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19 pages, 4296 KiB

Open AccessArticle

Biologicalization of Smart Manufacturing Using DNA-Based Computing

by Sharifu Ura and Lubna Zaman

Biomimetics 2023, 8(8), 620; https://doi.org/10.3390/biomimetics8080620 - 18 Dec 2023

Viewed by 1306

Smart manufacturing needs cognitive computing methods to make the relevant systems more intelligent and autonomous. In this respect, bio-inspired cognitive computing methods (i.e., biologicalization) can play a vital role. This article is written from this perspective. In particular, this article provides a general [...] Read more.

Smart manufacturing needs cognitive computing methods to make the relevant systems more intelligent and autonomous. In this respect, bio-inspired cognitive computing methods (i.e., biologicalization) can play a vital role. This article is written from this perspective. In particular, this article provides a general overview of the bio-inspired computing method called DNA-Based Computing (DBC), including its theory and applications. The main theme of DBC is the central dogma of molecular biology (once information of DNA/RNA has got into a protein, it cannot get out again), i.e., DNA to RNA (sequences of four types of nucleotides) and DNA/RNA to protein (sequence of twenty types of amino acids) are allowed, but not the reverse ones. Thus, DBC transfers few-element information (DNA/RAN-like) to many-element information (protein-like). This characteristic of DBC can help to solve cognitive problems (e.g., pattern recognition). DBC can take many forms; this article elucidates two main forms, denoted as DBC-1 and DBC-2. Using arbitrary numerical examples, we demonstrate that DBC-1 can solve various cognitive problems, e.g., “similarity indexing between seemingly different but inherently identical objects” and “recognizing regions of an image separated by a complex boundary.” In addition, using an arbitrary numerical example, we demonstrate that DBC-2 can solve the following cognitive problem: “pattern recognition when the relevant information is insufficient.” The remarkable thing is that smart manufacturing-based systems (e.g., digital twins and big data analytics) must solve the abovementioned problems to make the manufacturing enablers (e.g., machine tools and monitoring systems) more self-reliant and autonomous. Consequently, DBC can improve the cognitive problem-solving ability of smart manufacturing-relevant systems and enrich their biologicalization. Full article

(This article belongs to the Special Issue Bio-Inspired Computing: Theories and Applications)

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16 pages, 10112 KiB

Open AccessArticle

Ring Attractors as the Basis of a Biomimetic Navigation System

by Thomas C. Knowles, Anna G. Summerton, James G. H. Whiting and Martin J. Pearson

Biomimetics 2023, 8(5), 399; https://doi.org/10.3390/biomimetics8050399 - 01 Sep 2023

Viewed by 1103

Abstract

The ability to navigate effectively in a rich and complex world is crucial for the survival of all animals. Specialist neural structures have evolved that are implicated in facilitating this ability, one such structure being the ring attractor network. In this study, we [...] Read more.

The ability to navigate effectively in a rich and complex world is crucial for the survival of all animals. Specialist neural structures have evolved that are implicated in facilitating this ability, one such structure being the ring attractor network. In this study, we model a trio of Spiking Neural Network (SNN) ring attractors as part of a bio-inspired navigation system to maintain an internal estimate of planar translation of an artificial agent. This estimate is dynamically calibrated using a memory recall system of landmark-free allotheic multisensory experiences. We demonstrate that the SNN-based ring attractor system can accurately model motion through 2D space by integrating ideothetic velocity information and use recalled allothetic experiences as a positive corrective mechanism. This SNN based navigation system has potential for use in mobile robotics applications where power supply is limited and external sensory information is intermittent or unreliable. Full article

(This article belongs to the Special Issue Bio-Inspired Computing: Theories and Applications)

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20 pages, 28811 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning Vision System for Quadruped Robot Gait Pattern Regulation

by Christyan Cruz Ulloa, Lourdes Sánchez, Jaime Del Cerro and Antonio Barrientos

Biomimetics 2023, 8(3), 289; https://doi.org/10.3390/biomimetics8030289 - 03 Jul 2023

Viewed by 1730

Abstract

Robots with bio-inspired locomotion systems, such as quadruped robots, have recently attracted significant scientific interest, especially those designed to tackle missions in unstructured terrains, such as search-and-rescue robotics. On the other hand, artificial intelligence systems have allowed for the improvement and adaptation of [...] Read more.

