윤성로 교수

MicroRNAs (miRNAs) are a class of small RNAs of 19-23 nucleotides that regulate gene expression through target mRNA degradation or translational gene silencing. The miRNAs are reported to be involved in many biological processes, and the discovery of miRNAs has been provided great impacts on computational biology as well as traditional biology. Most miRNA-associated computational methods comprise the prediction of miRNA genes and their targets,and increasing numbers of computational algorithms a

We present ACMI, an FM-based indoor localization that does not require proactive site profiling. ACMI constructs the fingerprint database based on the pure estimation of indoor RSS distribution, where the signals transmitted from commercial FM radio stations are used. For this, ACMI makes use of our signal model harnessing public transmission information of FM stations in a combination with a floorplan of a building. Using the fingerprint database as the knowledge base, ACMI actively performs mu

Spectrum sensing, the task of discovering spectrum usage at a given location, is a fundamental problem in dynamic spectrum access networks. While sensing in narrow spectrum bands is well studied in previous work, wideband spectrum sensing is challenging since a wideband radio is generally too expensive and power consuming for mobile devices. Sequential scan, on the other hand, can be very slow if the wide spectrum band contains many narrow channels. In this paper, we propose an analog-filter bas

We present ACMI, an FM-based indoor localization system that does not require proactive site profiling. ACMI constructs the fingerprint database based on pure estimation of indoor received signal strength (RSS) distribution, where only the signals transmitted from commercial FM radio stations are used. Based on extensive field measurement study, we established our own signal propagation model that harnesses FM radio characteristics and open information of FM transmission towers in combination wi

A practical protocol jointly considering PHY and MAC for MIMO based concurrent transmissions in wireless ad hoc networks, called Contrabass, is presented. Concurrent transmissions refer to simultaneous transmissions by multiple nodes over the same carrier frequency within the same interference range. Contrabass is the first-to-date open-loop based concurrent transmission protocol which implements simultaneous channel training for concurrently transmitting links without any control message exchan

There have been a lot of research and investigation to improve handover latency of mobile IPv6 (MIPv6). The handover latency is one of the most important factors because performance of TCP flows as well as realtime traffic degrades sharply as the latency increases. The MIPv6 handover process consists of movement detection, duplicate address detection (DAD), and binding update. Among the three components, DAD has been identified as the most critical factor. In this paper, we introduce novel schem

본 연구는 국내 모바일 앱을 통한 이용자 정보 수집 및 트래킹 현황을 실증적으로 조사하였다. 조사 결과, 분석 대상 유·무료 앱 886개 중 92.6%인 820개 앱이 광고 식별자(ADID) 정보를 서버로 전송하고 있음을 확인하였다. 이는 국내 모바일 앱 생태계에서 광고 식별자를 통한 광범위한 이용자 트래킹이 이루어지고 있을 가능성을 시사한다. 또한, 본 연구는 모바일 앱을 통해 수집되는 이용자 정보가 소수의 사업자로 집중되고 있음을 발견하였다. 나아가 샘플링 조사 결과 이용자에 관한 정보들이 광고 식별자 정보와 함께 수집되고 있음을 확인하였다. 본 연구는 위와 같은 실증 조사를 기반으로 하여 국내에서 광고 식별자에 의한 프라이버시 침해 위협이 실질적으로 발생할 가능성이 있는지, 만약 있다면 어떤 위험이 존재하는지 살펴보았다. 하지만 잠재적 프라이버시 위협 가능성에도 불구하고, 국내에서는 광고 식별자 정보 수집과 이용자 트래킹에 관한 명확한 법적 규율이 존재하지 않는 상황이다. 특히

Background and Objectives：Sinus surgery has been reported to improve pulmonary function and decrease the use of asthma medications in patients with chronic rhinosinusitis and asthma. Most studies, however, have used a small number of patients and were conducted over a short period. To demonstrate a causal relationship between sinus surgery and asthma, a sufficient amount of patient data observed over a long period is required. To address the limitations of the existing approaches, we conducted a

