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Hunting stability is a critical aspect of the dynamic performance of high-speed trains. However, the current evaluation criteria only address heavy hunting motion, and they require supplementation and improvement to meet the increasing requirements of dynamic performance. Therefore, a study was conducted to develop an identification method for small-amplitude bogie hunting (SABH) motion. A total of 39,816 samples were obtained from field-measured bogie lateral acceleration and manually labelled as normal and SABH. The distribution comparison, Pierce coefficient, and importance ranking showed that harmonic character-related features (the autocorrelation coefficient, approximate entropy and etc.) should be considered more for SABH identification than amplitude-related features (maximum and RMS). The most critical features for SABH identification were the autocorrelation coefficient and spectral frequency spread. The classifier performance comparison showed that the decision tree was the best-performing classifier, followed by the support vector machine, while the linear discriminant, K-nearest neighbour, and naive Bayes classifiers were less effective. Sensitivity analysis indicated that the proposed method worked well with a sampling frequency of 50–200 Hz and a window length of 5–10 s. This research provides theoretical support for hunting instability monitoring and real-time active control studies for high-speed trains. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. This paper is the first attempt to discuss the use of optimization spiking neural P systems to improve the threshold segmentation of brain tumor images. To be specific, a threshold segmentation approach based on optimization numerical spiking neural P systems with adaptive multi-mutation operators (ONSNPSamos) is proposed to segment brain tumor images. More specifically, an ONSNPSamo with a multi-mutation strategy is introduced to balance exploration and exploitation abilities. At the same time, an approach combining the ONSNPSamo and connectivity algorithms is proposed to address the brain tumor segmentation problem. Our experimental results from CEC 2017 benchmarks (basic, shifted and rotated, hybrid, and composition function optimization problems) demonstrate that the ONSNPSamo is better than or close to 12 optimization algorithms. Furthermore, case studies from BraTS 2019 show that the approach combining the ONSNPSamo and connectivity algorithms can more effectively segment brain tumor images than most algorithms involved. [ABSTRACT FROM AUTHOR]

To guarantee their locomotion, biped robots need to walk stably. The latter is achieved by a high performance in joint control. This article addresses this issue by proposing a novel human-simulated fuzzy (HF) membrane control system of the joint angles. The proposed control system, human-simulated fuzzy membrane controller (HFMC), contains several key elements. The first is an HF algorithm based on human-simulated intelligent control (HSIC). This HF algorithm incorporates elements of both multi-mode proportional-derivative (PD) and fuzzy control, aiming at solving the chattering problem of multi-mode switching while improving control accuracy. The second is a membrane architecture that makes use of the natural parallelisation potential of membrane computing to improve the real-time performance of the controller. The proposed HFMC is utilised as the joint controller for a biped robot. Numerical tests in a simulation are carried out with the planar and slope walking of a five-link biped robot, and the effectiveness of the HFMC is verified by comparing and evaluating the results of the designed HFMC, HSIC and PD. Experimental results demonstrate that the proposed HFMC not only retains the advantages of traditional PD control but also improves control accuracy, real-time performance and stability. [ABSTRACT FROM AUTHOR]

The model of Spiking Neural P systems (SNP systems) is a widespread computational model in the area of membrane computing. It has numerous applications, especially related to machine learning. Most of these applications require a custom variant of SNP systems, differing by the rule form and by semantics. The model of network of cells and the formal framework for SNP systems were developed to help the analysis of such custom models, to compare and relate them to each other and to other models of computing. The model specifies the data structure, the rules and the update procedure, while the formal framework concentrates on how the input, output and the choice of the update strategy are handled. Together, these concepts specify a concrete instance of a network of cells that strongly bisimulates the desired model, thus making easier the process of the creation of new models and the extension of existing ones. Since the formal framework is rather generic, it might be slightly complex to use it for concrete cases. This paper provides a tutorial that explains the model of networks of cells and the basic concepts used in the formal framework for SNP systems. It gives a series of examples for the analysis of existing models, their bisimulation and their extension by different features. [ABSTRACT FROM AUTHOR]

Biological brains have a natural capacity for resolving certain classification tasks. Studies on biologically plausible spiking neurons, architectures and mechanisms of artificial neural systems that closely match biological observations while giving high classification performance are gaining momentum. Spiking neural P systems (SN P systems) are a class of membrane computing models and third-generation neural networks that are based on the behavior of biological neural cells and have been used in various engineering applications. Furthermore, SN P systems are characterized by a highly flexible structure that enables the design of a machine learning algorithm by mimicking the structure and behavior of biological cells without the over-simplification present in neural networks. Based on this aspect, this paper proposes a novel type of SN P system, namely, layered SN P system (LSN P system), to solve classification problems by supervised learning. The proposed LSN P system consists of a multi-layer network containing multiple weighted fuzzy SN P systems with adaptive weight adjustment rules. The proposed system employs specific ascending dimension techniques and a selection method of output neurons for classification problems. The experimental results obtained using benchmark datasets from the UCI machine learning repository and MNIST dataset demonstrated the feasibility and effectiveness of the proposed LSN P system. More importantly, the proposed LSN P system presents the first SN P system that demonstrates sufficient performance for use in addressing real-world classification problems. [ABSTRACT FROM AUTHOR]

