Band selection is one of the main methods of reducing the number of dimensions in a hyperspectral image. Recently, various methods have been proposed to address this issue. However, these methods usually obtain the band subset in the perspective of a locally optimal solution. To achieve an optimal solution with a global perspective, this paper developed a novel method for hyperspectral band selection via optimal combination strategy (OCS). The main contributions are as follows: (1) a subspace partitioning approach is proposed which can accurately obtain the partitioning points of the subspace. This ensures that similar bands can be divided into the same subspace; (2) two candidate representative bands with a large amount of information and high similarity are chosen from each subspace, which can fully represent all bands in the subspace; and (3) an optimal combination strategy is designed to acquire the optimal band subset, which achieves an optimal solution with a global perspective. The results on four public datasets illustrate that the proposed method achieves satisfactory performance against other methods.

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In large equivalent explosion tests, the method of calculating blast wave parameters by the plastic deformation angle of a cantilever beam can effectively avoid the problem of power supply, storage, communication, and low survival rate of conventional blast wave sensors. In this paper, by means of theoretical analysis and numerical simulation, the deformation response of a pure aluminum cantilever beam with different scaled distances is calculated, and the relationship between the plastic bending angle and blast wave parameters is obtained, which provides a novel and effective method for the measurement of blast wave parameters. The experimental results show that the deformation of the cantilever beam is mainly dominated by the impulse load and the error of this measurement method is within 15%. This method can provide a complementary approach to more precise but complex sensor measurement and can effectively evaluate the impulse in the explosion accident outside.

Recently, flame detection has attracted great attention. However, existing methods have the issues of low detection rates, high false alarm rates, and lack of smoke anti-interference ability. In this letter, a novel dynamic attention-based network (DANet) is proposed for autonomous flame detection in various scenarios. To mitigate the disturbance of smoke in images, a dynamic attention strategy is proposed to discover the potential features among scale-awareness and spatial-awareness. Then, based on dynamic attention module, a decoupled detection head is presented, which can predict category, regression, and object score independently to boost the performance. A self-contained challenging flame dataset, which is multi-scene, multiscale, and multi-interference informative is constructed to evaluate the proposed model and organize the experiments. Extensive ablation and comparison studies on self-labelled dataset reveal the effectiveness of the proposed dynamic attention-based network.

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