do not fully consider the specific perceptual issues involved in 360-degree viewing. This paper proposes a 360-degree image full-reference quality assessment (FR-IQA) methodology based on a multi-channel architecture. The proposed 360-degree FR-IQA method further optimizes and identifies the distorted image quality using two easily obtained useful saliency and depth-aware image features. The convolutional neural network (CNN) is designed for training. Furthermore, the proposed method accounts for predicting user viewing behaviors within 360-degree images, which will further benefit the multi-channel CNN architecture and enable the weighted average pooling of the predicted FR-IQA scores. The performance is evaluated on publicly available databases to demonstrate the advantages brought by the proposed multi-channel model in performance evaluation and cross-database evaluation experiments, where it outperforms other state-of-the-art ones. Moreover, an ablation study exhibits good generalization ability and robustness.

In this paper, a parallel decoder and a word group prediction module are proposed to speed up decoding and improve the effect of captions. The features of the image extracted by the encoder are linearly projected to different word groups, and then a unique relaxed mask matrix is designed to improve the decoding speed and the caption effect. First, since image captioning is composed of many words, sentences can also be broken down into word groups or words according to their syntactic structure, and we achieve this function through constituency parsing. Second, we make full use of the extracted features to predict the size of word groups. Then, a new embedding representing the information of the word is proposed based on word embedding. Finally, with the help of word groups, we design a mask matrix to modify the decoding process so that each step of the model can produce one or more words in parallel. Experiments on public datasets demonstrate that our method can reduce the time complexity while maintaining competitive performance.

Public computer education systems provide students essential opportunities to enhance computer literacy and information skills. However, the widespread adoption of online education technology exposes the field to several critical security risks. Threats, such as malware infections, data breaches, and other network intrusions, are all challenging the security of education systems, posing potential hazards to students’ personal information and even the entire teaching environment. To spur further work into specialized anomaly detection techniques for computer education, this paper presents an anomaly detection framework tailored for network services in computer education environments to safeguard these systems. Specifically, the proposed approach learns from large-scale online educational traffic data to classify the security state into five alert levels, enabling more granular anomaly detection and analysis. To assess their detection performance, deep learning and traditional machine learning algorithms are implemented and compared for multi-class intrusion classification. The results show that the proposed framework provides an effective security solution to bolster the integrity and stability of computer education systems against evolving network threats, enhancing threat intelligence to inform proactive security by detecting and characterizing anomalies through multilevel classification.

Deep learning-based video coding methods have demonstrated superior performance compared to classical video coding standards in recent years. The vast majority of the existing deep video coding (DVC) networks are based on convolutional neural networks (CNNs), and their main drawback is that since CNNs are affected by the size of the receptive field, they cannot effectively handle long-range dependencies and local detail recovery. Therefore, how to better capture and process the overall structure as well as local texture information in the video coding task is the core issue. Notably, the transformer employs a self-attention mechanism that captures dependencies between any two positions in the input sequence without being constrained by distance limitations. This is an effective solution to the problem described above. In this paper, we propose end-to-end transformer-based adaptive video coding (TAVC). First, we compress the motion vector and residuals through a compression network built on the vision transformer (ViT) and design the motion compensation network based on ViT. Second, based on the requirement of video coding to adapt to different resolution inputs, we introduce a position encoding generator (PEG) as adaptive position encoding (APE) to maintain its translation invariance across different resolution video coding tasks. The experiment shows that for multiscale structural similarity index measurement (MS-SSIM) metrics, this method exhibits significant performance gaps compared to conventional engineering codecs, such as [Formula: see text], [Formula: see text], and VTM-15.2. We also achieved a good performance improvement compared to the CNN-based DVC methods. In the case of peak signal-to-noise ratio (PSNR) evaluation metrics, TAVC also achieves good performance.

Object detection plays a key role in helping to enable industrial quality control and safety monitoring. This article introduces a lightweight and efficient transformer-based object detection network called the encoder-free DEtection TRansformer (EF-DETR). This novel architecture enhances the DETR model through a redesigned network structure, leading to improved accuracy in object detection and a more lightweight network. To address the issue of suboptimal object detection accuracy, especially for small objects in the DETR model, we introduce a multiscale feature extractor and a high-efficiency feature fusion module. These components facilitate the direct extraction of fine-grained features, thereby enabling effective object detection. Departing from the use of a high-complexity encoder structure, we explore the utilization of an encoder-free neck structure to reduce the network's computational complexity. In addition, to expedite convergence, denoising training is incorporated into the decoder. This article presents extensive experiments, and the EF-DETR demonstrates strong performance on the MS COCO2017 dataset compared to other popular models.

