Strong f-f excitation in titanium silicate: Near-UV LED pumped red phosphors with outstanding temperature sensitivity

Optical thermometry and white light-emitting diodes (WLEDs) are the research hotspots for Eu³⁺ doped phosphors. In this study, a series of Sr₂TiSi₂O₈: Eu³⁺ red phosphors were developed in a solid-state reaction method. The luminescent properties of the Sr₂TiSi₂O₈: Eu³⁺ phosphor illustrate that Sr₂TiSi₂O₈: Eu³⁺ is effectively excited by the NUV LED chip and emit an intense red light with a peak of 618 nm due to the noninversion symmetry coordination of Eu³⁺ in Sr₂TiSi₂O₈. The local coordination environment of Eu³⁺ was investigated in detail using crystallographic data, spectroscopic data and Judd–Ofelt parameters. In addition, the emission intensity of Sr₂TiSi₂O₈:Eu³⁺ is extremely sensitive to temperature. The relative sensitivity of Sr₂TiSi₂O₈:Eu³⁺ is 1.1% k^-1@300 k, which is comparable to other Eu³⁺-activated temperature sensing materials. The possible mechanism of the temperature sensitive quenching characteristics was proposed. The results indicate that Sr₂TiSi₂O₈:Eu³⁺ is a potential temperature sensing material.     

Experimental study on the deformation and failure mechanism of overburden rock during coal mining using a comprehensive intelligent sensing method

Understanding the spatiotemporal evolution of overburden deformation during coal mining is still a challenge in engineering practice due to the limitation of monitoring techniques. Taking the Yangliu Coal Mine as an example, a similarity model test was designed and conducted to investigate the deformation and failure mechanism of overlying rocks in this study. Distributed fiber optic sensing (DFOS), high-density electrical resistivity tomography (HD-ERT) and close-range photogrammetry (CRP) technologies were used in the test for comprehensive analyses. The combined use of the three methods facilitates the investigation of the spatiotemporal evolution characteristics of overburden deformation, showing that the mining-induced deformation of overburden strata was a dynamic evolution process. This process was accompanied by the formation, propagation, closure and redevelopment of separation cracks. Moreover, the key rock stratum with high strength and high-quality lithology played a crucial role in the whole process of overburden deformation. There were generally three failure modes of overburden rock layers, including bending and tension, overall shearing, and shearing and sliding. Shear failure often leads to overburden falling off in blocks, which poses a serious threat to mining safety. Therefore, real-time and accurate monitoring of overburden deformation is of great significance for the safe mining of underground coal seams.     

Two-Phase Bidirectional Dual-Relay Selection Strategy for Wireless Relay Networks

In this article, we introduce a new bi-directional dual-relay selection strategy with its bit error rate (BER) performance analysis. During the first step of the proposed strategy, two relays out of a set of N relay-nodes are selected in a way to optimize the system performance in terms of BER, based on the suggested algorithm which checks if the selected relays using the max-min criterion are the best ones. In the second step, the chosen relay-nodes perform an orthogonal space-time coding scheme using the two-phase relaying protocol to establish a bi-directional communication between the communicating terminals, leading to a significant improvement in the achievable coding and diversity gain. To further improve the overall system performance, the selected relay-nodes apply also a digital network coding scheme. Furthermore, this paper discusses the analytical approximation of the BER performance of the proposed strategy, where we prove that the analytical results match almost perfectly the simulated ones. Finally, our simulation results show that the proposed strategy outperforms the current state-of-the-art ones.     

Hydrogen sensing by RGB investigation of gasochromic coloration of MoO3/Pd-coated polyester fabrics

Recently, colorimetric sensors capable of detecting explosive and toxic molecules have attracted much attention for applications in health and environmental monitoring. Here, we demonstrate the detection of hydrogen gas using gasochromic fabrics and evaluate their performance with a commercial RGB color sensor module. For this purpose, first a thin Pd film was sputter deposited onto the polyester fabric and then amorphous MoO₃ films (several microns) were deposited by RF reactive magnetrons in various O₂/Ar ratios (from 2.5 to 12.5 O₂). Field emission scanning electron microscope (FESEM) identified the morphological changes of the surface at each coating step and showed that the fibrous structure of the fabrics was preserved after coating of the films, which is suitable for better gas adsorption. Exposure to hydrogen caused the obtained MoO₃/Pd/polyester samples to turn blue-gray at room temperature and the UV–Vis spectra showed an increase in optical absorption. Their sensitivity was evaluated in the presence of different H₂ concentrations from 250 to 10,000 ppm by recording the time variation of color difference (RGB Euclidean distance). A significant correlation was found between absorption spectrum and RGB analysis with respect to oxygen content. The color variation showed a linear behavior with respect to hydrogen concentration for all samples. Response and recovery times, reversibility and reproducibility in the presence of hydrogen gas were also evaluated using the RGB readout data. Overall, the RGB module optimally depicts the sensing characteristics of the gasochromic fabrics in terms of reversibility, sensitivity and reproducibility, which may contribute to their commercial development.     

