Journal articles: 'Error state'

1

Fowler, A. G., and K. Goyal. "Topological cluster state quantum computing." Quantum Information and Computation 9, no. 9&10 (September 2009): 721–38. http://dx.doi.org/10.26421/qic9.9-10-1.

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The quantum computing scheme described by Raussendorf et. al (2007), when viewed as a cluster state computation, features a 3-D cluster state, novel adjustable strength error correction capable of correcting general errors through the correction of Z errors only, a threshold error rate approaching 1% and low overhead arbitrarily long-range logical gates. In this work, we review the scheme in detail framing the discussion solely in terms of the required 3-D cluster state and its stabilizers.

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2

Kieffer, Michel. "Distributed bounded-error state estimation." IFAC Proceedings Volumes 42, no. 10 (2009): 360–65. http://dx.doi.org/10.3182/20090706-3-fr-2004.00059.

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3

Osborn, Denise R. "A Note on Error Correction Mechanisms and Steady-State Error." Economic Journal 96, no. 381 (March 1986): 208. http://dx.doi.org/10.2307/2233435.

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4

Falcao, D. M., and S. M. de Assis. "Linear programming state estimation: error analysis and gross error identification." IEEE Transactions on Power Systems 3, no. 3 (1988): 809–15. http://dx.doi.org/10.1109/59.14526.

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Dao, Arik-Quang V., James R. Parkinson, and Steven J. Landry. "Identifying Human-Machine Interaction Problems in Continuous State Data." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 86–90. http://dx.doi.org/10.1177/1541931213601019.

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A set of studies has been focused on identifying “markers” in aircraft data that are indicative of human factors issues. In this paper we discuss an experiment that investigated if human error is predictable from the error observed from the combined human-machine system. Sixteen pilots flew simulated instrument approaches under varying levels of workload and control augmentation conditions. Operator control lag, gain, delay, and error extent were computed from aircraft lateral path errors. These parameters along with pupil diameter data were analyzed for differences across workload conditions. Main effects for workload were found with respect to all control parameters consistent with the experiment hypotheses, but the effects were very small. Operator delay in responding to errors appeared inversely correlated with workload. Statistically significant differences were also found with respect to error extent ad pupil diameter.

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KOSKENNIEMI, KIMMO. "Finite state morphology and information retrieval." Natural Language Engineering 2, no. 4 (December 1996): 331–36. http://dx.doi.org/10.1017/s1351324997001587.

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A source of potential systematic errors in information retrieval is identified and discussed. These errors occur when base form reduction is applied with a (necessarily) finite dictionary. Formal methods for avoiding this error source are presented, along with some practical complexities met in its implementation.

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von Clarmann, T. "Smoothing error pitfalls." Atmospheric Measurement Techniques 7, no. 9 (September 18, 2014): 3023–34. http://dx.doi.org/10.5194/amt-7-3023-2014.

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Abstract. The difference due to the content of a priori information between a constrained retrieval and the true atmospheric state is usually represented by a diagnostic quantity called smoothing error. In this paper it is shown that, regardless of the usefulness of the smoothing error as a diagnostic tool in its own right, the concept of the smoothing error as a component of the retrieval error budget is questionable because it is not compliant with Gaussian error propagation. The reason for this is that the smoothing error does not represent the expected deviation of the retrieval from the true state but the expected deviation of the retrieval from the atmospheric state sampled on an arbitrary grid, which is itself a smoothed representation of the true state; in other words, to characterize the full loss of information with respect to the true atmosphere, the effect of the representation of the atmospheric state on a finite grid also needs to be considered. The idea of a sufficiently fine sampling of this reference atmospheric state is problematic because atmospheric variability occurs on all scales, implying that there is no limit beyond which the sampling is fine enough. Even the idealization of infinitesimally fine sampling of the reference state does not help, because the smoothing error is applied to quantities which are only defined in a statistical sense, which implies that a finite volume of sufficient spatial extent is needed to meaningfully discuss temperature or concentration. Smoothing differences, however, which play a role when measurements are compared, are still a useful quantity if the covariance matrix involved has been evaluated on the comparison grid rather than resulting from interpolation and if the averaging kernel matrices have been evaluated on a grid fine enough to capture all atmospheric variations that the instruments are sensitive to. This is, under the assumptions stated, because the undefined component of the smoothing error, which is the effect of smoothing implied by the finite grid on which the measurements are compared, cancels out when the difference is calculated. If the effect of a retrieval constraint is to be diagnosed on a grid finer than the native grid of the retrieval by means of the smoothing error, the latter must be evaluated directly on the fine grid, using an ensemble covariance matrix which includes all variability on the fine grid. Ideally, the averaging kernels needed should be calculated directly on the finer grid, but if the grid of the original averaging kernels allows for representation of all the structures the instrument is sensitive to, then their interpolation can be an adequate approximation.

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Movassagh, Kiarash, Arif Raihan, Balakumar Balasingam, and Krishna Pattipati. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries." Energies 14, no. 14 (July 6, 2021): 4074. http://dx.doi.org/10.3390/en14144074.

