Electro-Pneumatic Wiring Software for Distance-Learning Students in Automation Control Laboratories

When teaching electro-pneumatic circuits in the automation control class for a distance-learning program, one of the main issues is how to provide hands-on experience for students. In most cases, they can only use simulation design software and watch videos solving laboratory problems without access to the actual equipment. In this paper, the authors developed an electrical wiring software and assembly platform which can help students enhance their hands-on experience when taking the course online. Results show that the performance between on-campus students and distance-learning (DL) students are very similar.Cockrell School of Engineerin

Teaching Robot Kinematics For Engineering Technology Students Using a Created Three-Dimensional Robot and Camera

Teaching robot kinematics is important to engineering technology students in the robot automation. The study can help students not only in the coordinate transformation principles from a joint to its following joint in a robot, but also in relating the coordinate systems between a robot and a machine vision system. While students can utilize math software to compute robot kinematic transformations, they have problems verifying their answers. In this paper, a three dimensional vertically articulated robot is created to help students visualize the location and orientation of the end effector. Students can check their robot kinematic answers based on the joint encoder values set up at each joint. In addition, a camera is also mounted on the robot for the students to relate an object location from the camera coordinate system to the robot world frame

Estimation of ergodic square-root diffusion under high-frequency sampling

Gaussian quasi-likelihood estimation of the parameter $\theta$ in the square-root diffusion process is studied under high frequency sampling. Different from the previous study of Overbeck and Ryd\'{e}n(1998) under low-frequency sampling, high-frequency of data provides very simple form of the asymptotic covariance matrix. Through easy-to-compute preliminary contrast functions, a practical two-stage manner without numerical optimization is formulated in order to conduct not only an asymptotically efficient estimation of the drift parameters, but also high-precision estimator of the diffusion parameter. Simulation experiments are given to illustrate the results

Hands-on Homework or Laboratory Development for Distance Learning Students in Programmable Logical Controller (PLC)

When teaching PLC classes in the distance-education (DE) program, one of the main problems is to provide hands-on experience to students. This is because students can only use design software to complete their homework and lab problems without being able to touch PLCs or wire ladder logic diagrams. In this paper, the authors develop a virtual laboratory to help DE students gain hands-on wiring experience in the automation control classes. The development significantly shortens the gap on the issue of hands-on experience between on-campus students and DE students. To increase the performance of safety and effectiveness, on-campus students can also take the advantage of using the software before implementing their laboratory works or submitting homework problems

An Integrated Simulation Design With Three-Dimensional Motions and a Hydraulic Stewart Simulator

This paper presents an integrated design process and tests of a Stewart simulator with a virtual visualization tool, which uses Virtools to create and generate three-dimensional motions. An inverse kinematic algorithm is written to convert each visualized motion to the displacements of six cylinders in a Stewart motion simulator. Information of the displacements is then transferred through the User Datagram Protocol (UDP) to a personal computer which has the LabVIEW software. An NI USB-6251 data acquisition device is applied to interact with the LabVIEW program and the Stewart hydraulic simulator. The approach presented in this paper to function an old Stewart hydraulic simulator can also be applied to other simulators

Efficient CNN-LSTM based Parameter Estimation of Levy Driven Stochastic Differential Equations

This study addresses the challenges in parameter estimation of stochastic differential equations driven by non-Gaussian noises, which are critical in understanding dynamic phenomena such as price fluctuations and the spread of infectious diseases. Previous research highlighted the potential of LSTM networks in estimating parameters of alpha stable Levy driven SDEs but faced limitations including high time complexity and constraints of the LSTM chaining property. To mitigate these issues, we introduce the PEnet, a novel CNN-LSTM-based three-stage model that offers an end to end approach with superior accuracy and adaptability to varying data structures, enhanced inference speed for long sequence observations through initial data feature condensation by CNN, and high generalization capability, allowing its application to various complex SDE scenarios. Experiments on synthetic datasets confirm PEnet significant advantage in estimating SDE parameters associated with noise characteristics, establishing it as a competitive method for SDE parameter estimation in the presence of Levy noise.Comment: 2023 International Conference on Machine Learning and Applications (ICMLA

Question Decomposition Tree for Answering Complex Questions over Knowledge Bases

Knowledge base question answering (KBQA) has attracted a lot of interest in recent years, especially for complex questions which require multiple facts to answer. Question decomposition is a promising way to answer complex questions. Existing decomposition methods split the question into sub-questions according to a single compositionality type, which is not sufficient for questions involving multiple compositionality types. In this paper, we propose Question Decomposition Tree (QDT) to represent the structure of complex questions. Inspired by recent advances in natural language generation (NLG), we present a two-staged method called Clue-Decipher to generate QDT. It can leverage the strong ability of NLG model and simultaneously preserve the original questions. To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA system called QDTQA. Extensive experiments show that QDTQA outperforms previous state-of-the-art methods on ComplexWebQuestions dataset. Besides, our decomposition method improves an existing KBQA system by 12% and sets a new state-of-the-art on LC-QuAD 1.0.Comment: Accepted by AAAI202