• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.33-40
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• 2021

Automatic Speech Recognition(ASR) is a technology that analyzes human speech sound into speech signals and then automatically converts them into character strings that can be understandable by human. Speech recognition technology has evolved from the basic level of recognizing a single word to the advanced level of recognizing sentences consisting of multiple words. In real-time voice conversation, the high recognition rate improves the convenience of natural information delivery and expands the scope of voice-based applications. On the other hand, with the active application of speech recognition technology, concerns about related cyber attacks and threats are also increasing. According to the existing studies, researches on the technology development itself, such as the design of the Automatic Speaker Verification(ASV) technique and improvement of accuracy, are being actively conducted. However, there are not many analysis studies of attacks and threats in depth and variety. In this study, we propose a cyber attack model that bypasses voice authentication by simply manipulating voice frequency and voice speed for AI voice recognition service equipped with automated identification technology and analyze cyber threats by conducting extensive experiments on the automated identification system of commercial smartphones. Through this, we intend to inform the seriousness of the related cyber threats and raise interests in research on effective countermeasures.

• Journal of Industrial Convergence
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• v.20 no.5
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• pp.39-59
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• 2022

We analyze the perceptions and requirements of early childhood teachers for artificial intelligence(AI) education to develop an AI education program for 5-year-olds. As for the research methodology, we conducted a survey and an in-depth interview to extract the AI educational elements centering on the analysis stage, the first stage of the ADDIE model. The research result is that first, it is necessary to design a curriculum that combines the contents of early childhood education and AI education to be naturally accepted as AI education for 5-year-olds. Second, an evaluation tool for AI education that can showcase the teacher's reflection should be developed systematically. Third, it is necessary to support a play-centered AI education support and environment for early childhood teachers. Lastly, it is essential to establish a system that can be continuously operated in the field of early childhood education in consideration of AI education in the non-curricular curriculum. It is expected that in the future, a play-oriented AI education program for 5-year-olds will be developed to spread awareness of AI education for infants and present an AI education approach for each age and stage of learners.

• Journal of Korean Home Economics Education Association
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• v.34 no.2
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• pp.153-166
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• 2022

The purpose of this study is to develop and evaluate a dietary education program to improve the caring ability and multicultural acceptance of middle school students. Based on the instructional system design of ADDIE model, the dietary education program was developed to contain five sessions including four theoretical lectures and one lab session. Visual thinking technique was used to train students to express their thoughts and emotion by writing and drawing. The dietary education program was conducted for four weeks (from November 19 to December 14, 2018) at a middle school located in Seoul on a total of 69 middle school students, out of which 34 were assigned to an experimental group and 35 were assigned to a control group. Separate paired t-test were conducted for the experimental group and the control group, respectively, to determine the changes in caring ability and multicultural acceptance scores before and after the dietary education. There were significant increases in caring ability (dietary-, emotional-, behavioral- and cognitive caring) and multicultural acceptance (diversity, relationship and universality) scores among the experimental group after the dietary program. However, no differences were observed among the control group. The results indicate that the dietary education program can be an effective tool to improve caring ability and multicultural acceptance of middle school students.

• Journal of Wetlands Research
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• v.24 no.2
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• pp.115-122
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• 2022

As the impermeable area of soil increases due to urbanization, the water circulation system of the city is deteriorating. The existing guidelines for low impact development (LID) facilities installed to solve these water problems or in previous studies, engineering aspects are more prominent than landscaping aspects. This study attempted to present an engineering and landscaping model for reducing pollutants by identifying the effects of vegetation on rainfall outflows and pollutant reduction in bioretention and the economic aspects of planting. Based on the results of artificial rainfall monitoring at Jeonju Seogok Park and the literature on vegetation rainfall runoff and pollutant reduction performance, the best vegetation for reducing pollution compared to cost was Lythrum salicaria L and Salix gracilistyla Miq. was the best vegetation for carbon storage. If you insist to design plants with only these two plantation, there is no choice but to take risks such as biodiversity. Herbaceous plants such as Lythrum salicaria L can be replaced by death of the plants or pests if considered planting various plants. The initial planting cost could expensive, but it is also necessary to mix and plant Salix gracilistyla Miq, which are woody plants that are advantageous in terms of maintenance, according to the surrounding environment and conditions. Based on the conclusions drawn in this study, it can be a reference material when considering the reduction of pollution by species and carbon storage of vegetation in LID facilities.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

