• The Korean Journal of Emergency Medical Services
• /
• v.19 no.1
• /
• pp.7-17
• /
• 2015

Purpose: We gathered information for the development of a blood pressure measurement education program by analyzing the accuracy of reading taken using a blood pressure simulator by Korean paramedic students. Methods: Data from 131 students were collected in November 12-20, 2013, and April 2-4, 2014. A 27-item questionnaire was administered, the accuracy of measurements confirmed using a blood pressure simulator (BT-CEAB), and the data analyzed (SPSS v 21.0). Results: The accuracy of systolic and diastolic blood pressure readings (${\leq}2mmHg$) was relatively low (27.5%). The mean blood pressure knowledge score was 67.61 points; significant differences were noted considering the sex (p = .001), hours of practice (p =.007), numbers of practice (p = .001), and reported self-confidence (p = .026). The blood pressure measurement accuracy group did not show a significant difference in its knowledge of blood pressure (p = .198). Conclusion: Most subjects needed several practice sessions to master the skill of measuring blood pressure. The feedback provided by individual assessment and the practice education program will serve as the basis for clinical and prehospital practice.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.2
• /
• pp.107-114
• /
• 1999

This paper presents an application of artificial neural networks(ANN) to assess the dynamic security of power systems. The basic role of ANN is to provide assessment of the system's stability based on training samples from off-line analysi. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of ANN is a mapping of the pre-fault, fault-on, and post-fault system conditions into the CCT's. In previous work, a feed forward neural network is used to learn this mapping by using the generation outputs during the fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault considered. In order to enhance the speed of security assessment, the bus data and line powers are used as the input data of the ANN in thil paper. Test results show that the proposed neural networks have the reasonable accuracy and can be used in on-line security assenssment efficiently.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.25 no.5
• /
• pp.415-425
• /
• 2007

In this study, we generated an orthophoto with both LIDAR data and aerial images and compared it with that generated from only the images. For the accuracy assessment of these orthophotos, we performed not only qualitative analysis based on visual inspection but also quantitative analysis by measuring horizontal inconsistency, boundary coordinates and similarity measures on buildings. Based on the visual inspection and horizontal inconsistency, the orthophoto based on LIDAR DSM appeared to be more closer to a true-orthophoto. However, the analysis on measurements of boundary coordinates and similarity measures indicates that the orthophoto based on LIDAR DSM is more vulnerable to double mapping on occluded areas. Accordingly, if we apply an effective solution on double mapping or use only the central areas of the aerial images where occluded areas are rarely founded, we can generate automatically true-orthophotos based on a LIDAR DSM.

• Journal of the Korean Society of Safety
• /
• v.37 no.1
• /
• pp.78-87
• /
• 2022

In the risk assessment of urban railway systems, a hazard log is created by identifying hazards from accident and failure data. Then, based on a risk matrix, evaluators analyze the frequency and severity of the occurrence of the hazards, conduct the risk assessment, and then establish safety measures for the risk factors prior to risk control. However, because subjective judgments based on the evaluators' experiences affect the risk assessment results, a more objective and automated risk assessment system must be established. In this study, we propose a risk assessment model in which an adaptive neuro-fuzzy inference system (ANFIS), which is combined in artificial neural networks (ANN) and fuzzy inference system (FIS), is applied to the risk assessment of urban railway systems. The newly proposed model is more objective and automated, alleviating the limitations of risk assessments that use a risk matrix. In addition, the reliability of the model was verified by comparing the risk assessment results and risk control priorities between the newly proposed ANFIS-based risk assessment model and the risk assessment using a risk matrix. Results of the comparison indicate that a high level of accuracy was demonstrated in the risk assessment results of the proposed model, and uncertainty and subjectivity were mitigated in the risk control priority.

