Application of Multimedia and Internet Technology in Learning Mould Design

With the use of multimedia which combines the use o f text, sound, images, motion video and animation, it is more efficient for studen ts to learn mould design interactively. A program is created using several multi media software to simulate the mechanism of moulding processes in order to let s tudents understand the concept of mould design. In addition, students can even access the program through the Internet. Therefore, the software is defined as Multimedia and Internet Technology (MIT) program. The MIT program consists of four sections: (i) simulation of mould mechanisms, ( ii) cooling system, (iii) material information and (iv) games for tutorials. Sec tion One covers the basic operations of different types of moulds such as two-p late mould, three-plate mould, split mould, side-core mould and mould with und ercuts. Section Two introduces different types of cooling systems such as bubble r, baffle, cooling circuits, etc. Section Three provides some useful material in formation for mould design. Section Four contains games of matching mould compon ents, mould design problem finding and multiple choice questions to test student s how much they understand mould design concept. Multimedia is highly effective particularly in teaching and learning. It changes the nature of learning itself. It makes reading dynamic by giving words an impo rtant new dimension. It allows students to see, hear and do simultaneously, thus significantly reducing average learning time. Furthermore, through cooperative learning on Internet, students can access the program, share data or search info rmation anytime anywhere. Therefore, Multimedia and Internet Technology is one o f the vital aspects to speed up the realization of information age in society.

Call for papers Journal of Control Theory and Applications Special issue on Approximate dynamic programming and reinforcement learning

Approximate dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain and nonconvex environments over time.

Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

The novel Coronavirus disease 2019(COVID-19)pandemic has begun in China and is still affecting thousands of patient livesworldwide daily.AlthoughChest X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases,applying Ultrasound(US)imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently.In this article,we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images,based on generative adversarial neural networks(GANs).The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary classifier.Each one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique,keeping its main function of defining the“realness”of tested images.Extensive tests have been successfully conducted on public dataset of US images acquired with a convex US probe.This study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 patients.The results of our proposed GAN models showed that high accuracy values above 91.0%were obtained under different sizes of limited training data,outperforming other deep learning-based methods,such as transfer learning models in the recent studies.Consequently,the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

COMPUTER ASSISTED INSTRUCTION AND ITS APPLICATION IN ENGLISH LEARNING

This paper briefly describes the development of computer assisted instruction(CAI) abroad and in China, lists the advantages of CAI and deals with its application in English learning. Some suggestions about how to make better use of CAI in ELT are also given.

Deep reinforcement learning:a survey

Deep reinforcement learning(RL)has become one of the most popular topics in artificial intelligence research.It has been widely used in various fields,such as end-to-end control,robotic control,recommendation systems,and natural language dialogue systems.In this survey,we systematically categorize the deep RL algorithms and applications,and provide a detailed review over existing deep RL algorithms by dividing them into modelbased methods,model-free methods,and advanced RL methods.We thoroughly analyze the advances including exploration,inverse RL,and transfer RL.Finally,we outline the current representative applications,and analyze four open problems for future research.

A Low Spectral Bias Generative Adversarial Model for Image Generation

We propose a systematic analysis of the neglected spectral bias in the frequency domain in this paper.Traditional generative adversarial networks(GANs)try to fulfill the details of images by designing specific network architectures or losses,focusing on generating visually qualitative images.The convolution theorem shows that image processing in the frequency domain is parallelizable and performs better and faster than that in the spatial domain.However,there is little work about discussing the bias of frequency features between the generated images and the real ones.In this paper,we first empirically demonstrate the general distribution bias across datasets and GANs with different sampling methods.Then,we explain the causes of the spectral bias through the deduction that reconsiders the sampling process of the GAN generator.Based on these studies,we provide a low-spectral-bias hybrid generative model to reduce the spectral bias and improve the quality of the generated images.