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Evaluating aircraft cockpit emotion through a neural network approach

Published online by Cambridge University Press:  05 November 2020

Yanhao Chen Open the ORCID record for Yanhao Chen [Opens in a new window] ,
Suihuai Yu ,
Jianjie Chu ,
Dengkai Chen  and
Mingjiu Yu
Yanhao Chen*
Affiliation:
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an Shaanxi710072, P.R. China
Suihuai Yu
Affiliation:
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an Shaanxi710072, P.R. China
Jianjie Chu
Affiliation:
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an Shaanxi710072, P.R. China
Dengkai Chen*
Affiliation:
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an Shaanxi710072, P.R. China
Mingjiu Yu
Affiliation:
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an Shaanxi710072, P.R. China
*
Author for correspondence: Yanhao Chen, E-mail: [email protected]
Author for correspondence: Yanhao Chen, E-mail: [email protected]
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Abstract

Studies show that there are shortcomings in applying conventional methods for the emotional evaluation of the aircraft cockpit. In order to resolve this problem, a more efficient cockpit emotion evaluation system is established in the present study to simply and quickly obtain the cockpit emotion evaluation value. To this end, the neural network is applied to construct an emotional model to evaluate the emotional prediction of the interior design of the aircraft cockpit. Moreover, several technologies and the Kansei engineering method are applied to acquire the cockpit interior emotional evaluation data for typical aircraft models. In this regard, the radical basis function neural network (RBFNN), Elman neural network (ENN), and the general regression neural network (GRNN) are applied to construct the sentimental prediction evaluation model. Then, the three models are comprehensively compared through factors such as the model evaluation criteria, network structure, and network parameters. Obtained experimental results indicate that the GRNN not only has the highest classification accuracy but also has the highest stability in comparison to the other two neural networks, so that it is a more appropriate method for the emotional evaluation of the aircraft cockpit. Results of the present study provide decision supports for the emotional evaluation of the cockpit interior space.

Keywords

Aircraft cockpit interioremotional evaluation modelKansei engineeringneural network prediction
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 1 , February 2021 , pp. 81 - 98
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Aghdam, MH (2019) Context-aware recommender systems using hierarchical hidden Markov model. Physica A 518, 89–98.Google Scholar
Alam, M, Samad, MD, Vidyaratne, L, Glandon, A and Iftekharuddin, KM (2020) Survey on deep neural networks in speech and vision systems. Neurocomputing 417, 302–321.CrossRefGoogle ScholarPubMed
Andonovski, G, Mušič, G, Blažič, S and Škrjanc, I (2018) Evolving model identification for process monitoring and prediction of non-linear systems. Engineering Applications of Artificial Intelligence 68, 214–221.CrossRefGoogle Scholar
Angelini, C and Cascini, G (2016) Services evaluation and improvement with systematic innovation tools. Procedia CIRP 39, 225–230.CrossRefGoogle Scholar
Axon, DR, Aljadeed, R, Potisarach, P, Forbes, S, DiLeo, J and Warholak, T (2020) Pilot study of focus groups exploring student pharmacists’ perceptions of a medication management center internship. Currents in Pharmacy Teaching and Learning 12, 1123–1128.CrossRefGoogle ScholarPubMed
Bae, S, Kang, KD, Kim, SW, Shin, YJ, Nam, JJ and Han, DH (2019) Investigation of an emotion perception test using functional magnetic resonance imaging. Computer Methods and Programs in Biomedicine 179, 1–6.CrossRefGoogle ScholarPubMed
Bartusiak, R, Augustyniak, Ł, Kajdanowicz, T, Kazienko, P and Piasecki, M (2019) WordNet2Vec: Corpora agnostic word vectorization method. Neurocomputing 326–327, 141–150.CrossRefGoogle Scholar
