• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.103-112
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• 2017

Due to the large size and high complexity of modern buildings, the interest and the studies about indoor spatial information are increasing. Previous studies related to indoor spatial information were mostly about relevant technologies, and the application of indoor spatial information has been less studied. In the present study, the public administrative work areas where indoor spatial information may be applied were identified by using a modified delphi technique. And the indoor LOD (Level of Detail) and indoor positioning accuracy for indoor spatial information applications considering user requirements was established as standards for efficiently establishing and providing services. The required LOD and positioning accuracy for services was established by reestablishing indoor LOD and positioning accuracy and classifying services with reference to those. The indoor LOD was reestablished from LOD 0 to 4 by focusing on service utilization and general recognition, and the positioning accuracy was reestablished in three levels by considering the accuracy of the present positioning technology and service utilization status.

• The Korean Journal of Emergency Medical Services
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• v.23 no.3
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• pp.41-51
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• 2019

Purpose: This dissertation is a descriptive research study analyzing the accuracy and reliability of the cardiopulmonary resuscitation (CPR) application developed in Korea. Methods: Two faculty members from the emergency medical services department and the emergency medicine department searched applications from July 1 to July 10, 2019 and selected a total of 13 applications. Twelve questions were assessed for information accuracy and three were assessed for reliability. In accordance with application types, the number and mean ± standard deviation were analyzed. The t-test was used to compare the accuracy of CPR information in accordance with information sources. Results: The results revealed the following errors: 7 cases (53.8%) identified the chest center lining from the nipple center-line as the chest pressure point; 5 cases (38.5%) did not provide information on the depth of 6 cm that should not be exceeded; and 4 cases (30.8%) did not provide advise to check respiration. When the CPR information sources were included, the information accuracy score was high; a significant difference was observed (p=.035). Conclusion: In the case of applications providing medical information regarding CPR, it is necessary to provide at least authoritarian, sources of information, and author transparency as well as continuous effort and attention.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1345-1352
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• 2014

Data mining is the representative methodology to obtain meaningful information in the era of big data.By Wikipedia, association rule learning is a popular and well researched method for discovering interesting relationship between itemsets in large databases using association thresholds. It is intended to identify strong rules discovered in databases using different interestingness measures. Unlike general association rule, inverse association rule mining finds the rules that a special item does not occur if an item does not occur. If two types of association rule can be simultaneously considered, we can obtain the marketing information for some related products as well as the information of specific product marketing. In this paper, we propose a balanced attributable relative accuracy applicable to these association rule techniques, and then check the three conditions of interestingness measures by Piatetsky-Shapiro (1991). The comparative studies with rule accuracy, relative accuracy, attributable relative accuracy, and balanced attributable relative accuracy are shown by numerical example. The results show that balanced attributable relative accuracy is better than any other accuracy measures.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.4 s.22
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• pp.51-60
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• 2002

As the use of GIS becomes more widespread, the quality and source of data is becoming more of a concern among users. But accuracy is a component of quality, and the positional accuracy is a component of total accuracy. If only we consider the positional accuracy, simultaneously collecting technology of location and attribute information, whether it be manually, using conventional surveying method, GPS, or remote sensing, is a practical way of insuring that location and attribute information are correctly correlated. This study analyse the positional accuracy from a view-point of user and supplier, which is the considerations that can ensure quality level and continuously maintain the road and underground utility information. The positional accuracy of road and underground utility information are considered as two categories - expected accuracy of data collection procedure, required accuracy of data usage process. And the project manager must consider the cost/benefit view of data generation in order to determine the surveying method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.155-161
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• 2018

Recently, the development of sensor technology has led to an increase in research on unmanned aerial photogrammetry in various fields such as digital mapping, monitoring, cadastral survey, coastal survey, and topographic survey. However, existing studies are mainly limited experiments and analysis of specific application field, which is insufficient to demonstrate the validity of unmanned aerial photogrammetry for geospatial information construction. In this study, the studies related to the accuracy of unmanned aerial photogrammetry were investigated. The flight altitude and accuracy of horizontal direction is proportional to the GSD by analyzing the results of the individual studies conducted on the unmanned aerial photogrammetry within the last 5 years. In addition, the accuracy of the evaluation results varied widely according to the experimental conditions, and the problems of the previous studies that lacked the number of samples to evaluate the results were identified. A total accuracy analysis of 322 checkpoints yielded an accuracy of 0.028m in the horizontal direction and 0.044m in the vertical direction. In the future, the results of this study can be used as a basis for the validity of spatial information construction using unmanned aerial photogrammetry.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.139-148
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• 2012