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In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge are known. The Coulomb counting approach, on the other hand, is prone to inaccuracies from a variety of sources, and the magnitude of these errors has not been explored in the literature. We formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources: (1) current measurement error; (2) current integration approximation error; (3) battery capacity uncertainty; and (4) timing oscillator error/drift. It is demonstrated that the state-of-charge error produced can be either time-cumulative or state-of-charge-proportional. Time-cumulative errors accumulate over time and have the potential to render the state-of-charge estimation utterly invalid in the long term.The proportional errors of the state of charge rise with the accumulated state of charge and reach their worst value within one charge/discharge cycle. The study presents methods for reducing time-cumulative and state-of-charge-proportional mistakes through simulation analysis.

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9

Li, Xin, Zhi Xiong Zhang, Jian Zhong Shang, and Yu Jun Cao. "Research on Angular Variation for Aeroplane-Assembly Base on State Space Model." Applied Mechanics and Materials 278-280 (January 2013): 149–54. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.149.

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Abstract. Variation modeling is one of the most significant tools for assembly variation analysis. Considering dimension and geometric errors, and part situation errors, the error source that affects assembly accuracy is classified into two types: error of geometric location and orientation, error of geometric form. And unify these different types of error or deviation by the concept of Virtual Fixture. So a rigid assembly state space model is developed for stream of variation analysis in multi-station assembly processes. And an aeroplane-cabin-assembly process is analyzed in this model. The developed methodology outperforms the current simulation based techniques in computation efficiency, the model is validated using Monte Carlo series Simulations.

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10

Xu, Lin, Kevin Tomsovic, and Anjan Bose. "Topology error identification using a two-stage DC state estimator." Electric Power Systems Research 74, no. 1 (April 2005): 167–75. http://dx.doi.org/10.1016/j.epsr.2004.10.005.

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11

Rozovskaya, Alla, and Dan Roth. "Building a State-of-the-Art Grammatical Error Correction System." Transactions of the Association for Computational Linguistics 2 (December 2014): 419–34. http://dx.doi.org/10.1162/tacl_a_00193.

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This paper identifies and examines the key principles underlying building a state-of-the-art grammatical error correction system. We do this by analyzing the Illinois system that placed first among seventeen teams in the recent CoNLL-2013 shared task on grammatical error correction. The system focuses on five different types of errors common among non-native English writers. We describe four design principles that are relevant for correcting all of these errors, analyze the system along these dimensions, and show how each of these dimensions contributes to the performance.

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12

Bernardi, Nicolò F., Floris T. Van Vugt, Ricardo Ruy Valle-Mena, Shahabeddin Vahdat, and David J. Ostry. "Error-related Persistence of Motor Activity in Resting-state Networks." Journal of Cognitive Neuroscience 30, no. 12 (December 2018): 1883–901. http://dx.doi.org/10.1162/jocn_a_01323.

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The relationship between neural activation during movement training and the plastic changes that survive beyond movement execution is not well understood. Here we ask whether the changes in resting-state functional connectivity observed following motor learning overlap with the brain networks that track movement error during training. Human participants learned to trace an arched trajectory using a computer mouse in an MRI scanner. Motor performance was quantified on each trial as the maximum distance from the prescribed arc. During learning, two brain networks were observed, one showing increased activations for larger movement error, comprising the cerebellum, parietal, visual, somatosensory, and cortical motor areas, and the other being more activated for movements with lower error, comprising the ventral putamen and the OFC. After learning, changes in brain connectivity at rest were found predominantly in areas that had shown increased activation for larger error during task, specifically the cerebellum and its connections with motor, visual, and somatosensory cortex. The findings indicate that, although both errors and accurate movements are important during the active stage of motor learning, the changes in brain activity observed at rest primarily reflect networks that process errors. This suggests that error-related networks are represented in the initial stages of motor memory formation.

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13

Zhao, Yan, Jing Zhang, Gaoge Hu, and Yongmin Zhong. "Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty." Sensors 20, no. 3 (January 22, 2020): 627. http://dx.doi.org/10.3390/s20030627.

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This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kalman filtering (KF) framework with the set-membership concept for nonlinear state estimation under systematic uncertainty consisted of both stochastic error and unknown but bounded (UBB) error. Upon the linearization of the nonlinear system model via a Taylor series expansion, this method introduces a new UBB error term by combining the linearization error with systematic UBB error through the Minkowski sum. Subsequently, an optimal Kalman gain is derived to minimize the mean squared error of the state estimate in the KF framework by taking both stochastic and UBB errors into account. The proposed SM-HKF handles the systematic UBB error, stochastic error as well as the linearization error simultaneously, thus overcoming the limitations of the extended Kalman filter (EKF). The effectiveness and superiority of the proposed SM-HKF have been verified through simulations and comparison analysis with EKF. It is shown that the SM-HKF outperforms EKF for nonlinear state estimation with systematic UBB error and stochastic error.

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14

von Clarmann, T. "Smoothing error pitfalls." Atmospheric Measurement Techniques Discussions 7, no. 4 (April 1, 2014): 3301–19. http://dx.doi.org/10.5194/amtd-7-3301-2014.

