Strong Redundancy Research Articles

Stability analysis and control parameter optimization for AC-DC hybrid spot networks

AC-DC hybrid spot networks contain strong redundancy. It further enhances the power supply reliability of spot network systems and is helpful for the integration of distributed energy resources (DERs). However, there are few stability analysis studies of AC-DC hybrid spot networks. Focusing on the AC-DC hybrid spot network, which adopts a unified control strategy, its stability problem is studied in this article. A mathematical model of each unit is constructed for this system. Via linearization of spatial state equations, a small-signal model of the AC-DC hybrid spot network is established under different operation modes. Moreover, corresponding eigenvalue criteria are obtained, and the stability problem is analyzed by using the eigenvalue analysis method when the AC-DC hybrid spot network is subjected to a slight interference under the unified control strategy. The dominant factors of each eigenvalue are studied. Based on this approach, the influence of different control parameters on the system stability is demonstrated. Then, the control parameters are selected for control optimization. A simulation model is constructed in MATLAB/Simulink software. The eigenvalue criteria and simulation results validate the applicability of the small-signal model constructed in this article and the effectiveness of the optimization conclusions.

Proximal Policy Optimization With Time-Varying Muscle Synergy for the Control of an Upper Limb Musculoskeletal System

Because of their unique adaptability, flexibility, and robustness, musculoskeletal robotic systems are regarded potentially as next-generation robots. However, motion learning and generation of such a robotic system are still challenging. This paper presents a neuromuscular control method, namely, TMS-PPO, based on time-varying muscle synergy (TMS) and proximal policy optimization (PPO). The electromyogram (EMG) activation signals of actual human motions are decomposed to obtain TMSs based on the temporal properties of the TMS. The weights of networks are trained to generate the scale and phase coefficients through the PPO. The coefficients modulate the TMSs to generate appropriate activation patterns to optimize motion learning of the musculoskeletal system. To verify the effectiveness of the proposed method, the TMSs are extracted from human upper limb muscle activation signals, and we compare TMS-PPO with PPO in the motion learning and generation process of an upper limb musculoskeletal system. The results show that TMS-PPO can complete the control tasks because the average errors of the joints are less than 0.05 rad. In the meantime, TMSs are used as motion primitives of the musculoskeletal system to simulate the process of the human CNS controlling muscles. It shows that TMS-PPO reduces the energy consumption and improves the learning rate significantly compared with the PPO. The learning episodes reduce from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">\(10^4\)</tex-math> </inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">\(10^3\)</tex-math> </inline-formula> , which indicates that TMS-PPO has a stronger learning ability and better physiological explanation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Due to the superiorities of the musculoskeletal system, humanoid robots that imitate human driven mechanisms are vigorously carried out worldwide. Taking advantages of human-like characteristics, the musculoskeletal robot provides new opportunities to understand and validate the human mechanisms of muscle control and motion learning, to compare the performance of the robot to that of humans as well as work in real world, e.g., human interactive robots, amusement robots and medical training robots in the future. However, strong redundancy, coupling, and nonlinearity of the system also raises many challenges for the investigation of the control problem. Inspired by how the human CNS controls a musculoskeletal system and realize motion generalization, a novel muscle-synergies-based neuromuscular control that combines time-varying muscle synergy (TMS) and Proximal Policy Optimization (PPO), namely, TMS-PPO is proposed in this paper. The learning efficiency of PPO and the physiological interpretation of the control process are improved during the motion learning and generation processes of the musculoskeletal system. Preliminary simulation experiments suggest that this method is feasible in terms of control accuracy and efficiency. Moreover, the performance of the TMS-PPO is comparable to the PPO without significant improvement. To solve this problem, in future work, we will introduce the cerebellar model into the control method which plays the role of adjusting and correcting the motions of the limbs to achieve accurate and stable control in the actions process of humans.

