In this paper, a method of 3D concrete printing is used to find the optimal path of the nozzle running path. We propose a path optimization algorithm based on graph theory to solve two key problems in 3D concrete printing. The partitioning algorithm based on graph theory was adopted to improve the forming quality of concrete components, and ant colony algorithm was used to reduce printing time. The method was evaluated with 3D concrete printing experiments after introducing the process of implementing the partition algorithm and ant colony algorithm. The experiment results show a significant reduction in the idle strokes and the nozzle head-up times of the running path planned by the method in this paper. This has a direct impact on shortening the printing time and improving the forming quality. Compared with the other three conventional algorithms, the idle strokes of the nozzle planned by the method in this paper are reduced by 18.94%, 37.88%, and 66.67%, and the nozzle head-up times are reduced by 1.59%, 2.15%, and 8.69%. It provides a practical reference for the path optimization of 3D concrete printing.

Nowadays, remote sensing (RS) change detection (CD) plays an important role in Earth observation applications. Recently, the value of cross-modal CD has gradually emerged because of the complementary features in content. However, existing domain adaption-based methods are generally limited to the optical domain and suffer from imbalanced information between modalities. In this article, a novel CD method based on dual image translation and dual contrastive learning (C3D) is proposed for heterogeneous RS images, including a translation module and a CD module. First, the translation module aims to learn a comparable representation between the different domains through a C3D structure based on feature samples, which can break the consistency constraint and better solve the imbalanced information. Then the similarity metric of patches is compared by contextual features at different scales in the CD module to achieve a more accurate classification of changed and unchanged pixels. The C3D is compared with state-of-the-art methods and validated by the basic experimental results on five datasets. In addition, further experiments on the translation module were also performed to explore the effectiveness of contrastive learning in the CD task.

Smoke image segmentation plays a vital role in the accuracy of target extraction. In order to improve the performance of the traditional fire image segmentation algorithm, a new smoke segmentation method based on improved double truncation distance self-adaptive density peak clustering(TSDPC) is proposed. Firstly, the smoke image is over-segmented into multiple superpixels to reduce the time cost, and the local density of sample points corresponding to each superpixel is redefined by location information and color space information. Secondly, TSDPC combines the information entropy theory to find the optimal double truncation distance. Finally, TSDPC uses trigonometric functions to determine clustering centers in the decision diagram, which can solve the problem of over-segmentation. Then, it assigns labels to the remain sample points for obtaining the clustering result. Compared with other algorithms, the accuracy of TSDPC is increased by 5.68% on average, and the $F_1$ value is increased by 6.69% on average, which shows its high accuracy and effectiveness. In public dataset, TSDPC has also demonstrated its effectiveness.

Wearable human activity recognition (HAR) systems have received much attention due to their robustness to environments and capability of privacy protection in human-centric applications such as health-care services and intelligent surveillance. Recently, researchers have made different attempts for accurate HAR model construction, while, from the perspectives of user needs and sensory data, there still exist two important issues in the current models. On one hand, they are always difficult to obtain interpretable recognition results for users in practical scenarios. On the other hand, there may exist uncertainties in sensor measurements such as the data imprecision caused by sensor displacement or movements with respect to the body, which also brings difficulty for the derivation of accurate recognition results. Motivated by the above considerations, a multi-framework evidential association rule fusion-based activity recognition method (Multi-EARC) is proposed by combining the knowledge characterizing the uncertain association relationships between activity features and classes under different recognition frameworks. To do so, the procedures of confusion matrix-based coarse class partition and adaptive threshold-based evidential association rule mining are successively proposed for generating the rules in the coarse-grained framework. After that, a rule set optimization strategy and a belief reasoning mechanism are then presented for obtaining accurate and interpretable recognition result for each unknown input instance. In addition, a self-collected dataset is provided for illustrating the feasibility and university of our proposal in real-world applications. Experiments on public UCI Smartphone and mHealth datasets demonstrate the superiority of our proposal on recognition accuracy and interpretability.

We present a novel method to select waypoints from aerial images of candidate flying regions via matching suitability analysis, which is based on visual attention mechanism and feature attribute classification. At first, visual attention mechanism is used to get the saliency map of the initial image by low-rank recovery and sparse coding. The salient regions are selected to be as preparatory results with threshold constraint and nonmaxima suppression. Then we use support vector machine (SVM) to divide the preparatory results into two classes for suitable or unsuitable waypoints based on their feature attributes, which can be represented by two edge-based descriptors and two correlation-based descriptors. The experimental results show that the proposed method is valid and effective.

Sparse representation (SR) model has been applied to hyperspectral image (HSI) classification based on the observation that any spectral pixel could be approximately represented by a linear combination of several common pixels, but its discriminative ability is not deeply explored due to an insufficient description on spatial-spectral information and less emphasis on the dictionary structure. In this letter, we propose a new algorithm of HSI classification based on discriminative eigenpixels-based dictionary learning. Instead of using neighbor pixels directly, we define a new homogeneous region for each pixel, respectively, to exploit more spatial-spectral information and extract eigenpixels from homogeneous regions to preserve the essentials for each class. For the representation-based model, a discriminative eigenpixels-based dictionary is learned in homogeneous regions, where the locality of pixels is exploited to enhance the discriminative ability. Finally, we code a homogeneous region associated with a query pixel on the learned dictionary to determine its label by use of both the nearest neighbor (NN) classifier and majority voting. Experiments are conducted on four HSIs to demonstrate the effectiveness of the proposed method.