• Journal of the Korean Society of Radiology
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• v.82 no.6
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• pp.1477-1492
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• 2021

Purpose Dedicated breast CT is an emerging volumetric X-ray imaging modality for diagnosis that does not require any painful breast compression. To improve the detection rate of weakly enhanced lesions, an adaptive image rescaling (AIR) technique was proposed. Materials and Methods Two disks containing five identical holes and five holes of different diameters were scanned using 60/100 kVp to obtain single-energy CT (SECT), dual-energy CT (DECT), and AIR images. A piece of pork was also scanned as a subclinical trial. The image quality was evaluated using image contrast and contrast-to-noise ratio (CNR). The difference of imaging performances was confirmed using student's t test. Results Total mean image contrast of AIR (0.70) reached 74.5% of that of DECT (0.94) and was higher than that of SECT (0.22) by 318.2%. Total mean CNR of AIR (5.08) was 35.5% of that of SECT (14.30) and was higher than that of DECT (2.28) by 222.8%. A similar trend was observed in the subclinical study. Conclusion The results demonstrated superior image contrast of AIR over SECT, and its higher overall image quality compared to DECT with half the exposure. Therefore, AIR seems to have the potential to improve the detectability of lesions with dedicated breast CT.

• Journal of the Korean Society for Marine Environment & Energy
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• v.9 no.1
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• pp.1-13
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• 2006

A field survey on the spatio-temporal distribution characteristics and origins of organic matter in surface sediments was carried out monthly at six stations in Gamak Bay, South Korea from April 2000 to March 2002. The range of ignition loss(IL) was $4.6{\sim}11.6%(7.1{\pm}1.6%)$, while chemical oxygen demand(CODs) ranged from $12.25{\sim}99.26mgO_2/g-dry(30.98{\pm}19.09mgO_2/g-dry)$, acid volatile sulfide(AVS) went from no detection(ND)${\sim}10.29mgS/g-dry(1.02{\pm}0.58mgS/g-dry)$, and phaeopigment was $6.84{\sim}116.18{\mu}g/g-dry(23.72{\pm}21.16{\mu}g/g-dry)$. The ranges of particulate organic carbon(POC) and particulate organic nitrogen(PON) were $5.45{\sim}23.24 mgC/g-dty(10.34{\pm}4.40C\;mgC/g-dry)$ and $0.71{\sim}2.99mgN/g-dry(1.37{\pm}0.58mgN/g-dry)$, respectively. Water content was in the range of $43.1{\sim}77.6%(55.8{\pm}5.6%)$, and mud content(silt+clay) was higher than 95% at all stations. The spatial distribution of organic matter in surface sediments was greatly divided between the northwestern, central and eastern areas, southern entrance area from the distribution characteristic of their organic matters. The concentrations of almost all items were greater at the northwestern and southern entrance area than at the other areas in Gamak Bay. In particular, sedimentary pollution was very serious at the northwestern area, because the area had an excessive supply of organic matter due to aquaculture activity and the inflow of sewage from the land. These materials stayed longer because of the topographical characteristics of such as basin and the anoxic conditions in the bottom seawater environment caused by thermocline in the summer. The tendency of temporal change was most prominently in the period of high-water temperatures than low-water ones at the northwestern and southern entrance areas. On the other hand, the central and eastern areas did not show a regular trend for changing the concentrations of each item but mainly showed a higher tendency during the low-water temperatures. This was observed for all but AVS concentrations which were higher during the period of high-water temperature at all stations. Especially, the central and eastern areas showed a large temporal increase of AVS concentration during those periods of high-water temperature where the concentration of CODs was in excess of $20mgO_2/g-dry$. The results show that the organic matters in surface sediments in Gamak Bay actually originated from autochthonous organic matters with eight or less in average C/N ratio including the organic matters generated by the use of ocean, rather than terrigenous organic matters. However, the formation of autochthonous organic matter was mainly derived from detritus than living phytoplankton, indicated the results of the POC/phaeopigment ratio. In addition, the CODs/IL ratio results demonstrate that the detritus was the product of artificial activities such as dregs feeding and fecal pellets of farm organisms caused by aquaculture activities rather than the dynamic of natural ocean activities.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.