UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is proposed.The clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid assessment.Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is proposed.The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics.The LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization sensitivity.Comparing experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient classification.Compared with the five kinds of assess-ments models,the SAE-LVQ model has the highest accuracy.Finally,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation assessment.The assessment results are in line with the actual situation.

Learning a discriminative high-fidelity dictionary for single channel source separation

Sparse-representation-based single-channel source separation,which aims to recover each source’s signal using its corresponding sub-dictionary,has attracted many scholars’attention.The basic premise of this model is that each sub-dictionary possesses discriminative information about its corresponding source,and this information can be used to recover almost every sample from that source.However,in a more general sense,the samples from a source are composed not only of discriminative information but also common information shared with other sources.This paper proposes learning a discriminative high-fidelity dictionary to improve the separation performance.The innovations are threefold.Firstly,an extra sub-dictionary was combined into a conventional union dictionary to ensure that the source-specific sub-dictionaries can capture only the purely discriminative information for their corresponding sources because the common information is collected in the additional sub-dictionary.Secondly,a task-driven learning algorithm is designed to optimize the new union dictionary and a set of weights that indicate how much of the common information should be allocated to each source.Thirdly,a source separation scheme based on the learned dictionary is presented.Experimental results on a human speech dataset yield evidence that our algorithm can achieve better separation performance than either state-of-the-art or traditional algorithms.

Investigations on femtosecond laser-induced surface modification and periodic micropatterning with anti-friction properties on Ti6Al4V titanium alloy

Titanium alloys have a wide application in aerospace industries as it has greater strength and low density, but it has poor tribological properties. To improve its friction and wear performance, in present work, a femtosecond laser is used to directly irradiate the Ti6Al4V titanium alloy surface in air conditioning, which results in localized ablation and the formation of periodic microstructures but also a strong pressure wave, propagating the material inside. Through the optimization of processing parameters, surface modification and periodic micropatterning with effective anti-friction properties were successfully induced on the surface. After a treatment of femtosecond laser-induced surface modification(FsLSM), the surface microhardness was improved by 16.6% and compressive residual stress reached-746 MPa. Besides, laser-induced periodic surface structures(LIPSS) with a titanium oxide outer coating were fabricated uniformly on the titanium alloy surface. Rotary ball-on-disk wear experiments revealed that the average coefficient of friction(COF) and wear mass loss of the specimen with Fs LSM treatment were largely reduced by 68.9% and 90% as compared to that of untreated specimens, respectively. It was analyzed that the reason for the remarkable wear resistance was attributed to the comprehensive action of the generation of LIPSS, the titanium oxide outer coating, high amplitude compressive residual stress and gradient grain size distribution on the subsurface during the laser surface treatment. Since the findings here are broadly applicable to a wide spectrum of engineering metals and alloys, the present results offer unique pathways to enhancing the tribological performance of materials.