Error Monitoring

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Actualities and Development of Heavy-Duty CNC Machine Tool Thermal Error Monitoring Technology

Actualities and Development of Heavy-Duty CNC Machine Tool Thermal Error Monitoring Technology

Abstract Thermal error monitoring technology is the key technological support to solve the thermal error problem of heavy-duty CNC (computer numerical control) machine tools. Currently, there are many review literatures intro- ducing the thermal error research of CNC machine tools, but those mainly focus on the thermal issues in small and medium-sized CNC machine tools and seldom introduce thermal error monitoring technologies. This paper gives an overview of the research on the thermal error of CNC machine tools and emphasizes the study of thermal error of the heavy-duty CNC machine tool in three areas. These areas are the causes of thermal error of heavy-duty CNC machine tool and the issues with the temperature moni- toring technology and thermal deformation monitoring technology. A new optical measurement technology called the ‘‘fiber Bragg grating (FBG) distributed sensing tech- nology’’ for heavy-duty CNC machine tools is introduced in detail. This technology forms an intelligent sensing and monitoring system for heavy-duty CNC machine tools. This paper fills in the blank of this kind of review articles
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Anxiety not only increases, but also alters early error-monitoring functions

Anxiety not only increases, but also alters early error-monitoring functions

Although the ERN/Ne is usually described as reflecting cognitive or learning processes (Bernstein, Scheffers, & Coles, 1995; Coles, Scheffers, & Holroyd, 2001; Falken- stein et al., 1991), several ERP studies have shown that the ERN also captures variations in affect or motivation. This observation is consistent with the assumption that er- rors not only provide important learning or cognitive sig- nals, but also convey an important emotional significance (Bush, Luu, & Posner, 2000; Gehring & Willoughby, 2002; Pailing & Segalowitz, 2004; Pourtois et al., 2010). For example, Hajcak, Moser, Yeung, and Simons (2005) suggested that an error is primarily a motivationally sa- lient event, since the ERN was significantly larger for er- rors related to high monetary value. More evidence on the relationship between affect and the ERN has come from studies looking at variations in trait affect. Several researchers have found that individuals scoring high on trait anxiety and negative affect are characterized by in- creased ERNs (Boksem, Tops, Wester, Meijman, & Lorist, 2006; Hajcak, McDonald, & Simons, 2003, 2004; Olvet & Hajcak, 2008). This increased sensitivity for errors in individuals with anxiety characteristics suggests that the ERN also somehow reflects an affective evaluation during error detection (Bush et al., 2000; Olvet & Hajcak, 2008). By contrast, later stages of error monitoring (reflected in the Pe) seem to be resistant to individual differences in affect (Hajcak et al., 2004; Holmes & Pizzagalli, 2008; Vocat et al., 2008), emphasizing the distinction between these two early error-related ERP components.
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Motivational incentives and methylphenidate enhance electrophysiological correlates of error monitoring in children with attention deficit/hyperactivity disorder

Motivational incentives and methylphenidate enhance electrophysiological correlates of error monitoring in children with attention deficit/hyperactivity disorder

In ADHD, there is evidence of reduced ERN (Albr- echt et al., 2008; Liotti, Pliszka, Perez, Kothmann & Woldorff, 2005; van Meel, Heslenfeld, Oosterlaan & Sergeant, 2007) and Pe (Groom, Cahill, Bates, Jackson, Calton, Liddle, et al. 2010; Jonkman, van Melis, Kemner & Markus, 2007; Shen, Tsai & Duann, 2011; Wiersema, van der Meere & Roeyers, 2005, 2009) amplitudes although there is inconsis- tency between studies as to which marker is affected (Shiels & Hawk, 2010). This could reflect patho- physiological heterogeneity in the ADHD population (Nigg, Willcutt, Doyle & Sonuga-Barke, 2005) but might also arise from differences in task design and performance given that error rate and RT are known to influence ERN amplitude (Hajcak et al., 2003). Differences in levels of motivation across experimen- tal paradigms may also play a role because recent models of ADHD have proposed that higher level cognitive deficits may be partly attributable to prob- lems with the regulation of arousal and motivational state (Sergeant, 2000) or reward processing (Tripp & Wickens, 2008). In healthy adults, the ERN responds to manipulations designed to modify the motivational salience of errors (Hajcak, Holroyd, Moser & Simons, 2005; Potts, 2011), raising the question of whether enhancing the motivational significance of errors can restore electrophysiological markers of error monitoring to typical levels in ADHD. If so, this could inform the development of behavioural strategies for optimising cognition in this population. Previously we reported significant effects of motivational incentives on amplitudes of two stimulus-locked ERPs, the N2 and P3, in the same groups described here (Groom, Scerif, Liddle, Batty, Liddle, Roberts et al., 2010). The incentives did not fully ‘normalise’ the N2 and P3 of the ADHD group compared with typically developing children raising the question of whether motivational incen- tives can be more effective when targeted at other parts of the cognitive control system, such as error monitoring.
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Electrophysiological correlates of error monitoring and feedback processing in second language learning

