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Biomimetics, Volume 8, Issue 6 (October 2023) – 61 articles

Cover Story (view full-size image): When studying biological materials like nacre, skull bones, or teeth under a microscope, one can see why these materials are so tough: they are built (at the microlevel) as stiff, aligned platelets connected by soft adhesive layers, containing nanoscale bridge-like fibers. Can we use this concept to develop extra-durable and separable composite structures? Considering composite laminates to be analogues of stiff platelets in biological composites, we can propose the use of structured fibrous adhesives for wind turbine blades. This adhesive would combine mechanical interlocking and chemical bonding, creating a dual, extra-strong adhesive bonding mechanism. On the other hand, once the wind turbine blade reaches the end of its service life, the polymer matrix within the adhesive can be dissolved, allowing for the separation of the fibrous interlocking. View this paper
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36 pages, 6667 KiB  
Article
A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems Based on Technical and Vocational Education and Training
by Marie Hubalovska and Stepan Major
Biomimetics 2023, 8(6), 508; https://doi.org/10.3390/biomimetics8060508 - 23 Oct 2023
Cited by 1 | Viewed by 1485
Abstract
In this paper, a new human-based metaheuristic algorithm called Technical and Vocational Education and Training-Based Optimizer (TVETBO) is introduced to solve optimization problems. The fundamental inspiration for TVETBO is taken from the process of teaching work-related skills to applicants in technical and vocational [...] Read more.
In this paper, a new human-based metaheuristic algorithm called Technical and Vocational Education and Training-Based Optimizer (TVETBO) is introduced to solve optimization problems. The fundamental inspiration for TVETBO is taken from the process of teaching work-related skills to applicants in technical and vocational education and training schools. The theory of TVETBO is expressed and mathematically modeled in three phases: (i) theory education, (ii) practical education, and (iii) individual skills development. The performance of TVETBO when solving optimization problems is evaluated on the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that TVETBO, with its high abilities to explore, exploit, and create a balance between exploration and exploitation during the search process, is able to provide effective solutions for the benchmark functions. The results obtained from TVETBO are compared with the performances of twelve well-known metaheuristic algorithms. A comparison of the simulation results and statistical analysis shows that the proposed TVETBO approach provides better results in most of the benchmark functions and provides a superior performance in competition with competitor algorithms. Furthermore, in order to measure the effectiveness of the proposed approach in dealing with real-world applications, TVETBO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite. The simulation results show that TVETBO provides an effective and superior performance when solving constrained optimization problems of real-world applications compared to competitor algorithms. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms)
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62 pages, 14637 KiB  
Article
Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems
by Mohammad Dehghani, Gulnara Bektemyssova, Zeinab Montazeri, Galymzhan Shaikemelev, Om Parkash Malik and Gaurav Dhiman
Biomimetics 2023, 8(6), 507; https://doi.org/10.3390/biomimetics8060507 - 23 Oct 2023
Cited by 3 | Viewed by 2346
Abstract
In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, [...] Read more.
In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms. Full article
(This article belongs to the Special Issue Bioinspired Algorithms)
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13 pages, 4355 KiB  
Article
Advancements in Complementary Metal-Oxide Semiconductor-Compatible Tunnel Barrier Engineered Charge-Trapping Synaptic Transistors for Bio-Inspired Neural Networks in Harsh Environments
by Dong-Hee Lee, Hamin Park and Won-Ju Cho
Biomimetics 2023, 8(6), 506; https://doi.org/10.3390/biomimetics8060506 - 23 Oct 2023
Cited by 1 | Viewed by 1635
Abstract
This study aimed to propose a silicon-on-insulator (SOI)-based charge-trapping synaptic transistor with engineered tunnel barriers using high-k dielectrics for artificial synapse electronics capable of operating at high temperatures. The transistor employed sequential electron trapping and de-trapping in the charge storage medium, facilitating [...] Read more.
This study aimed to propose a silicon-on-insulator (SOI)-based charge-trapping synaptic transistor with engineered tunnel barriers using high-k dielectrics for artificial synapse electronics capable of operating at high temperatures. The transistor employed sequential electron trapping and de-trapping in the charge storage medium, facilitating gradual modulation of the silicon channel conductance. The engineered tunnel barrier structure (SiO2/Si3N4/SiO2), coupled with the high-k charge-trapping layer of HfO2 and high-k blocking layer of Al2O3, enabled reliable long-term potentiation/depression behaviors within a short gate stimulus time (100 μs), even under elevated temperatures (75 and 125 °C). Conductance variability was determined by the number of gate stimuli reflected in the maximum excitatory postsynaptic current (EPSC) and the residual EPSC ratio. Moreover, we analyzed the Arrhenius relationship between the EPSC as a function of the gate pulse number (N = 1–100) and the measured temperatures (25, 75, and 125 °C), allowing us to deduce the charge trap activation energy. A learning simulation was performed to assess the pattern recognition capabilities of the neuromorphic computing system using the modified National Institute of Standards and Technology datasheets. This study demonstrates high-reliability silicon channel conductance modulation and proposes in-memory computing capabilities for artificial neural networks using SOI-based charge-trapping synaptic transistors. Full article
(This article belongs to the Special Issue Bio-Inspired Neural Networks)
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17 pages, 5530 KiB  
Article
CFD-Based Simulation Analysis for Motions through Multiphase Environments
by Shuqi Wang, Jizhuang Fan and Yubin Liu
Biomimetics 2023, 8(6), 505; https://doi.org/10.3390/biomimetics8060505 - 23 Oct 2023
Viewed by 999
Abstract
The motion process and force of the jumper crossing a multiphase environment are of great significance to the research of small amphibious robots. Here, CFD (Computational Fluid Dynamics)-based simulation analysis for motions through multiphase environments (water–air multiphase) is successfully realized by UDF (user-defined [...] Read more.
