Biomimetics最新文献

Unmanned aerial vehicle target tracking is a complex task that encounters challenges in scenarios involving limited computing resources, real-time requirements, and target confusion. This research builds on previous work and addresses challenges by proposing a grid-based beetle antennae search algorithm and designing a lightweight multi-target detection and positioning method, which integrates interference-sensing mechanisms and depth information. First, the grid-based beetle antennae search algorithm's rapid convergence advantage is combined with a secondary search and rollback mechanism, enhancing its search efficiency and ability to escape local extreme areas. Then, the You Only Look Once (version 8) model is employed for target detection, while corner detection, feature point extraction, and dictionary matching introduce a confusion-aware mechanism. This mechanism effectively distinguishes potentially confusing targets within the field of view, enhancing the system's robustness. Finally, the depth-based localization of the target is performed. To verify the performance of the proposed approach, a series of experiments were conducted on the grid-based beetle antennae search algorithm. Comparisons with four mainstream intelligent search algorithms are provided, with the results showing that the grid-based beetle antennae search algorithm excels in the number of iterations to convergence, path length, and convergence speed. When the algorithm faces non-local extreme-value-area environments, the speed is increased by more than 89%. In comparison with previous work, the algorithm speed is increased by more than 233%. Performance tests on the confusion-aware mechanism by using a self-made interference dataset demonstrate the model's high discriminative ability. The results also indicate that the model meets the real-time requirements.

Osseodensification enhances the stability of endosteal implants. However, pre-clinical studies utilizing osseodensification instrumentation do not account for the limited presence of trabeculae seen clinically. This study aimed to evaluate the effect of osseodensification instrumentation on osteotomy healing in scenarios with and without the presence of trabecular bone. A ~10 cm incision was made over the hip of twelve sheep. Trabecular bone was surgically removed from twelve sites (one site/animal; negative control (Neg. Ctrl)) and left intact at twelve sites (one site/animal; experimental group (Exp.)). All osteotomies were created using the osseodensification drilling protocol. Each osteotomy received an endosteal implant and was evaluated after 3 or 12 weeks of healing (n = 6 animals/time). Histology revealed increased woven and lamellar bone surrounding the implants in the Exp. group relative to the Neg. Ctrl group. The Exp. group demonstrated the presence of bone fragments, which acted as nucleating sites, thereby enhancing the bone formation and remodeling processes. Bone-to-implant contact (%BIC) and bone area fractional occupancy (%BAFO) were significantly higher in the Exp. group relative to the Neg. Ctrl group both at 3 weeks (p = 0.009 and p = 0.043) and 12 weeks (p = 0.010 and p = 0.008). Osseodensification instrumentation in the presence of trabecular bone significantly improved osseointegration. However, no negative influences such as necrosis, inflammation, microfractures, or dehiscence were observed in the absence/limited presence of trabeculae.

The wind-driven aerodynamic noise of super-high-rise building facades not only affects the experience of use inside the building but also reduces the life cycle of building facade materials to some extent. In this paper, we are inspired by the micro-groove structure of shark skin with damping and noise reduction properties and apply bionic skin to reduce the aerodynamic noise impact of super-high-rise buildings. The aerodynamic noise performance of smooth and super-high-rise building models with bionic grooves is simulated via CFD to investigate the noise reduction performance of different bionic groove patterns, such as I-shape, ∪-shape, V-shape, and ∩-shape patterns, and their corresponding acoustic noise reduction mechanisms. This study showed that the bionic shark groove skin has a certain noise reduction effect, and the I-shaped groove has the best noise reduction effect. By applying bionic skin, the aerodynamic noise of super-high-rise buildings can be effectively reduced to improve the use experience and environmental quality of the buildings and provide a new research idea and application direction for the aerodynamic noise reduction design of building facades.

