Convex Hull Research Articles

Research on segmentation and reconstruction of overlapping ore contours based on EAM-SOLOv2 and convex hulls

Research on segmentation and reconstruction of overlapping ore contours based on EAM-SOLOv2 and convex hulls

The Robust Communication Method Against Dependent Jamming Based on Convex Hull in Communication Countermeasures

The Robust Communication Method Against Dependent Jamming Based on Convex Hull in Communication Countermeasures

Existence of a Cauchy surface and compactness of causally convex hulls in a spacetime

In this paper it is shown that, if a spacetime contains a Cauchy surface then the causally convex hulls of compact sets are compact. The converse is not true in general, however if the spacetime is causal then the compactness of causally convex hulls of compact sets of the spacetime ascertain the existence of a Cauchy surface. Also, the convex hull operator is denoted and some results regarding it has been explored.

An alternative for the robust assessment of the repeatability and reproducibility of analytical measurements using bivariate dispersion

IntroductionAssessing repeatability and reproducibility in analytical chemistry is commonly based on parametric dispersion indicators, such as relative standard deviation and standard deviation, calculated for each detected variable using repeated measurements of Quality Control (QC) samples collected throughout the data acquisition sequence. However, their reliability strongly relies on the assumption of normality distribution. Knowing that analytical variability is conditional to many sources, the use of such parametric estimators is not always suitable. There is therefore a need for robust indicators of data quality independent of central values and any parametric assumption. MethodsThree specific indicators were developed: (i) intra-group dispersion, based on the median area of the convex hull of QC samples within an analytical batch; (ii) inter-group dispersion, defined as the gradient of the deviation between analytical batches; and (iii) dispersion index. Mathematical properties of these indicators, including positivity, stability, and translation invariance, were then evaluated using synthetic data under normal and non-normal distributions. Finally, the relevance of these indicators and the associated visualization methods were highlighted based on a metabolomics case study involving liquid chromatography coupled to mass spectrometry measurements of the NIST SRM1950 reference material analyzed over more than one year within different projects. ResultsThe proposed indicators were shown to be translation invariant and always positive, while first investigations performed on synthetic data revealed a high stability for multiplication. Moreover, their application to experimental data revealed specific behaviors depending on the characteristics of the signal associated with the different detected analytes, showing their ability to capture the variability observed either in parametric or non-parametric conditions. Moreover, this investigation showed different structures of sensitivity to analytical variability all along the data processing steps. The proposed indicators also allowed a visualization of the analytical drift in two dimensions, to facilitate result interpretation. ConclusionThese indicators open the way to a better and more robust assessment of repeatability and reproducibility but also to improvements of long-term data comparability involving suitability testing.

Matching the building footprints of different vector spatial datasets at a similar scale based on one-class support vector machines

Automatic matching of multisource data is an important technique for achieving change detection, fusion and updating spatial data. However, most current learning methods for building footprint matching require a large number of samples, and labeling these samples is costly in terms of labor and time. Moreover, multisource building footprint data are complex and diverse leading to recognizing the different matching relationships is a hard task. Thus, this study proposes a learning-based method for recognizing multisource building footprints matching relationships by using a one-class support vector machine (OCSVM). The OCSVM was trained using only positive samples. First, a set of geometric indicators was designed to train a model and realize initial matching recognition. Then, a contextual metric was calculated based on the rough matching results, and geometric and contextual metrics were combined to train the model and realize relaxed matching recognition. Relaxed matching is an optimization process implemented after initial matching to recognize more relaxed matching relationships. In relaxed matching, a convex hull is used to recognize matching relationships besides 1:1, such as 1:n, m:1 and m:n. The experimental results showed that the proposed method outperformed indicator-weighted (weighted average) and learning-based matching methods, such as traditional SVMs and decision trees (DTs). The precision scores of the proposed model were 97.1%, 95% and 97.2% for the Wuhan (China), Beijing (China) and Richmond Hill (Canada) datasets, respectively. Furthermore, the proposed model identified the matching relationships of buildings with complex geometric features and high-density spatial distributions.

Shortest closed curve to contain a sphere in its convex hull

AbstractWe show that in Euclidean 3‐space any closed curve which contains the unit sphere within its convex hull has length , and characterize the case of equality. This result generalizes the authors' recent solution to a conjecture of Zalgaller. Furthermore, for the analogous problem in dimensions, we include the estimate by Nazarov, which is sharp up to the constant .

