Article
Green & Sustainable Science & Technology
Qiang Shang, Yang Yu, Tian Xie
Summary: In this paper, a hybrid new traffic state classification method based on unsupervised clustering is proposed. The method utilizes traffic data for clustering to achieve traffic state classification, and it shows superior performance compared to other methods.
Article
Engineering, Industrial
Serap Ercan Comert, Harun Resit Yazgan, Gamze Turk
Summary: Due to the expansion of the distribution network, the emission of toxic gases from vehicles has increased, posing a threat to the environment and health. This study proposes a new method based on clustering algorithms and Hopfield Neural Network to minimize CO2 emissions in the green vehicle routing problem for a supermarket chain. The results show that the proposed approach produces very satisfactory results.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yue He, Zeshui Xu, Nana Liu
Summary: This paper investigates the relationships among different probabilistic-based expression formats and introduces transformation functions to unify them. It also proposes novel distance measures for probabilistic hesitant fuzzy sets and develops the K-medoids algorithm for fusing different information formats.
APPLIED INTELLIGENCE
(2022)
Article
Thermodynamics
Minwoo Lee
Summary: The study introduces a tool called CECS for categorizing dynamical states and detecting early warning signals of thermoacoustic instability in a combustor. By utilizing permutation entropy and Jensen-Shannon complexity, this tool can accurately identify the early warning stage of thermoacoustic instability in both experimental and numerical systems through k-medoids clustering. This framework demonstrates a novel approach for detecting the onset of impending thermoacoustic instabilities.
EXPERIMENTAL THERMAL AND FLUID SCIENCE
(2022)
Article
Engineering, Mechanical
Tianqi Gu, Hongxin Lin, Dawei Tang, Shuwen Lin, Tianzhi Luo
Summary: This article introduces a robust MTLS method, which can suppress multiple outliers within the whole domain by removing anomalous nodes through a two-step pre-process. The proposed method shows great robustness and accuracy in reconstructing simulation and experiment data.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Mathematics, Applied
Yali Zhang, Pengjian Shang
Summary: This article introduces the maximal information coefficient (MIC) as a statistical correlation analysis method and points out the limitations of existing methods in detecting relationship types. It proposes a maximum information coefficient based on K-Medoids clustering (KM-MIC) method to improve computational efficiency and optimize data partitioning.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Computer Science, Hardware & Architecture
Jian-zhao Sun, Kun Yang, Marcin Wozniak
Summary: A new data clustering algorithm is studied for wireless communication smart bracelets, which uses the K-medoids algorithm to calculate the intra-cluster, inter-cluster, or overall similarity of the bracelet data, determining the optimal number of clusters. The algorithm improves the data clustering by selecting closely surrounded and relatively dispersed data objects as initial clustering centers.
MOBILE NETWORKS & APPLICATIONS
(2023)
Article
Engineering, Industrial
Zequan Chen, Guofa Li, Jialong He, Zhaojun Yang, Jili Wang
Summary: A new parallel adaptive structure reliability analysis method called RBIK is proposed in this study, which incorporates a global convergence condition and optimal importance sampling function, and utilizes K-medoids clustering for candidate sample analysis to achieve parallel operation. RBIK distinguishes itself by focusing on rapidly satisfying the global convergence condition of the Kriging model, rather than individual candidate samples.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad Rezaei, Pasi Franti
Summary: We propose two new clustering algorithms, k-sets and k-swaps, for data with set objects. The algorithms calculate the mean of sets in a cluster and the distance between a set and the mean. The k-sets algorithm is derived from classical k-means principles and repeats assignment and update steps until convergence. We introduce the k-swaps variant as a wrapper around k-means to avoid local minima. Experimental results demonstrate that this algorithm provides more accurate clustering results compared to k-medoids and other competitive methods.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Information Systems
Panagiotis Antoniadis, Emmanouil Tsardoulias, Andreas Symeonidis
Summary: Automatic Speech Recognition (ASR) is widely used for its simplicity in human-computer interaction and intuitive communication. Building a general-purpose ASR system for less spoken languages, like Greek, is challenging due to limited speech datasets. However, in specific domains, narrow-scope ASR systems can be accurate and fast without requiring large datasets and extended training. Personalized models, developed through adaptation methods, can further enhance ASR accuracy, as demonstrated in the evaluation using a self-created database.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Ridam Srivastava, Prabhav Singh, K. P. S. Rana, Vineet Kumar
Summary: Automatic Text Summarization (ATS) is an essential field in natural language processing that helps condense large text documents for users to quickly assimilate information. This study proposed an unsupervised extractive summarization approach combining clustering with topic modeling, which outperformed similar recent works.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Krishna Kumar Sharma, Ayan Seal
Summary: Multi-view clustering is gaining more attention due to the presence of multiple views in real-world datasets, providing complementary and consensus information. An adaptive mixture similarity function based on geometric distance and S-divergence is introduced for uncertain data clustering, integrated with k-medoids to reduce the impact of outliers and noises. Extensive experimental results demonstrate the effectiveness and robustness of the proposed method against noise and outliers.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Saeed Badoozadeh, Nazila Nikdel, Sadjad Galvani, Mohammad Farhadi-Kangarlu
Summary: Renewable energy sources are integrated into power systems due to their advantages. Uncertainty and correlation between variables bring challenges to power system studies. A clustering method based on K-medoids technique is used for probabilistic assessment of power system.
IET RENEWABLE POWER GENERATION
(2023)
Article
Computer Science, Artificial Intelligence
Kalpathy Jayanth Krishnan, Kishalay Mitra
Summary: This study proposes a modified Self Organizing Map algorithm for clustering time series data. By modifying the original steps of the algorithm and using specific initialization methods and similarity measures, this algorithm outperforms other popular clustering algorithms in terms of clustering performance and computation time.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Omer N. Kenger, Zulal Diri Kenger, Eren Ozceylan, Beata Mrugalska
Summary: Smart cities are seen as a potential solution to urban problems, and it is important to evaluate their effectiveness. This study uses clustering algorithms to categorize smart cities, and the results suggest that this method is more effective than grouping cities based on indicators.