New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data

The inversion of Cu contents in rocks based on hyperspectral remote sensing is indicative of geochemical exploration as well as mineral resource exploration. Due to the strong redundancy of band information in hyperspectral data, characteristic band selection is the key to improving the accuracy of metal element inversion models in hyperspectral data. In this study, we proposed a framework that innovatively focuses on secondary screening method (SSM) as the core, allowing users to add different data preprocessing methods and machine learning algorithms to the framework based on expert experience, to obtain high-precision metal element inversion models suitable for sampling data. we first preprocessed the measured spectral curves using the first order derivative, second order derivative, continuum removal, multiple scattering correction and variance analysis to highlight band absorption characteristics. In addition, we performed SSM to obtain Cu-related characteristic bands, which were further used as input data to train and compare the performance of Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF) and Gradient Boosting Regression Tree (GBRT). The results show that both the spectral pretreatment and modeling method could potentially impact the inversion accuracy. The accuracy of the ensemble method applied in fresh faces data (i.e., a CR-SSM-RF with R2 values of 0.77 and CR-SSM-GBRT with R2 values of 0.81) performed better than other models with an accuracy improvement of up to 80%. Compared to the full band model, the accuracy of characteristic bands selection model increased 50%. This also illustrates that the CR-SSM-RF or CR-SSM-GBRT model significantly improves the accuracy of the inversion of Cu contents in rocks compared to the original spectral reflectance. Our study provides a new idea for selecting different spectral preprocessing methods as the input values for the future model and will also offer an executable framework for the inversion of Cu contents in rocks of different lithology on a large scale in the future.

Sensor self-diagnosis method based on a graph neural network

Many types of sensors are used in industrial processes, and their reliability is high. However, the traditional method of regularly detecting and evaluating their health status is time-consuming and laborious, and is not suitable for the development of intelligent sensors. In this work, the relative entropy method is first used to quantitatively evaluate the redundancy relationship between sensors, and a sensor graph network is established based on this relationship. Secondly, an unsupervised multi-sensor self-diagnosis model, called attention-based pruning graph convolutional network, is proposed. In order to capture the strong redundancy among sensors by the attention mechanism, multi-sensor timing prediction is realised using a graph convolutional neural network, and the health status of each sensor can be independently judged by the changes in redundancy among the sensors. Finally, a temperature measurement system in a nickel flash furnace is considered as a case study to verify the feasibility and effectiveness of the proposed method.

Effects of CASZ1, WNT2B and PTPRG SNPs on stroke susceptibility in the Chinese Han population.

Stroke is an important cause of death and disability worldwide, ranking second in the cause of death, and it is thought to be related to genetic factors. The purpose of our study is to investigate the association between CASZ1, WNT2B and PTPRG single nucleotide polymorphisms (SNPs) and stroke risk in the Chinese population. We recruited 1418 volunteers, comprised of 710 stroke cases and 708 controls in this study. We used MassARRAY iPLEX GOLD method to genotype the three SNPs on CASZ1, WNT2B and PTPRG. Logistic regression was used to analyse the association between these SNPs and stroke, and odds ratios (ORs) and 95% confidence intervals (CIs) were then calculated. What's more, the interactions among SNPs were predicted by multi-factor dimensionality reduction (MDR) analysis. This research demonstrated that CASZ1 rs880315 and PTPRG rs704341 were associated with reduced stroke susceptibility. More precisely, CASZ1 rs880315 was associated with reduced stroke susceptibility in people aged ≤64 years and women. PTPRG rs704341 was associated with reduced stroke susceptibility in people aged >64 years, women, non-smokers and non-drinkers. Conversely, WNT2B rs12037987 was related to elevated stroke susceptibility in people aged >64 years, women and non-smokers. In addition, CASZ1 rs880315, WNT2B rs12037987 and PTPRG rs704341 had a strong redundancy relationship. Our study concludes that CASZ1 rs880315, WNT2B rs12037987 and PTPRG rs704341 are associated with stroke, and the study provides a basis for assessing genetic variants associated with stroke risk in the Han Chinese population.

