• Journal of the Korean Society of Radiology
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• v.82 no.1
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• pp.12-28
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• 2021

Mammography is the primary imaging modality for breast cancer detection; however, a high level of expertise is needed for its interpretation. To overcome this difficulty, artificial intelligence (AI) algorithms for breast cancer detection have recently been investigated. In this review, we describe the characteristics of AI algorithms compared to conventional computer-aided diagnosis software and share our thoughts on the best methods to develop and validate the algorithms. Additionally, several AI algorithms have introduced for triaging screening mammograms, breast density assessment, and prediction of breast cancer risk have been introduced. Finally, we emphasize the need for interest and guidance from radiologists regarding AI research in mammography, considering the possibility that AI will be introduced shortly into clinical practice.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.254-259
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• 2006

It is increased electrical energy consumption with the development of intelligence society in the subway station and thus an energy conservation through efficient use of electricity became more important. This paper shows a reasonable design load density in subway stations, that was made by the systematic and statistical way considering actual conditions, such as investigated electric equipment capacity, peak power consumption, demand factor, etc., for 34 subway stations and 10 electrical design offices. In this dissertation, it is necessary to analyse the key features and general trend from the investigated data. It made an analysis of the feature parameters, such as average, standard deviation, median, maximum, maximum and thus it was carried linear and nonlinear regression analysis.

• Journal of Powder Materials
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• v.26 no.1
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• pp.58-69
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• 2019

High performance lithium-ion batteries (LIBs) have attracted considerable attention as essential energy sources for high-technology electrical devices such as electrical vehicles, unmanned drones, uninterruptible power supply, and artificial intelligence robots because of their high energy density (150-250 Wh/kg), long lifetime (> 500 cycles), low toxicity, and low memory effects. Of the high-performance LIB components, cathode materials have a significant effect on the capacity, lifetime, energy density, power density, and operating conditions of high-performance LIBs. This is because cathode materials have limitations with respect to a lower specific capacity and cycling stability as compared to anode materials. In addition, cathode materials present difficulties when used with LIBs in electric vehicles because of their poor rate performance. Therefore, this study summarizes the structural and electrochemical properties of cathode materials for LIBs used in electric vehicles. In addition, we consider unique strategies to improve their structural and electrochemical properties.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.161-170
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• 2023

We attempted to estimate the carbon accumulation of Hibiscus hamabo and Paliurus ramosissimus, semimangroves native to Jeju Island, by remote sensing and to build an artificial intelligence model that predicts its spatial variation with climatic factors. The aboveground carbon accumulation of semi-mangroves was estimated from the aboveground biomass density (AGBD) provided by the Global Ecosystem Dynamics Investigation (GEDI) lidar upscaled using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images. In Jeju Island, carbon accumulation per unit area was 16.6 t C/ha for H. hamabo and 21.1 t C/ha for P. ramosissimus. Total carbon accumulation of semi-mangroves was estimated at 11.5 t C on the entire coast of Jeju Island. Random forest analysis was applied to predict carbon accumulation in semi-mangroves according to environmental factors. The deviation of aboveground biomass compared to the distribution area of semi-mangrove forests in Jeju Island was calculated to analyze spatial variation of biomass. The main environmental factors affecting this deviation were the precipitation of the wettest month, the maximum temperature of the warmest month, isothermality, and the mean temperature of the wettest quarter. The carbon accumulation of semi-mangroves predicted by random forest analysis in Jeju Island showed spatial variation in the range of 12.0 t C/ha - 27.6 t C/ha. The remote sensing estimation method and the artificial intelligence prediction method of carbon accumulation in this study can be used as basic data and techniques needed for the conservation and creation of mangroves as carbon sink on the Korean Peninsula.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4214-4230
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• 2020

This paper introduces a pilot allocation scheme for massive MIMO systems based on deep convolutional neural network (CNN) learning. This work is an extension of a prior work on the basic deep learning framework of the pilot assignment problem, the application of which to a high-user density nature is difficult owing to the factorial increase in both input features and output layers. To solve this problem, by adopting the advantages of CNN in learning image data, we design input features that represent users' locations in all the cells as image data with a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying proper rule, we construct output layers with a linear space complexity according to the number of users. We also develop a theoretical framework for the network capacity model of the massive MIMO systems and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which takes into account shift invariant characteristics. Through extensive simulation, we demonstrate that the proposed work realizes about a 98% theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.

