With the continuous maturation of technology and the deepening of human-centered design philosophy, eye tracking technology has gradually been applied in research studies for the optimization of flight deck design in commercial aviation. While these studies have focused solely on Pilot Flying (PF), without considering the visual attention distribution of Pilot Monitoring (PM). The purpose of this study was to analyze and compare the visual attention distribution of PF and PM during different flight phases, providing more comprehensive guidance and optimization suggestion to the flight deck design in commercial aviation. This study was conducted in the A320 full-motion flight simulator, with eye tracking technology recording eye movements of PF and PM in the same flight crew simultaneously. The result showed significant differences in three eye movement metrics between PF and PM, not only throughout the whole common manual circuit but also within specific flight phases. Additionally, the results indicated that in certain flight phases, PF and PM exhibited statistically significant correlations in certain eye movement metrics for specific areas of the flight deck. However, in most other instances, no statistically significant correlations were observed between the eye movement metrics of PF and PM. Therefore, the future design of flight decks may benefit from considering differentiation between PF and PM sides in certain flight phases or areas. Additionally, it can be beneficial to make dynamic adjustments to the current flight deck display and control interface while maintaining the existing layout design.

Wearable sensing has gained considerable attention for its effectiveness in gesture recognition, offering convenient, natural, and efficient interaction in human-machine systems. However, practical deployment in resource-constrained settings remains challenging due to data-intensive acquisition demands, high training overhead, and limited computing capacity—factors that hinder large-scale applicability. To address these issues, we proposed a lightweight model for fine-grained gesture recognition under sparse-site sensing conditions. Experiments were conducted on a custom multimodal dataset comprising 15 static and 18 dynamic gestures. Modal assessments across diverse gesture types revealed the complementarity of wrist surface electromyography and kinematic signals, validating the effectiveness of multimodal fusion. In addition, we leveraged transfer learning to improve model generalization to unseen subjects while reducing data acquisition burden and computational overhead. To achieve the Accuracy-Cost trade-off, we formulated an objective function and validated its generalizability across diverse sensing configurations, hardware platforms, and standardization strategies. Experimental results showed that an average accuracy sacrifice of 3.8% can yield substantial resource savings, with an average reduction of 87.8% in data collection and fine-tuning time, facilitating resource-efficient allocation of gesture recognition systems in Internet of Things (IoT) environments. Finally, an online gesture recognition prototype was developed and evaluated, demonstrating the method’s robustness and practical utility in cross-domain transfer scenarios. These findings provide both theoretical and practical foundations for deploying gesture recognition-based rehabilitation systems within IoT settings.

Wearable sensing technology offers a natural and convenient means of human-computer interaction, particularly for gesture recognition, yet domain shifts in wrist-worn single-site sensing pose significant challenges for cross-domain gesture recognition. To address this, we proposed a generalized cross-domain framework for fine-grained gesture recognition using wrist-worn single-site sensing. Concretely, we presented a Multi-Branch Network, which combines feature-level multimodal fusion with enhanced inter-modal interaction to effectively capture fine-grained gestures. To this end, we constructed a multimodal dataset, which comprises fifteen static and eighteen dynamic gestures. Furthermore, we developed five fine-tuning strategies and evaluated them across the paradigms of cross-session, cross-subject, cross-gesture, and cross-modality. Through comprehensive analyses, this study provides valuable insights into the selection of optimal fine-tuning strategies and elucidates the internal mechanisms underlying multiple cross-domain paradigms. To investigate the intricate trade-off between recognition accuracy and computational cost, we applied nonlinear least squares to construct the Accuracy-Cost trade-off functions. Experimental findings indicated that the optimal transfer learning ratios for these cross-domain paradigms ranged from 6.1% to 9.0%, with most clustering around 9.0%, offering a valuable reference for determining optimal transfer learning ratios within diverse cross-domain scenarios. Additionally, we implemented a real-time online gesture recognition system, validating the feasibility of our approach through preliminary tests in real-world scenarios. In conclusion, this study serves as a preliminary investigation into the application of wrist-worn single-site sensing for fine-grained gesture recognition.

Smart product-service systems (Smart PSS) are receiving increasing attention due to their potentials to satisfy customer needs and create sustainability to meet hybrid concerns. As a value co-creation business paradigm, various sustainable value propositions (SVPs) and their assessments come from stakeholders' subjective judgments, which values are imprecise, vague, and even inconsistent. The reliability of these values also varies with the confidence and cognitive bias of the respective stakeholder. How to evaluate these co-creative SVPs to ensure an objective and reliable result under such uncertain environments becomes a critical issue. To fill this gap, this paper proposes a rough-Z-number extended multi-criteria group decision-making framework integrating the rough-Z-number, DEMATEL (decision making trial and evaluation laboratory), and group decision-making strategy to evaluate the co-creative SVPs for Smart PSS. A novel rough-Z-number is presented to deal with the uncertainty and reliability representation of the stakeholder's individual assessment as well as the aggregation and subjectivity manipulation of these risk assessments from different stakeholders. A rough-Z-number extended DEMATEL is then proposed to determine the risk prioritization of the co-creative SVPs. Experimental results and comparative studies demonstrate the superiority of the proposed rough-Z-number-based DEMATEL in handling the uncertainty and reliability characterization as well as the subjectivity manipulation during the evaluation of the co-creative SVPs.

