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With the fast development of giant LEO constellations, the effective spectrum utilization has been regarded as one of the key orientations for satellite communications. This paper focuses on improving the spectrum utilization efficiency of satellite communications by proposing a non-continuous orthogonal frequency division multiplexing (NC-OFDM) method. Based on the models of NC-OFDM system, we first propose a sub-carrier allocation method by using spectrum sensing to efficiently perceive and utilize the spectrum holes in the satellite communication system. Then, a hybrid genetic particle swarm optimization method is adopted to allocate the channel resources effectively. Finally, simulation results verify that the proposed algorithm can significantly improve the spectrum efficiency of satellite communications.

This paper aims to explore torque optimization control issue in the turning of EV (Electric Vehicles) with motorized wheels for reducing energy consumption in this process. A three-degree-of-freedom (3-DOF) vehicle dynamics model is used to analyze the total longitudinal force of the vehicle and explain the influence of torque vectoring distribution (TVD) on turning resistance. The Genetic Algorithm-Particle Swarm Optimization Hybrid Algorithm (GA-PSO) is used to optimize the torque distribution coefficient offline. Then, a torque optimization control strategy for obtaining minimum turning energy consumption online and a torque distribution coefficient (TDC) table in different cornering conditions are proposed, with the consideration of vehicle stability and possible maximum energy-saving contribution. Furthermore, given the operation points of the in-wheel motors, a more accurate TDC table is developed, which includes motor efficiency in the optimization process. Various simulation results showed that the proposed torque optimization control strategy can reduce the energy consumption in cornering by about 4% for constant motor efficiency ideally and 19% when considering the motor efficiency changes in reality.

Wake re-categorization (RECAT) has been implemented to improve runway capacity, and consequently, aircraft arrival runway occupancy time has become a crucial factor influencing runway capacity. Accurate prediction of the runway occupancy time can assist controllers in determining aircraft separation, thereby enhancing the operational efficiency of the runway. In this study, the GA–PSO algorithm is utilized to optimize the Back Propagation neural network prediction model using Quick access recorder data from various domestic airports, achieving high-precision prediction. Additionally, the SHapley Additive explanation model is applied to quantify the effect of each characteristic parameter on the arrival runway occupancy time, resulting in the prediction of aircraft arrival runway occupancy time. This model can provide a foundation for improving runway operation efficiency and technical support for the design of airport runway and taxiway structure.

Fiber orientation induced by injection molding of short-fiber-reinforced composites causes anisotropy in material properties and produces warping. Fiber-orientation distribution is very important to research for mold design and quality to produce sound molded parts. In this study, three kinds of methods are used to solve the optimization problem. Fiber-orientation distribution is described by fiber-orientation tensor variation. The objective function is a minimum problem of the fiber-orientation tensor variation. Parameters such as fiber content, fiber aspect ratio, melting temperature, injection pressure, holding pressure, and filling time are considered as design variables. Based on orthogonal experiment design, Moldflow software is used in the fiber-reinforced acrylonitrile butadiene styrene composite injection molding. The effects of process parameters for the plastic part are studied using the signal-to-noise ratio. The most important design parameter influencing fiber-orientation tensor variation is determined by finite element analysis results based on the analysis of variance. The optimization model is established on the basis of the back propagation neural network. The Taguchi, the particle swarm optimization, and genetic algorithm–particle swarm optimization hybrid algorithm are used to find the minimum fiber-orientation tensor variation value. Results show that the quality index of the fiber-orientation tensor variation in the part is improved.

Face recognition (FR) from video offers a challenging issue in the area of image exploration along with computer visualization, furthermore, as such recognized heaps of deem over the previous years on account of its numerous applications in the scope of domains. The chief challenges in the video centered FR are the restraint of the camera hardware, the random poses captured by means of the camera as the subject is noncooperative, and changes in the resolutions owing to disparate lighting conditions, noise along with blurriness. Numerous FR algorithms were generated in the previous decennium, although these approaches are much better, the image’s accuracy is less only. To trounce such difficulties, an efficient FR system centered on hybrid optimized Kernel ELM is proposed. The proposed work encompasses five phases, explicitly (i) preprocessing, (ii) Face detection, (iii) Feature Extraction, (iv) Feature Reduction, and (v) Classification. In the preliminary phase, the data-base video clips are converted in to the frames in which pre-processing are performed utilizing a Modified wiener filter to eliminate the noise. The succeeding phase is employed for detecting the pre-processed image via the viola–jones (V-J). With this technique, the face is identified. After that, the features are extorted. The extracted ones then will be provided as the input to the Modified PCA approach. Then, perform classification operation using hybrid (PSO-GA) optimized Kernel ELM approach. The similar process is replicated for query images (QI). At last, the recognized image is found. Experimental results contrasted with the previous ANFIS classifier and existing methods concerning precision, accuracy, recall, F-measure, sensitivity along with specificity. The proposed FR system indicates better accuracy when compared with the prevailing methods.

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although these methods are approximate methods (i.e. their solutions are good, but probably not optimal), they do not require the derivatives of the objective function and constraints. Also, the heuristics use probabilistic transition rules instead of deterministic rules. Here, an evolutionary algorithm based on the hybrid genetic algorithm (GA) and particle swarm optimization (PSO), denoted by HGAPSO, is developed in order to solve force method-based simultaneous analysis and design problems for frame structures. Suitability of the HGAPSO algorithm is compared to both GA and PSO for all the design examples, demonstrating its efficiency and superiority, especially for frames with a larger number of redundant forces.