• 지질공학
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• 제34권2호
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• pp.173-191
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• 2024

Landslides pose significant threats to many countries globally, yet the development and implementation of effective landslide early warning systems (LEWS) remain challenging due to multifaceted complexities spanning scientific, technological, and political domains. Addressing these challenges demands a holistic approach. Technologically, integrating thresholds, such as rainfall thresholds, with real-time data within accessible, open-source software stands as a promising solution for LEWS. This article introduces LandScient_EWS, a PHP-based program tailored to address this need. The software facilitates the comparison of real-time measured data, such as rainfall, with predefined landslide thresholds, enabling precise calculations and graphical representation of real-time landslide advisory levels across diverse spatial scales, including regional, basin, and hillslope levels. To illustrate its efficacy, the program was applied to a case study in Medellin, Colombia, where a rainfall event on August 26, 2008, triggered a shallow landslide. Through pre-defined rainfall intensity and duration thresholds, the software simulated advisory levels during the recorded rainfall event, utilizing data from a rain gauge positioned within a small watershed and a single grid cell (representing a hillslope) within that watershed. By identifying critical conditions that may lead to landslides in real-time scenarios, LandScient_EWS offers a new paradigm for assessing and responding to landslide hazards, thereby improving the efficiency and effectiveness of LEWS. The findings underscore the software's potential to streamline the integration of rainfall thresholds into both existing and future landslide early warning systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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• pp.1053-1057
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• 1987

In this paper, we have studied a communication protocol which may provide Audio/Video data transmission in real time. Auido/Video data have its own characteristics. A new transport protocol with realtime constraint has been designed and implemented which performs dynamic error control and flow control depending on the characteristics of transmitted Audio/Video data. Since the receiving data can be predicted from the previously received data using the prediction function in Auido/Video data transmission, these functions are introduced in our transport protocol that may possibly improve the speed of data transmission and give a real time response. We have tested our transport protocol and measured the performance by the simulation. We assume that our transport protocol would be used in LAN environment. Our prime purpose is to provide a reliable and real time Auido/Video data transmission service.

• 한국컴퓨터그래픽스학회논문지
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• 제22권4호
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• pp.21-30
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• 2016

위험관리 시스템은 단 시간에 의사결정하기 위해 스트림 데이터를 실시간으로 분석 할 수 있어야 한다. 많은 데이터 분석 시스템은 CPU와 디스크 데이터베이스로 구성되어 있다. 하지만, cpu 기반 시스템은 스트림 데이터를 실시간으로 분석하는데 어려움이 있다. 스트림 데이터는 1ms부터 1시간, 1일까지 생성주기가 다양하다. 한 개의 센서가 생성하는 데이터는 작다. 하지만 수 만개의 센서가 생성하는 데이터는 매우 크다. 예를 들어 10만개 센서가 1초에 1GB 데이터를 생성한다면, CPU 기반 시스템은 이를 분석 할 수 없다. 이러한 이유로 실시간 스트림 데이터 분석 시스템은 빠른 처리 속도와 확장성이 필요하다. 본 논문에서는 GPU와 하이브리드 데이터베이스를 이용한 시각화 가속 기술을 제안한다. 제안한 기술을 평가하기 위해 우리는 지하 파이프라인에 설치된 센서와 트윗 데이터를 활용하여 실시간 릭 탐지 시각적 분석 시스템에 적용했다.

• 디지털콘텐츠학회 논문지
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• 제10권1호
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• pp.55-60
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• 2009

인터넷의 발달로 인하여 주변에 많은 정보들이 존재하며 이러한 정보의 양은 기하급수적으로 증가하고 있다. 이러한 방대한 정보를 관리하기 위해 등장한 정보관리 시스템들은 사용자가 원하는 정보를 보다 정확하고 효율적으로 검색하여 사용자의 요구를 만족시킨다. 그러나 모바일의 경우 자체 내장된 파일 시스템을 이용한 한정된 정보 관리 및 실시간 자료 처리 능력에 대한 한계를 갖고 있다. 본 논문은 이러한 모바일 환경에서 대용량 자료에 대한 효과적인 관리와 실시간 데이터 처리를 가능하게 하는 미들웨어 시스템을 제안한다. 제안된 미들웨어 시스템의 활용 가능성을 보이기 위해 개인 스케쥴링과 관련된 웹 데이터베이스 와 모바일 폰을 연동하는 실시간 알람 스케쥴러 시스템을 구현하였다.

• 천문학회보
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• 제44권1호
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• pp.78.1-78.1
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• 2019

Using a method to correct redshift space distortion (RSD) for individual galaxies, we mapped the real space distributions of galaxies in the Sloan Digital Sky Survey (SDSS) Data Release 7(DR7). We use an ensemble of mock catalogs to demonstrate the reliability of this extension, showing that it allows for an accurate recovery of the real-space correlation functions and galaxy biases. We also demonstrate that, using an iterative method applied to intermediate scale clustering data, we can obtain an unbiased estimate of the growth rate of structure $f\sigma_8$, which is related to the clustering amplitude of matter, to an accuracy of $\sim 10\%$. Applying this method to the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7), we construct a real-space galaxy catalog spanning the redshift range $0.01 \leq z \leq 0.2$, which contains 584,473 galaxies in the North Galactic Cap (NGC). Using this data we, infer $0.376 \pm 0.038$ at a median redshift z=0.1, which is consistent with the WMAP9 cosmology at $1\sigma$ level. By combining this measurement with the real-space clustering of galaxies and with galaxy-galaxy weak lensing measurements for the same sets of galaxies, we are able to break the degeneracy between $f$, $\sigma_8$ and $b$. From the SDSS DR7 data alone, we obtain the following cosmological constraints at redshift $z=0.1$ for galaxies.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.336-338
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• 2015

