Exploration on the Training Mode of Computer Professionals Based on the Concept of “New Engineering”

With the national industrial upgrading and technological innovation in recent years, the construction industry is leading in the direction of informatization, industrialization, intelligence and international integration, which puts forward new requirements for the current traditional mode of computer talent training. The innovation of talent training mode, the improvement of education and teaching, the improvement of education resources and so on have become the urgent problems of new engineering computer professional talent training Absolutely. This paper analyzes the current situation of "new engineering" talent demand and training, points out the shortcomings of the current computer talent training in the teaching concept, teaching mode, teachers and so on, and explores the new engineering computer talent training mode. And take the practice of Henan University School of civil engineering and architecture in the new engineering personnel training as an example, hope to have a certain reference significance for the new engineering computer professional personnel training. Keywords: new engineering; computer; interdisciplinary training; subject integration DOI: 10.7176/JEP/12-8-03 Publication date:March 31st 202

System Design of a Cheetah Robot Toward Ultra-high Speed

High-speed legged locomotion pushes the limits of the most challenging problems of design and development of the mechanism, also the control and the perception method. The cheetah is an existence proof of concept of what we imitate for high-speed running, and provides us lots of inspiration on design. In this paper, a new model of a cheetah-like robot is developed using anatomical analysis and design. Inspired by a biological neural mechanism, we propose a novel control method for controlling the muscles' flexion and extension, and simulations demonstrate good biological properties and leg's trajectory. Next, a cheetah robot prototype is designed and assembled with pneumatic muscles, a musculoskeletal structure, an antagonistic muscle arrangement and a J-type cushioning foot. Finally, experiments of the robot legs swing and kick ground tests demonstrate its natural manner and validate the design of the robot. In the future, we will test the bounding behaviour of a real legged system

Sticky Actuator: Free-Form Planar Actuators for Animated Objects

We propose soft planar actuators enhanced by free-form fabrication that are suitable for making everyday objects move. The actuator consists of one or more inflatable pouches with an adhesive back. We have developed a machine for the fabrication of free-from pouches; squares, circles and ribbons are all possible. The deformation of the pouches can provide linear, rotational, and more complicated motion corresponding to the pouch's geometry. We also provide a both manual and programmable control system. In a user study, we organized a hands-on workshop of actuated origami for children. The results show that the combination of the actuator and classic materials can enhance rapid prototyping of animated objects.National Science Foundation (U.S.) (Grant 1240383)National Science Foundation (U.S.) (Grant 1138967

Adaptive visual–tactile fusion recognition for robotic operation of multi-material system

The use of robots in various industries is evolving from mechanization to intelligence and precision. These systems often comprise parts made of different materials and thus require accurate and comprehensive target identification. While humans perceive the world through a highly diverse perceptual system and can rapidly identify deformable objects through vision and touch to prevent slipping or excessive deformation during grasping, robot recognition technology mainly relies on visual sensors, which lack critical information such as object material, leading to incomplete cognition. Therefore, multimodal information fusion is believed to be key to the development of robot recognition. Firstly, a method of converting tactile sequences to images is proposed to deal with the obstacles of information exchange between different modalities for vision and touch, which overcomes the problems of the noise and instability of tactile data. Subsequently, a visual-tactile fusion network framework based on an adaptive dropout algorithm is constructed, together with an optimal joint mechanism between visual information and tactile information established, to solve the problem of mutual exclusion or unbalanced fusion in traditional fusion methods. Finally, experiments show that the proposed method effectively improves robot recognition ability, and the classification accuracy is as high as 99.3%

A Magnetically and Thermally Controlled Liquid Metal Variable Stiffness Material

Smart materials that can actively tune their stiffness are of great interest to many fields, including the construction industry, medical devices, industrial machines, and soft robotics. However, developing a material that can offer a large range of stiffness change and rapid tuning remains a challenge. Herein, a liquid metal variable stiffness material (LMVSM) that can actively and rapidly tune its stiffness by applying an external magnetic field or by changing the temperature is developed. The LMVSM is composed of three layers: a gallium–iron magnetorheological fluid (Ga–Fe MRF) layer for providing variable stiffness, a nickel–chromium wire layer for Joule heating, and a soft heat dissipation layer for accelerating heating and rapid cooling. The stiffness can be rapidly increased by 4 times upon the application of a magnetic field or 10 times by solidifying the Ga–Fe MRF. Finally, the LMVSM is attached to a pneumatically controlled soft robotic gripper to actively tune its load capacity, demonstrating its potential to be further developed into smart components that can be widely adopted by smart devices

Gait parameter fitting and adaptive enhancement based on cerebral blood oxygen information

Accurate recognition of patients’ movement intentions and real-time adjustments are crucial in rehabilitation exoskeleton robots. However, some patients are unable to utilize electromyography (EMG) signals for this purpose due to poor or missing signals in their lower limbs. In order to address this issue, we propose a novel method that fits gait parameters using cerebral blood oxygen signals. Two types of walking experiments were conducted to collect brain blood oxygen signals and gait parameters from volunteers. Time domain, frequency domain, and spatial domain features were extracted from brain hemoglobin. The AutoEncoder-Decoder method is used for feature dimension reduction. A regression model based on the long short-term memory (LSTM) model was established to fit the gait parameters and perform incremental learning for new individual data. Cross-validation was performed on the model to enhance individual adaptivity and reduce the need for individual pre-training. The coefficient of determination (R2) for the gait parameter fit was 71.544%, with a mean square error (RMSE) of less than 3.321%. Following adaptive enhancement, the coefficient of R2 increased by 6.985%, while the RMSE decreased by 0.303%. These preliminary results indicate the feasibility of fitting gait parameters using cerebral blood oxygen information. Our research offers a new perspective on assisted locomotion control for patients who lack effective myoelectricity, thereby expanding the clinical application of rehabilitation exoskeleton robots. This work establishes a foundation for promoting the application of Brain-Computer Interface (BCI) technology in the field of sports rehabilitation

Learning adaptive reaching and pushing skills using contact information

In this paper, we propose a deep reinforcement learning-based framework that enables adaptive and continuous control of a robot to push unseen objects from random positions to the target position. Our approach takes into account contact information in the design of the reward function, resulting in improved success rates, generalization for unseen objects, and task efficiency compared to policies that do not consider contact information. Through reinforcement learning using only one object in simulation, we obtain a learned policy for manipulating a single object, which demonstrates good generalization when applied to the task of pushing unseen objects. Finally, we validate the effectiveness of our approach in real-world scenarios

Design and Fabrication of a MEMS Flow Sensor and Its Application in Precise Liquid Dispensing

A high speed MEMS flow sensor to enhance the reliability and accuracy of a liquid dispensing system is proposed. Benefitting from the sensor information feedback, the system can self-adjust the open time of the solenoid valve to accurately dispense desired volumes of reagent without any pre-calibration. First, an integrated high-speed liquid flow sensor based on the measurement of the pressure difference across a flow channel is presented. Dimensions of the micro-flow channel and two pressure sensors have been appropriately designed to meet the static and dynamic requirements of the liquid dispensing system. Experiments results show that the full scale (FS) flow measurement ranges up to 80 μL/s, with a nonlinearity better than 0.51% FS. Secondly, a novel closed-loop control strategy is proposed to calculate the valve open time in each dispensing cycle, which makes the system immune to liquid viscosity, pressure fluctuation, and other sources of error. Finally, dispensing results show that the system can achieve better dispensing performance, and the coefficient of variance (CV) for liquid dispensing is below 3% at 1 μL and below 4% at 100 nL