• 한국사회정책
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• v.23 no.3
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• pp.79-108
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• 2016

This study is focused on the historical route in which reconciliation between work and family has been operating in the United States whose welfare standards are low, by using analytic narratives, from late 19th century to early 20th century. The first step saw an increase in the social advancement of unmarried women due to the increase in employment in the occupation of white-collar, as well as the increase of women in the Academy and in educational institutions. In the second step, the social advancement of married women was increased by the enforcement of the New Deal, consumption capitalism, and World War II. In the third step, the sphere of highly-educated women was expanded to a professional one through active measures aimed at gender equality, and the women's liberation movement of the 1960s took place. The United States' path model was completed in the fourth step. This study show that the most important factors have a significant influence to complete route of the American's Route were individual efforts of women (capacity building through the academic and the powerful women's movement) and the individual employment relationship in labor market. This study shows that individual efforts of women, especially in building capacity through the academic and the powerful women's movement, and the labor market, whose individual employment relationship is well-developed, have a significant influence on completing the route of the highly educated middle-class women in America.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.646-661
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• 2020

Technological innovations and fast-growing new internet businesses are changing the paradigm of traditional business management, having various impacts on society. The development of internet technology is also increasing the adverse effects on technological innovation, and in particular, cybercrime related to computers continues to increase with each technological innovation. The purpose of this study is to construct a cybercrime business model (CBM) by using the business model canvas (BMC) theory for cybercrime in order to reduce cybercrime, and this model is applied and analyzed based on types of Korean cybercrimes. For this study, a systematic literature review was conducted to determine the components of cybercrime, and 60 relevant documents were classified through a keyword-based literature search. Besides, qualitative research in the classified literature has led to the derivation of cybercrime into 18 sub-blocks and nine building blocks. This study applies BMC theory to this derivation of cybercrime and builds the CBM through proper redefinition. Lastly, the developed CBM could be applied to cybercrime in Korea to help cyber incident-response staff understand cybercrimes analytically. This study contributes to the development of a new analysis framework that can reduce cybercrime.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.392-399
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• 2013

Due to the development of IT technology and the information age, a dependency of the network over the most of our lives have grown to a greater extent. Although it provides us to get various useful information and service, it also has negative effectiveness that can provide network intruder with vulnerable roots. In other words, we need to urgently cope with theses serious security problem causing service disableness or system connected to network obstacle with exploiting various packet information. Many experts in a field of security are making an effort to develop the various security solutions to respond against these threats, but existing solutions have a lot of problems such as lack of storage capacity and performance degradation along with the massive increase of packet data volume. Therefore we propose the packet analysis model to apply issuing Big Data technology in the field of security. That is, we used NoSQL which is technology of massive data storage to collect the packet data growing massive and implemented the packet analysis model based on K-means clustering using MapReudce which is distributed programming framework, and then we have shown its high performance by experimenting.

• Communications of Mathematical Education
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• v.30 no.4
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• pp.499-514
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• 2016

As people get to aware that the traditional teacher-centered education can not develop individual students' diversity and creativity and cope with the rapidly changing future society, Korean government has emphasized the learner-centered education since the 7th curriculum. Under this background, we have analyzed the problems of mathematics education that teachers recognized and the features of mathematics textbooks that they developed within the framework of leaner-centered education on the basis of the resources developed from 'Student-centered mathematics textbook improvement teacher research group in 2015.' As a result of using the framework of 'Learner-centered psychological principles (APA, 1997)' for analysis, teachers pointed out the problems related to the principles of Motivational and emotional influences on learning, Individual differences in learning, Developmental influences on learning, Nature of the learning process, and Construction of knowledge, in order. The features of textbook teachers developed reflected the principles of Nature of the learning process, Construction of knowledge, and Motivational and emotional influences on learning, in order. Finally, as we have compared teachers' recognition of the problems with the features of the textbooks developed, most of the problems teachers recognized are reflected in the textbooks; however, the Cognitive and metacognitive factor takes higher possession on the textbooks compared with the problems being recognized, and the Motivational and affective factor takes lower possession on the textbooks compared with the problems being recognized. Accordingly, we have been able to search for the solution to realize the learner-centered education through math textbooks.

