This paper proposes a metasurface filter integrated with a base station antenna for sub-6 GHz 5G communications. The metasurface filter, composed of a pixel-based geometric structure, is optimized using the conformati...
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ISBN:
(纸本)9781665494182
This paper proposes a metasurface filter integrated with a base station antenna for sub-6 GHz 5G communications. The metasurface filter, composed of a pixel-based geometric structure, is optimized using the conformational space annealing (CSA) algorithm. The proposed metasurface filter exhibits bandpass characteristics around the 3.5 GHz frequency, commonly used in sub-6 GHz 5G networks. The performance of the metasurface filter combined with a 4x1 patch antenna array was measured in a fully anechoic chamber. The measured gain pattern shows that the metasurface filter passes the signal with a small insertion loss in the passband and effectively suppresses out-of-band frequencies, providing frequency-selective performance.
In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes...
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In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes a Grid-based Variable Probability Rapidly-exploring Random Tree algorithm (GVP-RRT) for narrow passages. The algorithm introduced in this paper preprocesses the map through gridization to extract features of different path regions. Subsequently, it employs random growth with variable probability density based on the features of path regions using various strategies based on grid, probability, and guidance to enhance the probability of growth in narrow passages, thereby improving the completeness of the algorithm. Finally, the planned route is subjected to path re-optimization based on the triangle inequality principle. The simulation results demonstrate that the planning success rate of GVP-RRT in complex narrow channels is increased by 11.5-69.5% compared with other comparative algorithms, the average planning time is reduced by more than 50%, and the GVP-RRT has a shorter average planning path length.
Accurate identification of coal and gangue is a crucial guarantee for efficient and safe mining of top coal caving face. This article proposes a coal-gangue recognition method based on an improved beluga whale optimiz...
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Accurate identification of coal and gangue is a crucial guarantee for efficient and safe mining of top coal caving face. This article proposes a coal-gangue recognition method based on an improved beluga whalea* optimization algorithm (IBWO), convolutional neural network, and long short-term memory network (CNN-LSTM) multi-modal fusion model. First, the mutation and memory library mechanisms are introduced into the beluga whale optimization to explore the solution space fully, prevent falling into local optimum, and accelerate the convergence process. Subsequently, the image mapping of the audio signal and vibration signal is performed to extract Mel-Frequency Cepstral Coefficients (MFCC) features, generating rich sample data for CNN-LSTM. Then the multi-head attention mechanism is introduced into CNN-LSTM to speed up the training speed and improve the classification accuracy. Finally, the IBWO-CNN-LSTM coal-gangue recognition model is constructed by the optimal hyperparameter combination obtained by IBWO to realize the automatic recognition of coal-gangue. The benchmark function proves that IBWO is superior to othera* optimization algorithms. By building an experimental platform for the impact of coal and gangue falling on the tail beam of hydraulic support, multiple experimental data collection is carried out. The experimental results show that the proposed coal-gangue recognition model has better performance than other recognition models, and the accuracy rate reaches 95.238%. The multi-modal fusion strategy helps to improve the accuracy and robustness of coal-gangue recognition.
In this letter, we consider an air-and-ground cooperative network, where several aerial base stations (ABS) help terrestrial base stations (TBS) for coverage enhancement. In this network, we first quantify the space-t...
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In this letter, we consider an air-and-ground cooperative network, where several aerial base stations (ABS) help terrestrial base stations (TBS) for coverage enhancement. In this network, we first quantify the space-time coverage ratio (STCR) by fully considering the antenna models and the dynamic of the ABS, and then formulate a joint ABS deployment and TBS antenna downtilt optimization problem with the objective to maximize the STCR of the concerned area. The objective function involves many control variables and judgement operations, which make the problem very complex. To solve the problem effectively, we first adopt the genetic algorithm (GA). Using the solutions of the GA as training samples, we propose a deep neural network architecture to further reduce the computational time. Simulation results indicate that the proposed GA significantly improves the coverage ratio and the deep neural network (DNN) architecture achieves orders of magnitude acceleration in computational time with acceptable performance.
