Owing to the availability of a wide range of emotion recognition applications in our lives, such as for mental status calculation, the demand for high-performance emotion recognition approaches remains uncertain. Nevertheless, the wearing of facial masks has been indispensable during the COVID-19 pandemic. In this study, we propose a graph-based emotion recognition method that adopts landmarks on the upper part of the face. Based on the proposed approach, several pre-processing steps were applied. After pre-processing, facial expression features need to be extracted from facial key points. The main steps of emotion recognition on masked faces include face detection by using Haar–Cascade, landmark implementation through a media-pipe face mesh model, and model training on seven emotional classes. The FER-2013 dataset was used for model training. An emotion detection model was developed for non-masked faces. Thereafter, landmarks were applied to the upper part of the face. After the detection of faces and landmark locations were extracted, we captured coordinates of emotional class landmarks and exported to a comma-separated values (csv) file. After that, model weights were transferred to the emotional classes. Finally, a landmark-based emotion recognition model for the upper facial parts was tested both on images and in real time using a web camera application. The results showed that the proposed model achieved an overall accuracy of 91.2% for seven emotional classes in the case of an image application. Image based emotion detection of the proposed model accuracy showed relatively higher results than the real-time emotion detection.

/pdf/development-of-real-time-landmark-based-emotion-recognition-1pctc9af.pdf

Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces

The application of Additive Manufacturing (AM) within industrial settings brings new challenges to operational research. This paper provides in-depth analyses of nesting and scheduling problems in the context of AM. Using bibliometric and systematic review methods, we map the progression of knowledge in this territory and provide a detailed characterization of the existing mathematical models and approaches. Our findings indicate the fundamental decisions that predominate in these models and reveal the relationships between the critical studies, which form the backbone of the research. While the nesting and scheduling problem can be categorized into six types of decision, we have also found other emerging and interrelated optimization problems associated with nesting and scheduling decisions. To provide further details, an ample number of models are systematized in a reference framework for modeling nesting and scheduling in AM. Finally, pressing future research avenues that have emerged from our analysis and that need to be addressed to further advance the field are presented. 

Nesting and scheduling optimization of additive manufacturing systems: Mapping the territory

With the rapid development of Additive Manufacturing (AM) technology, the demand for batch printing is increasing, whose central issue is the printing layout. In order to solve the problem of insufficient utilization of space under self-supporting parts by existing nesting algorithms and reduce time spent searching for the placement position and rotation angle, a new 3D layout method is proposed based on the Convex-concave Coding Similarity of the Voxelized model (VCCCS) for AM. The voxelization method is innovatively used to generally show and standardize the geometric convexity and concavity of part surface and layout space. The Orthogonal Convex-concave Surface (OCCS) coding method is proposed to digitize the convexity and concavity of the voxelized model and layout space for the first time. Through finding Convex-concave Similarity Coding Sub-blocks (CCSCSBs), the positions in the similar geometric characteristics between the part and space can be obtained to guide the placement. The VCCCS placement method can make use of the placeable space generated by the self-supporting property of the materials, which avoids the loss of surface accuracy by stacking the parts at the same time. Experiments and corresponding analyses verify the effectiveness of the proposed algorithm and its industrial values. 

A 3D nesting method based on the convex-concave coding similarity of the voxelized model for additive manufacturing

This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature's significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system. 

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network.

The quality of the filling algorithm in additive manufacturing directly affects the strength of the part. The commonly used 3D printing filling algorithm at this stage is the classic filling algorithm. The density of each part in the filling region is the same, and there is a cavity structure in the transverse direction, which makes the strength of the part in the transverse direction lower. Therefore, this paper proposed a new filling algorithm—variable density filling algorithm based on Delaunay triangulation. First, we performed concave-polygon-convex decomposition on the filling area to form printing sub-regions; then, the filling density value was set according to the required intensity of each region, and we used the Poisson disk sampling algorithm to generate the filling point set. Finally, Delaunay triangulation was performed on the generated point set to generate filled traces. The comparison with the two commonly used classical filling algorithms proves that the algorithm can improve the strength of the part to a certain extent, and the printing time and the consumption of consumables will not increase significantly.

/pdf/variable-density-filling-algorithm-based-on-delaunay-7fe4rxgm.pdf

Variable Density Filling Algorithm Based on Delaunay Triangulation

A nesting optimization method based on digital contour similarity matching for additive manufacturing

Automatic selection system of the building orientation based on double-layer priority aggregation multi-attribute decision-making

Triangular meshes play critical roles in many applications, such as numerical simulation and additive manufacturing. However, the triangular meshes transformed from computer-aided design models using common algorithms may have many undesirable narrow triangles, which tends to affect the downstream applications. In this paper, we proposed two algorithms for Delaunay mesh construction and simplification to improve the quality of the triangular meshes. Two improved mesh operations of inserting vertices and collapsing vertices based on the principle of minimum volume destruction were designed. The improved vertex inserting operation is able to modify the local mesh so that it will conform to the local Delaunay property. The improved vertex collapsing operation can realize the simplification of the original mesh while maintaining the local Delaunay property. The results of visualized rendering and thermal diffusion simulations verified the improvement of the proposed algorithms in the aspects of the quantity and quality of the meshes.

/pdf/delaunay-mesh-construction-and-simplification-with-feature-201to01e.pdf

Delaunay Mesh Construction and Simplification with Feature Preserving Based on Minimal Volume Destruction

With the development of 3D printing technology, people put forward higher requirements for molding quality and efficiency. In the whole printing system, the design of scanning path directly affects the geometric accuracy, strength and molding efficiency of products. Simple parallel path and contour path can no longer meet the needs of manufacturing industry. In this paper, a new filling algorithm is proposed. On the basis of continuous filling without pause, the connection of sub-regions is realized by using Fermat spiral. The new algorithm integrates convex decomposition, PCA, variable spacing filling and other operations to further improve the printing efficiency. Finally, the superiority of the new algorithm is verified by comparing with the common algorithm.

An Optimal Filling Path Planning Based on Principal Component Analysis

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