Autor: Yang, Jianfeng - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Open-source Software Reliability Modeling with Stochastic Impulsive Differential Equations
Autorzy:: Zheng, Zhoutao
Yang, Jianfeng
Hu, Yao
Wang, Xibing
Powiązania:: https://bibliotekanauki.pl/articles/24200824.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:: open-source software
reliability modeling
random impulse
stochastic impulsive differential equations
Opis:: In reality, sudden updates of software, attacks of hackers, influence of the Internet market, etc. can cause a surge in the number of open-source software (OSS) faults (this moment is the time when impulse occurs), which results in impulsive phenomenon. For the existing software reliability models, dynamic process of software fault is considered to be continuous when assessing reliability, but continuity of the process can be disrupted with appearance of random impulses. Thus, to more accurately assess software reliability, we proposed an OSS reliability model with SIDE. In the model, dynamic process of software fault is divided into a continuous and a skipped part, described the continuous part of the process with SDE, and described destruction of the continuity caused by unpredictable random events with random impulses. Finally, the proposed model is verified with two datasets from real OSS project, and the results show that the proposed model is more in line with reality and has better fitting effect than the existing models.
Źródło:: Eksploatacja i Niezawodność; 2023, 25, 2; art. no. 166342
1507-2711
Pojawia się w:: Eksploatacja i Niezawodność
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A single upper limb pose estimation method based on the improved stacked hourglass network
Autorzy:: Peng, Gang
Zheng, Yuezhi
Li, Jianfeng
Yang, Jin
Powiązania:: https://bibliotekanauki.pl/articles/1838179.pdf
Data publikacji:: 2021
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: convolutional neural network
stacked hourglass network
skeleton key point
single upper limb pose estimation
human machine coordination
Opis:: At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation. For use in human–machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model is designed. A deconvolution layer is employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps. Integral regression is used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments show that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 123-133
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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