Zobrazeno 1 - 10
of 759
pro vyhledávání: '"convolutional block attention module"'
Publikováno v:
Jin'gangshi yu moliao moju gongcheng, Vol 44, Iss 5, Pp 588-598 (2024)
Objectives: With the improvement of production technology, the traditional diamond particle cleanliness detection method can no longer meet the requirements of high precision, high quality and high automation in the diamond industry due to its low ef
Externí odkaz:
https://doaj.org/article/bb9a74c9d06d4de9beb6f83c31214f95
Publikováno v:
水下无人系统学报, Vol 32, Iss 5, Pp 846-854 (2024)
Underwater target detection is often more susceptible to domain shift and reduced detection accuracy. In response to this phenomenon, this article proposed a domain-adaptive underwater target detection method based on graph-induced prototype alignmen
Externí odkaz:
https://doaj.org/article/ff80c1935cc447408f7a0f9096c82a71
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract While the globe continues to struggle to recover from the devastation brought on by the COVID-19 virus's extensive distribution, the recent worrying rise in human monkeypox outbreaks in several nations raises the possibility of a novel world
Externí odkaz:
https://doaj.org/article/bbe812895fd04297af0433ba1a508bde
Publikováno v:
Tongxin xuebao, Vol 45, Pp 101-114 (2024)
In high-throughput multi-beam satellite systems, the dimensionality of the spectrum resource allocation problem increased drastically with the number of satellite beams and service users, which caused an exponential rise in the complexity of the solu
Externí odkaz:
https://doaj.org/article/2ff0de8decd448329151ca59d6a612a0
Publikováno v:
Frontiers in Zoology, Vol 21, Iss 1, Pp 1-13 (2024)
Abstract Background Rapid identification and classification of bats are critical for practical applications. However, species identification of bats is a typically detrimental and time-consuming manual task that depends on taxonomists and well-traine
Externí odkaz:
https://doaj.org/article/d71e6a3634454d4d84a85924d1c96dc1
Autor:
Mohammed Zakariah, Abeer Alnuaim
Publikováno v:
Egyptian Informatics Journal, Vol 27, Iss , Pp 100536- (2024)
Human Activity Recognition (HAR) is crucial for the advancement of applications in smart environments, communication, IoT, security, and healthcare monitoring. Convolutional neural networks (CNNs) have made substantial contributions to human activity
Externí odkaz:
https://doaj.org/article/8fff162838174ba6868b28234382bd44
Non-intrusive residential load identification based on load feature matrix and CBAM-BiLSTM algorithm
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
With the increasing demand for the refined management of residential loads, the study of the non-invasive load monitoring (NILM) technologies has attracted much attention in recent years. This paper proposes a novel method of residential load identif
Externí odkaz:
https://doaj.org/article/7d11336ed672488a8b551fb52db7b667
Publikováno v:
Frontiers in Bioinformatics, Vol 4 (2024)
The identification of cancer subtypes plays a very important role in the field of medicine. Accurate identification of cancer subtypes is helpful for both cancer treatment and prognosis Currently, most methods for cancer subtype identification are ba
Externí odkaz:
https://doaj.org/article/4f014307b12741179c935d5f6d0a9626
Publikováno v:
IEEE Access, Vol 12, Pp 181997-182009 (2024)
Under high-intensity rail operations, rail tracks endure considerable stresses resulting in various defects such as corrugation and spellings. Failure to effectively detect defects and provide maintenance in time would compromise service reliability
Externí odkaz:
https://doaj.org/article/86b05ba3325f4f2280cd86b06ab9bae2
Publikováno v:
IEEE Access, Vol 12, Pp 172730-172741 (2024)
Corn is a major cereal crop, and accurate monitoring of corn planting areas is crucial for agricultural structural adjustments and ensuring food security. This study proposes an improved HRNet network that utilizes the spectral and spatial features o
Externí odkaz:
https://doaj.org/article/305ebc5afa954c4cbdab2229704eb72e