Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (522)
  • Open Access

    ARTICLE

    Boundedness and Positivity Preserving Numerical Analysis of a Fuzzy-Parameterized Delayed Model for Foot and Mouth Disease Dynamics

    Muhammad Tashfeen1, Fazal Dayan1, Muhammad Aziz ur Rehman1, Thabet Abdeljawad2,3,4,5,*, Aiman Mukheimer2

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2527-2554, 2024, DOI:10.32604/cmes.2024.056269 - 31 October 2024

    Abstract Foot-and-mouth disease (FMD) is a viral disease that affects cloven-hoofed animals including cattle, pigs, and sheep, hence causing export bans among others, causing high economic losses due to reduced productivity. The global effect of FMD is most felt where livestock rearing forms an important source of income. It is therefore important to understand the modes of transmission of FMD to control its spread and prevent its occurrence. This work intends to address these dynamics by including the efficacy of active migrant animals transporting the disease from one area to another in a fuzzy mathematical modeling… More >

  • Open Access

    ARTICLE

    Fuzzy Comprehensive Analysis of Static Mixers Used for Selective Catalytic Reduction in Diesel Engines

    Xin Luan1,*, Guoqing Su1, Hailong Chen1, Min Kuang1,2

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2459-2473, 2024, DOI:10.32604/fdmp.2024.054621 - 28 October 2024

    Abstract The proper selection of a relevant mixer generally requires an effective assessment of several models against the application requirements. This is a complex task, as traditional evaluation methods generally focus only on a single aspect of performance, such as pressure loss, mixing characteristics, or heat transfer. This study assesses a urea-based selective catalytic reduction (SCR) system installed on a ship, where the installation space is limited and the distance between the urea aqueous solution injection position and the reactor is low; therefore, the static mixer installed in this pipeline has special performance requirements. In particular,… More >

  • Open Access

    ARTICLE

    Multi-Lever Early Warning for Wind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

    Huan Ma1, Linlin Ma2, Zengwei Wang3,*, Zhendong Li3, Yuanzhen Zhu1, Yutian Liu3

    Energy Engineering, Vol.121, No.11, pp. 3133-3160, 2024, DOI:10.32604/ee.2024.055051 - 21 October 2024

    Abstract With the increasing penetration of renewable energy in power system, renewable energy power ramp events (REPREs), dominated by wind power and photovoltaic power, pose significant threats to the secure and stable operation of power systems. This paper presents an early warning method for REPREs based on long short-term memory (LSTM) network and fuzzy logic. First, the warning levels of REPREs are defined by assessing the control costs of various power control measures. Then, the next 4-h power support capability of external grid is estimated by a tie line power prediction model, which is constructed based More > Graphic Abstract

    Multi-Lever Early Warning for Wind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

  • Open Access

    ARTICLE

    Seasonal Short-Term Load Forecasting for Power Systems Based on Modal Decomposition and Feature-Fusion Multi-Algorithm Hybrid Neural Network Model

    Jiachang Liu1,*, Zhengwei Huang2, Junfeng Xiang1, Lu Liu1, Manlin Hu1

    Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

    Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

  • Open Access

    ARTICLE

    Distributed Robust Scheduling Optimization of Wind-Thermal-Storage System Based on Hybrid Carbon Trading and Wasserstein Fuzzy Set

    Gang Wang*, Yuedong Wu, Xiaoyi Qian, Yi Zhao

    Energy Engineering, Vol.121, No.11, pp. 3417-3435, 2024, DOI:10.32604/ee.2024.052268 - 21 October 2024

    Abstract A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems. A hybrid carbon trading mechanism that combines short-term and long-term carbon trading is constructed, and a fuzzy set based on Wasserstein measurement is proposed to address the uncertainty of wind power access. Moreover, a robust scheduling optimization method for wind–fire storage systems is formed. Results of the multi scenario comparative analysis of practical cases show that the More >

  • Open Access

    ARTICLE

    A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

    Chandraumakantham Om Kumar1,*, Sudhakaran Gajendran2, Suguna Marappan1, Mohammed Zakariah3, Abdulaziz S. Almazyad4

