Fang Yu¹, Wenbin Bi², Ning Cao^3,*, Jun Luo⁴, Diantang An⁵, Liqiang Ding⁴, Russell Higgs⁶

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3921-3931, 2023, DOI:10.32604/cmc.2023.028330

Abstract The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric encryption. Aiming at the problem of dynamic permission management in common access control methods, a new access control method based on smart contract under blockchain is proposed, which improves the intelligence level under blockchain technology. Firstly, the Internet attribute access control model based on smart contract is established. For the dynamic access of heterogeneous devices, the management contract, permission judgment contract and access control contract are designed; Secondly,… More >

Loc Q. Huynh¹, Ly V. Tran¹, Phuc N. K. Phan¹, Zhiqiu Yu², Son V. T. Dao^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2281-2300, 2023, DOI:10.32604/cmc.2023.034872

Abstract Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles. In this paper, we propose a novel path planning algorithm–Intermediary RRT*-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer, Particle swarm optimization (PSO), for fine-tuning and enhancement. In Phase 1, the start and goal trees are initialized at the starting and goal positions, respectively, and the intermediary tree is initialized at a random unexplored region of the search space. The… More >

Shuaijun Wang¹, Wenqiong Tu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09926

Abstract Cellulose nanofibrils (CNFs) and the continuously assembled microfibers have shown transversely isotropic behavior in many studies. Due to fact that the size of CNFs and the assembled microfibers is at the nano and micro scale, respectively, the characterization of their mechanical properties is extremely challenge. That greatly hinders the accurate multi-scale modeling and design of CNFs-based materials. In our study, we have characterized the elastic constants of both CNFs microfibers and CNFs through a Multi-scale Optimization Inversion technology. Through the tensile test of CNFs microfibers reinforced resin with different volume fractions and the micromechanics model of microfibers reinforced resin, the… More >

Kaili Shao¹, Hui Fu¹, Ying Song², Bo Wang^3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2259-2274, 2023, DOI:10.32604/csse.2023.041454

Abstract To serve various tasks requested by various end devices with different requirements, end-edge-cloud (E2C) has attracted more and more attention from specialists in both academia and industry, by combining both benefits of edge and cloud computing. But nowadays, E2C still suffers from low service quality and resource efficiency, due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility. To address these issues, this paper focuses on task offloading, which makes decisions that which resources are allocated to tasks for their processing. This paper first formulates the problem into binary non-linear programming and… More >

Xujun Li^*, Guodong Fang, Liming Rao, Tengze Zhang

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2671-2688, 2023, DOI:10.32604/csse.2023.038154

Abstract To improve the tracking accuracy of persons in the surveillance video, we proposed an algorithm for multi-target tracking persons based on deep learning. In this paper, we used You Only Look Once v5 (YOLOv5) to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) to do cascade matching and Intersection Over Union (IOU) matching of person targets between different frames. To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification (ReID) network in the process of cascade matching, we introduced Spatial Relation-aware… More >

Mohammad S. Al-kahtani¹, Zahid Mehmood^2,3,*, 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) and genetic algorithm (GA)) for… More >

Junfeng Sun¹, Chenghai Li¹, Yafei Song^1,*, 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, a prediction model of TCAN-BiGRU… More >

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. Field observations confirmed well these… More >

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 average loss of photovoltaic output… More >

Qi Zhuang^a,* , Zhuo Chen^b, Dong Liu^c, Yangyang Tian^d

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 of the model is evaluated… More >