No abstract

International audienceThe fracture of a turbojet engine turbine blade was investigated. Visual and surface examination showed that the turbine blade had initially cracked by a fatigue mechanism over a period of time and then failed by overload at the last moment. The fatigue crack initiated on a surface damaged by rubbing. Also the cracked turbine blade was severly damaged by hot gas flow and discolored. This heat damage made the gamma prime phase in the matrix (Ni-base superalloy) coarsen and lowered the fatigue strength of the base material assisting to the premature fatigue fracture. The decrease of the strength of the material due to degradation of gamma prime phase was verified by hardness measurements. The possible relevance of other parts attached to the engine shaft to the fracture was reviewed. From this reviewit is inferred that the root cause of cracking and excessive heat damage might be attributed to eccentricity of the shaft resulting from various reasons—shaft misalignment, uneven wear of bearing elements and mismatch in clearance, etc. However this is an assumption that should be verified by positive supporting evidence from condition monitoring of engines

Considering the results of this study showing sex differences in weight perception and weight control behaviors, sex-specific overweight prevention programs are needed to achieve accurate weight perception and healthy weight control behaviors.

In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a disadvantage of low recognition rate in the tilted and low-resolution images, as it is trained with images acquired from the front of the license plate. Furthermore, the vehicle images acquired by using CCTV have issues such as limitation of resolution and perspective distortion. Such factors make it difficult to apply the commonly used deep learning model. To improve the recognition rate, an algorithm which is a combination of the super-resolution generative adversarial network (SRGAN) model, and the perspective distortion correction algorithm is proposed in this paper. The accuracy of the proposed algorithm was verified with a character recognition algorithm YOLO v2, and the recognition rate of the vehicle license plate image was improved 8.8% from the original images.

PurposeThe purpose of this study is to propose a systematic method for the diffusion of forecasting technology in the pre‐launch stage.Design/methodology/approachThe authors designed survey question items that are familiar to interviewees as well as algebraically transformable into the parameters of a logistic diffusion model. In addition, they developed a procedure that reduces inconsistency in interviewee responses, removes outliers, and verifies conformability, in order to reduce the error and yield robust estimation results.FindingsThe results show that the authors' method performed better in the empirical cases of digital media broadcasting and internet protocol television in terms of sum of squared error compared with an existing survey‐based method, a regression method, and the guessing‐by‐analogy method. Specifically, the authors' method can reduce the error by using the conformability and outlier tests, while the consistency factor contributes to determining the final estimate with personal estimates.Research limitations/implicationsThe procedure proposed in this study is confined to the presented logistic model. Future research should aim to extend its application to other representative diffusion models such as the Bass model and the Gompertz model.Practical implicationsThe authors' method provides a better quality of forecasting for innovative new products and services compared with the guessing‐by‐analogy method, and it contributes to managerial decisions such as those in production planning.Originality/valueThe authors introduce the concepts of conformability and consistency in order to reduce the error from personal biases and mistakes. Based on these concepts, they develop a procedure to yield robust estimation results with less error.