Assessing the Effect of Considering Multiple Data Properties on Detecting Potential Errors in Pavement Condition Data

2017 ◽  
Vol 2639 (1) ◽  
pp. 39-45
Author(s):  
Salar Zabihi Siabil ◽  
Nasir G. Gharaibeh
Keyword(s):  
Error Detection ◽  
False Negative ◽  
Data Set ◽  
Pavement Condition ◽  
Detection Techniques ◽  
Multiple Data ◽  
Texas Department ◽  
Maintenance And Rehabilitation ◽  
Current Error

The quality of pavement condition data can affect the assessment of current condition, predictions of future condition, and the reliability of maintenance and rehabilitation plans and funding need estimates at the network level. Thus, improving the quality of pavement condition data is an ongoing process for transportation agencies. Detecting potential errors in network-level pavement condition data is a primary step in assessing and enhancing the accuracy of this data. Current error detection techniques tend to focus on analyzing time series trends in pavement condition to identify unexpected changes that may denote data errors. However, there are additional properties of these data that can be used to identify potential errors, including variability within uniform performance families and consistency between multiple performance indicators. This paper assesses the effect of considering those data properties on detecting potential errors in pavement condition data. Three case analyses were defined such that each considered a different combination of these properties to identify likely errors. The analyses were performed on a pavement condition data set representing the Brownwood District roadway network of the Texas Department of Transportation. The results of this investigation indicate that considering such properties in a combined manner can reduce the numbers of false positive errors and false negative errors.

scholarly journals Low-Voltage Energy Efficient Neural Inference by Leveraging Fault Detection Techniques

2021 ◽  
Author(s):  
Mehdi Safarpour ◽  
Tommy Z. Deng ◽  
John Massingham ◽  
Lei Xun ◽  
Mohammad Sabokrou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Error Detection ◽  
Energy Savings ◽  
Low Voltage ◽  
Building Blocks ◽  
Feedback Mechanism ◽  
Total System ◽  
Data Set ◽  
Detection Techniques ◽  
Energy Efficiency Improvement

This paper presents simple techniques to significantly reduced energy consumption of DNNs: Operating at reduced voltages offers substantial energy efficiency improvement but at the expense of increasing the probability of computational errors due to hardware faults. In this context, we targeted Deep Neural Networks (DNN) as emerging energy hungry building blocks in embedded applications. Without an error feedback mechanism, blind voltage down-scaling will result in degraded accuracy or total system failure. To enable safe voltage down-scaling, in this paper two solutions based on Self-Supervised Learning (SSL) and Algorithm Based Fault Tolerance (ABFT) were developed. A DNN model trained on MNIST data-set was deployed on a Field Programmable Gate Array (FPGA) that operated at reduced voltages and employed the proposed schemes. The SSL approach provides extremely low-overhead (≈0.2%) fault detection at the cost of lower error coverage and extra training, while ABFT incurs less than 8%overheads at run-time with close to 100% error detection rate. By using the solutions, substantial energy savings, i.e., up to 40.3%,without compromising the accuracy of the model was achieved

scholarly journals Low-Voltage Energy Efficient Neural Inference by Leveraging Fault Detection Techniques

2021 ◽  
Author(s):  
Mehdi Safarpour ◽  
Tommy Z. Deng ◽  
John Massingham ◽  
Lei Xun ◽  
Mohammad Sabokrou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Error Detection ◽  
Energy Savings ◽  
Low Voltage ◽  
Building Blocks ◽  
Feedback Mechanism ◽  
Total System ◽  
Data Set ◽  
Detection Techniques ◽  
Energy Efficiency Improvement

Operating at reduced voltages offers substantial energy efficiency improvement but at the expense of increasing the probability of computational errors due to hardware faults. In this context, we targeted Deep Neural Networks (DNN) as emerging energy hungry building blocks in embedded applications. Without an error feedback mechanism, blind voltage down-scaling will result in degraded accuracy or total system failure. To enable safe voltage down-scaling, in this paper two solutions based on Self-Supervised Learning (SSL) and Algorithm Based Fault Tolerance (ABFT) were developed. A DNN model trained on MNIST data-set was deployed on a Field Programmable Gate Array (FPGA) that operated at reduced voltages and employed the proposed schemes. The SSL approach provides extremely low-overhead (≈0.2%) fault detection at the cost of lower error coverage and extra training, while ABFT incurs less than 8%overheads at run-time with close to 100% error detection rate. By using the solutions, substantial energy savings, i.e., up to 40.3%,without compromising the accuracy of the model was achieved

scholarly journals Low-Voltage Energy Efficient Neural Inference by Leveraging Fault Detection Techniques

2021 ◽  
Author(s):  
Mehdi Safarpour ◽  
Tommy Z. Deng ◽  
John Massingham ◽  
Lei Xun ◽  
Mohammad Sabokrou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Error Detection ◽  
Energy Savings ◽  
Low Voltage ◽  
Building Blocks ◽  
Feedback Mechanism ◽  
Total System ◽  
Data Set ◽  
Detection Techniques ◽  
Energy Efficiency Improvement

