YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.     

Optimization of hydrogen enrichment via palladium membrane in vacuum environments using Taguchi method and normalized regression analysis

In this study, the separation of hydrogen from gas mixtures using a palladium membrane coupled with a vacuum environment on the permeate side was studied experimentally. The gas mixtures composed of H₂, N₂, and CO₂ were used as the feed. Hydrogen permeation fluxes were measured with membrane operating temperature in the range of 320–380 °C, pressures on the retentate side in the range of 2–5 atm, and vacuum pressures on the permeate side in the range of 15–51 kPa. The Taguchi method was used to design the operating conditions for the experiments based on an orthogonal array. Using the measured H₂ permeation fluxes from the Taguchi approach, the stepwise regression analysis was also employed for establishing the prediction models of H₂ permeation flux, followed by the analysis of variance (ANOVA) to identify the significance and suitability of operating conditions. Based on both the Taguchi approach and ANOVA, the H₂ permeation flux was mostly affected by the gas mixture composition, followed by the retentate side pressure, the vacuum degree, and the membrane temperature. The predicted optimal operating conditions were the gas mixture with 75% H₂ and 25% N₂, the membrane temperature of 320 °C, the retentate side pressure of 5 atm, and the vacuum degree of 51 kPa. Under these conditions, the H₂ permeation flux was 0.185 mol s^?1 m^?2. A second-order normalized regression model with a relative error of less than 7% was obtained based on the measured H₂ permeation flux.     

Increased hydroxyl concentration by tungsten oxide modified h-BN promoted catalytic performance in partial oxidation of methane

Hexagonal boron nitride (h-BN) as a layered inorganic nonmetallic material has been widely used. Hydrogen peroxide (H₂O₂) modification can trigger exfoliation and afford abundant B–OH active sites at edge of h-BN, which can enhance methane activation ability. Introducing tungsten oxide (WO₃) to h-BN produces a similar effect, because doping WO₃ into h-BN resulted in electron transfer to N, inducing fracture of B–N bond, resulting in N vacancy (triboron center), exposing more B sites and promoting the generation of B–OH. Significantly, the introduction of WO₃ on the modified h-BN dramatically increased the concentration of B–OH compared with the unmodified h-BN, because H₂O₂ modification weakened B–N bond. By means of XRD, TEM, XPS,EPR, FT-IR, it is proved that the high concentration of B–OH active sites contributed to activating C–H bond, thus methane conversion and CO and H₂ selectivity were significantly improved.     

Experimental research on combined effect of obstacle and local spraying water fog on hydrogen/air premixed explosion

In order to reveal the mechanism of water fog explosion suppression and research the combined effect of water fog and obstacle on hydrogen/air deflagration, multiple sets of experiments were set up. The results show that the instability of thermal diffusion under lean combustion conditions is the main influencing factor of hydrogen/air flame surface instability, and the existence of water fog will aggravate the hydrogen/air flame surface instability. When obstacle is not considered, 8 μm, 15 μm, 30 μm water fog can significantly reduce the flame velocity and explosion overpressure of hydrogen/air, 45 μm fine water fog plays the opposite role. When considering the relative position of the water fog release position and the obstacle, the 8 μm, 15 μm, 30 μm water fog has almost no suppression effect when released near the obstacle, but a significant suppression effect occur, when using the 45 μm water fog. In the field of theoretical research, the research results not only provide an experimental basis for the fine water fog to reduce the consequences of hydrogen explosion accidents, and the optimal diameter range used by the water fog, but also provide experimental reference for the numerical simulation of hydrogen/air explosion suppression in semi-open space, and promote the development of hydrogen explosion suppression theory. In terms of engineering applications, this study can provide a theoretical basis for the layout of fire fighting equipment in the engine room of nuclear power plants or hydrogen-powered ships.     