In recent years, classification problems have been widely studied as one of the critical research directions in artificial intelligence. Efficiently simulating the human brain's ability to solve deterministic classification problems has become a prominent research branch in the field of classification problems. Numerical spiking neural P systems (NSN P systems) with the characteristics of the highly flexible spiking neural P (SNP)systems architecture and addressing numerical variants have special advantages for solving classification problems in the real world. This paper proposes, a novel NSN P system, namely, a learning numerical spiking neural P system (LNSN P system), is proposed to solve classification problems. In LNSN P systems, each neuron contains not only discrete spikes and rules that must be described by regular expressions but also numerical variables and production functions that can be in any form, overcoming the discontinuity of traditional SN P systems. In addition, an ascending structure without an outer encoder has been designed in this study to encode information from low-dimensional indivisible space to high-dimensional divisible space. At the same time, an adaptive learning mechanism has been introduced to improve algorithm convergence and precision. Finally, the experimental results obtained using eight benchmark datasets from the machine learning repository demonstrate the feasibility and effectiveness of the proposed LNSN P system. Moreover, the proposed LNSN P system exhibits lower space complexity and better generalisation ability than the existing SN P systems with the learning ability to solve different classification problems. • An LNSN P system is proposed to solve classification problems. • An ascending structure without an outer encoder is designed to encode information. • An adaptive learning mechanism is introduced to improve algorithm performance. [ABSTRACT FROM AUTHOR]

Membrane computing models are parallel and distributed natural computing models. These models are often referred to as P systems. This paper proposes a novel multi-behaviors co-ordination controller model using enzymatic numerical P systems for autonomous mobile robots navigation in unknown environments. An environment classifier is constructed to identify different environment patterns in the maze-like environment and the multi-behavior co-ordination controller is constructed to coordinate the behaviors of the robots in different environments. Eleven sensory prototypes of local environments are presented to design the environment classifier, which needs to memorize only rough information, for solving the problems of poor obstacle clearance and sensor noise. A switching control strategy and multi-behaviors coordinator are developed without detailed environmental knowledge and heavy computation burden, for avoiding the local minimum traps or oscillation problems and adapt to the unknown environments. Also, a serial behaviors control law is constructed on the basis of Lyapunov stability theory aiming at the specialized environment, for realizing stable navigation and avoiding actuator saturation. Moreover, both environment classifier and multi-behavior coordination controller are amenable to the addition of new environment models or new behaviors due to the modularity of the hierarchical architecture of P systems. The simulation of wheeled mobile robots shows the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

Several variants of spiking neural P systems (SNPS) have been presented in the literature to perform arithmetic operations. However, each of these variants was designed only for one specific arithmetic operation. In this paper, a complete arithmetic calculator implemented by SNPS is proposed. An application of the proposed calculator to information fusion is also proposed. The information fusion is implemented by integrating the following three elements: (1) an addition and subtraction SNPS already reported in the literature; (2) a modified multiplication and division SNPS; (3) a novel storage SNPS, i.e. a method based on SNPS is introduced to calculate basic probability assignment of an event. This is the first attempt to apply arithmetic operation SNPS to fuse multiple information. The effectiveness of the presented general arithmetic SNPS calculator is verified by means of several examples. [ABSTRACT FROM AUTHOR]

Multiple problems such as target assignment timeliness and path energy optimization make it challenging for USVs traversing targets. This paper proposes a novel multiple targets traversing path planning algorithm for USVs, which includes Event-Triggered bundled (ET-Bundled) genetic target assignment method and dynamic elliptical guidance region (DEGR) path planning method. In terms of solving above problems, comparative simulation studies with the state-of-the-art methods demonstrate that ET-Bundled can not only ensures that the traversing time and path length do not exceed the constraint range, but also reduces the average CPU time of membrane structure by 60%, and DEGR can optimize the path energy consumption of EGR path by 34%. [ABSTRACT FROM AUTHOR]

An artificial compound eye (ACE) is a bio-inspired vision sensor which mimics a natural compound eye (typical of insects). This artificial eye is able to visualize large fields of the outside world through multi-aperture. Due to its functioning, the ACE is subject to optical flow, that is an apparent motion of the object visualized by the eye. This paper proposes a method to estimate the optical flow based on capturing multiple images (multi-aperture). In this method, based on descriptors-based initial optical flows, a unified global energy function is presented to incorporate the information of multi-aperture and simultaneously recover the optical flows of multi-aperture. The energy function imposes a compound flow fields consistency assumption along with the brightness constancy and piecewise smoothness assumptions. This formula efficiently binds the flow field in time and space, and further enables view-consistent optical flow estimation. Experimental results on real and synthetic data demonstrate that the proposed method recovers view-consistent optical flows crossed multi-aperture and performs better than other optical flow methods on the multi-aperture images. [ABSTRACT FROM AUTHOR]

