This paper presents a case study of Copelia, an augmented reality application targeted for smart glasses. Copelia was developed to optimize the workflow of COPEL (Companhia Paranaense de Energia), a major Brazilian electricity company. The application assists its users in the maintenance of reclosers with features such as object recognition, augmented reality (AR), and remote calls. These features allow generalist electricians to perform tasks that would otherwise be exclusively executed by skilled professionals. Copelia was evaluated in laboratory and field tests. Results show that users resisted the application due to its novel interaction paradigm. Most of the reported issues were related to the usability of smart glasses or the graphics interface. A list of good practices was derived from the discussion of these issues. The features themselves proved to be useful, especially the guided instructions. Despite an initial negative reaction, Copelia gained appraisal by its users over time, who highlighted its usefulness for training and maintenance.

Brazilian energy distribution companies are investing in automatic circuit reclosers (ACRs) to optimize their energy grids. These devices often require specialists to maintain. Training electricians can be problematic, as there are very similar models and provide different commands for the same tasks. Based on this problem, an application was developed in this work that allows generalists to carry out maintenance on reclosers and make training less confusing. This paper describes the object detection module of this application, which employs Deep Learning to identify four different recloser models. Were tested some state of art neural networks implementations in object detection field in the recognition of the ACRs supported models and in the tests was reached the best neural network obtained approximately 89% in Mean Average Precision (mAP). This work aim apply the object detection in energy area, focused in maintenance and training scenarios, showing in effective to detect and differentiate ACRs similar models, thus helping general electricians to provide a more accurate service and reducing company costs.

This paper proposes a solution using Deep Learning to increase efficiency in operation and maintenance of automatic circuit reclosers (ACRs). After the automatic detection of an ACR, the solution presents documentation and maintenance procedures in the format of checklists. Each step of those checklists is illustrated in augmented reality with 3D animated models of the detected ACR. The best neural network obtained a Mean Average Precision (mAP) of approximately 91.2% in tests. The solution will aid operators and maintenance professionals in decision making, providing them information and instructions compatible with each of the supported ACR models.

We present a study examining the impact of physical and cognitive challenge on reported immersion for a mixed reality game called Beach Pong. Contrary to prior findings for desktop games, we find significantly higher reported immersion among players who engage physically, regardless of their actual game performance. Building a mental map of the real, virtual, and sensed world is a cognitive challenge for novices, and this appears to influence immersion: in our study, participants who actively attended to both physical and virtual game elements reported higher immersion levels than those who attended mainly or exclusively to virtual elements. Without an integrated mental map, in-game cognitive challenges were ignored or offloaded to motor response when possible in order to achieve the minimum required goals of the game. From our results we propose a model of immersion in mixed reality gaming that is useful for designers and researchers in this space.