http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Object Connection Hypergraphs-an Approach for Nested Object Query Optimization
Hung Hoang Bao,Phuong Ngo Viet,Thanh Le Manh 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.5
In Object-Oriented Databases (OODB), nested object queries are used regularly. Nested structures are put in conditional expressions of the queries in two forms: nested sub-queries or path expression containing hidden joins – nested predicates in WHERE clauses. For nested queries, when analyzing the estimated cost of the nested algebraic expression, the expression evaluation result gives out an ineffective cost. Therefore, our method proposed in this paper will resolve the problems by leveling nested sub-queries in the nested queries. This method will increase the effectiveness of the query processing cost – We use object connection hypergraphs to present nested queries.
FGW-FER: Lightweight Facial Expression Recognition with Attention
Huy-Hoang Dinh,Hong-Quan Do,Trung-Tung Doan,Cuong Le,Ngo Xuan Bach,Tu Minh Phuong,Viet-Vu Vu 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.9
The field of facial expression recognition (FER) has been actively researched to improve human-computer interaction. In recent years, deep learning techniques have gained popularity for addressing FER, with numerous studies proposing end-to-end frameworks that stack or widen significant convolutional neural network layers. While this has led to improved performance, it has also resulted in larger model sizes and longer inference times. To overcome this challenge, our work introduces a novel lightweight model architecture. The architecture incorporates three key factors: Depth-wise Separable Convolution, Residual Block, and Attention Modules. By doing so, we aim to strike a balance between model size, inference speed, and accuracy in FER tasks. Through extensive experimentation on popular benchmark FER datasets, our proposed method has demonstrated promising results. Notably, it stands out due to its substantial reduction in parameter count and faster inference time, while maintaining accuracy levels comparable to other lightweight models discussed in the existing literature.
Sampath Natarajan,강린우,김진광,Tae-Kyun Jung,Thanh Thi Ngoc Doan,Ho-Phuong-Thuy Ngo,홍명기,Seunghwan Kim,Viet Pham Tan,Seok Joon Ahn,이상희,한예선,안예진 한국분자세포생물학회 2012 Molecules and cells Vol.33 No.1
Xanthomonas oryzae pv. oryzae (Xoo) is a plant bacterial pathogen that causes bacterial blight (BB) disease, resulting in serious production losses of rice. The crystal structure of malonyl CoA-acyl carrier protein transacylase (XoMCAT), encoded by the gene fabD (Xoo0880) from Xoo, was determined at 2.3 Å resolution in complex with N-cyclohexyl-2-aminoethansulfonic acid. Malonyl CoA-acyl carrier protein transacylase transfers malonyl group from malonyl CoA to acyl carrier protein (ACP). The transacylation step is essential in fatty acid synthesis. Based on the rationale, XoMCAT has been considered as a target for antibacterial agents against BB. Protein-protein interaction between XoMCAT and ACP was also extensively investigated using computational docking, and the proposed model revealed that ACP bound to the cleft between two XoMCAT subdomains.