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Abstract. The difference due to the content of a priori information between a constrained retrieval and the true atmospheric state is usually represented by the so-called smoothing error. In this paper it is shown that the concept of the smoothing error is questionable because it is not compliant with Gaussian error propagation. The reason for this is that the smoothing error does not represent the expected deviation of the retrieval from the true state but the expected deviation of the retrieval from the atmospheric state sampled on an arbitrary grid, which is itself a smoothed representation of the true state. The idea of a sufficiently fine sampling of this reference atmospheric state is untenable because atmospheric variability occurs on all scales, implying that there is no limit beyond which the sampling is fine enough. Even the idealization of infinitesimally fine sampling of the reference state does not help because the smoothing error is applied to quantities which are only defined in a statistical sense, which implies that a finite volume of sufficient spatial extent is needed to meaningfully talk about temperature or concentration. Smoothing differences, however, which play a role when measurements are compared, are still a useful quantity if the involved a priori covariance matrix has been evaluated on the comparison grid rather than resulting from interpolation. This is, because the undefined component of the smoothing error, which is the effect of smoothing implied by the finite grid on which the measurements are compared, cancels out when the difference is calculated.

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15

Haah, Jeongwan, and Matthew B. Hastings. "Measurement sequences for magic state distillation." Quantum 5 (January 20, 2021): 383. http://dx.doi.org/10.22331/q-2021-01-20-383.

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Magic state distillation uses special codes to suppress errors in input states, which are often tailored to a Clifford-twirled error model. We present detailed measurement sequences for magic state distillation protocols which can suppress arbitrary errors on any part of a protocol, assuming the independence of errors across qubits. Provided with input magic states, our protocol operates on a two-dimensional square grid by measurements of ZZ on horizontal pairs of qubits, XX on vertical pairs, and Z,X on single qubits.

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16

Rantung, Greis M. "KESALAHAN BERBAHASA INDONESIA DALAM TESIS MAHASISWA PROGRAM STUDI MANAJEMEN PENDIDIKAN PASCASARJANA UNIMA TAHUN 2008." Kadera Bahasa 2, no. 2 (January 29, 2019): 109–25. http://dx.doi.org/10.47541/kaba.v2i2.57.

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This study is aimed to (1) describe the spelling errors in usage and the error forms indiction of Pos-Graduate students thesis of Manado State University in Education Management Program 2008 and (2) describe the error forms in the diction of Unima Post-Graduate student thesis in Education Management Program 2008. This research descriptive with the following three stages, namely: (I) data provision stage, (2) data analysis stage, and (3) presenting the data analysis stage. The method in analysis speaking error method. The dominant error in spelling is the writing of unstandardized words, and the using of the word pairs or correlative conjunctions in diction are also complicated.

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17

Fitriani, Aula, Ervy Festina Mahardika, Muhammad Yusach Maulana, and Chafit Ulya. "Analisis kesalahan berbahasa tataran ejaan pada majalah â€œMuliaâ€ edisi April 2020." Jurnal Genre (Bahasa, Sastra, dan Pembelajarannya) 3, no. 1 (September 4, 2021): 1–9. http://dx.doi.org/10.26555/jg.v3i1.3391.

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This study aims to describe the form of the error-speaking state of spelling in the writing of magazine Mulia edition April 2020 which include: (1) errors in the use of letters; (2) the error of writing the word; (3) errors in the use of punctuation; (4) error writing element uptake; and (5) the use of the word is not raw. The object of this writing is the words, phrases, and sentences. this research is a descriptive qualitative research. Data collection techniques used in this research is documentation study. The results of the discussion showed that the overall number of the word containing the error-speaking state of spelling in the writing of magazine Mulia edition April 2020; 33 the error with the details of the 5 errors in the use of letters, 11 error writing the words, 1 usage errors punctuation, 1 error writing element uptake, 15 the use of the word is not raw.

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18

Bennett, Charles H., David P. DiVincenzo, John A. Smolin, and William K. Wootters. "Mixed-state entanglement and quantum error correction." Physical Review A 54, no. 5 (November 1, 1996): 3824–51. http://dx.doi.org/10.1103/physreva.54.3824.

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19

Sheen, W. H., and G. L. Stuber. "Error probability for reduced-state sequence estimation." IEEE Journal on Selected Areas in Communications 10, no. 3 (April 1992): 571–78. http://dx.doi.org/10.1109/49.127779.

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20

Sainz, I., and A. B. Klimov. "Error estimation in the direct state tomography." EPL (Europhysics Letters) 116, no. 1 (October 1, 2016): 10002. http://dx.doi.org/10.1209/0295-5075/116/10002.

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21

D’Ariano, G. M., and C. Macchiavello. "Loss-error compensation in quantum-state measurements." Physical Review A 57, no. 4 (April 1, 1998): 3131–33. http://dx.doi.org/10.1103/physreva.57.3131.

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22

Shang, Jiangwei, Hui Khoon Ng, Arun Sehrawat, Xikun Li, and Berthold-Georg Englert. "Optimal error regions for quantum state estimation." New Journal of Physics 15, no. 12 (December 13, 2013): 123026. http://dx.doi.org/10.1088/1367-2630/15/12/123026.

