539 research outputs found
Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble
Precipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant nonlinear variation characteristics. The mean generating function (MGF) is used to generate the continuation factor matrix, and the principal component analysis technique is employed to reduce the dimension of the continuation matrix, and the effective data features are extracted. Finally, the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June, July and August, and a comparative experiment is carried out by using ELM, long-term and short-term memory neural network (LSTM) and back propagation neural network based on genetic algorithm (GA-BP). The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models, and it has high stability and reliability, which provides a reliable method for precipitation prediction
Multi-objective Optimization of Space-Air-Ground Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms
As an attractive enabling technology for next-generation wireless
communications, network slicing supports diverse customized services in the
global space-air-ground integrated network (SAGIN) with diverse resource
constraints. In this paper, we dynamically consider three typical classes of
radio access network (RAN) slices, namely high-throughput slices, low-delay
slices and wide-coverage slices, under the same underlying physical SAGIN. The
throughput, the service delay and the coverage area of these three classes of
RAN slices are jointly optimized in a non-scalar form by considering the
distinct channel features and service advantages of the terrestrial, aerial and
satellite components of SAGINs. A joint central and distributed multi-agent
deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving
the above problem to obtain the Pareto optimal solutions. The algorithm first
determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the
inter-slice sub-channel and power sharing by relying on a centralized unit.
Then it optimizes the intra-slice sub-channel and power allocation, and the
virtual base station (vBS)/vUAV/virtual low earth orbit (vLEO) satellite
deployment in support of three classes of slices by three separate distributed
units. Simulation results verify that the proposed method approaches the
Pareto-optimal exploitation of multiple RAN slices, and outperforms the
benchmarkers.Comment: 19 pages, 14 figures, journa
Nanoindentation characterization on local plastic response of Ti-6Al-4V under high-load spherical indentation
After high-load spherical indentation treatment, the variations of hardness on the plastic zone of Ti-6Al-4V were investigated via nanoindentation method. The hardness within the center of plastic zone was measured by nanoindenter, and the magnitude decreased gradually along the depth, which were caused by the different extent of plastic deformation under the residual imprint. The microstructure of indentation were observed using scanning electron microscope (SEM) before and after surface etching, and the results showed that the microhardness revealed the average hardness of Ī± and Ī² phases of Ti-6Al-4V. The maximum hardness reached 6.438āÆGPa in the depth of 132āÆĪ¼m. In addition, the two and three dimensional contour profiles of residual imprint introduced by high-load spherical indentation were measured by the white-light interferometer and the shape of residual imprint was obtained. All results were discussed in detail
Efficient Fully Homomorphic Encryption with Circularly Secure Key Switching Process
Fully homomorphic encryption (FHE) has important applications in cloud computing. However, almost all fully homomorphic encryption schemes share two common flaws that they all use large-scale secret keys and some operations inefficient. In this paper, the āspecial bā variant of the Learning With Errors problem (bLWE) is presented, and helps us construct the first circularly secure key switching process which can replace the key switching process and similar re-linearization process used by the existing FHE schemes. Then, we present an efficient FHE. Compared with Brakerskiās scheme, our scheme reduces L secret keys to one and is more efficient. Finally, we prove the chosen-plaintext attack (CPA) security of the fully homomorphic scheme and the circular security of key switching process in standard model under the learning with errors problem (LWE) assumption
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