Mobile Video Object Detection with Temporally-Aware Feature Maps

This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices. Our approach combines fast single-image object detection with convolutional long short term memory (LSTM) layers to create an interweaved recurrent-convolutional architecture. Additionally, we propose an efficient Bottleneck-LSTM layer that significantly reduces computational cost compared to regular LSTMs. Our network achieves temporal awareness by using Bottleneck-LSTMs to refine and propagate feature maps across frames. This approach is substantially faster than existing detection methods in video, outperforming the fastest single-frame models in model size and computational cost while attaining accuracy comparable to much more expensive single-frame models on the Imagenet VID 2015 dataset. Our model reaches a real-time inference speed of up to 15 FPS on a mobile CPU.Comment: In CVPR 201

Stability of Zero-gap CO2 Electrolyzers for Electrochemical CO Production

The unchecked massive emissions of the greenhouse gas CO₂ through the continued use of fossil fuels is the main cause of global warming of the earth's atmosphere with dramatic consequences for our environment. Reducing global CO₂ emissions and controlling CO₂ concentrations in the atmosphere are currently among the greatest global challenges facing our society. The electrochemical CO₂ reduction reaction (referred to as ec-CO₂RR), capable of converting polluting CO₂ back into valuable chemicals such as carbon monoxide (CO) and formate using excess renewable energy, offers a promising approach to mitigate this problem. Socio-economic assessments have shown that carbon monoxide is a high-value ec-CO₂RR product and a platform chemical in the chemical industry. This is why this PhD project focused on the optimization of CO₂-to-CO conversion by electrochemical means. To achieve industrially relevant current densities in the order of several hundred milliamperes per square centimeter, the use of so-called gas diffusion electrodes (GDEs) is essential. One of the biggest problems currently preventing the use of this technology on a large scale is the stability of these GDEs. In this PhD project, the stability of GDEs is therefore systematically investigated using nanoparticulate silver catalysts in combination with a so-called semi-zero-gap test cell configuration (Arenz design). Using novel analytical approaches that can detect and quantify the transport of electrolyte through GDEs during CO₂ electrolysis (referred to as perspiration), various experimental factors (e.g., the use of binder materials, harmful use of capping agents, defectivity of GDEs, chemical nature of alkali metal cations, etc.) that stabilize or destabilize the GDEs could be identified. Active water/electrolyte management has been shown to be essential for prolonged operation of this kind of electrolyzers. Furthermore, a novel concept for stabilizing the nanoparticulate silver catalysts was developed. This is based on embedding the nanoparticles in a matrix of anion-conductive binder materials, which largely suppresses cathodic corrosion of the catalyst material during extended electrolysis. The results of this PhD thesis pave the way for a rational design of GDEs for future industrial applications of electrochemical CO₂ conversion

Analytical modeling of Lamb wave propagation in composite laminate bonded with piezoelectric actuator based on Mindlin plate theory

Dynamic analysis of plate structures based on the Mindlin plate theory has become one of the usual modeling methods for the structural health monitoring (SHM) of composite structures in recent years. Compared to the classical plate theory (CPT) based on Kirchhoff hypothesis, the Mindlin plate theory considers the influence of transverse shear deformation and moment of inertia on displacements. Thus it is more suitable for dynamic analysis of composite laminate with low transverse shear stiffness and large transverse shear deformation. Combining the adhesive layer coupling model of the piezoelectric actuator with the Mindlin plate theory, the dispersion curve of Lamb wave in any direction and mechanical parameters of any point in the composite are obtained, and thus after the substitution of boundary condition, the modeling of piezoelectric wafer excited Lamb wave propagating in composite laminate is realized. The validation experiment is performed on a carbon fiber composite laminate. It proves that the analytical modeling effectively reflects the propagation characteristics of Lamb wave in composite laminate and promotes the engineering application of SHM

DOUBLE-SIDED ULTRASONIC TESTING AND IMAGING OF MULTILAYER COMPOSITES WITH OUT-OF-PLANE FIBER WRINKLING

For fiber reinforced polymers (FRPs), ply stacking errors and fiber waviness during manufacturing will have a great influence on the mechanical properties of composites, which requires non-destructive testing and evaluation (NDT&E) for potential structure defects. Herein, this study analyzes the ultrasonic echo signals in pulse-echo (PE) mode to assess the layered structure and sub-structure of laminates, analytically and numerically. Based on recursive stiffness matrix method and analytic-signal technology, analytical modeling is performed to investigate the propagation of ultrasound and detect the layered structure in flat laminates. In addition, a numerical finite element model is built to further study the propagation of ultrasound in wavy composites. Based on the circular variances of ply angles extracted by structure tensor analysis, the double-sided ultrasonic testing and B-scan imaging method is proposed to correct the imaging artifact areas of wavy composites. After statistical validation, this weighted sum method can indeed reduce the influence of ultrasonic beam deviation caused by out-of-plane fiber wrinkling, thereby improving the imaging effect in wavy composites

