Redundant Model Research Articles

논스톱 포워딩 기능을 지원하는 네트워크 시스템에 대한 다중화 모형의 가용도 분석

In this paper, we analyse the effect of redundancy types and non-stop forwarding scheme on network service availability. We use stochastic reward net models as enabling modeling approach for the analytical evaluation. We first design stochastic reward nets for redundancy models with or without non-stop forwarding and then evaluate their availability using Stochastic Petri Net Package.

Fusion modeling for wind power forecasting based on redundant method elimination

In general, an individual forecasting model for wind power cannot always attain a sufficient degree of precision. To further improve the forecasting accuracy, this paper develops a new wind power forecasting model using a fusion model based on the concept of optimal selection and the Shapley Value. The fusion modeling method first establishes an individual forecasting model base. Next, higher-precision models are optimally selected using grey incidence analysis based on the collective evaluation index, and the redundancy of these models is judged on the basis of the information matrix of forecasting errors. This allows redundant models to be eliminated so as to simplify the establishment of the fusion model. Eliminating redundant models avoids negative degree of fusion. Finally, the fusion degree of each optimally selected individual model is calculated according to the Shapley Value. Models, which include the proposed model, seven individual models, and two other combined models, are tested to forecast the wind power by using practical data from two wind farms. The simulation results show that the proposed approach effectively decreases the appearance of larger errors and improves the forecasting accuracy. Thus, the proposed fusion model for wind power forecasting can provide a better foundation for the efficient use of wind power.

Generalized Context Modeling With Multi-Directional Structuring and MDL-Based Model Selection for Heterogeneous Data Compression

This paper proposes generalized context modeling (GCM) for heterogeneous data compression. The proposed model extends the suffix of predicted subsequences in classic context modeling to arbitrary combinations of symbols in multiple directions. To address the selection of contexts, GCM constructs a model graph with a combinatorial structuring of finite order combination of predicted symbols as its nodes. The estimated probability for prediction is obtained by weighting over a class of context models that contain all the occurrences of nodes in the model graph. Moreover, separable context modeling in each direction is adopted for efficient prediction. To find optimal class of context models for prediction, the normalized maximum likelihood (NML) function is developed to estimate their structures and parameters, especially for heterogeneous data with large sizes. Furthermore, it is refined by context pruning to exclude the redundant models. Such model selection is optimal in the sense of minimum description length (MDL) principle, whose divergence is proven to be consistent with the actual distribution. It is shown that upper bounds of model redundancy for GCM are irrelevant to the size of data. GCM is validated in an extensive field of applications, e.g., Calgary corpus, executable files, and genomic data. Experimental results show that it outperforms most state-of-the-art context modeling algorithms reported.

A lesson from associative learning: asymmetry and productivity in multiple-slot constructions

AbstractNon-redundant taxonomic models of construction grammar posit that only fully productive patterns qualify as constructions because they license an infinity of expressions. Redundant models claim that, despite subregularities and exceptions, partially productive patterns also count as constructions, providing the overall meanings of such patterns are not the strict sums of their parts. Because productivity is a major bone of contention between redundant and non-redundant construction grammar taxonomies, I examine the productivity of

The heuristic value of redundancy models of aging

Molecular studies of aging aim to unravel the cause(s) of aging bottom-up, but linking these mechanisms to organismal level processes remains a challenge. We propose that complementary top-down data-directed modelling of organismal level empirical findings may contribute to developing these links. To this end, we explore the heuristic value of redundancy models of aging to develop a deeper insight into the mechanisms causing variation in senescence and lifespan. We start by showing (i) how different redundancy model parameters affect projected aging and mortality, and (ii) how variation in redundancy model parameters relates to variation in parameters of the Gompertz equation. Lifestyle changes or medical interventions during life can modify mortality rate, and we investigate (iii) how interventions that change specific redundancy parameters within the model affect subsequent mortality and actuarial senescence. Lastly, as an example of data-directed modelling and the insights that can be gained from this, (iv) we fit a redundancy model to mortality patterns observed by Mair et al. (2003; Science 301: 1731–1733) in Drosophila that were subjected to dietary restriction and temperature manipulations. Mair et al. found that dietary restriction instantaneously reduced mortality rate without affecting aging, while temperature manipulations had more transient effects on mortality rate and did affect aging. We show that after adjusting model parameters the redundancy model describes both effects well, and a comparison of the parameter values yields a deeper insight in the mechanisms causing these contrasting effects. We see replacement of the redundancy model parameters by more detailed sub-models of these parameters as a next step in linking demographic patterns to underlying molecular mechanisms.