Robots with bio-inspired locomotion systems, such as quadruped robots, have recently attracted significant scientific interest, especially those designed to tackle missions in unstructured terrains, such as search-and-rescue robotics. On the other hand, artificial intelligence systems have allowed for the improvement and adaptation of the locomotion capabilities of these robots based on specific terrains, imitating the natural behavior of quadruped animals. The main contribution of this work is a method to adjust adaptive gait patterns to overcome unstructured terrains using the ARTU-R (A1 Rescue Task UPM Robot) quadruped robot based on a central pattern generator (CPG), and the automatic identification of terrain and characterization of its obstacles (number, size, position and superability analysis) through convolutional neural networks for pattern regulation. To develop this method, a study of dog gait patterns was carried out, with validation and adjustment through simulation on the robot model in ROS-Gazebo and subsequent transfer to the real robot. Outdoor tests were carried out to evaluate and validate the efficiency of the proposed method in terms of its percentage of success in overcoming stretches of unstructured terrains, as well as the kinematic and dynamic variables of the robot. The main results show that the proposed method has an efficiency of over 93% for terrain characterization (identification of terrain, segmentation and obstacle characterization) and over 91% success in overcoming unstructured terrains. This work was also compared against main developments in state-of-the-art and benchmark models. Full article

(This article belongs to the Special Issue Bio-Inspired Computing: Theories and Applications)

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17 pages, 4751 KiB

Open AccessArticle

Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition

by Zhengyao Wen, Wenzhong Lin, Tao Wang and Ge Xu

Biomimetics 2023, 8(2), 199; https://doi.org/10.3390/biomimetics8020199 - 11 May 2023

Cited by 62 | Viewed by 5113

Abstract

This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception. Firstly, multiple facial expression classes share inherently similar underlying facial appearance, and their differences could be [...] Read more.

This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception. Firstly, multiple facial expression classes share inherently similar underlying facial appearance, and their differences could be subtle. Secondly, facial expressions simultaneously exhibit themselves through multiple facial regions, and for recognition, a holistic approach by encoding high-order interactions among local features is required. To address these issues, this work proposes DAN with three key components: Feature Clustering Network (FCN), Multi-head Attention Network (MAN), and Attention Fusion Network (AFN). Specifically, FCN extracts robust features by adopting a large-margin learning objective to maximize class separability. In addition, MAN instantiates a number of attention heads to simultaneously attend to multiple facial areas and build attention maps on these regions. Further, AFN distracts these attentions to multiple locations before fusing the feature maps to a comprehensive one. Extensive experiments on three public datasets (including AffectNet, RAF-DB, and SFEW 2.0) verified that the proposed method consistently achieves state-of-the-art facial expression recognition performance. The DAN code is publicly available. Full article

(This article belongs to the Special Issue Bio-Inspired Computing: Theories and Applications)

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22 pages, 1552 KiB

Open AccessArticle

Building an Effective Classifier for Phishing Web Pages Detection: A Quantum-Inspired Biomimetic Paradigm Suitable for Big Data Analytics of Cyber Attacks

by Saad M. Darwish, Dheyauldeen A. Farhan and Adel A. Elzoghabi

Biomimetics 2023, 8(2), 197; https://doi.org/10.3390/biomimetics8020197 - 09 May 2023

Cited by 3 | Viewed by 1781

Abstract

To combat malicious domains, which serve as a key platform for a wide range of attacks, domain name service (DNS) data provide rich traces of Internet activities and are a powerful resource. This paper presents new research that proposes a model for finding [...] Read more.