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a data-driven soft sensor, which can supplement the physical sensor. Artificial Neural Networks have long been used for time series forecasting given their ability to recognize patterns in the data. Long Short-Term Memory (LSTM) neural networks are equipped with memory gates to help them learn time dependencies in a data series and have been proven to outperform other type of networks in predicting water levels in urban drainage systems. When used for soft sensing, neural networks typically receive antecedent observations as input, as these are good predictors of the current value. However, the antecedent observations may be missing due to transmission errors or deemed anomalous due to errors that are not easily explained. This study quantifies and compares the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. We applied these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark. We observed that i) LSTM predictions generally displayed large variability across training runs, which may be reduced by improving the selection of hyperparameters (non-trainable parameters); ii) when the most recent observations were known, adding information on the past did not improve the prediction accuracy; iii) when gaps were introduced in the antecedent water depth observations, LSTM networks were capable of compensating for the missing information with the other available input features (time of the day and rainfall intensity); iv) LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors. Therefore, we concluded that LSTM neural network may be trained to act as soft sensors in urban drainage systems even when observations from the physical sensors are missing.     

镁质熔剂性球团矿相的多重分形谱

 球团矿相微观结构决定其冶金物理化学性能，为透视球团矿微观结构与其宏观冶金物理化学性能之间的依赖关系，需要解决的首要关键问题是矿相微观视觉特征的提取与定量表征。以镁质熔剂性球团微观矿相为研究对象，在直观分析矿相视觉特征的基础上，采用多重分形谱分析方法，提取矿相的微观多重分形谱定量表征值f（α），并分别深度挖掘由f（α）衍生出的对称度特征值Δf、谱宽特征值Δα、容量维特征值D（0）随碱度变化的迁移规律和随矿相检测位置变化的迁移规律。研究结果表明，矿相微观视觉特征在HSV颜色空间上呈现的颜色特征未在碱度变化和位置变化上存在显著差异性；矿相微观多重分形谱分析中，Δf、Δα、D（0） 3个特征值在碱度变化和位置变化上有显著的差异性，可以很好地表征碱度迁移规律和位置迁移规律。     

用于发光温度传感的金属有机框架材料

金属有机框架(Metal organic frameworks,MOFs)由于其显著的结构多样性和可调的发光性能,为制备不同种类的发光传感器提供了良好契机。近年来,利用发光MOFs探测温度传感技术受到了人们的广泛关注。结合对发光测温的描述后,总结了发光型MOF温度计的最新研究进展,重点介绍了双发射型MOF在温度传感领域中的广泛应用。     

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

The work reported in this article describes the research advances and the lessons learned by the Robotics, Perception and Real‐Time group over a decade of research in the field of ground robotics in confined environments. This study has primarily focused on localization, navigation, and communications in tunnel‐like environments. As will be discussed, this type of environment presents several special characteristics that often make well‐established techniques fail. The aim is to share, in an open way, the experience, errors, and successes of this group with the robotics community so that those that work in such environments can avoid (some of) the errors made. At the very least, these findings can be readily taken into account when designing a solution, without needing to sift through the technical details found in the papers cited within this text.     

基于流形学习的光学遥感图像分类

随着光学遥感图像技术的快速发展与广泛应用,对光学遥感图像的准确分类具有深远的研究意义。传统特征提取方式提取的高维特征中夹杂着许多冗余信息,分类过程可能导致过拟合现象,针对传统的线性降维算法不足以保持原始数据的内部结构,容易造成数据失真这一问题,提出基于流形学习的光学遥感图像分类算法。该算法首先提取出图像的SIFT特征,然后将流形学习运用于特征降维,最后结合支持向量机进行训练和识别。实验结果表明,在Satellite、NWPU和UCMerced实验数据中,冰川、建筑群和海滩分类精度得到了有效提高,达到85%左右;针对沙漠、岩石、水域等特殊环境遥感图像,分类精度提高了10%左右。总而言之,基于流形学习的分类算法对通过降维之后的数据能够保持在原高维空间中的拓扑结构,相似特征点能得到有效聚合,预防了"维数灾难",减少了计算量,保证了分类精度。     

Docker容器化下的遥感算法程序集成方法

目的 近年来，随着我国遥感技术的快速发展，遥感数据呈现出大数据的特点，遥感数据的时效性增强，针对新环境下遥感算法编程语言众多，程序运行和部署环境需求多样，程序的集成和部署困难的问题，提出了一种遥感算法程序快速封装与Docker容器化系统集成架构。方法 该系统架构主要包括：1）遥感算法程序的镜像自动化封装制作；2）镜像的分发管理，达到算法程序镜像的共享；3）遥感信息产品生产流程的容器化编排服务，将相关联的算法程序镜像串联，以满足特定遥感信息产品的生产；4）容器的调度运行，调用镜像，实现特定遥感产品的容器化运行。本文在上述容器化系统集成架构下，以Landsat5数据的NDVI、NDWI信息产品的生产作为容器化生产实例，并同物理机、KVM （kernel-based virtual machine）虚拟机在运行时间、内存占用量、部署效率等性能进行了对比。结果 Docker容器虚拟化环境下的产品生产和物理机环境下在运行时间和内存占用量上几无差别，优于KVM虚拟机。Docker容器虚拟化环境和KVM虚拟机环境下在部署上能够节省大量时间，相比于物理机环境能够提高部署效率。结论 容器化的系统集成方式能够有效解决遥感算法程序集成和部署困难的问题，有利于遥感算法程序的复用和流程的共享，提高系统集成效率，具备较强的遥感数据实时快速处理能力。