Neutrosophic Modeling and Control

Quite recently, Neutrosophic Logic has been proposed by Florentine Smarandache which is based on non- standard analysis that was given by Abraham Robinson in 1960s. Neutrosophic Logic was developed to represent mathematical model of uncertainty, vagueness, ambiguity, imprecision, incompleteness, inconsistency, redundancy and contradiction in data (4),(5),(6),(7). All the factors stated are very integral to human thinking, as it is very rare that we tend to conclude/judge in definite environments. This paper discusses how neutrosophic logic can be utilized for modeling and control for which block diagram of neutrosophic inference system is proposed, to illustrate this designing of relatively simple neutrosophic classifier has been attempted. Current problems and future directions for neutrosophic approaches are also addressed.

Potential Use of Transport and Dispersion Model Ensembles for Forecasting Applications

AbstractUsing ensembles to improve the simulation of plume dispersion is becoming a more common practice. One of the biggest challenges in creating ensembles is developing the appropriate member selection process to get the most accurate results, quantify ensemble uncertainty, and use computing resources more efficiently by avoiding the use of redundant model information. In this work, two reduction techniques are tested: one that is independent of observations and is based on the exclusion of redundant members by using uncorrelation as a measure of distance among the members and a second one based on measured data, which minimizes the mean square error (MSE) of the average of all possible model combinations. These techniques are applied to a 24-member ensemble, created by varying the boundary layer parameterizations in the meteorological WRF Model and dispersion Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) simulating six releases of the Cross-Appalachian Tracer Experiment (CAPTEX). Applying the first technique produced results statistically comparable to the full ensemble for four out of six releases, while the second technique shows a similar or superior performance for all cases. Furthermore, to mimic a forecast application, the first day of the tracer release is used to select the ensemble members and the subsequent days are utilized as a forecast proxy to evaluate their performance. The reduced ensembles chosen by applying the technique based on the minimization of the MSE statistically perform similarly to or better than the full ensemble for the forecasting time periods. This suggests that when observational data are available, the application of ensemble reduction techniques provides a potentially promising tool for improving the dispersion forecast.

Does plant species richness guarantee the resilience of local medical systems? A perspective from utilitarian redundancy.

Resilience is related to the ability of a system to adjust to disturbances. The Utilitarian Redundancy Model has emerged as a tool for investigating the resilience of local medical systems. The model determines the use of species richness for the same therapeutic function as a facilitator of the maintenance of these systems. However, predictions generated from this model have not yet been tested, and a lack of variables exists for deeper analyses of resilience. This study aims to address gaps in the Utilitarian Redundancy Model and to investigate the resilience of two medical systems in the Brazilian semi-arid zone. As a local illness is not always perceived in the same way that biomedicine recognizes, the term “therapeutic targets” is used for perceived illnesses. Semi-structured interviews with local experts were conducted using the free-listing technique to collect data on known medicinal plants, usage preferences, use of redundant species, characteristics of therapeutic targets, and the perceived severity for each target. Additionally, participatory workshops were conducted to determine the frequency of targets. The medical systems showed high species richness but low levels of species redundancy. However, if redundancy was present, it was the primary factor responsible for the maintenance of system functions. Species richness was positively associated with therapeutic target frequencies and negatively related to target severity. Moreover, information about redundant species seems to be largely idiosyncratic; this finding raises questions about the importance of redundancy for resilience. We stress the Utilitarian Redundancy Model as an interesting tool to be used in studies of resilience, but we emphasize that it must consider the distribution of redundancy in terms of the treatment of important illnesses and the sharing of information. This study has identified aspects of the higher and lower vulnerabilities of medical systems, adding variables that should be considered along with richness and redundancy.