Dragonflies can independently control the movement of their forewing and hindwing to achieve the desired flight. In comparison with previous studies that mostly considered the same kinematics of the fore- and hindwings, this paper focuses on the aerodynamic interference of three-dimensional tandem flapping wings when the forewing kinematics is different from that of the hindwing. The effects of flapping amplitude (Φ₁), flapping mean angle (ϕ1¯), and pitch rotation duration (Δtr₁) of the forewing, together with wing spacing (L) are examined numerically. The results show that Φ₁ and ϕ1¯ have a significant effect on the aerodynamic forces of the individual and tandem systems, but Δtr₁ has little effect. At a small L, a smaller Φ₁, or larger ϕ1¯ of the forewing can increase the overall aerodynamic force, but at a large L, smaller Φ₁ or larger ϕ1¯ can actually decrease the force. The flow field analysis shows that Φ₁ and ϕ1¯ primarily alter the extent of the impact of the previously revealed narrow channel effect, downwash effect, and wake capture effect, thereby affecting force generation. These findings may provide a direction for designing the performance of tandem flapping wing micro-air vehicles by controlling forewing kinematics.

Urban freshwater ecosystems have many critical functions, such as providing water to all living things and supporting biodiversity. Factors such as water pollution, increased water consumption, habitat loss, climate change, and drought threaten the health of urban freshwater ecosystems. Looking for solutions to these challenges, this article aims to recycle water and return it to its life cycle using a climate-sensitive water collection strategy. The model focuses on the biomimetic method as a basic strategy. In this regard, the concept of water-harvesting has been examined in detail by conducting a deep literature review, including architecture and engineering disciplines. With all these data obtained, a synthesis/integration study was carried out by developing a model proposal based on adaptive building façade elements to solve the water problems experienced in cities. The model proposal, which is directly related to the titles of "Clean Water and Sanitation (SDG 6)" and "Sustainable Cities and Communities (SDG 11)", which are among the Sustainable Development Goals (SDGs), aims to provide different perspectives on the disciplines with its superficial and functional features. In this context, it is anticipated that the article will become an indispensable resource for other researchers working on the subject.

A spatial redundantly actuated parallel mechanism (RAPM) constrained by two point contact higher kinematic pairs (HKPs) has been designed, arising from the inspiration of mastication in human beings: the end effector is the lower jaw, the six kinematic chains are the primary chewing muscles, and the constraints at HKPs are the temporomandibular joints. In this paper, firstly, the constrained motions of the mechanism are described in detail; thereafter, five models are formulated by the well-known Newton-Euler's law, the Lagrangian equations, and the principle of virtual work, to explore its rigid-body inverse dynamics. The symbolic results show that the model structures based on these approaches are quite different: the model via the Newton-Euler law well reflects the nature of the mechanism in terms of the constraint forces from HKPs with six equations and eight unknowns, and the existence of reaction forces at the spherical joints is tightly dependent on the number of kinematic chains. In comparison, from the latter two methods, the constraint forces and the reaction forces at spherical joints do not appear in the four models in which there are only four equations and six unknowns. Further, by using the dynamics model of the non-redundantly actuated counterpart as the core in both the second models from the energy and virtual work-related methods, their computational cost is only about 16.7% and 36.63% of the two first models, respectively. Finally, the comparisons between the dynamics models of the RAPM and its counterpart clarify that the HKP constraints greatly alter the model structures and raise the technical difficulties.

