Design of a new kind of chemical experiment container with virtual reality fusion

Background At present,the teaching of experiments in primary and secondary schools is affected by cost and security factors.Existing research on virtual experiment platforms has alleviated these problems.However,the lack of real experimental equipment and use of a single channel to understand user intentions weaken these platforms operationally and degrade the naturalness of interactions.Methods To solve these problems,we propose an intelligent experimental container structure and a situational awareness algorithm,both of which are verified and applied to a chemical experiment involving virtual-real fusion.First,the acquired images are denoised in the visual channel using the maximum diffuse reflection chroma to remove overexposure.Second,container situational awareness is realized by segmenting the image liquid level and establishing a relation-fitting model.Then,strategies for constructing complete behaviors and making priority comparisons among behaviors are adopted for information complementarity and independence,respectively.A multichannel intentional understanding model and an inter-active paradigm that integrates vision,hearing,and touch are proposed.Results The experimental results show that the accuracy of the intelligent container situation awareness proposed in this paper reaches 99%,and the accuracy of the proposed intention understanding algorithm reaches 94.7%.The test shows that the intelligent experimental system based on the new interaction paradigm also has better performance and a more realistic sense of operation experience in terms of experimental efficiency.Conclusion The results indicate that the proposed experimental container and algorithm can achieve a natural level of human-computer interaction in a virtual chemical experiment platform,enhance the user′s sense of operation,and achieve high levels of user satisfaction.

Data fusion methods in multimodal human computer dialog

In multimodal human computer dialog,non-verbal channels,such as facial expression,posture,gesture,etc,combined with spoken information,are also important in the procedure of dialogue.Nowadays,in spite of high performance of users*single channel behavior computing,it is still great challenge to understand users1 intention accurately from their multimodal behaviors.One reason for this challenge is that we still need to improve multimodal information fusion in theories,methodologies and practical systems.This paper presents a review of data fusion methods in multimodal human computer dialog.We first introduce the cognitive assumption of single channel processing,and then discuss its implementation methods in human computer dialog;for the task of multi-modal information fusion,serval computing models are presented after we introduce the principle description of multiple data fusion.Finally,some practical examples of multimodal information fusion methods are introduced and the possible and important breakthroughs of the data fusion methods in future multimodal human-computer interaction applications are discussed.

AI for Supporting the Freedom of Drawing

Artificial intelligence(AI)has recently been developing rapidly in image processing and generation.AI is not only starting to take over repetitive and tedious tasks but is also involved in creative activities.Drawing is one of the areas with the greatest potential for collaboration between humans and AI.It is an important approach to express various information,but due to the lack of appropriate knowledge and skills,ordinary people without long-time training are unable to draw freely.Although there have been various attempts at human-AI collaboration in drawing,it is difficult for researchers to consider the wide variety of specific problems and develop univer-sal methods due to the openness,improvisation,and individuality of drawing.In this paper,we first analysed the contents of drawing and the general creation process in detail.Second,we have described a mechanism for using AI to enable people to regain the freedom of drawing collaboratively.Finally,we have developed a framework that describes methods for analysing specific problems and quickly finding solutions by building connections between the influencing factors in drawing,the demands of humans,and possible implementa-tion options.The framework also reveals a broad scope of possibilities for applying AI to support people in drawing.