In recent years, much attention has been paid to the electronic cluster eye (eCley), a new type of artificial compound eyes, because of its small size, wide field of view (FOV) and sensitivity to motion objects. An eCley is composed of a certain number of optical channels organized as an array. Each optical channel spans a small and fixed field of view (FOV). To obtain a complete image with a full FOV, the images from all the optical channels are required to be fused together. The parallax from unparallel neighboring optical channels in eCley may lead to reconstructed image blurring and incorrectly estimated depth. To solve this problem, this paper proposes a geometry based three-dimensional image processing method (G3D) for eCley to obtain a complete focused image and dense depth map. In G3D, we derive the geometry relationship of optical channels in eCley to obtain the mathematical relation between the parallax and depth among unparallel neighboring optical channels. Based on the geometry relationship, all of the optical channels are used to estimate the depth map and reconstruct a focused image. Subsequently, by using an edge-aware interpolation method, we can further gain a sharply focused image and a depth map. The effectiveness of the proposed method is verified by the experimental results. [ABSTRACT FROM AUTHOR]

With the development of intelligent substations, inspection robots are widely used to ensure the safe and stable operation of substations. Due to the prevalence of grass around the substation in the external environment, the inspection robot will be affected by grass when performing the inspection task, which can easily lead to the interruption of the inspection task. At present, inspection robots based on LiDAR sensors regard grass as hard obstacles such as stones, resulting in interruption of inspection tasks and decreased inspection efficiency. Moreover, there are inaccurate multiple object-detection boxes in grass recognition. To address these issues, this paper proposes a new assistance navigation method for substation inspection robots to cross grass areas safely. First, an assistant navigation algorithm is designed to enable the substation inspection robot to recognize grass and to cross the grass obstacles on the route of movement to continue the inspection work. Second, a three-layer convolutional structure of the Faster-RCNN network in the assistant navigation algorithm is improved instead of the original full connection structure for optimizing the object-detection boxes. Finally, compared with several Faster-RCNN networks with different convolutional kernel dimensions, the experimental results show that at the convolutional kernel dimension of 1024, the proposed method in this paper improves the mAP by 4.13% and the mAP is 91.25% at IoU threshold 0.5 in the range of IoU thresholds from 0.5 to 0.9 with respect to the basic network. In addition, the assistant navigation algorithm designed in this paper fuses the ultrasonic radar signals with the object recognition results and then performs the safety judgment to make the inspection robot safely cross the grass area, which improves the inspection efficiency. [ABSTRACT FROM AUTHOR]

Automatic design of P systems is an attractive research topic in the community of membrane computing. Differing from the previous work that used evolutionary algorithms to fulfill the task, this paper presents the design of a (deterministic transition) P system (without input membrane) of degree 1, capturing the value of an arbitrary k-order ( $$k\ge 2$$ ) polynomial p( n) by using a reasoning method. Specifically, the values of p( n) corresponding to a natural number t is equal to the multiplicity of a distinguished object of the system (the output object) in the configuration at instant t. We also discuss the descriptive computational resources required by the designed k-order polynomial P system. [ABSTRACT FROM AUTHOR]

This paper deals with cooperative multi-task traversing scheme for multiple unmanned surface vehicles (USVs) under complex marine environments. Due to the dynamic characteristics of the marine environment, the factors including the assignment efficiency, path length, safety and optimization cost are critical issues that cannot be ignored in cooperative multi-task traversing for multi-USV. The main contributions of this paper are as follows: (1) Based on genetic algorithm, a membrane reward-mechanism genetic-optimization (M-RM-GO) algorithm that can enhance the task assignment speed and achieve priority is proposed to solve the cooperative multi-task assignment problem. (2) In the case of marine environmental loads, a deflection factor is introduced into the fast marching method to make the avoidance path in the traversal process adapt to the dynamic characteristics of environmental loads. (3) In order to decrease the optimization cost of the above collision avoidance paths, an avoidance path optimization strategy with membrane structure is designed. Comparative studies with the state-of-the-art methods demonstrate that the proposed cooperative multiple task traversing scheme has superior performance. • Based on genetic algorithm, a membrane reward mechanism genetic-optimization (M-RM-GO) algorithm to enhance the task assignment speed and achieve priority. • In the case of marine environmental loads, a deflection factor is introduced into the fast marching method. • In order to decrease the optimization cost of the above collision avoidance paths, an avoidance path optimization strategy with membrane structure is designed. [ABSTRACT FROM AUTHOR]

