Article
Computer Science, Artificial Intelligence
Esteban Jove, Jose-Luis Casteleiro-Roca, Hector Quintian, Juan-Albino Mendez-Perez, Jose Luis Calvo-Rolle
Summary: The method of implementing non-convex boundaries to address the issue of geometric boundaries in anomaly detection systems has been successfully evaluated and compared against common one-class techniques with promising results.
INFORMATION FUSION
(2021)
Article
Plant Sciences
Xiaobao Liu, Biao Xu, Wenjuan Gu, Yanchao Yin, Hongcheng Wang
Summary: This study proposes a plant leaf veins coupling feature representation and measurement method based on DeepLabV3+ to address the issues of slow segmentation, partial occlusion of leaf veins, and low measurement accuracy. By using a lightweight network and an improved algorithm, the proposed method achieves high segmentation accuracy and speed, and accurately measures the length and width of leaf veins.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
A. Farshidvard, F. Hooshmand, S. A. MirHassani
Summary: This paper introduces a novel two-phase method to tackle the challenges of imbalanced data classification. The method combines under-sampling and ensemble-based approaches, partitioning the majority class into clusters to preserve the general pattern of data in the feature space and reduce the possibility of changing the data distribution.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Siyuan Wang, Jian Liu, Rui Bo, Yonghong Chen
Summary: Pumped storage hydro (PSH) plant is a valuable resource with storage and fast ramp capabilities to manage renewable energy intermittency. This paper proposes a hypograph-relaxation-based input-output curve modeling framework for accurate evaluation of PSH plant's generating/pumping capability. By decomposing the input-output curve and approximating each component with its respective convex hull, the proposed model achieves a trade-off between accuracy and computation time. Numerical experiments using real data demonstrate the computational advantage of the proposed approach for profit maximization problems.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
David Novoa-Paradela, Oscar Fontenla-Romero, Bertha Guijarro-Berdinas
Summary: OCENCH is an intuitive, robust and efficient One-Class Classification algorithm that represents the target class using subdivisible and expandable non-convex hulls. It can handle non-convex and disjointed shapes, and the execution can be parallelized to reduce the execution time.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Xiaohan Yuan, Shuyu Chen, Han Zhou, Chuan Sun, Lu Yuwen
Summary: In this paper, a novel and simple convex hull-based SMOTE (CHSMOTE) algorithm is proposed to overcome the weaknesses of SMOTE and alleviate class imbalance problem. CHSMOTE selects the border minority samples as initial samples, identifies the synthesis area based on convex hull, and generates more effective samples by enlarging the generation range. Extensive experiments demonstrate the effectiveness and superiority of the proposed algorithm.
INFORMATION SCIENCES
(2023)
Article
Thermodynamics
Ningning Li, Yan Gao
Summary: This paper designs a real-time pricing mechanism for the smart grid to meet the changing electricity demand, balance supply and demand, and maximize social welfare.
Article
Computer Science, Software Engineering
Peijing Liu, Salar Fattahi, Andres Gomez, Simge Kucukyavuz
Summary: In this paper, we study convex quadratic optimization problems with indicator variables and sparse matrix. By using a graphical representation of the support of the matrix, we prove that if the graph is a path, the associated problem can be solved in polynomial time. This allows us to construct a compact extended formulation for the closure of the convex hull of the mixed-integer convex problem's epigraph. Furthermore, we propose a novel decomposition method for a class of sparse strictly diagonally dominant matrices, inspired by inference problems with graphical models, leveraging the efficient algorithm for the path case. Our computational experiments demonstrate the effectiveness of the proposed method compared to state-of-the-art mixed-integer optimization solvers.
MATHEMATICAL PROGRAMMING
(2023)
Article
Computer Science, Information Systems
Xu Wei, Jiyu Li, Bo Long, Xiaodan Hu, Han Wu, Huifen Li
Summary: The proposed method using adaptive iterative binarization and mean filtering can effectively extract the shape and area of vortex without manual intervention. Experimental results show good accuracy of this method under different experimental conditions.