A Dynamic Representation of mRNA Nucleotides Clarifies the Conundrum of Codon Redundancy

The deciphering of the genetic code takes place through the reading of the nitrogenous bases, which are four in number. In most cases, the bases are taken three by three, thus generating 64 possible combinations with repetition. Each combination (codon) allows for the synthesis of a specific amino acid. Since the latter are only 21 in number, the codon-amino acid conversion table shows a strong redundancy. Countless efforts have been made to understand the true encryption mechanism. Here, we want to add our version, which consists of associating a periodic sound based on three notes to each codon. RNA now becomes a dynamic object and not just a list of static instructions. In addition to a different interpretation of the genetic code, there is also a considerable reduction in redundancy, given that the number of periodic sounds that can be produced with three notes drops to 20 (with the addition of four pure frequencies). Finally, we discuss the possibility of how these sounds can be generated and travel inside the double helix, and possibly emitted as biophotons.

Criticality-based allocation of active redundancies for asymmetric components in coherent systems

The active redundancy technique is widely used in reliability engineering to enhance the performance of reliability systems. In this paper, we utilize the criticality ordering to study optimal allocations of active redundancies in coherent systems comprising independent components. Firstly, we establish optimal allocation strategies for two heterogeneous active redundancies for components ranked by the criticality ordering, which is characterized by the structure relationships induced from the associated minimal cut sets. Our findings demonstrate that, under certain conditions where the criticality ordering (and reliability performances) between any two original components is required, the stronger redundancy should be allocated to the more critical component. Secondly, we investigate the best allocation policies for a batch of homogeneous active spares for different components while preserving the criticality ordering. It is shown that allocating more active redundancies to the more critical component results in a more reliable coherent system. Additionally, we prove that under certain conditions, an imbalance in the numbers of allocated spares leads to even greater reliability in the system. Thirdly, we study optimal allocations for any two components in coherent systems when their locations are quantified by minimal path sets. The optimal allocation policies are further derived, most of which are similar to those obtained by applying minimal cut sets. Numerical examples are provided to illustrate the main findings, and we apply our findings to a real-world scenario in an airplane cockpit system to enhance its reliability.

Redundancy Attack: Breaking Logic Locking Through Oracleless Rationality Analysis

During the last decade, logic locking has been proposed to protect integrated circuits against piracy and reverse engineering threats. Functional pruning attacks, such as the Boolean satisfiability (SAT) attack, have greatly challenged the security of logic locking techniques, yet they require access to an oracle and suffer from muted efficacy on inherently SAT-hard circuits. In this article, we present a novel oracleless attack on both XOR and MUX-based logic locking by analyzing the redundancy level deviation under different keys. Our fundamental insight is that incorrect keys would produce circuits that violate universally followed design principles, prominent among which stands the minimization of logic redundancy. We leverage the redundancy-level deviation produced by individual and pairs of key bits to recover key bit values and establish pairwise equivalence, leading to an efficient nearly linear time attack algorithm. We experimentally verify that the proposed attack quickly unveils more than half of the key bits with high accuracy on 21 ISCAS’85 and MCNC circuits locked with a variety of locking techniques. To fortify logic locking in the face of this successful redundancy attack, we supplement this article with a key gate insertion methodology that smooths the deviation in redundancy level. We achieve this goal by selecting key gates that exhibit pairwise dissociation among a set of individually secure locations, which effectively breaks down correlations in arbitrary sets of key gates and, thus, imposes a consistent redundancy level throughout the key space. Experimental results confirm the strong redundancy attack resilience of the proposed defense strategy.

Genomic methylation patterns in pre-meiotic gynoecia of wild-type and RdDM mutants of Arabidopsis.

Although DNA methylation patterns are generally considered to be faithfully inherited in Arabidopsis thaliana (Arabidopsis), there is evidence of reprogramming during both male and female gametogenesis. The gynoecium is the floral reproductive organ from which the ovules develop and generate meiotically derived cells that give rise to the female gametophyte. It is not known whether the gynoecium can condition genomic methylation in the ovule or the developing female gametophyte. We performed whole genome bisulfite sequencing to characterize the methylation patterns that prevail in the genomic DNA of pre-meiotic gynoecia of wild-type and three mutants defective in genes of the RNA-directed DNA methylation pathway (RdDM): ARGONAUTE4 (AGO4), ARGONAUTE9 (AGO9), and RNA-DEPENDENT RNA POLYMERASE6 (RDR6). By globally analyzing transposable elements (TEs) and genes located across the Arabidopsis genome, we show that DNA methylation levels are similar to those of gametophytic cells rather than those of sporophytic organs such as seedlings and rosette leaves. We show that none of the mutations completely abolishes RdDM, suggesting strong redundancy within the methylation pathways. Among all, ago4 mutation has the strongest effect on RdDM, causing more CHH hypomethylation than ago9 and rdr6. We identify 22 genes whose DNA methylation is significantly reduced in ago4, ago9 and rdr6 mutants, revealing potential targets regulated by the RdDM pathway in premeiotic gyneocia. Our results indicate that drastic changes in methylation levels in all three contexts occur in female reproductive organs at the sporophytic level, prior to the alternation of generations within the ovule primordium, offering a possibility to start identifying the function of specific genes acting in the establishment of the female gametophytic phase of the Arabidopsis life cycle.