Electrophysiological correlates of error monitoring and feedback processing in second language learning

Generally, the ERN can be considered a sign of the ability to monitor one’s actions, but presumably only when there is little uncertainty about what the correct action is (i.e., after learning). In contrast, the FRN mostly arises when error monitoring still depends on external feedback (i.e., before learning has taken place). The learning process can thus be understood as a transition from relying on external feedback to developing an internal representation based on which one can monitor one’s own performance. This is typically characterized by an inverse relationship between ERN and FRN components (Bellebaum and Daum, 2008; Heldmann et al., 2008b). When feedback becomes redundant, the size of the FRN decreases, as the external error signal becomes less surprising (Holroyd and Coles, 2002). Synchronously, the size of the ERN increases, indicating the correct response is known and the brain is able to detect errors independently. However, when experimental conditions do not allow for any learning to take place, the FRN remains large and no ERN is present (Eppinger et al., 2008; Glienke et al., 2015). Because internal error detection is only possible when correct representations are available, it is worthwhile to examine how such correct representations are formed. The L2 learning process seems a perfect example of incomplete learning, yet L2 learning has rarely been investigated from this perspective.
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Abnormal Error Monitoring in Math-Anxious Individuals: Evidence from Error-Related Brain Potentials.

Abnormal Error Monitoring in Math-Anxious Individuals: Evidence from Error-Related Brain Potentials.

committed a numerical error, as compared to an error in the non-numerical task. The relationship between the Pe component and math anxiety deserve further research, especially because of the great consequences that this possible lack of error consciousness could have in the process of mathematical learning. Regarding source localization analysis, the ERN activity mainly involved the Anterior cingulate cortex (Brodmann areas 24, 32 and 33) [9,11], and the medial and middle frontal gyrus (Brodmann area 6) [36], corresponding to the Supplemental motor area (SMA) (adjacent to the caudal part of the Anterior cingulate cortex) which has also been suggested to be a generator of the ERN [48]. The activation as well of some voxels at the insula, precuneus or posterior cingulate areas suggests a distributed error processing in the human system (see also 49). As for the CRN, it did not activate anterior cingulated brain areas. Hence, our results corroborate previous evidence suggesting that the CRN and the ERN involve different neural generators, with a greater involvement of posterior cingulate areas for the CRN and of anterior cingulated areas for the ERN [31]. The Pe showed activation mainly of the Anterior cingulate cortex (Brodmann area 24) and cingulate gyrus [36] and voxel activation seemed to be restricted to those areas that showed activation for the ERN, suggesting that these components were generated by the same ACC regions (see also 50,51).
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Motivation and semantic context affect brain error-monitoring activity: An event related brain potentials study

Motivation and semantic context affect brain error-monitoring activity: An event related brain potentials study

