Journal of Bionic Engineering最新文献

This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic (PV) models. The objective is to address challenges related to the detection and maintenance of PV systems and the improvement of conversion efficiency. RSWTLBO combines adaptive parameter w, Single Solution Optimization Mechanism (SSOM), and Weight Probability Exploration Strategy (WPES) to enhance the optimization ability of TLBO. The algorithm achieves a balance between exploitation and exploration throughout the iteration process. The SSOM allows for local exploration around a single solution, improving solution quality and eliminating inferior solutions. The WPES enables comprehensive exploration of the solution space, avoiding the problem of getting trapped in local optima. The algorithm is evaluated by comparing it with 10 other competitive algorithms on various PV models. The results demonstrate that RSWTLBO consistently achieves the lowest Root Mean Square Errors on single diode models, double diode models, and PV module models. It also exhibits robust performance under varying irradiation and temperature conditions. The study concludes that RSWTLBO is a practical and effective algorithm for identifying unknown parameters in PV models.

The interferences, such as the background, eyebrows, eyelashes, eyeglass frames, illumination variations, and specular lens reflection pose challenges for pupil localization in natural scenes. In this paper, we propose a novel method comprising improved YOLOv8 and Illumination Adaptive Algorithm (IAA), for fast and accurate pupil localization in natural scenes. We introduced deformable convolution into the backbone of YOLOv8 to enable the model to extract the eye regions more accurately, thus avoiding the interference of background outside the eye on subsequent pupil localization. The IAA can reduce the interference of illumination variations and lens reflection by adjusting automatically the grayscale of the image according to the exposure. Experimental results verified that the improved YOLOv8 exhibited an eye detection accuracy (IOU(ge)0.5) of 90.2%, while the IAA leads to a 9.15% improvement on 5-pixels error ratio ({varvec{e}}_{5}) with processing times in the tens of microseconds on GPU. Experimental results on the benchmark database CelebA show that the proposed method for pupil localization achieves an accuracy of 83.05% on ({varvec{e}}_{5}) and achieves real-time performance of 210 FPS on GPU, outperforming other advanced methods.

Hammer mill is widely used in the feed processing industry. During its operation, the material is thrown against the inner wall of the sieve after being broken by the hammer. Limited by the annular structure sieve, the grinded material tends to produce a “air- material circulation layer” on the inner wall of the sieve, leading to problems such as low grinding efficiency and high grinding energy consumption. Considering the disruptive characteristics of the special profile structure of a pigeon’s wing on the airflow field, we extract the geometric characteristics of the coupling element and optimize the related structural parameters. Based on the principles of bionics, a new wing sieve is then designed, and its efficient grinding mechanism is studied. Compared to the commercial sieve, the experimental results indicate the bio-inspired sieve can significantly improve the material productivity and grinding quality.

The power sector is an important factor in ensuring the development of the national economy. Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption. In this paper, a Multi-strategy Hybrid Coati Optimizer (MCOA) is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz) to realize the simulation and prediction of China’s daily electricity consumption. Firstly, a novel MCOA is proposed in this paper, by making the following improvements to the Coati Optimization Algorithm (COA): (i) Introduce improved circle chaotic mapping strategy. (ii) Fusing Aquila Optimizer, to enhance MCOA's exploration capabilities. (iii) Adopt an adaptive optimal neighborhood jitter learning strategy. Effectively improve MCOA escape from local optimal solutions. (iv) Incorporating Differential Evolution to enhance the diversity of the population. Secondly, the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm, the improved optimization algorithm, and the hybrid algorithm on the CEC2019 and CEC2020 test sets. Finally, in this paper, MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz), and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models, including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz), and seven forecasting models. The experimental results show that the error of the proposed method is minimized, which verifies the validity of the proposed method.

Researching the cooperative operation and functional expansion of multiple minirobot assemblies has the potential to bring about significant advancements in the practical applications of minirobots. In this study, we present a novel assembly system comprised of arc-shaped NdFeB magnetic minirobots. These minirobots can be individually utilized as assembly units, allowing for function expansion and comprehensive capability enhancement. We fabricate four Semicircular Arc Magnetic Minirobots (SAMM) arranged in different configurations and analyze their force and motion characteristics. Furthermore, by using this unit as a base, various expansion structures such as latches, petals, and rings can be assembled through reasonable combinations. We define the comprehensive reinforcement interval by comparatively analyzing changes in the unit’s motion characteristics and operational capabilities. Precise motion manipulation is employed to verify the rationality of the basic unit structure and the feasibility of the assembly scheme. Our proposed self-assembly scheme for magnetic minirobots exhibits great potential and may be used as a paradigm for future research on expanding the functionality of minirobots.

