Current Issue : January-March Volume : 2025 Issue Number : 1 Articles : 5 Articles
Fluid-driven soft robotic systems, typically powered by bulky and rigid pumps, face significant limitations in agility and adaptability. Addressing this, various soft pumps have been developed, aiming to achieve better compatibility with soft robotics while ensuring sufficient performance. However, finding an optimal balance between flow rate, pumping pressure, efficiency, and the ability to seamlessly integrate with soft robotic structures remains challenging. Herein, a trielectrode electrostatically driven soft pump is presented, featuring a central diaphragm for active bidirectional pumping of gases and dielectric liquids. This design surpasses previous dual-electrode soft pumps in electrostatic driving frequency, offering an improved flow rate of up to 330 mLmin1 and pressure of 15.96 kPa, within a compact form measuring 5.42 cm3 in volume and weighing only 11.2 g. In addition, constructed entirely from compliant materials, this pump is fully functional under bending, compression, and torsion, enhancing its integration with soft robotics. To demonstrate its practical utility, the pump is integrated into a soft gripper, enabling the manipulation of various objects. The introduced trielectrode design enables high-frequency electrostatic actuation, resulting in a compact, high-performance, and deformation-resilient soft pump, advancing highly integrated and practical soft robotics....
Predicting slope stability is important for preventing and mitigating landslide disasters. This paper examines the existing approaches for analyzing slope stability. There are several established conventional approaches for slope stability analysis that can be applied in this context. However, in recent decades, soft computing methods has been extensively developed and employed in stochastic slope stability analysis, notably as surrogate models to improve computing efficiency in contrast to traditional approaches. Soft computing methods can deal with uncertainty and imprecision, which may be quantified using performance indices like coefficient of determination, in regression and accuracy in classification. This review study focuses on conventional methods such as the Bishop’s method and Janbu’s method, as well as soft computing models such as support vector machine, artificial neural network, Gaussian process regression, decision tree, etc. The advantages and limitations of soft computing techniques in relation to conventional methods have also been thoroughly covered in this paper. The achievements of soft computing methods are summarized from two aspects—predicting factor of safety and classification of slope stability. Key potential research challenges and future prospects are also given....
The interaction between microbes and drugs encompasses the sourcing of pharmaceutical compounds, microbial drug degradation, the development of drug resistance genes, and the impact of microbial communities on host drug metabolism and immune modulation. These interactions significantly impact drug efficacy and the evolution of drug resistance. In this study, we propose a novel predictive model, termed GCGACNN.We first collected microbe, disease, and drug association data from multiple databases and the relevant literature to construct three association matrices and generate similarity feature matrices using Gaussian similarity functions. These association and similarity feature matrices were then input into a multi-layer Graph Neural Network for feature extraction, followed by a two-dimensional Convolutional Neural Network for feature fusion, ultimately establishing an effective predictive framework. Experimental results demonstrate that GCGACNN outperforms existing methods in predictive performance....
Engineers frequently aim to streamline environmental factors to facilitate the effective operation of robots. However, in nature, environmental considerations play a crucial role in shaping the embodiment of organisms. To comply robots with the complexity of real-world environments, embedding similar intelligence is key. In the field of soft robotics, various approaches offer insight into how intelligence can be integrated into artificial agents. A discussed topic is the intricate relationship between the brain and the body at the core of intelligence in robots. The goal of this article is, therefore, to unravel the strategies to implement different types of intelligence currently adopted in soft robots. A classification is made by making a distinction between agents that adapt to their environment by 1) their adaptive shape, 2) their adaptive functionality, and 3) their adaptive mechanics. Additionally, the perspectives on intelligence based on their computational approach are distinguished: centralized computation, decentralized computation, or embedded computation. It is concluded that a tailored robotic design approach attuned to specific environmental demands is needed. To unlock the full potential of soft robots, a fresh perspective on embodied intelligence is described, so-called mechanical intelligence, emphasizing the robot’s responsiveness to changing external conditions of a real-world environment....
The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector’s hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively....
Loading....