• 제목/요약/키워드: Smart Learning Quality

검색결과 142건 처리시간 0.026초

Visual Positioning System based on Voxel Labeling using Object Simultaneous Localization And Mapping

  • Jung, Tae-Won;Kim, In-Seon;Jung, Kye-Dong
    • International Journal of Advanced Culture Technology
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    • 제9권4호
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    • pp.302-306
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    • 2021
  • Indoor localization is one of the basic elements of Location-Based Service, such as indoor navigation, location-based precision marketing, spatial recognition of robotics, augmented reality, and mixed reality. We propose a Voxel Labeling-based visual positioning system using object simultaneous localization and mapping (SLAM). Our method is a method of determining a location through single image 3D cuboid object detection and object SLAM for indoor navigation, then mapping to create an indoor map, addressing it with voxels, and matching with a defined space. First, high-quality cuboids are created from sampling 2D bounding boxes and vanishing points for single image object detection. And after jointly optimizing the poses of cameras, objects, and points, it is a Visual Positioning System (VPS) through matching with the pose information of the object in the voxel database. Our method provided the spatial information needed to the user with improved location accuracy and direction estimation.

Research on AI Painting Generation Technology Based on the [Stable Diffusion]

  • Chenghao Wang;Jeanhun Chung
    • International journal of advanced smart convergence
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    • 제12권2호
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    • pp.90-95
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    • 2023
  • With the rapid development of deep learning and artificial intelligence, generative models have achieved remarkable success in the field of image generation. By combining the stable diffusion method with Web UI technology, a novel solution is provided for the application of AI painting generation. The application prospects of this technology are very broad and can be applied to multiple fields, such as digital art, concept design, game development, and more. Furthermore, the platform based on Web UI facilitates user operations, making the technology more easily applicable to practical scenarios. This paper introduces the basic principles of Stable Diffusion Web UI technology. This technique utilizes the stability of diffusion processes to improve the output quality of generative models. By gradually introducing noise during the generation process, the model can generate smoother and more coherent images. Additionally, the analysis of different model types and applications within Stable Diffusion Web UI provides creators with a more comprehensive understanding, offering valuable insights for fields such as artistic creation and design.

Refinement of Ground Truth Data for X-ray Coronary Artery Angiography (CAG) using Active Contour Model

  • Dongjin Han;Youngjoon Park
    • International journal of advanced smart convergence
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    • 제12권4호
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    • pp.134-141
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    • 2023
  • We present a novel method aimed at refining ground truth data through regularization and modification, particularly applicable when working with the original ground truth set. Enhancing the performance of deep neural networks is achieved by applying regularization techniques to the existing ground truth data. In many machine learning tasks requiring pixel-level segmentation sets, accurately delineating objects is vital. However, it proves challenging for thin and elongated objects such as blood vessels in X-ray coronary angiography, often resulting in inconsistent generation of ground truth data. This method involves an analysis of the quality of training set pairs - comprising images and ground truth data - to automatically regulate and modify the boundaries of ground truth segmentation. Employing the active contour model and a recursive ground truth generation approach results in stable and precisely defined boundary contours. Following the regularization and adjustment of the ground truth set, there is a substantial improvement in the performance of deep neural networks.

스마트 팩토리 모니터링을 위한 빅 데이터의 LSTM 기반 이상 탐지 (LSTM-based Anomaly Detection on Big Data for Smart Factory Monitoring)

  • ;;김진술
    • 디지털콘텐츠학회 논문지
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    • 제19권4호
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    • pp.789-799
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    • 2018
  • 이 논문에서는 이러한 산업 단지 시스템에서의 비정상적인 동작이 일어날 때, 시간 계열의 데이터를 분석하기 위하여 Big 데이터를 이용한 접근을 기반으로 하는 머신 러닝을 보여줍니다. Long Short-Term Memory (LSTM) 네트워크는 향상된 RNN버전으로서 입증되었으며 많은 작업에 유용한 도움이 되었습니다. 이 LSTM 기반 모델은 시간적 패턴뿐만 아니라 더 높은 레벨의 시간적 특징을 학습 한 다음, 미래의 데이터를 예측하기 위해 예측 단계에 사용됩니다. 예측 오차는 예측 인자에 의해 예측 된 결과와 실제 예상되는 값의 차이입니다. 오차 분포 추정 모델은 가우스 분포를 사용하여 관찰 스코어의 이상을 계산합니다. 이러한 방식으로, 우리는 하나의 비정상적 데이터의 개념에서 집단적인 비정상적 데이터 개념으로 바뀌어 갑니다. 이 작업은 실패를 최소화하고 제조품질을 향상시키는 Smart Factory의 모니터링 및 관리를 지원할 수 있습니다.

