• Journal of Korean Library and Information Science Society
• /
• v.49 no.4
• /
• pp.219-236
• /
• 2018

This study investigated the image of library in our society by analyzing newspaper articles related to libraries. A total of 3065 newspaper articles reported in Kyunghyang, Hankyoreh, Chosun, and Joongang from 2000 to 2017 were analyzed using time series analysis. The results of the time series analysis showed the number of newspaper articles related to library declined to a peak in 2009. Also, there were differences in the content of newspaper articles by year except for those content related to books. There were many articles about 'information' and 'use' in early 2000, about 'library operation and opening', 'culture' from 2006, and about 'residents of the area', 'culture' after 2012. These content of newspaper articles may reflects the image of library in our society. Finally, this study dis cussed practical ways of public library promotion.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.15 no.2
• /
• pp.121-125
• /
• 2015

In this paper, we propose a method that extracts features from character patterns using the fractal dimension of chaos theory. The input character pattern image is converted into time-series data. Then, using the modified Henon system suggested in this paper, it determines the last features of the character pattern image after calculating the box-counting dimension, natural measure, information bit, and information (fractal) dimension. Finally, character pattern recognition is performed by statistically finding each information bit that shows the minimum difference compared with a normalized character pattern database.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.10
• /
• pp.91-99
• /
• 2019

In this paper, we propose partial denoising boundary matching based on an index for faster matching in very large image databases. Attempts have recently been made to convert boundary images to time-series with the objective of solving the partial denoising problem in boundary matching. In this paper, we deal with the disk I/O overhead problem of boundary matching to support partial denoising in a large image database. Although the solution to the problem superficially appears trivial as it only applies indexing techniques to boundary matching, it is not trivial since multiple indexes are required for every possible denoising parameters. Our solution is an efficient index-based approach to partial denoising using $R^*-tree$ in boundary matching. The results of experiments conducted show that our index-based matching methods improve search performance by orders of magnitude.

• Smart Media Journal
• /
• v.10 no.2
• /
• pp.16-21
• /
• 2021

Financial time series analysis plays a very important role economically and socially in modern society and is an important task affecting global development, but due to difficulties such as a lot of noise and uncertainty, financial time series analysis prediction is a difficult research topic. In this paper, we propose a market prediction method (MPIL) by converting unstructured data and structured data into images. For market prediction, it analyzes SNS and news data, which is unstructured data for n days, and converts the market data, which is structured data, to an image with the GADF algorithm, and predicts an ultra-short market that predicts the price of n+1 days through image learning. MPIL has an average accuracy of 56%, which is higher than the 50% average accuracy of the model that predicts the market with LSTM by using sentiment analysis used for existing market forecasting.

• Journal of Navigation and Port Research
• /
• v.47 no.6
• /
• pp.376-385
• /
• 2023

Fishing boat capsizing accidents account for more than half of all capsize accidents. These can occur for a variety of reasons, including inexperienced operation, bad weather, and poor maintenance. Due to the size and influence of the industry, technological complexity, and regional diversity, fishing ships are relatively under-researched compared to commercial ships. This study aimed to predict the rolling motion time series of fishing boats using an image-based deep learning model. Image-based deep learning can achieve high performance by learning various patterns in a time series. Three image-based deep learning models were used for this purpose: Xception, ResNet50, and CRNN. Xception and ResNet50 are composed of 177 and 184 layers, respectively, while CRNN is composed of 22 relatively thin layers. The experimental results showed that the Xception deep learning model recorded the lowest Symmetric mean absolute percentage error(sMAPE) of 0.04291 and Root Mean Squared Error(RMSE) of 0.0198. ResNet50 and CRNN recorded an RMSE of 0.0217 and 0.022, respectively. This confirms that the models with relatively deeper layers had higher accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.2
• /
• pp.97-107
• /
• 2020

To analyze temporal and spatial changes in vegetation, it is necessary to determine the associated continuous distribution and conduct growth observations using time series data. For this purpose, the normalized difference vegetation index, which is calculated from optical images, is employed. However, acquiring images under cloud cover and rainfall conditions is challenging; therefore, time series data may often be unavailable. To address this issue, La et al. (2015) developed a multilinear simulation method to generate missing images on the target date using the obtained images. This method was applied to a small simulation area, and it employed a simple analysis of variables with lower constraints on the simulation conditions (where the environmental characteristics at the moment of image capture are considered as the variables). In contrast, the present study employs variables that reflect the growth characteristics of vegetation in a greater simulation area, and the results are compared with those of the existing simulation method. By applying the accumulated temperature, the average coefficient of determination (R²) and RMSE (Root Mean-Squared Error) increased and decreased by 0.0850 and 0.0249, respectively. Moreover, when data were unavailable for the same season, R² and RMSE increased and decreased by 0.2421 and 0.1289, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.28 no.2
• /
• pp.239-246
• /
• 2010

Satellite image has capability of getting a broad data rapidly. It is possible that acquisition of change information about topography, land, ecosystem and urbanization etc. from multi-temporal satellite Images. In this study, the time-series change of vegetation has detected using four period Landsat Imageries. Also, NDVI was used to recognize the vitality of vegetation. Time series change of vegetation about study area was able to detect effectively by the results of classification and NDVI. It is expected that this study should be utilized as the decision making related to the effective management and plan establishment.

• Journal of Korean Society for Geospatial Information Science
• /
• v.23 no.1
• /
• pp.123-128
• /
• 2015

In this study, experimented how to increase corresponding points which are obtained through stereo matching for dense 3D reconstruction. After extracting a snapshot image from the images acquired through stereo CCTVs, the matching points obtained using the SIFT matching and RANSAC procedure were gradually overlapped. In conclusion, it was confirmed that as images are overlapped, the number of matching points continues to grow.

• Korean Journal of Remote Sensing
• /
• v.22 no.5
• /
• pp.421-425
• /
• 2006

It can be important framework data to monitor the change of land-use pattern of river boundary in design and management of river. This study analyzed the change of land-use pattern of Gab and Yudeung River using time-series aerial images. To do this, we carried out radiation and geometric correction of image, and estimated land-use changes in inland and floodplain. As the analysis of inland, the ratio of residential, commercial, industrial, educational and public area, that is urbanized element, increases, but that of agricultural area shows a decline on the basis of 1990. Also, Minimum Distance Method, which is a kind of supervised classification method, is applied to extract water-body and sand bar layer in floodplain. As the analysis of land-use, the ratio of level-upped riverside land and water-body increases, but that of sand bar decreases. These time-series land use information can be important decision making data to evaluate the urbanization of river boundary, and especially it gives us goodness in river development project such as the composition of ecological habitat.

• Korean Journal of Remote Sensing
• /
• v.18 no.2
• /
• pp.117-125
• /
• 2002

Multitemporal analysis with remotely sensed data is complicated by numerous intervening factors, including atmospheric attenuation and occurrence of clouds that obscure the relationship between ground and satellite observed spectral measurements. Using an adaptive reconstruction system, dynamic compositing approach was developed to recover missing/bad observations. The reconstruction method incorporates temporal variation in physical properties of targets and anisotropic spatial optical properties into image processing. The adaptive system performs the dynamic compositing by obtaining a composite image as a weighted sum of the observed value and the value predicted according to local temporal trend. The proposed system was applied to the sequence of NDVI images of AVHRR observed on the Korean Peninsula from 1999 year to 2000 year. The experiment shows that the reconstructed series can be used as an estimated series with complete data for the observations including bad/missing values. Additionally, the gradient image, which represents the amount of temporal change at the corresponding time, was generated by the proposed system. It shows more clearly temporal variation than the data image series.