http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Zaijian Wang (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Dynamic threshold location algorithm based on fingerprinting method
Xuxing Ding,Bingbing Wang,Zaijian Wang 한국전자통신연구원 2018 ETRI Journal Vol.40 No.4
The weighted K‐nearest neighbor (WKNN) algorithm is used to reduce positioning accuracy, as it uses a fixed number of neighbors to estimate the position. In this paper, we propose a dynamic threshold location algorithm (DH‐KNN) to improve positioning accuracy. The proposed algorithm is designed based on a dynamic threshold to determine the number of neighbors and filter out singular reference points (RPs). We compare its performance with the WKNN and Enhanced K‐Nearest Neighbor (EKNN) algorithms in test spaces of networks with dimensions of 20 m × 20 m, 30 m × 30 m, 40 m × 40 m and 50 m × 50 m. Simulation results show that the maximum position accuracy of DH‐KNN improves by 31.1%, and its maximum position error decreases by 23.5%. The results demonstrate that our proposed method achieves better performance than other well‐known algorithms.
-
One-dimensional CNN Model of Network Traffic Classification based on Transfer Learning
Lingyun Yang,Yuning Dong,Zaijian Wang,Feifei Gao 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.2
There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.
ScienceON
KISS연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