The motion process and force of the jumper crossing a multiphase environment are of great significance to the research of small amphibious robots. Here, CFD (Computational Fluid Dynamics)-based simulation analysis for motions through multiphase environments (water–air multiphase) is successfully realized by UDF (user-defined function). The analytical model is first established to investigate the jumping response of the jumpers with respect to the jump angle, force, and water depth. The numerical model of the jumper and its surrounding fluid domain is conducted to obtain various dynamic parameters in the jumping process, such as jumping height and speed. Satisfactory agreements are obtained by comparing the error of repeated simulation results (5%). Meanwhile, the influence of the jumper’s own attributes, including mass and structural size, on the jumping performance is analyzed. The flow field information, such as wall shear and velocity when the jumper approaches and breaks through the water surface, is finally extracted, which lays a foundation for the structural design and dynamic underwater analysis of the amphibious robot. Full article
(This article belongs to the Special Issue Bio-Inspired Underwater Robot)
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32 pages, 6527 KiB  
Article
Biowelding 3D-Printed Biodigital Brick of Seashell-Based Biocomposite by Pleurotus ostreatus Mycelium
by Yomna K. Abdallah and Alberto T. Estévez
Biomimetics 2023, 8(6), 504; https://doi.org/10.3390/biomimetics8060504 - 23 Oct 2023
Viewed by 4208
Abstract
Mycelium biocomposites are eco-friendly, cheap, easy to produce, and have competitive mechanical properties. However, their integration in the built environment as durable and long-lasting materials is not solved yet. Similarly, biocomposites from recycled food waste such as seashells have been gaining increasing interest [...] Read more.
Mycelium biocomposites are eco-friendly, cheap, easy to produce, and have competitive mechanical properties. However, their integration in the built environment as durable and long-lasting materials is not solved yet. Similarly, biocomposites from recycled food waste such as seashells have been gaining increasing interest recently, thanks to their sustainable impact and richness in calcium carbonate and chitin. The current study tests the mycelium binding effect to bioweld a seashell biocomposite 3D-printed brick. The novelty of this study is the combination of mycelium and a non-agro–based substrate, which is seashells. As well as testing the binding capacity of mycelium in welding the lattice curvilinear form of the V3 linear Brick model (V3-LBM). Thus, the V3-LBM is 3D printed in three separate profiles, each composed of five layers of 1 mm/layer thickness, using seashell biocomposite by paste extrusion and testing it for biowelding with Pleurotus ostreatus mycelium to offer a sustainable, ecofriendly, biomineralized brick. The biowelding process investigated the penetration and binding capacity of the mycelium between every two 3D-printed profiles. A cellulose-based culture medium was used to catalyse the mycelium growth. The mycelium biowelding capacity was investigated by SEM microscopy and EDX chemical analysis of three samples from the side corner (S), middle (M), and lateral (L) zones of the biowelded brick. The results revealed that the best biowelding effect was recorded at the corner and lateral zones of the brick. The SEM images exhibited the penetration and the bridging effect achieved by the dense mycelium. The EDX revealed the high concentrations of carbon, oxygen, and calcium at all the analyzed points on the SEM images from all three samples. An inverted relationship between carbon and oxygen as well as sodium and potassium concentrations were also detected, implying the active metabolic interaction between the fungal hyphae and the seashell-based biocomposite. Finally, the results of the SEM-EDX analysis were applied to design favorable tessellation and staking methods for the V3-LBM from the seashell–mycelium composite to deliver enhanced biowelding effect along the Z axis and the XY axis with <1 mm tessellation and staking tolerance. Full article
(This article belongs to the Special Issue Biomimicry and 3D Printing of Living Materials: 2nd Edition)
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40 pages, 7326 KiB  
Article
Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data
by Dinesh Chellappan and Harikumar Rajaguru
Biomimetics 2023, 8(6), 503; https://doi.org/10.3390/biomimetics8060503 - 22 Oct 2023
Viewed by 1158
Abstract
In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares [...] Read more.
In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier’s performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier’s performance emphasizes the role of feature selection methods. Full article
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14 pages, 625 KiB  
Review
Research Progress on Low-Surface-Energy Antifouling Coatings for Ship Hulls: A Review
by Zhimin Cao and Pan Cao
Biomimetics 2023, 8(6), 502; https://doi.org/10.3390/biomimetics8060502 - 21 Oct 2023
Cited by 1 | Viewed by 1683
Abstract
The adhesion of marine-fouling organisms to ships significantly increases the hull surface resistance and expedites hull material corrosion. This review delves into the marine biofouling mechanism on marine material surfaces, analyzing the fouling organism adhesion process on hull surfaces and common desorption methods. [...] Read more.
The adhesion of marine-fouling organisms to ships significantly increases the hull surface resistance and expedites hull material corrosion. This review delves into the marine biofouling mechanism on marine material surfaces, analyzing the fouling organism adhesion process on hull surfaces and common desorption methods. It highlights the crucial role played by surface energy in antifouling and drag reduction on hulls. The paper primarily concentrates on low-surface-energy antifouling coatings, such as organic silicon and organic fluorine, for ship hull antifouling and drag reduction. Furthermore, it explores the antifouling mechanisms of silicon-based and fluorine-based low-surface-energy antifouling coatings, elucidating their respective advantages and limitations in real-world applications. This review also investigates the antifouling effectiveness of bionic microstructures based on the self-cleaning abilities of natural organisms. It provides a thorough analysis of antifouling and drag reduction theories and preparation methods linked to marine organism surface microstructures, while also clarifying the relationship between microstructure surface antifouling and surface hydrophobicity. Furthermore, it reviews the impact of antibacterial agents, especially antibacterial peptides, on fouling organisms’ adhesion to substrate surfaces and compares the differing effects of surface structure and substances on ship surface antifouling. The paper outlines the potential applications and future directions for low-surface-energy antifouling coating technology. Full article
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15 pages, 13070 KiB  
Article
Bio-Inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception
by Xiaobo Liu, Xudong Han, Ning Guo, Fang Wan and Chaoyang Song
Biomimetics 2023, 8(6), 501; https://doi.org/10.3390/biomimetics8060501 - 21 Oct 2023
Cited by 1 | Viewed by 1456
Abstract
In-hand object pose estimation is challenging for humans and robots due to occlusion caused by the hand and object. This paper proposes a soft finger that integrates inner vision with kinesthetic sensing to estimate object pose inspired by human fingers. The soft finger [...] Read more.
