WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

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.     

不同盐沼湿地类型脱氮除磷效能及影响因素分析

采用沉水植物表面流湿地(沉水组)、挺水植物表面流湿地(挺水组)和浮床湿地(浮床组)3种盐沼湿地对长江口近岸低污染水体进行脱氮除磷效能的研究。结果表明,HRT为3 d时,水组、挺水组、浮床组对NO3^--N的去除率在高温时段分别为79.9%±13.2%、71.8%±15.2%、77.2%±13.2%,中温时段分别为39.4%±13.7%、31.5%±8.5%、18.4%±16.6%,低温时段分别为15.6%±14.6%、19.7%±8.6%、2.%5±8.6%。沉水组和挺水组对TP的去除率受温度影响较小,分别为66.4%±32.4%、55.5%±29.4%;而浮床组除磷效果受温度影响较大。当HRT缩短为1.5 d时,3组湿地系统在高温时段仍可达到相近的脱氮除磷效果,在中低温时段脱氮除磷效果都有不同程度的下降。     

食品安全国家标准中维生素的测定方法及进展

人体所必需的营养素除了蛋白质、脂类、糖类等能量物质外,维生素也是不可或缺的营养物质,是评价其营养质量的关键指标。检测食品中的维生素含量,是有效保证其质量与安全的必要条件。因此,对现有国家标准食品中维生素的检测方法进行综述,总结各方法的前处理及检出限等,为增强检测工作效率,以及食品检测方法的研究和发展提供参考。     

一种新型的室温快速固化胶黏剂的研制

本文开发了一种新型的方舱夹芯板用室温固化高强度环氧结构胶黏剂,验证了其物化特性、相关力学性能和环境适应性。结果表明此胶黏剂具有优良性能,可以满足方舱用大板胶黏剂的使用需求。     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.     

Thiol-Functionalized Covalent Organic Frameworks as Thermal History Indictor for Temperature and Time History Monitoring

Various products, including foods and pharmaceuticals, are sensitive to temperature fluctuations. Thus, temperature monitoring during production, transportation, and storage is critical. Facile indicators are required to monitor temperature conditions via color changes in real time. This study aimed to prepare and apply thiol-functionalized covalent organic frameworks (COFs) as a novel indicator for monitoring thermal history and temperature abuse. The COFs underwent obvious color changes from bright yellow to purple after exposure to different temperatures for varying durations. The reaction kinetics are analyzed under isothermal conditions, which reveal that the order of reaction rates is k_−20°C < k_4°C < k_20°C < k_35°C < k_55°C. The activation energy (E_a) of the COFs is calculated using the Arrhenius equation as 50.71 kJ moL⁻¹. The COFs are capable of sensitive color changes and offer a broad temperature tracking range, thereby demonstrating their application potential for the monitoring of temperature and time exposure history during production, transportation, and storage. This excellent performance thermal history indicator also shows promise for expanding the application field of COFs.     

Superior Low-Temperature Reversible Adhesion Based on Bio-Inspired Microfibrillar Adhesives Fabricated by Phenyl Containing Polydimethylsiloxane Elastomers

Gecko-inspired microfibrillar adhesives have achieved great progress in microstructure design and adhesion improvement over the past two decades. Space applications nowadays show great interest in this material for the characteristics of reversible adhesion and universal van der Waals interactions. However, the impact of harsh environment of space on the performance of microfibrillar adhesives, especially the extreme low temperature, is rarely addressed. Herein, microfibrillar adhesives fabricated by phenyl containing polydimethylsiloxane (p-PDMS) elastomers with superior low-temperature reversible adhesion is proposed. p-PDMS elastomers are synthesized through one-pot anionic ring-opening copolymerization, and the resulting elastomers become non-crystallizable with excellent low-temperature elasticity. Low-temperature adhesion tests demonstrate that the adhesion strength of microfibrillar adhesives fabricated by p-PDMS elastomers can be well maintained to as low as −120 °C. In contrast, the adhesion strength of pure PDMS microfibrillar adhesive reduces more than 50% below its crystallization temperature. The low-temperature cyclic adhesion tests further demonstrate that p-PDMS microfibrillar adhesives exhibit superior reversible adhesion compared to that of PDMS microfibrillar adhesives, owing to the sustainable conformal contact and even distribution of loads over repeated cycles. This study provides a new fabrication strategy for microfibrillar adhesives, and is beneficial for the practical application of microfibrillar adhesives.