Uneven illumination surface defects inspection based on convolutional neural network

Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface image defects based on convolutional neural network, which is based on the adjustment of convolutional neural networks, training parameters, changing the structure of the network, to achieve the purpose of accurately identifying various defects. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image, and correct identification of various types of image defects affected by uneven illumination, thus overcoming the drawbacks of traditional machine vision inspection methods under uneven illumination

Correlations of flow harmonics in 2.76A TeV Pb--Pb collisions

Using the event-by-event viscous hydrodynamics VISH2+1 with MC-Glauber, MC-KLN, and AMPT initial conditions, we investigate the correlations of flow harmonics, including the symmetric cumulants $SC^{v}(m, n)$, the normalized symmetric cumulants $NSC(m, n)$, and the Pearson correlation coefficients $C(v_{m}^{2}, v_{n}^{2})$ in 2.76A TeV Pb--Pb collisions. We find $SC^{v}(m, n)$ is sensitive to both initial conditions and the specific shear viscosity $\eta/s$. A comparison with the recent ALICE data show that our hydrodynamic calculations can qualitatively describe the data of $SC^{v}(3, 2)$ and $SC^{v}(4, 2)$ for various initial conditions, which demonstrate that $v_2$, $v_4$ are correlated and $v_2$, $v_3$ are anti-correlated. Meanwhile, the predicted symmetric cumulants $SC^{v}(5, 2)$, $SC^{v}(5, 3)$, and $SC^{v}(4, 3)$ reveal that $v_2$ and $v_5$, $v_3$ and $v_5$ are correlated, $v_3$ and $v_4$ are anti-correlated in most centrality classes. We also find $NSC^{v}(3, 2)$ and $C(v_{3}^{2}, v_{2}^{2})$, which are insensitive to $\eta/s$, are mainly determined by corresponding $NSC^{\varepsilon}(3, 2)$ and $C(\varepsilon_{3}^{2}, \varepsilon_{2}^{2})$ correlators from the initial state. In contrast, other $NSC^{v}(m, n)$ and $C(v_{m}^{2}, v_{n}^{2})$ correlators are influenced by both initial conditions and $\eta/s$, which illustrates the non-linear mode couplings in higher flow harmonics with $n \geq 4$.Comment: 10 pages, 7 figure

Investigating the correlations of flow harmonics in 2.76A TeV Pb--Pb collisions

This proceeding briefly summarizes our recent investigations on the correlations of flow harmonics in 2.76A TeV Pb--Pb collisions with viscous hydrodynamics {\tt VISH2+1}. We calculated both the symmetric cumulants $SC^{v}(m, n)$ and the normalized symmetric cumulants $NSC^{v}(m, n)$, and found $v_{2}$ and $v_{4}$, $v_{2}$ and $v_{5}$, $v_{3}$ and $v_{5}$ are correlated, $v_{2}$ and $v_{3}$, $v_{3}$ and $v_{4}$ are anti-correlated. We also found $NSC^{v}(3, 2)$ are insensitive to the QGP viscosity, which are mainly determined by the initial conditions.Comment: SQM2016 proceeding, 4pages, 2 figure

Motion Planning for Mobile Robots

This chapter introduces two kinds of motion path planning algorithms for mobile robots or unmanned ground vehicles (UGV). First, we present an approach of trajectory planning for UGV or mobile robot under the existence of moving obstacles by using improved artificial potential field method. Then, we propose an I-RRT* algorithm for motion planning, which combines the environment with obstacle constraints, vehicle constraints, and kinematic constraints. All the simulation results and the experiments show that two kinds of algorithm are effective for practical use

A Proposed Priority Pushing and Grasping Strategy Based on an Improved Actor-Critic Algorithm

