Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios

Benefiting from the rapid development of convolutional neural networks, the performance of car license plate detection and recognition has been largely improved. Nonetheless, challenges still exist especially for real-world applications. In this paper, we present an efficient and accurate framework to solve the license plate detection and recognition tasks simultaneously. It is a lightweight and unified deep neural network, that can be optimized end-to-end and work in real-time. Specifically, for unconstrained scenarios, an anchor-free method is adopted to efficiently detect the bounding box and four corners of a license plate, which are used to extract and rectify the target region features. Then, a novel convolutional neural network branch is designed to further extract features of characters without segmentation. Finally, recognition task is treated as sequence labelling problems, which are solved by Connectionist Temporal Classification (CTC) directly. Several public datasets including images collected from different scenarios under various conditions are chosen for evaluation. A large number of experiments indicate that the proposed method significantly outperforms the previous state-of-the-art methods in both speed and precision

Lessons learned from China’s mitigation strategies in fighting COVID-19

In late 2019, the first SARS-CoV-2 case was reported in Wuhan, China. It has been known as a deadly virus that could cause many severe health complications, particularly respiratory illnesses. With its extraordinary ability to transmit between humans, the coronavirus disease 2019 (COVID-19) has spread worldwide, including Antarctica and the Arctic region. On 11th March 2020, the World Health Organization (WHO) declared the COVID-19 as a public health emergency worldwide (global pandemic) to raise global awareness of the dangerous virus. With immediate and efficient public health interventions, progress has been seen in many countries such as China and New Zealand. Therefore, in this review, we summarized the important public health risk mitigation measures applied in China

A Study of the Identification, Fragmentation Mode and Metabolic Pathways of Imatinib in Rats Using UHPLC-Q-TOF-MS/MS

In this study, The metabolites, metabolic pathways, and metabolic fragmentation mode of a tyrosine kinase inhibitor- (TKI-) imatinib in rats were investigated. The samples for analysis were pretreated via solid-phase extraction, and the metabolism of imatinib in rats was studied using ultra-high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS). Eighteen imatinib metabolites were identified in rat plasma, 21 in bile, 18 in urine, and 12 in feces. Twenty-seven of the above compounds were confirmed as metabolites of imatinib and 9 of them were newly discovered for the first time. Oxidation, hydroxylation, dealkylation, and catalytic dehydrogenation are the main metabolic pathways in phase I. For phase II, the main metabolic pathways were N-acetylation, methylation, cysteine, and glucuronidation binding. The fragment ions of imatinib and its metabolites were confirmed to be produced by the cleavage of the C-N bond at the amide bond. The newly discovered metabolite of imatinib was identified by UHPLC-Q-TOF-MS/MS. The metabolic pathway of imatinib and its fragmentation pattern were summarized. These results could be helpful to study the safety of imatinib for clinical use

Chatter-free and high-quality end milling for thin-walled workpieces through a follow-up support technology

When the thickness and weight of aircraft skins and rocket tank walls are reduced in end milling, chatter, forced vibration and deformation are prone to occur in the milling area along the tool axis. To truly reveal the vibration state of thin-walled workpieces during end milling, the model of axial regenerative chatter of the workpiece generated by the bottom edge is proposed for the first time in the tool axial plunge and horizontal feed. Subsequently, to achieve chatter-free and high-quality machining, this paper presents a new follow-up support technology, which can simultaneously provide stiffness and damping on both sides of thin-walled workpieces around the milling area and on the opposite side of the tool milling. As an implementation of this technology, a novel magnetic follow-up support device which can be installed at the end of the hybrid robot and adsorbed on the opposite side of the workpiece is designed, which solves the shortcomings of large space occupied by the support equipment. On this basis, a multi-modal time-varying dynamic model of the workpiece with the follow-up device is established to predict three-dimensional stability lobe diagrams. Meanwhile, the influences of the device and different support force on the dynamic characteristics of the workpiece are analyzed. A series of experiments prove that the combination of chatter prediction model and vibration suppression technology can realize the selection of chatter-free process parameters and high-quality mirror milling with high material removal rate for large thin-walled workpieces

Vibration and deformation suppression in mirror milling of thin-walled workpiece through a magnetic follow-up support fixture