Precision Phenotyping of Wild Rocket (Diplotaxis tenuifolia) to Determine Morpho-Physiological Responses under Increasing Drought Stress Levels Using the PlantEye Multispectral 3D System

Precision Phenotyping of Wild Rocket (Diplotaxis tenuifolia) to Determine Morpho-Physiological Responses under Increasing Drought Stress Levels Using the PlantEye Multispectral 3D System

An efficient method for modeling and evaluating the bench terrain of open-pit mines

In order to quantitatively analyze the roughness of the bench floor during open-pit mine blasting, this study proposes a real-time measuring method for the three-dimensional terrain of the bench floor during the excavation process. Real-time monitoring is conducted at the boundary and discrete internal points of the workbench floor during electric shovel operation, utilizing real-time kinematic global navigation satellite system (RTK-GNSS) positioning technology. An improved convex hull algorithm is introduced to automatically extract the optimal boundary of discrete point clouds based on their spatial distribution characteristics. This study establishes a digital elevation model (DEM) using five interpolation algorithms for 3D terrain visualization simulation. Through cross-validation, a comparative analysis of the DEM accuracy, the simulation results of the ordinary kriging interpolation algorithm were found to be optimized. The optimized interpolation algorithm is applied to simulate the 3D terrain in the Dexing open-pit copper mine, and the relevant terrain parameters were calculated. This dataset can serve as a precise foundation for the real-time path planning of elevation blasting design and ground leveling operations.

Colour gamut analysis of low‐cost dye‐sensitised solar cells using natural dyes

AbstractDue to the increasing effects of greenhouse gases and environmental pollution, the need for new clean energies like solar energy attracts much attention. Although the characteristics and the efficiency of green photovoltaic devices have been researched, the colour gamut achievable from those devices has not been studied. This study investigates the colour gamut of low‐cost dye‐sensitised solar cells (DSSCs). To do this, 14 natural dye extracts were obtained from herbal resources including dragon fruit, blueberry, mango, radish, yellow rose, red cabbage, sour pomegranate, beetroot, olive, green cabbage pepper, eggplant, parsley, bramble and cherry, and employed as photosensitiser in solar cells. Then, the colorimetric attributes of the photosensitisers were studied in three‐dimensional (3D)‐colour space, that is, CIELab, CIELCH and CIE1931 chromaticity diagram. Additionally, the convex hull method was employed to determine the colour gamut boundary and the corresponding colour gamut volume. Results showed that the majority of samples benefited from approximately 3°–82° of hue angle in a*b* diagram of CIELab colour space and showed the yellowish to reddish tint effects. In CIELCH colour order system, cherry and parsley showed the lowest and highest lightness attributes while the chroma property of samples varied from minimum 1.2 for eggplant to maximum 60.2 for the dragon fruit. Moreover, the results of using the convex hull method showed the volume of 7.73 × 104 that is bounded by the colour gamut of 3D colour points over the CIELab colour space.

Rectilinear convex hull of points in 3D and applications

AbstractLet P be a set of n points in $$\mathbb {R}^3$$ R 3 in general position, and let RCH(P) be the rectilinear convex hull of P. In this paper we obtain an optimal $$O(n\log n)$$ O ( n log n ) time and O(n) space algorithm to compute RCH(P). We also obtain an efficient $$O(n\log ^2 n)$$ O ( n log 2 n ) time and $$O(n\log n)$$ O ( n log n ) space algorithm to compute and maintain the set of vertices of the rectilinear convex hull of P as we rotate $${\mathbb {R}}^3$$ R 3 around the Z-axis. We study some combinatorial properties of the rectilinear convex hulls of point sets in $$\mathbb {R}^3$$ R 3 . Finally, as an application of the obtained results, we show an approximation algorithm to an optimization fitting problem in $$\mathbb {R}^3$$ R 3 .

Collaborative orchard pesticide spraying routing problem with multi-vehicles supported multi-UAVs

