Interactive Physically-Based Simulation of Roadheader Robot

Roadheader is an engineering robot widely used in underground engineering and mining industry. Interactive dynamics simulation of roadheader is a fundamental problem in unmanned excavation and virtual reality training. However, current research is only based on traditional animation techniques or commercial game engines. There are few studies that apply real-time physical simulation of computer graphics to the field of roadheader robot. This paper aims to present an interactive physically-based simulation system of roadheader robot. To this end, an improved multibody simulation method based on generalized coordinates is proposed. First, our simulation method describes robot dynamics based on generalized coordinates. Compared to state-of-the-art methods, our method is more stable and accurate. Numerical simulation results showed that our method has significantly less error than the game engine in the same number of iterations. Second, we adopt the symplectic Euler integrator instead of the conventional fourth-order Runge-Kutta (RK4) method for dynamics iteration. Compared with other integrators, our method is more stable in energy drift during long-term simulation. The test results showed that our system achieved real-time interaction performance of 60 frames per second (fps). Furthermore, we propose a model format for geometric and robotics modeling of roadheaders to implement the system. Our interactive simulation system of roadheader meets the requirements of interactivity, accuracy and stability

A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training

The successful application of large pre-trained models such as BERT in natural language processing has attracted more attention from researchers. Since the BERT typically acts as an end-to-end black box, classification systems based on it usually have difficulty in interpretation and low robustness. This paper proposes a visual interpretation-based self-improving classification model with a combination of virtual adversarial training (VAT) and BERT models to address the above problems. Specifically, a fine-tuned BERT model is used as a classifier to classify the sentiment of the text. Then, the predicted sentiment classification labels are used as part of the input of another BERT for spam classification via a semi-supervised training manner using VAT. Additionally, visualization techniques, including visualizing the importance of words and normalizing the attention head matrix, are employed to analyze the relevance of each component to classification accuracy. Moreover, brand-new features will be found in the visual analysis, and classification performance will be improved. Experimental results on Twitter's tweet dataset demonstrate the effectiveness of the proposed model on the classification task. Furthermore, the ablation study results illustrate the effect of different components of the proposed model on the classification results

Design and Test of the Clearing and Covering of a Minimum-Tillage Planter for Corn Stubble

Conservation tillage technology can reduce wind erosion and soil erosion, improve soil fertility, avoid straw burning and relieve ecological pressure. It is an important measure to achieve sustainable agricultural development. In northeast China, there is a large amount of straw covering the ground after the corn machine harvest, which can easily lead to the blockage of the soil-touching parts during no-tillage seeding, affecting sowing quality and crop yield. In order to solve the above problems, the clearing and covering of a minimum-tillage planter for corn stubble was developed. The machine can complete multiple processes, such as seedbed preparation, seeding, fertilization, covering and suppression, straw covering, etc., in a single entity. This paper focuses on the design of the straw cleaning device and uses discrete element method software (EDEM 2018, Altair Engineering, Troy, MI, USA) to establish the straw cleaning device–straw–soil discrete element simulation model. The quadratic-regression orthogonal center-of-rotation combination test method is used to optimize the parameter combination of the machine, using the operating speed, the speed of the knife roller and the penetration depth of the knife as the test factors and using the rate of cleaning straw and the equivalent power consumption as the evaluation index. The results show that each factor has a significant influence on the performance evaluation indices, and the order of influence of each factor on the rate of cleaning straw is operation speed > penetration depth of knife > speed of knife roller, and the order of influence of each factor on the equivalent power consumption is penetration depth of knife > speed of knife roller > operation speed. The optimal combination of parameters is a 5.5–6.2 km/h operation speed, a 500 rpm speed of the knife roller, a 40 mm penetration depth of the knife, a straw-cleaning rate of more than 90% and an equivalent power consumption of less than 8 kW. This study provides technical and equipment support for the promotion of conservation tillage technology in Northeast China