• Korean Journal of Human Ecology
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• v.21 no.3
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• pp.453-468
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• 2012

The number of Healthy Family Support Centers has dramatically increased during the past eight years since the Framework Act on Healthy Families was enacted. This phenomenal growth is largely credited to Certified Healthy Family Specialists (CHFSs). Despite their contributions, the job and working conditions of the CHFSs have rarely been explored from the insiders' perspective. In this study, we aim to delineate CHFSs' job and working conditions from their own narratives in order to improve an understanding of CHFSs' profession and work environment. We conducted in-depth interviews with nine CHFSs and a focus-group interview with five CHFSs. Our findings revealed that CHFSs took pride in their professions, internalized their professional mission of enhancing family strengths, and highlighted CHFSs' unique professional role in comparison to other human services professionals. In conclusion, CHFSs showed a strong professional identity consisting of rich professional knowledge, solid career goals, and integrated socio-political values. Contrary to the positive perception of the CHFSs' job, CHFSs expressed challenges in their working conditions in terms of small-scale organizations at local Healthy Family Support Centers, a heavy workload, hierarchical relationships with local government officers, and the unsatisfactory payroll and promotion system. This study contributes to a better understanding of CHFSs' job and their working conditions and provides insights on how to enhance professionalism among CHFSs and their work environment. As for policy implications, we suggest advancing qualifications for CHFSs, improving professional training programs for current CHFSs, and expanding small-scale organizations.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.21-30
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• 2007

Simulation means modeling structures or behaviors of the various objects, and experimenting them on the computer system. And the major approaches are DEVS(Discrete Event Systems Specification). Petri-net or Automata and so on. But, the simulation problems are getting more complex or complicated these days, so that an intelligent agent-based is being studied. In this paper, we are describing an intelligent agent-based simulation tool, which can supports the simulation experiment more efficiently. The significances of our system can be described as follows. First, the system can provide some AI algorithms through the system libraries. Second, the system supports simple method of designing the simulation model, since it's been built under the Finite State Machine (FSM) structure. And finally, the system acts as a simulation framework by supporting user not only the simulation engine, but also user-friendly tools, such as modeler scriptor and simulator. The system mainly consists of main simulation engine, utility tools, and some other assist tools, and it is tested and showed some efficient results in the three different problems.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.485-492
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• 2001

Recently, character agents are used as a user-friendly interface. In this paper, we have studied a generic framework for emotional character agents which are designed to infer emotions from diverse personalities, situations, user behaviors and to express them. The method of emotion inference is based on blackboard systems which are used to solve the problems in AI. Because it keeps independence between knowledge sources which are rules of emotions, a blackboard-based inference engine is easy to manage knowledge sources, Blackboard-based systems gave the system flexibility. So we can adapt the engine to various application systems. Each emotional agent monitors user behavior, learns user profile and infers user behavior. And it generates characters emotions according to the user profile. So, in case of same situations, the agent can generate different emotions according to users. We have studied to build an personalized emotional character agent which according to situations and user modeling.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.165-171
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• 2018

The most threatening attack that has become a hot topic of recent IT security is APT Attack.. So far, there is no way to respond to APT attacks except by using artificial intelligence techniques. Here, we have implemented a machine learning algorithm for analyzing cyber threat data using machine learning method, using a data set that collects cyber attack cases using Scikit Learn, a big data machine learning framework. The result showed an attack classification accuracy close to 70%. This result can be developed into the algorithm of the security control system in the future.

• Informatization Policy
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• v.27 no.4
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• pp.47-66
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• 2020

The rapid development of artificial intelligence technologies such as machine learning and deep learning is expanding its impact in the public administrative and public policy sphere. This paper is an exploratory study on policy decision-making in the age of artificial intelligence to design automated configuration and operation through data analysis and algorithm development. The theoretical framework was composed of the types of policy problems according to the degree of problem structuring, and the success and failure cases were classified and analyzed to derive implications. In other words, when the problem structuring is more difficult than others, the greater the possibility of failure or side effects of decision-making using artificial intelligence. Also, concerns about the neutrality of the algorithm were presented. As a policy suggestion, a subcommittee was proposed in which experts in technical and social aspects play a professional role in establishing the AI promotion system in Korea. Although the subcommittee works independently, it suggests that it is necessary to establish governance in which the results of activities can be synthesized and integrated.

• The Korean Journal of Franchise Management
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• v.11 no.4
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• pp.31-44
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• 2020

Purpose: The purpose of this study is to examine the effects of CEO's self-determination on entrepreneurship, business performance (operational and financial performance). Also, this research provide some strategic insights for improving business performance. In the proposed model, self-determination consists of autonomy, competence, and relatedness, and entrepreneurship consists of innovation, initiative and risk sensitivity, and proactiveness. More specifically, this study proposes a framework that entrepreneurship and operational performance will play mediating roles between self-determination and financial performance. Research design, data, methodology: In this study, an online survey was conducted on SME CEOs for analysis, and a total of 122 samples were used. In the analysis process for hypothesis verification and evaluation, frequency analysis was first performed to identify the demographic characteristics of the respondents, and confirmatory factor analysis was conducted to assess the reliability and validity of the measurement model. In addition, a structural model analysis was conducted to examine the structural relationships between CEO's self-determination, entrepreneurship, and business performance (operational and financial performance) using SmartPLS 3.0. Results: The findings and summary are as follows. First, the autonomy of self-determination has a positive effect on entrepreneurship. Second, the competence of self-determination affects entrepreneurship and operational performance. Third, it affects the innovation, initiative and risk sensitivity of the CEO's entrepreneurship, and ultimately, its operational performance. The results show that the business performance of Start-up also increases when self-determination can be a factor in increasing entrepreneurship in three sub-dimensionalities. Conclusions: The conclusion of this study is that in order for SMEs to develop into a sustainable company by securing competitiveness after start-up, external motivation such as external help and support from the state (local government) is important, but competence and relationship, which are components of self-determination. The intrinsic motivation of the CEO may be more important. To this end, CEO's should prioritize learning for competency development, and the government should pay attention to providing various educational programs through establishment of education policies and education systems to enhance the competency of start-up CEO's.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.777-788
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• 2021