• Explosives and Blasting
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• v.40 no.4
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• pp.15-26
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• 2022

With the recent increase in old and dangerous buildings, the demand for technology in the field of structure demolition is rapidly increasing. In particular, in the case of structures with severe deformation of damage, there is a risk of deterioration in stability and disaster due to changes in the load distribution characteristics in the structure, so rapid structure demolition technology that can be efficiently dismantled in a short period of time is drawing attention. However, structural deformation such as unauthorized extension or illegal remodeling occurs frequently in many old structures, which is not reflected in structural information such as building drawings, and acts as an obstacle in the demolition design process. In this study, as an effective way to overcome the discrepancy between the structural information of old structures and the actual structure, access to actual structures through 3D modeling was considered. 3D point cloud data inside and outside the building were obtained through LiDAR and drone photography for buildings scheduled to be blasting demolition, and precision matching between the two spatial data groups was performed using an open-source based spatial information construction system. The 3D structure model was completed by importing point cloud data matched with 3D modeling software to create structural drawings for each layer and forming each member along the structure slab, pillar, beam, and ceiling boundary. In addition, the modeling technique proposed in this study was verified by comparing it with the actual measurement value for selected structure member.

• Journal of the Korean Geotechnical Society
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• v.25 no.7
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• pp.35-46
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• 2009

Pressure grouting is a common technique in geotechnical engineering to increase the stiffness and strength of the ground mass and to fill boreholes or void space in a tunnel lining and so on. Recently, the pressure grouting has been applied to a soil-nailing system which is widely used to improve slope stability. The soil-nailing design has been empirically performed in most geotechnical applications because the interaction between pressurized grouting paste and the adjacent ground mass is complicated and difficult to analyze. The purpose of this study is to analyze the increase of pullout resistance induced by pressurized grouting with the aid of performing laboratory model tests and field tests. In this paper, two main causes of pullout resistance increases induced by pressurized grouting were verified: the increase of mean normal stress and the increase of coefficient of pullout friction. From laboratory tests, it was found that dilatancy angle could be estimated by modified cavity expansion theory using the measured wall displacements. The radial displacement increases with dilatancy angle decrease and the dilatancy angle increases with injection pressure increase. The measured pullout resistance obtained from field tests is in good agreement with the estimated one from the modified cavity expansion theory.

• The Korean Journal of Franchise Management
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• v.8 no.4
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• pp.21-32
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• 2017

Purpose - In hotel business, how to build the relationship between leader and employees is very important, because it affects on the customer satisfaction. Thus, this research examines the effect of authentic leadership on job performance in the context of hotel industry and identifies mediating roles of organizational engagement, job satisfaction, and creativity in the relationship between authentic leadership and job performance. This study suggests the guidelines for how hotel companies should improve employee productivity and build a desirable organizational culture by presenting employee attitudes and behavioral models that explain the relationship between leaders and employees. Research design, data, and methodology - This study examines the structural relationship between authentic leadership, organizational engagement, job satisfaction, creativity, and job performance from the employee's perspective. Authentic leadership divide into four sub-dimensions such as self-awareness, balanced process of informations, internalized moral perspective, and relational transparency. In order to test the purposes of this study, research model and hypotheses were developed. All constructs were measured with multiple items developed and tested in the previous studies. The data were collected from 114 franchise hotel employees and were analyzed using SPSS 21.0 and Smart PLS 3.0. program. Result - The results of this study are as follows. First, authentic leadership have significant impacts on organizational engagement and creativity, but does not have impact on job satisfaction directly. Second, organizational engagement have significant impacts on job satisfaction and job performance, but does not have impact on creativity directly. Third, job satisfaction has significant impact on creativity, but does not have impact on job performance. Fourth, creativity has significant impact on job performance. Conclusions - The findings of this study indicate that hotel leaders should properly implement the authentic leadership and consider how to build a corporate culture to improve an organizational and employee productivity through authentic leadership. Due to the nature of the hotel industry, which relies heavily on human resources, hotel companies must manage their employees with authenticity in order to increase organizational engagement, job satisfaction, and creativity that affect hotel and employee productivity. If hotel employees perceive their leader's authentic leadership, they show more organizational engagement that increases creativity and leads to job performance. Finally, hotel employees can propose creative ideas only if they will be satisfied with their jobs. Therefore, the leader should develop non-monetary or monetary reward system for the employees and, make an efforts to foster creativity of the employees.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.137-144
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• 2023