PURPOSES : The accuracy of travel time information is a key measure of effectiveness and reliability of advanced traveler information systems. This study aims at investigating drivers' perception on the acceptable level of information accuracy and their corresponding valuations. METHODS : A questionnaire survey was executed for collecting driver perception data to capture the expectation level of travel time information provided and their willingness to pay for the information. A Tobit model was adopted for exploring the relationship among the acceptable level, driver socioeconomic characteristics and travel attributes. Since drivers' willingness to pay for accurate travel time information can be different according to their travel lengths, a piecewise linear regression model was developed to capture the sensitivity of values of travel time information to travel length. RESULTS : The analysis results suggest that trip purpose and travel time are two dominant factors to determine drivers' acceptable level of travel time information. For business and short trips, drivers want more accurate information than for non-business and long trips. Drivers' willingness to pay for travel time information also varies depending on their incomes, trip purposes and travel lengths. The results also show that drivers' valuation of travel time information provided is sensitive to their travel length. For longer trips, drivers are less sensitive to travel time information and then put less value on the information provided. CONCLUSIONS : Censored nonlinear regression models are developed to estimate drivers' acceptable accuracy for travel time information and their valuation using questionnaire survey data. The findings on drivers perception to the required accuracy of travel time information and their corresponding willingness to pay can be used in the design and deployment of advanced traveler information system to improve its effectiveness and usefulness through high compliance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.493-502
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• 2013

For various geospatial information such as planimetric and topographic features, the required accuracy may be defined depending on the purpose of GIS applications. Also, the accuracy of the geospatial information have a major impact on the quality of the raw surveying data. In order to be usefully applied the precise geospatial information, the accuracy standards must be appropriately set so that the digital map as base map can be accurately made. Before computer mapping and GIS technology existed, paper maps were drawn by hand. So, the map scale was a significant contributor to the map accuracy. As such the past, the accuracy of maps is determined the scale at which the map would be drawn, but recent trends are to treat accuracy as a one of quality elements, rather than a specification for producing the map. Therefore, the purpose of this paper is to set the new korean map accuracy standards appropriate for the construction and application of the precise geospatial information on behalf of the current representation of korean digital maps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.729-748
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• 2021

Vocal detection is one of the fundamental steps in musical information retrieval. Typically, the detection process consists of feature extraction and classification steps. Recently, neural networks are shown to outperform traditional classifiers. In this paper, we report our study on how to improve detection accuracy further by carefully choosing the parameters of the deep network model. Through experiments, we conclude that a feature-classifier model is still better than an end-to-end model. The recommended model uses a spectrogram as the input plane and the classifier is an 18-layer convolutional neural network (CNN). With this arrangement, when compared with existing literature, the proposed model improves the accuracy from 91.8% to 94.1% in Jamendo dataset. As the dataset has an accuracy of more than 90%, the improvement of 2.3% is difficult and valuable. If even higher accuracy is required, the ensemble learning may be used. The recommend setting is a majority vote with seven proposed models. Doing so, the accuracy increases by about 1.1% in Jamendo dataset.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.809-819
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• 2020

Gender prediction accuracy increases as convolutional neural network (CNN) architecture evolves. This paper compares voting and ensemble schemes to utilize the already trained five CNN models to further improve gender prediction accuracy. The majority voting usually requires odd-numbered models while the proposed softmax-based voting can utilize any number of models to improve accuracy. The ensemble of CNN models combined with one more fully-connected layer requires further tuning or training of the models combined. With experiments, it is observed that the voting or ensemble of CNN models leads to further improvement of gender prediction accuracy and that especially softmax-based voters always show better gender prediction accuracy than majority voters. Also, compared with softmax-based voters, ensemble models show a slightly better or similar accuracy with added training of the combined CNN models. Softmax-based voting can be a fast and efficient way to get better accuracy without further training since the selection of the top accuracy models among available CNN pre-trained models usually leads to similar accuracy to that of the corresponding ensemble models.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1083-1096
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• 2021

Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value u_op by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and u_op. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.