This research is to propose an applicable framework for real-time health surveillance and safety monitoring at construction sites. First this study aims at finding (1) a framework for health surveillance that is likely to benefit employers and employees in the industry, (2) a valid way to identify factors or conditions with potential health concerns that can occur under particular work conditions, (3) An effective way to apply wireless/mobile sensors to construction workers using real-time/live data transmission methods, and (4) A relationship between a worker's vital signs and job site environment. Biosensors for physiological response and devices for weather/work related data are to collect real-time data. Relationships between jobs and physiological responses are analyzed and factors that touched particularly contributing to certain responses are identified. When data are incorporated with tasks, factors affecting tasks can be identified to estimate the magnitude of the factors. By comparing work and normal responses possible precautionary actions can be considered. In addition, the study would be lead to improving (1) trade-specific dynamic work schedules for workers which would be based on various factors affecting worker health level and (2) reevaluating worker productivity with health status and work schedule, thereby seeking ways to maximize worker productivity. Through a study, the paper presents expected benefits of implementing health monitoring.

• ETRI Journal
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• 제45권4호
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• pp.603-614
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• 2023

Advancements in remote medical technologies and smart devices have led to expectations of contactless rehabilitation. Conventionally, rehabilitation requires clinicians to perform routine muscle function assessments with patients. However, assessment results are difficult to cross-reference owing to the lack of a gold standard. Thus, the application of remote smart rehabilitation systems is significantly hindered. This study analyzes the factors affecting the real-time evaluation of muscle function based on biometric sensor data so that we can provide a basis for a remote system. We acquired real clinical stroke patient data to identify the meaningful features associated with normal and abnormal musculature. We provide a system based on these emerging features that assesses muscle functionality in real time via streamed biometric signal data. A system view based on the amount of data, data processing speed, and feature proportions is provided to support the production of a rudimentary remote smart rehabilitation system.

• 전기학회논문지
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• 제57권11호
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• pp.2080-2085
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• 2008

KEPCO's AMR (Automatic Meter Reading) is a system that performs the real-time inspection and management of the 15-minute load profile of electric power consumption through a wired and/or wireless network such as CDMA. It has been utilized widely for real-time collection and data analysis. So far, KEPCO has focused on establishing wireless networks using CDMA and collecting data in real time but failed to consider sufficiently performances that can improve the quality of the original data required in terms of data utilization as well as establish the summary information. In this paper, we are going to show the functions that improve data quality by recording the final renewal time of any erroneous data and maintaining such data lists to use them in the rebuilding of summary information. The goals are to reduce any load applied mainly on the DBMS (Database Management System) of AMR, to enable the real-time performance of establishment in the summary information, and to obtain high-quality inspection data. The performance evaluation result has revealed a 10-fold improvement compared to the traditional disk-based DBMS system when the summary information is established.

• 한국신뢰성학회지:신뢰성응용연구
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• 제18권3호
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• pp.250-259
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• 2018

Purpose: Previously, missile reliability prediction is based on theoretical failure prediction model. It has shown that the predicted reliability is inadequate to real field data. Although an MTTF based reliability prediction method using real field data has recently been studied to overcome this issue. In this paper, we present a more realistic method, considering MTBF concept, to predict missile reliability. Methods: In this paper we proposed a modified survival model. This model is considering MTBF as its core concept, and failed missiles in the model are to be repaired and redeployed. We compared the modified model (MTBF) and the previous model (MTTF) in terms of fitness against the real failure data. Results: The reliability prediction result of MTBF based model is closer to fields failure data set than that of MTTF based model. Conclusion: The proposed MTBF concept is more fitted to real failure data of missile than MTTF concept. The methodology of this study can be applied to analyze field failure data of other similar missiles.

• International journal of advanced smart convergence
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• 제6권3호
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• pp.29-37
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• 2017

The current behavior recognition system don't match data formats between sensor data measured by user's sensor module or device. Therefore, it is necessary to support data processing, sharing and collaboration services between users and behavior recognition system in order to process sensor data of a large capacity, which is another formats. It is also necessary for real time interaction with users and behavior recognition system. To solve this problem, we propose fog cloud based behavior recognition system for human body sensor data processing. Fog cloud based behavior recognition system solve data standard formats in DbaaS (Database as a System) cloud by servicing fog cloud to solve heterogeneity of sensor data measured in user's sensor module or device. In addition, by placing fog cloud between users and cloud, proximity between users and servers is increased, allowing for real time interaction. Based on this, we propose behavior recognition system for user's behavior recognition and service to observers in collaborative environment. Based on the proposed system, it solves the problem of servers overload due to large sensor data and the inability of real time interaction due to non-proximity between users and servers. This shows the process of delivering behavior recognition services that are consistent and capable of real time interaction.