Since aerosol has a relatively short duration and significant spatial variation, satellite observations become more important for the spatially and temporally continuous quantification of aerosol. However, optical remote sensing has the disadvantage that it cannot detect AOD (Aerosol Optical Depth) for the regions covered by clouds or the regions with extremely high concentrations. Such missing values can increase the data uncertainty in the analyses of the Earth's environment. This paper presents a spatial gap-filling framework using a univariate statistical method such as DCT-PLS (Discrete Cosine Transform-based Penalized Least Square Regression) and FMM (Fast Matching Method) inpainting. We conducted a feasibility test for the hourly AOD product from AHI (Advanced Himawari Imager) between January 1 and December 31, 2019, and compared the accuracy statistics of the two spatial gap-filling methods. When the null-pixel area is not very large (null-pixel ratio < 0.6), the validation statistics of DCT-PLS and FMM techniques showed high accuracy of CC=0.988 (MAE=0.020) and CC=0.980 (MAE=0.028), respectively. Together with the AI-based gap-filling method using extra explanatory variables, the DCT-PLS and FMM techniques can be tested for the low-resolution images from the AMI (Advanced Meteorological Imager) of GK2A (Geostationary Korea Multi-purpose Satellite 2A), GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI2 (Geostationary Ocean Color Imager) of GK2B (Geostationary Korea Multi-purpose Satellite 2B) and the high-resolution images from the CAS500 (Compact Advanced Satellite) series soon.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1044-1058
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• 2021

Tempered electronic contents have multiplied in last few years, thanks to the emergence of sophisticated artificial intelligence(AI) algorithms. Deepfakes (fake footage, photos, speech, and videos) can be a frightening and destructive phenomenon that has the capacity to distort the facts and hamper reputation by presenting a fake reality. Evidence of ownership or authentication of digital material is crucial for combating the fabricated content influx we are facing today. Current solutions lack the capacity to track digital media's history and provenance. Due to the rise of misrepresentation created by technologies like deepfake, detection algorithms are required to verify the integrity of digital content. Many real-world scenarios have been claimed to benefit from blockchain's authentication capabilities. Despite the scattered efforts surrounding such remedies, relatively little research has been undertaken to discover where blockchain technology can be used to tackle the deepfake problem. Latest blockchain based innovations such as Smart Contract, Hyperledger fabric can play a vital role against the manipulation of digital content. The goal of this paper is to summarize and discuss the ongoing researches related to blockchain's capabilities to protect digital content authentication. We have also suggested a blockchain (smart contract) dependent framework that can keep the data integrity of original content and thus prevent deepfake. This study also aims at discussing how blockchain technology can be used more effectively in deepfake prevention as well as highlight the current state of deepfake video detection research, including the generating process, various detection algorithms, and existing benchmarks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.726-741
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• 2022

The purpose of this study is to provide instructive theoretical models for art (music) education institutions especially when unpredictable risks, such as pandemics, occur again. Based on the customer behavior theory of the business-to-business-to-customer (B2B2C) platform, and in combination with the technology acceptance model (TAM) and expectation confirmation model (ECM), this study proposes an online education model from the perspective of art education. The framework is based on the three decision-making processes of the customer, and includes the product owner, content owner, and customer area. This paper highlights the factors that influence customers in making decisions when art education institutions are product owners. Regression analysis was introduced to study the factors influencing the expectation confirmation, and the overall fitting testing and six hypotheses testing of 385 effective samples were performed using the structural equation modeling (SEM). The results show that the course-design and after-service positively influenced the expectation confirmation, and the domain image positively influenced the continuance behavior. Negative emotions skipped the mediator (expectation confirmation) and directly exerted a significant negative impact on customers' willingness to continue system usage (continuance behavior). In addition, expectation confirmation positively influenced continuance behavior. The paths of detailed items comprising course-design, after-service, and negative emotion were also analyzed and discussed. In this path analysis, ordinary art learners did not believe that AI partners can play a very good auxiliary role. The findings contribute to the scope of information systems acting as an art education platform academically, and provide effective and theoretical support for the actual operation of art education institutions.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.35-43
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• 2023

Emotion recognition while driving is an essential task to prevent accidents. Furthermore, in the era of autonomous driving, automobiles are the subject of mobility, requiring more emotional communication with drivers, and the emotion recognition market is gradually spreading. Accordingly, in this research plan, the driver's emotions are classified into seven categories using psychological and behavioral data, which are relatively easy to collect. The latent vectors extracted through the auto-encoder model were also used as features in this classification model, confirming that this affected performance improvement. Furthermore, it also confirmed that the performance was improved when using the framework presented in this paper compared to when the existing EEG data were included. Finally, 81% of the driver's emotion classification accuracy and 80% of F1-Score were achieved only through psychological, personal information, and behavioral data.