Biological inspiration evaluation has been widely acknowledged as one of the most important phases in biologically inspired design (BID) as it substantially determines the direction of the following-up design activities. However, it is inherently an interdisciplinary assessment, which includes both the engineering domain and the biological systems. Due to the lack of knowledge at the early stage of product design, the risk assessments mainly depend on experts' subjective judgments, which values are vague, imprecise, and even inconsistent. How to objectively evaluate the biological inspiration under such uncertain and interdisciplinary scenarios remains an open issue. To bridge such gaps, this study proposes a fuzzy rough number extended multi-criteria group decision-making (MCGDM) to evaluate the biological inspiration for BID. A fuzzy rough number is introduced to represent the individual decision maker's risk assessment and aggregate respective evaluation values within the decision-making group. A fuzzy rough number extended decision-making trial and evaluation laboratory is presented to determine the criteria weights and a fuzzy rough number extended multi-attribute ideal real comparative analysis is proposed to rank the candidate biological inspirations. Experimental results and comparative analysis validate the superiority of the proposed MCGDM in handling the subjectivity and uncertainty in biological inspiration evaluation.

In this paper, we introduce an approach for visual tracking in videos that predicts the bounding box location of a target object at every frame. This tracking problem is formulated as a sequential decision-making process where both historical and current information are taken into account to decide the correct object location. We develop a deep reinforcement learning based strategy, via which the target object position is predicted and decided in a unified framework. Specifically, a RNN based prediction network is developed where local features and global features are fused together to predict object movement. Together with the predicted movement, some predefined possible offsets and detection results form into an action space. A decision network is trained in a reinforcement manner to learn to select the most reasonable tracking box from the action space, through which the target object is tracked at each frame. Experiments in an existing tracking benchmark demonstrate the effectiveness and robustness of our proposed strategy.

This paper presents a parameter coordination approach based on constraints network to support multidisciplinary collaborative design. Firstly, from the point of view of collaborative design, the model of constraints network for parameter coordination is studied. In this model, interval boxes are adopted to describe the uncertainty of design parameters quantitatively to support multidisciplinary collaborative and enhance the design robustness. Secondly, a general consistency algorithm is designed to predict conflict and refine the intervals for parameter coordination, which verify the design process early in the process and assist the designers in determining design variables to reduce the multidisciplinary iterations in collaborative design. Finally, the method is demonstrated by a bogie parameter design problem

Knowledge-based engineering (KBE) and simulation analysis have been used widely in multidisciplinary concurrent and collaborative design process. However, the acquisition of knowledge keeps bottleneck yet in building knowledge base in KBE. In this paper, a framework of knowledge discovery from multidisciplinary simulation data is proposed. Correspondingly, a data mining algorithm named fuzzy-rough algorithm is developed to deal with the simulation data by combining the fuzzy set theory and rough set theory. The proposed knowledge discovery process is applied respectively to obtain some useful, implicit production rules with efficient measure. Finally, the method is demonstrated by a metal forming simulation problem. The results prove that knowledge discovery from simulation data is feasible, and the proposed method can be applied in other disciplinary simulation

This paper describes an uncertainty management method using generalized dynamic constraint networks (GDCN). Uncertainty of parameter in the concurrent and collaborative design is analyzed, and GDCN is presented to manage the uncertainty. Firstly, the model of GDCN including domain level constraints and knowledge level constraints is established. Secondly, the modeling of domain level constraints and knowledge level constraints are introduced; thirdly, the clearing up strategy of conflicts among constraints and consistency algorithm are put forward to gain consistency parameter intervals. Finally, a design example is analyzed to show effectiveness of this proposed method

The development process of a complex product should consider synthetically manufacturing and assembly process, and may be involved with many disciplines, such as mechanics, dynamics, vibration analysis, and so on. Conventionally, parameter design is carried out prior to tolerance design for economic considerations, and designers determined the parameter values separately, which may bring difficulties to other designers and lead to frequent redesign due to conflicts on some product specifications. This paper presents an approach based on constraint network to support concurrent and collaborative design. Firstly, from the point of view of concurrent engineering, the model of constraints network for current parameter and tolerance design, which consider the process of assembly and manufacture, is studied; Secondly, from the point of view of collaborative design, the model of constraints network for multidisciplinary collaborative design is studied. Finally, constraints network-based design method is studied, and optimization approach is presented, which synthetically consider parameter and tolerance, and enhance the design robustness. The method is demonstrated by a gear pump design problem.