The evaluation metrics of the spatial robustness evaluation (SRE) and the temporal robustness evaluation (TRE) are listed in the graphical abstract. In the long term tracking, the deformation of target is considered to be difficult and challenge. Our proposed algorithm is better than other algorithms. [Display omitted] Automatic target detection and tracking systems are used extensively in complex scenes. In long-term tracking, some visual attributes of objects are changing, such as illumination, size, profile, and so on. To address the issue, it is particularly important to describe the essential properties of the objects in tracking. An enhanced kernelized correlation filter tracking strategy fused multiple features with location prediction is proposed. To make the object appearance models more accuracy and robustness, based on the original histogram of oriented gradient features, we integrate the hue, saturation, value, and grayscale information to construct a new descriptor to represent the target appearance. Moreover, location prediction and bi-linear interpolation are employed to obtain the more accurate target position. Experiments show that the proposed strategy can obtain superior or competitive performance in challenging benchmark data sets. In practice, the algorithm is applied to track shuttle bus targets in the airport apron. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) has become a prevalent tool for global optimization problems since it was proposed in 1995. As usual, when applying DE to a specific problem, determining the most proper strategy and its associated parameter values is time-consuming. Moreover, to achieve good performance, DE often requires different strategies combined with different parameter values at different evolution stages. Thus integrating several strategies in one algorithm and determining the application rate of each strategy as well as its associated parameter values online become an ad-hoc research topic. This paper proposes a novel DE algorithm, called multicriteria adaptive DE (MADE), for global numerical optimization. In MADE, a multicriteria adaptation scheme is introduced to determine the trial vector generation strategies and the control parameters of each strategy are separately adjusted according to their most recently successful values. In the multicriteria adaptation scheme, the impacts of an operator application are measured in terms of exploitation and exploration capabilities and correspondingly a multi-objective decision procedure is introduced to aggregate the impacts. Thirty-eight scale numerical optimization problems with various characteristics and two real-world problems are applied to test the proposed idea. Results show that MADE is superior or competitive to six well-known DE variants in terms of solution quality and convergence performance. [ABSTRACT FROM AUTHOR]

This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results. [ABSTRACT FROM PUBLISHER]

Membrane systems (also called P systems) refer to the computing models abstracted from the structure and the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other populations of cells. Spiking neural P systems (SNPS) are a class of distributed and parallel computing models that incorporate the idea of spiking neurons into P systems. To attain the solution of optimization problems, P systems are used to properly organize evolutionary operators of heuristic approaches, which are named as membrane-inspired evolutionary algorithms (MIEAs). This paper proposes a novel way to design a P system for directly obtaining the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators like in the case of MIEAs. To this aim, an extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking neural P system (OSNPS), are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems. Extensive experiments on knapsack problems have been reported to experimentally prove the viability and effectiveness of the proposed neural system. [ABSTRACT FROM AUTHOR]

As one of the most widely studied membrane systems, a spiking neural P system consists of three fundamental elements: initial spikes, evolution rules and connection between neurons. The automatic design of an arithmetic operation spiking neural P system (SN P system) in the literature usually considered the selection of the set of redundant rules under the condition of known initial spikes and connection between neurons. In this work, an automatic design method of arithmetic operation SN P systems including the encoding method and evolutionary strategy of arithmetic operation SN P systems is proposed to evolve an SN P system to achieve arithmetic operation. Experimental results show that the automatic design method of arithmetic operation SN P systems is feasible and effective for arithmetic operations. [ABSTRACT FROM AUTHOR]

Even though the giant panda's extinction status is downgraded from endangered to vulnerable, but the animals still face an uphill battle for survival. Moreover, the number of giant pandas is also rare. With the increase of captive individuals, wild release has become an inevitable task that human face. It is important to evaluate at least how much giant pandas are remained for being released to the wild. Several methods have been presented in the literature to assess minimum viable population (MVP) of giant pandas. However, each of these methods is designed only based on population sizes. In this paper, a membrane computing model based on behavioral biology is proposed to evaluate the MVP of the species. An application of the proposed data processing to binary tree whose nodes are composed of population sizes is also proposed. An binary tree is generated by integrating the following three techniques: (a) a random technique to generate the vast majority of populations; (b) a modified binary search method to obtain an order population size sequence; (c) a modified inorder traversal to generate an order binary tree and a parent tree. This is the first attempt to apply membrane computing model to assess the MVP of giant pandas. The effectiveness of the presented general PDP system is verified by means of the numbers or amounts of subpopulations. Besides, we also provide the population distribution of the obtained MVP. [ABSTRACT FROM AUTHOR]

Food quality assessment is an important part of the food industry. The traditional food quality assessment technologies have the limitations of inconsistent and different technical defects for each method. Data mining technology has significant advantages in dealing with the problems of uncertainty and fuzziness. Therefore, this study proposes a food quality assessment model based on data mining, which aims to realize the standardization and consistency of food quality assessment, and can achieve or exceed the accuracy of existing technologies, so as to solve the obvious problems existing in traditional assessment methods. The core of the proposed model is to design a deep learning framework based on double layer rough-refinement optimization. The first layer is rough optimization, which introduces the thought of multi-objective optimization to optimize the topological structure of neural networks with various candidate types and candidate depths. The second layer is refinement adjustment, which uses meta heuristic algorithm to globally optimize the weight parameters of the network model. The combination of rough and refinement optimization can greatly reduce the computation of overall simultaneous optimization and globally optimize the neural network model with the highest accuracy from the neural network type, topology, and network parameters. Two kinds of food quality assessment problems are used to simulate and verify the proposed deep learning framework. The results prove that the framework is effective, feasible, and adaptability, and the proposed assessment model can well solve different types of food quality assessments. [ABSTRACT FROM AUTHOR]