The application of UAVs in orchard management presents a substantial opportunity to enhance efficiency and promote environmentally friendly agricultural production. While individual UAVs encounter limitations, such as endurance constraints, these challenges can be mitigated through collaborative efforts involving multiple vehicles and UAVs. This study addresses the issue of multi-UAV cooperative orchard path planning with multiple vehicles by formulating a mathematical optimization model aimed at minimizing the maximum UAV operation time. In the proposed model, the vehicle solely provides batteries and supplies to the UAV. To efficiently solve this model, we propose an improved Simulated Annealing-Lin Kernighan Helsgaun (SA-LKH) algorithm. The proposed algorithm utilizes the K-means and convex hull algorithm to generate the initial solution. Subsequently, the paths of each group of UAVs and vehicles are optimized based on the LKH algorithm. Additionally, we construct a directed perturbation operator that perturbs the maximum and minimum time-consuming paths to achieve fast convergence of the algorithm. The results from four different cases demonstrate that, compared to the general variable neighborhood search algorithm, the improved Ant Colony Algorithm and Improved Genetic Algorithm, the proposed algorithm has demonstrated an average improvement in solution quality of 3.41%, 5.72% and 9.67%, respectively. The proposed method effectively enhances the solution quality, which can provide valuable insights into green cleaning management of orchards.

Dissipative control for quaternion-valued fuzzy memristive neural networks: Nonlinear scalarization approach

This paper deals with the dissipative control for a class of quaternion-valued fuzzy memristive neural networks. By constructing proper Lyapunov functionals and using adaptive controller, the strictly (Q,S,R)-dissipative are characterized parametrically. Then, based on the algebraic inequality and linear matrix inequality (LMI) approach, sufficient conditions for the existence of the dissipative controllers are obtained. In addition, the nonlinear scalarization approach is developed, which can be employed to compare the “size” of two different quaternions, in this way, the convex closure proposed by the quaternion weights are meaningful. Finally, simulation examples are given to show the efficiency of the proposed methods.

Abstract PO2-07-04: Applying the Alliance Trial Guidelines in Multi-focal Breast Disease Using an Artificial Intelligence Computational Platform: Economic Analysis and Cosmetic Sensitivity

Abstract Background: Traditionally, multi-focal breast cancer results in mastectomy. The Alliance Trial offers a paradigm shift in surgical options available for multi-focal breast cancer patients in the context of adjuvant chemotherapy. In the trial, patients with multi-focal disease (< 3 tumors) who underwent breast conservation surgery (BCS) were found to have similar outcomes to patients undergoing mastectomy. BCS for large volume tumors ( >30%) has been cited as having a high potential for cosmetic defect, and hence represents a typical upper limit for potential tissue removal in BCS. Here, we evaluated a patient cohort to better understand the economic impact of the Alliance trial and further categorize patients that would most benefit without suffering cosmetic impact. We employed a novel computational technology to quantify the ratio of tumor size to breast tissue volume. Methods: Using a publicly available, single site cohort (n=243, DUMC) of breast cancer patients that underwent mastectomy, we segmented the tumors using our TumorSight Viz software platform. This platform uses artificial intelligence to segment the tumor and surrounding tissues and allows for a volumetric and morphologic assessment in 3D space. We then applied relevant inclusion/exclusion criteria from the Alliance Trial to the cohort (Saha et al, 2018). In trial eligible patients, we used TumorSight Viz to create a convex hull (CH) around the multi-focal disease using dilations of 1 cm and 2 cm. The volume of the CH, corresponding to proposed surgical extirpation, and the overall breast volume (BV) were then computationally assessed in 3D. The ratio of CH to BV (CH:BV) was calculated and a cutoff of 30% (high potential for cosmetic deformity) was applied. A cost analysis was then carried out. We determined the aggregate per annum savings that could potentially be realized by transforming a subset of mastectomies to BCS by tabulating total costs of mastectomy+reconstruction vs. BCS+WBI (whole-breast irradiation), as well as adjusted for relative rates of adjuvant therapy (~80%) across the nationwide patient population. Results: We found that 19.3% of adjuvant mastectomy patients were eligible for BCS based on Alliance Trial criteria. Of those, 68% had tumor CH:BV < 30% when using a 1 cm dilation around the tumor. When using a 2 cm dilation, 56% had tumor CH:BV < 30%. Together, these results indicate that of all adjuvant mastectomy patients, an estimated 10.8-13.1% are eligible for BCS based on volumetric measures of cosmetically acceptable breast tissue removal. Our economic analysis of BCS vs. mastectomy revealed an estimated $28,500 cost savings for patients with private insurance, suggesting that both decreased costs and improved quality-of-life (QOL) can be mutually aligned. By assessing the nationwide number of patients receiving adjuvant therapy for breast cancer, alongside the percentage potentially eligible for BCS using the above cosmetic defect analysis, we estimate that BCS conversion from mastectomy offers to provide a net savings of $300-350 million annually. Conclusion: The Alliance Trial guidelines unveiled the potential option of BCS in ~20% of patients with multi-focal disease in our cohort, demonstrating considerable cost-savings. Computational tools can further differentiate individuals who may not be best candidates for BCS in this setting, ensuring high QOL and informed decision-making. Citation Format: John Pfeiffer, Matthew Biancalana, Dorys Lopez-Ramos, Bradley Feiger, Anuja Antony. Applying the Alliance Trial Guidelines in Multi-focal Breast Disease Using an Artificial Intelligence Computational Platform: Economic Analysis and Cosmetic Sensitivity [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-07-04.