Cyberattack technology is evolving to an unpredictable degree, and it is a situation that can happen 'at any time' rather than 'someday'. Infrastructure that is becoming hyper-connected and global due to cloud computing and the Internet of Things is an environment where cyberattacks can be more damaging than ever, and cyberattacks are still ongoing. Even if damage occurs due to external influences such as cyberattacks or natural disasters, intelligent self-recovery must evolve from a cyber resilience perspective to minimize downtime of cyber assets (OS, WEB, WAS, DB). In this paper, we propose an intelligent self-recovery technology to ensure sustainable cyber resilience when cyber assets fail to function properly due to a cyberattack. The original and updated history of cyber assets is managed in real-time using timeslot design and snapshot backup technology. It is necessary to secure technology that can automatically detect damage situations in conjunction with a commercialized file integrity monitoring program and minimize downtime of cyber assets by analyzing the correlation of backup data to damaged files on an intelligent basis to self-recover to an optimal state. In the future, we plan to research a pilot system that applies the unique functions of self-recovery technology and an operating model that can learn and analyze self-recovery strategies appropriate for cyber assets in damaged states.

• Korean Journal of Radiology
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• v.22 no.8
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• pp.1240-1252
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• 2021

Objective: To compare the accuracy for detecting breast cancer in the diagnostic setting between the use of digital breast tomosynthesis (DBT), defined as DBT alone or combined DBT and digital mammography (DM), and the use of DM alone through a systematic review and meta-analysis. Materials and Methods: Ovid-MEDLINE, Ovid-Embase, Cochrane Library and five Korean local databases were searched for articles published until March 25, 2020. We selected studies that reported diagnostic accuracy in women who were recalled after screening or symptomatic. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to estimate pooled sensitivity and specificity. We compared the diagnostic accuracy between DBT and DM alone using meta-regression and subgroup analyses by modality of intervention, country, existence of calcifications, breast density, Breast Imaging Reporting and Data System category threshold, study design, protocol for participant sampling, sample size, reason for diagnostic examination, and number of readers who interpreted the studies. Results: Twenty studies (n = 44513) that compared DBT and DM alone were included. The pooled sensitivity and specificity were 0.90 (95% confidence interval [CI] 0.86-0.93) and 0.90 (95% CI 0.84-0.94), respectively, for DBT, which were higher than 0.76 (95% CI 0.68-0.83) and 0.83 (95% CI 0.73-0.89), respectively, for DM alone (p < 0.001). The area under the summary receiver operating characteristics curve was 0.95 (95% CI 0.93-0.97) for DBT and 0.86 (95% CI 0.82-0.88) for DM alone. The higher sensitivity and specificity of DBT than DM alone were consistently noted in most subgroup and meta-regression analyses. Conclusion: Use of DBT was more accurate than DM alone for the diagnosis of breast cancer. Women with clinical symptoms or abnormal screening findings could be more effectively evaluated for breast cancer using DBT, which has a superior diagnostic performance compared to DM alone.