This letter presents a novel robust prony method for power system oscillatory modes monitoring that is immune to synchrophasor measurement noise and outliers. The conventional least squares for polynomial coefficients estimation is reformulated as a structural weighted total least square (WTLS) estimation problem to mitigate the impact of measurement noise. To enhance the robustness of WTLS for handling gross measurement errors, i.e., outliers, Huber type M-estimator is advocated and solved by means of an iteratively reweighted regression algorithm. Numerical results on the simplified realistic WECC 179-bus system demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

This letter proposes a statistical consistency check-based imperfect false data injection attacks detector that is more effective than the conventional residual-based methods. It is shown that the proposed detector could detect attacks with high probability by using a limited number of secure PMU measurements even if the probability of the false alarm is low. Numerical results validate its effectiveness and practicability. [ABSTRACT FROM AUTHOR]

This paper presents a membrane algorithm, called MAQIS, by appropriately combining concepts and principles of membrane computing and quantum-inspired evolutionary approach. MAQIS has four distinct features from the membrane algorithms reported in the literature: initial solutions are only inside the skin membrane; different regions separated by membranes have different components of the algorithm; all the components inside membranes cooperate to produce offspring in a single evolutionary generation; communication rules are performed in a single evolutionary step. Extensive experiments conducted on knapsack problems show that MAQIS outperforms five counterpart approaches and our previous work. The effectiveness of MAQIS is also verified in the application of image processing. [ABSTRACT FROM AUTHOR]

This paper proposes a framework named multi-objective ant colony optimization based on decomposition (MoACO/D) to solve bi-objective traveling salesman problems (bTSPs). In the framework, a bTSP is first decomposed into a number of scalar optimization subproblems using Tchebycheff approach. To suit for decomposition, an ant colony is divided into many subcolonies in an overlapped manner, each of which is for one subproblem. Then each subcolony independently optimizes its corresponding subproblem using single-objective ant colony optimization algorithm and all subcolonies simultaneously work. During the iteration, each subproblem maintains an aggregated pheromone trail and an aggregated heuristic matrix. Each subcolony uses the information to solve its corresponding subproblem. After an iteration, a pheromone trail share procedure is evoked to realize the information share of those subproblems solved by common ants. Three MoACO algorithms designed by, respectively, combining MoACO/D with AS, MMAS and ACS are presented. Extensive experiments conducted on ten bTSPs with various complexities manifest that MoACO/D is both efficient and effective for solving bTSPs and the ACS version of MoACO/D outperforms three well-known MoACO algorithms on large bTSPs according to several performance measures and median attainment surfaces. [ABSTRACT FROM AUTHOR]

This study aimed to estimate the dietary exposure towards mycotoxins of residents in Gansu province, China, from 2014–2020 through surveillance data on mycotoxins in grains and grain products. Fumonisin B1 (FB1), Deoxynivalenol (DON), 3- and 15-Acetyl-deoxynivalenol (3-ADON and 15-ADON), Tentoxin (TEN), Tenuazonic acid (TeA) and Zearalenone (ZEN) in 863 grains and grain products were detected by HPLC-MS and UPLC-MS. DON was the most detected mycotoxin of all samples. For women, the average dietary exposure to DON was 1.49 μg/kg bw/day, with 55.8% of the individuals eating dried noodles exceeding tolerable daily intake. The hazard quotient values were 1.24–12.60, so greater than 1 for DON at the average, 90th percentile, 95th percentile, and maximum levels: 44.6% of the HQ values for men and 45.7% for women were greater than 1. [ABSTRACT FROM AUTHOR]