In this paper, we present two novel methods to enable concurrent communications in MIMO networks. First method enables an 802.11n MIMO-OFDM receiver to decode independent data streams from two independent 802.11n transmitters concurrently. It is implemented on real time 802.11n based MIMO-OFDM testbeds and the performance of the technique is examined through both simulation and field trials. Second method is a low overhead Concurrent Communications scheme based on Adaptive Interference Cancellat

A PHY and MAC protocol for MIMO concurrent transmissions, called Contrabass, is presented. Concurrent transmissions, also referred to as multi-user MIMO, are simultaneous transmissions by multiple interfering nodes over the same carrier frequency. Concurrent transmissions technique has the potential of mitigating the overhead of MAC protocols by amortizing protocol overhead among multiple packets. However, existing proposals for concurrent transmissions could not achieve this as MIMO channel tra

딥 러닝(Deep learning)은 기존 인공 신경망 내 계층 수를 증가시킴과 동시에 효과적인 학습방법론을 제시함으로써 객체/음성 인식 및 자연어 처리 등 고수준 문제 해결에 있어 괄목할만한 성과를 보이고 있다. 그러나 학습에 필요한 시간과 리소스가 크다는 한계를 지니고 있어, 이를 줄이기 위한 연구가 활발히 진행되고 있다. 본 연구에서는 아파치 스파크 기반 클러스터 컴퓨팅 프레임워크 상에서 딥 러닝을 분산화하는 두 가지 툴(DeepSpark, SparkNet)의 성능을 학습 정확도와 속도 측면에서 측정하고 분석하였다. CIFAR-10/CIFAR-100 데이터를 사용한 실험에서 SparkNet은 학습 과정의 정확도 변동 폭이 적은 반면 DeepSpark는 학습 초기 정확도는 변동 폭이 크지만 점차 변동 폭이 줄어들면서 SparkNet 대비 약 15% 높은 정확도를 보였고, 조건에 따라 단일 머신보다도 높은 정확도로 보다 빠르게 수렴하는 양상을 확인할 수 있었다.

ABSTRACT Genome rearrangements often result in copy number alterations of cancer-related genes and cause the formation of cancer-related fusion genes. Current structural variation (SV) callers, however, still produce massive numbers of false positives (FPs) and require high computational costs. Here, we introduce an ultra-fast and high-performing somatic SV detector, called ETCHING, that significantly reduces the mapping cost by filtering reads matched to pan-genome and normal k -mer sets. To re

Because sensor nodes have very limited resources, energy efficiency is thought to be one of the most essential parts of wireless sensor networks. Many schemes including routing and MAC protocols have been proposed which attempt to make the operation of sensor networks more energy-efficient. However, while most of these schemes assume that precise amount of residual energy is already known, it is never easy to find out remaining life of sensor nodes in reality. In this paper, we investigate the p

본 논문에서는 서울시에 속하는 25개의 지역구로부터 측정된 미세먼지 시계열(time series) 정보의 상관도를 정보이론(information theory)의 엔트로피(entropy)로 정량화하고, 이를 그래프로 표현하는 서울시 지역구 미세먼지 전이 모델을 만들어 지역별 유사성과 영향력을 분석하는 방법을 제안한다. 먼저, 각각의 미세먼지 농도 시계열을 가지는 지역구의 모든 쌍마다 전이 엔트로피(transfer entropy)를 계산하여 그래프의 노드간 연결 강도를 구한다. 이 그래프에 전통적인 커뮤니티 검출(community detection) 기법인 모듈성 기반 군집화(on modularity-based clustering) 알고리즘을 적용하여 전체 지역구들에 생성되는 커뮤니티를 검출하였다. 이를 통해 지역적인 근접 정도가 높은 지역과 차량 이동이 많은 지역 간의 미세 먼지 전이성이 높은 것을 확인하였으며, 더불어 제안된 방법은 기존 미세먼지의 기상모델 분석과 다른 정보이론 관점