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.     

Experimental study on the propagation characteristics of hydrogen/methane/air premixed flames in a narrow channel

This work is focused on the explosion characteristics of premixed gas containing different volume fractions of hydrogen in a narrow channel (1000 mm × 50 mm × 10 mm) under the circumstance of stoichiometric ratio. The ignition positions were set in the closed end and the middle of the pipeline respectively. The results showed that when the gas was ignited at the pipeline closed end, the propagating flame was tulip structure for different premixed gas. When the hydrogen volume fraction was less than 40%, the flame propagation speed increased significantly with the rise of hydrogen volume fraction, and the overpressure peak also appeared obviously in advance. However, when the volume fraction of hydrogen was more than 40%, the increase of flame propagation speed and the overpressure peak occurrence time varied slightly. Furthermore, when the ignition position was placed in the middle of the pipeline, the flame propagation speed propagating to the opening end was much faster than that propagating to the closing end, and there was no tulip shape when the flame propagates to the opening end. The flame propagating to the closed end appeared tulip shape under the influence of airflow, and high-frequency flame oscillation occurred during the propagation. This work shows that the hydrogen volume fraction and ignition position significantly affected the flame structure, flame front speed, and explosion overpressure.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

梁结构振动支承约束反力控制

支承或连接构件对梁结构的动力学性能有至关重要影响,必须保证其在振动过程中不发生破坏或者失效。通过合理设计和布局附加弹性支承可以实现对这些重要连接构件所承受约束反力的控制。应用微分变换法推导含附加支承的梁结构支承约束反力及其对于附加支承位置和刚度的灵敏度表达式,并通过优化设计附加支承位置和刚度实现具有弹性约束端的简支梁结构各支承约束反力的平衡,可提高结构的动力学性能。     

自适应融合层级特征的混合退化图像复原算法

多种退化类型混合的图像比单一类型的退化图像降质更严重,很难建立精确模型对其复原,研究端到端的神经网络算法是复原的关键.现有的基于操作选择注意力网络的算法(operation-wiseattentionnetwork,OWAN)虽然有一定的性能提升,但是其网络过于复杂,运行较慢,复原图像缺乏高频细节,整体效果也有提升的空间.针对这些问题,提出一种基于层级特征融合的自适应复原算法.该算法直接融合不同感受野分支的特征,增强复原图像的结构;用注意力机制对不同层级的特征进行动态融合,增加模型的自适应性,降低了模型冗余;另外,结合L1损失和感知损失,增强了复原图像的视觉感知效果.在DIV2K,BSD500等数据集上的实验结果表明,该算法无论是在峰值信噪比和结构相似性上的定量分析,还是在主观视觉质量方面,均优于OWAN算法,充分证明了该算法的有效性.     

Understanding the asymmetric perceptions of smartphone security from security feature perspective: A comparative study

Smartphones are being used and relied on by people more than ever before. The open connectivity brings with it great convenience and leads to a variety of risks that cannot be overlooked. Smartphone vendors, security policy designers, and security application providers have put a variety of practical efforts to secure smartphones, and researchers have conducted extensive research on threat sources, security techniques, and user security behaviors. Regrettably, smartphone users do not pay enough attention to mobile security, making many efforts futile. This study identifies this gap between technology affordance and user requirements, and attempts to investigate the asymmetric perceptions toward security features between developers and users, between users and users, as well as between different security features. These asymmetric perceptions include perceptions of quality, perceptions of importance, and perceptions of satisfaction. After scoping the range of smartphone security features, this study conducts an improved Kano-based method and exhaustively analyzes the 245 collected samples using correspondence analysis and importance satisfaction analysis. The 14 security features of the smartphone are divided into four Kano quality types and the perceived quality differences between developers and users are compared. Correspondence analysis is utilized to capture the relationship between the perceived importance of security features across different groups of respondents, and results of importance-satisfaction analysis provide the basis for the developmental path and resource reallocation strategy of security features. This article offers new insights for researchers as well as practitioners of smartphone security.     

基于多自由度气动机械手位置伺服系统稳定性控制研究

基于传统易碎薄板机械手位置伺服控制系统稳定性低、自动化分拣效率低等不足,设计了一种基于笛卡尔坐标式的气动码垛机器手和位置伺服稳定性控制系统。首先,设计并介绍笛卡尔坐标式码垛机械手的基本组成结构,对机械手末端吸盘气动回路控制系统进行设计和分析;然后分别对机械手X、Y、Z三个方向的伺服电机控制原理进行分析并设计了一种位置伺服系统的前馈自适应控制算法;最后,将传统位置闭环PID算法和前馈自适应控制算法进行位置跟踪稳定性对比试验。实验结果表明该笛卡尔坐标式的气动码垛机器手和位置伺服稳定性控制系统设计合理,满足实际生产要求。     

磁悬浮控制力矩陀螺高速转子的高精度位置控制

在受到陀螺效应、动框架效应等影响后产生的磁力非线性问题是磁悬浮控制力矩陀螺(MSCMG)高速转子位置精度下降的主要因素。为解决以上问题,提高转子位置精度,本文分析了转子所受磁力的特性,建立了转子系统非线性动力学模型,提出了神经网络滑模控制方法。设计滑模控制律,采用径向基函数神经网络逼近控制律中的非线性模型,自适应算法根据误差在线调整神经网络的权值,同时可以保证整个系统的稳定性。仿真和实验结果表明,所提出方法的转子位置精度达到99%,稳态误差为0.000 2 mm。神经网络滑模控制可以实现MSCMG转子系统的高精度位置控制。     

气体裂变产物88Kr半衰期测量

为准确测定气体裂变产物⁸⁸Kr的半衰期，本工作从辐照铀靶中分离得到3个放化纯的⁸⁸Kr气体测量源。以⁸⁵Kr作内标监督源，¹³⁷Cs或⁵⁷Co作外标监督源，使用多个HPGe探测器分别采用单探测器位置接力法和双探测器位置接力法跟踪测量⁸⁸Kr 196.3 keV能量的特征γ射线，跟踪时间均在8个半衰期以上，以获得其半衰期数据。对3次独立测量数据用多种方法进行处理，最终得到⁸⁸Kr半衰期测定结果为（2.796±0.015） h。     

基于梯度提升决策树的车轮轮缘厚度磨耗预测

轮对在列车走行过程中起着导向、承受以及传递载荷的作用，其踏面及轮缘磨耗对地铁列车运行安全性和钢轨的寿命都将产生重要影响。根据地铁列车车轮磨耗机理，分析车轮尺寸数据特点，针对轮缘厚度这一型面参数，基于梯度提升决策树算法构建轮缘厚度磨耗预测模型。在该模型的基础上，任意选取某轮对数据进行验证分析，结果表明：基于梯度提升决策树的轮对磨耗预测模型具有较好的预测精度，可预测出1~6个月的轮缘厚度变化趋势范围，预测时间范围较长，可为地铁维保部门对轮对的维修方式由状态修转为预防修提供指导性建议。