Large thin-walled workpieces have large deformation and vibration during end milling due to their weak rigidity, which can lead to degradation of the surface quality of workpiece. To this end, this paper proposes a new method to suppress vibration and deformation during mirror milling of thin-walled workpieces by using a magnetic follow-up support fixture. The main idea is to design a fixture that can clamp around the milling area of the workpiece in real time, follow the tool motion and continuously provide a controlled air pressure force to the opposite side of the milling area without providing the mirroring support fixture that occupies a large space to the opposite side of workpiece. First, to accurately characterize the motion of the fixture, a mathematical model of the axial magnetic force considering the lateral offset is developed. On this basis, to efficiently investigate the capability of the fixture in improving the dynamic characteristics and dynamic response of workpiece, the finite element method (FEM) is used to analyze the frequency response functions (FRF) and the mode shapes of workpiece under the action of the fixture. Then, the vibration amplitude and deformation of workpiece on the milling path are simulated and studied by considering the variation of the air pressure and the position of the fixture. Finally, the prototype of the fixture is developed and then the effectiveness and feasibility of the proposed method are verified under the experimental tests with different machining parameters

Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term

Influence of impregnation-vacuum filtration conditions on Ni dispersion and activity of Ni-cordierite for hydrogenation

The influence of the preparation conditions of the impregnation-vacuum filtration method was investigated systematically on the Ni dispersion and the activity of Ni-cordierite structured catalysts in hydrogenation of m-dinitrobenzene to m-phenylenediamine. H2-TPD measurement showed that the Ni dispersion has close relationship with the impregnation solution concentration of nickel nitrate, the impregnation time, the vacuum degree, the vacuum filtration time and the calcination temperature. The hydrogenation activity test and nitrogen physisorption investigation showed that the catalytic performance of Ni-cordierite is dependent upon the Ni dispersion and the chemisorption mode of m-dinitrobenzene on Ni particles

Experience of Online Learning from COVID-19: Preparing for the Future of Digital Transformation in Education

COVID-19 has affected traditional instructional activities. Home-based isolation and restrictive movement measures have forced most learning activities to move from an offline to an online environment. Multiple studies have also demonstrated that teaching with virtual tools during the COVID-19 pandemic is always ineffective. This study examines the different characteristics and challenges that virtual tools brought to online education in the pre-pandemic and pandemic era, with the aim of providing experience of how virtual tools supported purely online learning during a health crisis. By searching keywords in public databases and review publications, this study tries to summarize the major topics related to the research theme. These topics are the characteristics of learning supported by technologies in pre-pandemic and pandemic era, the challenges that education systems have faced during the COVID-19 pandemic. This study also compares the functions, advantages and limitations of typical virtual tools, which has rarely been done in previous studies. This study tries to present the features of virtual tools that support online learning and the challenges regarding real-life risk scenarios, and tries to provide educational institutions with a distinct perspective for efficient teaching and learning in future potential health crises

SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting

Recently, deep learning has enabled a huge leap forward in image inpainting. However, due to the memory and computational limitation, most existing methods are able to handle only low-resolution inputs, typically less than 1 K. With the improvement of Internet transmission capacity and mobile device cameras, the resolution of image and video sources available to users via the cloud or locally is increasing. For high-resolution images, the common inpainting methods simply upsample the inpainted result of the shrinked image to yield a blurry result. In recent years, there is an urgent need to reconstruct the missing high-frequency information in high-resolution images and generate sharp texture details. Hence, we propose a general deep learning framework for high-resolution image inpainting, which first hallucinates a semantically continuous blurred result using low-resolution inpainting and suppresses computational overhead. Then the sharp high-frequency details with original resolution are reconstructed using super-resolution refinement. Experimentally, our method achieves inspiring inpainting quality on 2K and 4K resolution images, ahead of the state-of-the-art high-resolution inpainting technique. This framework is expected to be popularized for high-resolution image editing tasks on personal computers and mobile devices in the future

Study on the Light Field Regulation of UVC-LED Disinfection for Cold Chain Transportation

In this paper, the pain point that cold chain transportation urgently needs for an efficient disinfection method is pointed out. Thus, this work aims at solving the problems and improving the disinfection efficiency in cold chain transportation. While Ultraviolet-C (UVC) irradiation is an effective method by which to kill viruses, it is difficult to apply the commonly used UVC-LED disinfection light source to ice-covered cold chain transportation due to its uneven light field distribution. Thus, the light field regulation of UVC-LED disinfection for cold chain transportation is studied. A UVC-LED chip with a wavelength of 275 nm was used as a light source, and parallel light was obtained by collimating lenses. Then, microlens array homogenization technology was used to shape the UVC light into a uniform light spot, with an energy space uniformity rate of 96.4%. Moreover, a simulation was conducted to compare the effects of the ice layer on the absorption of UVC light. Finally, an experiment was carried out to verify that the disinfection efficiency can be increased nearly by 30% with the proposed system by disinfecting E. coli (Escherichia coli), and the results indicate that the proposed system is an effective disinfection solution during cold chain transportation