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23

Bae, Joonwoo. "Structure of minimum-error quantum state discrimination." New Journal of Physics 15, no. 7 (July 18, 2013): 073037. http://dx.doi.org/10.1088/1367-2630/15/7/073037.

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24

Guţă, M., J. Kahn, R. Kueng, and J. A. Tropp. "Fast state tomography with optimal error bounds." Journal of Physics A: Mathematical and Theoretical 53, no. 20 (April 30, 2020): 204001. http://dx.doi.org/10.1088/1751-8121/ab8111.

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25

Eul, H. J., and B. Schiek. "Error-corrected two-state unidirectional network analyser." Electronics Letters 24, no. 19 (1988): 1197. http://dx.doi.org/10.1049/el:19880813.

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26

Ramadhani, Syahri, Junaid Ur Rehman, and Hyundong Shin. "Quantum Error Mitigation for Quantum State Tomography." IEEE Access 9 (2021): 107955–64. http://dx.doi.org/10.1109/access.2021.3101214.

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27

Aham-Onyebuchi, UO, OO Jagun, A. Betiku, O. Olijide, and M. Leshi. "The Prevalence and Pattern of Refractive error in Ogun State, Nigeria." Babcock University Medical Journal (BUMJ) 1, no. 2 (June 30, 2015): 20–26. http://dx.doi.org/10.38029/bumj.v1i2.9.

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Objective: To determine the prevalence and pattern of refractive errors in Ogun State, Nigeria. The prevalence of blindness was also determined. Method: A cross sectional descriptive survey. Participants aged 15years and above were randomly selected from 1125 attendees of different community medical outreaches conducted in 5 Local government areas of Ogun State between August 2012 and March 2013. Participants underwent ocular examination and refraction (automated objective and subjective assessments). Results: The sample consisted of 780 participants. Their mean age was 49.14 (±18.37), and 52.8% were females. About two-thirds [67.7%] had presenting visual acuity [VA] of at least 6/18, while 14.3% were legally blind [VA worse than 3/60].Astigmatism was the most prevalent refractive error [40%] but on conversion tospherical equivalents, hyperopia [28.5%] became the most prevalent error. Apart from refractive errors, cataract and glaucoma were found to be the other major causes of visual impairment amongst the respondents. Conclusion: The prevalence of refractive errors and blindness in the study population was higher than previously documented with hyperopia being the most prevalent spherical equivalent refractive error. An urgent integration of primary eye care into the existing primary health care system for prevention of blindness is advocated.

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28

Bystrova, I. V., and B. P. Podkopaev. "Functional Diagnosis of Digital State Automation Networks." Journal of the Russian Universities. Radioelectronics, no. 2 (June 5, 2018): 12–19. http://dx.doi.org/10.32603/1993-8985-2018-21-2-12-19.

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The problem of functional diagnosis of digital devices forming a network of state automata is considered. This task for the network components is supposed to be solved, and the corresponding diagnostic devices for them are provided. The possibility of their population transformation into the tools for the entire network diagnostics is shown. The result of the transformation with the restrictions on the number of components with errors is simplified in compare with the original population. A procedure is proposed allowing to find analytical expressions defining an auxiliary control and an error discriminator for the most probable case of error localization (all errors are concentrated in a certain component of the network). The first part of the article provides a solution of the functional diagnosis problem for the case when the parity functions of all components are scalar, and the class of detectable errors is given by unit multiplicity. Next, the result generalizes for the case of vector functions, where multiple errors can occur. The procedure minimizes the sought for functional diagnosis devices with respect to the order when preserving the initial detecting ability within any network component. The obtained results are illustrated by an example of construction of functional diagnosis equipment for ranging signal processing device in broadband short-range radio engineering navigation system.

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29

Geraldine, Vincensia. "ERROR ANALYSIS ON THE USE OF CONJUNCTION IN STUDENTS’ WRITING RECOUNT TEXT AT VOCATIONAL STATE SCHOOL 41 JAKARTA." Journal of Language and Literature 6, no. 2 (2018): 97–104. http://dx.doi.org/10.35760/jll.2018.v6i2.2488.

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This research is conducted to find out the conjunction usage within the students’ writing assignments of vocational state school. The aims of this research are to find out the common errors on the use of conjunction in their writing, investigate types of errors that occur most frequently in students’ writing proposed by Ong in Taxonomy Cohesion, and categorizing the causes of errors using the theory proposed by Richard. The research discussed about error analysis on the use of adversative (but), additive (and), and causal (because, so) in students’ writing. This research used the qualitative method and the data source comes from the tenth-grade students’ assignments. The result of this research shows that the common errors in the use of conjunction in students’ writing were a conjunction and, followed by but, so, and because. Furthermore, frequent types of error occurred in the text error of misuse is the most types of errors occur within the students’ writing assignments and then followed by the error of redundant repetition, omission, and unnecessary addition. There are three causes of errors, which are; incomplete application of rules, ignorance of rules restriction, and over-generalization. The errors of misuse and omission found out in the data, is most likely caused by the incomplete application of rules. The error of unnecessary addition and redundant repetition were caused by the ignorance of rules restriction and over-generalization.

