Analysis and improvement of Boolean chaos robustness to noise

2021 ◽  
pp. 106064
Author(s):  
HaiFang Liu ◽  
YunCai Wang ◽  
GuoDong Zhang ◽  
JianGuo Zhang
Keyword(s):  
Robustness To Noise

Diagnosis of Turbine Engine Transient Performance With Model-Based Parameter Estimation Techniques

1994 ◽  
Author(s):  
Jeff W. Bird ◽  
Howard M. Schwartz
Keyword(s):  
Gas Turbine ◽  
Test Data ◽  
Physical Models ◽  
Turbine Engine ◽  
Robustness To Noise ◽  
Linear Effects ◽  
Diagnostic Capabilities ◽  
Engine Components ◽  
And Control ◽  
Parameter Estimation Techniques

This review surveys knowledge needed to develop an improved method of modelling the dynamics of gas turbine performance for fault diagnosis applications. Aerothermodynamic and control models of gas turbine processes are examined as complementary to models derived directly from test data. Extensive, often proprietary data are required for physical models of components, while system identification (SI) methods need data from specially-designed tests. Current methods are limited in: tuning models to test data, non-linear effects, component descriptions in SI models, robustness to noise, and inclusion of control systems and actuators. Conclusions are drawn that SI models could be formulated, with parameters which describe explicitly the functions of key engine components, to offer improved diagnostic capabilities.

scholarly journals Design of a biochemical circuit motif for learning linear functions

2014 ◽  
Vol 11 (101) ◽  
pp. 20140902 ◽  
Author(s):  
Matthew R. Lakin ◽  
Amanda Minnich ◽  
Terran Lane ◽  
Darko Stefanovic
Keyword(s):  
Internal State ◽  
Linear Functions ◽  
Learning Performance ◽  
Adaptive Behaviour ◽  
Biological Processes ◽  
Biochemical System ◽  
Biochemical Systems ◽  
Robustness To Noise ◽  
Novel Design

Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective.

scholarly journals Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

2021 ◽  
Vol 10 (11) ◽  
pp. 757
Author(s):  
Pin Nie ◽  
Zhenjie Chen ◽  
Nan Xia ◽  
Qiuhao Huang ◽  
Feixue Li
Keyword(s):  
Traffic Management ◽  
Similarity Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Motion Direction ◽  
Maritime Traffic ◽  
A Cell ◽  
Robustness To Noise ◽  
Trajectory Similarity

Automatic Identification System (AIS) data have been widely used in many fields, such as collision detection, navigation, and maritime traffic management. Similarity analysis is an important process for most AIS trajectory analysis topics. However, most traditional AIS trajectory similarity analysis methods calculate the distance between trajectory points, which requires complex and time-consuming calculations, often leading to substantial errors when processing AIS trajectory data characterized by substantial differences in length or uneven trajectory points. Therefore, we propose a cell-based similarity analysis method that combines the weight of the direction and k-neighborhood (WDN-SIM). This method quantifies the similarity between trajectories based on the degree of proximity and differences in motion direction. In terms of its effectiveness and efficiency, WDN-SIM outperformed seven traditional methods for trajectory similarity analysis. Particularly, WDN-SIM has a high robustness to noise and can distinguish the similarities between trajectories under complex situations, such as when there are opposing directions of motion, large differences in length, and uneven point distributions.

scholarly journals Image denoising using anisotropic second and fourth order diffusions based on gradient vector convolution

2012 ◽  
Vol 9 (4) ◽  
pp. 1493-1511 ◽  
Author(s):  
Huaibin Wang ◽  
Yuanquan Wang ◽  
Wenqi Ren
Keyword(s):  
Image Denoising ◽  
Fourth Order ◽  
Numerical Stability ◽  
Anisotropic Diffusion ◽  
Computational Cost ◽  
Gradient Vector ◽  
Order Model ◽  
Robustness To Noise ◽  
Fourth Order Model ◽  
Low Computational Cost

In this paper, novel second order and fourth order diffusion models are proposed for image denoising. Both models are based on the gradient vector convolution (GVC) model. The second model is coined by incorporating the GVC model into the anisotropic diffusion model and the fourth order one is by introducing the GVC to the You-Kaveh fourth order model. Since the GVC model can be implemented in real time using the FFT and possesses high robustness to noise, both proposed models have many advantages over traditional ones, such as low computational cost, high numerical stability and remarkable denoising effect. Moreover, the proposed fourth order model is an anisotropic filter, so it can obviously improve the ability of edge and texture preserving except for further improvement of denoising. Some experiments are presented to demonstrate the effectiveness of the proposed models.

scholarly journals Quantum learning with noise and decoherence: a robust quantum neural network

2019 ◽  
Author(s):  
Elizabeth Behrman ◽  
Nam Nguyen ◽  
James Steck
Keyword(s):  
Neural Network ◽  
Quantum Computing ◽  
Large Scale ◽  
Learning Approaches ◽  
Quantum Neural Network ◽  
Quantum Learning ◽  
Learning With Noise ◽  
Robustness To Noise ◽  
Robust To Noise ◽  
Arbitrary Quantum

