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一种适用于无人潜器的水下对接装置
No full text本发明涉及水下对接装置，具体地说是一种适用于无人潜器的水下对接装置，对接装置头部为对接头，对接头的上方开有对接通孔；对接装置的内部为空心式结构，在对接装置的对接头内部的空心结构中，存在一个保压密封装置；被对接装置的头部为导向罩，导向罩上设有灯源，导向罩后侧为对接筒，对接筒内设有可以夹紧和放松的电动夹爪；在被对接装置的内部也有一个保压密封装置。本发明为前头移动式对接，解决了水下设备移动性差、方向性差的笨重问题，符合了水下结构控制学的基本要求；同时，本发明可以使水下无人潜器有效地设置精密仪器的使用环境，并保证对接中的稳定性，具有方便进行水下无人潜器与其它装置的信息、能源传输的优势
一种面向化工的优先级升序可行性判定与软约束调整方法
No full text本发明涉及过程工业控制领域，具体是一种面向化工的优先级升序可行性判定与软约束调整方法，包括以下步骤：可行性判定与软约束调整初始化；硬约束可行性判定；逐级软约束调整并固化；确定稳态优化可行域。本发明针对化工过程现有优先级升序可行性判定与软约束调整的不完备性问题，在每一优先级下，同时引入所有优先级的硬约束以及当前优先级的软约束进行判定及调整，而后逐级完成直至所有优先级均实现可行性判定与软约束调整。该发明解决了现有优先级升序策略的不完备性，可有效提高相关过程控制工业软件的稳定性和可靠性，进而为相关化工过程的稳定操作运行提供保障
Infrared small target detection based on multiscale local contrast learning networks
No full textRecently, model-driven deep networks have achieved excellent detection performance on infrared small targets in cluttered environments. However, its detection performance is sensitive to the hyperparameters in the embedded model-driven module. Therefore, we propose a novel multiscale local contrast learning network (MLCL-Net), which is an end-to-end fully convolutional infrared small target detection network. By constructing a local contrast learning (LCL) structure, it can learn to generate local contrast feature maps during training. Considering the difference in target size, we further build a multiscale local contrast learning (MLCL) module based on LCL. By extracting and fusing local contrast information of different scales from feature maps of the same level, the feature information of targets is fully excavated. At the same time, due to the small size of the target, a slight pixel shift will cause a severe loss of accuracy. We propose a bilinear feature pyramid network (BFPN) based on the feature pyramid network (FPN). Compared to state-of-the-art methods, the proposed MLCLNet achieves superior performance with an intersection-over-union (IoU) of 0.772 and normalized IoU (nIoU) of 0.755 on the public SIRST dataset
Infrared and visible image fusion using improved generative adversarial networks
No full text红外与可见光图像融合技术能够同时提供红外图像的热辐射信息和可见光图像的纹理细节信息，在智能监控、目标探测和跟踪等领域具有广泛的应用。两种图像基于不同的成像原理，如何融合各自图像的优点并保证图像不失真是融合技术的关键，传统融合算法只是叠加图像信息而忽略了图像的语义信息。针对此问题，提出一种改进的生成对抗网络，生成器设计了局部细节特征和全局语义特征两路分支捕获源图像的细节和语义信息；在判别器中引入谱归一化模块，解决传统生成对抗网络不易训练的问题，加速网络收敛；引入了感知损失，保持融合图像与源图像的结构相似性，进一步提升了融合精度。实验结果表明，本文方法在主观评价与客观指标上均优于其他代表性方法，对比基于全变分模型方法，平均梯度和空间频率分别提升了55.84%和49.95%。</p
Event-Triggered Cooperative Output Regulation of Heterogeneous Multi-Agent Systems with Adaptive Fault-Tolerant Control
No full textThis paper studies the cooperative output regulation problem for heterogeneous multi-agent systems with actuator faults. A new two-layer distributed control strategy is proposed: (i) Two types of distributed observers are designed for agents to estimate the exogenous signal, and an adaptive event-triggering mechanism is introduced to reduce unnecessary information transmission between agents; (ii) A decentralized adaptive fault-tolerant control scheme is proposed to achieve the cooperative output regulation of heterogeneous multi-agent systems and compensate for the actuator faults automatically. Finally, a numerical example is given to verify the feasibility of the proposed algorithm. IEEE</p
AMF-Net: An adaptive multisequence fusing neural network for multi-modality brain tumor diagnosis
No full textTo precisely diagnose the brain tumor types and grades, magnetic resonance imaging (MRI), which is a kind of multisequence imaging technology, is usually applied. However, with the limitations of databases, most current computer-aided brain tumor diagnosis methods employ only a single MRI sequence, and the generalizability of these methods is not ideal. To improve the brain tumor diagnosis performance, an adaptive multisequence fusing neural network (AMF-Net), which can merge the characteristics of different MRI sequences with adaptive weights, is proposed. Inspired by the approximate horizontal symmetry of brains and manual diagnosis process, normalized horizontal differential images are adopted as the spatial attention mechanism, and dense skip connections from