The accuracy of the frontal extent in stereoscopic environments: A comparison of direct selection and virtual cursor techniques.

This experiment investigated the accuracy of distance judgment and perception of the frontal extent in a stereoscopic environment. Eight virtual targets were projected in a circular arrangement with two center-to-center target distances (18 cm and 36 cm) and three target sizes (0.6 cm, 1.5 cm, and 3.7 cm). Fourteen participants judged the positions of virtual targets presented at a distance of 90 cm from them by employing two different interaction techniques: the direct selection technique and the virtual cursor technique. The results showed overall higher accuracy with the virtual cursor technique than with the direct selection technique. It was also found that the target size significantly affected the frontal extent accuracy. In addition, significant interactions between technique and center-to-center target distance were observed. The direct selection technique was more accurate at the 18 cm center-to-center target distance along the horizontal (x) and vertical (y) axes, while the virtual cursor technique was more accurate for the 36 cm center-to-center target distance along the y axis. During the direct selection, estimations tended to converge to the center of the virtual space; however, this convergence was not observed in the virtual cursor condition. The accuracy of pointing estimations suffered on the left side of participants. These findings could provide direction for virtual reality developers in selecting proper interaction techniques and appropriately positioning virtual targets in stereoscopic environments

Exocentric Distance Judgment and Accuracy of Head-Mounted and Stereoscopic Widescreen Displays in Frontal Planes

An experiment was done to explore the effects of two virtual display systems on the accuracy of exocentric distance judgment and position. Sixteen participants viewed animated virtual targets using either a head-mounted display (HMD) or a stereoscopic widescreen display (SWD). The virtual targets have been shown, one at a time, at three depth levels and with two corresponding exocentric distances and three target sizes at each target distance and, afterward, via pointing by holding a stick to estimate the exocentric distance and position of each target. The position data were collected using an OptiTrack motion capture system. The results showed that the accuracy of exocentric distance judgment was higher with the head-mounted displays than with the stereoscopic widescreen displays. In addition, higher position accuracy in the X-direction was obtained from the stereoscopic widescreen displays, whereas no significant difference was observed in position accuracy in the Y-direction. However, it is possible that the HMD could give better accuracy in both exocentric distance and position judgments in the frontal plane, if the HMD had been perfectly mounted and flawlessly fit the participant’s eyes. The result also revealed that exocentric distance judgment was significantly higher at the farthest target distances than at the nearest distance. Similarly, the position accuracy significantly increased as exocentric distance decreased. Moreover, engineers may allude to the findings as the evidence from the study suggests that the intermediate target distances might be fitting or ideal distances to design and structure 3D applications

LSTM-based framework with metaheuristic optimizer for manufacturing process monitoring

Quick process shift detection and lower out-of-control run length are essential for monitoring the production process, especially in modern smart manufacturing. Specifically, the out-of-control run length is one of the most critical performance measures to evaluate the manufacturing process monitoring (MPM) model. The sooner the out-of-control is detected, the better the model is. However, developing a monitoring model which can provide quick shift detection for various data dimensions and volumes is challenging. In this research, single (1_LSTM) and stacked (S_LSTM) long-short-term memory (LSTM) based models with metaheuristic optimizer were proposed to detect process shifts quickly in the manufacturing domain. Based on the literature, three metaheuristic methods: Clustering-based organism search (CSOS), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) that are suitable for high-dimensional optimization were utilized in the proposed method to optimize weights in the LSTM-based network. The proposed models were evaluated based on average out-of-control run length (ARL1) against benchmark methods on various synthesized multivariate normal and real-world datasets. Also, the performances of CSOS, PSO, and SA were compared. The results show that CSOS_S_LSTM outperforms other methods with lower ARL1. The result also confirmed the effectiveness and applicability of the proposed models for real-world problems. The experimental results showed that the response time of detection can be improved by 33.19% and 38.77% on average using the proposed 1_LSTM and CSOS LSTM-based metaheuristics models, respectively