• The Journal of Society for e-Business Studies
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• v.15 no.3
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• pp.25-44
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• 2010

In recent years, Ubiquitous City (u-City), the integrated and convergence of ubiquitous services, infrastructure, technologies and city management of the new future development city, is being initiated by the Korean government and local authorities as their new national growth engine. However, a performance measurement system for evaluating and monitoring the impacts of U-City implementation is yet to be established. This paper aims to develop an integrated performance management system (PMS) and extensively used as a tool for managing performance activities to support the visions and goals of the u-City operations. Based on current reviews on the literature and interviews with experts drew Critical Success Factors (CSF) and Key Performance Indicators (KPI) by four different measurement domains including U-City services, infrastructure, technologies, management and developed into an integrated performance measurement system based on the Balanced Scored Card (BSC) perspective. The system also provides number of examples of 'u-City Strategy Map' which illustrates a causal relationship between CSFs to execute u-City visions and goals.

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.2 s.4
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• pp.89-98
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• 1994

Digital road map - which plays an essential role in giving accurate location of the vehicle, optimum route guidance, destination searching, and topographic feature query functions - is the most fundamental element of the vehicle navigation system. Unfortunately, there is not a nation-wide digital map in Korea such as U.S. TIGER fie, that is easily applied to digital road database production. Therefore, producing new digital road map is inevitable in Korea For establishing digital road map for vehicle navigation, this paper puts forth the necessary condition to stabilize the digital road map qualify, and to keep up the compatibility and the economical use. As a result, the standards of coordinate and map accuracy arc presented, and the Items and the structures of database arc decided.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1189-1195
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• 2008

We introduce a MEMS-based pedestrian dead reckoning (PDR) system. A walking navigation algorithm for pedestrians is presented and map-matching algorithm for the navigation system based on dead reckoning (DR) is proposed. The PDR is equipped on the human body and provides the position information of pedestrians. And this is able to be used in ubiquitous sensor network (USN), U-hearth monitoring system, virtual reality (VR) and etc. The PDR detects a step using a novel technique and simultaneously estimates step length. Also an azimuth of the pedestrian is calculated using a fluxgate which is the one of magnetometers. Map-matching algorithm can be formulated to integrate the positioning data with the digital road network data. Map-matching algorithm not only enables the physical location to be identified from navigation system but also improves the positioning accuracy. However most of map-matching algorithms which are developed previously are for the car navigation system (CNS). Therefore they are not appropriate to implement to pedestrian navigation system based on DR system. In this paper, we propose walking navigation system and map-matching algorithm for PDR.

• Journal of Korean Academy of Nursing Administration
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• v.21 no.5
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• pp.511-518
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• 2015

Purpose: The purpose of this research was to develop and evaluate the effect of a mind map for relief of emotional labor and burnout among nurses in outpatient departments in cancer hospitals. Methods: We developed a mind map to reduce emotional labor and burnout. A quasi-experimental study was used with a nonequivalent control group pretest-posttest design. Data were collected from December 2012 to April 2013. Participants were 35 nurses working in the outpatient department of a cancer hospital. The experimental group participated in the mind map program biweekly for 10 weeks. Data were analyzed using $x^2$-test, Mann-Whitney U test, paired t-test, and Wilcoxon sign rank test with the SPSS 21.0 program. Results: The physical burnout and total burnout scores decreased significantly in the intervention group which took the mind map program. Conclusion: Findings indicate that the mind map is an effective intervention to reduce burnout in outpatient department nurses.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.551-556
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• 2023

Angiography equipment is used to evaluate and treat coronary artery disease. As a common feature of equipment, radiation is used, and function development for dose reduction is being carried out by each company. Therefore, the difference depending on whether DCR installed in angiography equipment is used is analyzed from a radiological point of view to prove the effect. Among 431 patients who underwent coronary artery intervention from March 2021 to February 2023, 250 patients with retrospective data were selected. And than among the 250 subjects obtained, 91 patients used the cardiovascular roadmap function during single-vessel intervention, and 159 patients did not use the roadmap. When DCR was used, total dose area product (34.57 uGy/m² : 69.15 uGy/m²), total air kerma dose (688.47 mGy : 1640.4 mGy), fluoroscopy dose (23.87 uGy/m² : 49.91 uGy/m²) and fluoroscopy time (723.55 s : 366.03 s), total number of images (17 : 26) showed lower values and were statistically significant than those not used. The use of DCR function in single vessel coronary intervention is thought to be radiologically safer as single vessel coronary intervention using dynamic cardiovascular DCR showed lower perspective time and perspective dose than procedures performed without the DCR.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.23-32
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• 2021

Semantic image segmentation technology in the field of computer vision is a technology that classifies an image by dividing it into pixels. This technique is also rapidly improving performance using a machine learning method, and a high possibility of utilizing information in units of pixels is drawing attention. However, this technology has been raised from the early days until recently for 'lack of detailed segmentation' problem. Since this problem was caused by increasing the size of the label map, it was expected that the label map could be improved by using the edge map of the original image with detailed edge information. Therefore, in this paper, we propose a post-processing algorithm that maintains semantic image segmentation based on learning, but modifies the resulting label map based on the edge map of the original image. After applying the algorithm to the existing method, when comparing similar applications before and after, approximately 1.74% pixels and 1.35% IoU (Intersection of Union) were applied, and when analyzing the results, the precise targeting fine segmentation function was improved.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.142-145
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• 2008

This paper proposes a computationally efficient algorithm for color image segmentation using self-organizing map(SOM) and grassfire algorithm. We reduce a computation time by decreasing the number of input neuron and input data which is used for learning at SOM. First converting input image to CIE $L^*u^*v^*$ color space and run the learning stage with the SOM-input neuron size is three and output neuron structure is 4by4 or 5by5. After learning, compute output value correspondent with input pixel and merge adjacent pixels which have same output value into segment using grassfire algorithm. The experimental results with various images show that proposed method lead to a good segmentation results than others.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.326-338
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• 2009

In this paper, we argue that characteristics of ubiquitous business are different from those of e-business or m-business. We have developed a u-business model development framework incorporating the concept of value co-creation experiences, which is a core of the ubiquitous business paradigm. The framework of u-business model development presented in this paper consists of eight steps such as defining of domain, discovery of opportunity nucleus, defining of potential co-creation experience environment, deriving candidates of u-business services, selecting u-business killer services, defining value propositions of u-business killer services, defining u-business killer service offerings, and drawing a resource map of u-business killer services. The proposed u-business model development framework offers useful guidelines for practitioners to develop successful u-business models under a ubiquitous business paradigm.