Aircraft detection holds significant importance in both civilian and military fields, such as air transportation control and battlefield situational awareness. Deep learning-based methods for aircraft detection can obtain promising detection performance with sufficient and labeled samples. However, current public aircraft datasets are mainly from top-down perspective images, lacking multi-perspectives samples, which limits their applicability. Therefore, we construct a multi-perspectives aircraft dataset (MAD), encompassing 10 distinct classes, and making up of 13,205 images and 18,908 instances. Moreover, the imbalanced and similar classes are common issues in aircraft detection. We design an adaptive threshold focal loss (ATFL) function to alleviate the class imbalance through dynamically adjusting the loss weights between different classes. Meanwhile, the dynamic visual center (DVC) module which can effectively capture both local and global information of target is proposed to distinguish the aircraft classes that share similar feature. Finally, we evaluated several state-of-the-art methods on the constructed MAD dataset as baselines for future research.The experimental results verify that the recognition performance of the current state-of-the-art model can be improved after the application of ATFL and DVC. The dataset and code are available athttps://github.com/YangBo0411/aircraft-detection.

Object detection accuracy degrades seriously in visually degraded scenes. A natural solution is to first enhance the degraded image and then perform object detection. However, it is suboptimal and does not necessarily lead to the improvement of object detection due to the separation of the image enhancement and object detection tasks. To solve this problem, we propose an image enhancement guided object detection method, which refines the detection network with an additional enhancement branch in an end-to-end way. Specifically, the enhancement branch and detection branch are organized in a parallel way, and a feature guided module is designed to connect the two branches, which optimizes the shallow feature of the input image in the detection branch to be as consistent as possible with that of the enhanced image. As the enhancement branch is frozen during training, such a design plays a role in using the features of enhanced images to guide the learning of object detection branch, so as to make the learned detection branch being aware of both image quality and object detection. When testing, the enhancement branch and feature guided module are removed, and so no additional computation cost is introduced for detection. Extensive experimental results, on underwater, hazy, and low-light object detection datasets, demonstrate that the proposed method can improve the detection performance of popular detection networks (YOLO v3, Faster R-CNN, DetectoRS) significantly in visually degraded scenes.

Foggy days limit the functionality of outdoor surveillance systems. However, it is still a challenge for existing methods to maintain the uniformity of defogging between image regions with a similar depth of field and large differences in appearance. To address above problem, this article proposes a regional gradient constrained prior (RGCP) for defogging that uses the piecewise smoothing characteristic of the scene structure to achieve accurate estimation and reliable constraint of the transmission. RGCP first derives that when adjacent similar pixels in the fog image are aggregated and spatially divided into regions, clusters of region pixels in RGB space conform to a chi-square distribution. The offset of the confidence boundary of the clusters can be regarded as the initial transmission of each region. RGCP further uses a gradient distribution to distinguish different regional appearances and formulate an interregional constraint function to constrain the overestimation of the transmission in the flat region, thereby maintaining the consistency between the estimated transmission map and the depth map. The experimental results demonstrate that the proposed method can achieve natural defogging performance in terms of various foggy conditions.

RGBT tracking is gaining popularity due to its ability to provide effective tracking results in a variety of weather conditions. However, feature specificity and complementarity have not been fully used in existing models that directly fuse the correlation filtering response, which leads to poor tracker performance. In this article, we propose correlation filters with adaptive modality weight and cross-modality learning (AWCM) ability to solve multimodality tracking tasks. First, we use weighted activation to fuse thermal infrared and visible modalities, and the fusion modality is used as an auxiliary modality to suppress noise and increase the learning ability of shared modal features. Second, we design modal weights through average peak-to-correlation energy coefficients to improve model reliability. Third, we propose consistency in using the fusion modality as an intermediate variable for joint learning consistency, thereby increasing tracker robustness via interactive cross-modal learning. Finally, we use the alternating direction method of multipliers algorithm to produce a closed solution and conduct extensive experiments on the RGBT234, VOT-TIR2019, and GTOT tracking benchmark datasets to demonstrate the superior performance of the proposed AWCM against compared to existing tracking algorithms. The code developed in this study is available at the following website. 1

