• Title/Summary/Keyword: human movement

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A theoretical foundation study for the promotion of a social and emotional competencies of children (초등학생들의 사회·정서적 능력 함양을 위한 이론적 토대 연구)

  • Lee, In Jae
    • The Journal of Korean Philosophical History
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    • no.25
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    • pp.7-40
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    • 2009
  • The aim of this paper is to establish the theoretical foundation on "the integrative study of the character education for the promotion of social and emotional competencies of children.". Based on the social and emotional learning(SEL), this paper is tried to find out the effective ways to develop children's good character. According to SEL, social and emotional competence is the ability to understand, manage, and express the social and emotional aspects of one's life in ways that enable the successful management of life tasks such as learning, forming relationships, solving everyday problems, and adapting to the complex demands of growth and development. And it is also the process of acquiring and effectively applying the knowledge, attitudes, and skills necessary to recognize and manage emotions. Five key competencies such as self-awareness, social awareness, responsible decision making, self-management, relationship skills are taught, practiced, and reinforced through SEL programming. Both the social and emotional learning movement and the character education share in common the idea that much of human character can be modified for the better through learning. While character educators engage in developing civic virtue and moral character in our youth for more compassionate and responsible society, SEL educators engage in educating for a safe, secure, caring society. To effectively teach social and emotional competencies, the teachers themselves must embrace a teaching and learning philosophy that models the attitudes, feelings, and behaviors we aim to teach.

Deep Learning based Fish Object Detection and Tracking for Smart Aqua Farm (스마트 양식을 위한 딥러닝 기반 어류 검출 및 이동경로 추적)

  • Shin, Younghak;Choi, Jeong Hyeon;Choi, Han Suk
    • The Journal of the Korea Contents Association
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    • v.21 no.1
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    • pp.552-560
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    • 2021
  • Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

Legal and Economic Analysis of Changes in Customer Value of Fintech and Financial Services

  • Lee, Jung Woo
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.12
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    • pp.279-291
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    • 2020
  • It has already been a few years since the word Fintech in Korea started to attract attention. These days, they believed that Fintech was just a boom, but these days it is recognized as a catalyst for financial transformation. Large venture companies are also launching demonstration experiments by creating new organizations that can respond to Fintech. It feels like a big tide is coming to the cautious and conservative financial industry. Finance is made up of digital information. Fintech is an evolutionary process in which finance, expressed by digital information, is transformed into information technology (IT) and human economic activities are reorganized. It is FinTech. You won't be able to understand the real effects of Fintech by sticking to individual applications like remittance payments or household account book services. Fintech is an innovation that changes the structure of economic activity itself. In fact, it is from now on that a big impact will come. In other words, now is the time when we are thinking of a dream that we have not yet dreamed of. In this paper, I will examine how fintech originated, spread to Korea, and how it intends to change Korea's finance in the future. Financial institutions have used the fruits of information technology advances in the direction of pursuing stability and stability, without major changes in the way they work. However, the movement of Fintech that started in Silicon Valley in the United States shows that the fruit can be used in other directions. The fruit of technological progress is expected to expand year by year in the future. It is a request of the times to use it to improve user convenience and to pursue innovation that is beneficial to society. We expect the flow of Fintech to accelerate innovation in the Korean financial industry.

Partitioning Interwell Tracer Test and Analysis Method for Estimating Oil Pollutants in the Underground (지중 유류오염량 추정을 위한 분배추적자 시험 및 해석방법)

