Underwater image super-resolution using multi-stage information distillation networks

Recently, single image super-resolution (SISR) has been widely applied in the fields of underwater robot vision and obtained remarkable performance. However, most current methods generally suffered from the problem of a heavy burden on computational resources with large model sizes, which limited their real-world underwater robotic applications. In this paper, we introduce and tackle the super resolution (SR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present a novel lightweight multi-stage information distillation network, named MSIDN, for better balancing performance against applicability, which aggregates the local distilled features from different stages for more powerful feature representation. Moreover, a novel recursive residual feature distillation (RRFD) module is constructed to progressively extract useful features with a modest number of parameters in each stage. We also propose a channel interaction & distillation (CI&D) module that employs channel split operation on the preceding features to produce two-part features and utilizes the inter channel-wise interaction information between them to generate the distilled features, which can effectively extract the useful information of current stage without extra parameters. Besides, we present USR-2K dataset, a collection of over 1.6K samples for large-scale underwater image SR training, and a testset with an additional 400 samples for benchmark evaluation. Extensive experiments on several standard benchmark datasets show that the proposed MSIDN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.     

Influence of temperature on acoustic emission source location accuracy in underground structure

An acoustic emission (AE) experiment was carried out to explore the AE location accuracy influenced by temperature. A hollow hemispherical specimen was used to simulate common underground structures. In the process of heating with the flame, the pulse signal of constant frequency was stimulated as an AE source. Then AE signals received by each sensor were collected and used for comparing localization accuracy at different temperatures. Results show that location errors of AE keep the same phenomenon in the early and middle heating stages. In the later stage of heating, location errors of AE increase sharply due to the appearance of cracks. This provides some beneficial suggestions on decreasing location errors of structural cracks caused by temperature and improves the ability of underground structure disaster prevention and control.     

煤矿5G分布式MIMO组网技术研究（增刊）

根据煤矿井下的安全防爆要求，天线的射频发射功率受到严格限制，导致煤矿5G的基站部署密度显著高于地面场景。为了解决由此引发的交叉覆盖问题，本文提出了煤矿5G分布式MIMO组网技术，利用分布在不同位置的天线实现对用户数据的联合并行发送，不仅避免了由交叉覆盖导致的乒乓切换等负面效应，还提升了边缘区域的信号覆盖效果。在乌兰木伦煤矿的300米采煤工作面内，利用分布式MIMO组网技术实现了5G信号的无边界覆盖，上行速率不低于500Mbps，保障了煤机数据的实时有效传输。     

基于包装功能的时间–温度指示器与食品新鲜度指示器研究进展

目的 从包装功能实现的角度分析研究时间–温度指示器和食品新鲜度指示器的原理、类型与发展趋势，指出开发设计时间–温度指示器和食品新鲜度指示器应完成的包装功能，为有效实现包装交流功能提供研究思路和技术支持。方法 列出现有食品包装功能在物理环境、周围环境和人类环境下的实现矩阵，通过研究时间–温度指示器和食品新鲜度指示器的实现原理和所满足的包装交流功能，探讨这2种技术提高矩阵中分值较低元素的可行性。结果 时间–温度指示器监测食品所经历的时间、温度的完整历程，有助于提高实现矩阵中ComA元素的分值;食品新鲜度指示器提供生理变化或微生物生长引起的食品质量变化的定性或半定量信息，有助于提高实现矩阵中ComH元素的分值。结论 时间–温度指示器和食品新鲜度指示器能够有效提升食品包装的交流功能，保证消费者安全，减少资源浪费，具有广阔的发展前景。     

象山隧道液压凿岩台车开挖斜孔掏槽施工技术

新建的龙岩至厦门单线铁路象山隧道断面比较小,使用液压凿岩台车开挖,斜孔掏槽方案受到很大限制。本文介绍了使用液压凿岩台车钻爆开挖象山隧道,选择斜孔掏槽方案实现快速开挖,为类似的隧道工程施工提供经验。     

Reasonable construction of proton conducting channel via biomimetic caterpillar-like alumina fiber to improve the properties of its composite proton exchange membrane