In-hand object pose estimation is challenging for humans and robots due to occlusion caused by the hand and object. This paper proposes a soft finger that integrates inner vision with kinesthetic sensing to estimate object pose inspired by human fingers. The soft finger has a flexible skeleton and skin that adapts to different objects, and the skeleton deformations during interaction provide contact information obtained by the image from the inner camera. The proposed framework is an end-to-end method that uses raw images from soft fingers to estimate in-hand object pose. It consists of an encoder for kinesthetic information processing and an object pose and category estimator. The framework was tested on seven objects, achieving an impressive error of 2.02 mm and 11.34 degrees for pose error and 99.05% for classification. Full article
(This article belongs to the Special Issue Design, Fabrication and Control of Bioinspired Soft Robots)
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24 pages, 5069 KiB  
Review
Bioinspired Design Rules from Highly Mineralized Natural Composites for Two-Dimensional Composite Design
by Anamika Prasad, Vikas Varshney, Dhriti Nepal and Geoffrey J. Frank
Biomimetics 2023, 8(6), 500; https://doi.org/10.3390/biomimetics8060500 - 20 Oct 2023
Cited by 1 | Viewed by 1312
Abstract
Discoveries of two-dimensional (2D) materials, exemplified by the recent entry of MXene, have ushered in a new era of multifunctional materials for applications from electronics to biomedical sensors due to their superior combination of mechanical, chemical, and electrical properties. MXene, for example, can [...] Read more.
Discoveries of two-dimensional (2D) materials, exemplified by the recent entry of MXene, have ushered in a new era of multifunctional materials for applications from electronics to biomedical sensors due to their superior combination of mechanical, chemical, and electrical properties. MXene, for example, can be designed for specialized applications using a plethora of element combinations and surface termination layers, making them attractive for highly optimized multifunctional composites. Although multiple critical engineering applications demand that such composites balance specialized functions with mechanical demands, the current knowledge of the mechanical performance and optimized traits necessary for such composite design is severely limited. In response to this pressing need, this paper critically reviews structure–function connections for highly mineralized 2D natural composites, such as nacre and exoskeletal of windowpane oysters, to extract fundamental bioinspired design principles that provide pathways for multifunctional 2D-based engineered systems. This paper highlights key bioinspired design features, including controlling flake geometry, enhancing interface interlocks, and utilizing polymer interphases, to address the limitations of the current design. Challenges in processing, such as flake size control and incorporating interlocking mechanisms of tablet stitching and nanotube forest, are discussed along with alternative potential solutions, such as roughened interfaces and surface waviness. Finally, this paper discusses future perspectives and opportunities, including bridging the gap between theory and practice with multiscale modeling and machine learning design approaches. Overall, this review underscores the potential of bioinspired design for engineered 2D composites while acknowledging the complexities involved and providing valuable insights for researchers and engineers in this rapidly evolving field. Full article
(This article belongs to the Special Issue Bio-Inspired Design for Structure Applications)
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35 pages, 31733 KiB  
Article
Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs
by Mahmoud Badawy, Hossam Magdy Balaha, Ahmed S. Maklad, Abdulqader M. Almars and Mostafa A. Elhosseini
Biomimetics 2023, 8(6), 499; https://doi.org/10.3390/biomimetics8060499 - 19 Oct 2023
Cited by 1 | Viewed by 1476
Abstract
The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces [...] Read more.
The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with ’ImageNet’ weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a ’normal’ class with 2494 images and an ’OSCC’ (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis. Full article
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14 pages, 3947 KiB  
Article
Optimization of Fixations for Additively Manufactured Cranial Implants: Insights from Finite Element Analysis
by Fariha Haque, Anthony F. Luscher, Kerry-Ann S. Mitchell and Alok Sutradhar
Biomimetics 2023, 8(6), 498; https://doi.org/10.3390/biomimetics8060498 - 19 Oct 2023
Cited by 2 | Viewed by 1239
Abstract
With the emergence of additive manufacturing technology, patient-specific cranial implants using 3D printing have massively influenced the field. These implants offer improved surgical outcomes and aesthetic preservation. However, as additive manufacturing in cranial implants is still emerging, ongoing research is investigating their reliability [...] Read more.
With the emergence of additive manufacturing technology, patient-specific cranial implants using 3D printing have massively influenced the field. These implants offer improved surgical outcomes and aesthetic preservation. However, as additive manufacturing in cranial implants is still emerging, ongoing research is investigating their reliability and sustainability. The long-term biomechanical performance of these implants is critically influenced by factors such as implant material, anticipated loads, implant-skull interface geometry, and structural constraints, among others. The efficacy of cranial implants involves an intricate interplay of these factors, with fixation playing a pivotal role. This study addresses two critical concerns: determining the ideal number of fixation points for cranial implants and the optimal curvilinear distance between those points, thereby establishing a minimum threshold. Employing finite element analysis, the research incorporates variables such as implant shapes, sizes, materials, the number of fixation points, and their relative positions. The study reveals that the optimal number of fixation points ranges from four to five, accounting for defect size and shape. Moreover, the optimal curvilinear distance between two screws is approximately 40 mm for smaller implants and 60 mm for larger implants. Optimal fixation placement away from the center mitigates higher deflection due to overhangs. Notably, a symmetric screw orientation reduces deflection, enhancing implant stability. The findings offer crucial insights into optimizing fixation strategies for cranial implants, thereby aiding surgical decision-making guidelines. Full article
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24 pages, 11830 KiB  
Article
Probabilistic Dual-Space Fusion for Real-Time Human-Robot Interaction
by Yihui Li, Jiajun Wu, Xiaohan Chen and Yisheng Guan
Biomimetics 2023, 8(6), 497; https://doi.org/10.3390/biomimetics8060497 - 19 Oct 2023
Viewed by 1098
Abstract
For robots in human environments, learning complex and demanding interaction skills from humans and responding quickly to human motions are highly desirable. A common challenge for interaction tasks is that the robot has to satisfy both the task space and the joint space [...] Read more.