  • Jeong, Chan-Duck;Kim, Yong-Cheol;Myeong, Woo-Ho;Bang, Sung-Su;Lee, Gyu-Sang;Song, Sung-Ho
    • Journal of Soil and Groundwater Environment
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    • v.27 no.spc
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    • pp.99-112
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    • 2022
  • From early 2000, many researchers in the groundwater and soil environment remediation project tried to calculate the pollution level and pollution remediation cost and reflect it in the design. In addition, by identifying the movement characteristics of oil pollutants in the underground environment, many researchers tried to derive design factors necessary for pollution purification. However, although the test should be conducted in an area contaminated with oil, the toxicity and risk are too great for testing by deliberately leaking pollutants that are harmful to the human body. And as oil-contaminated areas are promoted by military units such as returned US military bases, there is a limit to access by the general public. In addition, since the indoor simulation test and the field application test have been carried out separately from each other, it was difficult to compare and review various simulation tests Therefore, in this study, PITT (Partitioning Interwell Tracer Test) and analysis methods were specifically presented through actual tests so that field workers could easily use them with the help of the military base and the Korea Rural Community Corporation Soil Environment Restoration Team. However, in order to directly reflect the distribution tracer test results in the pollution remediation design, it is necessary to reduce the analysis errors by comparing the analysis results of the existing soil pollution survey, physical exploration, and numerical modeling. In addition, it is judged to be cautious in the analysis because errors can easily occur due to various factors such as the type of oil at the polluted site, the hydraulic conductivity of the aquifer, and the skill of the researcher.

Public Sentiment Analysis and Topic Modeling Regarding COVID-19's Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia

  • Alamoodi, A.H.;Baker, Mohammed Rashad;Albahri, O.S.;Zaidan, B.B.;Zaidan, A.A.;Wong, Wing-Kwong;Garfan, Salem;Albahri, A.S.;Alonso, Miguel A.;Jasim, Ali Najm;Baqer, M.J.
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.7
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    • pp.2169-2190
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    • 2022
  • The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.

Application and Analysis of Remote Sensing Data for Disaster Management in Korea - Focused on Managing Drought of Reservoir Based on Remote Sensing - (국가 재난 관리를 위한 원격탐사 자료 분석 및 활용 - 원격탐사기반 저수지 가뭄 관리를 중심으로 -)

  • Kim, Seongsam;Lee, Junwoo;Koo, Seul;Kim, Yongmin
    • Korean Journal of Remote Sensing
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    • v.38 no.6_3
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    • pp.1749-1760
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    • 2022
  • In modern society, human and social damages caused by natural disasters and frequent disaster accidents have been increased year by year. Prompt access to dangerous disaster sites that are inaccessible or inaccessible using state-of-the-art Earth observation equipment such as satellites, drones, and survey robots, and timely collection and analysis of meaningful disaster information. It can play an important role in protecting people's property and life throughout the entire disaster management cycle, such as responding to disaster sites and establishing mid-to long-term recovery plans. This special issue introduces the National Disaster Management Research Institute (NDMI)'s disaster management technology that utilizes various Earth observation platforms, such as mobile survey vehicles equipped with close-range disaster site survey sensors, drones, and survey robots, as well as satellite technology, which is a tool of remote earth observation. Major research achievements include detection of damage from water disasters using Google Earth Engine, mid- and long-term time series observation, detection of reservoir water bodies using Sentinel-1 Synthetic Aperture Radar (SAR) images and artificial intelligence, analysis of resident movement patterns in case of forest fire disasters, and data analysis of disaster safety research. Efficient integrated management and utilization plan research results are summarized. In addition, research results on scientific investigation activities on the causes of disasters using drones and survey robots during the investigation of inaccessible and dangerous disaster sites were described.

Why Does Historical Drama Need Romance? -Focused on the Television Drama Mr. Sunshine (역사드라마는 왜 로맨스를 필요로 하는가 -<미스터 션샤인>(2018)을 중심으로)

