Offline hand-drawn circuit component recognition using texture and shape-based features

CAR models for the Fuzi–Gancao herb pair were constructed by BP, SVR, GA and PSO, and used to fit experimental data. The main active antitumor components were recognized from MIVs based on the optimal CAR model.

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An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Mathematical Problems in Engineering ◽

10.1155/2020/2940286 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuanyuan Xu ◽

Genke Yang ◽

Jiliang Luo ◽

Jianan He

Keyword(s):

Deep Learning ◽

Characteristic Curve ◽

Area Under The Curve ◽

Principal Component ◽

Recognition Algorithm ◽

Electronic Component ◽

Support Vector ◽

Learning Networks ◽

Component Recognition ◽

And Performance

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6 level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

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Consideration of Environmentally Assisted Fatigue in Austenitic Stainless Steel: Calculation and Practical Application

Volume 3: Design and Analysis ◽

10.1115/pvp2012-78107 ◽

2012 ◽

Author(s):

Sven H. Reese ◽

Johannes Seichter ◽

Dietmar Klucke

Keyword(s):

Primary Circuit ◽

Stress And Strain ◽

Loading Conditions ◽

Practical Application ◽

Circuit Component ◽

Detailed Assessment ◽

The Mean ◽

Fatigue Evaluation ◽

Assessment Procedures

The influence of LWR coolant environment to the lifetime of materials has been discussed recent years. Nowadays the consideration of environmentally assisted fatigue is under consideration in Codes and Standards like ASME and the German KTA Rules (e.g. Standard No. 3201.2 and Standard No. 3201.4) by means of so called attention thresholds. Basic calculation procedures in terms of quantifying the influence of LWR coolant environment by the Fen correction factor were proposed by Higuchi and others and are given in NUREG/CR-6909. This paper deals with the application of the proposed assessment procedures of ANL and the application to plant conditions. Therefore conservative assessment procedures are introduced without assuming the knowledge of detailed stress and strain calculations or temperature transients. Additionally, detailed assessment procedures based on Finite-Element calculations, respecting in-service temperature measurements including thermal reference transients and complex operational loading conditions are carried out. Fatigue evaluation of a PWR primary circuit component is used in order to evaluate the influence of plant like conditions numerically. Conclusions regarding the practical application are drawn by means of comparing the ANL approach considering laboratory conditions, conservative assessment procedures for the determination of cumulative fatigue usage factors of plant components and detailed assessment procedures. Plant like loading conditions, complex component geometries, loading scenarios and reference temperature transients shall be taken into account. Practical issues like the determination of the mean temperature or the strain rate have to be considered adequately.

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