강승우 교수

Current methods of predicting mutation-induced protein stability change are imprecise. Machine learning methods have been introduced for this prediction recently; however, the available experimental data used for training these predictors are biased. Abundant data are available for several frequently occurring amino acid substitutions, whereas only limited data have been accumulated for some other mutation types. Generally, current statistical models do not account for this bias toward the commo

In the Traditional Chinese Medicine (TCM) cross-sectional survey conducted by our team, we were interested in determining the risk factors of osteoporosis. To analyze this TCM study, we had to deal with three statistical problems: (1) a very large number of potential risk factors, (2) interactions among potential risk factors, and (3) nonlinear effects of some continuous-scale risk factors. To address these analytic issues, we used two data mining methods, support vector machine recursive featur

Network functions virtualization (NFV) has become the platform for decomposing the sequence of virtual network functions (VNFs), which can be grouped as a forwarding graph of service function chaining (SFC) to serve multi-service slice requirements. NFV-enabled SFC consists of several challenges in reaching the reliability and efficiency of key performance indicators (KPIs) in management and orchestration (MANO) decision-making control. The problem of SFC fault tolerance is one of the most criti

In order to maintain stable communication in 5G wireless networks, the link between a 5G base station and user equipment (UE) should be constantly monitored and adapted to the time-varying wireless channel. The use of UE for seamless information exchange is based on obtaining a target reference signal. The method used to obtain the reference signal involves identifying the index of the reference signal received from the 5G base stations. However, the existing index identification method employed

In this paper, we present a light-weight deep learning framework specifically designed and implemented for embedding in 5G software modems. The framework is developed completely using the C language to operate in real time by being mounted on a software modem. The framework incorporates an imagification process proposed by the authors which can enhance efficient reference signal classification in constrained environments. Imagification is the proposed technique that converts radio signal data, w

A new measure to assess the centrality of vertices in an undirected and connected graph is proposed. The proposed measure, L1 centrality, can adequately handle graphs with weights assigned to vertices and edges. The study provides tools for graphical and multiscale analysis based on the L1 centrality. Specifically, the suggested analysis tools include the target plot, L1 centrality-based neighborhood, local L1 centrality, multiscale edge representation, and heterogeneity plot and index. Most imp

We introduce novel measures, L1 prestige and L1 centrality, for quantifying the prominence of each vertex in a strongly connected and directed graph by utilizing the concept of L1 data depth (Vardi and Zhang, Proc. Natl. Acad. Sci. U.S.A. 97(4):1423–1426, 2000). The former measure quantifies the degree of prominence of each vertex in receiving choices, whereas the latter measure evaluates the degree of importance in giving choices. The proposed measures can handle graphs with both edge and verte

This paper addresses the challenge of memory-efficient time-series forecasting in resource-constrained environments. To this end, an imagification method is proposed that enables lightweight convolutional neural network CNN-based prediction by transforming multivariate time-series data into image representations. The method consists of three steps: rearranging features using the Pearson correlation coefficient to enhance local associations, generating images through a sliding window technique al

금융기관의 양해가 있는 차명대출 사안에서, 대법원은 명의대여자와 금융기관 사이의 대출계약을 통정허위표시로서 무효라고 보면서도 다른 한편으로는 그 대출계약의 당사자는 명의대여자와 금융기관이 아니라 명의차용자와 금융기관이라고 하고 있다. 학설상으로는 이러한 경우의 차명대출 사안에서 이를 통정허위표시의 문제로 검토하는 견해와 자연적 해석방법에 따른 계약당사자 확정의 문제로 검토하는 견해가 나뉘고 있다. 그런데 이러한 두 방향에서의 접근은 양립 가능한 것이 아니라 상호 모순되는 것이다. 이를 계약당사자 확정의 문제로 보는 접근방법은 1개의 대출계약을 전제로 한 것으로서 그때는 통정허위표시가 문제될 여지가 없고, 이를 통정허위표시의 문제로 보는 접근방법은 가장행위와 은닉행위가 각각 존재하는 것으로 파악하는 것이어서 자연적 해석에 의한 계약당사자 확정이 문제되지 않는다. 당사자들이 합의하에 표시된 것과 다른 법률행위를 하기로 의욕하는 경우에는, 표시된 문언대로의 법률행위를 무효로 하기로 하는

기업은 내/외부의 다양한 정보 유출 시도로부터 내부 자산을 보호하기 위한 여러 방법론을 적용해 악의적인 행위를 분석하며 위협을 차단 및 예방하는 활동을 하고 있다. 하지만 대부분 정형화된 로그 분석에 중점을 두고 있어 기업 내부의 텍스트, 이미지 파일과 같은 비정형 데이터에 의한 정보 유출 행위를 탐지하기에는 제한적이다. 이와 관련된 다양한 연구가 진행되고 있지만 공개된 과거의 데이터셋을 활용하며 실시간성이 고려되지 않은 한계점을 보인다. 본 논문에서는 텍스트 및 이미지 파일을 대상으로 실시간 데이터 유출 탐지가 가능한 딥러닝 기반 데이터 유형 분류 자동화 및 정보 유출 탐지 방법을 제안한다.

Artificial intelligence has recently permeated every field, and current research trends in wireless communications naturally involve leveraging machine learning (ML) for communications processing. Numerous previous research studies have theoretically demonstrated that intelligentizing physical layer holds the promise of enhancing performance for the 6G era. In this article, we demonstrate the practical implementation of intelligentizing random access (RA) through real-time channel learning (CL).