ySpiking neural P systems with anti-spikes (ASN P systems, for short) are a variant of spiking neural P systems, which are inspired by inhibitory impulses/spikes in biological neural systems. In general ASN P systems,...
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ySpiking neural P systems with anti-spikes (ASN P systems, for short) are a variant of spiking neural P systems, which are inspired by inhibitory impulses/spikes in biological neural systems. In general ASN P systems, spikes and anti-spikes can annihilate with each other when they meet in a neuron. The annihilation has priority to using spiking and forgetting rules, and takes no time to finish. In this work, we consider ASN P systems without annihilating priority with neurons working in a 'flip-flop' way, that is each neuron can only produce spikes by anti-spikes or produce anti-spikes from spikes. As results, such systems achieve the Turing completeness as number generator. This gives a positive answer to an open problem left in IJCCC (2009, Vol. IV, No. 3, pp. 273-282).
The design of DNA sequences is one of the most practical and important research topics in DNA computing. We adopt taboo search algorithm and improve the method for the systematic design of equal-length DNA sequences, ...
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The design of DNA sequences is one of the most practical and important research topics in DNA computing. We adopt taboo search algorithm and improve the method for the systematic design of equal-length DNA sequences, which can satisfy certain combinatorial and thermodynamic constraints. Using taboo search algorithm, our method can avoid trapping into local optimization and can nd a set of good DNA sequences satisfying required constraints.
Zernike moments (ZMs) are widely used in many image analysis and pattern recognition problems because of their superiority compared with other moments. However, they suffer from high computation cost and inherent erro...
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Zernike moments (ZMs) are widely used in many image analysis and pattern recognition problems because of their superiority compared with other moments. However, they suffer from high computation cost and inherent error. Previous researches have shown that the algorithm, computing ZMs in polar system, improves the ZMs accuracy of the reconstruction and invariance properties dramatically. In this study, the authors firstly modify a direct method for computing ZMs in polar coordinates and present a recursive relation. Then, this study presents an algorithm for fast computation of ZMs, based on the improved polar pixel tiling scheme. Owing to the symmetrical property, ZMs can be obtained by computing only one-sixteenth circle of the radial polynomials, which means that the number of pixels involved in the computation of ZMs is only 6.25% of the previous method. This leads to a significant reduction in the computational complexity requirements. A comparison with the other conventional method is performed in detail. The obtained results show the superiority of the proposed method.
Pneumatic muscle (PM) has many advantages such as light weight, high power to weight ratio and low price. However, it has strong time varying characteristic. The complex nonlinear dynamics of PM system poses some chal...
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Pneumatic muscle (PM) has many advantages such as light weight, high power to weight ratio and low price. However, it has strong time varying characteristic. The complex nonlinear dynamics of PM system poses some challenges for achieving accurate modeling and control. To solve these problems, we propose nonlinear internal model control (IMC) using echo state network (ESN) for PM system in this paper. The ESN based IMC is termed ESNBIMC, which fully embodies the virtues of ESN and IMC. In ESNBIMC, the dynamic model of PM system is identified by an ESN. The other ESN is trained to learn the inverse dynamics of the system, and then it can be used as a nonlinear controller. Recursive Least Square (RLS) algorithm can be applied to online training the ESN without affecting the previous weight structure, which is very suitable for real-time control problems. By using the identification ability of ESN and RLS, high accurate plant model of PM system without detailed model information can be built. In addition, strong robustness also can be attained by online self-tuning of controller and internal model. Experiment demonstrates the effectiveness of the proposed control algorithm. The results show that ESNBIMC achieves satisfactory tracking performance for PM system.
Recently, experiments have demonstrated that simple binary arithmetic and logical operations can be computed by the process of self- assembly of DNA tiles. In this paper, we show how the tile assembly process can be u...
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Recently, experiments have demonstrated that simple binary arithmetic and logical operations can be computed by the process of self- assembly of DNA tiles. In this paper, we show how the tile assembly process can be used for subtraction and division. In order to achieve this aim, four systems, including the comparator system, the duplicator system, the subtraction system, and the division system, are pro- posed to compute the difference and quotient of two input numbers using the tile assembly model. This work indicates that these systems can be carried out in polynomial time with optimal O(1) distinct tile types in parallel and at very low cost. Furthermore, we provide a scheme to factor the product of two prime numbers, and it is a breakthrough in basic biological operations using a molecular computer by self-assembly.
Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate t...
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Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.
This study investigates the consensus problem of second-order multi-agent systems (MASs) via impulsive control using position-only information with communication delays. The communication delays between any two distin...
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Presently pneumatic muscles (PMs) are used in various applications due to their simple construction, lightweight and high force to weight ratio. However, pneumatic muscles are facing various problems due to their nonl...
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High-precision semantic segmentation methods require global information and more detailed local features. It is difficult for ordinary convolutional neural networks to efficiently use this information. In response to ...
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High-precision semantic segmentation methods require global information and more detailed local features. It is difficult for ordinary convolutional neural networks to efficiently use this information. In response to the above issues, this paper uses the attention to scale method and proposes a novel attention model for semantic segmentation, which aggregates multi-scale and context features to refine prediction. Specifically, the skeleton convolutional neural network framework takes in multiple different scales inputs, by which means the CNN can get representations in different scales. The proposed attention model will handle the features from different scale streams respectively and integrate them. Then location attention branch of the model learns to softly weight the multi-scale features at each pixel location. Moreover, we add an recalibrating branch, parallel to where location attention comes out, to recalibrate the score map per class. We achieve quite competitive results on PASCAL VOC 2012 and ADE20K datasets, which surpass baseline and related works.
This paper focuses on the discrete-time automatic pipeline, inventory and order-based production control system (APIOBPCS), a well-established production and inventory control model. The feedback mechanism within the ...
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This paper focuses on the discrete-time automatic pipeline, inventory and order-based production control system (APIOBPCS), a well-established production and inventory control model. The feedback mechanism within the replenishment rule enables the model to mitigate the bullwhip effect, but introduces a stability problem. In this research, a comprehensive stability analysis is conducted for arbitrary lead times using difference equation theory. On the basis of stability, a state space approach is advocated to analyse the impact of replenishment parameters, demand processes, and lead times on the robustness of the bullwhip effect. The stability results demonstrate that the production control system can easily be destabilised without incorporating the work-in-progress (WIP) feedback loop. Furthermore, it reveals that the stability problem for long lead times can be simplified with the stability condition independent of the lead time. The results obtained in this study provide useful guidelines for the selection of replenishment parameters to guarantee stability and mitigate the bullwhip effect.
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