The optimization of robot controller parameters is a crucial task for enhancing robot performance, yet it often presents challenges due to the complexity of multi-objective, multi-dimensional multi-parameter optimization. This paper introduces a novel approach aimed at efficiently optimizing robot controller parameters to enhance its motion performance. While spiking neural P systems have shown great potential in addressing optimization problems, there has been limited research and validation concerning their application in continuous numerical, multi-objective, and multi-dimensional multi-parameter contexts. To address this research gap, our paper proposes the Entropy-Weighted Numerical Gradient Optimization Spiking Neural P System, which combines the strengths of entropy weighting and spiking neural P systems. First, the introduction of entropy weighting eliminates the subjectivity of weight selection, enhancing the objectivity and reproducibility of the optimization process. Second, our approach employs parallel gradient descent to achieve efficient multi-dimensional multi-parameter optimization searches. In conclusion, validation results on a biped robot simulation model show that our method markedly enhances walking performance compared to traditional approaches and other optimization algorithms. We achieved a velocity mean absolute error at least 35% lower than other methods, with a displacement error two orders of magnitude smaller. This research provides an effective new avenue for performance optimization in the field of robotics. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel Reinforcement Learning (RL) algorithm for robotic motion control, that is, a constrained Deep Deterministic Policy Gradient (DDPG) deviation learning strategy to assist biped robots in walking safely and accurately. The previous research on this topic highlighted the limitations in the controller's ability to accurately track foot placement on discrete terrains and the lack of consideration for safety concerns. In this study, we address these challenges by focusing on ensuring the overall system's safety. To begin with, we tackle the inverse kinematics problem by introducing constraints to the damping least squares method. This enhancement not only addresses singularity issues but also guarantees safe ranges for joint angles, thus ensuring the stability and reliability of the system. Based on this, we propose the adoption of the constrained DDPG method to correct controller deviations. In constrained DDPG, we incorporate a constraint layer into the Actor network, incorporating joint deviations as state inputs. By conducting offline training within the range of safe angles, it serves as a deviation corrector. Lastly, we validate the effectiveness of our proposed approach by conducting dynamic simulations using the CRANE biped robot. Through comprehensive assessments, including singularity analysis, constraint effectiveness evaluation, and walking experiments on discrete terrains, we demonstrate the superiority and practicality of our approach in enhancing walking performance while ensuring safety. Overall, our research contributes to the advancement of biped robot locomotion by addressing gait optimisation from multiple perspectives, including singularity handling, safety constraints, and deviation learning. [ABSTRACT FROM AUTHOR]

Enzymatic Numerical P Systems (ENPSs) are a model of membrane computing that is well-suited for the simulation of physical processes and that has been used for the design and the implementation of motion controllers for wheeled robots and flying drones. The ENPSs model has been proven to be Turing universal and the theoretical effort was focused on minimizing various descriptional complexity parameters. In this paper, we explore the minimum number of enzymatic rules needed to achieve universality in ENPSs, specifically focusing on the all-parallel derivation mode where all applicable rules are applied at the same time. We show that in the case of a linear restriction for production functions, the universality can be obtained using 21 enzymatic rules, substantially improving previously known results. If production functions are allowed to be polynomials of degree 2, we show that a single enzymatic rule is sufficient to achieve universality. To obtain these results, a new proof method is introduced based on the translation of ENPSs to systems of conditional recurrences. • The number of enzymatic rules with linear production functions for ENPSs to reach universality is reduced to 21 from 39. • Only one enzymatic rule is sufficient for ENPSs to achieve universality when using degree-2 polynomials in nonlinear production functions. • Conditional recurrences are introduced to prove the universality of ENPSs. [ABSTRACT FROM AUTHOR]

Computing models are a good and effective way to study population dynamics of endangered species like giant pandas. Until now, a variety of computing models were proposed for giant pandas, but no survey on computing models for population dynamics of giant panda ecosystems has yet appeared in the specialised literature. It is necessary to provide an overview of the state-of-the-art of this topic so as to allow newcomers to the area to obtain a clear understanding of developments, key research problems, properties of computing models in this field, including those that are currently under way. This paper proposes a unified framework to clearly summarise the computing models used for studying the population dynamics of threatened species with respect to theoretical and application aspects and presents a comprehensive and systematic survey of the state-of-the-art computing models. This paper also introduces basic concepts of computing models, surveys their theoretical developments and applications, sketches the differences between various computing model variants, and compares the advantages and limitations of the models. Comparing with single-factor computing models and double-factor computing models, multi-factor computing models, especially multi-environment population dynamics P systems, are more suitable for investigating giant panda ecosystems, because the use of bottom-up way to consider evolutionary behaviours influencing giant pandas' population. • Computing models for population dynamics of giant pandas. • Unified framework of computing models for giant pandas. • Comprehensive and systematic survey of the state-of-the-art computing models. • Differences, advantages and limitations of computing models. [Display omitted] [ABSTRACT FROM AUTHOR]

In recent years, incremental sampling-based motion planning algorithms have been widely used to solve robot motion planning problems in high-dimensional configuration spaces. In particular, the Rapidly-exploring Random Tree (RRT) algorithm and its asymptotically-optimal counterpart called RRT * are popular algorithms used in real-life applications due to its desirable properties. Such algorithms are inherently iterative, but certain modules such as the collision-checking procedure can be parallelized providing significant speedup with respect to sequential implementations. In this paper, the RRT and RRT * algorithms have been adapted to a bioinspired computational framework called Membrane Computing whose models of computation, a.k.a. P systems, run in a non-deterministic and massively parallel way. A large number of robotic applications are currently using a variant of P systems called Enzymatic Numerical P systems (ENPS) for reactive controlling, but there is a lack of solutions for motion planning in the framework. The novel models in this work have been designed using the ENPS framework. In order to test and validate the ENPS models for RRT and RRT * , we present two ad-hoc implementations able to emulate the computation of the models using OpenMP and CUDA. Finally, we show the speedup of our solutions with respect to sequential baseline implementations. The results show a speedup up to 6x using OpenMP with 8 cores against the sequential implementation and up to 24x using CUDA against the best multi-threading configuration. [ABSTRACT FROM AUTHOR]