Multidirectional jumping is commonly found in living creatures and desirable to be integrated into mobile robots for enhanced agility. Existing jumping robots mostly employ complex or cumbersome structures and modular designs to achieve multidirectional jumping. There is a lack of a simple, lightweight, and compact actuator design for multidirectional jumping robots. Here, we present a multidirectional jumping soft robot (MDJSR) driven by a biaxial electrohydraulic actuator (BEHA). The BEHA has a simple structure, i.e., a thin plastic frame-guided film pouch with four pairs of distributed electrodes and enclosed with a dielectric liquid. Inspired by gall midge larvae, the MDJSR exhibits two switchable locomotion strategies, including continuous non-energy-storing jumping to move fast and energy-storing jumping to cross obstacles. Its multidirectional jumping capability was demonstrated in the navigation through a labyrinth with two ways of obstaclecrossing and obstacle-circumventing in different terrain environments. In addition, the robot can be deployed to detect unknown space and collect environmental factors. This work provides an enabling solution to miniature and lightweight multimodal jumping soft robots for various robotic tasks

In this paper, we propose a lightweight multi-scale based attention network (MBAN) for single-image super-resolution (SISR). First, a deep feature transform block (DFTB) is designed for multi-scale feature extraction; this block combines group convolution and improved channel attention (ICA) for performance purposes while remaining sufficiently lightweight. Second, a dual multi-scale attention block (DMAB) is proposed for long-range information interaction; this block employs different window sizes for self-attention (SA) and short connections between different branches to achieve multiscale attention interaction. Finally, our MBAN is constructed by cascaded multi-scale based attention blocks (MBABs) that perform detail restoration; these blocks simultaneously extract multi-scale local features and integrate multi-scale global features with the DFTBs and DMABs. Extensive experiments suggest the superiority of our MBAN over the state-of-the-art (SOTA) lightweight SR methods in terms of both quantitative metrics and visual quality.

Various domain adaptation (DA) methods have been proposed to address distribution discrepancy and knowledge transfer between the source and target domains. However, many DA models focus on matching the marginal distributions of two domains and cannot satisfy fault-diagnosed-task requirements. To enhance the ability of DA, a new DA mechanism, called deep joint distribution alignment (DJDA), is proposed to simultaneously reduce the discrepancy in marginal and conditional distributions between two domains. A new statistical metric that can align the means and covariances of two domains is designed to match the marginal distributions of the source and target domains. To align the class conditional distributions, a Gaussian mixture model is used to obtain the distribution of each category in the target domain. Then, the conditional distributions of the source domain are computed via maximum-likelihood estimation, and information entropy and Wasserstein distance are employed to reduce class conditional distribution discrepancy between the two domains. With joint distribution alignment, DJDA can achieve domain confusion to the highest degree. DJDA is applied to the fault transfer diagnosis of a wind turbine gearbox and cross-bearing with unlabeled target-domain samples. Experimental results verify that DJDA outperforms other typical DA models.

Adaptively reconstruction of the skin surface covering the skeletons of aircrafts, ships, high-speed trains, buildings with steel frames, etc., based on on-site measured point clouds is an important issue in both industry and construction. However, the skeleton has the characteristics of long and narrow shape and variable structure, resulting in a narrow and sparse distribution of measured point cloud on its surface with variable shape in different areas, which brings great challenges to the reconstruction of skin surface. In this article, a method for accurately reconstructing the skin surface covering the skeleton structures based on the arbitrary distributed sparse on-site measured points of the skeleton surface is proposed. At first, a robust B-spline curve fitting method based on the least square principle is proposed to construct the boundary curves of the point cloud. Then, a Coons-B-spline surface fitting method based on the generated boundary curves is proposed to generate an initial skin surface. Next, the initial skin surface is considered as a curved thin plate with stiffness, and a method of surface deformation considering the target points of deformation and the tensile and shear stiffness of the surface is proposed to obtain the surface with high accuracy and good smoothness. To show the feasibility of the proposed method, simulations and experiments are carried out. It is proved that the proposed method can achieve better accuracy while maintaining smoothness.

