• Journal of Information Processing Systems
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• 제16권6호
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• 정보과학회 논문지
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• 제43권12호
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• pp.1420-1427
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• 2016

본 논문은 통신 환경이 불안정하고 토폴로지가 수시로 변하는 CV(Connected Vehicle) 환경에서 Fog computing과 SDN의 장점을 활용하는 방법에 대해 제시한다. 이를 위해서 먼저 중앙의 컨트롤러는 최신의 네트워크 토폴로지를 유지함으로써 현재 네트워크 상황을 파악할 수 있어야한다. 특히 모바일 환경에서는 컨트롤러가 수집하는 정보 중에서 노드의 움직임 정보가 중요하기 때문에 본 논문에서는 움직임 정보를 세 가지 종류로 세분화하여 관리하고 해당 정보를 효율적으로 활용하고자한다. 본 논문에서 제안하는 모바일 노드의 움직임 정보의 활용 방안은 크게 두 가지로 컨트롤 메시지 횟수를 조절함으로써 컨트롤 오버헤드를 줄이는 것과 통신 단절 시 효율적으로 복구할 수 있는 복구 프로세스를 제안하는 것이다. 복구 프로세스는 두 가지로 모바일 노드의 움직임 정보를 활용하여 연결 상태를 효율적으로 복구하는 방법과 cloud level과 fog level을 구별하여 경로 복구를 수행하는 방법이다. 시뮬레이션 결과, 주어진 환경에서 본 논문이 제안한 방법이 기존 방법에 비해 55% 가량의 컨트롤 오버헤드를 줄이고 통신 단절 시 끊김 시간을 5% 가량 단축시킬 수 있음을 확인하였다.

• 한국전자통신학회논문지
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• 제13권3호
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• pp.669-676
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• 2018

GOCI(: Geostationary Ocean Color Imager)는 8개 밴드의 레일리 보정 반사도를 이용하여 수평 $500m{\times}500m$의 높은 공간 해상도로 해무를 탐지한다. 가시광선과 근적외선은 지표면의 특성을 강하게 반영하여 구름과 안개 판별에 오차를 유발한다. Band7 반사도의 임계값을 설정하여 육지로 유입되는 해무를 탐지할 수 있었다. Band4 반사도가 Band8보다 크게 나타나는 영역이 구름으로 판별되는 경우는 주변 영역과 평균 반사도의 비교를 통해 해무로 탐지되는 오류를 보정하였다. 개선된 알고리즘은 천리안위성(COMS: Communication, Ocean, Meteorological Satellite)의 안개 영상 및 기상청 시정계 자료와 비교하여 검증되었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.294-297
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• 2007

Steam and advection fogs are frequently observed in the Yellow Sea located between Korea and China during the periods of March-April and June-July respectively. This study uses the remote sensing (RS) data for monitoring sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided an informative synopsis for the occurrence of steam and advection fogs through a ground truth. The RS data used in this study was GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and near-IR channel of GOES-9 and MTSAT-1R satellites was applied to estimate the extension of the sea fog. For the days examined, it was found that not only the DCD but also the texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind is used to provide a weak wind area less than threshold under stable condition of the surface wind around a fog event. The Laplacian computation for a measurement of the homogeneity was designed. A new combined method of DCD, QuikSCAT wind speed and Laplacian was applied in the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and Laplacian are -2.0 K, 8 m $s^{-1}$ and 0.1, respectively. The validation methods such as Heidke skill score, probability of detection, probability of false detection, true skill score and odds ratio show that the new combined method improves the detection of sea fog rather than DCD method.

• 대한원격탐사학회지
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• 제24권1호
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• pp.1-16
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• 2008

Steam and advection fogs are frequently observed in the Yellow Sea from March to July except for May. This study uses remote sensing (RS) data for the monitoring of sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided a valuable information for the occurrence of steam and advection fogs as a ground truth. The RS data used in this study were GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and shortwave IR channel of GOES-9 and MTSAT-1R satellites was applied to detect sea fog. The results showed that DCD, texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind data was used to provide the wind speed criteria for a fog event. The laplacian computation was designed for a measurement of the homogeneity. A new combined method, which includes DCD, QuikSCAT wind speed and laplacian computation, was applied to the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and laplacian are -2.0 K, $8m\;s^{-1}$ and 0.1, respectively. The validation results showed that the new combined method slightly improves the detection of sea fog compared to DCD method: improvements of the new combined method are $5{\sim}6%$ increases in the Heidke skill score, 10% decreases in the probability of false detection, and $30{\sim}40%$ increases in the odd ratio.

• International Journal of Internet, Broadcasting and Communication
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• 제9권4호
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• pp.44-50
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• 2017

To understand whether Falling, which is one of the causes of injuries, occurs, various behavior recognition research is proceeding. However, in most research recognize only the fact that Falling has occurred and provide the service. As well as the occurrence of the Falling, the risk varies greatly based on the type of Falling and the situation before and after the Falling. Therefore, when Falling occurs, it is necessary to infer the user's current situation and provide appropriate services. In this paper, we propose to base on Fog Computing and Cloud Computing to design Context-aware System using analysis of behavior data and process sensor data in real-time. This system solved the problem of increase latency and server overload due to large capacity sensor data.

