Journal of Bionic Engineering最新文献

The averaged equation of motion for linear acceleration in the BCF swimming mode was derived using the Elongated Body Theory (EBT) through time averaging. An analytical solution for the linear acceleration swimming velocity was obtained, revealing that the average velocity follows a hyperbolic tangent function of time, which can be considered a semi-empirical formula for linear acceleration swimming. The formula’s parameters, such as the steady swimming velocity and the acceleration time constant, can be determined by conducting experiments on linear acceleration, enabling the estimation of drag coefficient, effective added mass, thrust, and drag force. We developed a tensegrity robotic tuna and conducted a linear acceleration experiment. The results confirmed both the averaged equation of motion and its empirical formula, indicating that the formula is not limited by EBT and can be extended to large amplitude swimming and thunniform swimmers with large aspect ratio caudal fins. This provides researchers with an efficient and easy-to-implement method to estimate the swimming thrust and drag forces through linear acceleration experiments, without the need for complex and expensive flow field and force measurement equipment.

This paper presents a literature review exploring the potential of piezoelectric field-effect transistors (piezo-FETs) as bionic microelectromechanical systems (MEMS). First, piezo-FETs are introduced as bionic counterparts to natural mechanoreceptors, highlighting their classic configuration and working principles. Then, this paper summarizes the existing research on piezo-FETs as sensors for pressure, inertial, and acoustic sensors. Material selections, design characteristics, and key performance metrics are reviewed to demonstrate the advantage of piezo-FETs over traditional piezoelectric sensors. After identifying the limitations in these existing studies, this paper proposes using bionic piezoelectric coupling structures in piezo-FETs to further enhance the sensing capabilities of these artificial mechanoreceptors. Experimentally validated manufacturing methods for the newly proposed piezo-FET structures are also reviewed, pointing out a novel, feasible, and impactful research direction on these bionic piezoelectric MEMS sensors.

The anthropometric differences between European/American and Chinese population are remarkable and have significant influences on pedestrian kinematics and injury response in vehicle crashes. Therefore, the current study aims to develop and validate a Finite Element (FE) human body model representing the anthropometry of Chinese 50th percentile adult male for pedestrian safety analysis and development of Chinese ATDs (Anthropomorphic Test Devices). Firstly, a human body pedestrian model, named as C-HBM (Chinese Human Body Model), was developed based on the medical image data of a volunteer selected according to both anthropometry and anatomy characteristics of 50th percentile Chinese adult male. Then, the biofidelity of the C-HBM pedestrian model was validated against cadaver impact test data reported in the literature at the segment and full-body level. Finally, the validated C-HBM pedestrian model was employed to predict Chinese pedestrian injuries in real world vehicle crashes. The results indicate that the C-HBM pedestrian model has a good capability in predicting human body mechanical response in cadaver tests and Chinese leg and thorax injuries in vehicle crashes. Kinematic analysis shows that the C-HBM pedestrian model has less sliding on the hood surface, shorter movement in the horizontal direction, and higher pelvis displacement in the vertical direction than cadavers and the pedestrian model in the anthropometry of westerner due to anthropometric differences in the lower limbs. The currently developed C-HBM pedestrian model provides a basic tool for vehicle safety design and evaluation in China market, and for development of Chinese ATDs.

Micro- and nano-structures are intentionally incorporated into various biological surfaces, such as fish scales, snakeskin, and burr-covered plant leaves, to enhance their interactions with other surfaces. The mechanical anisotropy affects friction, interlocking, propulsion, and mobility on substrates. This study investigates a novel method for developing a robust, stratified, soft, lubricating coating on a surface. 3-Methacryloyloxypropyl-trimethoxysilane (MPS) is a cohesive adhesion promoter that functions by infiltrating Polydimethylsiloxane (PDMS) silicone elastomers to maintain low friction levels and high mechanical load-bearing capacity. MPS makes it easier for organic and inorganic materials to adhere to the surface of the initiator layer P(AAm-co-AA-co-PDMS/Fe). We investigate how the tough hydrogel layer of the module impacts the lubricating ability of the multilayer coating when the tough hydrogel layer of the module adheres to the bio-based polyurethane substrate. After 1,000 sliding cycles with a 1 N load, the improved PDMS’s Coefficient of Friction (COF) remains steady and low (COF < 0.81). We recommend using the suggested structure and a standard set of optimal variables to enhance the functional efficiency of such systems. In conclusion, we have demonstrated the optimal simulation of these parameters for stimulus-responsive, adjustable surface systems.

This paper proposes an improved version of the Partial Reinforcement Optimizer (PRO), termed LNPRO. The LNPRO has undergone a learner phase, which allows for further communication of information among the PRO population, changing the state of the PRO in terms of self-strengthening. Furthermore, the Nelder-Mead simplex is used to optimize the best agent in the population, accelerating the convergence speed and improving the accuracy of the PRO population. By comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function, the convergence accuracy of the LNPRO has been verified. The accuracy and stability of simulated data and real data in the parameter extraction of PV systems are crucial. Compared to the PRO, the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components, and it is also superior to other excellent algorithms. To further verify the parameter extraction problem of LNPRO in complex environments, LNPRO has been applied to three types of manufacturer data, demonstrating excellent results under varying irradiation and temperatures. In summary, LNPRO holds immense potential in solving the parameter extraction problems in PV systems.