Article
Biology
Sunera Chandrasiri, Thumula Perera, Anjala Dilhara, Indika Perera, Vijini Mallawaarachchi
Summary: Metagenomics enables culture-independent analysis of micro-organisms in environmental samples. We propose the CH-Bin method, which utilizes convex hull distance to group high dimensional feature vectors representing contigs, and experimental evidence shows improved binning results.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2022)
Article
Geochemistry & Geophysics
Zhenchuan Liang, Junjie Chen, Junzheng Jiang
Summary: This letter proposes an innovative multi-featured detection method utilizing graph signal processing (GSP) theory to effectively detect floating small targets in complex marine environments. Two graph representations of standardized Doppler power spectrum (SDPS) are proposed to capture the correlation of radar data in the Doppler domain. Three quantitative graph features, graph Laplacian regularizer, trace of Laplacian matrix, and variance of self-loop weight, are developed to distinguish target returns from sea clutter. A detector based on the graph features is constructed using the fast convex hull learning algorithm, and experiments confirm the effectiveness of the proposed method.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Statistics & Probability
Alberto Rodriguez-Casal, Paula Saavedra-Nieves
Summary: This study proposes a method for estimating density level sets using a random sample of points and the r-convexity assumption. A stochastic algorithm is used to select the value of r, and the resulting reconstruction achieved minimax rates for Hausdorff metric and distance.
ANNALS OF STATISTICS
(2022)
Article
Mathematics, Applied
Xue Zhang, Changzhong Wang, Xiaodong Fan
Summary: This paper proposes a distance metric learning method for image classification based on regularized convex hulls, using RPCHD and RCHCHD to measure distances between images and learning distance metric matrix through SVM models, the experiments show that the proposed method effectively improves image classification performance.
COMPUTATIONAL & APPLIED MATHEMATICS
(2021)
Article
Computer Science, Theory & Methods
Sun-Young Ihm, So-Hyun Park, Young-Ho Park
Summary: This paper proposes an unbalanced-hierarchical layer method that hierarchically divides the dimensions of input data to increase the total number of layers and reduce the index building time, aiming to improve the efficiency of data retrieval in cloud computing.
Article
Computer Science, Interdisciplinary Applications
Sivakumar Ayyasamy, Palaniappan Ramu, Isaac Elishakoff
Summary: This study develops an efficient convexity approach to construct uncertainty models with bounds, addressing challenges in engineering posed by unknown-but-bounded uncertainty and insufficient data. The method utilizes convex hull geometry and Chebyshev inequality to handle uncertainty, demonstrating superiority over existing approaches in uncertainty quantification. The proposed method provides conservative results compared to Monte Carlo simulations, avoiding issues of under- or over-design in engineering.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Plant Sciences
Makoto Yanagisawa, Anastasia S. Desyatova, Samuel A. Belteton, Eileen L. Mallery, Joseph A. Turner, Daniel B. Szymanski
Article
Biochemical Research Methods
Diana L. Delibaltov, Utkarsh Gaur, Jennifer Kim, Matthew Kourakis, Erin Newman-Smith, William Smith, Samuel A. Belteton, Daniel B. Szymanski, B. S. Manjunath
BMC BIOINFORMATICS
(2016)
Article
Plant Sciences
Samuel A. Belteton, Megan G. Sawchuk, Bryon S. Donohoe, Enrico Scarpella, Daniel B. Szymanski
Article
Plant Sciences
Jeh Haur Wong, Takehide Kato, Samuel A. Belteton, Rie Shimizu, Nene Kinoshita, Takumi Higaki, Yuichi Sakumura, Daniel B. Szymanski, Takashi Hashimoto
Article
Plant Sciences
Wenlong Li, Sedighe Keynia, Samuel A. Belteton, Faezeh Afshar-Hatam, Daniel B. Szymanski, Joseph A. Turner
Summary: The mechanical properties, size and geometry of cells, and internal turgor pressure play a significant role in cell morphogenesis. This study presents an experimental-computational approach to analyze the elastic bending behavior of Arabidopsis pavement cells and measure turgor pressure under different osmotic conditions. The results reveal the nonuniform distribution of wall modulus and provide insights into the heterogeneity and anisotropy of plant cell walls.
Correction
Plant Sciences
Samuel A. Belteton, Wenlong Li, Makoto Yanagisawa, Faezeh A. Hatam, Madeline I. Quinn, Margaret K. Szymanski, Matthew W. Marley, Joseph A. Turner, Daniel B. Szymanski
Article
Plant Sciences
Samuel A. Belteton, Wenlong Li, Makoto Yanagisawa, Faezeh A. Hatam, Madeline Quinn, Margaret K. Szymanski, Mathew W. Marley, Joseph A. Turner, Daniel B. Szymanski
Summary: Research has shown that the morphogenesis of pavement cells in plant leaves is regulated by cell wall tensile stress and the microtubule-cellulose synthase system, which drives leaf expansion and lobe formation. Lobe formation in leaves is a conserved trait associated with important agronomic traits.
Proceedings Paper
Imaging Science & Photographic Technology
Jiaxiang Jiang, Po-Yu Kao, Samuel A. Belteton, Daniel B. Szymanski, B. S. Manjunath
2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
(2019)