Article
Mechanics
Dong Han, Xu Jia, Huajun Zhang, Xiguang Gao, Xiao Han, Li Sun, Zhikang Zheng, Lu Zhang, Fang Wang, Yingdong Song
Summary: This study investigated the impact damage and penetration mechanisms of CVI-fabricated SiC/SiC composite plates through impact testing and microscopic observation, and explored the effect of FOD on the tensile behavior of the composite material.
COMPOSITE STRUCTURES
(2022)
Article
Computer Science, Artificial Intelligence
Carlo Baldassi
Summary: We introduce an evolutionary algorithm called recombinator-k-means for optimizing the highly nonconvex kmeans problem. Its defining feature is that its crossover step involves all the members of the current generation, stochastically recombining them with a repurposed variant of the k-means++ seeding algorithm. The recombination also uses a reweighting mechanism that realizes a progressively sharper stochastic selection policy and ensures that the population eventually coalesces into a single solution. We compare this scheme with a state-of-the-art alternative, a more standard genetic algorithm with deterministic pairwise-nearest-neighbor crossover and an elitist selection policy, of which we also provide an augmented and efficient implementation. Extensive tests on large and challenging datasets (both synthetic and real word) show that for fixed population sizes recombinator-k-means is generally superior in terms of the optimization objective, at the cost of a more expensive crossover step. When adjusting the population sizes of the two algorithms to match their running times, we find that for short times the (augmented) pairwise-nearest-neighbor method is always superior, while at longer times recombinator-k-means will match it and, on the most difficult examples, take over. We conclude that the reweighted whole-population recombination is more costly but generally better at escaping local minima Moreover, it is algorithmically simpler and more general (it could be applied even to k-medians or k-medoids, for example).
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yi-Cheng Chen, Yen-Liang Chen, Jyun-Yun Lu
Summary: K-Means algorithm is one of the most famous and popular clustering algorithms in the world, known for its simple structure, easy implementation, high efficiency, and fast convergence speed. This article introduces an improvement to past variants of K-Means used in evolutionary clustering, considering both past and future clustering results, and extending K-Means to multiple cycles, resulting in more consistent, stable, and smooth clustering results.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Materials Science, Ceramics
Clifton H. Bumgardner, Frederick M. Heim, David C. Roache, Morgan C. Price, Christian P. Deck, Xiaodong Li
Summary: A novel methodology using in situ stereoscopic digital image correlation was employed to assess the fracture mechanics of SiC/SiC ceramic matrix composites. The study revealed that cracks propagate along the specimen length during hermeticity testing, and that heat treatment in open air results in decreased flexural properties and brittle failure.
JOURNAL OF THE AMERICAN CERAMIC SOCIETY
(2021)
Article
Automation & Control Systems
Uri Stemmer
Summary: This research presents a new algorithm operating in the local model of differential privacy for solving the Euclidean k-means problem, significantly reducing additive error while maintaining multiplicative error. The study shows that the obtained additive error in handling the k-means objective is almost optimal in terms of its dependency on the database size.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Mechanics
Lala Bahadur Andraju, Gangadharan Raju
Summary: This study employs acoustic emission (AE) and digital image correlation (DIC) techniques to describe the evolution of intra/inter-laminar damage modes in CFRP laminates under different loading conditions. Different stacking sequences and specimens are investigated to distinguish various damage modes and their occurrence sequence. Unsupervised k-means clustering technique and DIC analysis are used to classify the AE data and evaluate the surface displacement and strain data for understanding the damage evolution. By using post failure analysis and fractography studies, a taxonomy of damage modes, their sequence of occurrence, and failure strains is obtained, which is useful for structural health monitoring and progressive damage modeling of composite laminates.
ENGINEERING FRACTURE MECHANICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Ahmed Fahim
Summary: The k-means method divides N objects into k clusters based on mean values, with linear time complexity and dependence on knowing the number of clusters and initial centers. This research introduces a method able to detect near-optimal values for k and initial centers without prior knowledge, resulting in improved final result quality. The proposed method combines DBSCAN and k-means to converge to global minima and has a time complexity of o(n log n).