To combat malicious domains, which serve as a key platform for a wide range of attacks, domain name service (DNS) data provide rich traces of Internet activities and are a powerful resource. This paper presents new research that proposes a model for finding malicious domains by passively analyzing DNS data. The proposed model builds a real-time, accurate, middleweight, and fast classifier by combining a genetic algorithm for selecting DNS data features with a two-step quantum ant colony optimization (QABC) algorithm for classification. The modified two-step QABC classifier uses K-means instead of random initialization to place food sources. In order to overcome ABCs poor exploitation abilities and its convergence speed, this paper utilizes the metaheuristic QABC algorithm for global optimization problems inspired by quantum physics concepts. The use of the Hadoop framework and a hybrid machine learning approach (K-mean and QABC) to deal with the large size of uniform resource locator (URL) data is one of the main contributions of this paper. The major point is that blacklists, heavyweight classifiers (those that use more features), and lightweight classifiers (those that use fewer features and consume the features from the browser) may all be improved with the use of the suggested machine learning method. The results showed that the suggested model could work with more than 96.6% accuracy for more than 10 million query–answer pairs. Full article

(This article belongs to the Special Issue Bio-Inspired Computing: Theories and Applications)

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22 pages, 5668 KiB

Open AccessArticle

Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

by Li Cao, Haishao Chen, Yaodan Chen, Yinggao Yue and Xin Zhang

Biomimetics 2023, 8(2), 186; https://doi.org/10.3390/biomimetics8020186 - 29 Apr 2023

Cited by 7 | Viewed by 1824

Abstract

A TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimization [...] Read more.

A TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimization mechanism on the basis of enhancing the performance of the original algorithm. To obtain a better fitness value throughout the optimization process and increase the algorithm’s optimization accuracy, the fitness function based on maximum likelihood estimation is modified. In order to speed up algorithm convergence and decrease needless global search without compromising population diversity, an initial solution is simultaneously added to the starting population location. Simulation findings demonstrate that the suggested method outperforms the TDOA/AOA algorithm and other comparable algorithms, including Taylor, Chan, PSO, CPSO, and basic CSA algorithms. The approach performs well in terms of robustness, convergence speed, and node positioning accuracy. Full article

(This article belongs to the Special Issue Bio-Inspired Computing: Theories and Applications)

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42 pages, 6181 KiB

Open AccessArticle

Subtraction-Average-Based Optimizer: A New Swarm-Inspired Metaheuristic Algorithm for Solving Optimization Problems

by Pavel Trojovský and Mohammad Dehghani

Biomimetics 2023, 8(2), 149; https://doi.org/10.3390/biomimetics8020149 - 06 Apr 2023

Cited by 27 | Viewed by 3121

Abstract

This paper presents a new evolutionary-based approach called a Subtraction-Average-Based Optimizer (SABO) for solving optimization problems. The fundamental inspiration of the proposed SABO is to use the subtraction average of searcher agents to update the position of population members in the search space. [...] Read more.

This paper presents a new evolutionary-based approach called a Subtraction-Average-Based Optimizer (SABO) for solving optimization problems. The fundamental inspiration of the proposed SABO is to use the subtraction average of searcher agents to update the position of population members in the search space. The different steps of the SABO’s implementation are described and then mathematically modeled for optimization tasks. The performance of the proposed SABO approach is tested for the optimization of fifty-two standard benchmark functions, consisting of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results show that the proposed SABO approach effectively solves the optimization problems by balancing the exploration and exploitation in the search process of the problem-solving space. The results of the SABO are compared with the performance of twelve well-known metaheuristic algorithms. The analysis of the simulation results shows that the proposed SABO approach provides superior results for most of the benchmark functions. Furthermore, it provides a much more competitive and outstanding performance than its competitor algorithms. Additionally, the proposed approach is implemented for four engineering design problems to evaluate the SABO in handling optimization tasks for real-world applications. The optimization results show that the proposed SABO approach can solve for real-world applications and provides more optimal designs than its competitor algorithms. Full article

