• 한국인터넷방송통신학회논문지
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• 제10권6호
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• pp.225-233
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• 2010

최근, 스마트 그리드, 스마트 홈 네트워크, 유비쿼터스 컴퓨팅 등의 분야에서 필요한 정보를 수집 및 가공하여 실시간 양방향으로 교환하고, 제어 및 감시하는 스마트 정보 모니터링 기술에 대한 연구를 계속 해 왔다. 본 논문에서는 에너지, U-Farm, 차량정보 및 홈 네트워크에 관한 스마트 정보 모니터링 기술의 응용 제품 및 최근 동향들을 알아본다. 특히, 스마트 그리드의 핵심부분인 스마트 미터와 실시간으로 정보를 교환하는 구글 파워미터, 유비쿼터스 농업을 위한 실시간 모니터링 시스템, 차량상태 정보를 위한 실시간 모니터링 시스템, 저전력, 저가격의 ZigBee 기반 스마트 정보 모니터링 기술 응용 및 관련사례에 대하여 기술한다. 마지막으로 스마트그리드 제주 실증단지 구축현황에 대하여 기술한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권1호
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• pp.99-118
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• 2013

Electrocardiograms (ECGs) are widely used by clinicians to identify the functional status of the heart. Thus, there is considerable interest in automated systems for real-time monitoring of arrhythmia. However, intra- and inter-patient variability as well as the computational limits of real-time monitoring poses significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is therefore a promising new intelligent diagnostic tool.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제16권4호
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• pp.447-451
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• 2010

유비쿼터스 지능공간 모니터링 시스템은 유비쿼터스 지능공간 내에서의 다양한 스마트 객체의 기능, 성능 및 상태를 모니터링하고 분석함으로써 사용자 지수, 서비스의 통합 동작 상황, 서비스의 상태를 판단할 수 있는 정보를 실시간으로 제공해준다. 또한 최적화 및 자기 관리를 통하여 서비스 성능을 향상시킬 수 있는 기능을 제공한다. 이러한 실시간 모니터링 시스템의 적용범위를 확장하기 위해서는 유동적인 대량의 데이터 처리가 필요하다. 본 논문에서는 모니터링 시스템을 구성하는 스마트 객체의 데이터 발생에 따라 유발되는 에이전트들의 부하를 해결하기 위해 부하를 예측하여 미리 분산시키는 기법을 제안한다. 제안된 기법이 적용된 시스템은 실험을 통해 기존의 시스템에 비해 전체 데이터 마감시간 초과율이 매개변수가 1일 경우, 80%이상 감소됨을 보여준다.

• 센서학회지
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• 제17권1호
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• pp.81-85
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• 2008

This paper addressed real-time urine monitoring device for intensive care patients. The device was developed to detect and count each urine drop using optical sensor and calculate the current urine output volume and its hourly rate. In experiment, the water volume scale of drainage bottle was observed and compared with the count of the device so that the volume of each drop was found to vary with the dropping rate per minute. From this measurement, the relationship equation was derived to estimate the total water volume from the drop rate (correlation coefficient : r= 0.99). The developed device could be applied to count patient's urine drop successfully. Therefore, this device can be used to monitor intensive care patient's urine status in real-time.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.917-918
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• 2013

최근 내비게이션 혹은 포털사이트에서는 길 찾기 안내 서비스에 집중 호우에 따른 도로의 실시간 정보를 제공하고 있다. 특히 '교통알림e' 에서는 도로의 침수 및 사고 유무에 따른 교통정보를 제공하고 있다. 본 연구는 기상청의 실시간 강우정보와 포털 사이트 등에서 제공하는 상태 정보를 연계하여 운전자에게 도로의 실시간 상태정보를 제공하고자 한다. 이를 위해서 실시간 강우정보와 포털 정보를 연계한 위험도로 평가 방안을 제시한다.

• Journal of Positioning, Navigation, and Timing
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• 제11권4호
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• pp.269-277
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• 2022

In this paper, we overview the system development status of the national maritime precise point positioning-real-time kinematic (PPP-RTK) service in Korea, also known as the Precise POsitioning and INTegrity monitoring (POINT) system. The development of the POINT service began in 2020, and the open service is scheduled to start in 2025. The architecture of the POINT system is composed of three provider-side facilities-a reference station, monitoring station, and central control station-and one user-side receiver platform. Here, we propose the detailed functionality of each component considering unidirectional broadcasting of augmentation data. To meet the centimeter-level user positioning accuracy in maritime coverage, new reference stations were installed. Each reference station operates with a dual receiver and dual antenna to reduce the risk of malfunctioning, which can deteriorate the availability of the POINT service. The initial experimental results of a testbed from corrections generated from the testbed network, including newly installed reference stations, are presented. The results show that the horizontal and vertical accuracies satisfy 2.63 cm and 5.77 cm, respectively. For the purpose of (near) real-time broadcasting of POINT correction data, we designed a correction message format including satellite orbit, satellite clock, satellite signal bias, ionospheric delay, tropospheric delay, and coordinate transformation parameters. The (near) real-time experimental setup utilizing (near) real-time processing of testbed network data and the designed message format are proposed for future testing and verification of the system.

• 한국BIM학회 논문집
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• 제11권2호
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2801-2815
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• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.

• 한국정보처리학회논문지
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• 제4권10호
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• pp.2470-2476
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• 1997

본 연구에서는 실시간 휴리스틱 탐색 알고리즘을 개발하고 이것을 기존의 mini-min lookahead 알고리즘과 비교하였다. 많은 실시간 휴리스틱 탐색의 접근 방법에서 종종 전체 문제를 몇 개의 부 문제로 문제를 분할한다. 본 연구에서는 분할된 부 문제에서 마감시간을 적용할 뿐만 아니라 전체 해를 구하는데 있어서도 마감시간을 적용하는 알고리즘을 제안한다. 실시간 휴리스틱 탐색 알고리즘으로 제안된 $RTA^{\ast}$, SARTS, DYNORA 등의 알고리즘들은 탐색에 필요한 시간의 예측을 휴리스틱 평가 함수로부터 얻기 때문에 휴리스틱 평가의 정확도가 그 알고리즘의 성능을 보장하게 된다. 그러나 실세계의 문제에서 정확한 휴리스틱 평가 함수를 구하는 것은 매우 어려운 일이므로 부 문제 공간에서의 탐색 상황을 반영한 마감시간을 적용할 필요가 있다. 본 연구에서는 동적 마감시간 전략인 cut-off 방법을 사용하는 새로운 알고리즘을 제안한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.249-252
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• 2004

The automatic determination of vehicle operation status as well as continuous tracking of vehicle location with intelligent management is one of major elements to achieve the goals. Especially, vehicle operation status can only be analyzed in terms of expert experiences with real-time location data with scheduling information. However the scheduling information of individual vehicle is very difficult to be interpreted immediately because there are hundreds of thousand vehicles are run at the same time in the national wide range workplace. In this paper, we propose the location-based knowledge management system(LKMs) using the active trajectory analysis method with routing and scheduling information to cope with the problems. This system uses an inference technology with dispatching and geographic information to generate the logistics knowledge that can be furnished to the manager in the central vehicle monitoring and controlling center.