Research on Virtual Reality Based Civil Aircraft Cabin Customization System

2013 ◽  
Vol 694-697 ◽  
pp. 3122-3125
Author(s):  
Nan Ma ◽  
Hu Liu ◽  
Yong Liang Tian ◽  
Jin Peng Bai ◽  
Zhe Wu

Based on the aircraft cabin customization requirement, the paper compares and analyzes the civil aircraft cabin customization systems of Boeing and Airbus and proposes a frame design scheme of customization systems. Furthermore, it particularly discusses the selection process and results management, and realizes the cabin customization demonstration by virtual reality.

2014 ◽  
Vol 13 (13) ◽  
pp. 2165-2169
Author(s):  
Zhang Xin ◽  
Liang Gongqian ◽  
Sun Lei

2014 ◽  
Vol 651-653 ◽  
pp. 1511-1514 ◽  
Author(s):  
Xian Li ◽  
Hu Liu

With the rapid development of the air transport industry, more attention is paid to interior ergonomics analysis which directly affects the time and cost spent during design. To provide a quick and human-in-loop accessibility way to evaluate accessibility in aircraft cabin, a new method based on virtual reality is proposed. In this method, the digital mock-up model is converted to other format which is need to build a virtual environment first, then virtual environment is built and a virtual hand is driven through tracking hand’s position by A.R.T(Advanced Realtime Tracking ) system to carry on accessibility evaluation in the virtual environment. Based on above-mentioned method the aircraft cabin accessibility evaluation system is designed and realized, which is verified by one case of a certain aircraft cabin. The result shows this method is simple and useful, offering a new way for accessibility evaluation in aircraft cabin.


2020 ◽  
Author(s):  
Jing LIU ◽  
Suihuai YU ◽  
Jianjie CHU

Abstract Comfort is becoming one of the most important principles in the process of design and evaluation of civil aircraft cabin. However, the comprehensive quantitative evaluation of comfort in an aircraft cabin is a complicated issue, because of the subjectivity of comfort perception and a large amount of factors involved in the whole complex cabin system. A hybrid model combined with Decision Making Trial and Evaluation Laboratory (DEMATEL) method and fuzzy comprehensive evaluation is proposed, which considers both the interrelation between the criteria and the fuzziness of subjective comfort perception concurrently. The result of empirical study from questionnaire survey in flight was consistent with that of the hybrid model. The proposed model is effective. It could provide a more reasonable priority to improve comfort in the aircraft cabin. According to the measured results of cabin environment, cabin facilities and layout, seat and service, the specific differences between the criteria can be displayed clearly, which is helpful to improve the cabin comfort level.


2019 ◽  
Vol 41 (1) ◽  
pp. 25-33
Author(s):  
Millana Pagnussat ◽  
Theresa Hauge ◽  
Eduardo da Silva Lopes ◽  
Rosa Maria Martins de Almeida ◽  
Alana Naldony

The great complexity of the operation of wood harvesting machines and unpredictable differences of performance between operators must be reflected in the industry recruitment techniques. This work aimed to carry out an evaluation of the bimanual motor skill in candidates for the position of harvester operators using a virtual reality simulator to generate information that can contribute to and improve the selection process. The work was developed at the Forest Operators Training Center (CENFOR), at the State University of the Center–West, in Irati, PR. A sample of 12 individuals was studied and distributed into three levels of performance. The motor ability of the individuals was evaluated through the variables: »run time«, »fall direction«, and »cutting height«, assessed at different points during a 4-hour practice – 0.5; 1.0; 1.5; 2.0; 3.0 and 4.0 hours – practice in a virtual harvester simulator. The data were analyzed by variance and means, as well as compared to a Tukey test at the 5% level of significance. The individuals had a significant difference in the variables »run time« and »cutting height«, and could be accurately used to predict bimanual motor skill/performance. There was a significant gain in the performance of the operators up to 1.5 hours after the beginning of the skill test, and all those who demonstrated greater and lesser ability in the first half hour of the test maintained this behavior until the end of the training period. The virtual reality simulator can be used as a tool to assess bimanual motor skills during the selection of harvester operators.


Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1722
Author(s):  
Qianwen Fu ◽  
Jian Lv ◽  
Shihao Tang ◽  
Qingsheng Xie

To effectively organize design elements in virtual reality (VR) scene design and provide evaluation methods for the design process, we built a user image space cognitive model. This involved perceptual engineering methods and optimization of the VR interface. First, we studied the coupling of user cognition and design features in the VR system via the Kansei Engineering (KE) method. The quantitative theory I and KE model regression analysis were used to analyze the design elements of the VR system’s human–computer interaction interface. Combined with the complex network method, we summarized the relationship between design features and analyzed the important design features that affect users’ perceptual imagery. Then, based on the characteristics of machine learning, we used a convolutional neural network (CNN) to predict and analyze the user’s perceptual imagery in the VR system, to provide assistance for the design optimization of the VR system design. Finally, we verified the validity and feasibility of the solution by combining it with the human–machine interface design of the VR system. We conducted a feasibility analysis of the KE model, in which the similarity between the multivariate regression analysis of the VR intention space and the experimental test was approximately 97% and the error was very small; thus, the VR intention space model was well correlated. The Mean Square Error (MSE) of the convolutional neural network (CNN) prediction model was calculated with a measured value of 0.0074, and the MSE value was less than 0.01. The results show that this method can improve the effectiveness and feasibility of the design scheme. Designers use important design feature elements to assist in VR system optimization design and use CNN machine learning methods to predict user image values in VR systems and improve the design efficiency. Facing the same design task requirements in VR system interfaces, the traditional design scheme was compared with the scheme optimized by this method. The results showed that the design scheme optimized by this method better fits the user’s perceptual imagery index, and thus the user’s task operation experience was better.


2021 ◽  
Author(s):  
Mara K. Fuchs ◽  
Florian Beckert ◽  
Jörn Biedermann ◽  
Björn Nagel

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