Effect of micro-cracks on the in-plane electronic conductivity of proton exchange membrane fuel cell catalyst layers based on lattice Boltzmann method

Micro-cracks commonly occur on the catalyst layers (CLs) during the manufacturing of catalyst coated membranes (CCMs). However, the crack shape parameters effect on CLs in-plane (IP) electronic conductivity λ_s is not clear. In this work, the relationship between crack parameters and the λ_s is obtained based on the two-dimensional (2D) multiple-relaxation time (MRT) lattice Boltzmann method (LBM). The LBM numerical model is validated by the normalized λ_s experiment applied on three different home-made cracked CLs, and the parameter study focus on crack width, length, quantity and phase angle are carried out. The results show that the decrease of λ_s has different sensitivity |k| to the parameters above. The crack width has little effect on λ_s decrease, and the |k_w| is 0.038. However, crack arm length and quantity show more significant impact, which |k_l| and |k_N| are 0.753 and 0.725, respectively. The CLs with different crack propagation directions show significant anisotropy on λ_s, and a 53.53% decrease in λ_s is observed between 0° and 90° crack phase angle change. To manufacture a high electronic conductivity CL, crack initiation and migration mitigation are highly encouraged.     

Volatile and non-volatile compound profiles of commercial sweet pickled mango and its correlation with consumer preference

Sweet pickled mango named Ma-Muang Bao Chae-Im is a traditional preserved mango from Hat Yai, Thailand. This study investigated (I) volatile and non-volatile compound profiles of commercial Ma-Muang Bao Chae-Im and (II) their relationship to consumer preference. Untargeted metabolomics profiling was performed by gas chromatography-mass quadrupole-time of flight analysis. There were 117 volatile and 44 non-volatile compounds annotated in six commercial brands of Ma-Muang Bao Chae-Im. Furthermore, 46 volatile and 19 non-volatile compounds’ discriminant markers were found by Partial least square discriminant analysis. Among those markers, sorbic and benzoic acid were observed in several brands; moreover, the combination of both compounds altered the volatile profile, especially the ester group. Partial least square regression revealed that overall consumer liking is correlated to 1-heptanol; 1-octanol; acetoin; acetic acid, 2-phenylethyl ester; D-manitol; terpenes and terpenoids, while firmness to sucrose and L-(-)-sorbofuranose. On the other hand, most ester compounds were not related to consumer preference.     

PID Control of Planar Nonlinear Uncertain Systems in the Presence of Actuator Saturation

This paper investigates PID control design for a class of planar nonlinear uncertain systems in the presence of actuator saturation. Based on the bounds on the growth rates of the nonlinear uncertain function in the system model, the system is placed in a linear differential inclusion. Each vertex system of the linear differential inclusion is a linear system subject to actuator saturation. By placing the saturated PID control into a convex hull formed by the PID controller and an auxiliary linear feedback law, we establish conditions under which an ellipsoid is contractively invariant and hence is an estimate of the domain of attraction of the equilibrium point of the closed-loop system. The equilibrium point corresponds to the desired set point for the system output. Thus, the location of the equilibrium point and the size of the domain of attraction determine, respectively, the set point that the output can achieve and the range of initial conditions from which this set point can be reached. Based on these conditions, the feasible set points can be determined and the design of the PID control law that stabilizes the nonlinear uncertain system at a feasible set point with a large domain of attraction can then be formulated and solved as a constrained optimization problem with constraints in the form of linear matrix inequalities (LMIs). Application of the proposed design to a magnetic suspension system illustrates the design process and the performance of the resulting PID control law.      

基于卷积神经网络的5G蜂窝网络无线定位方法

5G蜂窝网络发展迅猛，其覆盖面积将逐渐增大，因此使用5G蜂窝网络进行定位是有研究潜力的研究方向。本文提出一种新的深度学习技术来实现高效、高精度和低占用的定位，以代替传统指纹定位过程中繁重的指纹库生成以及距离计算。该方法建立了一个特殊的卷积神经网络，并根据5G天线信号的接收信号强度指示、相位和到达角等特征量，选择合适的输入数据格式构造样本组建训练集，对该卷积神经网络进行训练。训练得到的卷积神经网络可以替代指纹定位中的庞大指纹库，非常有利于直接在5G移动设备端实现定位。虽然卷积神经网络在训练过程中需要大量时间，但在训练完毕后直接进行分类定位的速度非常快，可以保障定位实现的实时性。本文所实现的卷积神经网络权重与偏置所占内存不到0.5 MB，且能够在实际应用环境中以95%的定位准确率以及0.1 m的平均定位精度实现高精度定位。     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。