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.     

Influences of assembly pressure and flow channel size on performances of proton exchange membrane fuel cells based on a multi-model

In this work, assembly pressure and flow channel size on proton exchange membrane fuel cell performance are optimized by means of a multi-model. Based on stress-strain data of the SGL-22BB GDL obtained from its initial compression experiments, Young's modulus with different ranges of assembly pressure fits well through modeling. A mechanical model is established to analyze influences of assembly pressure on various gas diffusion layer parameters. Moreover, a CFD calculation model with different assembly pressures, channel width, and channel depth are established to calculate PEMFC performances. Furthermore, a BP neural network model is utilized to explore optimal combination of assembly pressure, channel width and channel depth. Finally, the CFD model is used to validate effect of size optimization on PEMFC performance. Results indicate that gap change of GDL below bipolar ribs is more remarkable than that below channels under action of the assembly pressure, making liquid water easily transported under high porosity, which is conducive to liquid water to the channels, reduces the accumulation of liquid water under the ribs, and enhances water removal in the PEMFC. Affected by the assembly force, change of GDL porosity affects its diffusion rate, permeability and other parameters, which is not conducive to mass transfer in GDL. Optimizing the depth and different dimensions through width of the flow field can effectively compensate for this effect. Therefore, the PEMFC performance can be enhanced through the comprehensive optimization of the assembly force, flow channel width and flow channel depth. The optimal parameter is obtained when assembly pressure, channel width and channel depth are set as 0.6 MPa, 0.8 mm, and 0.8 mm, respectively. The parameter optimization enhances the mass transfer, impedance, and electrochemical characteristics of PEMFC. Besides, it effectively enhances the quality transfer efficiency inside GDL, prevents flooding, and reduces concentration loss and ohmic loss.     

Effect of high hydrostatic pressure pretreatment on flavour and physicochemical properties of freeze-dried carambola slices

Ripe carambolas are hard to store and transport, while freeze-dried ones are easy to store. However, its long production time leads to higher costs. This study shows that high hydrostatic pressure (HHP) treatment could shorten the freeze-drying time of carambola slices. After HHP treatment (25–250 MPa), the drying time of the fresh sample can be shortened by 33.3–44.4% and the distribution of water and pigment in tissues is much uniform. With the increment of the pressure, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydroxyl radical scavenging rate are increased. At 250 MPa, the total phenolic content (TPC) increased from 11.34 to 13.36 mg GAE g⁻¹, and the total flavonoid content (TFC) of the control sample was increased from 10.77 to 12.73 mg RE g⁻¹. Compared with the untreated sample, HHP treatment can enhance the flavour and shorten the freeze-drying time. This work guides the application of HHP technology for drying food processing.     

Effects of high-pressure-modified soy 11S globulin on the gel properties and water-holding capacity of pork batter

The effects of high-pressure-modified soy 11S globulin (0.1, 200, and 400 MPa) on the gel properties, water-holding capacity, and water mobility of pork batter were investigated. The high-pressure-modified soy 11S globulin significantly increased (P < 0.05) the emulsion stability, cooking yield, hardness, springiness, chewiness, resilience, cohesiveness, the a^* and b^* values, and the G′ and G′′ values of pork batter at 80 °C, compared with those of 0.1 MPa-modified globulin. In contrast, the centrifugal loss and initial relaxation time of T_2b, T₂₁, and T₂₂ significantly decreased (P < 0.05). Meanwhile, the microstructure was denser, and the voids were smaller and more uniform compared with those of 0.1 MPa-modified globulin. In addition, the sample with 11S globulin modified at 400 MPa had the best water-holding capacity, gel structure, and gel properties among the samples. Overall, the use of high-pressure-modified soy 11S globulin improved the gel properties and water-holding capacity of pork batter, especially under 400 MPa.     

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.     

Metabolic Profiling of VOCs Emitted by Bacteria Isolated from Pressure Ulcers and Treated with Different Concentrations of Bio-AgNPs

Considering the advent of antibiotic resistance, the study of bacterial metabolic behavior stimulated by novel antimicrobial agents becomes a relevant tool to elucidate involved adaptive pathways. Profiling of volatile metabolites was performed to monitor alterations of bacterial metabolism induced by biosynthesized silver nanoparticles (bio-AgNPs). Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae and Proteus mirabilis were isolated from pressure ulcers, and their cultures were prepared in the presence/absence of bio-AgNPs at 12.5, 25 and 50 µg mL⁻¹. Headspace solid phase microextraction associated to gas chromatography–mass spectrometry was the employed analytical platform. At the lower concentration level, the agent promoted positive modulation of products of fermentation routes and bioactive volatiles, indicating an attempt of bacteria to adapt to an ongoing suppression of cellular respiration. Augmented response of aldehydes and other possible products of lipid oxidative cleavage was noticed for increasing levels of bio-AgNPs. The greatest concentration of agent caused a reduction of 44 to 80% in the variety of compounds found in the control samples. Pathway analysis indicated overall inhibition of amino acids and fatty acids routes. The present assessment may provide a deeper understanding of molecular mechanisms of bio-AgNPs and how the metabolic response of bacteria is untangled.     

浅谈压力铸造的全自动化生产

近年来,国家对制造业的数字化、网络化、智能化越来越重视,特别是《中国制造2025》的发布,更是促进整个制造业向智能制造迈进,铸造行业也应该紧跟步伐。在传统压力铸造行业中压铸机的加料、取件,切边机的上件、下件,加工机床的上件、下件,都是依靠人来完成,大大制约了行业的发展。本文阐述了压力铸造的全自动化生产,为压力铸造行业的转型升级提供借鉴。     

Influence of pulse-jet cleaning pressure on performance of compact dust collector with pleated filter operated in clean-on-time mode

To operate a bag filter continuously, pulse-jet cleaning of dust particles from the filter medium is commonly required, and the pulse-jet pressure significantly affects the filter performance. In this study, the accumulation structure of residual dust particles inside and on the surface of a filter medium at different pulse-jet pressures was investigated by constructing a simple model, and the influence of the dust structure on the filter performance was clarified. Using a simple model, we determined the effective ratio of filtration area β, which represents the ratio of the filterable area to the total filtration area, the true resistance coefficient due to the primary dust layer ζ_p’ thinly deposited on the filter surface, and the true resistance coefficient inside the filter media itself ζ_f’_. The effective ratio of filtration area β decreased with operation time for all pulse-jet pressures; however, it maintained a high value when the pulse-jet pressure was high. The validity of β analyzed by the model was verified using two different methods, and the results showed good agreement, indicating that the model is effective in identifying real conditions. The true resistance coefficient due to the primary dust layer ζ_p’ decreased as the pulse-jet pressure increased; however, the true resistance coefficient inside the filter media itself ζ_f’ was the highest at 0.5 MPa. In addition, the dust collection efficiency was different at each pulse-jet pressure, which was considered to be caused by the difference in the dust particle accumulation structure.     

水压爆破拆除超大容积水池

简要介绍了组合式超大容积（８５０ｍ３）水池在水压爆破拆除过程中爆破方案选择、参数选取等方面的经验，并对爆破效果进行了分析，为同类工程施工提供了可借鉴的经验。     

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.