Deep learning technology can automatically learn features from large amounts of data, with powerful feature extraction and pattern recognition capabilities, thereby improving the accuracy and efficiency of object detection. [The objective of this study]: In order to improve the accuracy and speed of mask wearing deep learning detection models in the post pandemic era, the [Problem this study aimed to resolve] was based on the fact that no research work has been reported on standardized detection models for mask wearing with detecting nose targets specially. [The topic and method of this study]: A mask wearing normalization detection model (towards the wearing style exposing the nose to outside, which is the most obvious characteristic of non-normalized style) based on improved YOLOv5s (You Only Look Once v5s is an object detection network model) was proposed. [The improved method of the proposed model]: The improvement design work of the detection model mainly includes (1) the BottleneckCSP (abbreviation of Bottleneck Cross Stage Partial) module was improved to a BottleneckCSP-MASK (abbreviation of Bottleneck Cross Stage Partial-MASK) module, which was utilized to replace the BottleneckCSP module in the backbone architecture of the original YOLOv5s model, which reduced the weight parameters' number of the YOLOv5s model while ensuring the feature extraction effect of the bonding fusion module. (2) An SE module was inserted into the proposed improved model, and the bonding fusion layer in the original YOLOv5s model was improved for better extraction of the features of mask and nose targets. [Results and validation]: The experimental results indicated that, towards different people and complex backgrounds, the proposed mask wearing normalization detection model can effectively detect whether people are wearing masks and whether they are wearing masks in a normalized manner. The overall detection accuracy was 99.3% and the average detection speed was 0.014 s/pic. Contrasted with original YOLOv5s, v5m, and v5l models, the detection results for two types of target objects on the test set indicated that the mAP of the improved model increased by 0.5%, 0.49%, and 0.52%, respectively, and the size of the proposed model compressed by 10% compared to original v5s model. The designed model can achieve precise identification for mask wearing behaviors of people, including not wearing a mask, normalized wearing, and wearing a mask non-normalized.

Emotion is an intricate cognitive state that, when identified, can serve as a crucial component of the brain-computer interface. This study examines the identification of two categories of positive and negative emotions through the development and implementation of a dry electrode electroencephalogram (EEG). To achieve this objective, a dry EEG electrode is created using the silver-copper sintering technique, which is assessed through Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Analysis (EDXA) evaluations. Subsequently, a database is generated utilizing the designated electrode, which is based on the musical stimulus. The collected data are fed into an improved deep network for automatic feature selection/extraction and classification. The deep network architecture is structured by combining type 2 fuzzy sets (FT2) and deep convolutional graph networks. The fabricated electrode demonstrated superior performance, efficiency, and affordability compared to other electrodes (both wet and dry) in this study. Furthermore, the dry EEG electrode was examined in noisy environments and demonstrated robust resistance across a diverse range of Signal-To-Noise ratios (SNRs). Furthermore, the proposed model achieved a classification accuracy of 99% for distinguishing between positive and negative emotions, an improvement of approximately 2% over previous studies. The manufactured dry EEG electrode is very economical and cost-effective in terms of manufacturing costs when compared to recent studies. The proposed deep network, combined with the fabricated dry EEG electrode, can be used in real-time applications for long-term recordings that do not require gel.

Humanoid robots are increasingly being utilized in various activities involving humans, as they can facilitate certain tasks and provide benefits to users. Humanoid service robots possess capabilities akin to human performance, often proving advantageous due to their operational speed and immunity to fatigue. Within the scope of this study, Pepper, a humanoid robot renowned for its fidelity in mimicking human gestures and behavior, serves as the focal point. Tasked with aiding office occupants in object manipulation and relocation, Pepper underwent a targeted reliability assessment. This assessment encompassed the development of a reliability block diagram (RBD), alongside meticulous analyses of individual components and system functionality across diverse operational scenarios.

Northern Goshawk Optimization (NGO) is an efficient optimization algorithm, but it has the drawbacks of easily falling into local optima and slow convergence. Aiming at these drawbacks, an improved NGO algorithm named the Multi-Strategy Improved Northern Goshawk Optimization (MSINGO) algorithm was proposed by adding the cubic mapping strategy, a novel weighted stochastic difference mutation strategy, and weighted sine and cosine optimization strategy to the original NGO. To verify the performance of MSINGO, a set of comparative experiments were performed with five highly cited and six recently proposed metaheuristic algorithms on the CEC2017 test functions. Comparative experimental results show that in the vast majority of cases, MSINGO's exploitation ability, exploration ability, local optimal avoidance ability, and scalability are superior to those of competitive algorithms. Finally, six real world engineering problems demonstrated the merits and potential of MSINGO.