CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation

Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for these methods, such as indistinguishable weak defects and defect-like interference in the background. To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer. CINFormer presents a simple yet effective feature integration mechanism that injects the multi-level CNN features of the input image into different stages of the transformer network in the encoder. This can maintain the merit of CNN capturing detailed features and that of transformer depressing noises in the background, which facilitates accurate defect detection. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects, so as to further reduce the impact of the redundant background. Extensive experiments conducted on the surface defect datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer achieves state-of-the-art performance in defect detection

A Metamorphic Testing Approach for Assessing Question Answering Systems

Question Answering (QA) enables the machine to understand and answer questions posed in natural language, which has emerged as a powerful tool in various domains. However, QA is a challenging task and there is an increasing concern about its quality. In this paper, we propose to apply the technique of metamorphic testing (MT) to evaluate QA systems from the users’ perspectives, in order to help the users to better understand the capabilities of these systems and then to select appropriate QA systems for their specific needs. Two typical categories of QA systems, namely, the textual QA (TQA) and visual QA (VQA), are studied, and a total number of 17 metamorphic relations (MRs) are identified for them. These MRs respectively focus on some characteristics of different aspects of QA. We further apply MT to four QA systems (including two APIs from the AllenNLP platform, one API from the Transformers platform, and one API from CloudCV) by using all of the MRs. Our experimental results demonstrate the capabilities of the four subject QA systems from various aspects, revealing their strengths and weaknesses. These results further suggest that MT can be an effective method for assessing QA systems

Study on the Microstructure and Binding Mechanisms of Selective Laser Sintered Wood Plastic Composite

A new type of wood plastic composite, Eucalyptus/PES(Polyethersulfone) blend, was developed. It was designed to be used in LS (Laser Sintering) with reduced energy usage with cost savings. The preparation of Eucalyptus/PES blend and part sintering were under fixed processing conditions and parameters. Through mechanical testing, it was discovered that the strength of prototypes is low due to the formation of a segregated structure in the composite and a weak wood fiber–plastic particle interface. The specific focus of this paper was on investigating the microstructure and binding mechanisms between these two materials. Combining SEM technology and infrared spectrum analysis, the interface binding form of the wood fiber and PES was confirmed to be mechanical interlock. Also the size and distribution of wood fiber in the PES matrix, which can affect the binding and strengthening mechanism, were analyzed during sintering and crack extension.Mechanical Engineerin

Processing and Characterizations of Eucalyptus-PA12 Composite by Laser Sintering

To meet the existing requirements to make Laser Sintering (LS) technology more economical and environmentally friendly, a new type of low cost sustainable material (eucalyptus-polyamide 12 (EPA12) composite) was developed. This paper presents initial research into the LS PA12 with wood powder additions. EPA12 mixed in a ratio of 1:2 by volume has been shown to be extremely processable by LS. Before sintering experiments, thermal conductivity (which is an important component in understanding and optimizing the processing of laser sintering EPA 12) was measured. During LS processing, a variety of laser powers were chosen to investigate the effect of the energy input on the densification, mechanical properties and forming accuracy of the material. The dispersion of eucalyptus in the LS specimens of the composite powder was examined by scanning electron microscopy (SEM). The microstructure of sintered EPA12 was observed. By comparing the microstructures, observable differences based on varying levels of laser power were also present. The maximum tensile strength and the flexure strength of prototypes are 3.7 MPa and 38 MPa, respectively. These values increased from the minimum with increased energy input. On the contrary, the forming accuracy was high at a relatively low laser power.Mechanical Engineerin

Study on the Ingredient Proportions and After-Treatment of Laser Sintering Walnut Shell Composites

To alleviate resource shortage, reduce the cost of materials consumption and the pollution of agricultural and forestry waste, walnut shell composites (WSPC) consisting of walnut shell as additive and copolyester hot melt adhesive (Co-PES) as binder was developed as the feedstock of selective laser sintering (SLS). WSPC parts with different ingredient proportions were fabricated by SLS and processed through after-treatment technology. The density, mechanical properties and surface quality of WSPC parts before and after post processing were analyzed via formula method, mechanical test and scanning electron microscopy (SEM), respectively. Results show that, when the volume fraction of the walnut shell powder in the WSPC reaches the maximum (40%), sintered WSPC parts have the smallest warping deformation and the highest dimension precision, although the surface quality, density, and mechanical properties are low. However, performing permeating resin as the after-treatment technology could considerably increase the tensile, bending and impact strength by 496%, 464%, and 516%, respectively

Research and Analysis of Permanent Magnet Transmission System Controls on Diesel Railway Vehicles

As the energy crisis and environmental pollution continue to be a gradual threat, the energy saving of transmission systems has become the focus of railway vehicle research and design. Due to their high-power density and efficiency features, permanent magnet synchronous motors (PMSM) have been gradually applied in railway vehicles. To improve the efficiency of the transmission system of diesel railway vehicles, it is a good option to use PMSM as both a generator and traction motor to construct a full permanent magnet transmission system (FPMTS). Due to the application of the new FPMTS, some of the original control strategies for diesel railway vehicle transmission systems are no longer applicable. Therefore, it is necessary to adjust and improve the control strategies to meet the needs of FPMTS. We studied several key issues that affect the reliability and comfort of the vehicles. As such, this paper introduced the FPMTS control strategy, including the coordinated control strategy of the diesel and the traction motor, the two degrees of freedom (2DOF) decoupling current regulator, the maximum torque control of the standardized unit current, the wheel slip protection control, and the fault protection strategy. The experiment was carried out on the test platform and the test run of the diesel shunting locomotive equipped with the FPMTS. The results showed that the control strategy described in this paper met the operation characteristics of the FPMTS and that the control performance was superior. The study of FPMTS lays the foundation for the subsequent application of permanent magnet motors in high-powered diesel locomotives and high-speed diesel multi-units

Study on the Characteristics of Walnut Shell/Co-PES/Co-PA Powder Produced by Selective Laser Sintering

Agricultural and forestry wastes are used as materials for selective laser sintering (SLS) to alleviate resource shortage, reduce the pollution of the environment, lower the cost of materials, and improve the accuracy of parts produced by SLS. However, the mechanical properties of wood–plastic parts are poor, and thus they cannot be applied widely. In order to improve the mechanical properties of wood–plastic parts, a new type of walnut shell polymer composite (WSPC) was prepared by a polymer mixing method and was used to produce parts via SLS. Additionally, the dimensional accuracy, morphologies, density, and mechanical properties of the WSPC parts were studied. The results showed that the addition of a small amount of copolyamide (Co-PA) powder could effectively improve the mechanical properties and decrease the density of the WSPC parts. By increasing the amount of Co-PA powder and decreasing that of copolyester (Co-PES) powder, the mechanical properties first increased, then decreased, and finally increased again; in addition, the density first decreased then increased. By increasing the preheating temperature, the mechanical properties and density of the WSPC parts were enhanced

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