sEMG(surface electromyography) signals have been widely used in rehabilitation medicine in the past decades because of their non-invasive, convenient and informative features, especially in human action recognition, which has developed rapidly. However, the research on sparse EMG in multi-view fusion has made less progress compared to high-density EMG signals, and for the problem of how to enrich sparse EMG feature information, a method that can effectively reduce the information loss of feature signals in the channel dimension is needed. In this article, a novel IMSE (Inception-MaxPooling-Squeeze- Excitation) network module is proposed to reduce the loss of feature information during deep learning. Then, multiple feature encoders are constructed to enrich the information of sparse sEMG feature maps based on the multi-core parallel processing method in multi-view fusion networks, while SwT (Swin Transformer) is used as the classification backbone network. By comparing the feature fusion effects of different decision layers of the multi-view fusion network, it is experimentally obtained that the fusion of decision layers can better improve the classification performance of the network. In NinaPro DB1, the proposed network achieves 93.96% average accuracy in gesture action classification with the feature maps obtained in 300ms time window, and the maximum variation range of action recognition rate of individuals is less than 11.2%. The results show that the proposed framework of multi-view learning plays a good role in reducing individuality differences and augmenting channel feature information, which provides a certain reference for non-dense biosignal pattern recognition.

Autonomous driving has gradually become a research hotspot in recent years. Visual Simultaneous Localization and Mapping (SLAM) technology can help unmanned vehicles accurately explore the environment at a lower cost, and the readability of the map can be improved by integrating target detection algorithms. However, the location and 3D shape of the object in the map were not obtained. The method of RGBD-SLAM based on object detection with two-stream YOLOv4-MobileNetv3 convolutional neural network is proposed in this paper. RGBD SLAM algorithm and target detection algorithm are combined to build an algorithm model that can generate the global sparse map and build target dense map quickly. The two-stream network is integrated to obtain 2D information about the target, and further combined with the camera pose after the front-end key frame detection of the SLAM algorithm in this paper, and the dense 3D point cloud of the target and the center point position of the object is obtained. Then, the sparse point cloud of the SLAM system and the dense point cloud of the target can be obtained. The experimental results show that the number of point clouds decreases by about 50% and the time for mapping accounts for about 60% of the global dense mapping time. The method of this paper can efficiently decrease the computational space and improve the speed of semantic mapping, which verifies its feasibility and superiority. It can be used to achieve large-area mapping and the ability to update maps during autonomous driving.

Summary As the development of deep learning and the continuous improvement of computing power, as well as the needs of social production, target detection has become a research hotspot in recent years. However, target detection algorithm has the problem that it is more sensitive to large targets and does not consider the feature‐feature interrelationship, which leads to a high false detection or missed detection rate of small targets. An small target detection method (C‐SSD) based on improved SSD is proposed, that replaces the backbone network VGG‐16 of the SSD network with the improved dense convolution network (C‐DenseNet) network to achieves further feature fusion through fast connections between dense blocks. The Introduction of residuals in the prediction layer and DIoU‐NMS further improves the detection accuracy. Experimental results demonstrate that C‐SSD outperforms other networks at three different image scales and achieves the best performance of 83. A 8% accuracy on the PASCAL VOC2007 test set, proving the effectiveness of the algorithm. C‐SSD achieves a better balance of speed and accuracy, showing excellent performance in rapid detection of small targets.

Summary The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down‐sampling of low‐level features and up‐sampling of deconvolution of high‐level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small‐scale target.

Summary Target localization in unknown environment is one of the development directions of mobile robots. Simultaneous localization and mapping (SLAM) can be used to build maps in unknown environments, but it has the problem of poor readability and interactivity. In this article, target detection and SLAM are combined to search and locate the target by using rich RGBD images information. The determined position in the global map is conducive to the follow‐up operation of the target by mobile robots. By establishing a local dense point cloud map of the target object, the current state of the target object is directly displayed, the readability of the map is improved, and the disadvantages of difficult understanding of the global sparse map and slow construction of the global dense map are avoided. A target localization algorithm under the framework of yolov4 is designed to apply in the process of SLAM global mapping. Our works are helpful for obtaining positions of objects in three‐dimensional space. The experimental results show that the time‐consuming of this method in dense mapping is reduced by 50%–70%, and the number of point clouds is also reduced by 60%–70%.