• Parasites, Hosts and Diseases
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• v.54 no.4
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• pp.497-502
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• 2016

To know the infection status of zoonotic trematode metacercariae in brackish water fish, we surveyed mullets collected from 18 coastal areas in the Republic of Korea. The metacercariae of Pygidiopsis summa were detected in 236 (68.2%) out of 346 mullets examined. They were found in mullets from 15 areas except for those from Boseong-gun (Jeollanam-do), Pohang-si, and Uljin-gun (Gyeongsangbuk-do). Especially in mullets from Taean-gun (Chungcheongnam-do) and Geoje-si (Gyeongsangnam-do), their prevalences were 100% and 95.5%, and the average metacercarial density was more than 1,000 per fish. They were also detected in mullets from 3 coastal lakes, Gyeongpoho, Songjiho, and Hwajinpoho, in Gangwon-do, and their average densities were 419, 147, and 672 per infected fish, respectively. The metacercariae of 5 other heterophyid species, including Heterophyes nocens, Heterophyopsis continua, Metagonimus sp., Stictodora fuscata, and Stictodora lari, were found in the mullets examined. The metacercariae of H. nocens were detected in 66.7, 100, 28.6, 81.6, 3.9, 61.5, and 27.3% of mullets from Muan-gun, Shinan-gun, Haenam-gun, Gangjin-gun, and Boseong-gun (Jeollanam-do), Hadong-gun, and Geoje-si (Gyeongsangnam-do), and their metacercarial intensities were 64, 84, 119, 99, 1, 24, and 24 per fish infected, respectively. From the above results, it has been confirmed that P. summa metacercariae are heavily infected in mullets from coastal areas of Korea. It is suggested that residents who frequently consume raw mullet dish can be easily infected with heterophyid flukes.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2018.06a
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• pp.102-102
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• 2018

Electronic industry had required the finer size and the higher performance of the device. Therefore, 3-D die stacking technology such as TSV (through silicon via) and micro-bump had been used. Moreover, by the development of the 3-D die stacking technology, 3-D structure such as chip to chip (c2c) and chip to wafer (c2w) had become practicable. These technologies led to the appearance of HBM (high bandwidth memory). HBM was type of the memory, which is composed of several stacked layers of the memory chips. Each memory chips were connected by TSV and micro-bump. Thus, HBM had lower RC delay and higher performance of data processing than the conventional memory. Moreover, due to the development of the IT industry such as, AI (artificial intelligence), IOT (internet of things), and VR (virtual reality), the lower pitch size and the higher density were required to micro-electronics. Particularly, to obtain the fine pitch, some of the method such as copper pillar, nickel diffusion barrier, and tin-silver or tin-silver-copper based bump had been utillized. TCB (thermal compression bonding) and reflow process (thermal aging) were conventional method to bond between tin-silver or tin-silver-copper caps in the temperature range of 200 to 300 degrees. However, because of tin overflow which caused by higher operating temperature than melting point of Tin ($232^{\circ}C$), there would be the danger of bump bridge failure in fine-pitch bonding. Furthermore, regulating the phase of IMC (intermetallic compound) which was located between nickel diffusion barrier and bump, had a lot of problems. For example, an excess of kirkendall void which provides site of brittle fracture occurs at IMC layer after reflow process. The essential solution to reduce the difficulty of bump bonding process is copper to copper direct bonding below $300^{\circ}C$. In this study, in order to improve the problem of bump bonding process, copper to copper direct bonding was performed below $300^{\circ}C$. The driving force of bonding was the self-annealing properties of electrodeposited Cu with high defect density. The self-annealing property originated in high defect density and non-equilibrium grain boundaries at the triple junction. The electrodeposited Cu at high current density and low bath temperature was fabricated by electroplating on copper deposited silicon wafer. The copper-copper bonding experiments was conducted using thermal pressing machine. The condition of investigation such as thermal parameter and pressure parameter were varied to acquire proper bonded specimens. The bonded interface was characterized by SEM (scanning electron microscope) and OM (optical microscope). The density of grain boundary and defects were examined by TEM (transmission electron microscopy).

• Sleep Medicine and Psychophysiology
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• v.9 no.1
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• pp.18-23
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• 2002

Alexithymia represents deficits in the cognitive processing and regulation of emotions. It is observed in many cases of psychosomatic disease, anorexia nervosa, panic disorder, depression etc. Many studies have shown that alexithymia is associated with maladaptive styles of emotion regulation, low emotional intelligence, interhemispheric transfer deficit, and reduced rapid eye movement density. Psychotherapies that enhance emotional awareness may be effective in alleviating the difficulties of alexithymic individuals. Aexithymia is useful for constructing the role of personality and emotions in the pathogenesis of psychiatric disorders. It may serve as a bridge between neurobiology and psychology. We review recent alexithymia theory and research and their implications for treatment of psychosomatic disorders.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.537-539
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• 2019

Recently, large-scale fires have been generated as urban buildings have become more and more density. Especially, the expansion of smoke in buildings due to high-rise is an problem, and the smoke is the main cause of death in fires. Therefore, in this paper, the image-based smoke detection is proposed through deep learning-based artificial intelligence techniques to prevent possible damage if existing detectors are not detected. In addition, the detection model was not configured simply through only the smoke data set, but the data set in the haze form was additionally composed together to compensate for the accuracy.