Abstract ：

Motion blur can easily affect the quality of QR code image, making it difficult to recognize QR codes on moving objects. This paper proposes an algorithm for the recognition of motion-blurred QR codes based on generative adversarial network and attention mechanism. Firstly, a multi-scale feature extraction framework for motion defuzzification is designed using deep convolutional neural networks, and enhanced multi-scale residual blocks and multi-scale feature extraction modules are utilized to capture rich local and global features. Secondly, the efficient channel attention module is added to strengthen the weights of effective features and suppress invalid features by modeling the correlations between channels. In addition, training stability is achieved through the use of the WGAN-div loss function, leading to the generation of higher quality samples. Finally, the proposed algorithm is evaluated through qualitative and quantitative comparisons with several recent methods on both the GOPRO public dataset and a self-constructed QR code dataset, respectively. The experimental results demonstrate that, compared to the other methods, the proposed algorithm has shown significant improvements in both processing time and recognition accuracy when dealing with the task of severe motion-blurred QR code recognition.

Keyword ：

Motion deblurring Motion deblurring Attention mechanism Attention mechanism Generative adversarial network Generative adversarial network QR code identification QR code identification

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Abstract ：

Traditional manufacturing enterprises cannot adjust their production line structure in the short term. They face significant challenges in adapting to the rapidly changing market environment and meeting various variable batch production requirements. Building a suitable and convenient multi-layer planning and scheduling model is an important goal to solve the efficient operation of manufacturing enterprises. This paper proposes a planning and scheduling design that meets the needs of enterprise and the production workshop using the APERT-VC model through a top-down design methodology. APERT is an enterprise-level plan that uses attention mechanisms to collect job plan time and decomposes project plans into workshop plans through PERT technology. virtual command is workshop level plan management, which converts workshop plans into time series vectors and achieves rapid and comprehensive guidance of workshop resource planning for enterprises through multiple classification and decision-making. Through experiments, the algorithm achieved production scheduling accuracy improvement of over 30% compared to previous algorithms and a decision accuracy rate of over 90%. The first half of the new model solves the problem of collecting work time for multi variety and variable batch products, and improves the accuracy of algorithm input. The second half of the new algorithm innovatively combines image recognition technology with dispatcher behavior, achieving efficient simulation results.

Keyword ：

variable batches variable batches APERT-VC APERT-VC multiple varieties multiple varieties mixed production lines mixed production lines machine learning machine learning

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Abstract ：

In modern industrial manufacturing, there are uncertain dynamic disturbances between processing machines and jobs which will disrupt the original production plan. This research focuses on dynamic multi-objective flexible scheduling problems such as the multi-constraint relationship among machines, jobs, and uncertain disturbance events. The possible disturbance events include job insertion, machine breakdown, and processing time change. The paper proposes a conv-dueling network model, a multidimensional state representation of the job processing information, and multiple scheduling objectives for minimizing makespan and delay time, while maximizing the completion punctuality rate. We design a multidimensional state space that includes job and machine processing information, an efficient and complete intelligent agent scheduling action space, and a compound scheduling reward function that combines the main task and the branch task. The unsupervised training of the network model utilizes the dueling-double-deep Q-network (D3QN) algorithm. Finally, based on the multi-constraint and multi-disturbance production environment information, the multidimensional state representation matrix of the job is used as input and the optimal scheduling rules are output after the feature extraction of the conv-dueling network model and decision making. This study carries out simulation experiments on 50 test cases. The results show the proposed conv-dueling network model can quickly converge for DQN, DDQN, and D3QN algorithms, and has good stability and universality. The experimental results indicate that the scheduling algorithm proposed in this paper outperforms DQN, DDQN, and single scheduling algorithms in all three scheduling objectives. It also demonstrates high robustness and excellent comprehensive scheduling performance.

Keyword ：

Conv-dueling network model Conv-dueling network model Deep reinforcement learning Deep reinforcement learning Scheduling problem Scheduling problem Dynamic disturbance Dynamic disturbance

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Abstract ：

Humans are experts at making decisions for challenging driving tasks with uncertainties. Many efforts have been made to model the decision-making process of human drivers at the behavior level. However, limited studies explain how human drivers actively make trustworthy sequential decisions to complete interactive driving tasks in an uncertain environment. This paper argues that human drivers intently search for actions to reduce the uncertainty of their perception of the environment, i.e., perceptual uncertainty, to a low level that allows them to make a trustworthy decision easily. This paper provides a proof-of-concept framework to empirically reveal that human drivers' perceptual uncertainty decreases when executing interactive tasks with uncertainties. We first introduce an explainable-artificial intelligence approach (i.e., SHapley Additive exPlanation, SHAP) to determine the salient features on which human drivers base decisions. Then, we use entropy-based measures to quantify the drivers' perceptual changes in these ranked salient features across the decision-making process, reflecting the changes in uncertainties. The validation and verification of our proposed method are conducted in the highway on-ramp merging scenario with congested traffic using the INTERACTION dataset. Experimental results support that human drivers intentionally seek information to reduce their perceptual uncertainties in the number and rank of salient features of their perception of environments to make a trustworthy decision.