Soft sensor development for nonlinear and time‐varying processes based on supervised ensemble learning with improved process state partition

AbstractThe nonlinearities and time‐varying characteristics are two major causes of low performance of soft sensors in process systems. Motivated of solving the two problems, this paper proposes an adaptive soft sensing method under the ensemble learning framework. An improved process state partition scheme is proposed to construct independent local models, which not only inherits the merits of the original process state partition method but also possesses the function of detecting and deleting redundant models. These prepared local models are weighted by a supervised weighting mechanism and then combined via the Bayesian inference to predict the y‐value of the query sample. Because the weighting mechanism can fully exploit the historical data set and quantify each local model's generalization ability for the query sample, it is potential to compute the combination weights more accurately. Simulations are conducted on two benchmark data sets from two real‐life chemical processes. Extensive performance evaluations of the proposed soft sensor are conducted, and results show its effectiveness. © 2015 Curtin University of Technology and John Wiley & Sons, Ltd.

Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models

This paper proposes an adaptive soft sensing method based on selective ensemble of local partial least squares models, referring to as the SELPLS, for quality prediction of nonlinear and time-varying chemical processes. To deal with the process nonlinearity, we partition the process state into local model regions upon which PLS models are constructed, through a statistical hypothesis testing based adaptive localization procedure. Two main delightful advantages of this localization strategy are that, redundant local models can be effectively detected and deleted and the local model set can be easily augmented online without retraining from scratch. In addition, a local model weighting mechanism is proposed to adaptively differentiate the contributions of local models by explicitly quantifying their generalization abilities for the current process dynamics. Finally, the selective ensemble learning strategy combines partial local models instead of all available models through Bayesian inference, which is able to reach a good equilibrium between the prediction bias and variance. The proposed SELPLS based soft sensor is applied to a simulated continuous stirred tank reactor and a real-life industrial sulfur recovery unit. Extensive simulation results demonstrate the effectiveness of the proposed scheme in contrast with several state-of-the-art adaptive soft sensing approaches.

Multi-timescale and correlated dynamic adaptive chemistry modeling of ignition and flame propagation using a real jet fuel surrogate model

A new correlated dynamic adaptive chemistry (CO-DAC) method is developed and integrated with the hybrid multi-timescale (HMTS) method for computationally efficient modeling of ignition and unsteady flame propagation of real jet fuel surrogate mixtures with a detailed and comprehensively reduced kinetic mechanism. A concept of correlated dynamic adaptive chemistry (CO-DAC) method in both time and space coordinates is proposed by using a few key phase parameters which govern the low, intermediate, and high temperature chemistry, respectively. Correlated reduced mechanisms in time and space are generated dynamically on the fly from the detailed kinetic mechanism by specifying thresholds of phase parameters of correlation and using the multi-generation path flux analysis (PFA) method. The advantages of the CO-DAC methods are that it not only provides the flexibility and accuracy of kinetic model and chemistry integration but also avoids redundant model reduction in time and space when the chemistry is frequently correlated in phase space. To further increase the computational efficiency in chemistry integration, the hybrid multi-timescale (HMTS) method is integrated with the CO-DAC method to solve the stiff ordinary differential equations (ODEs) of the reduced chemistry generated on the fly by CO-DAC. The present algorithm is compared and validated against the conventional VODE solver, DAC and HMTS/DAC methods for simulating ignition and unsteady flame propagation of real jet fuel surrogate mixtures consisting of four component fuels, n-dodecane, iso-octane, n-propyl benzene, and 1,3,5-trimethyl benzene. The results show the present HMTS/CO-DAC algorithm is not only computationally efficient but also robust and accurate. Moreover, it is shown that compared to the DAC and HMTS/DAC methods, the computation time of model reduction in CO-DAC is almost negligible even for a large kinetic mechanism involving hundreds of species. In addition, the results show that computation efficiency of CO-DAC increases from homogeneous ignition to one-dimensional flame propagation for both the first and second generation PFA reduction. Therefore, the present HMTS/CO-DAC method can enable high-order model reduction and achieve higher computation efficiency for multi-dimensional numerical modeling.