For robots in human environments, learning complex and demanding interaction skills from humans and responding quickly to human motions are highly desirable. A common challenge for interaction tasks is that the robot has to satisfy both the task space and the joint space constraints on its motion trajectories in real time. Few studies have addressed the issue of hyperspace constraints in human-robot interaction, whereas researchers have investigated it in robot imitation learning. In this work, we propose a method of dual-space feature fusion to enhance the accuracy of the inferred trajectories in both task space and joint space; then, we introduce a linear mapping operator (LMO) to map the inferred task space trajectory to a joint space trajectory. Finally, we combine the dual-space fusion, LMO, and phase estimation into a unified probabilistic framework. We evaluate our dual-space feature fusion capability and real-time performance in the task of a robot following a human-handheld object and a ball-hitting experiment. Our inference accuracy in both task space and joint space is superior to standard Interaction Primitives (IP) which only use single-space inference (by more than 33%); the inference accuracy of the second order LMO is comparable to the kinematic-based mapping method, and the computation time of our unified inference framework is reduced by 54.87% relative to the comparison method. Full article
(This article belongs to the Special Issue Intelligent Human-Robot Interaction)
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14 pages, 1060 KiB  
Review
Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy
by Joanna Jiang, Wei-Lun Chao, Troy Cao, Stacey Culp, Bertrand Napoléon, Samer El-Dika, Jorge D. Machicado, Rahul Pannala, Shaffer Mok, Anjuli K. Luthra, Venkata S. Akshintala, Thiruvengadam Muniraj and Somashekar G. Krishna
Biomimetics 2023, 8(6), 496; https://doi.org/10.3390/biomimetics8060496 - 19 Oct 2023
Cited by 2 | Viewed by 1552
Abstract
Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, [...] Read more.
Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65–75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs. Full article
(This article belongs to the Section Bioinspired Sensorics, Information Processing and Control)
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15 pages, 4612 KiB  
Article
An Admittance Control Method Based on Parameters Fuzzification for Humanoid Steering Wheel Manipulation
by Tuochang Wu, Junkai Ren, Chuang Cheng, Xun Liu, Hui Peng and Huimin Lu
Biomimetics 2023, 8(6), 495; https://doi.org/10.3390/biomimetics8060495 - 19 Oct 2023
Viewed by 1161
Abstract
Developing a human bionic manipulator to achieve certain humanoid behavioral skills is a rising problem. Enabling robots to operate the steering wheel to drive the vehicle is a challenging task. To address the problem, this work designs a novel 7-DOF (degree of freedom) [...] Read more.
Developing a human bionic manipulator to achieve certain humanoid behavioral skills is a rising problem. Enabling robots to operate the steering wheel to drive the vehicle is a challenging task. To address the problem, this work designs a novel 7-DOF (degree of freedom) humanoid manipulator based on the arm structure of human bionics. The 3-2-2 structural arrangement of the motors and the structural modifications at the wrist allow the manipulator to act more similar to a man. Meanwhile, to manipulate the steering wheel stably and compliantly, an admittance control approach is firstly applied for this case. Considering that the system parameters vary in configuration, we further introduce parameter fuzzification for admittance control. Designed simulations were carried out in Coppeliasim to verify the proposed control approach. As the result shows, the improved method could realize compliant force control under extreme configurations. It demonstrates that the humanoid manipulator can twist the steering wheel stably even in extreme configurations. It is the first exploration to operate a steering wheel similar to a human with a manipulator by using admittance control. Full article
(This article belongs to the Special Issue Biorobotics: 2nd Edition)
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24 pages, 3053 KiB  
Article
SaMDE: A Self Adaptive Choice of DNDE and SPIDE Algorithms with MRLDE
by Pravesh Kumar and Musrrat Ali
Biomimetics 2023, 8(6), 494; https://doi.org/10.3390/biomimetics8060494 - 18 Oct 2023
Cited by 1 | Viewed by 807
Abstract
Differential evolution (DE) is a proficient optimizer and has been broadly implemented in real life applications of various fields. Several mutation based adaptive approaches have been suggested to improve the algorithm efficiency in recent years. In this paper, a novel self-adaptive method called [...] Read more.
Differential evolution (DE) is a proficient optimizer and has been broadly implemented in real life applications of various fields. Several mutation based adaptive approaches have been suggested to improve the algorithm efficiency in recent years. In this paper, a novel self-adaptive method called SaMDE has been designed and implemented on the mutation-based modified DE variants such as modified randomized localization-based DE (MRLDE), donor mutation based DE (DNDE), and sequential parabolic interpolation based DE (SPIDE), which were proposed by the authors in previous research. Using the proposed adaptive technique, an appropriate mutation strategy from DNDE and SPIDE can be selected automatically for the MRLDE algorithm. The experimental results on 50 benchmark problems taken of various test suits and a real-world application of minimization of the potential molecular energy problem validate the superiority of SaMDE over other DE variations. Full article
(This article belongs to the Section Development of Biomimetic Methodology)
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18 pages, 8533 KiB  
Article
Bio-Inspired Spotted Hyena Optimizer with Deep Convolutional Neural Network-Based Automated Food Image Classification
by Hany Mahgoub, Ghadah Aldehim, Nabil Sharaf Almalki, Imène Issaoui, Ahmed Mahmud and Amani A. Alneil
Biomimetics 2023, 8(6), 493; https://doi.org/10.3390/biomimetics8060493 - 18 Oct 2023
Viewed by 1168
Abstract
Food image classification, an interesting subdomain of Computer Vision (CV) technology, focuses on the automatic classification of food items represented through images. This technology has gained immense attention in recent years thanks to its widespread applications spanning dietary monitoring and nutrition studies to [...] Read more.
Food image classification, an interesting subdomain of Computer Vision (CV) technology, focuses on the automatic classification of food items represented through images. This technology has gained immense attention in recent years thanks to its widespread applications spanning dietary monitoring and nutrition studies to restaurant recommendation systems. By leveraging the developments in Deep-Learning (DL) techniques, especially the Convolutional Neural Network (CNN), food image classification has been developed as an effective process for interacting with and understanding the nuances of the culinary world. The deep CNN-based automated food image classification method is a technology that utilizes DL approaches, particularly CNNs, for the automatic categorization and classification of the images of distinct kinds of foods. The current research article develops a Bio-Inspired Spotted Hyena Optimizer with a Deep Convolutional Neural Network-based Automated Food Image Classification (SHODCNN-FIC) approach. The main objective of the SHODCNN-FIC method is to recognize and classify food images into distinct types. The presented SHODCNN-FIC technique exploits the DL model with a hyperparameter tuning approach for the classification of food images. To accomplish this objective, the SHODCNN-FIC method exploits the DCNN-based Xception model to derive the feature vectors. Furthermore, the SHODCNN-FIC technique uses the SHO algorithm for optimal hyperparameter selection of the Xception model. The SHODCNN-FIC technique uses the Extreme Learning Machine (ELM) model for the detection and classification of food images. A detailed set of experiments was conducted to demonstrate the better food image classification performance of the proposed SHODCNN-FIC technique. The wide range of simulation outcomes confirmed the superior performance of the SHODCNN-FIC method over other DL models. Full article
(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)
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30 pages, 10408 KiB  
Article
Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection
by Liguo Yao, Jun Yang, Panliang Yuan, Guanghui Li, Yao Lu and Taihua Zhang
Biomimetics 2023, 8(6), 492; https://doi.org/10.3390/biomimetics8060492 - 18 Oct 2023
Viewed by 1500
Abstract
The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has [...] Read more.
