Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (131)
  • Open Access

    ARTICLE

    Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

    Mohammad S. Al-kahtani1, Zahid Mehmood2,3,*, Tariq Sadad4, Islam Zada5, Gauhar Ali6, Mohammed ElAffendi6

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2279-2290, 2023, DOI:10.32604/iasc.2023.037673

    Abstract Cybersecurity threats are increasing rapidly as hackers use advanced techniques. As a result, cybersecurity has now a significant factor in protecting organizational limits. Intrusion detection systems (IDSs) are used in networks to flag serious issues during network management, including identifying malicious traffic, which is a challenge. It remains an open contest over how to learn features in IDS since current approaches use deep learning methods. Hybrid learning, which combines swarm intelligence and evolution, is gaining attention for further improvement against cyber threats. In this study, we employed a PSO-GA (fusion of particle swarm optimization (PSO) More >

  • Open Access

    ARTICLE

    Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

    Junfeng Sun1, Chenghai Li1, Yafei Song1,*, Peng Ni2, Jian Wang1

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 993-1021, 2023, DOI:10.32604/csse.2023.039215

    Abstract The accuracy of historical situation values is required for traditional network security situation prediction (NSSP). There are discrepancies in the correlation and weighting of the various network security elements. To solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network is proposed, which is optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO). This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data. Furthermore,… More >

  • Open Access

    ARTICLE

    GIS analysis of hypsometry and basin asymmetry factor in Htab river basin and tectonic implications (Central Atlas, Tunisia)

    Ali Chaieb, Noamen Rebai

    Revue Internationale de Géomatique, Vol.29, No.3, pp. 287-296, 2019, DOI:10.3166/rig.2019.00082

    Abstract The geomorphology of the Htab river watershed is mainly guided by the action of the E-W Kasserine fault. This activity influenced the installed hydrographic system. To see the impact of neotectonic on the Htab river watershed, four morphometric indices were applied: the elongation ratio, the hypsometric curve, the hypsometric integral, and the asymmetry factor. The processing and calculation of these indices were based on global DEMs (Digital Elevation Models). The result obtained shows an important link between the activity of the Kasserine fault, the geomorphological behavior of the Htab river watershed and the hydrographic network. More >

  • Open Access

    ARTICLE

    PSO-BP-Based Optimal Allocation Model for Complementary Generation Capacity of the Photovoltaic Power Station

    Zhenfang Liu*, Haibo Liu, Dongmei Zhang

    Energy Engineering, Vol.120, No.7, pp. 1717-1727, 2023, DOI:10.32604/ee.2023.027968

    Abstract To improve the operation efficiency of the photovoltaic power station complementary power generation system, an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed. Particle Swarm Optimization and BP neural network are used to establish the forecasting model, the Markov chain model is used to correct the forecasting error of the model, and the weighted fitting method is used to forecast the annual load curve, to complete the optimal allocation of complementary generating capacity of photovoltaic power stations. The experimental results show that this method reduces the More >

  • Open Access

    ARTICLE

    PREDICTING THE WAX DEPOSITION RATE BASED ON EXTREME LEARNING MACHINE

    Qi Zhuanga,* , Zhuo Chenb, Dong Liuc, Yangyang Tiand

    Frontiers in Heat and Mass Transfer, Vol.19, pp. 1-8, 2022, DOI:10.5098/hmt.19.19

    Abstract In order to improve the accuracy and efficiency of wax deposition rate prediction of waxy crude oil in pipeline transportation, A GRA-IPSO-ELM model was established to predict wax deposition rate. Using Grey Relational Analysis (GRA) to calculate the correlation degree between various factors and wax deposition rate, determine the input variables of the prediction model, and establish the Extreme Learning Machine (ELM) prediction model, improved particle swarm optimization (IPSO) is used to optimize the parameters of ELM model. Taking the experimental data of wax deposition in Huachi operation area as an example, the prediction performance More >

  • Open Access

    ARTICLE

    Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

    Firas M. Makahleh1, Ayman Amer2, Ahmad A. Manasrah1, Hani Attar2, Ahmed A. A. Solyman3, Mehrdad Ahmadi Kamarposhti4,*, Phatiphat Thounthong5

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3317-3337, 2023, DOI:10.32604/cmc.2023.035690

    Abstract In this paper, the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, their distribution and generation are investigated. First, the optimal placement and capacity of the energy storage is taken into consideration, then, the charge-discharge strategy for this equipment is determined. Here, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to calculate the minimum and maximum load in the network with the presence of energy storage systems. The energy storage systems were utilized in a distribution system with the aid of a peak load More >

  • Open Access

    ARTICLE

    BN-GEPSO: Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization

    Muhammad Saad Salman1, Ibrahim M. Almanjahie2,3, AmanUllah Yasin1, Ammara Nawaz Cheema1,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4217-4229, 2023, DOI:10.32604/cmc.2023.034960

    Abstract At present Bayesian Networks (BN) are being used widely for demonstrating uncertain knowledge in many disciplines, including biology, computer science, risk analysis, service quality analysis, and business. But they suffer from the problem that when the nodes and edges increase, the structure learning difficulty increases and algorithms become inefficient. To solve this problem, heuristic optimization algorithms are used, which tend to find a near-optimal answer rather than an exact one, with particle swarm optimization (PSO) being one of them. PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds (how they search… More >

  • Open Access

    ARTICLE

    Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

    Caiyun Liu, Peng Liu*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3221-3242, 2023, DOI:10.32604/iasc.2023.035114

    Abstract Shared manufacturing is recognized as a new point-to-point manufacturing mode in the digital era. Shared manufacturing is referred to as a new manufacturing mode to realize the dynamic allocation of manufacturing tasks and resources. Compared with the traditional mode, shared manufacturing offers more abundant manufacturing resources and flexible configuration options. This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment, and the characteristics of shared manufacturing resource allocation. The execution of manufacturing tasks, in which candidate manufacturing resources enter or exit at various More >

  • Open Access

    ARTICLE

    An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II

    Lei Xing1, Yakui Liu2,3,*

    Energy Engineering, Vol.120, No.4, pp. 1023-1043, 2023, DOI:10.32604/ee.2023.025335

    Abstract The optimal allocation of integrated energy system capacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy, which has attracted many attentions. However, the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters. To solve the above problem, the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms. Firstly, an integrated energy system consisting of the photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and energy storage is established. Meanwhile, the minimum economic cost, the maximum wind and PV… More >

  • Open Access

    ARTICLE

    Comparative Analysis of Equal and Unequal Grounding Grid Configurations by Compression Ratio and Least Square Curve Fitting Techniques

    M. Soni*, Abraham George

    Energy Engineering, Vol.120, No.3, pp. 597-616, 2023, DOI:10.32604/ee.2023.021301

    Abstract The primary aim of the power system grounding is to safeguard the person and satisfying the performance of the power system to maintain reliable operation. With equal conductor spacing grounding grid design, the distribution of the current in the grid is not uniform. Hence, unequal grid conductor span in which grid conductors are concentrated more at the periphery is safer to practice than equal spacing. This paper presents the comparative analysis of two novel techniques that create unequal spacing among the grid conductors: the least-square curve fitting technique and the compression ratio technique with equal More >

Displaying 21-30 on page 3 of 131. Per Page