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Eko Mulyono. "ERRORS IN NARRATIVE TEXT COMMITTED BY STUDENTS IN GRADE XI OF VOCATIONAL HIGH SCHOOL (SMK) STATE 4 SURAKARTA." PREMISE JOURNAL:ISSN online: 2442-482x, ISSN printed: 2089-3345 5, no. 2 (May 8, 2017): 64. http://dx.doi.org/10.24127/pj.v5i2.816.

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This study aimes to identify the types of errors in the students’ writing, to know the frequencies of each type of errors, and to investigate the causes of errors. There are three types of errors occured in the students’ writing namely lexical error, syntactical error, and discourse error. The errors can be categorized into twenty four subcategories of errors: wrong spelling words (10,35%), wrong selection words (15,53%), omission verb (0,74%), omission v-ing after preposition for (0,55%), addition unnecessary verb (0,74%), using simple present tense refers to simple past (22,37%), use simple future instead of past future (2,40%), using irregular past verb tense after to infinitive (2,40%), addition final ed after to infinitive (1,85%), addition v-ing after to infinitive (1,11%), addition double marking verb (1,66%), omission to be (11,65%), addition to be (1,29%), omission s/es in the use of plural noun (2,40%), addition s in singular noun (1,29%), omission article (6,47%), addition unnecessary article (1,66%), wrong article (1,11%), wrong subject pronoun (2,03%), wrong object pronoun (0,55%), wrong possessive pronoun (2,03%), generic structure (2,96%), reference (2,03%), wrong selection conjunction (4,81%). The most dominant error is in syntactical error i.e. using simple present tense refers to simple past with the percentage 22,37%. Those errors are caused by four aspects, they are overgeneralization, incomplete application of rules, ignorance of rule restrictions, and false concept hypothesized.

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van Erp, Paul B. C., Victor L. Knoop, and Serge P. Hoogendoorn. "Macroscopic Traffic State Estimation: Understanding Traffic Sensing Data-Based Estimation Errors." Journal of Advanced Transportation 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/5730648.

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Traffic state estimation is a crucial element in traffic management systems and in providing traffic information to road users. In this article, we evaluate traffic sensing data-based estimation error characteristics in macroscopic traffic state estimation. We consider two types of sensing data, that is, loop-detector data and probe speed data. These data are used to estimate the mean speed in a discrete space-time mesh. We assume that there are no errors in the sensing data. This allows us to study the errors resulting from the differences in characteristics between the sensing data and desired estimate together with the incomplete description of the relation between the two. The aim of the study is to evaluate the dependency of this estimation error on the traffic conditions and sensing data characteristics. For this purpose, we use microscopic traffic simulation, where we compare the estimates with the ground truth using Edie’s definitions. The study exposes a relation between the error distribution characteristics and traffic conditions. Furthermore, we find that it is important to account for the correlation between individual probe data-based estimation errors. Knowledge related to these estimation errors contributes to making better use of the available sensing data in traffic state estimation.

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Balicka, Agnieszka, Ireneusz Balicki, Alexandra Trbolova, Mateusz Szadkowski, and Aleksandra Tomkowicz. "Refractive state of eyes in dogs." Medycyna Weterynaryjna 74, no. 3 (2018): 151–55. http://dx.doi.org/10.21521/mw.6083.

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The article discusses refractive errors in dogs. The refractive errors presented in the article are myopia, hyperopia, anisometropia and astygmatism. These disadvantages are discussed in individual dogs breeds. Analysis has proved that most of dogs are emmetropic an thus have normal visual acuity. In dogs and human alike the refraction defect may have a genetic compound, it may be congenital or acquired and may change with age. Ametropia directly affects the orientation of animals and their behavior, therefore examination for refractive error is an important element of vision testing. .

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Hinamoto, Takao, Shuji Karino, and Naoki Kuroda. "Error spectrum shaping in 2D state-space digital filters using error diagonal feedback." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 81, no. 1 (January 1998): 79–90. http://dx.doi.org/10.1002/(sici)1520-6440(199801)81:1<79::aid-ecjc8>3.0.co;2-1.

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34

Mou, J., and C. R. Liu. "An Adaptive Methodology for Machine Tool Error Correction." Journal of Engineering for Industry 117, no. 3 (August 1, 1995): 389–99. http://dx.doi.org/10.1115/1.2804345.

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An adaptive methodology for machine tool error correction is presented in this paper. The estimated errors resulting from the proposed methodology could be used to adjust the depth of cut on the finish pass or correct the probing data for on-machine inspection to improve the accuracy of workpiece. A generalized error model is derived, using rigid body kinematics, to describe the error motion between the cutting tool and workpiece. Process-intermittent gauging and state observation techniques are integrated to monitor the thermally induced errors and then to modify the error model’s coefficients as the cutting process proceeds. Analysis-of-variance (ANOVA) is used to identify the significant variables for thermal effect modeling. A multiple linear regression model is derived to identify the dynamics of the time-varying thermally induced errors. Multivariable state observers are constructed to track the thermal effect in real time and to fine tune the error model’s coefficients. Experimental results show that the time-varying machine tool errors can be estimated with desirable accuracy.

