• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2026-06 (Vol.75 No.6)
  4. 10.5370/KIEE.2026.75.6.1383
XML PDF INFO REF

References

1 
Korea Rural Economic Institute (KREI), "Agricultural Outlook 2025 Report," 2025. Google Search
2 
Statistics Korea, "Farm Households by Age of Farm Household Head(Census of Agriculture, Forestry and Fisheries)," 2023. Google Search
3 
Livestock Environmental Management Institute, "Comparison of the Proportion of Foreign Workers on Farms by Livestock Species," 2023. Google Search
4 
I. Traulsen, Art. no. 170, "Using Acceleration Data to Automatically Detect the Onset of Farrowing in Sows," Sensors, vol. 18, no. 1, 2018. DOI
5 
C. Lipori, B. F. A. Laurenssen, I. Reimert, N. M. Soede, A. Youssef, "A Wearable Software Sensor for Parturition Onset Prediction in Sows," pp. 1315-1323, 2024. Google Search
6 
E. Mayrhuber, K. Maschat, D. Brunner, S. M. Winkler, M. Oczak, Art. no. 104381, "Improved and interpretable accelerometer-based farrowing prediction," Biosystems Engineering, vol. 263, 2026. DOI
7 
M. Oczak, F. Bayer, S. Vetter, K. Maschat, J. Baumgartner, Art. no. 106517, "Comparison of the automated monitoring of the sow activity in farrowing pens using video and accelerometer data," Computers and Electronics in Agriculture, vol. 192, 2022. DOI
8 
X. Yang, C. Zheng, C. Zou, H. Gan, S. Li, S. Huang, Y. Xue, Art. no. 106139, "A CNN-based posture change detection for lactating sow in untrimmed depth videos," Computers and Electronics in Agriculture, vol. 185, 2021. DOI
9 
J. H. Witte, J. Gerberding, C. Lensches, I. Traulsen, "Using Deep Learning for automated birth detection during farrowing," pp. 141-154, 2022. Google Search
10 
M. Wutke, C. Lensches, U. Hartmann, I. Traulsen, "Towards automatic farrowing monitoring-A Noisy Student approach for improving detection performance of newborn piglets," PLOS ONE, vol. 19, no. 10, 2024. DOI
11 
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, R. Girshick, "Segment Anything," pp. 4015-4026, 2023. Google Search
12 
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, S. Xie, "A ConvNet for the 2020s," pp. 11976-11986, 2022. Google Search
13 
F. Chollet, "Xception: Deep learning with depthwise separable convolutions," pp. 1251-1258, 2017. Google Search
14 
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, "Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows," pp. 10012-10022, 2021. Google Search
15 
M. Ilse, J. Tomczak, M. Welling, "Attention-based Deep Multiple Instance Learning," pp. 2127-2136, 2018. Google Search
16 
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale," 2021. Google Search
17 
D. J. Araújo, "Key Patches Are All You Need: A Multiple Instance Learning Framework for Robust Medical Diagnosis," 2024. Google Search