Adaptive multi-modal feature fusion for far and hard object detection

In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is proposed,which makes use of multi-neighborhood information of voxel and image information.Firstly,design an improved ResNet that maintains the structure information of far and hard objects in low-resolution feature maps,which is more suitable for detection task.Meanwhile,semantema of each image feature map is enhanced by semantic information from all subsequent feature maps.Secondly,extract multi-neighborhood context information with different receptive field sizes to make up for the defect of sparseness of point cloud which improves the ability of voxel features to represent the spatial structure and semantic information of objects.Finally,propose a multi-modal feature adaptive fusion strategy which uses learnable weights to express the contribution of different modal features to the detection task,and voxel attention further enhances the fused feature expression of effective target objects.The experimental results on the KITTI benchmark show that this method outperforms VoxelNet with remarkable margins,i.e.increasing the AP by 8.78%and 5.49%on medium and hard difficulty levels.Meanwhile,our method achieves greater detection performance compared with many mainstream multi-modal methods,i.e.outperforming the AP by 1%compared with that of MVX-Net on medium and hard difficulty levels.

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

In-service aircraft engines turbine blades life prediction based on multi-modal operation and maintenance data

The in-service life of turbine blades directly affects the on-wing lifetime and operating cost of aircraft engines.It would be essential to accurately evaluate the remaining useful life of turbine blades for safe engine operation and reasonable maintenance decision-making.In this paper,a machine learning-based mechanism with multiple information fusion is proposed to predict the remaining useful life of high-pressure turbine blades.The developed method takes account of the in-service operating factors such as the high-pressure rotor speed and exhaust gas temperature,as well as the engine operating environments and performance degradation.The effectiveness of this method is demonstrated on simulated test cases generated by an integrated blade creep-life assessment model,which comprises engine performance,blade stress,thermal,and creep life estimation models.The results show that the proposed method provides a prospective result for in-service life evaluation of turbine blades and is of significance to evaluating the engine on-wing lifetime and making a reasonable maintenance plan.

基于深度学习的多模态地貌识别算法研究

为了提高地貌识别精度,本文基于深度学习融合多种模态地貌数据的特征对地貌进行识别。该方法利用深度学习网络分别从晕渲图、高程和坡度3种数据中提取地貌的物理和视觉特征,然后采用残差学习模型挖掘不同模态特征之间的深度关联,对多模态地貌数据特征进行学习并融合生成地貌深层特征,从而实现对地貌数据的联合表达。最后,使用3个全连接层和一个SoftMax分类器为每个样本数据生成一个地貌类别标签。实验结果表明,与以往的方法相比,基于深度学习的多模态地貌数据的地貌识别具有更好的性能。

基于多源数据融合的高精细实景三维建模技术

针对大面积错综复杂的油气管道建立高精细实景三维模型,常规的倾斜摄影数据获取方法对于复杂构件及微地貌,在后期建模效果上精细度得不到保证。多源数据融合建模是对倾斜摄影建模方法的进一步探索,此方法有效解决纹理细节、模型逼真等方面存在的问题,并且在实际生产中达到了"高精细、高效率、低成本"的目的。

基于多源数据融合的典型海岛三维形貌表征方法研究

随着海岛开发利用和基础设施建设的频繁,高效、精准地获取海岛形态基础数据就显得尤为迫切。三维地形地貌探测和构建技术的日臻完善,及时解决了海岛基础勘测时间长、效率低、精度小的问题。文章通过三维激光扫描和无人机航测手段,全覆盖扫描广西小庙墩(炮台口)海岛表面形态,结合RTK-DGPS实测验证和近景摄影测量,获取海岛实景,运用Geomagic Studio和Smart3D等软件,构建典型海岛三维数字高程模型(DEM),融合多源数据和海岛实景,还原海岛高精度仿真三维形态。为高精度勘测海岛提供有效手段,为海岛基础地理信息获取、地质灾害识别和生态环境修复提供基础数据支撑。

A solution of UAV localization problem using an interacting multiple nonlinear fuzzy adaptive H_(∞)models filter algorithm

The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigation scheme based on Interacting Multiple Nonlinear Fuzzy Adaptive H_∞ Models(IMM-NFAH_∞) filtering technique for UAV is presented. The proposed IMM-NFAH_∞ strategy switches between two different Nonlinear Fuzzy Adaptive H_∞(NFAH_∞) filters and each NFAH_∞ filter is based on different fuzzy logic inference systems. The newly proposed technique takes into consideration the high order Taylor series terms and adapts the nonlinear H_∞ filter based on different fuzzy inference systems via adaptive filter bounds(di),along with disturbance attenuation parameter c. Simulation analysis validates the performance of the proposed algorithm, and the comparison with nonlinear H_∞(NH_∞) filter and that with different NFAH_∞ filters demonstrate the effectiveness of UAV localization utilizing IMM-NFAH_∞ filter.