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35

Liu, Wei, Dan Song, Zhipeng Wang, and Kun Fang. "Comparative Analysis between Error-State and Full-State Error Estimation for KF-Based IMU/GNSS Integration against IMU Faults." Sensors 19, no. 22 (November 11, 2019): 4912. http://dx.doi.org/10.3390/s19224912.

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Considering the inertial measurement unit (IMU) faults risk of an unmanned aerial vehicle (UAV), this paper provides an analysis of the error overboundings of position estimation in a tightly coupled IMU/global navigation satellite system (GNSS) integrated architecture under the IMU fault conditions using an error-state EKF-based approach and provides a comparison to a recently published EKF-based full state method. Simulation results show that both the error overboundings of the error-state and full-state EKFs can fit the state error against the IMU faults, but the error-state EKF is more suitable for UAV navigation system integrity assurance due to its higher calculation effciency. This study will be extended to the integrity monitoring of multisensor systems.

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Lei, Lili, and Joshua P. Hacker. "Nudging, Ensemble, and Nudging Ensembles for Data Assimilation in the Presence of Model Error." Monthly Weather Review 143, no. 7 (July 1, 2015): 2600–2610. http://dx.doi.org/10.1175/mwr-d-14-00295.1.

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Abstract Objective data assimilation methods such as variational and ensemble algorithms are attractive from a theoretical standpoint. Empirical nudging approaches are computationally efficient and can get around some amount of model error by using arbitrarily large nudging coefficients. In an attempt to take advantage of the strengths of both methods for analyses, combined nudging-ensemble approaches have been recently proposed. Here the two-scale Lorenz model is used to elucidate how the forecast error from nudging, ensemble, and nudging-ensemble schemes varies with model error. As expected, an ensemble filter and smoother are closest to optimal when model errors are small or absent. Model error is introduced by varying model forcing, coupling between scales, and spatial filtering. Nudging approaches perform relatively better with increased model error; use of poor ensemble covariance estimates when model error is large harms the nudging-ensemble performance. Consequently, nudging-ensemble methods always produce error levels between the objective ensemble filters and empirical nudging, and can never provide analyses or short-range forecasts with lower errors than both. As long as the nudged state and the ensemble-filter state are close enough, the ensemble statistics are useful for the nudging, and fully coupling the ensemble and nudging by centering the ensemble on the nudged state is not necessary. An ensemble smoother produces the overall smallest errors except for with very large model errors. Results are qualitatively independent of tuning parameters such as covariance inflation and localization.

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Yuan, Zhigou, Peter A. Vanrolleghem, and Ghislain C. Vansteenkiste. "Modeling error identification of activated sludge models." Water Science and Technology 36, no. 5 (September 1, 1997): 81–88. http://dx.doi.org/10.2166/wst.1997.0170.

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Information about the location of modeling erros is crucial for the efficient improvement of an invalid model. This article discusses how to pinpoint modeling errors through comparison of experimental data with data obtained through simulation of the invalid model. An observer-based approach is presented. By designing a dedicated observer for the system using the invalid model, a signal vector is generated, on which each modeling error imposes an easily identifiable feature. An algorithm to analyze the featured signal is then presented. With this algorithm, the features of each of the modeling errors are extracted. The approach is illustrated for a denitrification reactor model in which errors in the dimension of the state vector, in the structure of the biokinetic relationship and in the values of the parameters could be identified.

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DE ILARRAZA, ARANTZA DÍAZ, KOLDO GOJENOLA, MAITE ORONOZ, and IÑAKI ALEGRIA. "Syntactic error detection and correction in date expressions using finite-state transducers." Natural Language Engineering 17, no. 2 (March 21, 2011): 145–61. http://dx.doi.org/10.1017/s1351324911000088.

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AbstractThis paper presents a set of experiments for the detection and correction of syntactic errors, exploring two alternative approaches. The first one uses an error grammar which combines a robust morphosyntactic analyser and two groups of finite-state transducers (one for the description of syntactic error patterns and the other for the correction of the detected errors). We have also experimented an alternative approach using a positive date grammar where deviations are detected by applying edit-distance techniques. The system has been tested on a corpus of real texts which contained both correct and incorrect sentences. Although the experiment was limited to one language, the results show that attainable performance is not the only criterion for preferring one solution over another.

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Mulyono, Eko. "ERRORS IN NARRATIVE TEXT COMMITTED BY STUDENTS OF GRADE XI OF VOCATIONAL HIGH SCHOOL STATE 4 SURAKARTA ACADEMIC YEAR 2014/2015." Jurnal Penelitian Humaniora 18, no. 2 (September 12, 2017): 25. http://dx.doi.org/10.23917/humaniora.v18i2.5189.