This study aims to develop a magnetorheological (MR) damper for semi-active knee prostheses to restore the walking ability of transfemoral amputees. The core dimensions of the MR damper were determined via theoretical magnetic field calculations, and the theoretical relationship between current and joint torque was derived through electromagnetic simulation. Then, a physical prototype of the semi-active prosthetic knee equipped with the MR damper was manufactured. Based on the data obtained from angle sensor, pressure sensor (loadcell), and inertial measurement unit (IMU) on the prosthesis, a matching control algorithm is developed. The joint torque of the MR damper can be adaptively adjusted according to the walking speed of the amputee, allowing the amputee to realize a natural gait. The effectiveness of the control program was verified by the ADAMS and MATLAB co-simulation. The results of the test and simulation show that the MR damper can provide sufficient torque needed for normal human activities.

In response to the shortcomings of Dwarf Mongoose Optimization (DMO) algorithm, such as insufficient exploitation capability and slow convergence speed, this paper proposes a multi-strategy enhanced DMO, referred to as GLSDMO. Firstly, we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation (EE). Secondly, the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum. In addition, in order to address the problem of low convergence efficiency of DMO, this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities, and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization, which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution (Gbest). Subsequently, the superiority of GLSDMO is verified on CEC2017 and CEC2019, and the optimization effect of GLSDMO is analyzed in detail. The results show that GLSDMO is significantly superior to the compared algorithms in solution quality, robustness and global convergence rate on most test functions. Finally, the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example. The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.

This study investigates the friction and deformation behavior of the skin in contact with a rigid massage ball and its influencing factors. Pressing and stretching experiments were conducted using a collaborative robot experimental platform. The experiments encompassed a loading normal force range of 2 N to 18 N and a sliding speed range of 10 mm/s to 20 mm/s. The friction response curve exhibits two different stages: static stick state and dynamic stick-slip stage, both of which have been mathematically modeled. By analyzing the experimental data, we analyzed the effects of elastic modulus, sliding speed and normal loading force on skin tangential friction and tensile deformation. The results indicate that as the normal load increases, both friction and deformation exhibit an increase. Conversely, they decrease with an increase in elastic modulus. Notably, while deformation diminishes with higher sliding speed, friction force remains relatively unaffected by velocity. This observation can be attributed to the strain rate sensitivity resulting from the viscoelastic characteristics of the skin under substantial deformation. This study advances the understanding of friction and deformation behavior during skin friction, offering valuable insights to enhance the operational comfort of massage robots.

Legged robots show great potential for high-dynamic motions in continuous interaction with the physical environment, yet achieving animal-like agility remains significant challenges. Legged animals usually predict and plan their next locomotion by combining high-dimensional information from proprioception and exteroception, and adjust the stiffness of the body’s skeletal muscle system to adapt to the current environment. Traditional control methods have limitations in handling high-dimensional state information or complex robot motion that are difficult to plan manually, and Deep Reinforcement Learning (DRL) algorithms provide new solutions to robot motioncontrol problems. Inspired by biomimetics theory, we propose a perception-driven high-dynamic jump adaptive learning algorithm by combining DRL algorithms with Virtual Model Control (VMC) method. The robot will be fully trained in simulation to explore its motion potential by learning the factors related to continuous jumping while knowing its real-time jumping height. The policy trained in simulation is successfully deployed on the bio-inspired single-legged robot testing platform without further adjustments. Experimental results show that the robot can achieve continuous and ideal vertical jumping motion through simple training

Within the consistent daily rhythm of human life, intervertebral discs endure a variety of complex loads beyond the influences of gravity and muscle forces, leading to significant morphological changes (in terms of volume, area, and height) as well as biomechanical alterations, including an increase in disc stiffness and a decrease in intradiscal pressure. Remarkably, the discs demonstrate an ability to regain their original morphological and biomechanical characteristics after a period of nocturnal rest. The preservation of normal disc function is critically dependent on this recovery phase, which serves to forestall premature disc degeneration. This phenomenon of disc recovery has been extensively documented through numerous in vivo studies employing advanced clinical techniques such as Magnetic Resonance Imaging (MRI), stadiometry, and intradiscal pressure measurement. However, the findings from in vitro studies present a more complex picture, with reports varying between full recovery and only partial recuperation of the disc properties. Moreover, research focusing on degenerated discs in vitro has shed light on the quantifiable impact of degeneration on the disc ability to recover. Fluid dynamics within the disc are considered a primary factor in recovery, yet the disc intricate multiscale structure and its viscoelastic properties also play key roles. These elements interact in complex ways to influence the recovery mechanism, particularly in relation to the overall health of the disc. The objective of this review is to collate, analyze, and critically evaluate the existing body of in vivo and in vitro research on this topic, providing a comprehensive understanding of disc recovery processes. Such understanding offers a blueprint for future advancements in medical treatments and bionic engineering solutions designed to mimic, support, and enhance the natural recovery processes of intervertebral discs.