Biomimicry is an interdisciplinary field that aims to provide sustainable solutions to technical issues. However, learners often encounter challenges in the application of biomimicry due to the multidisciplinary requisites and abstract thinking skills required. Although multiple hands-on activities and teaching strategies have been explored, significant obstacles remain. Recently, generative artificial intelligent tools have become readily accessible to the general public, among which is ChatGPT. ChatGPT is known for generating detailed responses to user inquiries and has demonstrated efficacy in enhancing learning, although its specific application to biomimicry education has yet to be explored. To bridge this knowledge gap, this study seeks to evaluate the capabilities of ChatGPT in helping its users identify biomimetic solutions. It is found that the effectiveness of ChatGPT in biomimicry education significantly depends on the user’s ability to formulate knowledgeable and effective prompts. Although, a novice user can use ChatGPT to get a fundamental overview of the technical challenge and explore potential sources of bioinspiration. The study proposes a theoretical framework to guide users in the effective use of ChatGPT for biomimicry education and application. In addition, users are cautioned against ChatGPT responses and advised to employ it as a tool to complement their own knowledge gaps. The results from this study can offer insights for teachers and self-directed learners on the effective use of prompts in ChatGPT for biomimicry education. Future investigations will seek to validate this framework by evaluating users’ experiences and feedback on its application in creating prototypes.

Compared to the traditional flapping-wing structure with single motion mode, a micro servoactuator driven Flapping-Wing Air Vehicle (FWAV) breaks free from the limitations imposed by the motion parameters of the crank-connecting rod mechanism. It allows for simultaneous control of wings’ position and velocity attitude through pulse width modulation, showcasing unrivaled controllability and promising extensive applications. However, this method of motion control also brings new challenges to the design of the wings’ motion parameters. This study seeks to investigate the relationship between the motion parameters of micro servoactuator driven FWAV and its aerodynamic characteristics, then explore a servo control method that can optimize its thrust-producing performance. To achieve this, this paper involves the establishment of Amplitude Loss Model (ALM), Flapping Wing Dynamic Model (FWDM), and Power Load Model (PLM), followed by motion capture experiments, dynamic monitoring experiments, and power monitoring experiments. Experimental results show that the proposed modeling method, which fully considers the amplitude loss effect and advanced twisting effect in flapping-wing motion, can accurately calculate thrust, power, and power load, with prediction errors of less than 10%, 5% and 13%, respectively. This high-precision model can effectively optimize motion parameters, allowing for better performance of flapping-wing motion.

Wall climbing robots have a wide range of applications in the field of transportation, petrochemicals, aerial construction, and other monitoring prospects; however, for complex defects on the wall, it is easy for the robot to fall off from attachment. This paper puts forward the bionic wheel-tracked rolling adsorption wall-climbing robots. The paper designs flexible material as sealing material for the negative pressure chamber of wall-climbing robots through the imitation of the biological mechanism of the insect adhesion pads. The material has the advantages of wear resistance, strong wall adaptability, large load, simple structure, etc., and it has a highly reliable and stable adsorption ability on unstructured and complex walls. The mathematical model of adsorption of the wall-crawling robot is constructed in different wall environments, and the kinematic analysis is carried out. The influence of the leakage on the adsorption capacity due to the deformation of the flexible sealing material, defects of the wall surface and the air ducts formed under different roughnesses is analyzed. Through the fabrication and experiment of the prototype, the correctness of the theoretical analysis is verified. The measured load capacity of the robot is 2.47 times its own weight, and it has great obstacle-crossing ability.

Convolutional Neural Networks (CNNs) have shown remarkable capabilities in extracting local features from images, yet they often overlook the underlying relationships between pixels. To address this limitation, previous approaches have attempted to combine CNNs with Graph Convolutional Networks (GCNs) to capture global features. However, these approaches typically neglect the topological structure information of the graph during the global feature extraction stage. This paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network (MGPN), which is designed explicitly for chest X-ray image classification. Our approach sequentially combines CNNs and GCNs, enabling the learning of both local and global features from individual images. Recognizing that different nodes contribute differently to the final graph representation, we introduce an NI-GTP module to enhance the extraction of ultimate global features. Additionally, we introduce a G-LFF module to fuse the local and global features effectively.

The Unit Commitment Problem (UCP) corresponds to the planning of power generation schedules. The objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand, which also minimizes the total operating cost while adhering to different constraints such as power generation limits, unit startup, and shutdown times. In this paper, four different binary variants of the Bald Eagle Search (BES) algorithm, were introduced, which used two variants using S-shape, U-shape, and V-shape transfer functions. In addition, the best-performing variant (using an S-shape transfer function) was selected and improved further by incorporating two binary operators: swap-window and window-mutation. This variation is labeled Improved Binary Bald Eagle Search (IBBESS2). All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-, 10-, 20-, 40-, 60-, 80-, and 100-unit. For comparative evaluation, 34 comparative methods from existing literature were compared, in which IBBESS2 achieved competitive scores against other optimization techniques. In other words, the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-, 40-, 60-, 80-, and 100-unit problems. Furthermore, IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms, especially in large-scale unit commitment problems. The Friedman statistical test further validates the results, where the proposed IBBESS2 is ranked the best. In conclusion, the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.