• Journal of the Korean Regional Science Association
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• v.34 no.4
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• pp.61-74
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• 2018

This study aims to analyze the convergence change of smart city industries in Korea. Industries of Smart city can be defined ICTs and Knowledge embedded construction industry. The input output model and structural path analysis have been done using the input output tables published by Bank of Korea in 1980 and 2014. GDP deflator was applied to the input output tables. 403 industries were reclassified into 27 industries and 8 industries categories: Agriculture and Mining(AM), Non-IT Manufacture(NITM), IT Manufacture(ITM), Energy Supply(EnS), Construction as smart city(C), IT Service(ITS), Knowledge Service(KS), Etc. Service(EtS). The results are as follows; First, the input output coefficient analysis showed that The information and communication service industry(ITS) and the energy supply industry(EnS) have increased input to the construction industry(C). On the other hands, knowledge service industry(KS) and etc. service industries(EtS) decreased. Second, the multiplier analysis revealed that construction industry(C) led by smart city had a great influence on ITS, EnS, ITM and NITM directly and indirectly. Furthermore, The IT industry had the biggest change from 1980 to 2014. Third, the smart city industry has created a new convergence of 117, and it has been leading to segmentation of the structure. Change of convergence has been proceeding mainly in the ITS and EnS, NITM, ITM industries.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.115-123
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• 2021

Pine nuts are Korea's representative nut forest products and profitable crops. However, pine nuts are harvested by climbing the trees themselves, thus the risk is high. In order to solve this problem, it is necessary to harvest pine nuts using a robot or an unmanned aerial vehicle(UAV). In this paper, we propose a deep learning based detection method for harvesting pine nut in UAV aerial images. For this, a video was recorded in a real pine forest using UAV, and a data augmentation technique was used to supplement a small number of data. As the data for 3D detection, Unity3D was used to model the virtual pine nut and the virtual environment, and the labeling was acquired using the 3D transformation method of the coordinate system. Deep learning algorithms for detection of pine nuts distribution area and 2D and 3D detection of pine nuts objects were used DeepLabV3+, YOLOv4, and CenterNet, respectively. As a result of the experiment, the detection rate of pine nuts distribution area was 82.15%, the 2D detection rate was 86.93%, and the 3D detection rate was 59.45%.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.337-351
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• 2022

With the full spread and rapid development of social media, the trend of decentralization of social media information propagation is becoming clearer day by day, and the segmentation of time by audiences using social media information is clearly progressing. Therefore, this study aims to study the influence relationship between social media attitudes toward fake news, social capital, risk perception, and discriminant intentions based on existing studies. Accordingly, the research model presented related research questions and organized a questionnaire to collect a total of 500 valid surveys. The SPSS 26.0 program and the AMOS 24.0 program were used to analyze the data. The research results are as follows. First, the more positive the user's attitude towards the fake news identification intention of social media, the more they want to use various methods or tools to identify the authenticity of online information. Second, the more positive the user's attitude towards social media fake news, the more aware of the potential threats social media fake news poses to their own physical, psychological, financial and so on. At the same time, by raising one's own awareness of the dangers, counterintelligence intentions against fake news on social media will also increase. Third, the richer the social capital the user has, the stronger the information literacy, and therefore the stronger the identification intention of social media fake news. Fourth, the higher the value of social capital Chinese users have, the greater the damage they have suffered from fake news, and the higher the risk awareness of fake news to protect their interests. Fifth, it means that Chinese users recognized information suspected of social media and took corresponding measures.

• Journal of the Korean Society of Radiology
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• v.16 no.2
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• pp.103-113
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• 2022

In this paper, a combined ¹⁸F-FDG(fluorodeoxyglucose) and MNP(magnetic nanoparticles) contrast agent was synthesized using N-(p-maleimidophenyl) isocyanate as the crosslinker for use in simultaneous PET-MRI scans. PET-MRI images were acquired and evaluated before and after injection of the combined contrast imaging agent (¹⁸F-FDG labeled MNP) from a glioma stem cell mouse model. After setting the region of interest (ROI) on each acquired image, the area of the lesion was calculated by segmentation. As a result, the PET image was larger than the MRI. In particular, the simultaneous PET-MRI images showed accurate lesions along with the surrounding soft tissue. The mean and standard deviation values were higher in the MRI images alone than in the PET images or the simultaneous PET-MRI images, regardless of whether the contrast agent was injected. In addition, the simultaneous PET-MRI image values were higher than for the PET images. For PSNR experiments, the original image was PET Image using 18F-FDG, MRI using MNPs, and MRI without contrast medium, and the target image was simultaneous PET-MRI image using 18F-FDG labeled MNPs contrast medium. As a result, all of them appeared significantly, suggesting that the 18F-FDG labeled MNPs contrast medium is useful. Future research is needed to develop an agent that can simultaneously diagnose and treat through SPECT-MRI imaging research that can use various nuclides.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.77-82
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• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.211-227
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• 2023