  • Yang, Geunae
    • Journal of Popular Narrative
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    • v.26 no.2
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    • pp.123-153
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    • 2020
  • As the importance of documented fact has weakened in historical dramas, the combination with other genres has become prominent. By reviewing the way romance is dealt with in historical dramas, this research examines how the properties of historical events adopted by historical dramas are related to the motif of love, and how the narrative of love and romance contributes to the historical effects, with a focus on the television drama Mr. Sunshine. Mr. Sunshine is the first historical drama written by Kim Eun-sook, combining deliberately rearranged history with the writer's unique grammar of romance. The failed resistance movement of the righteous army in the drama is matched with the love that cannot be achieved based on self-negation. The drama, which deals with the tyranny of Japanese imperialism and the independence of Joseon, fictionalizes key characters and events, transforming the desire of love into the passion of patriotism. Romance in Mr. Sunshine serves as a catalyst for emphasizing the tragedy of historical events and reconstitutes cultural memories. In historical dramas, the fictional plot of romance leads viewers to reflect on human life in history that flows from the past to the future. How does an individual's inner feelings contribute to the historical representation? This research is significant as it is the first attempt to examine the relationship between historical drama and romance in various ways.

Bone Health and L-ascorbic acid in Postmenopausal Women (폐경 여성의 골 건강과 L-ascorbic acid)

  • Kim, Bokyung;Kim, Mihyang
    • Journal of Life Science
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    • v.31 no.12
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    • pp.1142-1148
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    • 2021
  • As the average human lifespan has been extended, there has been a lot of interest in the quality of life of women after menopause. It is known that the average age of menopause among Korean women is 49.7 years, and the post-menopausal life of a woman takes up more than one third of her life. L-ascorbic acid (AsA) is known to be involved in the synthesis and maturation of collagen, a bone constituent protein. The aim of this review is to discuss the potential of AsA in bone health in postmenopausal women. AsA plays an important role in collagen biosynthesis, and collagen is a protein constituting bone and is a necessary material for calcification of the bone matrix. Collagen crosslinking is necessary for the stabilization and elasticity of collagen fibers during growth and matruation of animals, but an excessive increase is likely to lead to further aging because the movement of intercellular nutrients or waste is suppressed. AsA acts as a reducing agent to stabilize the immature collagen crosslinking and suppress pyridinoline production, a mature crosslinking. Therefore, AsA participates in collagen biosynthesis and helps bone tissue health, while regulating the excessive maturation of collagen crosslinking, it is expected to play an important role in bone-related problems that may occur in postmenopausal women.

Quantitative Comparison of Motion Artifacts in PET Images using Data-Based Gating (데이터 기반 게이팅을 이용한 PET 영상의 움직임 인공물의 정량적 비교)

  • Jin Young, Kim;Gye Hwan, Jin
    • Journal of the Korean Society of Radiology
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    • v.17 no.1
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    • pp.91-98
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    • 2023
  • PET is used effectively for biochemical or pathological phenomena, disease diagnosis, prognosis determination after treatment, and treatment planning because it can quantify physiological indicators in the human body by imaging the distribution of various biochemical substances. However, since respiratory motion artifacts may occur due to the movement of the diaphragm due to breathing, we would like to evaluate the practical effect by using the a device-less data-driven gated (DDG) technique called MotionFree with the phase-based gating correction method called Q.static scan mode. In this study, images of changes in moving distance (0 cm, 1 cm, 2 cm, 3 cm) are acquired using a breathing-simulated moving phantom. The diameters of the six spheres in the phantom are 10 mm, 13 mm, 17 mm, 22 mm, 28 mm, and 37 mm, respectively. According to maximum standardized uptake value (SUVmax) measurements, when DDG was applied based on the moving distance, the average SUVmax of the correction effect by the moving distance was improved by 1.92, 2.48, 3.23 and 3.00, respectively. When DDG was applied based on the diameter of the phantom spheres, the average SUVmax of the correction effect by the moving distance was improved by 2.37, 2.02, 1.44, 1.20, 0.42 and 0.52 respectively.

A Data-driven Classifier for Motion Detection of Soldiers on the Battlefield using Recurrent Architectures and Hyperparameter Optimization (순환 아키텍쳐 및 하이퍼파라미터 최적화를 이용한 데이터 기반 군사 동작 판별 알고리즘)

  • Joonho Kim;Geonju Chae;Jaemin Park;Kyeong-Won Park
    • Journal of Intelligence and Information Systems
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    • v.29 no.1
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    • pp.107-119
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    • 2023
  • The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.