A robust optimization method for new distribution systems based on adaptive data-driven polyhedral sets

In order to better describe the uncertainty of renewable energy output, this paper proposed a novel robust optimization method for new distribution systems based on adaptive data-driven polyhedral sets. First, an ellipsoidal uncertainty set was established using historical data on renewable energy output, and a data-driven convex hull polyhedral set was established by connecting high-dimensional ellipsoidal vertices; on this basis, an adaptive data-driven polyhedral set model was established to address the problem of high conservatism in the scaling process of convex hull polyhedral sets. Furthermore, a novel adaptive data-driven robust scheduling model for new distribution systems was established, and a column-and-constraint generation (C&CG) algorithm was used to solve the robust scheduling model. Finally, the improved IEEE-33 bus system simulation verification shows that the robust scheduling model for new distribution systems based on adaptive data-driven polyhedral sets can reduce conservatism and improve the robustness of optimization results.

Abstract PO2-02-11: Radiomics and an Erosion Model to Predict Post-Neoadjuvant Therapy Tumor Characteristics for Personalized Breast Cancer Treatment

Abstract Background: Neoadjuvant therapy (NAT) has become a standard-of-care (SOC) for patients diagnosed with locally advanced or early-stage breast cancer. A critical step towards optimizing treatment selection and coordinating surgical care is to estimate the tumor’s response to NAT. While pathological complete response (pCR) is commonly used to measure NAT effectiveness, patients who do not achieve pCR still benefit from NAT depending on the tumor’s response. A reduction in tumor extent could alter the surgical approach from mastectomy to breast conserving therapy and improve recurrence and overall survival. In this study we used pre-treatment imaging data to provide a comprehensive prediction of clinically relevant metrics including tumor volume, tumor convex hull volume, and spatially derived metrics; this research represents an important advancement in predicting NAT effectiveness. Methods: 262 breast cancer patients from 5 institutions who underwent NAT with pre- and post-NAT dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were included in this study. Each MRI was automatically segmented using a convolutional neural network with manual edits and verification. Radiomic features were extracted from the pre-NAT MRI and tumor segmentation. Significant radiomic features were selected based on their correlation with post-NAT volume, feature-to-feature correlation, and LASSO regression. Altogether, 12 and 17 radiomic features were selected for volume and convex hull volume prediction, respectively. The selected features were inputted into a Huber loss regression model to predict either the post-NAT tumor volume or the convex hull volume (surgical excision volume). To enable spatial and morphological comparison between the predicted and post-NAT tumor, an erosion model was developed in which the pre-NAT and post-NAT MRIs were co-registered, and the pre-NAT tumor was symmetrically eroded until its volume matched the predicted volume. Clinically relevant metrics such as the distance from the tumor to the nipple, skin, and chest were computed to assess the morphological and spatial prediction capabilities of the model. All models were tested on patients from cohort holdout sets (n=110) unseen during training. Results: The volume prediction analysis yielded promising results, with an R^2 value of 0.62 for post-NAT tumor volume prediction and an R^2 value of 0.56 for post-NAT tumor convex hull volume prediction. The mean volume and convex hull volume error was 1.1 cc and 1.3 cc, respectively. Table 1 provides an overview of the morphological and spatial comparisons between the predicted and post-NAT tumors. Overall, the predicted tumors demonstrated close agreement with their post-NAT counterparts. Conclusion: This study demonstrates a successful approach towards estimating post-NAT tumor volume, convex hull volume, and morphological and spatial characteristics. By providing reliable estimates of post-NAT tumor characteristics using SOC pre-NAT data, our predictive models enable personalized treatment planning and patient stratification to optimize patient care. Table 1. Predicted vs. post-NAT tumor comparison. Results demonstrate accurate predictions for clinically relevant distance metrics. Citation Format: Bradley Feiger, John Pfeiffer, John Cole, Anuja Antony, Joseph Peterson. Radiomics and an Erosion Model to Predict Post-Neoadjuvant Therapy Tumor Characteristics for Personalized Breast Cancer Treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-11.