Accurate traffic prediction is a critical yet challenging task in Intelligent Transportation Systems, benefiting a variety of smart services, e.g., route planning and traffic management. Although extensive efforts have been devoted to this problem, it is still not well solved due to the flexible dependency within traffic data along both spatial and temporal dimensions. In this paper, we explore the flexibility from three aspects, namely the time-varying local spatial dependency, the dynamic temporal dependency, and the global spatial dependency. Then we propose a novel Dual Graph Gated Recurrent Neural Network (DG2RNN) to effectively model all these dependencies and offer flexible (multi-step) predictions for future traffic flow. Specifically, we design a Dual Graph Convolution Module to capture the local spatial dependency from two perspectives, namely road distance and adaptive correlation. To model the dynamic temporal dependency, we firstly develop a Bidirectional Gated Recurrent Layer to capture the forward and backward sequential contexts of historical traffic flow, then combine the derived hidden states with their various contributions learned by a temporal attention mechanism. Besides, we further design a spatial attention mechanism to learn the latent global spatial dependency among all locations to facilitate the prediction. Extensive experiments on three types of real-world traffic datasets demonstrate that our model outperforms state-of-the-arts. Results also show our model has more stable performance for the flexible prediction with varying prediction horizons.

This article proposes a new multisensor fusion fault diagnosis method for gearbox, namely residual gated dynamic sparse network, to improve the multisensor feature learning and fusion ability. Considering that the fault sensitivity of the sensor varies with mounted location and complex transfer path modulation causes information from multisensor redundant, the lightweight channel attention unit is designed to strengthen the feature extraction ability of the network. The developed gated dynamic sparse unit is inserted into the deep architecture to eliminate ineffective components caused by high noise interference. Besides, the loss function is improved with multiple activation criteria to enhance convergence ability. The results of experiments and the engineering application show that the proposed method is more effective than other methods under varying degrees of noise interference.

In this article, facing the gaps that the traditional long short-term memory (LSTM) and convolution neural network (CNN) cannot differentially deal with the input data based on the corresponding trend and stage information in remaining useful life (RUL) prediction, a more accurate and robust RUL prediction model is constructed. First, a temporally multidifferential LSTM (TMLSTM) with the multitrend division unit and multicellular unit is proposed, and a spatially multidifferential CNN (SMCNN) with the multistage division unit and differentiated convolutions is designed. Then, by combining TMLSTM and SMCNN, a spatiotemporally multidifferential deep neural network is developed for predicting the equipment RUL, which enhances the ability of feature extraction from the spatiotemporal perspective by using the multitrend and multistage information. Via several evaluation indexes, the commercial modular aero propulsion system simulation dataset and the wind turbine gearbox bearing dataset are used to validate the superiority of the proposed method over several existing prediction methods.

Extracting robust and discriminative local features from images plays a vital role for long term visual localization, whose challenges are mainly caused by the severe appearance differences between matching images due to the day-night illuminations, seasonal changes, and human activities. Existing solutions resort to jointly learning both keypoints and their descriptors in an end-to-end manner, leveraged on large number of annotations of point correspondence which are harvested from the structure from motion and depth estimation algorithms. While these methods show improved performance over non-deep methods or those two-stage deep methods, i.e., detection and then description, they are still struggled to conquer the problems encountered in long term visual localization. Since the intrinsic semantics are invariant to the local appearance changes, this paper proposes to learn semantic-aware local features in order to improve robustness of local feature matching for long term localization. Based on a state of the art CNN architecture for local feature learning, i.e., ASLFeat, this paper leverages on the semantic information from an off-the-shelf semantic segmentation network to learn semantic-aware feature maps. The learned correspondence-aware feature descriptors and semantic features are then merged to form the final feature descriptors, for which the improved feature matching ability has been observed in experiments. In addition, the learned semantics embedded in the features can be further used to filter out noisy keypoints, leading to additional accuracy improvement and faster matching speed. Experiments on two popular long term visual localization benchmarks (Aachen Day and Night v1.1, Robotcar Seasons) and one challenging indoor benchmark (InLoc) demonstrate encouraging improvements of the localization accuracy over its counterpart and other competitive methods.

