• Proceedings of the Korean Society for Quality Management Conference
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• 2006.04a
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• pp.86-91
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• 2006

Companies had to be more intelligent in order to survive in the rapidly changing environments. We need to make a decision to build the Information System to support their decision making. But, how can we know the new system would be better than the old system in making us intelligent? The answer is we can do it with the concept of Intelligence Density. In this study, Intelligence Density concept will be introduced, and the way how it can be applied to the information system will be presented. I think Intelligence Density should be studiedmoretohelpmanagersmakerightdecisions.

• Journal of Korean Society for Quality Management
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• v.34 no.3
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• pp.31-40
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• 2006

Companies had to be more intelligent in order to survive in the rapidly changing environments. We need to make a decision to build the Information System to support the managers in their decision making. That is the reason many companies are tend to have Business Intelligence Systems. But, how can we know the new system would be better than the old system in making us intelligent? The answer is we can do it with the concept of Intelligence Density. In this study, Intelligence Density concept will be introduced, and the way how it can be applied to the information system will be presented. I think Intelligence Density should be studied more to help managers make right decisions for the DSS implementation.

• Korean Journal of Radiology
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• v.24 no.9
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• pp.849-859
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• 2023

Objective: The prognostic value of the volume and density of skeletal muscles in the abdominal waist of patients with colon cancer remains unclear. This study aimed to investigate the association between the automated computed tomography (CT)-based volume and density of the muscle in the abdominal waist and survival outcomes in patients with colon cancer. Materials and Methods: We retrospectively evaluated 474 patients with colon cancer who underwent surgery with curative intent between January 2010 and October 2017. Volumetric skeletal muscle index and muscular density were measured at the abdominal waist using artificial intelligence (AI)-based volumetric segmentation of body composition on preoperative pre-contrast CT images. Patients were grouped based on their skeletal muscle index (sarcopenia vs. not) and muscular density (myosteatosis vs. not) values and combinations (normal, sarcopenia alone, myosteatosis alone, and combined sarcopenia and myosteatosis). Postsurgical disease-free survival (DFS) and overall survival (OS) were analyzed using univariable and multivariable analyses, including multivariable Cox proportional hazard regression. Results: Univariable analysis showed that DFS and OS were significantly worse for the sarcopenia group than for the non-sarcopenia group (P = 0.044 and P = 0.003, respectively, by log-rank test) and for the myosteatosis group than for the non-myosteatosis group (P < 0.001 by log-rank test for all). In the multivariable analysis, the myosteatotic muscle type was associated with worse DFS (adjusted hazard ratio [aHR], 1.89 [95% confidence interval, 1.25-2.86]; P = 0.003) and OS (aHR, 1.90 [95% confidence interval, 1.84-3.04]; P = 0.008) than the normal muscle type. The combined muscle type showed worse OS than the normal muscle type (aHR, 1.95 [95% confidence interval, 1.08-3.54]; P = 0.027). Conclusion: Preoperative volumetric sarcopenia and myosteatosis, automatically assessed from pre-contrast CT scans using AI-based software, adversely affect survival outcomes in patients with colon cancer.

• Journal of Korean Academy of Nursing
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• v.53 no.1
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• pp.55-68
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• 2023

Purpose: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. Methods: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.698-701
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• 2005

We have investigated experimentally a nano patterning using electron beam lithography for the nickel stamper fabrication. Recently, DVD and Blu-ray disk(BD) have nano-scale patterns in order to increase the storage density. Specially, BD has 100nm-scale patterns which are generally fabricated by electron beam lithography. In this paper, we found optimum condition of electron-beam lithography for 100nm-scale patterning. We controlled various conditions of EHP(acceleration voltage), beam current, dose and aperture size in order to obtain optimum conditions. We used 100nm-thick PMMA layer on a silicon wafer as photoresist. We found that EHP was the most dominant factor in electron-beam lithography.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.155-159
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• 2022

After artificial intelligence was trained with the data on the concentration of essential mineral elements in hair, the age was predicted by the concentration of mineral elements in the hair of the subject, and the result was compared with the actual age of the subject, and the correlation was investigated. The total number of hair data was 296, of which 2/3 were used for AI learning and 1/3 was used as the subject data. There was a correlation of 0. 678 between the actual age of the young subjects under the age of 25 and the age predicted by the AI. There was almost no correlation in the middle-aged subjects group, and there was a weak correlation of 0.522 in the elderly subject group. In order to secure the usefulness of artificial intelligence using hair mineral element concentration data, it is necessary to provide a larger number of data to the artificial intelligence.

• ETRI Journal
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• v.34 no.5
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• pp.771-774
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• 2012

Typical channel allocation algorithms for secondary users do not include processes to reduce the frequency of switching from one channel to another caused by random interruptions by primary users, which results in high packet drops and delays. In this letter, with the purpose of decreasing the number of switches made between channels, we propose a nonparametric channel allocation algorithm that uses robust kernel density estimation to effectively schedule idle channel resources. Experiment and simulation results demonstrate that the proposed algorithm outperforms both random and parametric channel allocation algorithms in terms of throughput and packet drops.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.147-153
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• 2023

In recent times, an absence of effective crowd management has led to numerous stampede incidents in crowded places. A crucial component for enhancing on-site crowd management effectiveness is the utilization of crowd counting technology. Current approaches to analyzing congested scenes have evolved beyond simple crowd counting, which outputs the number of people in the targeted image to a density map. This development aligns with the demands of real-life applications, as the same number of people can exhibit vastly different crowd distributions. Therefore, solely counting the number of crowds is no longer sufficient. CSRNet stands out as one representative method within this advanced category of approaches. In this paper, we propose a crowd counting network which is adaptive to the change in the density of people in the scene, addressing the performance degradation issue observed in the existing CSRNet(Congested Scene Recognition Network) when there are changes in density. To overcome the weakness of the CSRNet, we introduce a system that takes input from the image's information and adjusts the output of CSRNet based on the features extracted from the image. This aims to improve the algorithm's adaptability to changes in density, supplementing the shortcomings identified in the original CSRNet.

• Journal of Magnetics
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• v.16 no.4
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• pp.431-434
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• 2011

The reduction of the Nb content in the $(Fe_{0.75}B_{0.20}Si_{0.05})_{96}Nb_4$ bulk metallic glass (BMG) has been studied. The glass-forming ability (GFA) is reduced by decreasing the Nb content, but it can be enhanced by replacing partially Fe by Co. Furthermore, the saturation magnetization of the $(Fe_{0.8}Co_{0.2})_{76}B_{18}Si_3Nb_3$ BMG is 1.35 T, being with 13% larger than that of the base alloy $(Fe_{0.75}B_{0.20}Si_{0.05})_{96}Nb_4$. $(Fe_{0.8}Co_{0.2})_{76}B_{18}Si_3Nb_3$ BMG exhibits slightly larger $B_{800}$ (the magnetic flux density at 800 A/m) and smaller core losses (20%-30%) compared with the commercial Fe-6.5 mass% Si steel.

• 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.