(This article belongs to the Special Issue Bio-Inspired Computing: Theories and Applications)

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16 pages, 4342 KiB

Open AccessArticle

Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm

by Xiaohua Xia, Haoming Xiang, Yusong Cao, Zhaokai Ge and Zainan Jiang

Biomimetics 2023, 8(2), 139; https://doi.org/10.3390/biomimetics8020139 - 29 Mar 2023

Cited by 3 | Viewed by 1383

Abstract

Imitating the visual characteristics of human eyes is one of the important tasks of digital image processing and computer vision. Feature correspondence of humanoid-eye binocular images is a prerequisite for obtaining the fused image. Human eyes are more sensitive to edge, because it [...] Read more.

Imitating the visual characteristics of human eyes is one of the important tasks of digital image processing and computer vision. Feature correspondence of humanoid-eye binocular images is a prerequisite for obtaining the fused image. Human eyes are more sensitive to edge, because it contains much information. However, existing matching methods usually fail in producing enough edge corresponding pairs for humanoid-eye images because of viewpoint and view direction differences. To this end, we propose a novel and effective feature matching algorithm based on edge points. The proposed method consists of four steps. First, the SUSAN operator is employed to detect features, for its outstanding edge feature extraction capability. Second, the input image is constructed into a multi-scale structure based on image pyramid theory, which is then used to compute simplified SIFT descriptors for all feature points. Third, a novel multi-scale descriptor is constructed, by stitching the simplified SIFT descriptor of each layer. Finally, the similarity of multi-scale descriptors is measured by bidirectional matching, and the obtained preliminary matches are refined by subsequent procedures, to achieve accurate matching results. We respectively conduct qualitative and quantitative experiments, which demonstrate that our method can robustly match feature points in humanoid-eye binocular image pairs, and achieve favorable performance under illumination changes compared to the state-of-the-art. Full article

(This article belongs to the Special Issue Bio-Inspired Computing: Theories and Applications)

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60 pages, 7662 KiB

Open AccessArticle

Green Anaconda Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

by Mohammad Dehghani, Pavel Trojovský and Om Parkash Malik

Biomimetics 2023, 8(1), 121; https://doi.org/10.3390/biomimetics8010121 - 14 Mar 2023

Cited by 17 | Viewed by 2915

Abstract

A new metaheuristic algorithm called green anaconda optimization (GAO) which imitates the natural behavior of green anacondas has been designed. The fundamental inspiration for GAO is the mechanism of recognizing the position of the female species by the male species during the mating [...] Read more.

A new metaheuristic algorithm called green anaconda optimization (GAO) which imitates the natural behavior of green anacondas has been designed. The fundamental inspiration for GAO is the mechanism of recognizing the position of the female species by the male species during the mating season and the hunting strategy of green anacondas. GAO’s mathematical modeling is presented based on the simulation of these two strategies of green anacondas in two phases of exploration and exploitation. The effectiveness of the proposed GAO approach in solving optimization problems is evaluated on twenty-nine objective functions from the CEC 2017 test suite and the CEC 2019 test suite. The efficiency of GAO in providing solutions for optimization problems is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that the proposed GAO approach has a high capability in exploration, exploitation, and creating a balance between them and performs better compared to competitor algorithms. In addition, the implementation of GAO on twenty-one optimization problems from the CEC 2011 test suite indicates the effective capability of the proposed approach in handling real-world applications. Full article

(This article belongs to the Special Issue Bio-Inspired Computing: Theories and Applications)

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Review

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36 pages, 638 KiB

Open AccessReview

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

by Minghai Xu, Li Cao, Dongwan Lu, Zhongyi Hu and Yinggao Yue

Biomimetics 2023, 8(2), 235; https://doi.org/10.3390/biomimetics8020235 - 03 Jun 2023

Cited by 7 | Viewed by 3469

Abstract

Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology [...] Read more.

Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected. Full article

(This article belongs to the Special Issue Bio-Inspired Computing: Theories and Applications)

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