• Convergence Security Journal
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• v.24 no.3
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• pp.95-104
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• 2024

The performance capability of the future battlefield depends on whether the next-generation technology of the Fourth Industrial Revolution, called ABCMS (AI, Bigdata, Cloud, Mobile, Security), can be applied to secure innovative defense capabilities It is no exaggeration to say. In addition, the future military operation environment is rapidly changing into a net work-oriented war (NCW) in which all weapon systems mutually share battlefield information and operate in real-time within a single integrated information and communication network based on the network and is expanding to the scope of operation of the manned and unmanned complex combat system. In particular, communication networks responsible for high-speed and hyperconnectivity require high viability and efficiency in power operation based on multi-tier (defense mobile, satellite, M/W, wired) networks for the connection of multiple combat elements and smooth distribution of information. From this point of view, this study is different from conventional single-media, single-path transmission with fixed specifications, It is an artificial intelligence-based transmission technology using RNN (Recurrent Neural Networks) algorithm and load distribution during traffic congestion using available communication wired and wireless infrastructure multimedia simultaneously and It is the development of MMMP-Multi-Media Multi-Path adaptive network technology.

• The Journal of Korea Robotics Society
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• v.19 no.3
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• pp.293-310
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• 2024

In this paper, we propose an agent model for Language-Driven Zero-Shot Object Navigation (L-ZSON) tasks, which takes in a freeform language description of an unseen target object and navigates to find out the target object in an inexperienced environment. In general, an L-ZSON agent should able to visually ground the target object by understanding the freeform language description of it and recognizing the corresponding visual object in camera images. Moreover, the L-ZSON agent should be also able to build a rich spatial context map over the unknown environment and decide efficient exploration actions based on the map until the target object is present in the field of view. To address these challenging issues, we proposes AML (Agent Model for L-ZSON), a novel L-ZSON agent model to make effective use of AI foundation models such as Large Language Model (LLM) and Vision-Language model (VLM). In order to tackle the visual grounding issue of the target object description, our agent model employs GLEE, a VLM pretrained for locating and identifying arbitrary objects in images and videos in the open world scenario. To meet the exploration policy issue, the proposed agent model leverages the commonsense knowledge of LLM to make sequential navigational decisions. By conducting various quantitative and qualitative experiments with RoboTHOR, the 3D simulation platform and PASTURE, the L-ZSON benchmark dataset, we show the superior performance of the proposed agent model.

• Journal of Digital Policy
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• v.2 no.4
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• pp.1-10
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• 2023

This study conducted preliminary study to identify effective ways to use ChatGPT in traffic policing by analyzing ChatGPT's responses to the driver's license test and the road traffic accident appraiser test. I collected ChatGPT responses for the driver's license test item pool and the road traffic accident appraiser test using the OpenAI API with Python code for 30 iterative experiments, and analyzed the percentage of correct answers by test, year, section, and consistency. First, the average correct answer rate for the driver's license test and the for road traffic accident appraisers test was 44.60% and 35.45%, respectively, which was lower than the pass criteria, and the correct answer rate after 2022 was lower than the average correct answer rate. Second, the percentage of correct answers by section ranged from 29.69% to 56.80%, showing a significant difference. Third, it consistently produced the same response more than 95% of the time when the answer was correct. To effectively utilize ChatGPT, it is necessary to have user expertise, evaluation data and analysis methods, design a quality traffic law corpus and periodic learning.