Protocol for the Generation of a Transcription Factor Open Reading Frame Collection (TFome)

The construction of a physical collection of open reading frames (ORFeomes) for genes of any model organism is a useful tool for the exploration of gene function, gene regulation, and protein-protein interaction. Here we describe in detail a protocol that has been used to develop the first collection of transcription factor (TF) and co-regulator (CR) open reading frames (TFome) in maize (Burdo et al., 2014). This TFome is being used to establish the architecture of gene regulatory networks (GRNs) responsible for the control of transcription of all genes in an organism. The protocol outlined here describes how to proceed when only an incomplete genome with partial annotation is available. TFome clones are made in a recombination-ready vector of the Gateway? system, allowing for the facile transfer of the ORFs to other Gateway?-compatible vectors, such as those suitable for expression in other host species. Although this protocol was developed for the maize TFome it can readily be applied to the generation of complete ORFeome collections in other eukaryotic species.[Protocol overview] An important aspect of successful TFome generation is the initial effort spent to establish a reliable set of gene models so that they can be subsequently amplified or synthesized. An actual TFome construction protocol for a particular species will depend on available resources such as a full-length cDNA (flcDNA) collection and a reliable reference genome (Figure 1). In the case of maize, a flcDNA collection and a draft genome was available, but the former provided only 30% of the needed clones, and the latter contained gaps and some erroneous gene models. In order to develop a near-complete set of target gene models for maize TFs, a bioinformatics pipeline was developed as described by Yilmaz et al. (2009). In brief, a two-pronged search process was developed. The first involved making a collection of protein sequences of TFs in other species and available from databases such as PlantTFDB, PlnTFDB and DBDTF. These sequences were then used to search gene models from the draft maize genome using BLASTP. The second process involved developing a collection of domains that define TF families and that are mostly annotated in the PFAM database (Finn et al., 2014). These domains were then used to search the draft maize genome using BLASTX. The number of TF families that exist and their naming is subject to change as new members are discovered and studied. Table 1 provides a list of known TF families with alternative names along with the respective PFAM domains whose presence or absence defines each TF family. HMM models for each domain can be obtained from the PFAM database (pfam.xfam.org). Following the BLAST search, redundant models are eliminated and then based on the TF motifs present in each gene model, gene models are assigned to a TF or Co-Regulator (CR) family according to the criteria specified in Table 1. Lastly, it is recommended to set up a database to store information on each TF family. The GRASSIUS (www.grassius.org) website was established to access the stored information on TF gene models for maize, sorghum, rice, Brachypodium, sugarcane and other grasses (Burdo et al., 2014). In the following section, an assumption is made that at least a draft genome or draft transcriptome is available and that a set of gene models is available that have been determined ab initio or with additional manual annotation. Familiarity with the use of PERL scripts is advantageous for the gene model assembly phase.Figure 1. Flowchart for the generation of a TFome project. Flowchart outlining the general strategy for template identification, PCR amplification and cloning of transcription factor (TF) full length (FL) open reading frames (ORFs). (modified from Burdo et al., 2014)

Stitching Measurement Technique for Large-aperture Aspheric Surface during Grinding Process