JOURNAL OF COMPUTATIONAL SCIENCE
(2021)
Article
Computer Science, Information Systems
Jing Liu, Fuyuan Cao, Jiye Liang
Summary: In this paper, a centroids-guided deep multi-view k-means clustering method is proposed, which incorporates deep representation learning into the multi-view k-means objective. The method produces more k-means-friendly representations by reducing the loss between each representation and its assigned cluster centroid.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Hongfu Liu, Junxiang Chen, Jennifer Dy, Yun Fu
Summary: K-means is a widely used clustering algorithm known for its simplicity and efficiency. This review paper focuses on generalizing K-means to solve challenging and complex problems. It unifies the available approaches in terms of data representation, distance measure, label assignment, and centroid updating. Concrete applications of modified K-means formulations are reviewed, including iterative subspace projection and clustering, consensus clustering, constrained clustering, domain adaptation, and outlier detection.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Avgoustinos Vouros, Stephen Langdell, Mike Croucher, Eleni Vasilaki
Summary: K-Means is a widely used algorithm for data clustering, but it has limitations such as only finding local minima and being sensitive to initial centroid positions. Various K-Means variations and initialization techniques have been proposed, with more sophisticated techniques reducing the need for complex clustering methods. Deterministic methods generally outperform stochastic methods, but there is a trade-off where simpler stochastic methods run multiple times can result in better clustering.
Article
Computer Science, Artificial Intelligence
Luc Giffon, Valentin Emiya, Hachem Kadri, Liva Ralaivola
Summary: K-means algorithm and Lloyd's algorithm have expanded beyond their original clustering purposes to play pivotal roles in various machine learning and data analysis techniques. QuicK-means is an efficient extension of K-means that reduces computational complexity through sparse matrix products, demonstrating benefits through experimental results.
Article
Computer Science, Artificial Intelligence
Peter Olukanmi, Fulufhelo Nelwamondo, Tshilidzi Marwala
Summary: A key drawback of k-means algorithm is its susceptibility to local minima. The authors propose a technique for comparing initializations directly and selecting the best one based on the maximum minimum inter-center distance. The experiments and mathematical analysis show significant efficiency gains and improved accuracy compared to repeated k-means.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Marco Capo, Aritz Perez, Jose A. Antonio
Summary: The K-means algorithm is a popular clustering method, but its performance depends heavily on the initialization phase. Researchers have developed various initialization techniques to address this issue. This article introduces a cost-effective Split-Merge step that can restart the K-means algorithm after reaching a fixed point, reducing error and computing fewer distances.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Miaomiao Li, Yi Zhang, Suyuan Liu, Zhe Liu, Xinzhong Zhu
Summary: Multiple kernel clustering (MKC) aims to determine the optimal kernel from several pre-computed basic kernels. A new algorithm called simple multiple kernel k-means with kernel weight regularization (SMKKM-KWR) is proposed to overcome the issue of sparse or over-selected kernel weight coefficients. Experimental results show that SMKKM-KWR achieves effective and efficient clustering performance.
INFORMATION FUSION
(2023)
Article
Automation & Control Systems
Luefeng Chen, Kuanlin Wang, Min Li, Min Wu, Witold Pedrycz, Kaoru Hirota
Summary: This article proposes a K-means clustering-based kernel canonical correlation analysis algorithm for multimodal emotion recognition in human-robot interaction. By fusing multimodal features from different modalities, the proposed method improves heterogeneity among modalities and enhances the accuracy of emotion recognition.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Chemistry, Physical
Wei Hu, Jinzi Huang, Chao Zhang, Tengfei Ren, Tianhao Guan, Kairong Wu, Bo Wang, Raza Muhammad Aamir, Muhammad Zakir Sheikh, Tao Suo
Article
Chemistry, Physical
Wei Hu, Jinzi Huang, Yihang Li, Lianyang Chen, Tianhao Guan, Yupeng Sun, Bo Wang, Xi Zhou, Tao Suo
Summary: This study investigates the high-temperature performance of pre-impacted 2D-C/SiC composites, revealing the correlation between weight loss and residual compressive strength. The pre-impact strengthening effect on compressive strength was found, providing new insights into the high-temperature damage tolerance of ceramic matrix composites.
Article
Materials Science, Composites
Huanfang Wang, Chuang Dong, Wei Hu, Haoyuan Dang, Chunlin Du, Yun Ou, Minxian Shi, Chao Zhang
Summary: This study investigated the thermal stability and mechanical properties of B4C-talc modified high-silica/boronphenolic composites. It was found that the compressive strength of the composite is sensitive to temperature, holding time, and fiber undulation. Different failure modes were observed at different temperatures, and the compressive strength showed different trends with varying holding time at different temperatures.
COMPOSITES SCIENCE AND TECHNOLOGY
(2022)