• Health Policy and Management
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• v.34 no.3
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• pp.309-318
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• 2024

Background: Sleep disorders in menopausal women can act as a factor that lowers the quality of life. This study aims to understand the effects of mental health, such as depression, generalized anxiety disorder, and stress, on sleep duration in menopausal women to contribute to the management of their mental health. Methods: Using data from the 2021 Korea National Health and Nutrition Examination Survey, we conducted a study on 549 naturally menopausal women aged 50-64 years. We applied frequency and weighted percentages, Rao-Scott chi-square tests, and logistic regression. Results: It was found that women with a university degree or higher had appropriate sleep duration (odds ratio [OR], 0.33; p=0.004). Women with mild depression were less likely to have appropriate sleep compared to those without depression (OR, 2.13; p=0.025). Additionally, women who experienced high levels of stress were less likely to have appropriate sleep compared to those with minimal stress (OR, 2.73; p=0.041). Conclusion: Depression and stress are related to sleep duration, and these mental health issues can lead to physiological problems due to their complex experiences. Therefore, individual attention and management of mental health are crucial, and it seems necessary to develop community-based treatment and prevention strategies.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.236-251
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• 2020

As remote sensing technologies are evolving, and more satellites are orbited, the demand for using satellite data for disaster monitoring is rapidly increasing. Although natural and social disasters have been monitored using satellite data, constraints on establishing an integrated satellite-based near real-time disaster monitoring system have not been identified yet, and thus a novel framework for establishing such system remains to be presented. This research identifies constraints on establishing satellite data-based near real-time disaster monitoring systems by devising and testing a new conceptual framework of disaster monitoring, and then presents a feasible disaster monitoring system that relies mainly on acquirable satellite data. Implementing near real-time disaster monitoring by satellite remote sensing is constrained by technological and economic factors, and more significantly, it is also limited by interactions between organisations and policy that hamper timely acquiring appropriate satellite data for the purpose, and institutional factors that are related to satellite data analyses. Such constraints could be eased by employing an integrated computing platform, such as Amazon Web Services(AWS), which enables obtaining, storing and analysing satellite data, and by developing a toolkit by which appropriate satellites'sensors that are required for monitoring specific types of disaster, and their orbits, can be analysed. It is anticipated that the findings of this research could be used as meaningful reference when trying to establishing a satellite-based near real-time disaster monitoring system in any country.

• Journal of the Korean Medical Association
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• v.61 no.12
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• pp.765-775
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• 2018

With growing interest in novel digital healthcare devices, such as artificial intelligence (AI) software for medical diagnosis and prediction, and their potential impacts on healthcare, discussions have taken place regarding the regulatory approval, coverage, and clinical implementation of these devices. Despite their potential, 'digital exceptionalism' (i.e., skipping the rigorous clinical validation of such digital tools) is creating significant concerns for patients and healthcare stakeholders. This white paper presents the positions of the Korean Society of Radiology, a leader in medical imaging and digital medicine, on the clinical validation, regulatory approval, coverage decisions, and clinical implementation of novel digital healthcare devices, especially AI software for medical diagnosis and prediction, and explains the scientific principles underlying those positions. Mere regulatory approval by the Food and Drug Administration of Korea, the United States, or other countries should be distinguished from coverage decisions and widespread clinical implementation, as regulatory approval only indicates that a digital tool is allowed for use in patients, not that the device is beneficial or recommended for patient care. Coverage or widespread clinical adoption of AI software tools should require a thorough clinical validation of safety, high accuracy proven by robust external validation, documented benefits for patient outcomes, and cost-effectiveness. The Korean Society of Radiology puts patients first when considering novel digital healthcare tools, and as an impartial professional organization that follows scientific principles and evidence, strives to provide correct information to the public, make reasonable policy suggestions, and build collaborative partnerships with industry and government for the good of our patients.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.189-198
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• 2024

AI is accepted not only in the private sector but also in the defense sector as a cutting-edge technology that must be introduced for the development of national defense. In particular, artificial intelligence has been selected as a key task in defense science and technology innovation, and the importance of data is increasing. As the national defense department shifts from a closed data policy to data sharing and activation, efforts are being made to secure high-quality data necessary for the development of national defense. In particular, we are promoting a review of the business budget system to secure data so that related procedures can be improved to reflect the unique characteristics of AI and big data, and research and development can begin with sufficient large quantities and high-quality data. However, there is a need to establish standardization and quality standards for structured data and unstructured data at the national defense level, but the defense department is still proposing standardization and quality standards for structured data, so this needs to be supplemented. In this paper, we propose an unstructured data set standard format for defense unstructured data sets, which are most needed in defense artificial intelligence, and based on this, we propose a standardization method for defense unstructured data sets.

