234 research outputs found
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 COâ‚‚ 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 COâ‚‚ emissions and controlling COâ‚‚ concentrations in the atmosphere are currently among the greatest global challenges facing our society.
The electrochemical COâ‚‚ reduction reaction (referred to as ec-COâ‚‚RR), capable of converting polluting COâ‚‚ 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-COâ‚‚RR product and a platform chemical in the chemical industry. This is why this PhD project focused on the optimization of COâ‚‚-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 COâ‚‚ 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 COâ‚‚ 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
Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO 2 reduction: A combined EDX/ICP–MS approach
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
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