Domain adaption models are widely applied to fault transfer diagnosis. However, the traditional domain adaption models can output only one high-dimensional transfer feature (TF); thus, it is difficult to capture domain-invariant information. Besides, using only one fully connected top classifier probably causes overfitting. Considering these two problems, in this article, we propose a multiscale transfer voting mechanism (MSTVM) to improve the classical domain adaption models and it can be universally applicable to any one of most domain adaption models. MSTVM consists of two substrategies: multiscale transfer mechanism (MSTM) and multiple transfer voting mechanisms (MTVM). The MSTM block includes several branches with multiscale convolutional and pooling operations, and it can output several multiscale TFs to strengthen the domain confusion. The MTVM block consists of multiple top classifiers and a plurality voting operation; thus, MTVM can effectively avoid overfitting and improve generalization ability. MSTVM has the advantages of MSTM and MTVM. Via two transfer diagnosis experiments, the advantage of MSTVM for improving various domain adaption models is verified.

This article reports an automated optically-induced dielectrophoresis (ODEP) system for characterizing the specific membrane capacitance (SMC) of individual cells. The simulation of cell motion is conducted to analyze the electrokinetic forces acting on the cell. A self-developed visual tracking algorithm for multicells is used to realize an automated process for determining the frequency-sweeping range, crossover frequencies, and cell radii. The SMC values of malignant bladder cancer cells (T24 and RT4) and normal urothelial cells (SV-HUC-1) were quantified using the automated system, demonstrating that the system has a measurement speed of ~1 cell/s, an accuracy of 1 kHz for the crossover frequency determination, and an accuracy of 0.2 μm for the cell radius measurement.

Driven by the emerging advanced information and communication technologies, e.g., artificial intelligence, 5G wireless communications, big data analytics, etc., industrial Internet serves as a key enabling technology to realize intelligent manufacturing, and has been attracting considerable attentions from academia and industry. However, the traditional industrial networks can hardly satisfy the quality of service (QoS) requirements for some mission-critical industrial applications (e.g., fault detection, advanced control, remote monitoring, predictive maintenance, etc.) due to network heterogeneity, traffic congestion, dynamic end-to-end latency, reliability issues, and so on. The emerging software-defined networking (SDN) has been considered as a promising architecture to improve the QoS of industrial applications by flexibly decoupling the control and data planes to control the network behaviours centrally. Owing to economy and policy considerations, a realistic solution is to incrementally deploy SDN in industrial networks instead of fully replacing traditional industrial routers with SDN-enabled switches. In this article, we consider a hybrid Industrial network consisting of conventional routers (e.g., running OSPF protocol) and SDN-enabled switches (e.g., running OpenFlow protocol), and propose an intelligent QoS-aware forwarding strategy to improve the QoS of industrial applications, by utilizing a single path minimum cost forwarding scheme and a K-path partition algorithm for multipath forwarding. Simulation results demonstrate that the proposed scheme not only guarantees the QoS requirements of industrial services, but also efficiently utilizes bandwidth resources by balancing traffic load in the SDN/OSPF hybrid industrial Internet.

This paper studies the compound learning control of disturbed uncertain strict-feedback systems. The design is using the dynamic surface control equipped with a novel learning scheme. This paper integrates the recently developed online recorded data-based neural learning with the nonlinear disturbance observer (DOB) to achieve good "understanding" of the system uncertainty including unknown dynamics and time-varying disturbance. With the proposed method to show how the neural networks and DOB are cooperating with each other, one indicator is constructed and included into the update law. The closed-loop system stability analysis is rigorously presented. Different kinds of disturbances are considered in a third-order system as simulation examples and the results confirm that the proposed method achieves higher tracking accuracy while the compound estimation is much more precise. The design is applied to the flexible hypersonic flight dynamics and a better tracking performance is obtained.

The unmanned surface vehicles (USVs) have become a major trend in the construction of naval equipment and its flexibility and intelligence making it widely used in real-scenes. For cooperative defense with multiple USVs to intercept intruders, it is proposed that planning the path with obstacle avoidance and protecting the target by task allocation actions. The particle swarm optimization based on probe mechanism (PM-PSO) is proposed for pathing planning with obstacle avoidance. With the consideration of the constraints of different defense schemes such as the path cost, the interception loss, the defense income and so on, it is proposed that the dispersed particle swarm optimization based on genetic algorithm (GA-PSO) for the interception task allocation. Furthermore, the fitness function is proposed to evaluate the feasibility of the interception path and the quality of the allocation scheme. Extensive simulation experiments are conducted and demonstrated the effectiveness, rationality and superiority of the proposed methods.