Looking Fast and Slow: Memory-Guided Mobile Video Object Detection

With a single eye fixation lasting a fraction of a second, the human visual system is capable of forming a rich representation of a complex environment, reaching a holistic understanding which facilitates object recognition and detection. This phenomenon is known as recognizing the "gist" of the scene and is accomplished by relying on relevant prior knowledge. This paper addresses the analogous question of whether using memory in computer vision systems can not only improve the accuracy of object detection in video streams, but also reduce the computation time. By interleaving conventional feature extractors with extremely lightweight ones which only need to recognize the gist of the scene, we show that minimal computation is required to produce accurate detections when temporal memory is present. In addition, we show that the memory contains enough information for deploying reinforcement learning algorithms to learn an adaptive inference policy. Our model achieves state-of-the-art performance among mobile methods on the Imagenet VID 2015 dataset, while running at speeds of up to 70+ FPS on a Pixel 3 phone

Large structural impact localization based on multi-agent system

In practical applications of structural health monitoring, a huge amount of distributed sensors are usually used to monitor structures of large dimensions. In order to obtain fast and accurate evaluation of a structure, a multi-agent system is introduced to manage different sensor sets and to fuse distributed information. In this paper, a multi-agent system based on impact location is presented to deal with the impact load localization problem for large-scale structures. The monitoring system firstly detects whether an impact event happens in the monitored subregion, and focuses on the impact source on the sub-region boundary to obtain the sensor network data with blackboard systems. Then the collaborative evaluation of both the acoustic emission and the inverse analysis localization method is employed to obtain precise and fast localization result. Finally, a reliable assessment for the whole structure is provided by fusing evaluation results from the sub-regions. The performance of the proposed multi-agent system is illustrated by means of experimental on a large aerospace aluminum plate structure. Extensive testing of the proposed system demonstrated its effectiveness for the impact load localization in each sub-region, particularly for impacts lying next to the borders of the sub-regions

Total Focusing Method for Imaging Defect in CFRP Composite with Anisotropy and Inhomogeneity

Fiber reinforced polymers (FRPs) are increasingly used in thick primary load-bearing structures, while manufacturing and in-service defects occur with a higher chance as the composite thickness increases, which entails the nondestructive detection and evaluation of potential structure defects. This study focuses on the imaging qualities of defects at different depth in thick FRPs via total focusing method (TFM), aiming at determining the optimum imaging strategy for thick FRPs (25 mm for discussion). Dynamic homogenization based on Floquet theory and numerical finite element analysis are performed to interrogate the wave propagation characteristics. The Frequency-dependent time correction method for TFM imaging (F-TFM) is proposed for accurate defect imaging in periodically layered crossply FRP. Finally, the results show that the proposed F-TFM method is able to detect and locate the defects of 2 mm size at all possible depth

Inhibition of TRPA1 Attenuates Doxorubicin-Induced Acute Cardiotoxicity by Suppressing Oxidative Stress, the Inflammatory Response, and Endoplasmic Reticulum Stress

The transient receptor potential ankyrin 1 (TRPA1) channel is expressed in cardiomyocytes and involved in many cardiovascular diseases. However, the expression and function of TRPA1 in doxorubicin- (Dox-) induced acute cardiotoxicity have not been elucidated. This study aimed at investigating whether blocking the TRPA1 channel with the specific inhibitor HC-030031 (HC) attenuates Dox-induced cardiac injury. The animals were randomly divided into four groups: control, HC, Dox, and Dox + HC. Echocardiography was used to evaluate cardiac function, and the heart was removed for molecular experiments. The results showed that the expression of TRPA1 was increased in the heart after Dox treatment. Cardiac dysfunction and increased serum CK-MB and LDH levels were induced by Dox, but these effects were attenuated by HC treatment. In addition, HC mitigated Dox-induced oxidative stress, as evidenced by the decreased MDA level and increased GSH level and SOD activity in the Dox + HC group. Meanwhile, HC treatment lowered the levels of the proinflammatory cytokines IL-1β, IL-6, IL-17, and TNF-α induced by Dox. Furthermore, HC treatment mitigated endoplasmic reticulum (ER) stress and cardiomyocyte apoptosis induced by Dox. These results indicated that inhibition of TRPA1 could prevent Dox-induced cardiomyocyte apoptosis in mice by inhibiting oxidative stress, inflammation, and ER stress