The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has drawbacks, such as being prone to falling into local optima, low search efficiency, and limited optimization accuracy due to limitations in some innate biological conditions. To address the corresponding shortcomings, this paper proposes three improved strategies: a novel opposition-based learning strategy, a novel exploration mechanism, and a biological elimination update mechanism. Based on the original SCSO, a multi-strategy improved sand cat swarm optimization (MSCSO) is proposed. To verify the effectiveness of the proposed algorithm, the MSCSO algorithm is applied to two types of problems: global optimization and feature selection. The global optimization includes twenty non-fixed dimensional functions (Dim = 30, 100, and 500) and ten fixed dimensional functions, while feature selection comprises 24 datasets. By analyzing and comparing the mathematical and statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, the results show that the proposed MSCSO algorithm has good optimization ability and can adapt to a wide range of optimization problems. Full article
(This article belongs to the Special Issue Bionic Artificial Neural Networks and Artificial Intelligence)
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19 pages, 5229 KiB  
Article
Robot Arm Reaching Based on Inner Rehearsal
by Jiawen Wang, Yudi Zou, Yaoyao Wei, Mengxi Nie, Tianlin Liu and Dingsheng Luo
Biomimetics 2023, 8(6), 491; https://doi.org/10.3390/biomimetics8060491 - 18 Oct 2023
Viewed by 1574
Abstract
Robot arm motion control is a fundamental aspect of robot capabilities, with arm reaching ability serving as the foundation for complex arm manipulation tasks. However, traditional inverse kinematics-based methods for robot arm reaching struggle to cope with the increasing complexity and diversity of [...] Read more.
Robot arm motion control is a fundamental aspect of robot capabilities, with arm reaching ability serving as the foundation for complex arm manipulation tasks. However, traditional inverse kinematics-based methods for robot arm reaching struggle to cope with the increasing complexity and diversity of robot environments, as they heavily rely on the accuracy of physical models. In this paper, we introduce an innovative approach to robot arm motion control, inspired by the cognitive mechanism of inner rehearsal observed in humans. The core concept revolves around the robot’s ability to predict or evaluate the outcomes of motion commands before execution. This approach enhances the learning efficiency of models and reduces the mechanical wear on robots caused by excessive physical executions. We conduct experiments using the Baxter robot in simulation and the humanoid robot PKU-HR6.0 II in a real environment to demonstrate the effectiveness and efficiency of our proposed approach for robot arm reaching across different platforms. The internal models converge quickly and the average error distance between the target and the end-effector on the two platforms is reduced by 80% and 38%, respectively. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot: 2nd Edition)
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27 pages, 5197 KiB  
Article
An Advanced Bio-Inspired Mantis Search Algorithm for Characterization of PV Panel and Global Optimization of Its Model Parameters
by Ghareeb Moustafa, Hashim Alnami, Sultan Hassan Hakmi, Ahmed Ginidi, Abdullah M. Shaheen and Fahad A. Al-Mufadi
Biomimetics 2023, 8(6), 490; https://doi.org/10.3390/biomimetics8060490 - 18 Oct 2023
Cited by 2 | Viewed by 1361
Abstract
Correct modelling and estimation of solar cell characteristics are crucial for effective performance simulations of PV panels, necessitating the development of creative approaches to improve solar energy conversion. When handling this complex problem, traditional optimisation algorithms have significant disadvantages, including a predisposition to [...] Read more.
Correct modelling and estimation of solar cell characteristics are crucial for effective performance simulations of PV panels, necessitating the development of creative approaches to improve solar energy conversion. When handling this complex problem, traditional optimisation algorithms have significant disadvantages, including a predisposition to get trapped in certain local optima. This paper develops the Mantis Search Algorithm (MSA), which draws inspiration from the unique foraging behaviours and sexual cannibalism of praying mantises. The suggested MSA includes three stages of optimisation: prey pursuit, prey assault, and sexual cannibalism. It is created for the R.TC France PV cell and the Ultra 85-P PV panel related to Shell PowerMax for calculating PV parameters and examining six case studies utilising the one-diode model (1DM), two-diode model (1DM), and three-diode model (3DM). Its performance is assessed in contrast to recently developed optimisers of the neural network optimisation algorithm (NNA), dwarf mongoose optimisation (DMO), and zebra optimisation algorithm (ZOA). In light of the adopted MSA approach, simulation findings improve the electrical characteristics of solar power systems. The developed MSA methodology improves the 1DM, 2DM, and 3DM by 12.4%, 44.05%, and 48.88%, 28.96%, 43.19%, and 55.81%, 37.71%, 32.71%, and 60.13% relative to the DMO, NNA, and ZOA approaches, respectively. For the Ultra 85-P PV panel, the designed MSA technique achieves improvements for the 1DM, 2DM, and 3DM of 62.05%, 67.14%, and 84.25%, 49.05%, 53.57%, and 74.95%, 37.03%, 37.4%, and 59.57% compared to the DMO, NNA, and ZOA techniques, respectively. Full article
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20 pages, 5062 KiB  
Article
Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis
by Xuqing Fan, Sai Deng, Zhengxing Wu, Junfeng Fan and Chao Zhou
Biomimetics 2023, 8(6), 489; https://doi.org/10.3390/biomimetics8060489 - 17 Oct 2023
Cited by 1 | Viewed by 1192
Abstract
Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and lightweight AlexNet. Initially, one-dimensional time [...] Read more.
Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and lightweight AlexNet. Initially, one-dimensional time series sensor signals are converted into two-dimensional images using the Gramian angular field method with sliding window augmentation. Next, weighted fusion methods are employed to combine Gramian angular summation field images and Gramian angular difference field images, allowing for the full utilization of image information. Subsequently, a lightweight AlexNet is developed to extract features and classify fused images for fault diagnosis with fewer parameters and a shorter running time. To improve diagnosis accuracy, the particle swarm optimization algorithm is used to optimize the weighted fusion coefficient. The results indicate that the proposed method achieves a fault diagnosis accuracy of 99.72% when the weighted fusion coefficient is 0.276. These findings demonstrate the effectiveness of the proposed method for diagnosing depth sensor faults in robotic fish. Full article
(This article belongs to the Special Issue Bionic Robotic Fish)
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15 pages, 10556 KiB  
Article
New PMMA-Based Hydroxyapatite/ZnFe2O4/ZnO Composite with Antibacterial Performance and Low Toxicity
by Olga Bakina, Natalia Svarovskaya, Ludmila Ivanova, Elena Glazkova, Nikolay Rodkevich, Vladyslav Evstigneev, Maxim Evstigneev, Andrey Mosunov and Marat Lerner
Biomimetics 2023, 8(6), 488; https://doi.org/10.3390/biomimetics8060488 - 14 Oct 2023
Cited by 1 | Viewed by 1204
Abstract
Polymethylmethacrylate (PMMA) is the most commonly used bone void filler in orthopedic surgery. However, the biocompatibility and radiopacity of PMMA are insufficient for such applications. In addition to insufficient biocompatibility, the microbial infection of medical implants is one of the frequent causes of [...] Read more.
Polymethylmethacrylate (PMMA) is the most commonly used bone void filler in orthopedic surgery. However, the biocompatibility and radiopacity of PMMA are insufficient for such applications. In addition to insufficient biocompatibility, the microbial infection of medical implants is one of the frequent causes of failure in bone reconstruction. In the present work, the preparation of a novel PMMA-based hydroxyapatite/ZnFe2O4/ZnO composite with heterophase ZnFe2O4/ZnO NPs as an antimicrobial agent was described. ZnFe2O4/ZnO nanoparticles were produced using the electrical explosion of zinc and iron twisted wires in an oxygen-containing atmosphere. This simple, highly productive, and inexpensive nanoparticle fabrication approach could be readily adapted to different applications. From the findings, the presented composite material showed significant antibacterial activity (more than 99% reduction) against P. aeruginosa, S. aureus, and MRSA, and 100% antifungal activity against C. albicans, as a result of the combined use of both ZnO and ZnFe2O4. The composite showed excellent biocompatibility against the sensitive fibroblast cell line 3T3. The more-than-70% cell viability was observed after 1–3 days incubation of the sample. The developed composite material could be a potential material for the fabrication of 3D-printed implants. Full article
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27 pages, 1405 KiB  
Review
Biomimetic Cardiac Tissue Models for In Vitro Arrhythmia Studies
by Aleria Aitova, Andrey Berezhnoy, Valeriya Tsvelaya, Oleg Gusev, Alexey Lyundup, Anton E. Efimov, Igor Agapov and Konstantin Agladze
Biomimetics 2023, 8(6), 487; https://doi.org/10.3390/biomimetics8060487 - 14 Oct 2023
Viewed by 2073
Abstract
Cardiac arrhythmias are a major cause of cardiovascular mortality worldwide. Many arrhythmias are caused by reentry, a phenomenon where excitation waves circulate in the heart. Optical mapping techniques have revealed the role of reentry in arrhythmia initiation and fibrillation transition, but the underlying [...] Read more.
Cardiac arrhythmias are a major cause of cardiovascular mortality worldwide. Many arrhythmias are caused by reentry, a phenomenon where excitation waves circulate in the heart. Optical mapping techniques have revealed the role of reentry in arrhythmia initiation and fibrillation transition, but the underlying biophysical mechanisms are still difficult to investigate in intact hearts. Tissue engineering models of cardiac tissue can mimic the structure and function of native cardiac tissue and enable interactive observation of reentry formation and wave propagation. This review will present various approaches to constructing cardiac tissue models for reentry studies, using the authors’ work as examples. The review will highlight the evolution of tissue engineering designs based on different substrates, cell types, and structural parameters. A new approach using polymer materials and cellular reprogramming to create biomimetic cardiac tissues will be introduced. The review will also show how computational modeling of cardiac tissue can complement experimental data and how such models can be applied in the biomimetics of cardiac tissue. Full article
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44 pages, 2459 KiB  
Article
Percentile-Based Adaptive Immune Plasma Algorithm and Its Application to Engineering Optimization
by Selcuk Aslan, Sercan Demirci, Tugrul Oktay and Erdal Yesilbas
Biomimetics 2023, 8(6), 486; https://doi.org/10.3390/biomimetics8060486 - 14 Oct 2023
Viewed by 1202
Abstract
The immune plasma algorithm (IP algorithm or IPA) is one of the most recent meta-heuristic techniques and models the fundamental steps of immune or convalescent plasma treatment, attracting researchers’ attention once more with the COVID-19 pandemic. The IP algorithm determines the number of [...] Read more.
The immune plasma algorithm (IP algorithm or IPA) is one of the most recent meta-heuristic techniques and models the fundamental steps of immune or convalescent plasma treatment, attracting researchers’ attention once more with the COVID-19 pandemic. The IP algorithm determines the number of donors and the number of receivers when two specific control parameters are initialized and protects their values until the end of termination. However, determining which values are appropriate for the control parameters by adjusting the number of donors and receivers and guessing how they interact with each other are difficult tasks. In this study, we attempted to determine the number of plasma donors and receivers with an improved mechanism that depended on dividing the whole population into two sub-populations using a statistical measure known as the percentile and then a novel variant of the IPA called the percentile IPA (pIPA) was introduced. To investigate the performance of the pIPA, 22 numerical benchmark problems were solved by assigning different values to the control parameters of the algorithm. Moreover, two complex engineering problems, one of which required the filtering of noise from the recorded signal and the other the path planning of an unmanned aerial vehicle, were solved by the pIPA. Experimental studies showed that the percentile-based donor–receiver selection mechanism significantly contributed to the solving capabilities of the pIPA and helped it outperform well-known and state-of-art meta-heuristic algorithms. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
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28 pages, 8105 KiB  
Review
A Retrospective of Project Robo Raven: Developing New Capabilities for Enhancing the Performance of Flapping Wing Aerial Vehicles
by Hugh A. Bruck and Satyandra K. Gupta
Biomimetics 2023, 8(6), 485; https://doi.org/10.3390/biomimetics8060485 - 12 Oct 2023
Cited by 1 | Viewed by 1440
Abstract
Flapping Wing Air Vehicles (FWAVs) have proven to be attractive alternatives to fixed wing and rotary air vehicles at low speeds because of their bio-inspired ability to hover and maneuver. However, in the past, they have not been able to reach their full [...] Read more.
