• Informatization Policy
• /
• v.21 no.3
• /
• pp.3-32
• /
• 2014

Recent developments of smart phone and ICT, and explosive developments of wireless sensor area drive radical changes on traditional health care service. To accommodate the changes, many researchers have studied to expand traditional healthcare service areas including home care for independent living and public care for preventive and collaborative wellness area. This study proposes a research framework for healthcare information technology area based on Mettler and Raptis's(2012) work. Then, the study analyze the research trends in the area based on the framework. The area of monitoring patients health status at home using smart phone, providing innovative healthcare service by out-patients monitoring, and implementing preventive healthcare services are identified most active and emerging research agenda. It is expected that the research framework and implications of this study can assist future research efforts and practical utilization of healthcare information technologies.

• Smart Structures and Systems
• /
• v.31 no.4
• /
• pp.325-334
• /
• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.291-292
• /
• 2012

본 연구에서는 모바일기반의 효율적인 건강정보 모니터링 수행하기 위하여 소프트웨어 디자인 패턴 중 생체신호 모니터링 프레임워크에 적합한 패턴을 제시하고자 한다. 이를 위해 Java언어 기반의 구글 안드로이드사의 Android를 이용한 모바일 환경에서 프레임워크를 설계 하였다. 또한 모니터링 프레임워크의 설계 및 구현을 하고 실험을 통해 프레임워크 기반의 모바일 생체신호 모니터링 애플리케이션의 유용성을 확인하였다.

• Journal of Computing Science and Engineering
• /
• v.7 no.4
• /
• pp.272-284
• /
• 2013

In this paper, we propose a new framework for anomaly detection in medical wireless sensor networks, which are used for remote monitoring of patient vital signs. The proposed framework performs sequential data analysis on a mini gateway used as a base station to detect abnormal changes and to cope with unreliable measurements in collected data without prior knowledge of anomalous events or normal data patterns. The proposed approach is based on the Mahalanobis distance for spatial analysis, and a kernel density estimator for the identification of abnormal temporal patterns. Our main objective is to distinguish between faulty measurements and clinical emergencies in order to reduce false alarms triggered by faulty measurements or ill-behaved sensors. Our experimental results on both real and synthetic medical datasets show that the proposed approach can achieve good detection accuracy with a low false alarm rate (less than 5.5%).

• Journal of The Institute of Information and Telecommunication Facilities Engineering
• /
• v.9 no.3
• /
• pp.102-106
• /
• 2010

Recently process industries from oil and gas procedures and mining companies to manufactures of chemicals, foods, and beverages has been exploring the USN (Ubiquitous Sensor Networks) technology to improve safety of production processes. However, to apply the USN technology in the large-scale plant industry, reliability and security issues are not fully addressed yet, and the absence of the industrial sensor networking standard causes a compatibility problem with legacy equipment and systems. Although this situation, process industry such as energy plants are looking for the secure wireless plant solution to provide detailed, accurate safety monitoring from previously hard-reach, unaccordable area. In this paper, SPSF (Smart Plant Safety Framework based on Reliable-Secure USN) is suggested to fulfill the requirements of high-risk industrial environments for highly secure, reliable data collection and plant monitoring that is resistant to interference. The SPSF consists of three main layers: 1) Smart Safety Sensing Layer, 2) Smart Safety Network Layers, 3) Plant Network System Layer.

• Korean Journal of Remote Sensing
• /
• v.20 no.3
• /
• pp.207-214
• /
• 2004

This study focused on providing a methodology to utilize temporal information obtained from remotely sensed data for monitoring a wide variety of targets on the earth's surface. Generally, a methodology in understanding of global changes is composed of mapping, quantifying, and monitoring changes in the physical characteristics of land cover. The selected processing and analysis technique affects the quality of the obtained information. In this research, feature extraction methodology is proposed based on segmentation. It requires a series of processing of multitempotal images: preprocessing of geometric and radiometric correction, image subtraction/thresholding technique, and segmentation/thresholding. It results in the mapping of the change-detected areas. Here, the appropriate methods are studied for each step and especially, in segmentation process, a method to delineate the exact boundaries of features is investigated in multiresolution framework to reduce computational complexity for multitemporal images of large size.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.493-495
• /
• 2016

The framework and application model for risk mitigation service based on network provides monitoring function of the risk event data to be inputted and analyses it for mitigation process. Furthermore, it performs the analysis of the manmade calamities such as accident, building destruction, natural calamities caused by climate change, and animal harms caused by bird flu and foot-and-mouth disease occurring in livestock and wild animals, and provides the mitigation service of it. The application model for risk mitigation is combined with network and carries out the real time acquisition and monitoring of risk events, and provides mitigation service for the risks caused by calamities and reduces economic losses.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.479-486
• /
• 2009

This paper presents an integrated real-time monitoring and control framework that facilitates decision making by enabling project managers to take corrective actions right after any deviation happens and mitigate the damage to the ongoing steel projects. The proposed framework employs the High Level Architecture (HLA) as its infrastructure. It is composed of several individual monitoring and control components called "Federates," which cooperate and interact with each other through the Real-time Infrastructure (RTI). Reusability, interoperability and extendibility of federates in the proposed project control system make this a unique system.

• Structural Monitoring and Maintenance
• /
• v.7 no.3
• /
• pp.261-281
• /
• 2020

Civil structures may experience progressive deterioration and damage under environmental and operational conditions over their service life. Finite element (FE) model updating method is one of the most important approaches for damage identification in structures due to its capabilities in structural health monitoring. Although various damage detection approaches have been investigated on structures, there are limited studies on large-sized space structures. Thus, this paper aims to investigate the applicability and efficiency of sensitivity-based FE model updating framework for damage identification in large space structures from a distinct point of view. This framework facilitates modeling and model updating in large and geometric complicated space structures. Considering sensitivity-based FE model updating and vibration measurements, the discrepancy between acceleration response data in real damaged structure and hypothetical damaged structure have been minimized through adjusting the updating parameters. The feasibility and efficiency of the above-mentioned approach for damage identification has finally been demonstrated with two numerical examples: a flat double layer grid and a double layer diamatic dome. According to the results, this method can detect, localize, and quantify damages in large-scaled space structures very accurately which is robust to noisy data. Also, requiring a remarkably small number of iterations to converge, typically less than four, demonstrates the computational efficiency of this method.

• Smart Structures and Systems
• /
• v.21 no.1
• /
• pp.123-137
• /
• 2018

Structural monitoring is the most important part of the construction and operation of the embankment dams. Appropriate instruments selection for dams is vital, as inappropriate selection causes irreparable loss in critical condition. Due to the lack of a systematic approach to determine adequate instruments, a framework based on three comparable Multi-Attribute Decision Making (MADM) methods, which are VIKOR, technique of order preference by similarity to ideal solution (TOPSIS) and Preference ranking organization method for enrichment evaluation (PROMETHEE), has been developed. MADM techniques have been widely used for optimizing priorities and determination of the most suitable alternatives. However, the results of the different methods of MADM have indicated inconsistency in ranking alternatives due to closeness of judgements from decision makers. In this study, 9 criteria and 42 geotechnical instruments have been applied. A new method has been developed to determine the decision makers' importance weights and an aggregation method has been introduced to optimally select the most suitable instruments. Consequently, the outcomes of the aggregation ranking correlate about 94% with TOPSIS and VIKOR, and 83% with PROMETHEE methods' results providing remarkably appropriate prioritisation of instruments for embankment dams.