There is a seeming contradiction in our results. On the one hand, we showed that the amplitude of the ERN increased in the semantically related context as opposed to the mixed context, indicating that the ERN is sensitive to the presence of conflict. On the other hand, we demonstrated that the ERN was higher in the high-motivation condition than low-motivation condition. In the high-motivation condition, errors had a higher significance than in the low-motivation condition, indicating that the ERN is sensitive to the motivational manipulation. It is unlikely that errors in the semantically related context had higher significance than in the mixed context since the financial reward was independent of semantic context (see above). Furthermore, after the experimental session participants reported that they attempted to name pictures as accurately as possible independently of the context in which pictures were presented. To our knowledge, conflict and motiva- tional accounts of the ERN are two mutually exclusive hypotheses in the existing literature, and it has not been shown so far that the ERN can be affected by both factors. We would like to propose here that possibly the conflict and motivational theories are closer related than previously thought. The detection of conflict or errors is likely to have direct affective consequences (Yeung, 2004). Ullsperger and Von Cramon (2004) also suggest that there might be a close interplay with emotional and motivational functions and performance monitoring. Therefore, a clear-cut distinction between theories that associate the ERN with a process of error/conflict detection and theories that associate it with a process giving rise to affective/motivational changes related to error or conflict detection may not be possible, since both may refer to one and the same process (Yeung, 2004).
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Evaluating Changes in Error-Monitoring Electrocortcial Responses as an Outcome of Attention Bias Modification Training

Evaluating Changes in Error-Monitoring Electrocortcial Responses as an Outcome of Attention Bias Modification Training

The author wishes to thank his graduate advisor and mentor, Dr. Joshua M. Carlson who provided guidance and insight throughout this project and many other aspects of my graduate schooling; his undergraduate mentor Dr. Lucy J. Troup who first sparked his interest in research and sitting on my thesis committee; Dr. Adam J. Prus for sitting on my thesis committee; Dr. Lin Fang, who was instrumental in helping make aspects of this thesis a reality and provided great feedback and assistance throughout this project; and the research assistants of the Cognitive x Affective Behavior & Integrated Neuroscience lab for their help collecting data, recruiting participants, and monitoring participant progress in this long study. Additionally, the author would like to thank the National Institute of Mental Health, as this thesis was part of a larger project (R15MH1109051) funded by this organization in an grant awarded to Dr. Carlson – without this funding this thesis would have not been possible.
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The use of error tags in ARTFL’s Encyclopédie: Does good error identification lead to good error correction?

The use of error tags in ARTFL’s Encyclopédie: Does good error identification lead to good error correction?

The actual performance of the spelling correction algorithm on words which include the error tag, while comparable to the performance on other words, is perhaps not as high as we might i[r]

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Error and Predicativity

Error and Predicativity

I would like to suggest a distinction between two types of error which can appear in mathematics. First of all there are what I should like to call “local” errors. These are errors which plague individual proofs, or possibly a relatively small group of proofs which share a similarity in structure. These mistakes are somehow confined to a small portion of the mathematical enterprise and, if corrigible, they can be amended without introducing any substantial revision of the underlying mathematical principles one appeals to when devising the given proof. Secondly, there are “foundational” mistakes, which instead relate to the very principles of proofs and the axioms; these occur when inconsistencies arise within our foundational systems.
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Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction

Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction

Until now, error type performance for Grammatical Error Correction (GEC) sys- tems could only be measured in terms of recall because system output is not anno- tated. To overcome this problem, we in- troduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automat- ically extract edits from parallel original and corrected sentences and classify them according to a new, dataset-agnostic, rule- based framework. This not only facilitates error type evaluation at different levels of granularity, but can also be used to reduce annotator workload and standardise exist- ing GEC datasets. Human experts rated the automatic edits as “Good” or “Accept- able” in at least 95% of cases, so we ap- plied ERRANT to the system output of the CoNLL-2014 shared task to carry out a de- tailed error type analysis for the first time.
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7.4 Error Report Rejection Conditions Error Codes

7.4 Error Report Rejection Conditions Error Codes

Error Reason(s) For Rejection AC4 ƒ A valid Referring/Requisitioning Health Care Provider number must be present for this service code ƒ The fee schedule code is C813, C815 and the referral number is not in the Midwife range ( 700000-722899 )

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Error Estimations, Error Computations, and Convergence Rates in FEM for BVPs

