This paper examines the current state of the Enterprise Marketing Ecosystem (EME), especially in the context of Digital Transformation (DT), highlighting both the challenges and opportunities it presents. A significan...
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This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the...
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This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the proposed SFS-ANFIS model, the predicting results were compared with ANFIS and three hybrid methodologies based on ANFIS combined with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Accurate prediction of uniaxial compressive strength (UCS) is of great significance for all geotechnical projects such as tunnels and dams. Hence, this study proposes the use of SFS-ANFIS, GA-ANFIS, DE-ANFIS, PSO-ANFIS, and ANFIS models to predict UCS. In this regard, the fresh water tunnel of Pahang-Selangor located in Malaysia was considered and the requirement data samples were collected. Different statistical metrics such as coefficient of determination (R-2) and mean absolute error were used to evaluate the models. Referring to the efficiency results of SFS-ANFIS, it can be found that the SFS-ANFIS (with the R-2 of 0.981) has higher ability than PSO-ANFIS, DE-ANFIS, GA-ANFIS, and ANFIS models in predicting the UCS.
Enhancing the control performance of the tractor-trailer air suspensions can improve the driving efficiency and handling ability (DP-HA) of the driver. Based on the dynamic model of a multi-axle tractor-trailer using ...
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Sidelobe calculation is a common procedure in phased array synthesis. However, the traditional sidelobe calculation method through pattern simulation and peak searching costs a large amount of time. In this letter, we...
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Sidelobe calculation is a common procedure in phased array synthesis. However, the traditional sidelobe calculation method through pattern simulation and peak searching costs a large amount of time. In this letter, we derive a formula for fast sidelobe level calculation in arbitrary 2-D phased arrays. The formula describes the relationship between sidelobe (change throughout the article) level and the element feeding excitations. The result of numerical experiments shows that the proposed method is as accurate as the search method. When applying our formula in a genetic algorithm for sidelobe suppression, the proposed method consumes 0.1% of the computing time compared with the search method in a nonuniformly distributed array of 50 elements.
In recent decades, Sensor nodes (SNs) are used in numerous uses of heterogeneous wireless sensor networks (HWSNs) to obtain a variety of sensing data sources. Sink mobility shows a significant part in the enhancement ...
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In recent decades, Sensor nodes (SNs) are used in numerous uses of heterogeneous wireless sensor networks (HWSNs) to obtain a variety of sensing data sources. Sink mobility shows a significant part in the enhancement of sensor system execution, energy utilization, and lifetime. To manage sink mobility, rendezvous points (RPs) are introduced where some SNs are chosen as RPs, and the non-RP nodes convey the information to the cluster heads (CHs). The CHs then forward their information to the nearby RPs. To determine the set of RPs and travelling path of mobile sinks (MSs) that visits these RPs is quite challenging. This work presents an energy-efficient SOSS based routing method that depends on RPs and multiple MSs in HWSNs. At first, all the heterogeneous nodes are distributed into the number of clusters using mean shift clustering (MSC). Then, the Bald eagle search (BES) algorithm is used for an optimal selection of CHs whereas multiple MS is employed for effective data gathering. The use of multiple MSs can enhance the data collection efficiency and decreases the energy utilization for HWSNs. Finally, the hybrid seagull optimization and salp swarm (SOSS) algorithm is used to find the RPs and travelling routes of MS. The entire simulation work of the heterogeneous network is simulated in the NS2 platform. The simulation outcomes display that the suggested method provides superior performance in HWSN than other current routing protocols.
Nowadays, numerous algorithms on power allocation have been proposed for maximizing the EE (Energy efficiency) and SE (Spectral efficiency) in the Distributed Antenna System (DAS). Moreover, the conservative technique...