• Journal of Information Technology Services
• /
• v.3 no.1
• /
• pp.21-41
• /
• 2004

The technical proposal suggested and published by MIC(Ministry of Information and Communication, henceforth MIC) contains too general assessment elements, which causes qualitative and subjective assessment of technical proposals. Thus, in terms of the technical proposal assessment. It lacks In fairness, validity and accuracy. Furthermore, it has a great deal of difficulty in assessment caused by the inconsistency between proposal planning and assessment methods. Also, each company has different writing format, so it is impossible to make use of its maneuvering data for the assessment. To overcome these weaknesses, our research focused on a quantitative evaluation on development organization, which is a part of organizational and administrative part of the technical proposal suggested and published by MIC. In this research, we divided development organization for the technical proposal into organization, teams, and team members, and then studied addition, deletion and merging for the assessment criteria. For the related study, we chose especially CMM(Capability Maturity Model) from a lot of international and national references, which is a model measuring the maturity of organization, and then we focused on Small-CMM which is available in the small-sized organization. We also suggested input form, description method, assessment elements for the quantitative assessment in the chosen developmental organization, and finally we proposed standard referencing criteria for the assessment. Our study concludes that our assessment method are valid and available in comparison with the previous Delphi method through a validity evaluation test.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1998.10a
• /
• pp.13-18
• /
• 1998

The assessment of machined surface is difficult because the freeform surface must be evaluated by surface fairness as well as dimensional accuracy. In this paper, the methodology of freeform surface generation using measured data on the machine tool is presented. The reliability of measured points data is obtained by measuring error compensation. The compensated data are formulated through Non-uniform G-spline surface modeling. In order to improve the surface fairness, the generated model si smoothened by parameterization The validity and usefulness of the proposed method are examined through computer simulation and experiments on the machine tool.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.34 no.5
• /
• pp.479-493
• /
• 2016

LiDAR(Light Detection and Ranging) data acquired from ALS(Airborne Laser Scanner) has been intensively utilized to reconstruct object models. Especially, researches for 3D modeling from LiDAR data have been performed to establish high quality spatial information such as precise 3D city models and true orthoimages efficiently. To reconstruct object models from irregularly distributed LiDAR point clouds, sensor calibration, noise removal, filtering to separate objects from ground surfaces are required as pre-processing. Classification and segmentation based on geometric homogeneity of the features, grouping and representation of the segmented surfaces, topological analysis of the surface patches for modeling, and accuracy assessment are accompanied by modeling procedure. While many modeling methods are based on the segmentation process, this paper proposed to extract key points directly for building modeling without segmentation. The method was applied to simulated and real data sets with various roof shapes. The results demonstrate feasibility of the proposed method through the accuracy analysis.

• Spatial Information Research
• /
• v.12 no.3
• /
• pp.275-283
• /
• 2004

In order to effectively, and accurately assess seawater intrusion in coastal area, multidisciplinary data including geophysical, well logging, and hydrogeochemical data should be managed in systematical way. Such systematical management of data is critical key to improve the re-usability of the data as well as the accuracy of the assessment by means of providing a method of synthetic analysis. Therefore, for systematical management of multidisciplinary data in seawater intrusion problem, we have developed a database management system and 3-D visualization interface based on geographic information system in this, study. All geophysical survey, well logging, hydrochemical, as well as drilling, data are classified as attribute data using Microsoft Access, and joined with spatial information based on ArcView. The database management system and 3-D visualization interface to handle these data, also, developed using the script language of ArcView. We think the development of database and 3-D visualization system will improve the efficiency of data management, user-friendliness of data access, and accuracy of data analysis.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.1-8
• /
• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.

• Journal of Climate Change Research
• /
• v.2 no.3
• /
• pp.203-219
• /
• 2011

Greenhouse gas(GHG) inventories were reported recently in various fields. It, however, has been rarely to mention about the accuracy and reliability of the GHG inventory results. Some reliable assessment methods were introduced to judge the accuracy of the GHG inventory results. It is, hence, critical to develop an evaluation methodology. This project was designed 1) to develop evaluation methodology for reliability of inventory results by GHG-CAPSS, 2) to check the feasibility of the developed evaluation methodology as a result of applying this methodology to two emission sources: liquid fossil fuel and landfill, and 3) to construct the technical roadmap for future role of GHG-CAPSS. Qualitative and quantitative assessment methodologies were developed to check the reliability and accuracy of the inventory results. Qualitative assessment methodology was designed to evaluate the accuracy and reliability of estimation methods of GHG emissions from emission and sink sources, activity data, emission factor, and quality management schemes of inventory results. On the other hand, quantitative assessment methodology was based on the uncertainty assessment of emission results. According to the results of applying the above evaluation methodologies to two emission sources, those seem to be working properly. However, it is necessary to develop source-specific rating systems because emission and sink sources exhibit source-specific characteristics of GHG emissions and sinks.