Abstract PO2-02-10: Predicting the feasibility of breast conserving surgery using pre-treatment standard of care DCE-MRI: a novel clinical decision support tool for breast cancer surgical planning

Abstract Background: The decision-making process regarding breast cancer treatment options, specifically breast conserving surgery (BCS) versus mastectomy, is a complex and sensitive matter that takes into account the patient's preferences and their eligibility for BCS. A range of anatomical factors, such as tumor size, breast size, and proximity of the tumor to the skin, can influence the surgeon's recommendation regarding BCS or mastectomy. While mastectomy may be the only viable option for patients with large tumors, those with smaller tumors often have the opportunity to collaboratively decide with their surgeon on the most appropriate approach to ensure oncologic success and minimize potential cosmetic deformity. To facilitate this decision-making process, we developed a tool capable of providing informed recommendations on BCS or mastectomy based on standard of care imaging patient data. By integrating these recommendation capabilities, we aim to enhance the decision-making process and improve patient outcomes in breast cancer treatment. Methods: To assist surgeons and patients in determining a patient’s eligibility for breast conserving surgery (BCS), we developed the BCS Feasibility Score. The BCS Feasibility Score is generated from a binary classifier machine learning model and is representative of the probability that a given patient could receive BCS. The score is based solely on the output from TumorSight Viz, a software platform that uses only pre-treatment T1-weighted DCE MRI and applies a suite of deep learning algorithms to segment the patient’s disease and the surrounding breast tissues. From the multi-tissue segmentation generated using TumorSight Viz, we extracted 25 spatial morphology features to use in the model development process. The training and testing sets were a subsample of a publicly available dataset (Saha, et al, 2018). We selected patients that received either BCS or mastectomy and had a viable T1-weighted DCE MRI. Bi-lateral cases were excluded. The total training/testing set consisted of n = 749 cases, 363 of which received mastectomy and 386 who received BCS. Before model training occurred, we split 20% of the sample to use as the test set. We then trained a random forest classifier across a range of pre-set hyper-parameters, using a total of 25 features as inputs, and using 5x cross-validation in the training set. We then assessed model performance in the testing set only. Results: In the test set, 76 cases received BCS versus 74 that received mastectomy. We successfully predicted BCS in 56 out of 76 cases (74%), and mastectomy in 46 out of 74 cases (62%). Overall, we observed an AUC = 0.76 and an F1 score = 0.66, indicating moderate to strong model performance. The most important features in the model, as measured by SHAP values, included the axis aligned tumor longest dimension (mm), the closest distance between the tumor and the nipple (mm), the volume of the tumor convex hull (mL; dilated uni-centrically), and the ratio of tumor convex hull volume to breast volume. We tested the model’s performance in a fully independent holdout validation set of 579 cases. Within this validation set, 335 patients received BCS versus 244 received mastectomy. We successfully predicted BCS in 254 out of 335 cases (76%), and mastectomy in 149 out of 244 cases (61%). Overall, we observed an AUC = 0.75 and an F1 score = 0.63. Validation set performance mirrored results observed in the test set, indicating strong generalizability. In the validation set, comparing patients who underwent BCS to those who underwent mastectomy, we observed significant differences across all four of the most predictive features. Conclusion: TumorSight Viz and the BCS Feasibility Score help empower both patients and clinicians with information and tools to facilitate surgical planning decisions. Citation Format: John Pfeiffer, Bradley Feiger, Anuja Antony, Joseph Peterson. Predicting the feasibility of breast conserving surgery using pre-treatment standard of care DCE-MRI: a novel clinical decision support tool for breast cancer surgical planning [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-10.

Carotid body ablation restores astrocyte/microglia morphology in the NTS and reduces hypertension following long-term intermittent hypoxia

