Efficient Determination of Fault Criticality for Manufacturing Test Set Optimization

Abstract. In this study, the detection of dairy cows with difficult calvings using artificial neural networks (ANN) and classification functions (CF) is presented. The set of 15 classification variables was used. The dependent variable was the class of calving difficulty: difficult or easy. Perceptrons with one (MLP1) and two (MLP2) hidden layers as well as radial basis function (RBF) networks were analyzed. The prepared classifiers were characterized by good quality. The accuracy amounted to 75–92 %. Only the RBF network had somewhat worse quality. The level of correct detection by ANN was also high. The sensitivity on a test set was 67–80 % at specificity of 61- 81 %. In the case of CF, a considerable disproportion between sensitivity (6 %) and specificity (99 %) was found. The variables with the greatest contribution to the determination of calving difficulty class were calving season, CYP19-PvuII genotype, pregnancy length and, to a lesser degree, other variables. The performed analyses proved the usefulness of ANN for the detection of cows with difficult calvings, whereas the detection by CF was inaccurate.

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Determination of Heavy Rain Damage-Triggering Rainfall Criteria Based on Data Mining

10.5194/nhess-2020-295 ◽

2020 ◽

Author(s):

Jongsung Kim ◽

Donghyun Kim ◽

Changhyun Choi ◽

Myungjin Lee ◽

Yonsoo Kim ◽

...

Keyword(s):

Data Mining ◽

Local Governments ◽

Heavy Rainfall ◽

Confusion Matrix ◽

Heavy Rain ◽

Test Set ◽

Independent Variables ◽

Damage Data ◽

Triggering Rainfall

Abstract. Heavy rainfall occurs over the Korean peninsula mainly because of typhoons and a localized heavy rainfall, leading to severe flooding and landslide risk. KMA (Korean Meteorological Administration) has the criteria for issuing a Heavy Rain Advisory (HRA) over the peninsula even though each region or local government has different conditions in capability of disaster prevention (CDP) and different characteristics in rainfall and heavy rain damage. Therefore, the aim of this study is to suggest the methodology for the determination of Heavy rain Damage-Triggering Rainfall Criteria (HD-TRC) that HRA can be issued in each region. The study regions are local governments in Gyeonggi-province, Seoul-city, and Incheon-city in Korea. HD-TRC can be determined based on rainfall and heavy rain damage data. The data from 2005 to 2018 are collected and then the data for flood or rainy season from June to September are extracted. The rainfall data is provided in KMA and heavy rain damage data during disaster periods (DPs) can be obtained from the statistical yearbook of natural disaster (SYND) published by MOIS (Minstry of Interior and Safety) every year. Training set of 2005 to 2014 is used for obtaining HD-TRC and test set of 2015 to 2018 is used for evaluating three criteria of HD-TRC, Advanced HD-TRC, and HRA. Analysis for determining the best criteria is performed through data mining processes as follows: (1) Maximum rainfalls in durations of 1 to 24-hr (X1) and antecedent rainfalls of 1 to 7-day (X2) are obtained and used as independent variables. Heavy rain damage data are divided into damage day (1) and no damage day (0) used as dependent variables (Y). Principal component analysis (PCA) is performed and PCs (principal components) are obtained as PC.X1 and PC.X2 for independent variables. Then Risk Index (RI) is defined as PC.X1 + PC.X2 and RIs become the candidates for HD-TRC. The predicted damage (Ŷ) is obtained based on RIs and confusion matrix is constructed then the best HD-TRC is determined through the evaluation of classification performance. (2) However, ‘abnormal days’ (ADs) in a DP that the damage is occurred exists. The ADs mean the days which we do not have rainfall or have small rainfall amount during DP. Say, ADs have too small rainfall to damage even during DP. The ADs are defined as days below rainfall of 20 mm and 5 cases of ADs are also defined as 0, 0–5, 0–10, 0–15, and 0–20 mm in this study. We count total days in all the DPs and in ADs for a case. The ratio of ADs to total days during DPs could be the occurrence probability or prior probability (PP) of ADs for a case and 5 PPs are obtained. Also, the average AD for each case can be obtained and defined as risk range (RR). Then we define Advanced HD-TRC using MCS (Monte Carlo Simulation) linked with PP, RR, and from HD-TRC for each case. Therefore, HD-TRC is determined based on RI and Advanced HD-TRC for each case based on PP and RR. Finally, three criteria of HD-TRC, Advanced HD-TRC, and HRA are compared based on performance evaluation by test set. As a result, Advanced HD-TRC shows the best performance and so the suggested methodology can be used for regional heavy rain damage warning information.

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Determination of Descriptors Which Influence the Toxicity of Organochlorine Compounds Using Qsar Method

Chemical Science International Journal ◽

10.9734/csji/2019/v27i130107 ◽

2019 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Massapihanhoro Ouattara Pierre ◽

Bamba Kafoumba ◽

N’guessan Nobel Kouakou ◽

Ziao Nahossé

Keyword(s):

Poecilia Reticulata ◽

Principal Component ◽

Zero Point ◽

Organochlorine Compounds ◽

Test Set ◽

Point Energy ◽

Physicochemical Descriptors ◽

The Family ◽

Qsar Models

Organochlorine Pesticides (OCP) are organic compounds obtained by the chlorination of various unsaturated hydrocarbons. They are very toxic and therefore belong to the family of persistent organic pollutants. If formerly these pesticides were used to fight against certain vectors of diseases and thus improve the productivity of the host, today they are considered as "enemy" of the environment. To understand the origin of the toxicity of organochlorine compounds, we used 73 molecules (test set: 50 and validation: 23) containing at least one chlorine atom and for which the toxicity (LogLC50) against Poecilia reticulata is known to establish QSAR models. Firstly, we used principal component analysis (PCA) to identify the best descriptors. Then, the different models were established using the method of multiple linear regression (MLR). Models established with quantum and physicochemical descriptors only showed satisfactory results. But the best model was determined with the combination of both quantum and physicochemical descriptors. The criteria of this model are as follows: R2 = 0.939 ; R2ajusted = 0.932 ; Pvalue < 0.0001; α = 0.05 R2CV = 0.935 ; R2 - R2CV = 0.004 ; MCE = 0.073; F = 134.701 These criteria show that the toxicity of organochlorine compounds is well described by the combination of quantum and physicochemical descriptors namely lipophilia (LogP), polarizability (pol), entropy (S), zero-point energy (ZPE) and the number of chlorine atoms (NCl).

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