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.     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

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.     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

Preparation of high-purity SiC ceramics with good plasma corrosion resistance by hot-pressing sintering

High-purity SiC ceramic devices are applied in semiconductor industry owing to their outstanding properties. Nevertheless, it is difficult to densify SiC ceramics without any sintering additive even by HP sintering. In this work, high-purity and dense SiC ceramics were fabricated by HP sintering with very low amounts of sintering aids. Residual B content was only 556 ppm and relative density was more than 99.5%. Furthermore, thermal conductivity of as-prepared SiC ceramics was improved from 155 W m^?1 K^?1 to 167 W m^?1 K^?1 by increasing holding time and their plasma corrosion resistance was promoted in the meantime. The as-prepared high-purity SiC ceramics have broad application prospects in the field of semiconductor industry.     

Learn decision trees with deep visual primitives

In this paper, we strive to propose a self-interpretable framework, termed PrimitiveTree, that incorporates deep visual primitives condensed from deep features with a conventional decision tree, bridging the gap between deep features extracted from deep neural networks (DNNs) and trees’ transparent decision-making processes. Specifically, we utilize a codebook, which embeds the continuous deep features into a finite discrete space (deep visual primitives) to distill the most common semantic information. The decision tree adopts the spatial location information and the mapped primitives to present the decision-making process of the deep features in a tree hierarchy. Moreover, the trained interpretable PrimitiveTree can inversely explain the constituents of the deep features, highlighting the most critical and semantic-rich image patches attributing to the final predictions of the given DNN. Extensive experiments and visualization results validate the effectiveness and interpretability of our method.     

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.     

Operation principles for hydrogen spark ignited direct injection engines for passenger car applications

The sustainable reduction of greenhouse gas emissions from road transport requires solutions to achieve net-zero carbon dioxide emissions. Therefore, in addition to vehicles with electrified powertrains, such as those implemented in battery electric of fuel cell vehicles, internal combustion engines fueled with e-fuels or biofuels are also under discussion. An e-fuel that has come into focus recently, is hydrogen due to its potential to achieve zero tank-to-wheel and well-to-wheel carbon dioxide emissions when the electrolysis is powered by electricity from renewable sources. Due to the high laminar burning velocity, hydrogen has the potential for engine operation with high cylinder charge dilution by e.g. external exhaust gas recirculation or enleanment, resulting in increased efficiency. On the other hand, the high burning velocity and high adiabatic flame temperatures pose a challenge for engine cooling due to increased heat losses compared to conventional fuels. To further evaluate the use of hydrogen for small passenger car engines, a series production 1 L 3 cylinder gasoline engine provided by Ford Werke GmbH was modified for hydrogen direct injection. The engine was equipped with a high pressure external exhaust gas recirculation system to investigate charge dilution at stoichiometric operation. Due to limitations of the turbocharging system, very lean operation, which can achieve nitrogen oxides raw emissions below 10 ppm, was limited to part load operation below BMEP = 8 bar. Thus, a reduction of the nitrogen oxides emission level at high loads compared to stoichiometric operation was not possible. At stoichiometric operation with external exhaust gas recirculation engine efficiency can be increased significantly. The comparison of stoichiometric hydrogen and gasoline operation shows a reduced indicated efficiency with hydrogen with significant faster combustion of hydrogen at comparable centers of combustion. However, higher boost pressures would allow to achieve even higher indicated efficiencies by charge dilution compared to gasoline engine operation.     

高深溜井井筒堵塞的爆破处理实践

溜井是采用平硐-溜井方式开拓矿山的运输咽喉,溜井正常与否对矿山生产影响极大。本文通过黑沟铁矿高深溜井井筒堵塞处理实例,对其堵塞爆破处理方法及经验做了系统分析。重点介绍的爆破冲击波破拱疏通高深溜井井筒高位堵塞的爆破方法,富有新意,可供国内外同类型矿山参考借鉴。     

Metal chlorides-promoted ammonia absorption of deep eutectic solvent

Ammonia is considered as a promising hydrogen or energy carrier. Ammonia absorption or adsorption is an important aspect for both ammonia removal, storage and separation applications. To these ends, a wide range of solid and liquid sorbents have been investigated. Among these, the deep eutectic solvent (DES) is emerging as a promising class of ammonia absorbers. Herein, we report a novel type of DES, i.e., metal-containing DESs for ammonia absorption. Specifically, the NH₃ absorption capacity is enhanced by ca. 18.1–36.9% when a small amount of metal chlorides, such as MgCl₂, MnCl₂ etc., are added into a DES composed of resorcinol (Res) and ethylene glycol (EG). To our knowledge, the MgCl₂/Res/EG (0.1:1:2) DES outperforms most of the reported DESs. The excellent NH₃ absorption performances of metal–containing DESs have been attributed to the synergy of Lewis acid–base and hydrogen bonding interactions. Additionally, good reversibility and high NH₃/CO₂ selectivity are achieved over the MgCl₂/Res/EG (0.1:1:2) DES, which enables it to be a potential NH₃ absorber for further investigations.