Article
Automation & Control Systems
Ngoc Kien Vu, Hong Quang Nguyen
Summary: This article introduces a new MOR algorithm that aims to reduce order by preserving the dominant poles of the original system, demonstrating its effectiveness through the order reduction of a high-order controller with simulation results confirming its correctness.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2021)
Article
Computer Science, Information Systems
Jikui Wang, Zhengguo Yang, Xuewen Liu, Bing Li, Jihai Yi, Feiping Nie
Summary: Graph optimization dimensionality reduction methods have gained popularity in machine learning, but the challenge lies in selecting proper neighbors for graph construction. This article presents a novel method called Projected Fuzzy c-means with Probabilistic Neighbors (PFCM) that combines graph optimization and Fuzzy c-means. The proposed method projects the data into an optimal subspace and learns the sparse weights matrix using probabilistic neighbors and membership matrix. Experimental results demonstrate the effectiveness of the approach in clustering tasks compared to other dimensionality reduction algorithms.
INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Santosh Kumar Suman, Awadhesh Kumar
Summary: This paper presents a novel methodology called dominant pole-based modified pole clustering method (DPMPCM) for model order reduction, which has shown improved approximation to the original system compared to other methods. The method ensures stability and maintains both transient and steady-state performance of the system, proving to be efficient and superior to existing techniques in the literature.
IETE JOURNAL OF RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Yue Yin, Yehua Sheng, Jiarui Qin
Summary: This study proposes a new fuzzy time-series (FTS) prediction model, IT2-FCM-FTS, which utilizes the interval type-2 (IT2) FCM algorithm to enhance model performance. Experimental results demonstrate that the proposed model achieves superior prediction accuracy compared to the traditional ARIMA model and the FCM-based model.
APPLIED SOFT COMPUTING
(2022)
Article
Geochemistry & Geophysics
Bo Yang, Kaijun Xu, Zhan Liu
Summary: Magnetotelluric (MT) inversion algorithms play a crucial role in exploration geophysics, yet face the challenge of non-uniqueness in inverse problems. Machine learning techniques have been utilized to address the ambiguity in geological interpretations caused by the smoothness of resistivity models in MT inversions. The study introduces an effective MT inversion method based on FCM clustering algorithm, integrating geophysical inversion and geological differentiation.
SURVEYS IN GEOPHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Seyed Emadedin Hashemi, Fatemeh Gholian-Jouybari, Mostafa Hajiaghaei-Keshteli
Summary: Big data is increasingly important in various research fields. Cluster analysis is recognized as an effective process, especially for big data. The whale optimization algorithm is applied to solve the convergence problem in fuzzy C-means clustering and find more suitable cluster centers. The algorithm is validated on large data sets and proves to be more powerful and efficient compared to other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jingwei Chen, Jianyong Zhu, Hongyun Jiang, Hui Yang, Feiping Nie
Summary: This article proposes a clustering method called P_SFCM that combines principal component analysis and membership learning to improve the robustness of noise. Experimental results show that P_SFCM is competitive with other methods.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Sezai Tokat, Kenan Karagul, Yusuf Sahin, Erdal Aydemir
Summary: Performance evaluations are crucial in assessing a company's strategy, especially in terms of warehouse efficiency and productivity. This study explores the use of artificial intelligence-assisted key performance indicators to enhance warehouse loading performance and analyze different scenarios.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Qiang Chen, Weizhong Yu, Xiaowei Zhao, Feiping Nie, Xuelong Li
Summary: In this paper, a rooted Mahalanobis distance based GK-FCM model is proposed, which has better clustering performance and superior robustness than traditional GK-FCM. Due to the introduction of rooted Mahalanobis distance, the optimization of the proposed model becomes non-trivial and a novel iterative converging algorithm is developed to optimize it based on the re-weighted method.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Shaohua Huang, Yu Guo, Nengjun Yang, Shanshan Zha, Daoyuan Liu, Weiguang Fang
Summary: This study proposes a clustering method based on density peak-weighted fuzzy C-means for abnormal detection in production process using IoT data, which improves accuracy and convergence speed through feature reduction and clustering model construction.
JOURNAL OF INTELLIGENT MANUFACTURING
(2021)
Article
Engineering, Electrical & Electronic
Kaixin Zhao, Yaping Dai, Zhiyang Jia, Ye Ji
Summary: The paper proposes a general FCM clustering algorithm based on contraction mapping (cGFCM) for more general cases using Minkowski metric, providing an analytical method for calculating the parameters. The algorithm's core is the construction of a contraction mapping to update prototypes, guided by the Banach contraction mapping principle, with proven correctness and feasibility. Furthermore, experimental studies show that the proposed cGFCM algorithm extends FCM to more general cases with improved performance and reduced running time compared to other clustering methods.
