In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.

The composite of MIL-101 and graphene oxide with remarkably improved acetone adsorption capacity was prepared. This composite exhibited enhanced porous structure and stronger adsorption interaction towards adsorbate. The mechanism of such improvement was discussed. Acetone adsorption on this composite was highly reversible.

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In this paper, a novel change detection technique is proposed based on multiscale superpixel segmentation and stacked denoising autoencoders (SDAE). This approach is designed to achieve superpixelbased change detection, in which the basic analysis unit is between pixel-based and object-based ones. Given two original images, the difference image (DI) is obtained by conventional DI generation methods. Then, we propose a multiscale superpixel segmentation which is guided by the changing degrees estimated from the DI. Different from traditional multiscale superpixel, the proposed multiscale superpixel segmentation is employed in a single map. In the proposed method, SDAE is used to learn the difference representation between bi-temporal superpixels. Bi-temporal superpixels are stacked and fed into SDAE for its pretraining, and then SDAE is fine-tuned according to pseudo labels generated by traditional unsupervised methods. After fine-tuned with back propagation, the SDAE can be used to classify all superpixel pairs into changed or unchanged ones. The experimental results on real remote sensing datasets have demonstrated the effectiveness of the proposed approach.

Energy saving in wireless sensor networks is a fundamental issue as most sensor nodes are powered by batteries. The deployment of mobile sinks can alleviate the imbalance of energy consumption among sensor nodes, thereby prolonging the network lifetime. In this paper, we study the energy management problem in sensor networks, using multiple mobile sinks. We first formulate the problem as a novel data collection problem and then propose an efficient algorithm for it. The key challenge in the design of the proposed algorithm is how to balance the workload among mobile sinks and the energy consumption among sensor nodes through the control of the movement of mobile sinks. We finally evaluate the performance of the proposed algorithm through experimental simulation. Experimental results show that the proposed algorithm is very promising, which can improve the energy efficiency and the quality of data transmission in the sensor network significantly.

In view of the fact that traditional air target threat assessment methods are difficult to reflect the combat characteristics of uncertain, dynamic and hybrid formation, an algorithm is proposed to solve the multi-target threat assessment problems. The target attribute weight is calculated by the intuitionistic fuzzy entropy (IFE) algorithm and the time series weight is gained by the Poisson distribution method based on multi-times data. Finally, assessment and sequencing of the air multi-target threat model based on IFE and dynamic VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is established with an example which indicates that the method is reasonable and effective.

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