Keyword ：

Task analysis Task analysis Merging Merging Vehicles Vehicles human driver human driver interaction interaction Uncertainty Uncertainty Decision making Decision making Data models Data models Trustworthy decision-making Trustworthy decision-making uncertainty uncertainty Predictive models Predictive models

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Abstract ：

Friction rolling additive manufacturing (FRAM) is an emerging solid-state additive manufacturing technology for high-strength aluminum alloys that are prone to produce solidification defects using fusion-based additive manufacturing method. However, the mechanisms responsible for good bonding between the FRAM interlayers remain unclear. This study successfully reveals such mechanisms for FRAM-produced hybrid AA2319 and AA6061 aluminum alloys using 3D X-ray computed tomography, electron backscatter diffraction, and trans-mission electron microscopy. Three findings concerning the quality of bonding between adjacent layers in FRAM were obtained. Firstly, material plastic flow and macroscopic migration behavior occurred simultaneously along the longitudinal and transverse directions at the interlayer, forming a macroscopic mechanically interlocking nonplanar interface. The convex thread at the tool head increased the material flow in the transverse direction, facilitating material mixing in the adjacent layer. Secondly, severe plastic deformation resulted in significant grain refinement on both sides of the interface. The average grain size reduction rates for the sides of AA2319 and AA6061 were 98.3% and 95.9%, respectively. Thirdly, direct contact with no obvious oxides resulted in complete metallurgical interface bonding. These findings can aid in the elucidation of the basic physical process of FRAM and provide guidance for process parameter optimization and tool head design in the future.

Keyword ：

Aluminum alloys Aluminum alloys Interlayer bonding mechanisms Interlayer bonding mechanisms Solid -state additive manufacturing Solid -state additive manufacturing Multiscale characterization Multiscale characterization

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Abstract ：

Polymeric micro devices are gaining huge market potential in broad areas of medical devices, diagnostic devices, drug delivery, and optical applications. Current research is focusing on developing functional polymeric micro devices on a mass-production scale. Microinjection moulding is a promising technique suitable for fabricating polymeric micro devices. This review aims to summarise the primary achievements that have been achieved in various aspects of microinjection moulding of polymer micro devices, consisting of micro parts and micro surface structures. The relationships of the machine, process, rheology, tooling, micro/nanoscale replication, morphology, properties, and typical applications are reviewed in detail. Finally, a conclusion and challenges are highlighted.

Keyword ：

nano structures nano structures polymer devices polymer devices micro micro microinjection moulding microinjection moulding

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Abstract ：

Currently existing credit risk models, e.g., Scoring Card and Extreme Gradient Boosting (XGBoost), usually have requirements for the capacity of modeling samples. The small sample size may result in the adverse outcomes for the trained models which may neither achieve the expected accuracy nor distinguish risks well. On the other hand, data acquisition can be difficult and restricted due to data protection regulations. In view of the above dilemma, this paper applies Generative Adversarial Nets (GAN) to the construction of small and micro enterprises (SMEs) credit risk model, and proposes a novel training method, namely G-XGBoost, based on the XGBoost model. A few batches of real data are selected to train GAN. When the generative network reaches Nash equilibrium, the network is used to generate pseudo data with the same distribution. The pseudo data is then combined with real data to form an amplified sample set. The amplified sample set is used to train XGBoost for credit risk prediction. The feasibility and advantages of the G-XGBoost model are demonstrated by comparing with the XGBoost model.

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Abstract ：

Thermal bubble-driven micropumps have the advantages of high reliability, simple structure and simple fabrication process. However, the high temperature of the thermal bubble may damage some biological or chemical properties of the solution. In order to reduce the influence of the high temperature of the thermal bubbles on the pumped liquid, this paper proposes a kind of heat insulation micropump driven by thermal bubbles with induction heating. The thermal bubble and its chamber are designed on one side of the main pumping channel. The high temperature of the thermal bubble is insulated by the liquid in the heat insulation channel, which reduces the influence of the high temperature of the thermal bubble on the pumped liquid. Protypes of the new micropump with heat source insulation were fabricated and experiments were performed on them. The experiments showed that the temperature of the pumped liquid was less than 35 degrees C in the main pumping channel.

Keyword ：

microfluidics microfluidics heat source insulation heat source insulation thermal bubble driven thermal bubble driven induction heating induction heating micropump micropump

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Abstract ：

Fusion-based metal additive manufacturing (AM) processes are becoming progressively mature in some material systems. However, the printing of high strength aluminum alloy remains a considerable challenge due to its unavoidable solidification defects. In this study, a novel solid-state additive manufacturing process, being referred to as Friction and Rolling based Additive Manufacturing (FRAM), was developed to print AA6061. The method developed here achieved heat generation and continuous material addition simultaneously, using a modified horizontal CNC machine. The results indicate that fully dense microstructure with defect-free and refined grain was successfully obtained using the FRAM method. The adjacent layers have achieved good bonding without melting because the material at the top surface of the previous layer experience a secondary friction, which can enhance the interface metallurgical bonding and eliminate the potential interface defects. The grain size of the deposited material was significantly reduced compared to that of feeding material because of dynamic recrystallization caused by severe plastic deformation. This also led to the significant increase in elongation of the deposited material. The deposited material showed ductile fracture in both horizontal and vertical directions. The proposed process has the potential to print other non-weldable engineering alloys that are sensitive to solidification defects.

Keyword ：

Mechanical properties Mechanical properties Solid-state Solid-state Additive manufacturing Additive manufacturing Microstructure Microstructure Friction stir processing Friction stir processing Aluminum alloy Aluminum alloy

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Abstract ：

导电微粒与弹性体混合所形成的复合材料既能承受较大的形变,又具备压阻效应,是制备柔性应变片、柔性接触传感器的有效方式。本文采用石墨微粒与乳胶混合形成导电高分子复合材料,以溶液喷涂法制备柔性传感薄膜,通过拉伸试验对柔性传感元件的灵敏度、重复性、粘弹性等性能进行测试与建模分析。

Keyword ：

压阻效应 压阻效应 石墨微粒 石墨微粒 粘弹性 粘弹性

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