Article
Mathematics
Karim El Moutaouakil, Vasile Palade, Safaa Safouan, Anas Charroud
Summary: Soft computing models based on fuzzy or probabilistic approaches provide decision system makers with the ability to handle imprecise and incomplete information. A new measurement method that combines fuzzy and probabilistic notions has been proposed, which evaluates both the degree of membership and the frequency of objects/events. This method has shown improvements in performance measures for both clustering and image compression tasks.
Article
Computer Science, Artificial Intelligence
Xin Tian, Cun Sun, Ying Sun, Yan Song, Guoliang Wei, Hui Yu, Ming Li
Summary: This paper proposes a novel fuzzy double c-means clustering method using sparse self-representation (PFD SSR) that combines the local information obtained from locality preserving projection (LPP) and global data distribution from sparse self-representation (SSR) to improve the clustering performance and handle high-dimensional biological data effectively.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Information Systems
Yasemin Eryoldas, Alptekin Durmusoglu
Summary: Metaheuristic algorithms are developed to find near-optimal solutions to optimization problems within acceptable times. Fine-tuning specific parameters of these algorithms can improve their performance. In this paper, a novel algorithm configuration method based on Latin Hypercube Hammersley Sampling and Fuzzy C-means Clustering is proposed and evaluated against state-of-the-art automatic parameter tuning methods in two experiments and four cases.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Prabha Verma, Mousumi Sinha, Siddhartha Panda
Summary: This study presents a semi-supervised outlier detection method based on fuzzy c-means clustering, using a novel threshold criterion to determine the authenticity of electronic nose responses and testing its effectiveness on experimental datasets.
IEEE SENSORS JOURNAL
(2021)
Article
Thermodynamics
Premananda Pany, R. K. Singh, R. K. Tripathi
ENERGY CONVERSION AND MANAGEMENT
(2016)
Article
Computer Science, Artificial Intelligence
Niraj Kumar Choudhary, Soumya Ranjan Mohanty, Ravindra Kumar Singh
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
(2017)
Article
Chemistry, Multidisciplinary
Vivek Gupta, R. S. Gupta, Ravindra Kumar Singh
JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE
(2014)
Proceedings Paper
Engineering, Electrical & Electronic
Sanjeev Sehgal, Surbhi Suman, Jaydeep Patel, Deepak S. Chauhan, Navneet K. Singh, Ravindra K. Singh
2017 INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON)
(2017)
Proceedings Paper
Engineering, Electrical & Electronic
Abhishek Kumar, Navin K. Paliwal, Asheesh K. Singh, Pradeep Kumar, Sanjeev Sehgal, Navneet K. Singh, Ravindra K. Singh
2017 INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON)
(2017)
Proceedings Paper
Computer Science, Theory & Methods
Sakshi Gupta, Ravindra K. Singh
2015 1ST INTERNATIONAL CONFERENCE ON FUTURISTIC TRENDS ON COMPUTATIONAL ANALYSIS AND KNOWLEDGE MANAGEMENT (ABLAZE)
(2015)
Proceedings Paper
Automation & Control Systems
V. Raviprasad, Ravindra Kumar Singh
2014 FIRST INTERNATIONAL CONFERENCE ON AUTOMATION, CONTROL, ENERGY & SYSTEMS (ACES-14)
(2014)
Proceedings Paper
Engineering, Electrical & Electronic
Niraj Kumar Choudhary, Soumya Ranjan Mohanty, Ravindra Kumar Singh
2014 INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON)
(2014)
Proceedings Paper
Automation & Control Systems
Nikhil K. Yadav, R. K. Singh
2012 2ND INTERNATIONAL CONFERENCE ON POWER, CONTROL AND EMBEDDED SYSTEMS (ICPCES 2012)
(2012)
Proceedings Paper
Automation & Control Systems
Ashish Kushwaha, R. K. Singh
2012 2ND INTERNATIONAL CONFERENCE ON POWER, CONTROL AND EMBEDDED SYSTEMS (ICPCES 2012)
(2012)
Proceedings Paper
Automation & Control Systems
Hrishitosh Bisht, R. K. Singh
2012 2ND INTERNATIONAL CONFERENCE ON POWER, CONTROL AND EMBEDDED SYSTEMS (ICPCES 2012)
(2012)
Article
Engineering, Multidisciplinary
BC Jha, K Patralekh, R Singh
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
(2000)