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 471-501, 2024, DOI:10.32604/cmc.2024.054966 - 15 October 2024

    Abstract The security of the wireless sensor network-Internet of Things (WSN-IoT) network is more challenging due to its randomness and self-organized nature. Intrusion detection is one of the key methodologies utilized to ensure the security of the network. Conventional intrusion detection mechanisms have issues such as higher misclassification rates, increased model complexity, insignificant feature extraction, increased training time, increased run time complexity, computation overhead, failure to identify new attacks, increased energy consumption, and a variety of other factors that limit the performance of the intrusion system model. In this research a security framework for WSN-IoT, through… More >

  • Open Access

    ARTICLE

    Fuzzy Machine Learning-Based Algorithms for Mapping Cumin and Fennel Spices Crop Fields Using Sentinel-2 Satellite Data

    Shilpa Suman1, Abhishek Rawat2,*, Anil Kumar3, S. K. Tiwari4

    Revue Internationale de Géomatique, Vol.33, pp. 363-381, 2024, DOI:10.32604/rig.2024.053981 - 18 September 2024

    Abstract In this study, the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means (PCM) and Noise Clustering (NC) classifiers were examined and mapped the cumin and fennel rabi crop. Two training sample selection approaches that have been investigated in this study are “mean” and “individual sample as mean”. Both training sample techniques were applied to the PCM and NC classifiers to classify the two indices approach. Both approaches have been studied to decrease spectral information in temporal data processing. The Modified Soil Adjusted Vegetation Index 2 (MSAVI-2) and Class-Based Sensor… More >

  • Open Access

    ARTICLE

    A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

    Jyun-Guo Wang*

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1149-1170, 2024, DOI:10.32604/csse.2024.052931 - 13 September 2024

    Abstract In many Eastern and Western countries, falling birth rates have led to the gradual aging of society. Older adults are often left alone at home or live in a long-term care center, which results in them being susceptible to unsafe events (such as falls) that can have disastrous consequences. However, automatically detecting falls from video data is challenging, and automatic fall detection methods usually require large volumes of training data, which can be difficult to acquire. To address this problem, video kinematic data can be used as training data, thereby avoiding the requirement of creating… More >

  • Open Access

    ARTICLE

    Fuzzy Multi-Criteria Decision Support System for the Best Anti-Aging Treatment Selection Process through Normal Wiggly Hesitant Fuzzy Sets

    Daekook Kang1, Ramya Lakshmanaraj2, Samayan Narayanamoorthy2, Navaneethakrishnan Suganthi Keerthana Devi2, Samayan Kalaiselvan3, Ranganathan Saraswathy4, Dragan Pamucar5,6,7,*, Vladimir Simic8,9

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4947-4972, 2024, DOI:10.32604/cmc.2024.055260 - 12 September 2024

    Abstract This socialized environment among educated and developed people causes them to focus more on their appearance and health, which turns them towards medical-related treatments, leading us to discuss anti-aging treatment methods for each age group, particularly for urban people who are interested in this. Some anti-aging therapies are used to address the alterations brought on by aging in human life without the need for surgery or negative effects. Five anti-aging therapies such as microdermabrasion or dermabrasion, laser resurfacing anti-aging skin treatments, chemical peels, dermal fillers for aged skin, and botox injections are considered in this… More >

  • Open Access

    ARTICLE

    Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

    Xianghui Hu1, Yiming Tang2,3, Witold Pedrycz3,4, Jiuchuan Jiang5,*, Yichuan Jiang1,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4917-4945, 2024, DOI:10.32604/cmc.2024.054775 - 12 September 2024

    Abstract Traditional Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) clustering algorithms are data-driven, and their objective function minimization process is based on the available numeric data. Recently, knowledge hints have been introduced to form knowledge-driven clustering algorithms, which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints. However, these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself; they require the assistance of evaluation indices. Moreover, knowledge hints are usually used as part of the data structure (directly replacing some clustering centers),… More >

Displaying 1-10 on page 1 of 522. Per Page