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This study aimes to identify the types of errors in the students' writing, to know the frequencies of each type of errors, and to investigate the causes of errors. There are three types of errors occured in the students' writing namely lexical error, syntactical error, and discourse error. The errors can be categorized into twenty four subcategories of errors: wrong spelling words (10,35%), wrong selection words (15,53%), omission verb (0,74%), omission v-ing after preposition for (0,55%), addition unnecessary verb (0,74%), using simple present tense refers to simple past (22,37%), use simple future instead of past future (2,40%), using irregular past verb tense after to infinitive (2,40%), addition final ed after to infinitive (1,85%), addition v-ing after to infinitive (1,11%), addition double marking verb (1,66%), omission to be (11,65%), addition to be (1,29%), omission s/es in the use of plural noun (2,40%), addition s in singular noun (1,29%), omission article (6,47%), addition unnecessary article (1,66%), wrong article (1,11%), wrong subject pronoun (2,03%), wrong object pronoun (0,55%), wrong possessive pronoun (2,03%), generic structure (2,96%), reference (2,03%), wrong selection conjunction (4,81%). The most dominant error is in syntactical error i.e. using simple present tense refers to simple past with the percentage 22,37%. Those errors are caused by four aspects, they are overgeneralization, incomplete application of rules, ignorance of rule restrictions, and false concept hypothesized.

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Chen, Zijun, Kevin J. Satzinger, Juan Atalaya, Alexander N. Korotkov, Andrew Dunsworth, Daniel Sank, Chris Quintana, et al. "Exponential suppression of bit or phase errors with cyclic error correction." Nature 595, no. 7867 (July 14, 2021): 383–87. http://dx.doi.org/10.1038/s41586-021-03588-y.

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AbstractRealizing the potential of quantum computing requires sufficiently low logical error rates1. Many applications call for error rates as low as 10−15 (refs. 2–9), but state-of-the-art quantum platforms typically have physical error rates near 10−3 (refs. 10–14). Quantum error correction15–17 promises to bridge this divide by distributing quantum logical information across many physical qubits in such a way that errors can be detected and corrected. Errors on the encoded logical qubit state can be exponentially suppressed as the number of physical qubits grows, provided that the physical error rates are below a certain threshold and stable over the course of a computation. Here we implement one-dimensional repetition codes embedded in a two-dimensional grid of superconducting qubits that demonstrate exponential suppression of bit-flip or phase-flip errors, reducing logical error per round more than 100-fold when increasing the number of qubits from 5 to 21. Crucially, this error suppression is stable over 50 rounds of error correction. We also introduce a method for analysing error correlations with high precision, allowing us to characterize error locality while performing quantum error correction. Finally, we perform error detection with a small logical qubit using the 2D surface code on the same device18,19 and show that the results from both one- and two-dimensional codes agree with numerical simulations that use a simple depolarizing error model. These experimental demonstrations provide a foundation for building a scalable fault-tolerant quantum computer with superconducting qubits.

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Hu, Peng-Hao, Ying-Jun Lei, and Yang-Kai Ou. "Analysis of Motion Errors of Linear Guide Pair Based on Parallel Mechanism." Machines 9, no. 2 (February 5, 2021): 33. http://dx.doi.org/10.3390/machines9020033.

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This paper systematically summarized the technical state of art and research results on the motion error of a linear guideway, corrected some misconceptions, and further clarified the relationship between the straightness error of the guide rail itself and the motion error of the linear stage. Moreover, a new method based on parallel mechanism is provided to study the motion errors of the linear guide pair. The basic idea is to abstract the structural relationship between the stage and the guide rail into a 4-bar parallel mechanism. Thus, the stage can be considered as a moving platform in the parallel mechanism. Its motion error analysis is also transferred to moving platform position analysis in the parallel mechanism. The straightness motion error and angular motion error of the stage can be analyzed simultaneously by using the theory of parallel mechanism. Some experiments were conducted on the linear guideway of a self-developed parallel coordinate measuring machine. The experimental data and analysis verify the feasibility and correctness of this method.

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Garimella, S. S., and K. Srinivasan. "Robust State Estimation for Linear Systems." Journal of Dynamic Systems, Measurement, and Control 115, no. 1 (March 1, 1993): 193–96. http://dx.doi.org/10.1115/1.2897397.

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Real-time state estimation of a linear dynamic system using an observer, in the presence of modeling errors in the system model used by the observer and uncertainty in the initial system states, is considered here. A guideline for designing observers for multioutput systems is established, based on an expression for an upper bound on the norm of the state estimation error derived in this paper. An example is presented to illustrate the usefulness of this guideline.

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Turner, A. J., and D. J. Jacob. "Balancing aggregation and smoothing errors in inverse models." Atmospheric Chemistry and Physics Discussions 15, no. 1 (January 13, 2015): 1001–26. http://dx.doi.org/10.5194/acpd-15-1001-2015.