Error Estimations, Error Computations, and Convergence Rates in FEM for BVPs

The a priori estimates only hold in asymptotic range and their derivation in the currently published literature are only valid for self adjoint operators in GM/WF when functional B ( ) ⋅ ⋅ , is symmetric, thus GM/WF has best approximation property in B-norm. New work presented in this paper establishes correspondence between best approximation property of an integral form in some norm and the variational consistency of the integral form and demonstrates that when one exists the other is ensured. Thus, for establishing a priori error estimates, varia- tional consistency becomes an essential property of the integral form. Of course best approximation property in some norm if it exists is equally good as best approximation property and variational consistency of integral form can not exist without each other, i.e. they co-exist. In case of GM/WF, VC integral form is possible for self adjoint operator and in case of LSP VC integral form is possible for all three classes of differential operators, hence a priori estimates for GM/WF for self adjoint operators and a priori estimates for LSP for all three classes of operators can be derived. The derivation of a priori error estimates presented in proposition 5.2 applies to GM/WF for self adjoint operators and in case of LSP for all three classes of operators as well as any other integral form resulting from a chosen method of approximation as long as the integral form is VC. Numerical studies for the model problems containing the three classes of operators confirm that when the integral form is VC, same a priori estimates and convergence rates hold. Thus, for the first time we have a priori error estimates for non-self adjoint and non-linear differential operators. Extensive numerical studies are presented for various p and k values for uniform h-refinements demonstrating that the theoretically derived convergence rates in a priori estimates are always in agreement with calculated values when the integral forms are VC. The a priori error es- timates derived here also hold for 2D and 3D BVPs as long as the integral forms in these BVPs are variationally consistent. This can be confirmed numerically and is in agreement with published literature for self adjoint op- erators.
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Network Topologies or Error Performance Monitoring in SDH Technology

Network Topologies or Error Performance Monitoring in SDH Technology

13. Loss of Sequence Synchronization (LSS) Out of service bit error measurements using pseudo-random sequences can only be performed if the reference sequence produced on the receiving side of the test set-up is correctly synchronized to the sequence coming from the object under test. In order to achieve compatible measurement results, it’s necessary that the sequence synchronization characteristics are specified. The following requirement is applicable to all ITU-T 0.150 Recommendations dealing with error performance measurements using pseudo-random sequences. Sequence synchronization shall be considered to be lost and re- synchronizations shall be started if: 1. The bit error ratio is>0.20 during an integration interval of 1 second; or
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Error Correction and Error Handling on PowerQUICC III Processors

Error Correction and Error Handling on PowerQUICC III Processors

(DCP_PERR), the address on the cache line with the error is placed in the MCAR register (the MCSRR0 register is not meaningful for this error). The recoverability for this error is determined depending on if the parity error is on modified data. If it is determined that the data was not modified, the recovery mechanism for this error is to invalidate the data cache line and resume operation by executing the rfmci instruction. However, the recovery code must be guaranteed not to generate an L1 cache parity error. When a machine check interrupt is taken, the device clears the MSR[ME] bit. In this state, any subsequent machine check interrupts cause the device to enter the checkstop state. Therefore, if the machine check handler recovery code generates an L1cache parity error, the device enters the checkstop state.
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Error Is Not Terror: Error Correction in Second Language Writing

Error Is Not Terror: Error Correction in Second Language Writing

Traditionally, writing teachers and students have regarded error correction as playing a driving force in improving L2 writing accuracy (Ferris & Roberts, 2001; Lee, 2004); however, several writers have questioned the ultimate effectiveness of error correction (Truscott, 1996; Chandler, 2003). Results of some scholarly studies (Kepner, 1991; Truscott & Hsu, 2008) have shown that error correction was not only ineffective, but also potentially deleterious to second language writing development. Truscott (1996) in his seminal work targeting the issue argued that error correction in L2 classes should be abolished. In an extensive review of past studies, he stated that error correction is pedagogically ineffective and unhelpful. In a similar vein, Kepner's (1991) study showed that students who were provided with error correction in their journal entries did not manifest a significantly improved performance in comparison to those who did not receive any written corrective feedback. This means that no matter how many times an erroneous structure is corrected; students are unable to transfer the correct structure to their L2 writing regularly. In Kepner's study, students were provided with two types of written feedback: message-related comments and surface-error corrections. It became clear that employing consistent L2 teacher's written error corrections was ineffective in L2 writing. In contrast, the consistent use of message related comments was facilitative in enhancing both overall quality and surface-level accuracy. Ferris (1999) argued against Truscott's claim addressing the likely utility of grammar correction. She carefully examined the Truscott's argument and identified two major pitfalls: (1) there are manifold approaches to error correction which are less or more effective; (2) his conclusions made based on the results attained by other writers were flawed and untenable.
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Quantum rms error and Heisenberg’s error-disturbance relation