Personal service robots, a type of social robot that has emerged with the aging population and technological advancements, are undergoing a transformation centered around technologies that can extend independent living for older adults in their homes. For older adults to accept and use social robot innovations in their daily lives on a long-term basis, it is crucial to have a deeper understanding of user perspectives, contexts, and emotions. This research aims to comprehensively understand older adults by utilizing a mixed-method approach that integrates quantitative and qualitative data. Specifically, we employ the Van Kaam phenomenological methodology to group conversations into nine categories based on emotional cues and conversation participants as key variables, using voice conversation records between older adults and social robots. We then personalize the conversations based on frequency and weight, allowing for user segmentation. Additionally, we conduct profiling analysis using demographic data and health indicators obtained from pre-survey questionnaires. Furthermore, based on the analysis of conversations, we perform K-means cluster analysis to classify older adults into three groups and examine their respective characteristics. The proposed model in this study is expected to contribute to the growth of businesses related to understanding users and deriving insights by providing a methodology for segmenting older adult s, which is essential for the future provision of social robots with caregiving functions in everyday life.

• Asia Marketing Journal
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• v.7 no.1
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• pp.21-42
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• 2005

Customer Relationship Management (CRM) is a corporate marketing strategy maintaining and managing customers. And with CRM companies maximize the customer's value through a series of processes of new customer retention, VIP customer retention, customer value increase, potential customer activation, and customers for lifetime by collecting the customer information and taking advantage of it effectively. In particular, as the competitive environment is changing rapidly and getting more intense, maintaining the customer retention through customer churn management becomes more important in order to increase the customer value for maximizing the company's profit and to build up the relationship with customers. For example, the financial industry has managed the customer churn with the concept of customer segmentation. Recently the customer retention and churn management is becoming increasingly important in all business fields as well as financial industry since the companies expect the effect of preventing the customer churn by identifying characteristics of customers. However, despite the increasing interest and importance of the management of the customer churn, not many of studies are systematically executed by analyzing the data of customer churn. In this study we analyze the actual data of CRM activities for the customer retention, specifically the data of TV home-shopping. By doing so, we hope to identify the differences of demographic attributes and transaction specific characteristics in consumer behaviors between the churning customer and the retained customers. In addition, we try to find out the variables which can impact the churning of the customers and to predict the churn rate of individual customer through our proposed model of customer churn. In the end, based on our findings we suggest the possible marketing strategies for TV home-shopping companies.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1321-1330
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• 2023

This study attempts to address the problem of 3D pose estimation for multiple human objects through a single image generated during the character development process that can be used in augmented reality. In the existing top-down method, all objects in the image are first detected, and then each is reconstructed independently. The problem is that inconsistent results may occur due to overlap or depth order mismatch between the reconstructed objects. The goal of this study is to solve these problems and develop a single network that provides consistent 3D reconstruction of all humans in a scene. Integrating a human body model based on the SMPL parametric system into a top-down framework became an important choice. Through this, two types of collision loss based on distance field and loss that considers depth order were introduced. The first loss prevents overlap between reconstructed people, and the second loss adjusts the depth ordering of people to render occlusion inference and annotated instance segmentation consistently. This method allows depth information to be provided to the network without explicit 3D annotation of the image. Experimental results show that this study's methodology performs better than existing methods on standard 3D pose benchmarks, and the proposed losses enable more consistent reconstruction from natural images.

• Korean Journal of Radiology
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• v.21 no.12
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• pp.1345-1354
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• 2020

Objective: To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer's disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use. Materials and Methods: PubMed MEDLINE and EMBASE were searched using the terms 'cognitive impairment' or 'Alzheimer' or 'dementia' and 'radiomic' or 'texture' or 'radiogenomic' for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS. Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. Results: The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer's Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence. Conclusion: The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.