Chronic intermittent hypoxia (CIH) heightened carotid body (CB) chemosensory discharges, which contributes to hypertension development. Previously, we reported that CB ablation significantly reduced blood pressure after 21 days of CIH; however, whether CB-mediated glial cell activation in the caudal Nucleus of the Solitary Tract (cNTS) following long-term CIH contribute to maintain high blood pressure and enhanced ventilatory chemoreflex remains unknown. To test this hypothesis, we exposed male C57BL6 mice to CIH (5% FIO2, 12 times/h, 8 h/day) for 60 days. At 45 days of CIH, CBs were selectively denervated, and animals returned to CIH for 15 more days. At the end of the experimental protocol, we measured arterial blood pressure, hypoxic ventilatory response (HVR) in awake mice and characterized astrocyte and microglia morphological features using Sholl analysis. CIH induces hypertension (MABP: 83.47±1.39 vs. 95.00±2.18 mmHg; Sham vs. CIH), increases the HVR (1.69±0.17 vs. 4.31±0.87 ΔVE/min; Sham vs. CIH), modify cNTS astrocytes phenotype (N° of branches: 13.01±0.66 vs. 11.31±0.52; cable length: 181.00±8.90 vs 148.10±1.48 μm, convex hull: 788.10±37.60 vs. 584.30±23.75 μm3; Sham vs. CIH), and microglia arborization (N° of branches: 196.10±8.35 vs. 376.30±16.80; cable length: 667.4±29.63 vs. 1267±60.47 μm, convex hull: 6177±362 vs. 9721±564 μm3; Sham vs. CIH). Importantly, CB denervation normalized the elevated blood pressure (MABP: 83.47±1.38 mmHg; CIHd), the altered peripheral chemoreflex response (HVR: 1.63±0.43 ΔVE/min; CIHd) and morphological changes in astrocytes (N °of branches: 17.23±0.88, cable length: 231.70±12.87, convex hull: 982.70±55.55 μm3) and microglia located in the cNTS (N° of branches 126.20±13.34; cable length: 126.2±13.3, Convex hull size: 3584±380 μm3; CIHd). Present results suggest that CBs play a crucial role in the maintenance of high arterial pressure and respiratory alterations induced by long-term CIH and may contribute to the formation of a neuroinflammatory niche at the cNTS by modifying glial cell activity. Supported by Fondecyt Grants 1211443 and 1220950. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification

Diagnosing liver disease presents a significant medical challenge in impoverished countries, with over 30 billion individuals succumbing to it each year. Existing models for detecting liver abnormalities suffer from lower accuracy and higher constraint metrics. As a result, there is a pressing need for improved, efficient, and effective liver disease detection methods. To address the limitations of current models, this method introduces a deep liver segmentation and classification system based on a Customized Mask-Region Convolutional Neural Network (cm-RCNN). The process begins with preprocessing the input liver image, which includes Adaptive Histogram Equalization (AHE). AHE helps dehaze the input image, remove color distortion, and apply linear transformations to obtain the preprocessed image. Next, a precise region of interest is segmented from the preprocessed image using a novel deep strategy called cm-RCNN. To enhance segmentation accuracy, the architecture incorporates the ReLU activation function and the modified sigmoid activation function. Subsequently, a variety of features are extracted from the segmented image, including ResNet features, shape features (area, perimeter, approximation, and convex hull), and enhanced median binary pattern. These extracted features are then used to train a hybrid classification model, which incorporates classifiers like SqueezeNet and DeepMaxout models. The final classification outcome is determined by averaging the scores obtained from both classifiers.

Simulating Thin Shells by Bicubic Hermite Elements

In this study, we present the bicubic Hermite element method (BHEM), a new computational framework devised for the elastodynamic simulation of thin-shell structures. The BHEM is constructed based on quadrilateral Hermite patches, which serve as a unified representation for shell geometry, simulation, collision avoidance, as well as rendering. Compared with the commonly utilized linear FEM, the BHEM offers higher-order solution spaces, enabling the capture of more intricate and smoother geometries while employing significantly fewer finite elements. In comparison to other high-order methods, the BHEM achieves conforming C1 continuity for Kirchhoff–Love (KL) shells with minimal complexity. Furthermore, by leveraging the subdivision and convex hull properties of Hermite patches, we develop an efficient algorithm for ray-patch intersections, facilitating collision handling in simulations and ray tracing in rendering. This eliminates the need for laborious remodeling of the pre-existing surface as the conventional approaches do. We substantiate our claims with comprehensive experiments, which demonstrate the high accuracy and versatility of the proposed method.

Consensus for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme

This paper studies the leader–follower consensus problem for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme. This adaptive mechanism allows parameters in triggered mechanism to change automatically compared to fixed ones in traditional strategies. The paper introduces two distinct approaches to address the issue of saturation. The first approach is sector bounded condition, while the second relies on convex hull representation. Compared with the former one, the latter one can reduce the conservatism of controller design effectively. Furthermore, it is assumed that the nonlinear function adheres to incremental quadratic limitations, thereby offering a more comprehensive depiction of its nonlinear properties. Finally, the validity of the proposed approaches is demonstrated by one numerical example in the background of mechanical systems.