Quantum rms error and Heisenberg’s error-disturbance relation

An irony observed in [13] is that the experiments [8, 9] confirming Ozawa’s inequality (4) use approximations for which the error ε no (A , M, ρ ) and disturbance η no (B , M, ρ ) are state-independent and in fact directly coupled to Δ(A, C) and Δ(B, D). As noted above, in such joint measurements the quantities ε no cease to be operationally significant and accessible. There are constellations of observables and states where these quantities are nonzero while the distributions in an individual state are identical, so that, say, Δ(A ρ , C ρ ) = 0. This confirms that ε no , η no do fail to indicate the absence of imprecision in an individual state that would be appropriate to distribution-comparison error measures. In the case of qubit observables of the form measured in the Toronto experiment one has ε no (A , M, ρ) = Δ(A , C) and η no (B , M, ρ) = Δ(B , D). (In this case the target observables commute
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Case study of error recovery and error propagation on ranger

Case study of error recovery and error propagation on ranger

lockups events. We found that Northbridge error , core and ECC error events are weakly correlated to soft lockup events on all 26 dates. This represents a recovery rate of 100%. Detailed diagnosis: When correlations of CPU & memory resource use counters and correlations of chipset & ECC errors occur on the same date, it shows that CPU memory usage activities are associated with the generation of chipset and memory errors. When the CPU accessed corrupted data stored in ECC memory, this triggered an ECC error which was subsequently corrected. Therefore, the correlated CPU & memory resource use counters and correlated chipset & memory errors can be used to monitor recovery from internal data corruption.
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Error Annotation of the Arabic Learner Corpus: A New Error Tagset

Error Annotation of the Arabic Learner Corpus: A New Error Tagset

As a result of the above limitations, we developed another error taxonomy based on ARIDA and other error-analysis studies [13-16]. The reason for relying on the ARIDA tagset is that it includes two comprehensively well-described categories, Style and Punctuation. The other four studies investigate different real types of error in Arabic learner production using the bottom-up method where they analyzed their own samples then extracted the corresponding error-type lists. These studies do not aim to develop an error-type tagset to be used for further projects, such as learner corpora. Nonetheless, their error taxonomies are valid and adaptable since they in- clude significant and comprehensive classes of learner error. Furthermore, we cannot overlook the authenticity of the texts from which these error types are derived; which adds to the validity of their taxonomies. The following is a brief overview:
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Grammatical Error Detection Using Error  and Grammaticality Specific Word Embeddings

Grammatical Error Detection Using Error and Grammaticality Specific Word Embeddings

and as it can be seen, FCE+E&GWE-L8 detected the error in contrast to FCE+W2V. Noun type er- rors are presented in Table 4(b). Here, FCE+W2V did not detect any error, while FCE+E&GWE- L8 could detect the mass noun error, frequently found in a learner corpus. Detection of “sale” and “cloths” was failed in both models, but they are hard to detect since the former requires syn- tactic information and the latter involves com- mon knowledge. In Table 4(c), FCE+W2V suc- ceeded in detection of a missing article error, but FCE+E&GWE-L8 did not. Even though proposed word embeddings learn substitution errors effec- tively, they cannot properly learn insertion and deletion errors. It is our future work to extend word embeddings to include these types of errors and focus on contextual errors that are difficult to deal with the model, for example, missing articles. Figure 3 visualizes word embeddings (FCE+W2V and FCE+E&GWE-L8) of fre- quently occurring errors in learning data using t-SNE. We plot prepositions and some typical verbs 5 , where FCE+E&GWE-L8 showed better
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Monitoring. Monitoring. Reinspection. Comprehensive. Monitoring. Monitoring. Monitoring

Monitoring. Monitoring. Reinspection. Comprehensive. Monitoring. Monitoring. Monitoring

BJ's Wholesale Club #71 413 Constant Friendship Blvd... Milton Wright High School.[r]

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