Toward robust process monitoring of complex process industries based on denoising sparse auto-encoder

Robust industrial process monitoring is crucial to ensure production safety and product quality stability. However, the process monitoring of high-dimensional, nonlinear, complex industrial processes is still challenging due to their inherent complexities, such as multi-phase, multi-field, and tight coupling of multiple sub-processes. In this article, a simple yet robust nonlinear process monitoring scheme based on a kind of denoising sparse auto-encoder (DSAE) is proposed. Specifically, a novel hybrid auto-encoder, namely DSAE, is established, to address the strong redundancy, nonlinearity and noise interference in process variables by integrating a sparse auto-encoder (SAE) and a denoising auto-encoder (DAE). Successively, an online process monitoring model is established by introducing two new process monitoring statistics computed on the DSAE-based feature representation space and residual space, respectively. Moreover, the Kernel density estimation (KDE) approach is adopted to determine the corresponding control limits of the monitoring statistics, which can avoid the conventional empirical assumption of F or Chi-square distribution on monitoring statistics. Extensive confirmative and comparative experiments conducted for the fault monitoring of a nonlinear numerical simulation system, the benchmark Tennessee Eastman process (TEP) and ventilation exhaust fans (VEFs) in the continuous casting process (CCP) from a top steel plant in China show that the proposed method is promising. Specifically, the proposed method performs favorably against the representative process monitoring methods, and it has especially a stronger robustness against noise and uncertain interference in nonlinear process systems.

High confidence and low accuracy in redundancy masking

Visual scenes typically contain redundant information. One mechanism by which the visual system compresses such redundancies is ‘redundancy masking’ – the reduction of the perceived number of items in repeating patterns. For example, when presented with three lines in the periphery, observers frequently report only two lines. Redundancy masking is strong in radial arrangements and absent in tangential arrangements. Previous studies suggested that redundancy-masked percepts predominate in stimuli susceptible to redundancy masking. Here, we investigated whether strong redundancy masking is associated with high confidence in perceptual judgements. Observers viewed three to seven radially or tangentially arranged lines at 10° eccentricity. They first indicated the number of lines, and then rated their confidence in their responses. As expected, redundancy masking was strong in radial arrangements and weak in tangential arrangements. Importantly, with radial arrangements, observers were more confident in their responses when redundancy masking occurred (i.e., lower number of lines reported) than when it did not occur (i.e., correct number of lines reported). Hence, observers reported higher confidence for erroneous than for correct judgments. In contrast, with tangential arrangements, observers were similarly confident in their responses whether redundancy masking occurred or not. The inversion of confidence in the radial condition (higher confidence when accuracy was low and lower confidence when accuracy was high) suggests that redundancy-masked appearance trumps ‘veridical’ perception. The often-reported richness of visual consciousness may partly be due to overconfidence in erroneous judgments in visual scenes that are subject to redundancy masking.

Avoiding redundant columns by adding classical Benders cuts to column generation subproblems

When solving the linear programming (LP) relaxation of a mixed-integer program (MIP) with column generation, columns might be generated that are not needed to express any integer optimal solution. Such columns are called strongly redundant and the dual bound obtained by solving the LP relaxation is potentially stronger if these columns are not generated. We introduce a sufficient condition for strong redundancy that can be checked by solving a compact LP. Using a dual solution of this compact LP we generate classical Benders cuts for the subproblem so that the generation of strongly redundant columns can be avoided. The potential of these cuts to improve the dual bound of the column generation master problem is evaluated computationally using an implementation in the branch-price-and-cut solver GCG. While their efficacy is limited on classical problems, the benefit of applying the cuts is demonstrated on structured models to which a temporal decomposition can be applied.