In this paper, we prepare a novel biomimetic caterpillar-like alumina fiber with the characteristic of continuous alumina backbone and fine needle whiskers spine. Then the high-performance caterpillar-like alumina fiber composite proton exchange membrane (CAPEM) is obtained by introducing the amino modified biomimetic caterpillar-like alumina fiber into sulfonated polysulfone (SPSF) matrix, which successfully reasonable construction of the proton conducting channels in both vertical and horizontal orientation. The properties of CAPEM, including proton conductivity, methanol permeability, etc. Are systematically studied. The results show that the proton conductivity of CAPEM increases with rising the temperature, which reaches the maximum of 0.263 S/cm at 80 °C and 100% RH, respectively. The excellent proton conductivity of CAPEM is attributed to the long-range continuous proton conducting channel formed by the horizontal continuous alumina skeleton in the in-plane direction and the vertical overlapped fine needle whiskers spine in the through-plane direction. In addition, the interfacial compatibility between amino modified caterpillar-like alumina fiber and SPSF matrix is enhanced through the reasonable construction of proton conducting channels, which effectively inhibits the methanol permeation of the composite membrane with 4.18 × 10^?7 cm² s^?1 and improves the comprehensive performance of the CAPEM.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

A survey on tracking control of unmanned underwater vehicles: Experiments-based approach

This paper aims to provide a review of the conceptual design and theoretical framework of the main control schemes proposed in the literature for unmanned underwater vehicles (UUVs). Additionally, the objective of the paper is not only to present an overview of the recent control architectures validated on UUVs but also to give detailed experimental-based comparative studies of the proposed control schemes. To this end, the main control schemes, including proportional–integral–derivative (PID) based, sliding mode control (SMC) based, adaptive based, observation-based, model predictive control (MPC) based, combined control techniques, are revisited in order to consolidate the principal efforts made in the last two decades by the automatic control community in the field. Besides implementing some key tracking control schemes from the classification mentioned above on Leonard UUV, several real-time experimental scenarios are tested, under different operating conditions, to evaluate and compare the efficiency of the selected tracking control schemes. Furthermore, we point out potential investigation gaps and future research trends at the end of this survey.     

MHD mixed convection of a Cu–water nanofluid flow through a channel with an open trapezoidal cavity and an elliptical obstacle

In the current work, numerical simulations are achieved to study the properties and the characteristics of fluid flow and heat transfer of (Cu–water) nanofluid under the magnetohydrodynamic effects in a horizontal rectangular canal with an open trapezoidal enclosure and an elliptical obstacle. The cavity lower wall is grooved and represents the heat source while the obstacle represents a stationary cold wall. On the other hand, the rest of the walls are considered adiabatic. The governing equations for this investigation are formulated, nondimensionalized, and then solved by Galerkin finite element approach. The numerical findings were examined across a wide range of Richardson number (0.1 ≤ Ri ≤ 10), Reynolds number (1 ≤ Re ≤ 125), Hartmann number (0 ≤ Ha ≤ 100), and volume fraction of nanofluid (0 ≤ φ ≤ 0.05). The current study's findings demonstrate that the flow strength increases inversely as the Reynolds number rises, which pushes the isotherms down to the lower part of the trapezoidal cavity. The Nu_avg rises as the Ri rise, the maximum Nu_avg = 10.345 at Ri = 10, Re = 50, ϕ = 0.05, and Ha = 0; however, it reduces with increasing Hartmann number. Also, it increase by increasing ϕ, at Ri = 10, the Nu_avg increased by 8.44% when the volume fraction of nanofluid increased from (ϕ = 0–0.05).     

基于改进SOA-VMD的磁声发射信号去噪算法

磁声发射（MAE）是铁磁性材料磁化过程中产生的声发射信号，在构件应力检测和微观损伤检测中有着广泛的应用。针对MAE信号非稳态、复杂性、衰减性等特点，提出海鸥算法结合变分模态分解（SOA-VMD）的去噪方法，为克服海鸥算法求解过程中易陷入局部最优解问题，利用柯西变异算子产生随机迭代过程，使改进算法即柯西变异海欧算法（CVSOA）跳出早熟收敛。采用以幅值谱熵为适应度函数，优化VMD算法中分解模态个数K和二次惩戒因子α两个参数，将含噪声的MAE信号进行VMD分解重构。经仿真信号和实际检测信号分析表明，改进后的CVSOA-VMD算法全局寻优能力和去噪性能优于传统的SOA-VMD算法，降噪后的MAE信号特征值对于不同应力下均方根、偏斜度特征值的重复性更好，可靠性更高。