스마트 디바이스 기반 수업분석 프로그램 설계 및 구현 -한국어 특성 반영과 교사활용도 증진을 위한 UI설계를 적용하여- (Design and Implementation of Smart Device Application for Instructional Analysis)

  • 강두봉;정주훈;김영환
    • 컴퓨터교육학회논문지
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    • 제18권4호
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    • pp.31-40
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    • 2015
  • 본 연구의 목적은 수업 개선을 위한 수업분석 프로그램을 스마트 디바이스 기반의 어플리케이션으로 설계 및 구현한 데 있다. 이를 위해 사용자 인터페이스(UI)를 간소화하고, 서술어가 뒤에 나오는 한국어 특성을 감안하였으며, 입체적 분석을 위해 Flanders의 '언어상호작용 분석법', Tuckman의 '수업분위기 분석법', Mcgraw의 '과업집중 분석법'의 3가지 방법을 통한 분석이 가능하게 설계하였다. 본 연구는 선행 연구들과 달리 위 3가지 특징을 적용하여 교사들이 손쉽게 스스로 자신의 수업을 분석할 수 있는 앱을 개발했다는데 의의가 있으며, 일선 교사들은 다른 교사의 도움이나 전문적 지식 없이도 수업을 분석하고 피드백 할 수 있어 자발적 수업개선에 도움이 될 수 있을 것이다. 향후, 최근의 수업 환경에 맞는 수업분석 기법 및 음성인식 시스템 등의 연구와 연계하여 수업분석 원리의 정립과 관련 어플리케이션의 개발에 기초가 될 수 있을 것이다.

Bridge Inspection and condition assessment using Unmanned Aerial Vehicles (UAVs): Major challenges and solutions from a practical perspective

  • Jung, Hyung-Jo;Lee, Jin-Hwan;Yoon, Sungsik;Kim, In-Ho
    • Smart Structures and Systems
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    • 제24권5호
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    • pp.669-681
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    • 2019
  • Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).

사물인터넷 환경에서 청소년수련시설 종사자의 사회적 지지가 서비스 질에 미치는 영향: 임파워먼트의 매개효과 중심으로 (The Effect of Social Support on Service Quality of Youth Training Facility Employees in Internet of Thing Environment: The Mediating Effect of Empowerment)

  • 윤기혁;이진열
    • 사물인터넷융복합논문지
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    • 제6권1호
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    • pp.31-38
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    • 2020
  • 본 연구는 청소년수련시설 종사자의 사회적 지지가 서비스 질에 미치는 영향에서 임파워먼트의 매개효과를 검증하고자 하였다. 본 연구결과를 통해 청소년수련시설 종사자의 서비스 질 향상에 필요한 기초자료를 제공하는 것을 목적으로 하였다. 본 연구 결과 첫째, 사회적 지지와 임파워먼트는 서비스 질에 정적인 영향을 제공하는 것으로 나타났고, 둘째, 임파워먼트의 부분매개효과를 확인할 수 있다. 본 연구결과에 기초하여 제언하면 다음과 같다. 첫째, 종사자의 사회적 지지를 위해 청소년수련시설의 중간관리자와 시설장은 인터넷, 스마트 폰 등 사물인터넷 환경을 활용해야 한다. 둘째, 임파워먼트 향상을 위해 업무와 관련된 정보지원, 보상과 관련된 물질지원, 업무 처리와 관련된 평가지원 등의 지원이 이루어져야 한다. 셋째, 종사자의 임파워먼트 향상을 위해 사물인터넷 환경을 활용한 증강현실(AR), 가상현실(VR), 플립러닝 프로그램 개발이 필요하다.

Optimal sensor placement for structural health monitoring based on deep reinforcement learning

  • Xianghao Meng;Haoyu Zhang;Kailiang Jia;Hui Li;Yong Huang
    • Smart Structures and Systems
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    • 제31권3호
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    • pp.247-257
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    • 2023
  • In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

Object detection in financial reporting documents for subsequent recognition

  • Sokerin, Petr;Volkova, Alla;Kushnarev, Kirill
    • International journal of advanced smart convergence
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    • 제10권1호
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    • pp.1-11
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    • 2021
  • Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.

정확히 재가중되는 온라인 전체 에러율 최소화 기반의 객체 추적 (Object Tracking Based on Exactly Reweighted Online Total-Error-Rate Minimization)

  • 장세인;박충식
    • 지능정보연구
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    • 제25권4호
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    • pp.53-65
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    • 2019
  • 영상 기반의 보안 시스템의 증가함에 따라 각 용도마다 다른 다양한 객체들에 대한 처리들이 중요해지고 있다. 객체 추적은 객체 인식, 검출과 같은 작업들과 함께 필수적인 작업으로 다뤄진다. 이 객체 추적을 달성하기 위해서 다양한 머신러닝이 적용될 수 있다. 성공적인 분류기로써 전체 에러율 최소화(total-error-rate minimization) 기반의 방법론이 사용될 수 있다. 이 전체 에러율 최소화 기반의 방법론은 오프라인 학습을 기반으로 하고 있다. 객체 추적은 실시간으로 처리하며 갱신해야하는 것이 필수적이므로 온라인 학습(online learning)을 기반으로 하는 것이 적합하다. 온라인 전체 에러율 최소화 방법론이 개발되었지만 점근적으로 재가중되는(approximately reweighted) 작업이 포함되어 에러를 누적시킬 수 있다는 단점이 있다. 본 논문에서는 정확하게 재가중되는(exactly reweighted) 방법론을 제안하면서 온라인 전체 에러율 최소화가 달성되었다. 이 제안된 온라인 학습 방법론을 객체 추적에 적용하여 총 8개의 데이터베이스에서 다른 추적 방법론들 보다 좋은 성능이 달성되었다.