• 한국산업정보학회논문지
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• 제20권2호
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• pp.45-56
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• 2015

본 논문에서는 스마트폰 카메라의 객체기반 자동초점 기능을 위해, 움직이는 물체의 고속 추적 방법을 제안한다. 사양이 낮은 플랫폼에서의 비-학습 제약을 고려하여 히스토그램 특징 기반의 슬라이딩 윈도우 검출 기법을 사용한다. 각 부분 윈도우에 대한 히스토그램의 계산 시간문제는 적분 히스토그램을 통해 해결한다. 본 논문에서는 지역적 후보 검출, 적응적 템플릿 크기 방법을 제안한다. 또한 추적 위치의 안정화를 위해 정합 함수에 안정화 항을 추가하는 기법을 제안한다. 자체 수집한 데이터에 대한 실험결과는 PC 환경에서 초당 100 프레임 수준의 높은 처리 속도 달성을 보여주었다.

• 한국산학기술학회논문지
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• 제17권3호
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• pp.591-598
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• 2016

자동차 운행에 있어서 최종적인 단계는 주차로 마무리 되는데, 특히 운전을 처음 배우는 사람들이 가장 어려워하며, 협소한 주차공간에서의 주차는 초보자뿐만 아니라 일반 운전자도 편하지는 않다. 이러한 문제점을 해결하기 위해 주차 조향 보조 시스템, 자동 항속 제어 시스템 등이 개발되어 운전자의 편의성을 향상시키고 있고, 초음파, 카메라, 열화상 카메라 또는 레이더 등을 이용하여 운전자에게 주변 환경을 보다 정확하게 인지할 수 있도록 주차 보조 시스템이 개발되고 있다. 본 연구에서는 카메라 위치가 차량 후면 어느 곳에 위치하든 중앙부에 위치했을 때와 동일하게 영상을 처리할 수 있도록 후진 회전 반경 식을 제안하고, 실차 검증을 통해 주차 가이드라인을 생성하고자 한다.

• 컴퓨터교육학회논문지
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• 제18권4호
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• pp.11-18
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• 2015

청력의 결여나 결손이 생기면 회화 음이나 환경 음을 청취하는 것이 어려우므로 언어 감각, 성격 등에 문제가 발생한다. 이러한 청각장애인들을 위하여 특별하게 발음 교정 교육을 할 수 있는 응용 프로그램을 설계하고 구현하였다. 시간과 장소에 제약이 없이 사용할 수 있도록 스마트폰 앱 형태로 제작하였고, 청각장애인의 발음 교정 실태를 고려하여 발음 습득 순서에 따라 난이도 순 훈련을 제공한다. 기본 발음 연습, 단어 발음 연습을 할 수 있고, 이에 대한 기록을 통하여 연습률과 성공률을 확인할 수 있다. 또한, 전면카메라를 통한 자가 발음 교정이 가능하도록 하여 발음이 향상되도록 하였다.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.632-634
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• 2013

현재 스마트시대에서 대학이나 교육기관의 수업 중 현장답사와 견학시스템 및 강의 일지 작성을 필요로 하는 과목이 증가하고 있다. 이러한 시점에서 교수들이 일일이 확인하고 일지를 작성, 데이터화 하는 과정의 편리성을 확보 하려고 한다. 이에, 본 논문에서는 안드로이드 위치정보 및 다양한 모듈로 현장답사 및 견학시스템의 자료화 및 인증화 하는 시스템을 도입하여, 보다 편리한 학습 환경 및 일지작성을 단순화 하는 작업시스템을 설계 및 구현하였다.

