scholarly journals A Model-Based Active Testing Approach to Sequential Diagnosis

2010 ◽  
Vol 39 ◽  
pp. 301-334 ◽  
Author(s):  
A. Feldman ◽  
G. Provan ◽  
A. Van Gemund
Keyword(s):  
Pattern Generation ◽  
Model Based ◽  
Trade Offs ◽  
Additional Input ◽  
Active Testing ◽  
Testing Algorithms ◽  
Input Variables ◽  
Testing Algorithm ◽  
Sequential Diagnosis ◽  
Test Vectors

Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or executing additional tests (sequential diagnosis/test sequencing). In this paper we combine the above approaches with techniques from Automated Test Pattern Generation (ATPG) and Model-Based Diagnosis (MBD) into a framework called FRACTAL (FRamework for ACtive Testing ALgorithms). Apart from the inputs and outputs that connect a system to its environment, in active testing we consider additional input variables to which a sequence of test vectors can be supplied. We address the computationally hard problem of computing optimal control assignments (as defined in FRACTAL) in terms of a greedy approximation algorithm called FRACTAL-G. We compare the decrease in the number of remaining minimal cardinality diagnoses of FRACTAL-G to that of two more FRACTAL algorithms: FRACTAL-ATPG and FRACTAL-P. FRACTAL-ATPG is based on ATPG and sequential diagnosis while FRACTAL-P is based on probing and, although not an active testing algorithm, provides a baseline for comparing the lower bound on the number of reachable diagnoses for the FRACTAL algorithms. We empirically evaluate the trade-offs of the three FRACTAL algorithms by performing extensive experimentation on the ISCAS85/74XXX benchmark of combinational circuits.

scholarly journals Performance of Cans Classification System for Different Conveyor Belt Speed using Naïve Bayes

2020 ◽  
Vol 5 (4) ◽  
pp. 111
Author(s):  
Yulia Resti ◽  
Firmansyah Burlian ◽  
Irsyadi Yani
Keyword(s):  
Classification System ◽  
Naive Bayes ◽  
Conveyor Belt ◽  
Original Model ◽  
Model Based ◽  
Accuracy Level ◽  
Conveyor Belts ◽  
Sorting Process ◽  
Input Variables

The classification system in the sorting process in the can recycling industry can be made based on digital images by exploring the basic color pixel values ​​of images such as R, G, and B as variable inputs. In real time, the classification of cans in the sorting process occurs when cans placed on a conveyor belt move at a certain speed. This paper discusses the performance of can classification systems using the Naïve Bayes method. This method can handle all types of variables, including when all variables are continuous. Two types of conveyor belts are designed to get different speeds, and all images of the cans are captured on both conveyor belts. Two models of Bayes naive are built on the basis of the different distribution assumptions; the original model (all Gaussian distributed) and the model based on the best distribution. Performance of the classification system is built by dividing data into the learning data and the testing data with a composition of 50:50 in which each data is designed into 50 groups with different percentages on each type of cans using sampling technique without replacement. The results obtained are, first, the speed of the conveyor belt when capturing an image affects the pixel values of red, green, and blue and ultimately affects the results of the classification of cans. Second, not all input variables are Gaussian distributed. The classification system was built using assumption the best distribution model for each input variable has the better average accuracy level than the model that assumes all input variables are Gaussian distributed, and the accuracy level of classification on the first speeds of conveyor belt with a gear ratio of 12:30 and a diameter of 35 mm has an accuracy that is better than the other speed, both on the original model and the model based on the best distribution. However, it is necessary to test more statistical distribution models to obtain significant results.

scholarly journals Development of a machine learning model to estimate biotic ligand model-based predicted no‐effect concentrations for copper in freshwater

2021 ◽  
Author(s):  
Jiwoong Chung ◽  
Geonwoo Yoo ◽  
Jinhee Choi ◽  
Jong-Hyeon Lee
Keyword(s):  
Risk Assessment ◽  
Electrical Conductivity ◽  
Environmental Risk ◽  
Biotic Ligand Model ◽  
Data Availability ◽  
Outcome Variable ◽  
Model Based ◽  
Biotic Ligand ◽  
Input Variables ◽  
Ligand Model

The copper biotic ligand model (BLM) has been used for environmental risk assessment by taking into account the bioavailability of copper in freshwater. However, the BLM-based environmental risk of copper has been assessed only in Europe and North America, with monitoring datasets containing all of the BLM input variables. For other areas, it is necessary to apply surrogate tools with reduced data requirements to estimate the BLM-based predicted no-effect concentration (PNEC) from commonly available monitoring datasets. To develop an optimized PNEC estimation model based on an available monitoring dataset, an initial model that considers all BLM variables, a second model that requires variables excluding alkalinity, and a third model using electrical conductivity as a surrogate of the major cations and alkalinity have been proposed. Furthermore, deep neural network (DNN) models have been used to predict the nonlinear relationships between the PNEC (outcome variable) and the required input variables (explanatory variables). The predictive capacity of DNN models in this study was compared with the results of other existing PNEC estimation tools using a look-up table and multiple linear and multivariate polynomial regression methods. Three DNN models, using different input variables, provided better predictions of the copper PNECs compared with the existing tools for four test datasets, i.e., Korean, United States, Swedish, and Belgian freshwaters. The adjusted r2 values in all DNN models were higher than 0.95 in the test datasets, except for the Swedish dataset (adjusted r2 > 0.87). Consequently, the most applicable model among the three DNN models could be selected according to the data availability in the collected monitoring database. Because the most simplified DNN model required only three water quality variables (pH, dissolved organic carbon, and electrical conductivity) as input variables, it is expected that the copper BLM-based risk assessment can be applied to monitoring datasets worldwide.