Flapping Wing Air Vehicles (FWAVs) have proven to be attractive alternatives to fixed wing and rotary air vehicles at low speeds because of their bio-inspired ability to hover and maneuver. However, in the past, they have not been able to reach their full potential due to limitations in wing control and payload capacity, which also has limited endurance. Many previous FWAVs used a single actuator that couples and synchronizes motions of the wings to flap both wings, resulting in only variable rate flapping control at a constant amplitude. Independent wing control is achieved using two servo actuators that enable wing motions for FWAVs by programming positions and velocities to achieve desired wing shapes and associated aerodynamic forces. However, having two actuators integrated into the flying platform significantly increases its weight and makes it more challenging to achieve flight than a single actuator. This article presents a retrospective overview of five different designs from the “Robo Raven” family based on our previously published work. The first FWAVs utilize two servo motors to achieve independent wing control. The basic platform is capable of successfully performing dives, flips, and button hook turns, which demonstrates the potential maneuverability afforded by the independently actuated and controlled wings. Subsequent designs in the Robo Raven family were able to use multifunctional wings to harvest solar energy to overcome limitations on endurance, use on-board decision-making capabilities to perform maneuvers autonomously, and use mixed-mode propulsion to increase payload capacity by exploiting the benefits of fixed and flapping wing flight. This article elucidates how each successive version of the Robo Raven platform built upon the findings from previous generations. The Robo Raven family collectively addresses requirements related to control autonomy, energy autonomy, and maneuverability. We conclude this article by identifying new opportunities for research in avian-scale flapping wing aerial vehicles. Full article
(This article belongs to the Special Issue Bio-Inspired Flight Systems and Bionic Aerodynamics 2.0)
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19 pages, 9705 KiB  
Article
Face Image Segmentation Using Boosted Grey Wolf Optimizer
by Hongliang Zhang, Zhennao Cai, Lei Xiao, Ali Asghar Heidari, Huiling Chen, Dong Zhao, Shuihua Wang and Yudong Zhang
Biomimetics 2023, 8(6), 484; https://doi.org/10.3390/biomimetics8060484 - 12 Oct 2023
Viewed by 1458
Abstract
Image segmentation methods have received widespread attention in face image recognition, which can divide each pixel in the image into different regions and effectively distinguish the face region from the background for further recognition. Threshold segmentation, a common image segmentation method, suffers from [...] Read more.
Image segmentation methods have received widespread attention in face image recognition, which can divide each pixel in the image into different regions and effectively distinguish the face region from the background for further recognition. Threshold segmentation, a common image segmentation method, suffers from the problem that the computational complexity shows exponential growth with the increase in the segmentation threshold level. Therefore, in order to improve the segmentation quality and obtain the segmentation thresholds more efficiently, a multi-threshold image segmentation framework based on a meta-heuristic optimization technique combined with Kapur’s entropy is proposed in this study. A meta-heuristic optimization method based on an improved grey wolf optimizer variant is proposed to optimize the 2D Kapur’s entropy of the greyscale and nonlocal mean 2D histograms generated by image computation. In order to verify the advancement of the method, experiments compared with the state-of-the-art method on IEEE CEC2020 and face image segmentation public dataset were conducted in this paper. The proposed method has achieved better results than other methods in various tests at 18 thresholds with an average feature similarity of 0.8792, an average structural similarity of 0.8532, and an average peak signal-to-noise ratio of 24.9 dB. It can be used as an effective tool for face segmentation. Full article
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26 pages, 7041 KiB  
Article
Derivation of Ultra-High Gain Hybrid Converter Families for HASEL Actuators Used in Soft Mobile Robots
by Tirthasarathi Lodh and Hanh-Phuc Le
Biomimetics 2023, 8(6), 483; https://doi.org/10.3390/biomimetics8060483 - 12 Oct 2023
Viewed by 1216
Abstract
This work proposes, analyzes, designs, and validates superior topologies of UHGH converters that are capable of supporting extremely large conversion ratios up to ∼2000× and output voltage up to ∼4–12 kV for future mobile soft robots from an input voltage as low as [...] Read more.
This work proposes, analyzes, designs, and validates superior topologies of UHGH converters that are capable of supporting extremely large conversion ratios up to ∼2000× and output voltage up to ∼4–12 kV for future mobile soft robots from an input voltage as low as the range of a 1-cell battery pack. Thus, the converter makes soft robots standalone systems that can be untethered and mobile. The extremely large voltage gain is enabled by a unique hybrid combination of a high-gain switched magnetic element (HGSME) and a capacitor-based voltage multiplier rectifier (CVMR) that, together, achieve small overall size, efficient operation, and output voltage regulation and shaping with simple duty-cycle modulation. With superior performance, power density, and compact size, the UHGH converters prove to be a promising candidate for future untethered soft robots. Full article
(This article belongs to the Special Issue Biology for Robotics and Robotics for Biology)
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24 pages, 5744 KiB  
Article
Implementation of Chaotic Reverse Slime Mould Algorithm Based on the Dandelion Optimizer
by Yi Zhang, Yang Liu, Yue Zhao and Xu Wang
Biomimetics 2023, 8(6), 482; https://doi.org/10.3390/biomimetics8060482 - 11 Oct 2023
Cited by 1 | Viewed by 1037
Abstract
This paper presents a hybrid algorithm based on the slime mould algorithm (SMA) and the mixed dandelion optimizer. The hybrid algorithm improves the convergence speed and prevents the algorithm from falling into the local optimal. (1) The Bernoulli chaotic mapping is added in [...] Read more.
