김유성 교수

Summary Content‐centric networking (CCN) has been recently proposed as an alternative to traditional IP‐based networking. In CCN, content is accessed by content name instead of a host identifier (locational identifier). This new type of access methodology rapidly and efficiently disseminates content in combination with the in‐network caching mechanism. For practical use of CCN, many network properties studied in IP‐based networking are being revisited, and new types of CCN architecture component

In this letter, we propose a novel deep learning-based spectrum sensing scheme using a multi-antenna receiver. Our main idea is constructing a correlation matrix composed of not only auto-correlation functions per each antenna but also cross-correlation functions between antennas. By using the rich informative matrix, with a simple convolutional neural network (CNN) structure, our model, DS2MA (Deep Spectrum Sensing with Multiple Antennas), can efficiently learn to detect the presence of a prima

We design a novel learning-based spectrum sensing model. Under the insight that an autocorrelation curve yields richer information than a single sum of received signal powers for detecting the presence of a primary user, we propose a convolutional neural network-based deep learning model, called deep spectrum sensing (DSS), that receives an autocorrelation curve as input. Extensive simulation results show that our DSS model has a higher performance than existing deep-learning-based models that u

In this study, the multidisciplinary aerodynamicstructural optimal design is carried out for the supersonic fighter Through the aeroelastic analyses of the various candidate wings, the aerodynamic and structural performances are calculated such as the lift coefficient, the drag coefficient and the deformation of the Based on the calculated performances, the supersonic fighter wing is designed by using response surface methodology to have better aerodynamic performances and less weight than the b

Pattern analysis of wafer maps in semiconductor manufacturing is critical for failure analysis aspects or activities that increase yield. As deep learning becomes more popular than ever, research on the wafer map classification is active. However, more accurate pattern classification and data processing methods are required for the accuracy of commonality analysis to find suspected facilities using wafer map classification. It is difficult to represent all types of wafer maps in dozens of forms,

Automatic defect classification (ADC) systems automatically classify defects that inevitably occur during semiconductor manufacturing processes. ADC is the beginning of defect management that increases the yield of semiconductor chip production, and prevents accidents in the process. It takes a lot of engineer’s labor to classify defects, but ADC can be the answer to classify all defects at low cost. ADC employs the defect image of a wafer surface, captured using scanning electron microscopy (SE

We present a scalable and topologically-aware application-layer multicast approach, specially designed for large-scale distributed applications. The proposed approach constructs topologically-aware data paths which are based on topological clustering of multicast group members. The approach does not require any exact network topology information, but instead requires the relative location information of members using landmarks. We partition the members into topologically-aware clusters based on

Mobile communication technology is evolving rapidly and becoming increasingly ubiquitous, thereby increasing the demand for uplink data-intensive applications (e.g., personal broadcasting and live augmented/virtual reality videos). Recently, to facilitate a cost-effective and smooth transition from 4G to 5G networks, most carriers leverage existing 4G infrastructures using a dual connectivity (DC) feature. DC increases uplink throughput and mobility robustness; however, it also causes unpreceden

Named Data Networking (NDN) is an emerging communication paradigm to resolve a traffic explosion problem due to repeated and duplicated delivery of large multimedia content. To make NDN being useful more widely, however, it should support various types of traffic and their Quality of Service (QoS) requirements. In this paper, we propose a differentiated services (diffserv) model for NDN. For scalability, the proposed diffserv model is designed to follow the guidelines from the IP diffserv model.

Reinforcement learning (RL) has achieved considerable success in many fields, but applying it to real-world problems can be costly and risky because it requires a lot of online interaction. Recently, offline RL has shown the possibility of extracting a solution through existing logged data without online interaction. In this work, we propose an offline hierarchical RL method, Guider (Guide to Control), that can efficiently solve long-horizon and sparse-reward tasks from offline data. The high-le