Virtual Reality Intelligent Hardware最新文献

Background

Digital Twins are becoming increasingly popular in a variety of industries to manage complex systems. As digital twins become more sophisticated, there is an increased need for effective training and learning systems. Teachers, project leaders, and tool vendors encounter challenges while teaching and training their students, co-workers, and users.

Methods

In this study, we propose a new method for training users in using digital twins by proposing a gamified and virtual environment. We present an overall architecture and discuss its practical realization.

Results

We propose a set of future challenges that we consider critical to enabling a more effective learning/training approach.

Digital Twin (DT) is a pivotal application under the industrial digital transformation envisaged by the fourth industrial revolution (Industry 4.0). DT defines intelligent and real-time faithful reflections of physical entities such as industrial robots, thus allowing their remote control. Relying on the latest advances in Information and Communication Technologies (ICT), namely Network Function Virtualization (NFV) and Edge-computing, DT can be deployed as an on-demand service in the factories close proximity and offered leveraging radio access technologies. However, with the purpose of achieving the well-known scalability, flexibility, availability and performance guarantees benefits foreseen by the latest ICT, it is steadily required to experimentally profile and assess DT as a Service (DTaaS) solutions. Moreover, the dependencies between the resources claimed by the service and the relative demand and work loads require to be investigated. In this work, an Edge-based Digital Twin solution for remote control of robotic arms is deployed in an experimental testbed where, in compliance with the NFV paradigm, the service has been segmented in virtual network functions. Our research has primarily the objective to evaluate the entanglement among overall service performance and VNFs resource requirements, and the number of robots consuming the service varies. Experimental profiles show the most critical DT features to be the inverse kinematics and trajectory computations. Moreover, the same analysis has been carried out as a function of the industrial processes, namely based on the commands imposed on the robots, and particularly of their ion-level, resulting in a novel trade-off between computing and time resources requirements and trajectory guarantees. The derived results provide crucial insights for the design of network service scaling and resource orchestration frameworks dealing with DTaaS applications. Finally, we empirically prove LTE shortage to accommodate the minimum DT latency requirements.

Background

All recent technological findings can be collectively used to strengthen the industrial Internet of things (IIoT) sector. The novel technology of multi-access edge computing or mobile edge computing (MEC) and digital twins have advanced rapidly in the industry. MEC is the middle layer between mobile devices and the cloud, and it provides scalability, reliability, security, efficient control, and storage of resources. Digital twins form a communication model that enhances the entire system by improving latency, overhead, and energy consumption.

Methods

The main focus in this study is the biggest challenges that researchers in the field of IIoT have to overcome to obtain a more efficient communication environment in terms of technology integration, efficient energy and data delivery, storage spaces, security, and real-time control and analysis. Thus, a distributed system is established in a local network, in which several functions operate. In addition, an MEC-based framework is proposed to reduce traffic and latency by merging the processing of data generated by IIoT devices at the edge of the network. The critical parts of the proposed IIoT system are evaluated by using emulation software.

Results

The results show that data delivery and offloading are performed more efficiently, energy consumption and processing are improved, and security, complexity, control, and reliability are enhanced.

Conclusions

The proposed framework and application provide authentication and integrity to end users and IoT devices.

Background

The advancements of Artificial Intelligence, Big Data Analytics, and the Internet of Things paved the path to the emergence and use of Digital Twins (DTs) as technologies to “twin” the life of a physical entity in different fields, ranging from industry to healthcare. At the same time, the advent of eXtended Reality (XR) in industrial and consumer electronics has provided novel paradigms that may be put to good use to visualize and interact with DTs. XR technologies can support human-to-human interactions for training and remote assistance and could transform DTs into collaborative intelligence tools.

Methods

We here present the Human Collaborative Intelligence empowered Digital Twin framework (HCLINT-DT) integrating human annotations (e.g., textual and vocal) to allow the creation of an all-in-one-place resource to preserve such knowledge. This framework could be adopted in many fields, supporting users to learn how to carry out an unknown process or explore others’ past experiences.

Results

The assessment of such a framework has involved implementing a DT supporting human annotations, reflected in both the physical world (Augmented Reality) and the virtual one (Virtual Reality).

Conclusions

The outcomes of the interface design assessment confirm the interest in developing HCLINT-DT-based applications. Finally, we evaluated how the proposed framework could be translated into a manufacturing context.

Background

The uncanny valley hypothesis states that users may experience discomfort when interacting with almost human-like artificial characters. Advancements in artificial intelligence, robotics, and computer graphics have led to the development of life-like virtual humans and humanoid robots. Revisiting this hypothesis is necessary to check whether they positively or negatively affect the current population, who are highly accustomed to the latest technologies.

Methods

In this study, we present a unique evaluation of the uncanny valley hypothesis by allowing participants to interact live with four humanoid robots that have varying levels of human-likeness. Each participant completed a survey questionnaire to evaluate the affinity of each robot. Additionally, we used deep learning methods to quantify the participants’ emotional states using multimodal cues, including visual, audio, and text cues, by recording the participant–robot interactions.

Results

Multi-modal analysis and surveys provided interesting results and insights into the uncanny valley hypothesis.

Background

Exploring correspondences across multiview images is the basis of various computer vision tasks. However, most existing methods have limited accuracy under challenging conditions.

Method

To learn more robust and accurate correspondences, we propose DSD-MatchingNet for local feature matching in this study. First, we develop a deformable feature extraction module to obtain multilevel feature maps, which harvest contextual information from dynamic receptive fields. The dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust correspondence. Second, we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching, which enables our method to produce more accurate correspondences.

Result

Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark, as well as on the visual localization benchmark. Specifically, our method achieved 91.3% mean matching accuracy on the HPatches dataset and 99.3% visual localization recalls on the Aachen Day-Night dataset.

Background

Virtual reality (VR) applications can be used to provide comprehensive training scenarios that are difficult or impossible to represent in physical configurations. This includes team training for emergency services such as firefighting. Creating a high level of immersion is essential for achieving effective virtual training. In this respect, motion-capture systems offer the possibility of creating highly immersive multi-user training experiences, including full-body avatars.

Methods

This study presents a preliminary prototype that helps extinguish a virtual fire on a container ship as a VR training scenario. The prototype provides a full-body and multi-user VR experience based on the synthesis of position data provided by the motion-capture system and orientation data from the VR headsets. Moreover, the prototype facilitates an initial evaluation of the results.

Results

The results confirm the value of using VR for training procedures that are difficult to implement in the real world. Furthermore, the results show that motion-capture-based VR technologies are particularly useful for firefighting training, in which participants can collaborate in difficult-to-access environments. However, this study also indicates that increasing the immersion in such training remains a challenge.

Conclusions

This study presents a prototypical VR application that enables the multi-user training of maritime firefighters. Future research should evaluate the initial results, provide more extensive training scenarios, and measure the training progress.

Background

Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players.

Methods

This study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology, and iStoryline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a teamʹs wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and RandomUnderSampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using Grid- SearchCV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive exPlanations (SHAP) model was introduced to enhance the interpretability of the model.

Results

The experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model.

Conclusions

This study combines data visualization and machine learning to design a National Basketball Association data visualization system to help the general audience visualize game data and predict all-star players; this can also be extended to other sports events or related fields.