In order to test large-aperture aspheric surface optice element accurately during grinding process, a comprehensive optimization data processing model of stitching measurement is proposed based on coordinate measuring. Firstly, the stitching model for two segments of surface shape contour is built based on multi-body system theory and least square principle; secondly, the rejecting redundant data model is built based on curvature principle and least square fitting of aspheric equation; then the comprehensive optimization processing model for multiple segments stitching is built; three generatrices of a non-axisymmetry aspherical optical element whose aperture is 176 mm is stitching measured by Taylor Hobson profilometer and the data is processed by the model mentioned in the text. The results show that the maximum average error of the stitching measurement is 0.25 μm, and the minimum average error of the stitching measurement is –0.22 μm which is compared with the single measurement. The error is an order of magnitude higher than the surface shape peak–valley value 2.77 μm of the optic element, and matches the measurement requirements. The experimental results prove the correctness and effectiveness of the comprehensive optimization data processing model of stitching measurement, and the model can be applied in engineering.

SPNP를 이용한 이중화 모델들의 가용도 비교분석

We suggest various supporting methods for seamless services to support the stable service of system and network. As a representative, the redundancy is the duplication for supporting the stable service of the system and networks. The various redundancy systems have been proposed, these may have a variety forms depending on the number of the service supporting systems. We analyze a availability of a various redundancy model about a redundancy systems. We use a SRN based SPNP as an analysis tool. In this paper, we explain a various redundancy models, then compare and analyze the availability of redundancy models according to a number of service units.

Determination of the Identifiable Parameters in Robot Calibration Based on the POE Formula

This paper presents an analytical approach to determine and eliminate the redundant model parameters in serial-robot kinematic calibration based on the product of exponentials formula. According to the transformation principle of the Lie algebra se(3) between different frames, the connection between the joints' twist errors and the links' geometric ones is established. Identifiability analysis shows that the redundant errors are simply equivalent to the commutative elements of the robot's joint twists. Using the Lie bracket operation of se(3), a linear partitioning operator can be constructed to analytically separate the identifiable parameters from the system error vector. Then, error models satisfying the completeness, minimality, and model continuity requirements can be obtained for any serial robot with all combinations and configurations of revolute and prismatic joints. The conventional conclusion that the maximum number of independent parameters is 4r + 2p + 6 in a generic serial robot with r revolute and p prismatic joints is verified. Using the quotient manifold of the Lie group SE(3), the links' geometric errors and the joints' offset errors can be integrated as a whole, such that all these errors can be identified simultaneously. To verify the effectiveness of the proposed method, calibration simulations and experiments are conducted on an industrial six-degree-of-freedom (DoF) serial robot.

What is the role of exotic medicinal plants in local medical systems? A study from the perspective of utilitarian redundancy

Medicinal plants are an important aspect of local medical systems. The composition of a medicinal plant collection is influenced by cultural and environmental factors. Additionally, the functionality of a local medical system can be threatened by the replacement of native species with exotic ones, as well as by cultural factors such as the erosion of knowledge. The objectives of this study are: 1) examine the composition of the medicinal plant collec- tion of two rural communities settled in the caatinga (savanna-like vegetation) of the state of Pernambuco (Brazil); 2) observe the role of exotic plants in the local medical systems; and 3) identify the profile of the species utilized according to the Utilitarian Redundancy Model. Similarities were observed between the medicinal floras of the communities studied, emphasizing the importance of the surrounding biome within the possibilities of species selection, although exotic species appear to contribute by increasing the diversity of species considered in the communities to be medicinal. The native species act broadly among the body systems recognized in the two communities, whereas exotic species act in specific body systems, for which there are few associated native species.

Preventive Maintenance Model Analysis Based on Condition

To improve operation maintenance management levels and increase economic profits for repairable devices, condition based preventive maintenance (CBM) has been widely applied in industrial enterprises in recent years. Compared with traditional time based maintenance (TBM), CBM not only can ensure the production process more safety but also dramatically reduce the examining-maintenance cost, which can be thought as a proactive maintenance scheme, practically. In this paper, we firstly construct the full life cycle model with four-state of the devices, and then simplified it into three-state model based on some practical considerations. On the basis of it, the paper analyzes its reliability operation characteristics under diverse maintenance models such as the proactive maintenance based on CBM, and the proactive maintenance based on TBM, and the passive maintenance, and the redundant maintenance scheme, and computes its desired maintenance cost and the investment payback period. The computing results show that the investments payback period is the shortest of the proactive maintenance rule based on CBM, and the one based on TBM comes second, and the one based on passive maintenance comes third, and the one of the redundant management model is the inferior. The investigations show that proactive maintenance based on CBM is a very effective operation maintenance management mode for repairable devices, and possesses very broad applications prosperity.