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Abstract. Inverse models use observations of a system (observation vector) to quantify the variables driving that system (state vector) by statistical optimization. When the observation vector is large, such as with satellite data, selecting a suitable dimension for the state vector is a challenge. A state vector that is too large cannot be effectively constrained by the observations, leading to smoothing error. However, reducing the dimension of the state vector leads to aggregation error as prior relationships between state vector elements are imposed rather than optimized. Here we present a method for quantifying aggregation and smoothing errors as a function of state vector dimension, so that a suitable dimension can be selected by minimizing the combined error. Reducing the state vector within the aggregation error constraints can have the added advantage of enabling analytical solution to the inverse problem with full error characterization. We compare three methods for reducing the dimension of the state vector from its native resolution: (1) merging adjacent elements (grid coarsening), (2) clustering with principal component analysis (PCA), and (3) applying a Gaussian mixture model (GMM) with Gaussian pdfs as state vector elements on which the native-resolution state vector elements are projected using radial basis functions (RBFs). The GMM method leads to somewhat lower aggregation error than the other methods, but more importantly it retains resolution of major local features in the state vector while smoothing weak and broad features.

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Turner, A. J., and D. J. Jacob. "Balancing aggregation and smoothing errors in inverse models." Atmospheric Chemistry and Physics 15, no. 12 (June 30, 2015): 7039–48. http://dx.doi.org/10.5194/acp-15-7039-2015.

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Abstract. Inverse models use observations of a system (observation vector) to quantify the variables driving that system (state vector) by statistical optimization. When the observation vector is large, such as with satellite data, selecting a suitable dimension for the state vector is a challenge. A state vector that is too large cannot be effectively constrained by the observations, leading to smoothing error. However, reducing the dimension of the state vector leads to aggregation error as prior relationships between state vector elements are imposed rather than optimized. Here we present a method for quantifying aggregation and smoothing errors as a function of state vector dimension, so that a suitable dimension can be selected by minimizing the combined error. Reducing the state vector within the aggregation error constraints can have the added advantage of enabling analytical solution to the inverse problem with full error characterization. We compare three methods for reducing the dimension of the state vector from its native resolution: (1) merging adjacent elements (grid coarsening), (2) clustering with principal component analysis (PCA), and (3) applying a Gaussian mixture model (GMM) with Gaussian pdfs as state vector elements on which the native-resolution state vector elements are projected using radial basis functions (RBFs). The GMM method leads to somewhat lower aggregation error than the other methods, but more importantly it retains resolution of major local features in the state vector while smoothing weak and broad features.

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Fowler, Austin G., David S. Wang, and Lloyd C. L. Hollenberg. "Surface code quantum error correction incorporating accurate error propagation." Quantum Information and Computation 11, no. 1&2 (January 2011): 8–18. http://dx.doi.org/10.26421/qic11.1-2-2.

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The surface code is a powerful quantum error correcting code that can be defined on a 2-D square lattice of qubits with only nearest neighbor interactions. Syndrome and data qubits form a checkerboard pattern. Information about errors is obtained by repeatedly measuring each syndrome qubit after appropriate interaction with its four nearest neighbor data qubits. Changes in the measurement value indicate the presence of chains of errors in space and time. The standard method of determining operations likely to return the code to its error-free state is to use the minimum weight matching algorithm to connect pairs of measurement changes with chains of corrections such that the minimum total number of corrections is used. Prior work has not taken into account the propagation of errors in space and time by the two-qubit interactions. We show that taking this into account leads to a quadratic improvement of the logical error rate.

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Hsu, Sheng-Ping, and Tzong-Shi Liu. "Modifications of Control Loop to Improve the Depth Response of Autonomous Underwater Vehicles." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/324813.

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During a constant depth maneuver of an autonomous underwater vehicle (AUV), its pitch attitude and stern plane deflections create forces and moments to achieve equilibrium in the vertical plane. If an AUV has a proportional controller only in its depth control loop, then different weights or centers of gravity will cause different steady-state depth errors at trimmed conditions. In general, a steady-state depth error can be eliminated by adding an integral controller in the depth control loop. However, an improper integrator may lead to a bad transient response, even though the steady-state depth error can finally be eliminated. To remove the steady-state depth error, this study proposes methods that adjust the depth command and add a switching integral controller in the depth control loop. Simulation results demonstrate that the steady-state depth error can be eliminated and the transient response can be improved.

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Bates, Alan T., Peter F. Liddle, Kent A. Kiehl, and Elton T. C. Ngan. "State dependent changes in error monitoring in schizophrenia." Journal of Psychiatric Research 38, no. 3 (May 2004): 347–56. http://dx.doi.org/10.1016/j.jpsychires.2003.11.002.

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Lee, Kyoung-Woong, and Han-Soo Choi. "Analysis of Steady State Error on Simple FLC." Journal of Institute of Control, Robotics and Systems 17, no. 9 (September 1, 2011): 897–901. http://dx.doi.org/10.5302/j.icros.2011.17.9.897.

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SAITO, Mitsuyuki, Kazuo YAMANAKA, and Naoya KAWASAKI. "On Admissible State-Observation Error in LQ Regulator." Transactions of the Society of Instrument and Control Engineers 40, no. 4 (2004): 477–79. http://dx.doi.org/10.9746/sicetr1965.40.477.

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Trivedi, Neeta, and N. Balakrishnan. "State and Error Estimation in Multisource Bayesian Tracking." Procedia Computer Science 19 (2013): 998–1003. http://dx.doi.org/10.1016/j.procs.2013.06.139.

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Journal articles on the topic 'Error state'

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