Beamforming is a key technology in millimeter-wave communication. By controlling the transmitter and receiver of a millimeter-wave antenna array, the signal-to-noise ratio (SNR) can be effectively improved, and interf...
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ISBN:
(纸本)9781728157337
Beamforming is a key technology in millimeter-wave communication. By controlling the transmitter and receiver of a millimeter-wave antenna array, the signal-to-noise ratio (SNR) can be effectively improved, and interference and noise can be suppressed. In order to further enhance the anti-interference ability of beamforming, under the condition of minimum variance distortionless response (MVDR) linear constraint, this paper uses the graywolfoptimization (GWO) algorithm numerical search capability to optimize the weights. An optimized minimum variance distortionless response (OMVDR) adaptive beamforming algorithm is presented. In the selection of the fitness function, the idea of separating amplitude and phase is adopted, and the fitness function is set to the weighted sum of the mean square error of the output signal and the input signal, thereby realizing the simultaneous optimization of the weight amplitude and phase. Simulation results show that OMVDR has lower sidelobe level and null and better anti-interference ability than traditional MVDR algorithms and PSO-MVDR.
With the development of wireless sensor networks, research on three-dimensional(3D) node localization algorithms is becoming more and more important. 3D Distance Vector Hop(DV-Hop) is a non-ranging-based 3D positionin...
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ISBN:
(纸本)9781728173276
With the development of wireless sensor networks, research on three-dimensional(3D) node localization algorithms is becoming more and more important. 3D Distance Vector Hop(DV-Hop) is a non-ranging-based 3D positioning algorithm with low positioning accuracy and large errors. Aiming at above problems, 3D DV-Hop localization based on improved lion swarm optimization(ILSO) algorithm is proposed. The wolf swarm hunting idea of gray wolf optimization algorithm and the herd interaction idea of sheep optimizationalgorithm are used to improve the lion swarm optimizationalgorithm. The ILSO algorithm is compared with several algorithms and performs well. Then it is applied to the optimization of unknown node coordinates. Simulation results show that the proposed algorithm has higher positioning accuracy than classic 3D DV-Hop algorithm and the 3D DV-Hop algorithm based on the original lion swarm optimizationalgorithm.
Grey wolfoptimizationalgorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is different from others in t...
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Grey wolfoptimizationalgorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is different from others in terms of model structure. It is a large-scale search method centered on three optimal samples, and which is also the research object of many scholars. In the course of its research, this paper find that GWO is flawed. It has good performance for the optimization problem whose optimal solution is 0, however, for other problems, its advantage is not as obvious as before or even worse. Then it is further found that when GWO solves the same optimization function, the farther the function's optimal solution is from 0, the worse its performance, and this flaw also appears in other optimizationalgorithms. Through the study of this defect, the analysis is carried out, and the reason is determined. Finally, although there is no way to make GWO normal, this paper provides a verification method to avoid the same problem, and hopes to help the development of the optimizationalgorithm. (C) 2019 Elsevier B.V. All rights reserved.
Solid backfill coal mining has become one of the main means of green mining in coal mines because of its ability to control surface subsidence and disposal of surface gangue. Mixed gangue backfill material (MGBM) is t...
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Solid backfill coal mining has become one of the main means of green mining in coal mines because of its ability to control surface subsidence and disposal of surface gangue. Mixed gangue backfill material (MGBM) is the key factor for stratum control in solid backfill mining, and its compact mechanical performance directly affects the efficiency of backfill mining. In order to better carry out backfill mining design and backfill effect evaluation, a new hybrid artificial intelligence model integrating support vector machines (SVM), differential evolution algorithm (DE) and gray wolf optimization algorithm (GWO) is proposed to predict the compaction property of MGBM. The cement, lime and fly ash materials are selected to be mixed with gangue backfill materials and a large number of compaction tests using a self-made circular cylinder barrel are carried out to provide the dataset for the DGWO-SVM hybrid model. The input variables of this model include cement content, lime content, fly ash content and overburden stress, and the output variable of the model is the compaction property of MGBM. The performance of the DGWO-SVM model is evaluated by R-2, MAE and RMSE. The predictive results indicate that the DGWO-SVM hybrid model can accurately predict the compaction property of MGBM, and the R-2 of the training set and the testing set are 0.9518, 0.9137. Meanwhile, the relative importance of each input variable is implemented using the MIV method, and the importance scores of cement content, lime content, fly ash content and overburden stress are 0.3266, 0.0738, 0.2448, 0.3548, respectively. The research results can provide guidance for the optimization design of solid backfill mining. (C) 2020 Elsevier Ltd. All rights reserved.
In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide)...
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In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with graywolfoptimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy.
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