Trying to Live Now: Chronotopic Figures in Jenny Watson’s A Painted Page Series

Between late 1979 and early 1980, Australian artist Jenny Watson painted a sequence of six works, each with the title A Painted Page. Combining gridded, painted reproductions of photographs, newspapers and department store catalogues with roughly painted fields of color, the series brought together a range of recent styles and painterly idioms: pop, photorealism, and non-objective abstraction. Watson’s evocation of styles considered dated, corrupted or redundant by contemporary critics was read as a sign of the decline of modernism and the emergence of a postmodernism inflected with irony and a cool, “new wave” sensibility. An examination of the Painted Pages in the context of Watson’s interest in autobiography and her association with the women’s art movement, however, reveals the works to be subjective, highly personal reflections on memory, self and artistic aspiration. Drawing on Bahktin’s model of the chronotope, this paper argues for a spatio-temporal reading of Watson’s Painted Pages rather than the crude model of stylistic redundancy and succession. Watson’s source images register temporal orders ranging across the daily, the seasonal and the epochal. Her paintings transpose Bahktin’s typology of quotidian, provincial and “adventuristic” time into autobiographical paintings of teenage memories, the vicissitudes of the art world and punk subcultures. Collectively, the Painted Pages established a chronotopic field; neither an aggregation of moments nor a collaged evocation of a period but a point at which Watson closed off one kind of time (an art critical time of currency and succession) and opened up another (of subjectivity and affective experience).

A Real-Time Performance Monitoring Tool for Dual Redundant and Resource Augmented Framework of Cruise Control System

The computing resources used in safety-critical systems have stringent timing requirements due to mission critical nature of their tasks. A fault in these systems could lead to mission failure and catastrophic consequences. To avoid this various redundancy schemes are built in to mission critical applications to ensure the overall success of the system. The usual industrial practice is to employ fault tolerance using hardware redundancy where costs are highly exorbitant depending on the mission. In this paper, a prototype tool has been designed and developed for testing and evaluation of a framework for adaptive fault tolerance on an existing dual hardware redundancy with resource augmentation. This proposed model gives enhanced resource management and improved system performance under normal runtime and provides minimal safe functionality under permanent fault condition. It has been implemented with a practical case study of Cruise Control System using NXP LPC2148 processors. The results demonstrate the better performance and process speedup (execution time of process) vis-a-vis over a traditional dual redundant processor system and the high performance that can accrue by applying this model to an m-processor redundancy model.

Supplier selection and vendor rating model in high innovation environment: a case study in aeronautical sector

Supplier selection refers to the critical process of finding suitable suppliers from a set of alternative ones in order to maximize the performances of the whole chain. It is widely recognized in the literature that this process plays a strategic role for any supply chain. Rating and selection activities are one of the most critical and strategic choices, especially in fields where the research and innovation capabilities of partners are supposed to be primary aspects for the chain’s success. This problem is highly complex because it involves a number of qualitative and quantitative factors with strong interdependencies. In this paper the relationship between a buyer and its suppliers in the aeronautical sector is studied and a model for supplier selection has been proposed. The supplier selection process can be classified as a multi-criteria decision problem. In this paper a model based on the Analytical Hierarchy Process (AHP) and Redundancy Model (RM) methodology is proposed. The evaluation criteria are developed and successfully applied into a case study of the aeronautic industry. A validation of the model has been conducted with a detailed step-by-step implementation and reported in the paper. Keywords: analytical hierarchy process; AHP; buyer-supplier relationship; supplier evaluation; innovation; RD vendor rating.