This paper presents a hybrid algorithm based on the slime mould algorithm (SMA) and the mixed dandelion optimizer. The hybrid algorithm improves the convergence speed and prevents the algorithm from falling into the local optimal. (1) The Bernoulli chaotic mapping is added in the initialization phase to enrich the population diversity. (2) The Brownian motion and Lévy flight strategy are added to further enhance the global search ability and local exploitation performance of the slime mould. (3) The specular reflection learning is added in the late iteration to improve the population search ability and avoid falling into local optimality. The experimental results show that the convergence speed and precision of the improved algorithm are improved in the standard test functions. At last, this paper optimizes the parameters of the Extreme Learning Machine (ELM) model with the improved method and applies it to the power load forecasting problem. The effectiveness of the improved method in solving practical engineering problems is further verified. Full article
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15 pages, 2636 KiB  
Article
A Path-Planning Method Based on Improved Soft Actor-Critic Algorithm for Mobile Robots
by Tinglong Zhao, Ming Wang, Qianchuan Zhao, Xuehan Zheng and He Gao
Biomimetics 2023, 8(6), 481; https://doi.org/10.3390/biomimetics8060481 - 10 Oct 2023
Cited by 1 | Viewed by 1537
Abstract
The path planning problem has gained more attention due to the gradual popularization of mobile robots. The utilization of reinforcement learning techniques facilitates the ability of mobile robots to successfully navigate through an environment containing obstacles and effectively plan their path. This is [...] Read more.
The path planning problem has gained more attention due to the gradual popularization of mobile robots. The utilization of reinforcement learning techniques facilitates the ability of mobile robots to successfully navigate through an environment containing obstacles and effectively plan their path. This is achieved by the robots’ interaction with the environment, even in situations when the environment is unfamiliar. Consequently, we provide a refined deep reinforcement learning algorithm that builds upon the soft actor-critic (SAC) algorithm, incorporating the concept of maximum entropy for the purpose of path planning. The objective of this strategy is to mitigate the constraints inherent in conventional reinforcement learning, enhance the efficacy of the learning process, and accommodate intricate situations. In the context of reinforcement learning, two significant issues arise: inadequate incentives and inefficient sample use during the training phase. To address these challenges, the hindsight experience replay (HER) mechanism has been presented as a potential solution. The HER mechanism aims to enhance algorithm performance by effectively reusing past experiences. Through the utilization of simulation studies, it can be demonstrated that the enhanced algorithm exhibits superior performance in comparison with the pre-existing method. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
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19 pages, 24141 KiB  
Article
YOLOv5-MS: Real-Time Multi-Surveillance Pedestrian Target Detection Model for Smart Cities
by Fangzheng Song and Peng Li
Biomimetics 2023, 8(6), 480; https://doi.org/10.3390/biomimetics8060480 - 09 Oct 2023
Cited by 6 | Viewed by 1757
Abstract
Intelligent video surveillance plays a pivotal role in enhancing the infrastructure of smart urban environments. The seamless integration of multi-angled cameras, functioning as perceptive sensors, significantly enhances pedestrian detection and augments security measures in smart cities. Nevertheless, current pedestrian-focused target detection encounters challenges [...] Read more.
Intelligent video surveillance plays a pivotal role in enhancing the infrastructure of smart urban environments. The seamless integration of multi-angled cameras, functioning as perceptive sensors, significantly enhances pedestrian detection and augments security measures in smart cities. Nevertheless, current pedestrian-focused target detection encounters challenges such as slow detection speeds and increased costs. To address these challenges, we introduce the YOLOv5-MS model, an YOLOv5-based solution for target detection. Initially, we optimize the multi-threaded acquisition of video streams within YOLOv5 to ensure image stability and real-time performance. Subsequently, leveraging reparameterization, we replace the original BackBone convolution with RepvggBlock, streamlining the model by reducing convolutional layer channels, thereby enhancing the inference speed. Additionally, the incorporation of a bioinspired “squeeze and excitation” module in the convolutional neural network significantly enhances the detection accuracy. This module improves target focusing and diminishes the influence of irrelevant elements. Furthermore, the integration of the K-means algorithm and bioinspired Retinex image augmentation during training effectively enhances the model’s detection efficacy. Finally, loss computation adopts the Focal-EIOU approach. The empirical findings from our internally developed smart city dataset unveil YOLOv5-MS’s impressive 96.5% mAP value, indicating a significant 2.0% advancement over YOLOv5s. Moreover, the average inference speed demonstrates a notable 21.3% increase. These data decisively substantiate the model’s superiority, showcasing its capacity to effectively perform pedestrian detection within an Intranet of over 50 video surveillance cameras, in harmony with our stringent requisites. Full article
(This article belongs to the Special Issue Bioinspired Artificial Intelligence Applications)
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18 pages, 6155 KiB  
Article
Human Operation Augmentation through Wearable Robotic Limb Integrated with Mixed Reality Device
by Hongwei Jing, Tianjiao Zheng, Qinghua Zhang, Kerui Sun, Lele Li, Mingzhu Lai, Jie Zhao and Yanhe Zhu
Biomimetics 2023, 8(6), 479; https://doi.org/10.3390/biomimetics8060479 - 08 Oct 2023
Viewed by 1429
Abstract
Mixed reality technology can give humans an intuitive visual experience, and combined with the multi-source information of the human body, it can provide a comfortable human–robot interaction experience. This paper applies a mixed reality device (Hololens2) to provide interactive communication between the wearer [...] Read more.
Mixed reality technology can give humans an intuitive visual experience, and combined with the multi-source information of the human body, it can provide a comfortable human–robot interaction experience. This paper applies a mixed reality device (Hololens2) to provide interactive communication between the wearer and the wearable robotic limb (supernumerary robotic limb, SRL). Hololens2 can obtain human body information, including eye gaze, hand gestures, voice input, etc. It can also provide feedback information to the wearer through augmented reality and audio output, which is the communication bridge needed in human–robot interaction. Implementing a wearable robotic arm integrated with HoloLens2 is proposed to augment the wearer’s capabilities. Taking two typical practical tasks of cable installation and electrical connector soldering in aircraft manufacturing as examples, the task models and interaction scheme are designed. Finally, human augmentation is evaluated in terms of task completion time statistics. Full article
(This article belongs to the Special Issue Intelligent Human-Robot Interaction)
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