Bolar, AA, Tesfamariam, S and Sadiq, R (2017) Framework for prioritizing infrastructure user expectations using Quality Function Deployment (QFD). International Journal of Sustainable Built Environment 6, 16–29.CrossRefGoogle Scholar
Brezonakova, A, Skvarekova, I, Pecho, P, Davies, R, Bugaj, M and Kandera, B (2019) The effects of back lit aircraft instrument displays on pilots fatigue and performance. Transportation Research Procedia 40, 1273–1280.CrossRefGoogle Scholar
Causse, M, Péran, P, Dehais, F, Caravasso, CF, Zeffiro, T, Sabatini, U and Pastor, J (2013) Affective decision making under uncertainty during a plausible aviationtask: an fMRI study. NeuroImage 71, 19–29.CrossRefGoogle Scholar
Chanyachatchawan, S, Yan, H-B, Sriboonchitta, S and Huynh, V-N (2017) A linguistic representation based approach to modelling Kansei data and its application to consumer-oriented evaluation of traditional products. Knowledge-Based Systems 138, 124–133.Google Scholar
Chen, Y, Yan, S and Tran, CC (2019) Comprehensive evaluation method for user interface design in nuclear power plant based on mental workload. Nuclear Engineering and Technology 51, 453–462.CrossRefGoogle Scholar
Chen, Z, Ming, X, Zhou, T, Chang, Y and Sun, Z (2020) A hybrid framework integrating rough-fuzzy best-worst method to identify and evaluate user activity-oriented service requirement for smart product service system. Journal of Cleaner Production 253, 1–19.CrossRefGoogle Scholar
Darekar, RV and Dhande, AP (2018) Emotion recognition from Marathi speech database using adaptive artificial neural network. Biologically Inspired Cognitive Architectures 23, 35–42.CrossRefGoogle Scholar
Détienne, F, Baker, M and Le Bail, C (2019) Ideologically-embedded design: community, collaboration and artefact. International Journal of Human-Computer Studies 131, 72–80.CrossRefGoogle Scholar
Dong, J, Zhao, Y, Liu, C, Han, Z-F and Leung, C-S (2019) Orthogonal least squares based center selection for fault-tolerant RBF networks. Neurocomputing 339, 217–231.Google Scholar
Gbededo, MA and Liyanage, K (2020) Descriptive framework for simulation-aided sustainability decision-making: a Delphi study. Sustainable Production and Consumption 22, 45–57.Google Scholar
Guo, D, Zhang, Y, Xiao, Z, Mao, M and Liu, J (2015) Common nature of learning between BP-type and Hopfield-type neural networks. Neurocomputing 167, 578–586.CrossRefGoogle Scholar
Guo, C, Lu, J, Tian, Z, Guo, W and Darvishan, A (2019) Optimization of critical parameters of PEM fuel cell using TLBO-DE based on Elman neural network. Energy Conversion and Management 183, 149–158.CrossRefGoogle Scholar
Guo, F, Qu, Q-X, Nagamachi, M and Duffy, VG (2020) A proposal of the event-related potential method to effectively identify Kansei words for assessing product design features in Kansei engineering research. International Journal of Industrial Ergonomics 106, 46–59.Google Scholar
Hai-bo, X and Zhi -sheng, Z (2012) Evaluation method for cockpit lighting of civil aircraft. Technology and Market 19, 12–13.Google Scholar
Hai-yu, X, An, Z, Zhi-li, T and Bin, C (2012) Comprehensive evaluation of aircraft cockpit human-machine interface. Science Technology and Engineering 12, 940–943.Google Scholar
Han, S-Z, Huang, L-H, Zhou, Y-Y and Liu, Z-L (2018) Mixed chaotic FOA with GRNN to construction of a mutual fund forecasting model. Cognitive Systems Research 52, 380–386.CrossRefGoogle Scholar
Han, S-Y, Kwak, N-S, Oh, T and Lee, S-W (2020) Classification of pilots’ mental states using a multimodal deep learning network. Biocybernetics and Biomedical Engineering 40, 324–336.CrossRefGoogle Scholar
He, C, Ma, M and Wang, P (2020) Extract interpretability-accuracy balanced rules from artificial neural networks: a review. Neurocomputing 387, 346–358.CrossRefGoogle Scholar
Hessels, RS and Hooge, ITC (2019) Eye tracking in developmental cognitive neuroscience – the good, the bad and the ugly. Developmental Cognitive Neuroscience 40, 1–11.CrossRefGoogle ScholarPubMed
Hofmeyr, DP (2020) Degrees of freedom and model selection for k-means clustering. Computational Statistics and Data Analysis 149, 1–14.CrossRefGoogle Scholar