Membrane computing is a branch of natural computing that studies models of computation inspired by the functioning of the living cell and by cooperation of cells organized in different ways, such as in tissues, organs, or populations. In "On the Power of Boundary Rule Application in Membrane Computing", Battyányi discusses how to simulate maximal parallelism by non-cooperative boundary rules applied only in sequential mode, as well as by membrane dissolution, promoters, and inhibitors. Organized since the year 2000, the International Conference on Membrane Computing is the main event covering membrane computing, where scientists meet to present current research results, to discuss actual trends and to exchange ideas on various research questions. [Extracted from the article]

With the demand for high-endurance lithium-ion batteries in new energy vehicles, communication and portable devices, high energy density lithium-ion batteries have become the main research direction of the battery industry. State of Charge (SoC), as a state parameter that must be accurately evaluated by the battery management system, enables online safety monitoring of the battery operation, and prolongs its service life. In this paper, an improved algorithm based on multi-hidden layer long short-term memory (MHLSTM) neural network and suboptimal fading extended Kalman filtering (SFEKF) is proposed for synthetic SoC estimation. First, the battery external measurable information is captured. The battery real data properties are matched with the network topology without additional battery model construction, and the battery SoC is roughly evaluated using an MHLSTM network. Then, a suboptimal fading factor is inserted into the extended Kalman filter (EKF) algorithm for iterative recursion and adaptive handling to smooth the prediction results of the MHLSTM network and enhance the accuracy of state estimation, system stability, and generality. Three customized electric vehicle (EV) driving conditions datasets are categorized into training and testing sets to fulfill the efficient estimation of synthetic SoC by the fusion algorithm and solve the time series problem. Using the maximum error (ME), mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), the results show that the maximum bias of the fusion algorithm to estimate the synthetic SoC is limited to within 1.2%, even under the abrupt change of the system. It can converge to the real value quickly and maintains an excellent tracking capability for data changes, reflecting the high accuracy estimation capability and the robustness possessed by the system. • A fusion model is presented for synthetic SoC estimation. • The MHLSTM network topology is matched with the actual battery measurement data. • The SFEKF algorithm is developed to adaptively smooth the MHLSTM model results. • The accuracy of the model is verified by simulation tests on different data sets. • The method features excellent generalization ability and can improve the estimation accuracy. [ABSTRACT FROM AUTHOR]

The document is an introduction to the topic of artificial neural networks, which are formal models based on how information is processed in the human brain. Different approaches have been studied, some incorporating new elements to solve complex problems, while others aim to closely resemble real-life neural networks. The focus of this introduction is on spiking neural networks, which allow information to spread in a non-synchronous way. The document discusses the spiking neuron model and learning methods specific to these networks, as well as their applications in various fields such as computer vision, robot control, and complex systems modeling. The special issue of the journal includes studies on spiking neural P systems, covering theoretical aspects, applications, and rigorous review processes. The guest editors are from universities in the UK, Italy, Spain, and China. [Extracted from the article]

Spiking Neural Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron.In the present work, a novel communication strategy among neurons of Spiking Neural Systems is proposed. In the resulting models, called Spiking Neural Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron).The Spiking Neural Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems. [ABSTRACT FROM AUTHOR]

Face recognition technology has been widely used in the field of artificial intelligence. The technology needs to be carried out normally under the appropriate light, however, there is not ideal light, even poor-lighted for the face recognition device, and with the head in deflect angle. The poor-lighted under various head poses will influence the face recognition significantly. To address the issue, we present a novel and practical architecture based on deep fully convolutional neural network and generative adversarial networks for illumination normalization of facial images. The proposed method is termed as illumination normalization generative adversarial network. Compared with previous methods based on deep learning, our approach does not require identity and illumination label as input. We preserve identities of faces by an elaborately-designed generator together with content loss. Moreover, the framework of our scheme is simpler than previous methods based on deep learning. It can address the illumination of frontal and non-frontal face. In order to fairly evaluate the proposed method against state-of-the-art models, the peak signal to noise ratio is employed to estimate the performance of illumination normalization algorithm. Experimental results show that the proposed method achieves favorable normalization results against previous models under various head poses and illumination challenges. [ABSTRACT FROM AUTHOR]

The highly endangered giant panda (Ailuropoda melanoleuca) is the world's most widely recognised conservation icon. Population dynamics models can get on track to a healthy population for giant pandas, and assess its development over time. This paper proposes a new way to study population dynamics of giant pandas in captivity by means of membrane computing, a bio-inspired computational paradigm based on processing multisets of objects within a cell structure, following a series of evolution rules. This framework is used to model the intrinsically stochastic real-life evolution processes of giant pandas, based on pedigree data. This is the first attempt to collect and analyse so complete source, and to investigate population dynamics of giant pandas based on them. Pedigree data consisting of the number of giant panda individuals per age are provided by Chengdu Research Base of Giant Panda Breeding of China. A special simulator has been developed to assist in the design and formal verification of the mathematical model presented. Particularly, the model feasibility, effectiveness, soundness and robustness have been validated by the simulator, which also enables decision-making based on the simulation results of conducted virtual experiments. [ABSTRACT FROM AUTHOR]