Abstract The manipulator workspace is an essential element in the field of manipulator research and is of great significance for manipulator motion planning. However, little research has been conducted on dividing the manipulator workspace into working subspaces. No precise division method has been proposed; the inverse kinematics of multiple solutions in manipulator trajectory planning may also cause abrupt joint changes, thus affecting the planned trajectory. The article proposes a working subspace division method for all ball‐wrist 6DOF(degree‐of‐freedom) manipulators that satisfy the Piper criterion to address the above problems. The kinematic model of the manipulator is established, and the Jacobi matrix of the manipulator is obtained. The space of joints of the manipulator is divided into unique domains containing only single inverse kinematic solutions by means of singular trajectory lines when the determinant of the Jacobi matrix is zero; The solution from the joint space to the workspace is achieved by a nonlinear mapping, which completes the partitioning of the work subspace, and each work subspace contains only unique inverse kinematic solutions. When trajectory planning is carried out from the independent area of a single workspace to the overlapping area of multiple workspaces, selecting the inverse kinematic solution in a single working subspace can effectively avoid abrupt changes in the joints of the manipulator and trajectory misalignment caused by numerous inverse solution selection problems and make the planned trajectory smooth and consistent with the operational requirements of each scene.

Summary Recently, most deep convolutional neural networks used for image super‐resolution have achieved impressive performance on ideal datasets. However, these methods always fail in real‐world super‐resolution, and the results are blurred and structurally deformed. In this paper, a multiscale generative adversarial network (MGAN) is proposed to alleviate these issues. The model's multiscale loss function can effectively reduce the solution space and obtain the best features to reconstruct the image. The degraded framework based on kernel estimation and noise injection is mainly applied to obtain LR images that share the same domain with real‐world pictures. Moreover, the gradient branch is presented to provide other structural priors for SR processing. Simultaneously, to obtain better visual effects, LPIPS is used for perceptual losses instead of Visual Geometry Group (VGG). The competitive results show that our MGAN model outperforms the state‐of‐the‐art methods, resulting in lower noise and better visual quality, and reflects the superiority in image structure restoration.

Abstract Under the background of economic globalization, enterprises face with more severe and uncertain environmental dynamism, and its lean production system redesign strategy is more critical. Firstly, given the dynamic environment that enterprises are facing, the redesign of the lean production system based on the environmental dynamism is proposed. Secondly, environmental dynamism is divided into two dimensions: market dynamics and technology dynamics, which is calculated by the objective method. Thirdly, by establishing the redesigned model of the lean production system based on environmental dynamism, the relationship between environmental dynamism and lean production level is analyzed. Fourth, the data of 251 listed companies from different industries in 2014 to 2017 were analyzed to verify the specific impact of environmental dynamism on the enterprise's lean production level. It was found that the relationship between environmental dynamism and the enterprise lean production level is presented as “U,” “S,” and other more complex relationships. At last, some suggestions are put forward to optimal the redesign of lean production systems under different environmental dynamism.

Vision-based gesture recognition accords with natural communication habits of human and can carry out long-distance and non-contact interactions. So it has become a hot direction in human-computer interaction research whose recognition effect largely depends on the performance of image preprocessing and recognition algorithms. In this paper, a gesture recognition method using color image and depth image combined is designed. For the influence of the angle on the same gesture, the skeleton algorithm is optimized based on the layer-by-layer stripping concept. The fast refinement algorithm improves the process of repeated scanning, extracts the key node information in the skeleton map of the hand, and establishes the spatial axis of the hand to determine the gesture direction. The gesture recognition experiment was performed based on convolutional neural network. The results showed the recognition accuracy rate was 96.01%, and the robustness and accuracy of the proposed recognition method were verified.

Aiming at the phenomenon of low lean level of Chinese enterprise and the over lean level of foreign enterprise, it is a significant to build an evaluation tool of the enterprise lean degree to guide the sustainablility of the enterprises' lean improvement. Combined with the current research results of lean, a design scheme of knowledge base system based on lean evaluation model with 5 layers structure is put forward. With the existing model representation method, a method of model knowledge representation are combined with the object-oriented and framework, and which makes the model knowledgeable. An UML technology is used to modelling the cases of the system, and the dynamic process and static class of the system are studied. Finally, a simulation example is given to verify the effectiveness of the system.