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This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

This study aims to investigate the noise-inducing characteristics during the start-up process of a mixed-flow pump and the impact of different start-up schemes on pump noise.

This study conducted numerical simulations on the mixed-flow pump under different start-up schemes and investigated the flow characteristics and noise distribution under these schemes.

The results reveal that the dipole noise is mainly caused by pressure fluctuations, while the quadrupole noise is mainly generated by the generation, development and breakdown of vortices. Additionally, the noise evolution characteristics during the start-up process of the mixed-flow pump can be divided into the initial stage, stable growth stage, impulse stage and stable operation stage.

The findings of this study can provide a theoretical basis for the selection of start-up schemes for mixed-flow pumps, reducing flow noise and improving the operational stability of mixed-flow pumps.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.

An impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.

The experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.

The system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.

Stiffness adjusting ability is essential for soft robotic arms to perform complex tasks. A soft state enables dexterous operation and safe interaction, while a rigid state enables large force output or heavy weight carrying. However, making a compact integration of soft actuators with powerful stiffness adjusting mechanisms is challenging. This study aims to develop a piston-like particle jamming mechanism for enhanced stiffness adjustment of a soft robotic arm.

The arm has two pairs of differential tendons for spatial bending, and a jamming core consists of four jamming units with particles sealed inside braided tubes for stiffness adjustment. The jamming core is pushed and pulled smoothly along the tendons by a piston, which is then driven by a motor and a ball screw mechanism.

The tip displacement of the arm under 150 N jamming force and no more than 0.3 kg load is minimal. The maximum stiffening ratio measured in the experiment under 150 N jamming force is up to 6–25 depends on the bending direction and added load of the arm, which is superior to most of the vacuum powered jamming method.

The proposed robotic arm makes an innovative compact integration of tendon-driven robotic arm and motor-driven piston-like particle jamming mechanism. The jamming force is much larger compared to conventional vacuum-powered systems and results in a superior stiffening ability.

This paper aims to focus on the research progress of intelligent self-healing anti-corrosion coatings in the aviation field in the past few years. The paper provides certain literature review supports and development direction suggestions for future research on intelligent self-healing coatings in aviation.

This mini-review uses a systematic literature review process to provide a comprehensive and up-to-date review of intelligent self-healing anti-corrosion coatings that have been researched and applied in the field of aviation in recent years. In total, 64 articles published in journals in this field in the last few years were analysed in this paper.

The authors conclude that the incorporation of multiple external stimulus-response mechanisms makes the coatings smarter in addition to their original self-healing corrosion protection function. In the future, further research is still needed in the research and development of new coating materials, the synergistic release of multiple self-healing mechanisms, coating preparation technology and corrosion monitoring technology.

To the best of the authors’ knowledge, this is one of the few systematic literature reviews on intelligent self-healing anti-corrosion coatings in aviation. The authors provide a comprehensive overview of the topical issues of such coatings and present their views and opinions by discussing the opportunities and challenges that self-healing coatings will face in future development.

This study aims to study the thermal identification issue by harvesting both solar energy and atmospheric thermal updraft for a solar-powered unmanned aerial vehicle (SUAV) and its electric energy performance under continuous soaring conditions.

The authors develop a specific dynamic model for SUAVs in both soaring and cruise modes. The support vector machine regression (SVMR) is adopted to estimate the thermal position, and it is combined with feedback control to implement the SUAV soaring in the updraft. Then, the optimal path model is built based on the graph theory considering the existence of several thermals distributed in the environment. The procedure is proposed to estimate the electricity cost of SUAV during flight as well as soaring, and making use of dynamic programming to maximize electric energy.

The simulation results present the integrated control method could allow SUAV to soar with the updraft. In addition, the proposed approach allows the SUAV to fly to the destination using distributed thermals while reducing the electric energy use.

Two simplified dynamic models are constructed for simulation considering there are different flight mode. Besides, the data-driven-based SVMR method is proposed to support SUAV soaring. Furthermore, instead of using length, the energy cost coefficient in optimization problem is set as electric power, which is more suitable for SUAV because its advantage is to transfer the three-dimensional path planning problem into the two-dimensional.

There is a growing interest in applying the principles of biophilic design to hotel lobbies to enhance positive user experiences. However, there is little empirical research in this area, particularly for abstract or indirect biophilic approaches. The current study analyzed the biophilic strategy of “organized complexity” (which entails structured hierarchical patterns) in a hotel lobby in relation to attention restoration outcomes.

A virtual reality approach was used to create three lobby designs, differing only in their degree of organized complexity. A between-subjects study was conducted with 91 participants, evaluating reactions to the lobby designs in terms of perceived attention restoration, perceived environmental complexity and environmental preferences.

A strong positive relationship was found between participants' perception of environmental complexity and feelings of restoration. However, no statistically significant correlation was found between restoration and the objective measures of organized complexity. This suggests that individual preferences or baseline responses may play a crucial role in the benefits associated with this aspect of biophilic design.

The findings suggest a novel possibility – that responses to organized complexity may not be universal but may instead depend on an individual’s background and personal preferences. While the study found that perceived complexity was associated with restoration, the objective features of the environment that incited these perceptions were different for different participants. This indicates that more research is needed into potentially relevant personal factors.

This study aims to investigate the importance and effect of asymmetric technological collaborations’ key success factors in developing countries. The number of collaborations between large enterprises and SMEs, known as asymmetric technological collaborations (ATC) is growing considerably. But this asymmetry in itself can increase the number and intensity of collaboration challenges. So far, limited studies have been conducted on the stability of ATCs, and most of them have been in the context of developed countries. Meanwhile, studying the strength and stability of collaboration in the nano industry with growing market value and increasing newcomers is of particular importance.

Here, with bionic engineering approach, we used chemistry for the first time to identify the main stability factors of ATCs and build our hypotheses and research model. To this end, we introduced the factors affecting the stability of the dative chemical bond as a bionic counterpart of corporate venture capital (CVC), which is a type of ATC, and proposed 4 hypotheses. We used structural equation modeling (SEM) with partial least squares (PLS) method to examine the hypothesized relationships.

The analysis of survey questionnaire data from 26 asymmetric collaborations in Iran’s nanotechnology industry shows that “learning of the acceptor company” with a negative effect, “network ties” and “development of the collaboration host region” with a positive effect and “diversity in the collaboration portfolio” with an inverted U-shaped effect are the most influential factors in the stability and continuity of CVCs, respectively.

The findings of this research can be the beginning of a broad path leading to exploring and getting inspiration from chemistry to analyze management issues.