The three-dimensional (3D) size and morphology of high-temperature metal components need to be measured in real time during manufacturing processes, such as forging and rolling. Since the surface temperature of a metal component is very high during the forming and manufacturing process, manually measuring the size of a metal component at a close distance is difficult; hence, a non-contact measurement technology is required to complete the measurement. Recently, machine vision technology has been developed, which is a non-contact measurement technology that only needs to capture multiple images of a measured object to obtain the 3D size and morphology information, and this technology can be used in some extreme conditions. Machine vision technology has been widely used in industrial, agricultural, military and other fields, especially fields involving various high-temperature metal components. This paper provides a comprehensive review of the application of machine vision technology in measuring the 3D size and morphology of high-temperature metal components. Furthermore, according to the principle and method of measuring equipment structures, this review highlights two aspects in detail: laser scanning measurement and multi-view stereo vision technology. Special attention is paid to each method through comparisons and analyses to provide essential technical references for subsequent researchers.

Definition of color edge was given, characteristics of color edge was expounded. Defined the direction information measure (DIM) used in the HSV color space, a new color edge detection approach based on the DIM was proposed. The defined DIM focused on the direction property of edge and the filter scale's effect on edge detection. Hue edge images, chromaticity edge images, saturation edge images, luminance edge images as well as combinatorial edge images which combined the hue, saturation and luminance edges could be got respectively by their respective weight. The results of this algorithm were compared against the Sobel edge detector used in the HSL color space. The experiments carried out show that more color edge information is observed and more noise is suppressed by the proposed method in this paper

In recent years, the problem of $k$ -core has attracted wide-spread research attention due to the popularity of the graph-related applications, such as social network analysis, community detection, and collaboration networks. To efficiently support group-based activity planning, the organizers need to guarantee the size and closeness of the group. Current applications of the $k$ -core only support searching for the maximum $k$ -core group or adding the minimum number of edges to obtain the $k$ -core. However, no research has focused on the problem of enlarging the $k$ -core. In reality, when new tasks arrive, a work team needs to not only recruit new team members based on the requirements but also guarantee sufficient closeness among the members for good cooperation. In this paper, we first formalize a new query, a social group enlarging query with size constraints (SGEQ), which aims to find $n$ users to enlarge a subgraph from a $k$ -core of size $m$ to a ( $k+\Delta $ )-core of size ( $m+n$ ) by inserting the minimum number of edges. We prove that the SGEQ problem is NP-hard. To solve this problem, we first propose a novel algorithm, namely, the maximum connection edges algorithm (MCEA), which searches for the inserted vertex that has the most edges with an induced subgraph every time. Then, we develop an optimizing algorithm, namely, the maximum contribution degree algorithm (MCDA), which on average adds a number of edges in the expanded query less than that obtained by the MCEA. Finally, we conduct extensive experiments on two real-world datasets, and the results demonstrate the efficiency of the proposed algorithms.

Before the graph neural network session recommendation system in order to ensure that the model can not smooth, and ignore the project long distance dependence, and most of the recommended method ignore the project sequence relationship, usually the session in the last project as the user's final interest, and lead to the session understanding is not comprehensive. In view of the above problems, this paper proposes a graph neural network recommendation method with sequence network. The main idea of this method is to regard the session as a graph structure, in which the nodes represent the items that the user interacts with, and the edges represent the interaction between the user and these items, such as click, purchase, etc. Then, the graph neural network can learn the embedding vector of each behavior in the graph. Comparing these behavioral vectors with the corresponding item vectors shows the user's interest in different items in the current session, and a self-attention mechanism is introduced into the graph neural network to distinguish long-distance dependencies in the learning sessions without oversmoothing. The sequence network can then be used to introduce bidirectional gating recurrent units and soft attention mechanisms when the vector representation of the item is fused into the vector representation of the session. In addition, combining contrast learning techniques to improve the expression of long tail items and indirectly improve the effect of the session recommendation model. The experimental results show that the recommendation algorithm of adding sequence network can reduce the complexity and improve the performance.