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Virtual 3D environment for exploring the spatial ability of students


Available Online:2020-10-14

Abstract (98) | PDF (21) | HTML (33)
Spatial ability is an unique type of intelligence; it can be distinguished from other forms of intelligence and plays an essential role in an individual's success in many academic fields, particular in this era of technology. Instruction-assisted 3D technology can display stereo graphics and promote students' understanding of the geometrical structure and characteristics of graphics. Spatial ability includes several aspects. Few software programs are available for training different aspects of spatial ability for senior high school students. This study aims to explore an effective method for training the spatial ability for senior high school students, and to promote the development of students' independent inquiry ability.
First, an inquiry design strategy to improve the spatial ability of students is proposed. Based on this strategy, unity3D was used to develop a 3D inquiry environment that can use leap motion to complete a gesture interaction. Finally, researchers carried out experience-based activities and issued user experience questionnaires to participants to verify the application effect of the spatial ability inquiry environment and used interviews to understand the user experience of participants exploring the leap motion device in a 3D inquiry environment.
32 learners participated in the experiment. learners have a high score for perceived usefulness and willingness to use. Compared with the perceived ease of use and perceived usefulness, the average score of the application effect is relatively low. In terms of willingness to use, most of the learners expressed their willingness to use a similar inquiry environment for spatial ability training in the future.
The spatial ability inquiry environment can help learners better understand different concepts. The users showed a strong willingness to continue using the device. The device also updates the teaching concept to a certain extent and emphasizes the dominant position of a student.
Effects of Virtual-real fusion on immersion, presence, and learning performance in laboratory education


Available Online:2020-09-11

Abstract (121) | PDF (5) | HTML (82)
Virtual-reality (VR) fusion techniques have become increasingly popular in recent years, and several previous studies have applied them to laboratory education. However, without a basis for evaluating the effects of virtual-real fusion on VR in education, many developers have chosen to abandon this expensive and complex set of techniques.
In this study, we experimentally investigate the effects of virtual-real fusion on immersion, presence, and learning performance. Each participant was randomly assigned to one of three conditions: a PC environment (PCE) operated by mouse; a VR environment (VRE) operated by controllers; or a VR environment running virtual-real fusion (VR-VRFE), operated by real hands.
The analysis of variance (ANOVA) and t-test results for presence and self-efficacy show significant differences between the PCE*VR-VRFE condition pair. Furthermore, the results show significant differences in the intrinsic value of learning performance for pairs PCE*VR-VRFE and VRE*VR-VRFE, and a marginally significant difference was found for the immersion group.
The results suggest that virtual-real fusion can offer improved immersion, presence, and self-efficacy compared to traditional PC environments, as well as a better intrinsic value of learning performance compared to both PC and VR environments. The results also suggest that virtual-real fusion offers a lower sense of presence compared to traditional VR environments.
Emotional dialog generation via multiple classifiers based on a generative adversarial network


Available Online:2020-08-12

Abstract (205) | PDF (6) | HTML (152)
Human-machine dialog generation is an essential topic of research in the field of natural language processing. Generating high-quality, diverse, fluent, and emotional conversation is a challenging task. Based on continuing advancements in artificial intelligence and deep learning, new methods have come to the forefront in recent times. In particular, the end-to-end neural network model provides an extensible conversation generation framework that has the potential to enable machines to understand semantics and automatically generate responses. However, neural network models come with their own set of questions and challenges. The basic conversational model framework tends to produce universal, meaningless, and relatively "safe" answers.
Based on generative adversarial networks (GANs), a new emotional dialog generation framework called EMC-GAN is proposed in this study to address the task of emotional dialog generation. The proposed model comprises a generative and three discriminative models. The generator is based on the basic sequence-to-sequence (Seq2Seq) dialog generation model, and the aggregate discriminative model for the overall framework consists of a basic discriminative model, an emotion discriminative model, and a fluency discriminative model. The basic discriminative model distinguishes generated fake sentences from real sentences in the training corpus. The emotion discriminative model evaluates whether the emotion conveyed via the generated dialog agrees with a pre-specified emotion, and directs the generative model to generate dialogs that correspond to the category of the pre-specified emotion. Finally, the fluency discriminative model assigns a score to the fluency of the generated dialog and guides the generator to produce more fluent sentences.
Based on the experimental results, this study confirms the superiority of the proposed model over similar existing models with respect to emotional accuracy, fluency, and consistency.
The proposed EMC-GAN model is capable of generating consistent, smooth, and fluent dialog that conveys pre-specified emotions, and exhibits better performance with respect to emotional accuracy, consistency, and fluency compared to its competitors.
A survey on monocular 3D human pose estimation


Available Online:2020-06-18

Abstract (500) | PDF (23) | HTML (215)
Recovering human pose from RGB images and videos has drawn increasing attention in recent years owing to minimum sensor requirements and applicability in diverse fields such as human-computer interaction, robotics, video analytics, and augmented reality. Although a large amount of work has been devoted to this field, 3D human pose estimation based on monocular images or videos remains a very challenging task due to a variety of difficulties such as depth ambiguities, occlusion, background clutters, and lack of training data. In this survey, we summarize recent advances in monocular 3D human pose estimation. We provide a general taxonomy to cover existing approaches and analyze their capabilities and limitations. We also present a summary of extensively used datasets and metrics, and provide a quantitative comparison of some representative methods. Finally, we conclude with a discussion on realistic challenges and open problems for future research directions.
A multichannel human-swarm robot interaction system in augmented reality


Available Online:2020-05-29

Abstract (323) | PDF (2) | HTML (264)
A large number of robots have put forward the new requirements for human-robot interaction. One of the problems in human-swarm robot interaction is how to naturally achieve an efficient and accurate interaction between humans and swarm robot systems. To address this, this paper proposes a new type of human-swarm natural interaction system.
Through the cooperation between three-dimensional (3D) gesture interaction channel and natural language instruction channel, a natural and efficient interaction between a human and swarm robots is achieved.
First, A 3D lasso technology realizes a batch-picking interaction of swarm robots through oriented bounding boxes. Second, control instruction labels for swarm-oriented robots are defined. The instruction label is integrated with the 3D gesture and natural language through instruction label filling. Finally, the understanding of natural language instructions is realized through a text classifier based on the maximum entropy model. A head-mounted augmented reality display device is used as a visual feedback channel.
The experiments on selecting robots verify the feasibility and availability of the system.
Object registration using an RGB-D camera for complex product augmented assembly guidance


Available Online:2020-05-19

Abstract (339) | PDF (8) | HTML (278)
Augmented assembly guidance aims to help users complete assembly operations more efficiently and quickly through augmented reality technology, breaking the limitations of traditional assembly guidance technology which is single in content and boring in way. Object registration is one of the key technologies in augmented assembly guidance process, which can affect the location and direction of virtual assembly guidance information in real assembly environment.
This paper presents an object registration method based on RGB-D camera, which combines Lucas-Kanade (LK) optical flow algorithm and Iterative Closet Point (ICP) algorithm. An augmented assembly guidance system for complex products through this method is built. Meanwhile, in order to compare the effectiveness of the proposed method, we also implemented object registration based on an open source augmented reality SDK Vuforia.
An engine model and a complex weapon cabin equipment are taken as an case to verify this work. The result shows that the registration method proposed in this paper is more accurate and stable compared with that based on Vuforia and the augmented assembly guidance system through this method greatly improves the user's time compared with the traditional assembly.
Therefore, we can conclude that the object registration method mentioned in this paper can be well applied in the augmented assembly guidance system, which can do enhance the efficiency of assembly considerably.