The most basic and primary skills of a robot are pushing and grasping. In cluttered scenes, push to make room for arms and fingers to grasp objects. We propose a modified Actor-Critic (A-C) framework for deep reinforcement learning, Cross-entropy Softmax A-C (CSAC), and use the Prioritized Experience Replay (PER) based on the theoretical foundation and main methods of deep reinforcement learning, combining the advantages of algorithms based on value functions and policy gradients. The grasping model is trained using self-supervised learning to achieve end-to-end mapping from image to propulsion and grasping action. A vision module and an action module have been created out of the entire algorithm framework. The prioritized experience replay is improved to further improve the CSAC-PER algorithm for model sample diversity and robot exploration performance during robot grasping training. The experience replay buffer is dynamically sampled using the prior beta distribution and the dynamic sampling algorithm based on the beta distribution (CSAC-beta) is proposed based on the CSAC algorithm. Despite its low initial efficiency, the experimental simulation results show that the CSAC-beta algorithm eventually achieves good results and has a higher grasping success rate (90%)

Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information

Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object detector. Many existing methods follow the standard multiple instance learning (MIL) paradigm and have achieved promising performance. However, the lack of deterministic information leads to part domination and missing instances. To address these issues, this paper focuses on identifying and fully exploiting the deterministic information in WSOD. We discover that negative instances (i.e. absolutely wrong instances), ignored in most of the previous studies, normally contain valuable deterministic information. Based on this observation, we here propose a negative deterministic information (NDI) based method for improving WSOD, namely NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and exploiting. In the collecting stage, we design several processes to identify and distill the NDI from negative instances online. In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively. Experimental results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO show that our method achieves satisfactory performance.Comment: 7 pages, 5 figure

Shell Analysis and Optimisation of a Pure Electric Vehicle Power Train Based on Multiple Software

Motor end cover mounting fracture is a problem recently encountered by novel pure electric vehicles. Regarding the study of the traditional vehicle engine mount bracket and on the basis of the methods of design and optimisation available, we have analysed and optimised the pure electric vehicle end cover mount system. Multi-body dynamic software and finite element software have been combined. First, we highlight the motor end cover mount bracket fracture engineering problems, analyse the factors that may produce fracture, and propose solutions. By using CATIA software to establish a 3D model of the power train mount system, we imported it into ADAMS multi-body dynamic software, conducted 26 condition analysis, obtained five ultimate load conditions, and laid the foundations for subsequent analysis. Next, a mount and shell system was established by the ANSYS finite element method, and modal, strength, and fatigue analyses were performed on the end cover mount. We found that the reason for fracture lies in the intensity of the end cover mount joint, which leads to the safety factor too small and the fatigue life not being up to standard. The main goal was to increase the strength of the cover mount junction, stiffness, safety coefficient, and fatigue life. With this aim, a topology optimisation was conducted to improve the motor end cover. A 3D prototype was designed accordingly. Finally, stiffness, strength, modal, and fatigue were simulated. Our simulation results were as follows. The motor end cover suspension stiffness increases by 20%, the modal frequency increases by 2.3%, the quality increases by 3%, the biggest deformation decreases by 52%, the maximum stress decreases by 28%, the minimum safety factor increases by 40%, and life expectancy increases 50-fold. The results from sample and vehicle tests highlight that the component fracture problem has been successfully solved and the fatigue life dramatically improved. Document type: Articl

Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances

Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. More than 300 papers are covered in this review. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision, and systematically review the corresponding methods developed in a hierarchical division manner. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD. Future research directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than 300 papers relevant to the RSOD filed were reviewed in this surve

ReDas: Supporting Fine-Grained Reshaping and Multiple Dataflows on Systolic Array

Current systolic arrays still suffer from low performance and PE utilization on many real workloads due to the mismatch between the fixed array topology and diverse DNN kernels. We present ReDas, a flexible and lightweight systolic array that can adapt to various DNN models by supporting dynamic fine-grained reshaping and multiple dataflows. The key idea is to construct reconfigurable roundabout data paths using only the short connections between neighbor PEs. The array with 128$\times$128 size supports 129 different logical shapes and 3 dataflows (IS/OS/WS). Experiments on DNN models of MLPerf demonstrate that ReDas can achieve 3.09x speedup on average compared to state-of-the-art work.Comment: 7 pages, 11 figures, conferenc