In speaker recognition tasks, convolutional neural network (CNN)-based approaches have shown significant success. Modeling the long-term contexts and efficiently aggregating the information are two challenges in speaker recognition, and they have a critical impact on system performance. Previous research has addressed these issues by introducing deeper, wider, and more complex network architectures and aggregation methods. However, it is difficult to significantly improve the performance with these approaches because they also have trouble fully utilizing global information, channel information, and time-frequency information. To address the above issues, we propose a lighter and more efficient CNN-based end-to-end speaker recognition architecture, ResSKNet-SSDP. ResSKNet-SSDP consists of a residual selective kernel network (ResSKNet) and self-attentive standard deviation pooling (SSDP). ResSKNet can capture long-term contexts, neighboring information, and global information, thus extracting a more informative frame-level. SSDP can capture short- and long-term changes in frame-level features, aggregating the variable-length frame-level features into fixed-length, more distinctive utterance-level features. Extensive comparison experiments were performed on two popular public speaker recognition datasets, Voxceleb and CN-Celeb, with current state-of-the-art speaker recognition systems and achieved the lowest EER/DCF of 2.33%/0.2298, 2.44%/0.2559, 4.10%/0.3502, and 12.28%/0.5051. Compared with the lightest x-vector, our designed ResSKNet-SSDP has 3.1 M fewer parameters and 31.6 ms less inference time, but 35.1% better performance. The results show that ResSKNet-SSDP significantly outperforms the current state-of-the-art speaker recognition architectures on all test sets and is an end-to-end architecture with fewer parameters and higher efficiency for applications in realistic situations. The ablation experiments further show that our proposed approaches also provide significant improvements over previous methods. [ABSTRACT FROM AUTHOR]

Spiking neural P systems (SN P systems), inspired by biological neurons, are introduced as symbolical neural-like computing models that encode information with multisets of symbolized spikes in neurons and process information by using spike-based rewriting rules. Inspired by neuronal activities affected by enzymes, a numerical variant of SN P systems called enzymatic numerical spiking neural P systems (ENSNP systems) is proposed wherein each neuron has a set of variables with real values and a set of enzymatic activation-production spiking rules, and each synapse has an assigned weight. By using spiking rules, ENSNP systems can directly implement mathematical methods based on real numbers and continuous functions. Furthermore, ENSNP systems are used to model ENSNP membrane controllers (ENSNP-MCs) for robots implementing wall following. The trajectories, distances from the wall, and wheel speeds of robots with ENSNP-MCs for wall following are compared with those of a robot with a membrane controller for wall following. The average error values of the designed ENSNP-MCs are compared with three recently fuzzy logical controllers with optimization algorithms for wall following. The experimental results showed that the designed ENSNP-MCs can be candidates as efficient controllers to control robots implementing the task of wall following. [ABSTRACT FROM AUTHOR]

Ticks rank second in the world as vectors of disease. Tick infestation is one of the factors threatening the health and survival of giant pandas. Here, we describe the mitogenomes of Ixodes acutitarsus and Ixodes ovatus parasitizing giant pandas, and perform comparative and phylogenetic genomic analyses on the newly sequenced and other available mitogenomes of hard ticks. All six newly determined mitogenomes contain a typical gene component and share an ancient Arthropoda gene arrangement pattern. Our study suggests that I. ovatus is a species complex with high genetic divergence, indicating that different clades of I. ovatus represent distinct species. Comparative mitogenomic analyses show that the average A + T content of Ixodidae mitogenomes is 78.08%, their GC-skews are strongly negative, while AT-skews fluctuate around 0. A large number of microsatellites are detected in Ixodidae mitogenomes, and the main microsatellite motifs are mononucleotide A and trinucleotide AAT. We summarize five gene arrangement types, and identify the trnY-COX1-trnS1-COX2-trnK-ATP8-ATP6-COX3-trnG fragment is the most conserved region, whereas the region near the control region is the rearrangement hotspot in Ixodidae mitogenomes. The phylogenetic trees based on 15 genes provide a very convincing relationship (Ixodes + (Robertsicus + ((Bothriocroton + Haemaphysalis) + (Amblyomma + (Dermacentor + (Rhipicentor + (Hyalomma + Rhipicephalus))))))) with very strong supports. Remarkably, Archaeocroton sphenodonti is embedded in the Haemaphysalis clade with strong supports, resulting in paraphyly of the Haemaphysalis genus, so in-depth morphological and molecular studies are essential to determine the taxonomic status of A. sphenodonti and its closely related species. Our results provide new insights into the molecular phylogeny and evolution of hard ticks, as well as basic data for population genetics assessment and efficient surveillance and control for the giant panda-infesting ticks. [ABSTRACT FROM AUTHOR]

The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods. [ABSTRACT FROM AUTHOR]