No abstract

In the field of pattern recognition, the traditional supervised learning methods and unsupervised learning methods are not always suitable for the practical applications. In some applications, the data obtained is neither no-information-given nor all-information-given. In addition, the data obtained usually contains some noises due to many interference factors in practical collection procedure and these noises are of great influence on the traditional clustering methods. In order to overcome the two problems mentioned above, based on the classical Maximal Entropy Clustering (MEC), we propose a semi-supervised MEC algorithm based on the maximized central distance and the compensation term for membership, i.e., CM-sSMEC algorithm. The experimental results on benchmarking UCI data sets show that it has a better performance than the traditional unsupervised clustering method.

Junna SHANG †a) , membership type and Ziyang YAO †b) , membership type SUMMARY With the arrival of 5G and the popularity of smart devices, indoor localization technical feasibility has been verified, and its market demands is huge. The channel state information (CSI) extracted from Wi-Fi is physical layer information which is more fine-grained than the received signal strength indication (RSSI). This paper proposes a CSI correction localization algorithm using DenseNet, which is termed CorFi. This method first uses isolation forest to eliminate abnormal CSI, and then constructs a CSI amplitude fingerprint containing time, frequency and antenna pair information. In an offline stage, the densely connected convolutional networks (DenseNet) are trained to establish correspondence between CSI and spatial position, and generalized extended interpolation is applied to construct the interpolated fingerprint database. In an online stage, DenseNet is used for position estimation, and the interpolated fingerprint database and K-nearest neighbor (KNN) are combined to correct the position of the prediction results with low maximum probability. In an indoor corridor environment, the average localization error is 0.536m.

We report a template-free hydrogen reduction approach to prepare cobalt nanoporous magnetic materials with various morphologies employing Co3O4 as precursors, which were obtained by thermal-decomposing CoCO3 intermediates. The kinetic control of experimental parameters of synthetic CoCO3 intermediates by a facile solvent-thermal route can be an effective strategy to tune the morphology of Co nanoporous structures. The microstructures, crystal structures or thermal characteristics of products at different stages were investigated to reveal the formation mechanism of the Co nanoporous structures. Magnetic measurement showed that the Co nanoporous structures with rhomb-like and prism-like morphology exhibited saturation magnetization (Ms) of 149.2 emu/g and 141.8 emu/g, and coercivity (Hcj) of 508.4 Oe and 554.9 Oe, respectively. The as-prepared Co ferromagnetic materials exhibited remarkably high coercivity values mainly due to the three dimensional nanoporous structures.

Infrared sensing technology can be well used for night observation, which is becoming an important measure for battlefield reconnaissance. It is a powerful way to implement precision strikes and situational awareness by improving the ability of target recognition based on infrared images. For the problem of infrared image recognition, the Light Gradient Boosting Machine (LightGBM) is employed to select the outline descriptors extracted based on the elliptic Fourier series (EFS), which is combined with sparse representation-based classification (SRC) to achieve target recognition. First, based on the target outlines in the infrared image, the multi-order outline descriptors are extracted to characterize the essential characteristics of the target to be recognized. Then, the LightGBM feature selection algorithm is used to screen the multi-order outline descriptors to reduce redundancy and improve the pertinence of features. Finally, the selected outline descriptors are classified based on SRC. The method effectively improves the effectiveness of the final features through the feature selection of LightGBM and reduces the computational complexity of classification at the same time, which is beneficial to improve the overall recognition performance. The mid-wave infrared (MWIR) dataset of various targets is employed to carry out verification experiments for the proposed method under three different conditions of original samples, noisy samples, and partially occluded samples. By comparing the proposed method with several types of existing infrared target recognition methods, the results show that the proposed method can achieve better performance.