Caching content with fog computing at the edge nodes has been a promising alternative to mitigate burdens of backbone networks and improve mobile users' quality of experience in mobile social networks (MSNs). However, as edge node may be vulnerable due to the attacks from malicious users, the design of secure caching schemes for the fog/edge enabled MSNs becomes a new challenge. In this paper, to tackle the above problem, we propose a secure caching scheme for disaster backup in MSNs with fog computing. Specifically, to protect the privacy, a partitioning and scrambling method is first designed to encrypt the contents. Then, the encrypted contents are replicated to multiple replicates, where these replicates are delivered and stored in different servers. Based on the recovery time objective and content delivery latency, an auction game model is developed to determine the optimal servers, where both edge nodes and cloud servers can obtain the maximum utilities. Extensive simulations are conducted to show the effectiveness and reliability of the proposed scheme.

Measuring cell motility and morphology is important for revealing their functional characteristics. This paper presents automation techniques that enable automated, non-invasive measurement of motility and morphology parameters of single sperm. Compared to the status quo of qualitative estimation of single sperm's motility and morphology manually, the automation techniques provide quantitative data for embryologists to select a single sperm for intracytoplasmic sperm injection. An adapted joint probabilistic data association filter was used for multi-sperm tracking and tackled challenges of identifying sperms that intersect or have small spatial distances. Since the standard differential interference contrast (DIC) imaging method has side illumination effect which causes inherent inhomogeneous image intensity and poses difficulties for accurate sperm morphology measurement, we integrated total variation norm into the quadratic cost function method, which together effectively removed inhomogeneous image intensity and retained sperm's subcellular structures after DIC image reconstruction. In order to relocate the same sperm of interest identified under low magnification after switching to high magnification, coordinate transformation was conducted to handle the changes in the field of view caused by magnification switch. The sperm's position after magnification switch was accurately predicted by accounting for the sperm's swimming motion during magnification switch. Experimental results demonstrated an accuracy of 95.6% in sperm motility measurement and an error <10% in morphology measurement.

The importance of mobile robots for the nuclear disaster response has been realized after Fukushima Dai-ichi nuclear power plant accident. In this paper, we propose a tracked robot for rescue and search in nuclear environment. A gamma-ray irradiation test of the robot's control system is conducted, in order to evaluate the performance of the robot in nuclear environment. The parallel test method is described and the test result is reported.

To improve the mobility of locomotive devices on loose, sandy terrain, protrusions or convex patterns called lugs (i.e., grousers) are attached to the surface of a locomotive modulus. Following our previous study, in which the effects of angular speed, lug sinkage length, and soil cumulative deformation on lug-soil interaction forces during the fixed-axis rotational motion were experimentally confirmed, this study proposed an approximation equation to formulize the relationship among the normal force, lug sinkage length, and lug rotational angle. Moreover, the measured tangential force is compared with values calculated from a conventional tangential force model for discussing its accuracy of predicting the tangential force. Conclusions from this study present the fundamental principles for understanding the lug-soil interaction mechanics for a lug that is performing arbitrary planar motion on sandy terrain.

The planar rimless wheel (PRW) is a classic and simple passive dynamic mechanism to simulate biped walking, different simplified PRW models have respective descriptions and limited applications. This paper focus on constructing the general PRW model, and analyzing the intrinsic and mathematical relation between different PRW models. Based that, the limit cycle of symmetric PRW and asymmetric PRW are proposed separately. Moreover, the stability in universal domain are investigated in detail. Furthermore, simulation and experiment results show that the 2-period limit cycle motion of the asymmetric PRW is more effective, flexible and self-adaptive compared with regular rimless wheel, and more actual applications would be achieved by adopting the general model.