<p>Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing. </p> <p> </p>

scholarly journals Selection against expression noise explains the origin of the hourglass pattern of Evo-Devo

2019 ◽  
Author(s):  
Jialin Liu ◽  
Michael Frochaux ◽  
Vincent Gardeux ◽  
Bart Deplancke ◽  
Marc Robinson-Rechavi
Keyword(s):  
Regulatory Elements ◽  
Mutational Robustness ◽  
Expression Variability ◽  
Expression Noise ◽  
Environmental Perturbations ◽  
Phylotypic Stage ◽  
Robustness To Noise ◽  
Selection For ◽  
Hourglass Model ◽  
Evo Devo

The evolution of embryological development has long been characterized by deep conservation. Both morphological and transcriptomic surveys have proposed a “hourglass” model of Evo-Devo1,2. A stage in mid-embryonic development, the phylotypic stage, is highly conserved among species within the same phylum3–7. However, the reason for this phylotypic stage is still elusive. Here we hypothesize that the phylotypic stage might be characterized by selection for robustness to noise and environmental perturbations. This could lead to mutational robustness, thus evolutionary conservation of expression and the hourglass pattern. To test this, we quantified expression variability of single embryo transcriptomes throughout fly Drosophila melanogaster embryogenesis. We found that indeed expression variability is lower at extended germband, the phylotypic stage. We explain this pattern by stronger histone modification mediated transcriptional noise control at this stage. In addition, we find evidence that histone modifications can also contribute to mutational robustness in regulatory elements. Thus, the robustness to noise does indeed contributes to robustness of gene expression to genetic variations, and to the conserved phylotypic stage.

scholarly journals Model Updating Using a Quadratic Form

2005 ◽  
Author(s):  
Pablo A. Tarazaga ◽  
Yoram Halevi ◽  
Daniel J. Inman
Keyword(s):  
Quadratic Form ◽  
Model Updating ◽  
Lower Dimension ◽  
Theoretic Analysis ◽  
Compression Method ◽  
Weighted Version ◽  
Weighting Matrix ◽  
Robustness To Noise ◽  
Free Parameters ◽  
Fixed Structure

The paper presents a method for model updating, called Quadratic Compression Method (QCM). The updated model has a fixed structure with some free parameters. Algebraic manipulations of the eigenvalue equation lead to a simplified equation with a lower dimension. This equation is then solved in a Least Squares sense. The method is shown to belong to the class of Minimization of the Error in the Characteristic Equation (MECE), with a particular choice of the weighting matrix. The paper presents also a weighted version of the method, called WQCM, which is motivated by reducing the effect of measuring noise. In addition to the theoretic analysis, the superior robustness to noise properties of QCM and WQCM are demonstrated by simulations and experimentally.

On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments

2021 ◽  
pp. 1-30
Author(s):  
Asieh Abolpour Mofrad ◽  
Samaneh Abolpour Mofrad ◽  
Anis Yazidi ◽  
Matthew Geoffrey Parker
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Large Set ◽  
Memory Architecture ◽  
Retrieval Performance ◽  
Storage And Retrieval ◽  
Memory Efficiency ◽  
Robustness To Noise ◽  
A Chain ◽  
Sequence Storage

Abstract Associative memories enjoy many interesting properties in terms of error correction capabilities, robustness to noise, storage capacity, and retrieval performance, and their usage spans over a large set of applications. In this letter, we investigate and extend tournament-based neural networks, originally proposed by Jiang, Gripon, Berrou, and Rabbat (2016), a novel sequence storage associative memory architecture with high memory efficiency and accurate sequence retrieval. We propose a more general method for learning the sequences, which we call feedback tournament-based neural networks. The retrieval process is also extended to both directions: forward and backward—in other words, any large-enough segment of a sequence can produce the whole sequence. Furthermore, two retrieval algorithms, cache-winner and explore-winner, are introduced to increase the retrieval performance. Through simulation results, we shed light on the strengths and weaknesses of each algorithm.

scholarly journals A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2170
Author(s):  
Vittoria Bruni ◽  
Michela Tartaglione ◽  
Domenico Vitulano
Keyword(s):  
Instantaneous Frequency ◽  
Individual Component ◽  
Stationary Process ◽  
Practical Applications ◽  
Time Frequency ◽  
Number Of Components ◽  
Multicomponent Signal ◽  
Multicomponent Signals ◽  
Robustness To Noise ◽  
Signal Complexity

Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise.

scholarly journals Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3997 ◽  
Author(s):  
Tam Nguyen ◽  
Xiaoli Qin ◽  
Anh Dinh ◽  
Francis Bui
Keyword(s):  
Template Matching ◽  
Moving Average ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Moving Average Filter ◽  
Ecg Signals ◽  
Low Resource ◽  
Peak Detection Algorithm ◽  
Robustness To Noise ◽  
Average Filter

A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices.

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