Bayesian optimization (BO) is one of the most powerful strategies to solve expensive blackbox optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of ...
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ISBN:
(数字)9781624107047
ISBN:
(纸本)9781624107047
Bayesian optimization (BO) is one of the most powerful strategies to solve expensive blackbox optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of the curse of dimensionality. In this paper, to solve high dimensional optimization problems, we propose to incorporate linear embedding subspaces of small dimension to efficiently perform the optimization. An adaptive learning strategy for these linear embeddings is carried out in conjunction with the optimization. The resulting BO method, named EGORSE, combines in an adaptive way both random and supervised linear embeddings. EGORSE has been compared to state-of-the-art algorithms and tested on academic examples with a number of design variables ranging from 10 to 600. The obtained results show the high potential of EGORSE to solve high-dimensional black-box optimization problems, both in terms of CPU time and number of calls to the expensive black-box.
The goal of this study is to identify the best geometry for a tactical pod to reduce viscous drag. To accomplish this, the body's geometry has been mathematically described and controlled by a set of shape paramet...
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ISBN:
(数字)9781624107047
ISBN:
(纸本)9781624107047
The goal of this study is to identify the best geometry for a tactical pod to reduce viscous drag. To accomplish this, the body's geometry has been mathematically described and controlled by a set of shape parameters. The baseline geometry can then be changed to produce different iterations of the pod. Then, a simple method is used to qualitatively predict the drag due to viscosity based on the momentum deficit of the boundary layer at the body's trailing edge. This technique has been improved by incorporating dynamic boundary layer transition calculation when evaluating different geometries. The viscous drag prediction method and a (1+1)-Evolution Strategya* optimization algorithm are used to iteratively alter the geometry by perturbing the shape parameters until a shape with the least drag is achieved. At the final stage, the results from the optimization process are compared to the solution of the Transition-SST viscous model for both the baseline and optimized geometries, from which it is confirmed that the viscous drag calculation method based on the momentum deficit of the boundary layer can be effectively used to optimize an axisymmetric body and that including the dynamic calculation of the boundary layer transition is a valid improvement to the method that could lead to better results during the optimization process.
This article presents a framework to reduce energy consumption in a floor shop press based on Industrializable Industrial Internet of Things (I3oT). The I3oT proposes the development of IIoT tools using the informatio...
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This article presents a framework to reduce energy consumption in a floor shop press based on Industrializable Industrial Internet of Things (I3oT). The I3oT proposes the development of IIoT tools using the information available in the system, without adding any additional sensors. Based on this philosophy, we proposed to develop the C360 criterion in our previous works, which allowed to extract all the information available in the stamping presses for the development of I3oT applications. In this article, we propose the development of a framework to optimize the parameters accessible from the C360 criterion for energy saving in the stamping process. Regarding the three parameters that can be modified and that affect energy consumption, that is, counterbalance pressure, tonnage and press speed, we will work with the first two in this paper. At the end of the article, the results obtained from the presses installed at Ford factory in Almussafes (Valencia) are shown based on their adjustment.
In order to assess the compressive strength (CS) of high-performance concrete (HPC) prepared with fly ash and blast furnace slag, several artificial-based analytics were applied. This study, it was employed the Chimp ...
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In order to assess the compressive strength (CS) of high-performance concrete (HPC) prepared with fly ash and blast furnace slag, several artificial-based analytics were applied. This study, it was employed the Chimp optimizer ( CO) to identify optimal values of determinative factors of Support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), which could be adjusted to improve performance. The suggested approaches were established using 1030 tests, eight inputs (a primary component of mix designs, admixtures, aggregates, and curing age), and the CS as the forecasting objective. The outcomes were then contrasted with those found in the body of existing scientific literature. Calculation results point to the potential benefit of combining CO - SVR and CO - ANFIS study. When compared to the CO - SVR, the CO - ANFIS showed much higher R2 and lower Root means square error values. Comparing the findings shows that the created CO- ANFIS is superior to anything that has previously been published. In conclusion, the suggested CO - ANFIS analysis might be used to determine the proposed approach for estimating the CS of HPC augmented with blast furnace slag and fly ash.
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