• Informatization Policy
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• v.31 no.2
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• pp.3-38
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• 2024

Hallucination is a significant barrier to the utilization of large-scale language models or multimodal models. In this study, we collected 654 computer science papers with "hallucination" in the abstract from arXiv from December 2022 to January 2024 following the advent of Chat GPT and conducted frequency analysis, knowledge network analysis, and literature review to explore the latest trends in hallucination research. The results showed that research in the fields of "Computation and Language," "Artificial Intelligence," "Computer Vision and Pattern Recognition," and "Machine Learning" were active. We then analyzed the research trends in the four major fields by focusing on the main authors and dividing them into data, hallucination detection, and hallucination mitigation. The main research trends included hallucination mitigation through supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), inference enhancement via "chain of thought" (CoT), and growing interest in hallucination mitigation within the domain of multimodal AI. This study provides insights into the latest developments in hallucination research through a technology-oriented literature review. This study is expected to help subsequent research in both engineering and humanities and social sciences fields by understanding the latest trends in hallucination research.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.61-72
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• 2020

The fear of crime, discussed in the early 1960s in the United States, is a psychological response, such as anxiety or concern about crime, the potential victim of a crime. These anxiety factors lead to the burden of the individual in securing the psychological stability and indirect costs of the crime against the society. Fear of crime is not a good thing, and it is a part that needs to be adjusted so that it cannot be exaggerated and distorted by the policy together with the crime coping and resolution. This is because fear of crime has as much harm as damage caused by criminal act. Eric Pawson has argued that the popular impression of violent crime is not formed because of media reports, but by official statistics. Therefore, the police should watch and analyze news related to fear of crime to reduce the social cost of fear of crime and prepare a preemptive response policy before the people have 'fear of crime'. In this paper, we propose a deep - based news classification system that helps police cope with crimes related to crimes reported in the media efficiently and quickly and precisely. The goal is to establish a system that can quickly identify changes in security issues that are rapidly increasing by categorizing news related to crime among news articles. To construct the system, crime data was learned so that news could be classified according to the type of crime. Deep learning was applied by using Google tensor flow. In the future, it is necessary to continue research on the importance of keyword according to early detection of issues that are rapidly increasing by crime type and the power of the press, and it is also necessary to constantly supplement crime related corpus.

• Health Policy and Management
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• v.24 no.3
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• pp.254-260
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• 2014

Background: The purpose of this study is to assume appropriate outpatient consultation time for each clinical department on the basis of measured outpatient consultation time and satisfaction of outpatient. Methods: We surveyed the feeling and satisfactory outpatient consultation time, satisfaction, revisiting intention and recommendation to others to 1,105 patients of single general hospital in Gyeonggi-do and measured their real outpatient consultation time from October 28 to November 27 in 2013. On the basis of satisfaction, we estimated appropriate outpatient consultation time through area under the receiver operating characteristic curve in logistic regression model. Results: Feeling outpatient consultation time was 5.1 minutess, satisfactory outpatient consultation time which was suggested by patient was 6.3 minutes, and real outpatient consultation time was 4.2 minutes. Department which had longest real outpatient consultation time was infection (7 minutes) and department which had longest satisfactory outpatient consultation time was neurology (9.4 minutes). From the univariate and the multiple linear regression analysis, real outpatient consultation time was longer in pulmonology patient, new patient and afternoon patient, satisfactory outpatient consultation time was longer in infection, neurology, neuropsychiatry, neurosurgery, and rehabilitation patient. Appropriate real outpatient consultation time was suggested as 5.6 minutes which differentiated high and low satisfied patient group. However, we could not assume appropriate outpatient consultation time for each clinical department because the number of patient who had bad satisfaction was too low. Conclusion: To improve patient's satisfaction, we hope outpatient reservation system is operated as each patient's outpatient consultation time is at least 5.6 minutes.