• International conference on construction engineering and project management
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• 2022.06a
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.329-342
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• 2017

This study investigates convergent influence on Self Factors(SF), Stress Factors(STF), Resilience & Control Factors(RCF), Test Anxiety(TA), Learning Flow(LF) and academic burnout among Health College Students in TOEIC class(HCST). The survey was administered to 291 HCST from 1 college located in J area during the period from April 3, 2017 to April 14, 2017. The structured self-administered questionaries were used. With the analysis of covariance structure, we could confirm relationship among the six factors such as SF, STF, RCF, TA, LF and academic burnout. The results of the study indicate that the efforts, to manage these factors, are required to decrease the academic burnout of HCST. Squared multiple correlations, which explain the academic burnout related with the stress from economic pressure and job seeking, test anxiety, learning flow and self factors, were 98.8%. The results are expected to be useful for the development of TOEIC learning curriculum and course to decrease the academic burnout of HCST. In the following study, the analysis about additional factors of influence on academic burnout will be needed.

• Journal of Broadcast Engineering
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• v.17 no.5
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• pp.817-826
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• 2012

This paper presents a robust viewpoint estimation algorithm for Moving Parallax Barrier mobile 3D display in sudden illumination changes. We analyze the previous viewpoint estimation algorithm that consists of the Viola-Jones face detector and the feature tracking by the Optical-Flow. The sudden changes in illumination decreases the performance of the Optical-flow feature tracker. In order to solve the problem, we define a novel performance measure for the Optical-Flow tracker. The overall performance can be increased by the selective adoption of the Viola-Jones detector and the Optical-flow tracker depending on the performance measure. Various experimental results show the effectiveness of the proposed method.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.55-70
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• 2009

Vision-based Human computer interaction is an emerging field of science and industry to provide natural way to communicate with human and computer. In that sense, inferring the emotional state of the person based on the facial expression recognition is an important issue. In this paper, we present a novel approach to recognize facial expression from a sequence of input images using emotional specific HMM (Hidden Markov Model) and facial motion tracking based on optical flow. Conventionally, in the HMM which consists of basic emotional states, it is considered natural that transitions between emotions are imposed to pass through neutral state. However, in this work we propose an enhanced transition framework model which consists of transitions between each emotional state without passing through neutral state in addition to a traditional transition model. For the localization of facial features from video sequence we exploit template matching and optical flow. The facial feature displacements traced by the optical flow are used for input parameters to HMM for facial expression recognition. From the experiment, we can prove that the proposed framework can effectively recognize the facial expression in real time.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.1-6
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• 2014

In this study, we propose optimized RBFNNs based on night vision face recognition simulator with the aid of $(2D)^2$ PCA algorithm. It is difficult to obtain the night image for performing face recognition due to low brightness in case of image acquired through CCD camera at night. For this reason, a night vision camera is used to get images at night. Ada-Boost algorithm is also used for the detection of face images on both face and non-face image area. And the minimization of distortion phenomenon of the images is carried out by using the histogram equalization. These high-dimensional images are reduced to low-dimensional images by using $(2D)^2$ PCA algorithm. Face recognition is performed through polynomial-based RBFNNs classifier, and the essential design parameters of the classifiers are optimized by means of Differential Evolution(DE). The performance evaluation of the optimized RBFNNs based on $(2D)^2$ PCA is carried out with the aid of night vision face recognition system and IC&CI Lab data.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.98-105
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• 2011

Recent advances in computer technologies support real-time image processing and special effects on personal computers. This paper presents and analyzes a real-time interactive video system. The motivation of this work is to realize an artistic concept that aims at transforming the timeline visual variations in a video of sea water waves into sound in order to provide an audience with an experience of overlapping themselves onto the nature. In practice, the video of sea water waves taken on a beach is processed using an optical flow algorithm in order to extract the information of visual variations between the video frames. This is then masked by the silhouette of an audience and the result is projected on a gallery space. The intensity information is extracted from the resulting video and translated into piano sounds accordingly. This work generates an interactive space realizing the intended concept.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.299-305
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• 2019

The purpose of study was to investigate the effects of career decision efficacy, learning Flow, and academic achievement on department adaptation of dental hygiene students. The subjects of the study are self-administered surveys of 200 students who indicated their intention to participate. 181 questionnaires were analyzed using SPSS 18.0. The results of this study show that career decision efficacy, learning flow, academic achievement affect to major satisfaction, major confidences, major attachment. The results of this study show that career decision efficacy, academic achievement affect to major vision. Some dental hygienists students need support systems such as learning methods and educational environment that can improve academic achievement and learning commitment in order to help students adapt to their department. In addition, education and career support for career decision efficacy should be continuously maintained.

• Journal of the Korean Solar Energy Society
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• v.32 no.5
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• pp.94-101
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• 2012

A Dokdo wind resource map has been drawn up for the Green Island Energy Master Plan according to Korea's national vision for 'Low Carbon Green Growth'. The micro-siting software WindSim v5.1,which is based on Computational Flow Analysis, is used with MERRA reanalysis data as synoptic climatology input data, and sensitivity analysis on turbulence model is accompanied. A wind resource assessment has been conducted for the Dokdo wind power dissemination plan, which consists of two 10kW wind turbines to be installed at the Dongdo dock and Dokdo guard building. It is evaluated that the capacity factors at Dongdo dock and Dokdo guard building are about 20% and 30% respectively, and annual and hourly variations of wind power generation have been analyzed, but summertime energy production is predicted to be only 40% of wintertime energy production.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.735-739
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• 2005

This paper presents a new system to estimate the head pose of human in interactive indoor environment that has dynamic illumination change and large working space. The main idea of this system is to suggest a new morphological feature for estimating head angle from stereo disparity map. When a disparity map is obtained from stereo camera, the matching confidence value can be derived by measurements of correlation of the stereo images. Applying a threshold to the confidence value, we also obtain the specific morphology of the disparity map. Therefore, we can obtain the morphological shape of disparity map. Through the analysis of this morphological property, the head pose can be estimated. It is simple and fast algorithm in comparison with other algorithm which apply facial template, 2D, 3D models and optical flow method. Our system can automatically segment and estimate head pose in a wide range of head motion without manual initialization like other optical flow system. As the result of experiments, we obtained the reliable head orientation data under the real-time performance.