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Inspection Optimization Using Probabilistic Criteria
Published in Don E. Bray, Roderic K. Stanley, Nondestructive Evaluation, 2018
Don E. Bray, Roderic K. Stanley
For large defects, P(R|x)→smallCTM =CM + CI
High Level Languages for Microcontrollers
Published in Zdravko Karakehayov, Knud Smed Christensen, Ole Winther, Embedded Systems Design with 8051 Microcontrollers, 2018
Zdravko Karakehayov, Knud Smed Christensen, Ole Winther
Here is an example of a small C program: /************************************************************/ /* compl_pl.c */ /* This program complements Port 1 */ /*********************************************************/ #pragma code=0x2000 /* Starting address of the code */ main () { do { if (P3.4) P1=0xFF; else P1=0x00; } while (1); }
Optimal warehouse location and size under risk of failure
Published in International Journal of Systems Science: Operations & Logistics, 2023
We selected 34 potential warehouse locations based on population and/or significant geographical location. The three potential capacities are small (c = 1), medium (c = 2) and large (c = 3). The corresponding capacity levels are , and . This set of locations includes the two existing warehouse locations, indexed by w = 1 and w = 2, at their current large capacity so that The land and fabrication costs were amortised over 15 years at with payments made every 45 days. The 45 day demand , in , was determined from historical data. The processing costs , transportation costs and the storage capacities were estimated by the company. As in Dey and Jenamani (2019), Li et al. (2013) the failure probabilities were generated from .
Estimation and prediction of screening efficiency of Sand Crumb Rubber (SCR) mix infill trench
Published in International Journal of Geotechnical Engineering, 2022
Abir Sarkar, Rahul Barman, Debjit Bhowmik
C is also known as the misclassification cost, a non-zero parameter that controls the maximum penalty imposed on margin-violating observations and prevents overfitting. Increasing the box constraint leads to fewer assignments of support vectors, but at the same time, it leads to longer training time. However, a large C gives a lower bias and higher variance. In contrast, a small C creates a hard margin allowing significantly less misclassification. The most common loss functions in machine learning are quadratic, least modulus, Huber and ԑ-insensitive. In the present study, ԑ-insensitive and quadratic functions were explored. The quadratic loss function represents the conventional least-squares error criterion. Addressing the issue of non-production of sparseness in the support vectors, Vapnik (1974) proposed a ԑ-insensitive loss function, which approximates Huber’s loss function. The quadratic loss function given by Equation (10) corresponds to the conventional least-squares error criterion. The ԑ-insensitive loss function is given by Equation (11). Vapnik proposed it since it helps us to obtain a sparse set of support vectors. The linear kernel is mostly used when there is a large set of sparse data vectors or for text characterization.
Dynamic shakedown limits for flexible pavement with cross-anisotropic materials
Published in Road Materials and Pavement Design, 2020
Jiangu Qian, Han Lin, Xiaoqiang Gu, Jianfeng Xue
It can be found that the tendencies of all cases are almost the same, but there are some differences between the cases due to the influence of anisotropic parameters and different methods for changing the anisotropic stiffness ratio. It is clear that the Eh1/Eh2 corresponding to the maximum shakedown limit tends to increase with increasing stiffness anisotropy Ev2/Eh2. For example, Eh1/Eh2 corresponding to the maximum shakedown limit is around 20 for Ev2/Eh2 = 0.25 while it is around 30 for Ev2/Eh2 = 1.0 at a cohesion ratio ch1/ch2 = 10. However, when ch1/ch2 is relatively small (ch1/ch2 ≤ 5), such effect can be negligible. It means that failure tends to occur in the first layer with increasing Young’s modulus anisotropy Ev2/Eh2 when ch1/ch2 is sufficiently large.