Based on the multi-agents system control theory and technology, this paper presents the cooperative defense process of multiple unmanned surface vehicles (USVs) operating in complicated sea environment, and explains the quantification of the battle effectiveness, cooperative strategy, task allocation and finally describes in detail the cooperative strategies on random graph. Then we point out the problems in the current cooperative defense process and the future development direction. The cooperative defense research of USVs in the sea environment has a great significance to the effective promotion of social and military efficiency.

Unmanned combat system is one of the development trend of modern weapons and equipment and has applied to military affairs. The major goal of the Unmanned Surface Vehicles (USVs in short) is to destroy protected targets in the shortest time. This paper originates from biology, putting forward a new attack strategy—The USV combat Based on Wolves Attack with Weight. Namely, using the characteristics of the wolves attack to study the process of attacking. It also discusses the attack measures from weights, velocity and firepower of USVs through weight distribution and summarizes the advantages of wolves attack in USV combat.

Based on the multi-agents system control theory and technology, this paper presents the construction of cooperative defense system of multiple unmanned surface vehicles (USVs) operating in complicated sea environment, develops the mathematical formula describing the trajectory equation when the USV intercepts the intruder, and explains the proposed coordination control method used in the corresponding defense system. Then we point out the future development requirement of the cooperative defense system. The cooperative defense system research of USVs in the sea environment has a great significance to the effective promotion of social and military efficiency, and it is the basis of the implement about cooperative strategies.

To gain high mobility on challenging terrains, a mobile robot based on eccentric paddle mechanism (ePaddle) with locomotion versatility has been proposed. In this paper, a paddle-aided stair-climbing motion is presented for this ePaddle-based robot. The robot can roll on the stair as a traditional wheeled vehicle and also can climb up the stair under the help of its paddles. Robot-stair interaction modes are presented and typical feasible postures of the robot in stair-climbing are discussed. Frictional requirements for the robot to hold a desired posture are evaluated by modelling statics of the robot. Analyzed results reveal that two critical scenarios in wheeled mode occur when the front-wheel is at the bottom of the riser, and when the rear-wheel is at the top of the riser, respectively. In contrast, frictional requirements of the paddle-aided stair-climbing postures confirm that the robot can climb up the stair with all feasible postures by touching the stair with the paddle, which verifies the effectiveness of the proposed paddle-aided stair-climbing.

An eccentric paddle locomotion mechanism (ePaddle) was proposed to enhance the mobility of amphibious robots for multi-terrains tasks. There are several feasible terrestrial and aquatic gaits for an ePaddle-based robot. In this paper, we present the method for modelling thrust in one of the aquatic gaits, namely the oscillating-paddling gait, for an ePaddle mechanism. The conception of the oscillating-paddling gait is introduced firstly and followed by the thrust model. In order to verify the proposed model, a thrust measuring facility is built. A series of experiments are carried out with this facility. From the results, we verify the thrust model for the oscillating-paddling gait. Furthermore, we characterize how the amplitude and direction of the generated net thrust force relate with the amplitude, period and oscillation ratio of the oscillating-paddling gait.

A human-robot cooperative approach to reliable planning and execution is presented. The human-robot system consists of three components: human user, wheelchair robot and the noninvasive brain-computer interface (BCI) which can represent limit types of user's intention patterns based on EEG signals, with insufficient decoding accuracy and time delay. To achieve efficient navigation and positioning under condition of decoding uncertainties of the BCI, three cooperative modes are proposed for specific situations based on trade-off of robot's autonomy and user's flexibility. The coding protocol in each mode is elucidated in detail, and strategies of mode switching are developed. To achieve continuous and smooth motion, a look-ahead visual feedback is applied, so that the user can adjust the intention and/or actively correct extraction error of the BCI before the robot reaches current path node, and consequently, reliable planning and execution are ensured. The effectiveness of the strategies is evaluated by simulations.

To enhance the mobility of amphibious robots for multi-terrains tasks, we have proposed an eccentric paddle locomotion mechanism (ePaddle) with several feasible terrestrial and aquatic gaits. In this paper, we present a rigid paddle model for predicting the thrust force in one of the aquatic gaits, namely the rotational paddling gait. Thrust forces calculated by this model demonstrate the idea that by relocating the paddle shaft eccentrically from its wheel center, the rotating paddles will generate vectored thrust force for swimming. The paddling motion and the validity of the rigid paddle model are verified by experiments in a water tank.