Hu, H, Wang, H, Bai, Y and Liu, M (2019) Determination of endometrial carcinoma with gene expression based on optimized Elman neural network. Applied Mathematics and Computation 341, 204–214.Google Scholar
Husemann, M, Schäfer, K and Stumpf, E (2018) Flexibility within flight operations as an evaluation criterion for preliminary aircraft design. Journal of Air Transport Management 71, 201–214.CrossRefGoogle Scholar
Jaina, DK, Shamsolmoali, P and Sehdev, P (2019) Extended deep neural network for facial emotion recognition. Pattern Recognition Letters 120, 69–74.CrossRefGoogle Scholar
Jeon, M, Walker, BN and Yim, J-B (2014) Effects of specific emotions on subjective judgment, driving performance, and perceived workload. Transportation Research Part F 24, 197–209.CrossRefGoogle Scholar
Jeon, M, Walker, BN and Gable, TM (2015) The effects of social interactions with in-vehicle agents on a driver's anger level, driving performance, situation awareness, and perceived workload. Applied Ergonomics 50, 185–199.CrossRefGoogle ScholarPubMed
Jiao, Y and Qu, Q-X (2019) A proposal for Kansei knowledge extraction method based on natural language processing technology and online product reviews. Computers in Industry 108, 1–11.CrossRefGoogle Scholar
Jude Hemanth, D, Anitha, J and Son, LH (2018) Brain signal based human emotion analysis by circular back propagation and Deep Kohonen Neural Networks. Comuters and Electrical Enineerin 68, 170–180.CrossRefGoogle Scholar
Kraemer, J and Süß, H-M (2015) Real time validation of online situation awareness questionnaires in simulated approach air traffic control. Procedia Manufacturing 3, 3152–3159.CrossRefGoogle Scholar
Kraemer, AD, Villani, E and Arjoni, DH (2019) Aircraft FDI and human factors analysis of a take-off maneuvre using SIVOR flight simulator. International Federation of Automatic Control 51, 184–189.Google Scholar
Kumar, V, Mishra, RK and Krishnapillai, S (2019) Study of pilot's comfortness in the cockpit seat of a flight simulator. International Journal of Industrial Ergonomics 71, 1–7.CrossRefGoogle Scholar
Lei, L, Chen, W, Xue, Y and Liu, W (2018) A comprehensive evaluation method for indoor air quality of buildings based on rough sets and a wavelet neural network. Building and Environment 162, 1–18.Google Scholar
Liang, C-C, Lee, Y-H, Ho, C-H and Chen, K-H (2019) Investigating vehicle interior designs using models that evaluate user sensory experience and perceived value. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 1–20.https://doi.org/10.1017/S0890060419000246.Google Scholar
Liu, Q, Sun, P, Fu, X, Zhang, J, Yang, H, Gao, H and Li, Y (2020) Comparative analysis of BP neural network and RBF neural network in seismic performance evaluation of pier columns. Mechanical Systems and Signal Processing 141, 1–14.CrossRefGoogle Scholar
Lu, L and Yuan, Y (2018) A novel TOPSIS evaluation scheme for cloud service trustworthiness combining objective and subjective aspects. The Journal of Systems & Software 143, 71–86.Google Scholar
Meza-Kubo, V, Morán, AL, Carrillo, I, Galindo, G and García-Canseco, E (2016) Assessing the user experience of older adults using a neural network trained to recognize emotions from brain signals. Journal of Biomedical Informatics 62, 202–209.CrossRefGoogle ScholarPubMed
Nagamachi, M (1995) Kansei Engineering: a new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics 15, 3–11.CrossRefGoogle Scholar
Nagamachi, M and Imada, AS (1995) Kansei Engineering: an ergonomic technology for product development. International Journal of Industrial Ergonomics 15, 1.CrossRefGoogle Scholar
Pandey, MM (2020) Evaluating the strategic design parameters of airports in Thailand to meet service expectations of Low-Cost Airlines using the Fuzzy-based QFD method. Journal of Air Transport Management 82, 1–9.CrossRefGoogle Scholar
Pejčev, AV and Spalević, MM (2014) Error bounds of the Micchelli–Sharma quadrature formula for analytic functions. Journal of Computational and Applied Mathematics 259, 48–56.CrossRefGoogle Scholar
Perez-Zarate, D, Santoyo, E, Acevedo-Anicasio, A, Díaz-Gonzalez, L and García-Lopez, C (2019) Evaluation of artificial neural networks for the prediction of deep reservoir temperatures using the gas-phase composition of geothermal fluids. Computers and Geosciences 129, 49–68.CrossRefGoogle Scholar