• 한국정보통신학회논문지
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• 제25권2호
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• pp.165-170
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• 2021

기계학습은 최근 대부분의 분야에서 적용하여 연구를 하고 있다. 이것은 기계학습의 결과가 결정된 것이 아니라 입력데이터의 학습으로 목적함수를 생성하고, 이를 통해 통하여 새로운 데이터에 대한 판단이 가능하기 때문이다. 또한, 축적된 데이터의 증가는 기계학습 결과의 정확도에 영향을 미친다. 이에 수집된 데이터는 기계학습에 중요한 요인이다. 제안하는 본 시스템은 서비스 제공을 위한 클라우드 시스템과 지역의 포그 시스템의 융합 시스템이다. 이에 클라우드 시스템은 서비스를 위한 머신러닝과 기반 구조를 제공하고, 포그 시스템은 클라우드와 사용자의 중간에 위치하여 데이터 수집 및 정제를 수행한다. 이를 적용하기 위한 데이터는 스마트기기에서 발생하는 센세 데이터로 한다. 이에 적용된 기계학습 기법은 분류를 위한 SVM알고리즘, 상태 인지를 위한 RNN 알고리즘을 이용한다.

• 대한원격탐사학회지
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• 제35권1호
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• pp.117-136
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• 2019

In this study, a hybrid-type of day fog detection algorithm (DFDA) was developed based on the optical and textural characteristics of fog top, using the Himawari-8 /Advanced Himawari Imager data. Supplementary data, such as temperatures of numerical weather prediction model and sea surface temperatures of operational sea surface temperature and sea ice analysis, were used for fog detection. And 10 minutes data from visibility meter from the Korea Meteorological Administration were used for a quantitative verification of the fog detection results. Normalized albedo of fog top was utilized to distinguish between fog and other objects such as clouds, land, and oceans. The normalized local standard deviation of the fog surface and temperature difference between fog top and air temperature were also assessed to separate the fog from low cloud. Initial threshold values (ITVs) for the fog detection elements were selected using hat-shaped threshold values through frequency distribution analysis of fog cases.And the ITVs were optimized through the iteration method in terms of maximization of POD and minimization of FAR. The visual inspection and a quantitative verification using a visibility meter showed that the DFDA successfully detected a wide range of fog. The quantitative verification in both training and verification cases, the average POD (FAR) was 0.75 (0.41) and 0.74 (0.46), respectively. However, sophistication of the threshold values of the detection elements, as well as utilization of other channel data are necessary as the fog detection levels vary for different fog cases(POD: 0.65-0.87, FAR: 0.30-0.53).

• 조형예술학연구
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• 제6권
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• pp.100-118
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• 2004

Mi Fu, who was associated with Wen Tong, invented the Mijoem Technique (Dotting Technique) in landscape painting with his son Mi Youren. His landscapes, which referred to Dong Yuan's landscape technique and was inspired by the scenery of Jiang Nan, illustrate the mood of a cloud-covered foggy landscape by liberally applying dots with ink. 'Chunsansoesong' which is considered done by Mi Fu, clearly shows the virtues of ink painting's spreading, absorbing and omission techniques. This simply rendered landscape - whose mountains and hills are wrapped in both clouds and fog - displays exquisiteness by using small dots. In 'Chunsansoesong', the characteristics of Song painting: a 'vital energy', a 'poem within the painting', a 'beauty of margin', a 'beauty of one brush stroke, and a 'display of inner meanings' are implicatively expressed This is because it's simple but connotatively delineative. There is the characteristic of a 'poem within the painting' when analyzing the both fragmented and combined 'Chunsansoesong'. The margins support an imaginative space as the height of the mountains get higher which result in deepening both the width and depth of the landscape space. Furthermore, the soft thickness of ink, clouds, pine trees, and pavilion evoke delineative feelings and a desire to write a poem Every thing in 'Chunsansoesong' is enveloped in both clouds and fog regardless of its distance and this delivers boundless feelings of Oriental mystery and urges a desire for 'writing a poem'. The pavilion that faces the cloud and fog-bound mountains especially flames the poetic urge further by inducing viewers' poetic imaginations. As we reviewed above, 'Chunsansoesong's cloud and fog-covered landscape is a good example that clearly showcases the characteristics of a 'Poem within the Painting'.

• 대한원격탐사학회지
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• 제33권4호
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• pp.359-375
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• 2017

We developed fog detection algorithm (KNU_FDA) based on the optical and textural properties of fog using satellite (COMS) and ground observation data. The optical properties are dual channel difference (DCD: BT3.7 - BT11) and albedo, and the textural properties are normalized local standard deviation of IR1 and visible channels. Temperature difference between air temperature and BT11 is applied to discriminate the fog from other clouds. Fog detection is performed according to the solar zenith angle of pixel because of the different availability of satellite data: day, night and dawn/dusk. Post-processing is also performed to increase the probability of detection (POD), in particular, at the edge of main fog area. The fog probability is calculated by the weighted sum of threshold tests. The initial threshold and weighting values are optimized using sensitivity tests for the varying threshold values using receiver operating characteristic analysis. The validation results with ground visibility data for the validation cases showed that the performance of KNU_FDA show relatively consistent detection skills but it clearly depends on the fog types and time of day. The average POD and FAR (False Alarm Ratio) for the training and validation cases are ranged from 0.76 to 0.90 and from 0.41 to 0.63, respectively. In general, the performance is relatively good for the fog without high cloud and strong fog but that is significantly decreased for the weak fog. In order to improve the detection skills and stability, optimization of threshold and weighting values are needed through the various training cases.