The lithology identification of cuttings based on mineral element content data plays an important role in oil and gas exploration. Currently, the method for acquiring the element content of cuttings is to air-dry cuttings obtained in the mud logging process, and then use x-ray fluorescence (XRF) technology to obtain the types and contents of the main elements in the cuttings. In this paper, a method for identifying the lithology of cuttings based on channel attention mechanism is proposed for the mineral element content data of cuttings obtained by XRF technology. Specifically, the existing one-dimensional data composed of mineral elements are input into the network model. The channels are first expanded to introduce more features. Then, the features obtained by the multi-channels are fused to obtain features that are more conducive to the identification of cuttings lithology. To avoid introducing too much noise during channel change, the SE module is improved and applied to the one-dimensional convolutional neural network in this paper. Additionally, the features of different channels are weighted by autonomous learning, ensuring that the features related to the current task have a higher contribution to the network. By reducing the influence of invalid features caused by changing channels, this method safeguards the reliability of the features used for debris classification. The experiment results show that the cuttings recognition algorithm proposed in this paper has higher accuracy than the comparison algorithm.
Deep convolutional neural networks have led to significant improvement over the previous salient object detection systems. The existing deep models are trained end-to-end and predicts salient objects by calculating pixel values, which results saliency maps are typically blurry. Our Pixel-wise Binary Classification Network (PBCN) focuses on binary classification in pixel level for salient object detection: saliency and background. In order to increase the resolution of output feature maps and get denser feature maps, Hybrid dilation convolution (HDC) is employed into PBCN. Then, Hybrid Dilation Spatial Pyramid Pooling (HDSPP) is proposed to extract denser multi-scale image representations. In HDSPP, it contains one 1×1 convolution and several dilated convolutions, with different rates and the output feature maps of the convolutions will be fused. Finally, softmax is introduced to implement the binary classification instead of sigmoid. Experiment, on four datasets, show that PBCN significantly improves the state-of-the-art.
Correspondence detection is a vital step in point cloud registration and it can help getting a reliable initial alignment. In this paper, we put forward an advanced point feature-based graph matching algorithm to solve the initial alignment problem of rigid 3D point cloud registration with partial overlap. Specifically, Fast Point Feature Histograms are used to determine the initial possible correspondences firstly. Next, a new objective function is provided to make the graph matching more suitable for partially overlapping point cloud. The objective function is optimized by the simulated annealing algorithm for final group of correct correspondences. Finally, we present a novel set partitioning method which can transform the NP-hard optimization problem into a O(n3)-solvable one. Experiments on the Stanford and UWA public data sets indicates that our method can obtain better result in terms of both accuracy and time cost compared with other point cloud registration methods.
KEYWORDS: Motion models, 3D modeling, Motion estimation, Rigid registration, Data modeling, Lithium, Distance measurement, 3D scanning, Fluctuations and noise, Sensors
Three-dimensional modeling of scene or object requires registration of multiple range scans, which are obtained by range sensor from different viewpoints. An approach is proposed for scaling registration of multiview range scans via motion averaging. First, it presents a method to estimate overlap percentages of all scan pairs involved in multiview registration. Then, a variant of iterative closest point algorithm is presented to calculate relative motions (scaling transformations) for these scan pairs, which contain high overlap percentages. Subsequently, the proposed motion averaging algorithm can transform these relative motions into global motions of multiview registration. In addition, it also introduces the parallel computation to increase the efficiency of multiview registration. Furthermore, it presents the error criterion for accuracy evaluation of multiview registration result, which can make it easy to compare results of different multiview registration approaches. Experimental results carried out with public available datasets demonstrate its superiority over related approaches.
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