深海直达波区卷积神经网络测距方法

深海声场通常可以看作不同掠射角的多途声线在接收器处的叠加,其中经海底反射的声线携带与海底参数有关的声场特征。利用深度卷积神经网络分别学习垂直阵声压域(CNN-Field)和垂直阵波束域(CNN-CBF)特征的方法被用来估计直达波区声源距离。该方法首先对仿真直达波区声场数据做预处理,然后将声压域和波束域的声场数据分别作为训练集训练深度卷积神经网络模型,最后输入测试集数据到训练完成的模型中估计声源距离.实测环境参数的仿真实验表明CNN-Field方法在不同海底参数的测试集下测距结果差异较大,CNN-CBF方法差异较小,而且在16阵元10 m等间距垂直阵的阵元域信噪比大于0dB时估计准确率可以达到97%.海试数据处理结果表明CNN-CBF方法的直达波区内测距准确率高于CNN-Field,在距离10 km以内的平均准确率可以达到93.16%.     

拓扑近藤绝缘体SmB6中的奇异电子性质

拓扑近藤绝缘体是一种本征的强关联拓扑电子体系,其体能隙来源于近藤关联效应。自2010年拓扑近藤绝缘体的理论概念被提出后,六硼化钐(SmB6) 作为第一种被预测为拓扑近藤绝缘体的材料在这十多年中被多种实验手段反复研究验证,被广泛接受认为是第一种拓扑近藤绝缘体。在这篇综述中,我们回顾了关于SmB6 的一些重要实验结果,比如电输运测量,角分辨光电子能谱(ARPES), 表面形貌分析(STM) 等,并论述了如何通过这些关键的实验证据证实SmB6 的拓扑近藤绝缘物相。同时,我们也展示了SmB6 这一关联电子体系的其他奇异物性,包括中间价态在表面和体内的分离现象,以及量子振荡发现的体振荡信号等等。这些性质表明我们对SmB6 这一材料的理解仍然不充分,其中还有更为丰富的物理值得挖掘。     

Research on a Rice Counting Algorithm Based on an Improved MCNN and a Density Map

The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.     

SensorAware: visual analysis of both static and mobile sensor information

Journal of Visualization - Sensor networks composed of static and mobile sensors are applicable for situation monitoring. In this paper, we propose SensorAware, an interactive system for...     

Visual exploration of dependency graph in source code via embedding-based similarity

Journal of Visualization -  A large software system contains millions of lines of source code, and the development often involves many developers over a long period. How to understand and...     

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.     

Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ESMA. First, the Levy flight method is used to improve the exploration ability of SMA. Second, quasi opposition-based learning is introduced to enhance the exploitation ability and balance the exploration and exploitation. Then, the superiority of the proposed work ESMA is confirmed concerning the 23 benchmark functions. Afterward, the ESMA is applied in multilevel thresholding image segmentation using minimum cross-entropy as the fitness function. We select eight greyscale images as the benchmark images for testing and compare them with the other classical and state-of-the-art algorithms. Meanwhile, the experimental metrics include the average fitness (mean), standard deviation (Std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and Wilcoxon rank-sum test, which is utilized to evaluate the quality of segmentation. Experimental results demonstrated that ESMA is superior to other algorithms and can provide higher segmentation accuracy.     

CaCu3Ti4O12 陶瓷介电模量响应特性的研究

由于CaCu₃Ti₄O₁₂巨介电常数陶瓷的低频区直流电导较大, 本文采用模量 M"-f频谱表征与分析了低频和高频的两个松弛极化过程. 研究认为, 这两个特征峰属于晶界区Schottky 势垒耗尽层边缘深陷阱的电子松弛过程, 其中高频松弛峰起源于晶粒本征缺陷的电子松弛过程, 而低频松弛峰则为与氧空位有关的松弛极化过程. 对于CaCu₃Ti₄O₁₂这类低频下具有高直流电导的陶瓷材料, 采用模量频谱能更有效地分析研究其损耗极化机理. 关键词： 3Ti₄O₁₂陶瓷')" href="#">CaCu₃Ti₄O₁₂陶瓷 模量 松弛过程 电导     

高生长速度条件下的“层片↔棒状”共晶转变机理研究

低速生长条件下, 共晶“层片↔棒状”转变只由两相的体积分数控制. 高速情况下, 这种转变有时亦发生, 其转变机理不清楚. 本文应用竞争生长准则, 结合高速生长条件下层片共晶和棒状共晶生长模型研究了生长速度引起的“层片↔棒状”转变机理. 结果显示: 体积分数在临界值附近很小的范围内, 生长速度和溶质配分系数的增大可引起“棒状→ 层片”共晶转变; 而当体积分数远离临界值时, 转变不发生. 生长速度名义上引起“层片↔棒状”共晶转变实际上是由生长速度变化引起的体积分数的变化导致的. 关键词： “层片↔棒状”共晶转变 竞争生长 生长速度 体积分数     

Simulation of pedestrian counter flow through bottlenecks by using an agent-based model

Considerable research has been conducted on the topic of unidirectional evacuations from exits. However, few studies aim at simulating counter flow through a bottleneck with complex conflict. This paper proposes an agent-based model to investigate bidirectional flow evacuation. Pedestrian speed is determined by the speed of the leading agent and the surrounding agents. The moving direction of pedestrian originates from four forces, namely, gradient force, repulsive force, resistance force, and random force. These four forces dominate the main stream of the pedestrian moving trajectory, the interaction between pedestrians and their local environment, the resistance or disinclination to movement, and the random variations and chaotic nature of pedestrian dynamics. The novelty of this research is in the agent-based model that combines the agent and forces while providing insights for the simulation of the pedestrian dynamic on the cognitive level. The experiment results show that the behavior that arises from this model is consistent with the observations from Guangzhou Metro and that this model could help capture the essence of pedestrian behavior near egresses.