Multivariate time series prediction of high dimensional data based on deep reinforcement learning

In order to improve the prediction accuracy of high-dimensional data time series, a high-dimensional data multivariate time series prediction method based on deep reinforcement learning is proposed. The deep reinforcement learning method is used to solve the time delay of each variable and mine the data characteristics. According to the principle of maximum conditional entropy, the embedding dimension of the phase space is expanded, and a multivariate time series model of high-dimensional data is constructed. Thus, the conversion of reconstructed coordinates from low-dimensional to high-dimensional can be kept relatively stable. The strong independence and low redundancy of the final reconstructed phase space construct an effective model input vector for multivariate time series forecasting. Numerical experiments of classical multivariable chaotic time series show that the method proposed in this paper has better forecasting effect, which shows the forecasting effectiveness of this method.

Hybrid Feature Selection for Wafer Acceptance Test Parameters in Semiconductor Manufacturing

Wafer acceptance test (WAT) is a key process of semiconductor manufacturing. The collected testing parameters can be used in identification of wafer defects, improvement of product yield, and control of production costs. However, WAT parameters regularly have characteristics such as high dimensions and strong redundancy, which prevent the wafer yield from accurate prediction and effective improvement. To overcome these shortcomings, a hybrid feature selection method is proposed to identify key WAT parameters influencing wafer yields. This method is composed of two stages, i.e. filter selection and wrapper selection. In filter selection, the minimum Redundancy Maximum Relevance (mRMR) filtering parameter pre-screening criterion based on mutual information (MI) is proposed. The relevance between each parameter and the wafer yield value is calculated by MI. At the same time, the criterion of MI is used to measure the redundancy between each parameter to select the minimum redundancy parameters, and reduce feature size for further searches. In wrapper selection, a wrapped key parameter identification model based on genetic algorithm (GA) and deep belief network (DBN) is designed. The coding and optimization of candidate input parameters are realized by GA. The wafer yield prediction error value of the DBN and the weight of the selected features are solved as the fitness function to realize the selection process of the combined parameters. In experiment, both testing data sets and industrial data are used to demonstrate the efficiency of this proposed method.

Steganalysis by Recombining Spatial Rich Model

Steganalysis is an important topic in the field of information security. Steganalysis is mainly based on the traditional machine learning method, represented by the spatial rich model, and has high detection performance. Aiming at the problems of high feature dimension, strong redundancy and long feature extraction time in the spatial rich model, this paper conducts a series of feature selection operations and explores four different model combination methods: single quantization factor combination method, single filter kernel type combination method, mixed selecting combination method, Pearson correlation coefficient combination method. In addition, a feature preprocessing means are added. The experiment proves that the combination of mixed selecting combination and Pearson correlation coefficient can be stable with the performance of steganalysis before dimension reduction, but the performance dimension ratio is higher.

A dynamical analysis on non-local autoregressive model and its application on image reconstruction

Abstract Natural images are highly structured, which reflects strong spatial redundancy underlying its pixels. In this paper, a dynamical analysis of the non-local autoregressive (NAR) model is researched for the first time. We prove that under the condition when the spectrum radius of NAR matrix is less than 1, the original image as the global stable fixed point of function system referring to a modified NAR model can be iteratively recovered via attractor reconstruction from an arbitrary start point.

Design and analysis of pseudo hadamard transformation and non-chaotic substitution based image encryption scheme

In this paper, Pseudo Hadamard Transformation (PHT) and non-chaotic substitution based image encryption scheme has been proposed. Images are characterized by intrinsic properties such as, strong redundancy and correlation between the adjacent pixels, hence more vulnerable to cyber-attacks. In the proposed technique, the redundancy and correlation have been effectively reduced by pixel position transformation using PHT and pixel value variation using non chaotic substitution, providing two stage security in encryption for images. Fifteen standard test images are considered for experimental analysis. Better average Number of Pixel Changing Rate (NPCR) and Unified Average pixel Changing Intensity (UACI) are obtained for a set of standard test images compared to more popular existing algorithms.