Operating at reduced voltages offers substantial energy efficiency improvement but at the expense of increasing the probability of computational errors due to hardware faults. In this context, we targeted Deep Neural Networks (DNN) as emerging energy hungry building blocks in embedded applications. Without an error feedback mechanism, blind voltage down-scaling will result in degraded accuracy or total system failure. To enable safe voltage down-scaling, in this paper two solutions based on Self-Supervised Learning (SSL) and Algorithm Based Fault Tolerance (ABFT) were developed. A DNN model trained on MNIST data-set was deployed on a Field Programmable Gate Array (FPGA) that operated at reduced voltages and employed the proposed schemes. The SSL approach provides extremely low-overhead (≈0.2%) fault detection at the cost of lower error coverage and extra training, while ABFT incurs less than 8%overheads at run-time with close to 100% error detection rate. By using the solutions, substantial energy savings, i.e., up to 40.3%,without compromising the accuracy of the model was achieved

Maintenance of a Genetic Data Set on Multiple Data Base and Statistical Analysis Systems

1982 ◽  
Vol 61 (s109) ◽  
pp. 34-34
Author(s):  
Samuel J. Agronow ◽  
Federico C. Mariona ◽  
Frederick C. Koppitch ◽  
Kazutoshi Mayeda
Keyword(s):  
Statistical Analysis ◽  
Data Base ◽  
Genetic Data ◽  
Data Set ◽  
Multiple Data

Real-time error detection techniques based on FPGA

2013 ◽  
Vol 33 (5) ◽  
pp. 1459-1462
Author(s):  
Xiaoming JU ◽  
Jiehao ZHANG ◽  
Yizhong ZHANG
Keyword(s):  
Real Time ◽  
Error Detection ◽  
Time Error ◽  
Detection Techniques

scholarly journals Calling for improved quality in the registration of traditional Chinese medicine during the public health emergency: a survey of trial registries for COVID-19, H1N1, and SARS

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhuoran Kuang ◽  
◽  
Xiaoyan Li ◽  
Jianxiong Cai ◽  
Yaolong Chen ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Secondary Outcome ◽  
Specific Information ◽  
Clinical Trial Registry ◽  
Data Set ◽  
Diagnosis Criteria

Abstract Objective To assess the registration quality of traditional Chinese medicine (TCM) clinical trials for COVID-19, H1N1, and SARS. Method We searched for clinical trial registrations of TCM in the WHO International Clinical Trials Registry Platform (ICTRP) and Chinese Clinical Trial Registry (ChiCTR) on April 30, 2020. The registration quality assessment is based on the WHO Trial Registration Data Set (Version 1.3.1) and extra items for TCM information, including TCM background, theoretical origin, specific diagnosis criteria, description of intervention, and outcomes. Results A total of 136 records were examined, including 129 severe acute respiratory syndrome coronavirus 2 (COVID-19) and 7 H1N1 influenza (H1N1) patients. The deficiencies in the registration of TCM clinical trials (CTs) mainly focus on a low percentage reporting detailed information about interventions (46.6%), primary outcome(s) (37.7%), and key secondary outcome(s) (18.4%) and a lack of summary result (0%). For the TCM items, none of the clinical trial registrations reported the TCM background and rationale; only 6.6% provided the TCM diagnosis criteria or a description of the TCM intervention; and 27.9% provided TCM outcome(s). Conclusion Overall, although the number of registrations of TCM CTs increased, the registration quality was low. The registration quality of TCM CTs should be improved by more detailed reporting of interventions and outcomes, TCM-specific information, and sharing of the result data.

Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

2021 ◽  
pp. 036119812110088
Author(s):  
Raul E. Avelar ◽  
Karen Dixon ◽  
Boniphace Kutela ◽  
Sam Klump ◽  
Beth Wemple ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Synthetic Data ◽  
Calibration Procedure ◽  
Safety Performance ◽  
Absolute Deviation ◽  
Data Set ◽  
Safety Database ◽  
Simple Index ◽  
Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

scholarly journals Evaluating Presence/Absence of Target Microbes in Microbiological Tests

2000 ◽  
Vol 83 (6) ◽  
pp. 1429-1434
Author(s):  
Robert J Blodgett ◽  
Anthony D Hitchins
Keyword(s):  
Performance Studies ◽  
Collaborative Study ◽  
False Negative ◽  
The Other ◽  
Test Results ◽  
Logistic Curve ◽  
Microbiological Method ◽  
Test Portion ◽  
Method Performance ◽  
Data Set

Abstract A typical qualitative microbiological method performance (collaborative) study gathers a data set of responses about a test for the presence or absence of a target microbe. We developed 2 models that estimate false-positive and false-negative rates. One model assumes a constant probability that the tests will indicate the target microbe is present for any positive concentration in the test portion. The other model assumes that this probability follows a logistic curve. Test results from several method performance studies illustrate these estimates.

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