Pinto, FST, Fogliatto, FS and Qannari, EM (2014) A method for panelists’ consistency assessment in sensory evaluations based on the Cronbach's alpha coefficient. Food Quality and Preference 32, 41–47.CrossRefGoogle Scholar
Rui, H, Yang, H, YunHao, Z and Huan, L (2020) Hyperspectral image quality evaluation using generalized regression neural network. Signal Processing: Image Communication 83, 1–7.Google Scholar
Ruwa, N, Mao, Q, Wang, L, Gou, J and Dong, M (2019) Mood-aware visual question answering. Neurocomputing 330, 305–316.CrossRefGoogle Scholar
Schutte, S and Eklund, J (2005) Design of rocker switches for work-vehicles—an application of Kansei Engineering. Applied Ergonomics 36, 557–567.CrossRefGoogle ScholarPubMed
Şenol, MB (2015) Anthropometric evaluation of cockpit designs. International Journal of Occupational Safety and Ergonomics 22, 1–26.Google Scholar
Thomas, PR (2018) Performance, characteristics, and error rates of cursor control devices for aircraft cockpit interaction. International Journal of Human-Computer Studies 109, 41–53.CrossRefGoogle Scholar
Vanhala, M, Lu, C, Peltonen, J, Sundqvist, S, Nummenmaa, J and Järvelin, K (2020) The usage of large data sets in online consumer behaviour: a bibliometric and computational text-mining-driven analysis of previous research. Journal of Business Research. doi:10.1016/j.jbusres.2019.09.009.CrossRefGoogle Scholar
Wang, C-H and Chin, H-T (2017) Integrating affective features with engineering features to seek the optimal product varieties with respect to the niche segments. Advanced Engineering Informatics 33, 350–359.CrossRefGoogle Scholar
Wang, P, Meng, P, Zhai, J-Y and Zhu, Z-Q (2013) A hybrid method using experiment design and grey relational analysisn for multiple criteria decision making problems. Knowledge-Based Systems 53, 100–107.CrossRefGoogle Scholar
Wang, WM, Li a, Z, Tian, ZG, Wang, JW and Cheng, MN (2018) Extracting and summarizing affective features and responses from online product descriptions and reviews: a Kansei text mining approach. Engineering Applications of Artificial Intelligence 73, 149–162.CrossRefGoogle Scholar
Wei, Z (2015) Music emotion classification and evaluation model based on BP neural network. Electronic Design Engineering 23, 71–74.Google Scholar
Weidong, H, Qiang, X and Qiulin, D (2008) On acquisition of product design knowledge by an ART-2 neural network. Mechanical Science and Technology for Aerospace Engineering 27, 399–404.Google Scholar
Xiao, YUAN, Dongjing, HAO, Haiyan, LIU, Zhefeng, JIN and Dayong, DONG (2017) The research of civil aircraft cockpit HMI evaluation method. Civil Aircraft Design ＆ Research 124, 17–22.Google Scholar
Xuecai, X, Gui, F, Yujingyang, X, Ziqi, Z, Ping, C, Baojun, L and Song, J (2019) Risk prediction and factors risk analysis based on IFOA-GRNN and apriori algorithms: application of artificial intelligence in accident prevention. Process Safety and Environmental Protection 122, 169–184.Google Scholar
Yan, W, Chen, C-H and Chang, W (2009) An investigation into sustainable product conceptualization using a design knowledge hierarchy and Hopfield network. Computers & Industrial Engineering 56, 1617–1626.CrossRefGoogle Scholar
Yu, C, Li, Y, Xiang, H and Zhang, M (2018) Data mining-assisted short-term wind speed forecasting by wavelet packet decomposition and Elman neural network. Journal of Wind Engineering & Industrial Aerodynamics 175, 136–143.CrossRefGoogle Scholar
Zhai, L-Y, Khoo, L-P and Zhong, Z-W (2009) Design concept evaluation in product development using rough sets and grey relation analysis. Expert Systems with Applications 36, 7072–7079.Google Scholar
Zhang, X and Yang, M (2019) Color image knowledge model construction based on ontology. Color Research and Application 44, 487–676.CrossRefGoogle Scholar
Zhou, Q, Wang, Y, Jiang, P, Shao, X, Choi, S-K, Hu, J, Cao, L and Meng, X (2017) An active learning radial basis function modeling method based on self-organization maps for simulation-based design problems. Knowledge-Based Systems 131, 10–27.CrossRefGoogle Scholar