Design and Implementation of Transformation and Non-Chaotic Substitution Based Image Cryptosystem

In this article, a Modified Pseudo Hadamard Transformation (MPHT) and non-Chaotic substitution based image encryption scheme has been proposed. Due to intrinsic properties such as, strong redundancy and correlation between the adjacent pixels, images are more vulnerable to cyber-attacks. In the proposed technique, the redundancy and correlation have been effectively reduced by pixel position transformation using MPHT and pixel value variation using non chaotic substitution, providing two stage security in encryption for secrete images. An average 99.6089% of Number of Pixel Changing Rate (NPCR) and 33.4328% of Unified Average pixel Changing Intensity (UACI) are obtained for a set of standard test images compared to more popular existing techniques

Practical Radiometric Compensation for Projection Display on Textured Surfaces using a Multidimensional Model

AbstractRadiometric compensation methods remove the effect of the underlying spatially varying surface reflectance of the texture when projecting on textured surfaces. All prior work sample the surface reflectance dependent radiometric transfer function from the projector to the camera at every pixel that requires the camera to observe tens or hundreds of images projected by the projector. In this paper, we cast the radiometric compensation problem as a sampling and reconstruction of multi‐dimensional radiometric transfer function that models the color transfer function from the projector to an observing camera and the surface reflectance in a unified manner. Such a multi‐dimensional representation makes no assumption about linearity of the projector to camera color transfer function and can therefore handle projectors with non‐linear color transfer functions(e.g. DLP, LCOS, LED‐based or laser‐based). We show that with a well‐curated sampling of this multi‐dimensional function, achieved by exploiting the following key properties, is adequate for its accurate representation: (a) the spectral reflectance of most real‐world materials are smooth and can be well‐represented using a lower‐dimension function; (b) the reflectance properties of the underlying texture have strong redundancies – for example, multiple pixels or even regions can have similar surface reflectance; (c) the color transfer function from the projector to camera have strong input coherence. The proposed sampling allows us to reduce the number of projected images that needs to be observed by a camera by up to two orders of magnitude, the minimum being only two. We then present a new multi‐dimensional scattered data interpolation technique to reconstruct the radiometric transfer function at a high spatial density (i.e. at every pixel) to compute the compensation image. We show that the accuracy of our interpolation technique is higher than any existing methods.

Development and applications of a DNA labeling method with magnetic nanoparticles to study the role of horizontal gene transfer events between bacteria in soil pollutant bioremediation processes.

Horizontal gene transfers are critical mechanisms of bacterial evolution and adaptation that are involved to a significant level in the degradation of toxic molecules such as xenobiotic pesticides. However, understanding how these mechanisms are regulated in situ and how they could be used by man to increase the degradation potential of soil microbes is compromised by conceptual and technical limitations. This includes the physical and chemical complexity and heterogeneity in such environments leading to an extreme bacterial taxonomical diversity and a strong redundancy of genes and functions. In addition, more than 99 % of soil bacteria fail to develop colonies in vitro, and even new DNA-based investigation methods (metagenomics) are not specific and sensitive enough to consider lysis recalcitrant bacteria and those belonging to the rare biosphere. The objective of the ANR funded project “Emergent” was to develop a new culture independent approach to monitor gene transfer among soil bacteria by labeling plasmid DNA with magnetic nanoparticles in order to specifically capture and isolate recombinant cells using magnetic microfluidic devices. We showed the feasibility of the approach by using electrotransformation to transform a suspension of Escherichia coli cells with biotin-functionalized plasmid DNA molecules linked to streptavidin-coated superparamagnetic nanoparticles. Our results have demonstrated that magnetically labeled cells could be specifically retained on micromagnets integrated in a microfluidic channel and that an efficient selective separation can be achieved with the microfluidic device. Altogether, the project offers a promising alternative to traditional culture-based approaches for deciphering the extent of horizontal gene transfer events mediated by electro or natural genetic transformation mechanisms in complex environments such as soil.