• Cartoon and Animation Studies
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• s.36
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• pp.643-667
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• 2014

Recently, shooting photographs have become highly popular among general public and been used by various media such as digital camera, mobile, and kiosk. We could find that users prefer to Funology which is combined by fun and hardware technology on emotional point of view. Shooting photographs attracts user participation and makes effect of design to expand. The goal of this study is to classify user actions in a electronic kiosk which includes digital photography function based on the perspective of Funology and to bulit user behaviors model. Therefore user group model will be defined, and then interaction design guidelines of shooting photographs will be proposed. For this research, first of all, the concepts of Funology and user interaction with taking photographs are classified to three types which is based on literature investigation. Secondly, "Phodosee" kiosk is examined with Funology design elements which have been categorized beforehand. Then user's behaviors which are shown their interaction with "Phodosee" kiosk are observed and analyzed using video ethnography based on Funology perspectives. Finally, four persona models are suggested based on user's behaviors as follows; 1) to avoid being taken photography, 2) to try to shoot photography, 3) to participate shooting photography and 4) to lead others to take photography. To summarize this study, effects and limitations of Funology design elements using digital photography are discussed and guideline is suggested to improve user experience design.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.6
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• pp.185-195
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• 2017

In recent years, unexpected bridge fire accidents have increased because of augmenting the number of traffic volumes and hazardous materials by the increment in traffics and distribution business. Furthermore, in accordance with the effort of using the under space of bridges, the ratio of occupied by combustible materials like oil tanker or lorry has been increased. As a result, the occurrence of bridge fire has been growing drastically. In order to mitigate the accident of bridge fire, risk assessment of bridge fire has been studied, however, practical risk models considering safety from users' viewpoints were scarce. This study represented quantitative risk assessment model applicable to national road bridges in Korea. The primary factors with significant impacts on bridge fire accidents was chosen such as clearance height, materials of bridges, arrival time of fire truck and fire intensity. The selected factors were used for Fire Dynamics Simulation (FDS) and the peak temperature calculated by FDS in accordance with the fire duration and fire intensity. The risk assessment model in bridge fire reflected the FDS analysis results, the fire damage criteria, and the grade of fire truck arrival time was established. Response plans for bridge fire accidents according to the risk assessment output has been discussed. Lastly, distances between bridges and fire stations were calculated by GIS network analysis. Based on the suggested assessment model and methodology, sample bridges were selected and graded for the risk assessment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.5
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• pp.163-171
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• 2011

In e-commerce environment, security should be considered as an essential factor for success. In this paper, we analyze security requirements for e-commerce system, and it is focused on the practical usage, not theoretical contribution, in the field of e-commerce security. To identify the security requirements being specific to e-commerce environment, the researches related to e-commerce security are surveyed and a phase of Delphi method and Analytic Hierarchy Process(AHP) are used to determine the relative importance of e-commerce security factors. Since researchers and practitioners can have significantly different views because of each different work environment, we divide the professionals into two respondents' group. This survey result can be useful security guidelines in the development of e-commerce service system from the initial system development step to the completion.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.2
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• pp.169-176
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• 2015

The mechanical properties of living tissues have been major subjects of interest in biomechanics. In particular, the characteristics of very soft materials such as the brain have not been fully understood because experiments are often severely limited by ethical guidelines. There are increasing demands for studies on remote medical operations using robots. We conducted compression tests on brain-like specimens made of gelatin to find substitutes with the mechanical properties of brain tissues. Using a finite element analysis, we compared our experimental data with existing data on the brain in order to establish material models for brain tissues. We found that our substitute models for brain tissues effectively simulated their mechanical behaviors.