T2-weighted (T2-W) sequences are implemented to emphasize the importance of the T2-W sequences. Moreover, to adaptively combine different MRI sequences, an innovative self-learning mechanism, namely adaptive sequence fusion (ASF) module, is proposed. The experimental results show that the average accuracies of the AMF-Net on two databases reach 98.1% and 92.1%, respectively, and the application of the proposed spatial attention mechanism and the ASF module can improve the average accuracy on two databases by 1.7%/1.7% and 1.3%/2.1%, respectively, which indicates that the proposed spatial attention mechanism and the ASF module can improve the performance for brain tumor diagnosis tasks.</p
Deep learning with laser-induced breakdown spectroscopy (LIBS) for the classification of rocks based on elemental imaging
No full textIn geological research, the identification and classification of rock lithology plays an important role in many fields such as resource exploration, earth evolution and paleontology research. Laser-induced breakdown spectroscopy (LIBS), which is capable of real-time, on-situ, micro-destructive determination of the elemental composition of any substance (solid, liquid, or gas), has been developed as a technology for ‘geochemical fingerprinting’ in a variety of geochemical applications. However, for rock samples with coarse grains, the bulk analysis based on the average spectrum is insufficient. This study proposes a new method for identifying multiple types of rocks, which utilizes the rapid multi-element compositional imaging capability of LIBS, and combines with the deep learning theory. The LIBS-based images characterizing the spatial distribution of elements on rock surface were achieved firstly, and then were classified by the Inception-v3 network combined with the transfer learning method. In addition, to solve the problem of the small scale of the image dataset obtained in the laboratory, specific data augmentation methods such as cutting-recombining and filtering were proposed. Moreover, the superior of this method was verified by the three classification experiments of shale, gneiss and granite.</p
Reducing self-absorption effect by double-pulse combination in laser-induced breakdown spectroscopy
No full textThe method of double-pulse laser-induced breakdown spectroscopy is usually employed to enhance the spectral signal intensity. However, in this study, double-pulse laser-induced breakdown spectroscopy is adopted to investigate the effect of the self-absorption reduction of the spectrum. This research explored that the influence of the change of the gas environment generated by the first laser beam on the self-absorption effect of the plasma spectrum by the second laser beam. Especially despite the different combinations of laser energy, for the three elements of Cu, Mn and Ni, the weakest spectral self-absorption effect can be obtained when the double-pulse delays are around 80 μs, 100 μs, and 110 μs, respectively. In addition, this paper also found that when the energy of the first laser beam is unchanged, the spectral self-absorption effect has a strong correlation with the double-pulse delay, and has a weak correlation with the change of the second laser energy.</p
Visual Measurement Method of Gap Width of Split Type Ammunition Based on Improved Ostu-Sobel
No full text在分体式炮弹产品的质量检测中，螺纹连接处间隙的高精度稳定测量是保证炮弹质量的重要指标之一。为了精确测量螺纹连接处间隙，利用机器视觉的方法，提出了一种基于改进Otsu-Sobel的分体式炮弹缝宽视觉测量方法。该方法根据图像特征生成自适应感兴趣区域，再通过单调化处理与Sobel算子确定缝隙边缘的粗定位区间，在局部利用图像梯度的离散度精确定位缝隙边缘。为解决机械安装、缝宽倒角等因素对精度的影响，利用了最小二乘拟合方法对测量结果进行修正。实验结果表明，该方法可精确检测0.1～0.7mm的缝宽，且其测量误差小于0.02mm。该方法解决了分体式炮弹螺纹连接处间隙精确测量的技术难题，可满足产品质量检测的需要。</p
Robust semantic SLAM with variable structure
No full text基于深度学习的飞速发展，语义信息逐渐成为SLAM（Simultaneous Location and Mapping）领域的研究热点。由于环境以及传感器本身带来的噪声问题，现有大多数语义SLAM算法所构建的语义地图中存在一些异常点，导致构建的语义地图缺乏一致性，并且影响算法精度。损失函数可以调整对异常点分配的权重，从而抑制异常点的存在。但是大多数语义SLAM算法使用的损失函数本身模型固定，不能很好地适应周围环境噪声的变化。为了解决此问题，提出了一种变结构的鲁棒语义SLAM算法，称为VS-SLAM。采用高斯混合相关熵权重函数作为损失函数，利用其可以通过调整参数，随周围环境噪声变化来改变其模型结构的特点，最大程度地拟合噪声的分布，更有利于降低算法对异常点的权重分配，提高对异常点的鲁棒性。在公开的KITTI数据集上的实验表明，比现有的先进方法有更高的精度。在建图的时间几乎相等的情况下，平均相对平移误差和旋转误差分别降低了5.36%和8.82%，并且构建的语义地图更加具有一致性。</p