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Nowadays, numerous algorithms on power allocation have been proposed for maximizing the EE (Energy efficiency) and SE (Spectral efficiency) in the Distributed Antenna System (DAS). Moreover, the conservative techniques employed for power allocation seem to be problematic, due to their high computational complexity. The main objective of this paper focuses on optimizing the power allocation in order to enhance the EE and SE along with the improved antenna capacity using an effective optimization approach with the clustering model. To obtain the optimized power allocation and antenna capacity, Multi-scale resource Grasshopper optimization Algorithm (Multi-scale resource GOA) scheme is proposed and employed. Furthermore, clustering is developed based on the Discriminative cluster-based Expectation maximization (DC-EM) clustering algorithms, which also helps to reduce the interference rate and computational complexity. The performance analysis is made under various scenarios and circumstances. The proposed system (DAS with GOA-EM) is assessed and compared with the existing approaches in terms of both the EE and SE, which demonstrates that its superiority.
Autonomous vehicles for intelligent surveillance in rural areas increasingly demand low-cost and reliable data collection technologies to perform dense monitoring across extended areas. Backscattering communication ha...
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Autonomous vehicles for intelligent surveillance in rural areas increasingly demand low-cost and reliable data collection technologies to perform dense monitoring across extended areas. Backscattering communication has been employed for this purpose, primarily for low-cost and energy efficiency reasons. This paper considers a backscattering data collection system empowered by unmanned aerial vehicles (UAVs) to overcome the challenge of wireless coverage and provide backscattering tags with physical-layer security. Relevant prior works only focused on the secrecy of backscattering communications, while the limited battery of UAVs was overlooked during the underlying vehicle control. This paper aims to jointly optimize the trajectory of multiple UAVs and choice of tags, as well as tags' reflection parameters, to manage data leakage and total energy consumed by UAVs during a round of data collection. Our specific contributions are threefold. (1) We propose a 3D multiUAV backscattering data collection framework and formulate an optimization problem to maximize the ratio of secrecy across all tags to the power consumption of UAVs subject to some practical constraints. (2) We show that our problem is non-convex and partition it into three sub-problems, transform objective functions, and relax certain constraints to obtain approximate convex problems that yield suboptimal solutions. (3) We evaluate the efficacy of our proposed intelligent security protocol for UAV-assisted data collection, compare its performance with some baseline schemes, our protocal achieve leading performance in terms of secrecy energy efficiency. We also provide the impact of parameters on the secrecy energy efficiency, as well as quantify its complexity via extensive simulations.
In this paper, we aim at the problem of rapid loss of population diversity encountered in the application of PSO algorithm. A feedback strategy is proposed to maintain population diversity. In order to balance detecti...
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In this paper, we aim at the problem of rapid loss of population diversity encountered in the application of PSO algorithm. A feedback strategy is proposed to maintain population diversity. In order to balance detection and development capabilities, the adjustment of inertia weights is also studied, and a new adaptive particle swarm optimization algorithm is proposed. Through the iterative comparison test of APSO algorithm and LDW algorithm, it is confirmed that APSO has higher robustness and accuracy.
In structural design of steel frames, in order to achieve proper safety, the effect of uncertainties in the design and loading parameters of the structure must be considered. This approach is obtained by defining a re...
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In structural design of steel frames, in order to achieve proper safety, the effect of uncertainties in the design and loading parameters of the structure must be considered. This approach is obtained by defining a reliability index. In this study, the Modified Dolphin Monitoring (MDM) operator was used to evaluate the reliability index of three well-known steel frame structures based on the Hasofer-Lind method. The reliability index was evaluated using the EVPS and VPS algorithms and with considering the MDM operator on them. The constraint of the last story drift is considered as limit state function. The random variables consist of external loads, modulus of elasticity, moment of inertia and cross-sectional areas. According to the number of evaluations of the limit state function, the results show the efficiency of this method in comparison to the Monte Carlo simulation method. Also, the values of the most probable point (MPP) are examined.
This paper addresses a new attempt of the AEFA to define the uncertain model parameters of TDM of PV units. Two commercial PV modules are investigated with intensive simulations and necessary analysis. The parameters ...
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This paper addresses a new attempt of the AEFA to define the uncertain model parameters of TDM of PV units. Two commercial PV modules are investigated with intensive simulations and necessary analysis. The parameters of AFEA based TDM are validated thru the empirical dataset points. Necessary performance assessments are made which signify the AEFA results compared to others. Dynamic simulations of MPP is performed.
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