• 로봇학회논문지
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• 제13권1호
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• pp.31-37
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• 2018

Recently, the safety in vehicle also has become a hot topic as self-driving car is developed. In passive safety systems such as airbags and seat belts, the system is being changed into an active system that actively grasps the status and behavior of the passengers including the driver to mitigate the risk. Furthermore, it is expected that it will be possible to provide customized services such as seat deformation, air conditioning operation and D.W.D (Distraction While Driving) warning suitable for the passenger by using occupant information. In this paper, we propose robust vehicle occupant detection algorithm based on RGB-Depth-Thermal camera for obtaining the passengers information. The RGB-Depth-Thermal camera sensor system was configured to be robust against various environment. Also, one of the deep learning algorithms, OpenPose, was used for occupant detection. This algorithm is advantageous not only for RGB image but also for thermal image even using existing learned model. The algorithm will be supplemented to acquire high level information such as passenger attitude detection and face recognition mentioned in the introduction and provide customized active convenience service.

• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.479-486
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• 2021

This study aims to explore the relationships between strategic planning, strategic maneuverability, and firm performance in the current dynamic business environment. It employs a quantitative research method and reports on a survey, using a questionnaire, of service companies in Indonesia's oil and gas industry. Of the 337 companies selected by simple random sampling from a vendor database, responses were received from 70 companies. The analysis was performed using Partial Least Square Structural Equation Modeling and SmartPLS software. The analysis consisted of descriptive statistics, evaluation of the measurement model, evaluation of the structural model, and hypotheses testing. The results show that both strategic planning and strategic maneuverability have a positive relationship with firm performance. In addition, there is a positive relationship between strategic planning and firm performance through the mediating role of strategic maneuverability. The findings suggest that the organizational agility, organizational flexibility, and organizational responsiveness that constitute strategic maneuverability have a positive direct and indirect effect on firm performance, namely financial performance, customer performance, internal process performance, and learning and growth. This study contributes to the strategic management literature and the theory of maneuvers by providing empirical evidence on the relationship between strategic planning, strategic maneuverability, and firm performance.

• 드라이브 ㆍ 컨트롤
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• 제19권4호
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• pp.97-103
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• 2022

Low fertility rates and increased life expectancy further exacerbate the process of an aging society. This is also reflected in the gradual increase in the proportion of vulnerable groups in the social population. The demand for improved mobility among vulnerable groups such as the elderly or the disabled has greatly driven the growth of the electric-assisted mobility device market. However, such mobile devices generally require a certain operating capability, which limits the range of vulnerable groups who can use the device and increases the cost of learning. Therefore, autonomous driving technology needs to be introduced to make mobility easier for a wider range of vulnerable groups to meet their needs of work and leisure in different environments. This study uses mini PC Odyssey, Velodyne Lidar VLP-16, electronic device and Linux-based ROS program to realize the functions of working environment recognition, simultaneous localization, map generation and navigation of electric powered mobile devices for vulnerable groups. This autonomous driving mobility device is expected to be of great help to the vulnerable who lack the immediate response in dangerous situations.

• Smart Structures and Systems
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• 제29권1호
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Smart Structures and Systems
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• 제29권2호
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• pp.361-374
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• 2022

As civil infrastructure has continued to age worldwide, its structural integrity has been threatened owing to material deteriorations and continual loadings from the external environment. Structural Health Monitoring (SHM) has emerged as a cost-efficient method for ensuring structural safety and durability. As SHM research has gradually addressed an increasing number of structure-related problems, it has become difficult to understand the changing research topic trends. Although previous review papers have analyzed research trends on specific SHM topics, these studies have faced challenges in providing (1) consistent insights regarding macroscopic SHM research trends, (2) empirical evidence for research topic changes in overall SHM fields, and (3) methodological validations for the insights. To overcome these challenges, this study proposes a framework tailored to capturing the trends of research topics in SHM through a bibliometric and network analysis. The framework is applied to track SHM research topics over 15 years by identifying both quantitative and relational changes in the author keywords provided from representative SHM journals. The results of this study confirm that overall SHM research has become diversified and multi-disciplinary. Especially, the rapidly growing research topics are tightly related to applying machine learning and computer vision techniques to solve SHM-related issues. In addition, the research topic network indicates that damage detection and vibration control have been both steadily and actively studied in SHM research.