ScholarWorks@Hongik: Song, Ha Yoon

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Song, Ha Yoon (송하윤)

Engineering (Department of Computer Engineering)

Research Area: Computer System

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Clustering:5||Human Mobility:4||Global Positioning System:4||MBS:3||Network:3||Localization:3||RSSI:3||Positioning data:2||Big Five Factors:2||L2E:2||DeadReckoning:2||R:2||Deep learning:2||Radial Basis Function:2||Expectation Maximization:2||Human Mobility Model:2||Asset Management System:2||One Class Support Vector Machine:2||DBSCAN:2||Human mobility model:2||Parameter Optimization:2||Financial IT:2||Frontoffice System:2||GMM:2||GPGPU:2||Markov Model:2||Computer Vision:2||Middleoffice System:2||BIRCH:2||Straight Through Processing(STP):2||Personality factors:2||Personalitylocation relationship:2||HAP:2||Mobile Sensor Network:2||군집화:2||EM Clustering Algorithm:2||HAP Telecommunication:2||Mobility model:2||Backoffice System:2||Human mobility pattern:1||Human mobility speed distribution:1||Human mobility state classification:1||Human speed:1||Hyperparameter optimization:1||Interactive TV:1||Inverse Cluster Duration:1||Inverse Cluster Frequency:1||Inverse Location Duration:1||Inverse Location Frequency:1||Java management extensions:1||KolmogorovSmirnov test:1||LoRa:1||Location Base Service:1||Location Prediction:1||Location Preference:1||Location data:1||Location preference:1||Location visiting duration inside area:1||Location visiting frequency inside area:1||Longest Common Route Subsequence algorithm:1||MRTree:1||MR트리:1||Machine learning:1||Map matching:1||Measures of Location Visit:1||Micro Mobility:1||Middleware:1||Middleware approach:1||Mobile sensor network:1||Mobility Model:1||Mobility modeling:1||Mobility pattern analysis:1||Mobility pattern prediction:1||Moving window:1||Multiscreen:1||Multimedia:1||Nand Flash Memory:1||Neural architecture search:1||Neutralino:1||Next location prediction:1||Number of hidden units:1||Operation clustering:1||Optimum logistics hub location:1||Parallel Computing:1||Parcel service:1||Particle Filter:1||Particle filter:1||Personal Mobility Model:1||Personal features:1||Personal mobility mode:1||Personal mobility model:1||Personality:1||Personality Factors:1||PersonalityLocation Relationship:1||Position Duration:1||Position Frequency:1||Positioning Data:1||Positioning Data Analytics:1||Positioning System:1||Positioning data analytics:1||Positioning data error:1||Positioning system:1||Probabilistic Approach:1||Probability distribution of human mobile speed:1||RSS1:1||Random forest:1||Rank of area visiting duration:1||Rank of area visiting frequency:1||Rank of location visiting duration:1||Rank of location visiting frequency:1||Recommender system:1||Recurrent location visit:1||Recurrent neural network:1||SUSY:1||Service oriented cluster servers:1||Settop box:1||Signalfinding algolithm:1||Simulation:1||Simulation(모의실험):1||Software platform:1||SpaceTime Analysis:1||Spearman&apos:1||Speed distributions:1||Stacking:1||System on chip:1||Taxi Transportation Data:1||Taxi catch:1||Taxi transportation data:1||Temporal mobility:1||Time weighted speed:1||Trajectory Pattern:1||Trajectory data:1||Trajectory pattern:1||TrajectoryPattern:1||Urban Mobility Model:1||Urban mobility model:1||Vote dispersion problem:1||WIMP:1||XGBoost:1||allocation algorithm:1||continuous time markov chain:1||daily location analysis:1||daily mobility model:1||data broadcasting:1||deadreckoning:1||delivery system:1||density estimation:1||future internet:1||graph fitting:1||human mobility:1||integrated mobility model:1||internet of things:1||location clustering:1||location information:1||middleware integration:1||middleware specifications:1||mixture model:1||outlier detection:1||parallel processing:1||positioning data:1||probability distribution:1||s Rank Correlation:1||settopbox middleware:1||smartphones:1||speed distribution:1||video server:1||금융IT:1||낸드플래시메모리:1||네트워크:1||데드레코닝:1||데이타베이스:1||데이타베이스 인덱스 성능:1||모바일 센서 네트워크:1||미들오피스시스템:1||미들웨어:1||백오피스시스템:1||병렬 컴퓨팅:1||분포 추정:1||서비스 지향 클러스터 서버:1||성층권 통신:1||스마트폰:1||연속시간 마르코프 연쇄:1||위치 군집화:1||위치 데이터:1||위치정보:1||유가증권시스템:1||이동 기지국:1||이동 상태 판단:1||이동기지국:1||이상점 판단:1||일일 기반 위치분석:1||일일 이동모델:1||자바 매니지먼트 익스텐션:1||지역화:1||집단 이동성 모델:1||컴퓨터비젼:1||클러스터 관리:1||통합 이동모델:1||파티클 필터:1||프론트오피스시스템:1||혼합 모형:1||Android:1||확률적 접근:1||BFF:1||Big data analytics:1||Big five factors:1||CASSD:1||Cluster management:1||Clustering(클러스터링):1||Computer vision:1||Continuous Time Markov Chain:1||Convolution neural network:1||Cyclic mobility pattern:1||DARTS:1||DGDARTS:1||Dark matter:1||Database:1||Database Indexing Performance:1||Deep belief network:1||Deep neural network:1||Digital Broadcasting:1||Digital TV:1||Distribution fitting:1||EM 클러스터링 알고리즘:1||Error correction:1||Error detection:1||Error filtering:1||Estimation of personality:1||Expectation Maximization Clustering:1||Feature selection:1||Geopositioning data:1||Group Mobility Model:1||HAP MBS Placement(고고도 플랫폼 이동 기지국 위치설정):1||HAP based Networking(고고도 플랫폼 기반 네트워크):1||HAPS:1||Hardware abstraction layer:1||Hidden Markov model:1||Human Location Preference:1||Human Mobile State Determination:1||Human location preference:1||Human mobility:1||

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