scholarly journals Evaluation of Modified Two-Tiered Testing Algorithms for Lyme Disease Laboratory Diagnosis Using Well-Characterized Serum Samples

2018 ◽  
Vol 56 (8) ◽  
Author(s):  
Adoracion Pegalajar-Jurado ◽  
Martin E. Schriefer ◽  
Ryan J. Welch ◽  
Marc R. Couturier ◽  
Tiffany MacKenzie ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Laboratory Testing ◽  
Laboratory Diagnosis ◽  
Serum Samples ◽  
Chemiluminescence Immunoassay ◽  
Testing Algorithms ◽  
Second Tier ◽  
Testing Algorithm ◽  
Low Sensitivity ◽  
Recommended Algorithm

ABSTRACTStandard two-tiered testing (STTT) is the recommended algorithm for laboratory diagnosis of Lyme disease (LD). Several limitations are associated with STTT that include low sensitivity in the early stages of disease, as well as technical complexity and subjectivity associated with second-tier immunoblotting; therefore, modified two-tiered testing (MTTT) algorithms that utilize two sequential first-tier tests and eliminate immunoblotting have been evaluated. Recently, a novel MTTT that uses a VlsE chemiluminescence immunoassay followed by a C6 enzyme immunoassay has been proposed. The purpose of this study was to evaluate the performance of the VlsE/C6 MTTT using well-characterized serum samples. Serum samples from the CDC Lyme Serum Repository were tested using three MTTTs, VlsE/C6, whole-cell sonicate (WCS)/C6, and WCS/VlsE, and three STTTs (immunoblotting preceded by three different first-tier assays: VlsE, C6, and WCS). Significant differences were not observed between the results of the MTTTs assessed; however, the VlsE/C6 MTTT resulted in the highest specificity (100%) when other diseases were tested and the lowest sensitivity (75%) for LD samples. Significant differences were present between the results for various MTTTs and STTTs evaluated. Specifically, all MTTTs resulted in higher sensitivities than the STTTs for all LD groups combined and were significantly more accurate (i.e., higher proportion of correct classifications) for this group, with the exception of the WCS/ViraStripe STTT. Additionally, when other diseases were tested, only the results of the VlsE/C6 MTTT differed significantly from those of the WCS/ViraStripe STTT, with the VlsE/C6 MTTT resulting in a 6.2% higher accuracy. Overall, the VlsE/C6 MTTT offers an additional laboratory testing algorithm for LD with equivalent or enhanced performance compared to that of the other MTTTs and STTTs evaluated in this study.

Genetic-Algorithm-based Test Pattern Generation for Crosstalk Faults between On-Chip Aggressor and Victim

2015 ◽  
Vol 25 (03) ◽  
pp. 1640018
Author(s):  
Kishore Duganapalli ◽  
Ajoy K. Palit ◽  
Walter Anheier
Keyword(s):  
Supply Voltage ◽  
Fitness Function ◽  
Vlsi Design ◽  
Pattern Generation ◽  
Test Automation ◽  
Process Technology ◽  
Noise Margin ◽  
Aspect Ratios ◽  
On Chip ◽  
Test Vectors

With the shrinking feature size and increasing aspect ratios of interconnects in DSM chips, the coupling noise between adjacent interconnects has become a major signal integrity (SI) issue, giving rise to crosstalk failures. In older technologies, SI issues have been ignored because of high noise immunity of the CMOS circuits and the process technology. However, as CMOS technologies lower down the supply voltage as well as the threshold voltage of a transistor, digital designs are more and more susceptible to noise because of the reduction of noise margin. The genetic algorithms (GAs) have been applied earlier in different engineering disciplines as potentially good optimization tools and for various applications in VLSI design, layout, EDIF digital system testing and also for test automation, particularly for stuck-at-faults and crosstalk-induced delay faults. In this paper, an elitist GA has been developed that can be used as an ATPG tool for generating the test patterns for crosstalk-induced faults between on-chip aggressor and victim and as well as for stuck-at-faults. It has been observed that the elitist GA, when the fitness function is properly defined, has immense potential in extracting the suitable test vectors quickly from randomly generated initial patterns.

scholarly journals VALIDATION OF A SURVIVABLE PUBLISH-SUBSCRIBE SYSTEM

2014 ◽  
pp. 77-94
Author(s):  
Sankalp Singh ◽  
Adnan Agbaria ◽  
Fabrice Stevens ◽  
Tod Courtney ◽  
John F. Meyer ◽  
...  
Keyword(s):  
Top Down ◽  
Model Based ◽  
Trade Offs ◽  
Validation Procedure ◽  
High Level

We describe, with respect to high-level survivability requirements, the validation of a survivable publish subscribe system that is under development. We use a top-down approach that methodically breaks the task of validation into manageable tasks, and for each task, applies techniques best suited to its accomplishment. These efforts can be largely independent and use a variety of validation techniques, and the results, which complement and supplement each other, are seamlessly integrated to provide a convincing assurance argument. We also demonstrate the use of model-based validation techniques, as a part of the overall validation procedure, to guide the system’s design by exploring different configurations and evaluating trade-offs.

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