• Journal of Distribution Science
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• v.9 no.3
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• pp.55-64
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• 2011

This study is important in its focus to find key clues in the marketing strategy, consumer behavior, and communication processes that define the infantile obesity market. The study, the first of its kind, surveyed a target audience, purchasing group, and housewives in their quest to determine purchasing decisions and effective branding strategy planning for the infantile obesity market. Another key component of the study was to focus on the key direct and/or indirect distribution channels for the subject market. Recently, obesity has emerged as a major social concern; some studies show that the onslaught of an adverse eating culture in Korea emanates from the prevalence of fast-food dining establishments. Obesity among children leads to adult obesity, especially if the young people's parents are overweight; notably, if either one or both of the parents are obese, the percentage of young people eventually being obese is approximately 80 to 85 percent. Because obesity is the cause of many major health concerns later in life, the struggle for a healthy life is considerably adversely affected by parents' consumer behavior. Infantile obesity, resulting in adult obesity, is also an important national economic and social issue. The sizable direct and indirect economic costs, as well as the tremendous social costs of obesity, cannot be overstated. Effective food branding and advertising centered on food preferences and dietary behaviors, especially to children, creates an effective marketing effort that, ultimately, leads to positive results. Thus, the purpose of this study is to demonstrate that the treatment of childhood obesity in Korea, through the activation of a brand and retail market, can effectively solve social and economic problems that result from infantile and childhood obesity. In this study, obesity markets and distribution channels in the purchase decision-making factors determining factor based on it effective inspection and branding strategies and brand marketing communications strategy proposed measures contribute to the obesity drug market and further enable the childhood obesity problem is intended to assist in solving.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.393-400
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• 2021

Body weight of livestock is a crucial indicator for assessing feed requirements and nutritional status. This study was performed to estimate the body weight of Korean cattle (Hanwoo) using body volume determined from three-dimensional (3-D) image. A TOF camera with a resolution of 640×480 pixels, a frame rate of 44 fps and a field of view of 47°(H)×37°(V) was used to capture the 3-D images for Hanwoo. A grid image of the body was obtained through preprocessing such as separating the body from background and removing outliers from the obtained 3-D image. The body volume was determined by numerical integration using depth information to individual grid. The coefficient of determination for a linear regression model of body weight and body volume for calibration dataset was 0.8725. On the other hand, the coefficient of determination was 0.9083 in a multiple regression model for estimating body weight, in which the age of Hanwoo was added to the body volume as an explanatory variable. Mean absolute percentage error and root mean square error in the multiple regression model to estimate the body weight for validation dataset were 8.2% and 24.5kg, respectively. The performance of the regression model for weight estimation was improved and the effort required for estimating body weight could be reduced as the body volume of Hanwoo was used. From these results obtained, it was concluded that the body volume determined from 3-D of Hanwoo could be used as an effective variable for estimating body weight.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.23-41
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• 2019

This study understand the subject domain of book which has been read by men and woman elderly by analizying the PFNET using library big data and confirm the difference between adult at age 30-40. This study extract co-occurrence matrix of book lending on the popular book list from library big data, for 4 group, men/woman elderly, men/woman adult. With these matrix, this study performs FP network analysis. And Pearson Correlation Analysis based on the Triangle Betweenness Centrality calculated on the loan book was performed to understand the correlation among the 4 clusters which has been created by PNNC algorithm. As a result, reading trend which has been focused on modern korean novel has been revealed in elderly regardless gender, among them, men elderly show extreme tendency concentrated on modern korean long series novel. In the correlation analysis, the male elderly showed a weak negative correlation with the adult male of r = -0.222, and the negative direction of all the other groups showed that the tendency of male elderly's loan book was opposite.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.58-70
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• 2019

In the case of a pedestrian traffic accident, it has a large-scale danger directly connected by a fatal accident at the time of the accident. The domestic ITS is not used for intelligent risk classification because it is used only for collecting traffic information despite of the construction of good quality traffic infrastructure. The CNN based pedestrian detection classification model, which is a major component of the proposed system, is implemented on an embedded system assuming that it is installed and operated in a restricted environment. A new model was created by improving YOLO's artificial neural network, and the real-time detection speed result of average accuracy 86.29% and 21.1 fps was shown with 20,000 iterative learning. And we constructed a protocol interworking scenario and implementation of a system that can connect with the ITS. If a pedestrian accident prevention system connected with ITS will be implemented through this study, it will help to reduce the cost of constructing a new infrastructure and reduce the incidence of traffic accidents for pedestrians, and we can also reduce the cost for system monitoring.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.495-505
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• 2019

Typical algorithms of deep learning include CNN(Convolutional Neural Networks), which are mainly used for image recognition, and RNN(Recurrent Neural Networks), which are used mainly for speech recognition and natural language processing. Among them, CNN is able to learn from filters that generate feature maps with algorithms that automatically learn features from data, making it mainstream with excellent performance in image recognition. Since then, various algorithms such as R-CNN and others have appeared in object detection to improve performance of CNN, and algorithms such as YOLO(You Only Look Once) and SSD(Single Shot Multi-box Detector) have been proposed recently. However, since these deep learning-based detection algorithms determine the success of the detection in the still images, stable object tracking and detection in the video requires separate tracking capabilities. Therefore, this paper proposes a method of combining Kalman filters into deep learning-based detection networks for improved object tracking and detection performance in the video. The detection network used YOLO v2, which is capable of real-time processing, and the proposed method resulted in 7.7% IoU performance improvement over the existing YOLO v2 network and 20 fps processing speed in FHD images.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.132-141
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• 2019

The necessity of transmitting video data over a narrow-bandwidth exists steadily despite that video service over broadband is common. In this paper, we propose a scalable video coding framework for low-resolution video transmission over a very narrow-bandwidth network by super-resolution of decoded frames of a base layer using a convolutional neural network based super resolution technique to improve the coding efficiency by using it as a prediction for the enhancement layer. In contrast to the conventional scalable high efficiency video coding (SHVC) standard, in which upscaling is performed with a fixed filter, we propose a scalable video coding framework that replaces the existing fixed up-scaling filter by using the trained convolutional neural network for super-resolution. For this, we proposed a neural network structure with skip connection and residual learning technique and trained it according to the application scenario of the video coding framework. For the application scenario where a video whose resolution is $352{\times}288$ and frame rate is 8fps is encoded at 110kbps, the quality of the proposed scalable video coding framework is higher than that of the SHVC framework.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.585-594
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• 2021

This Quasi-experimental study was started under the assumption that the stress of students who participated in Buddhism and Mind Healing Lectures based on an understanding of the scriptures will be relieved through the lectures, thereby enhancing their psychological stability, thinking ability, and enhancing understanding. Stress can be confirmed through a self-report test, but in this study, quantitative EEG was measured to evaluate the stress level and secure objectivity. To this end, the difference between the 1st week as pre and 15th week as post quantitative EEG was verified for the experimental group taking the Buddhism and Mind Healing Lecture held from March to June 2019 at S University in G-gu, Seoul, and the control group who did not. The Mann Whitney U test and Wilcoxon code ranking test were used as analysis methods because the number of subjects was 14. As a result, there was a significant difference in the beta wave (F7, T3, 4, T5) and the high beta wave (F7, F8, T3, T4) in the experimental group. The coherence was also improved, while there was no significant difference in the control group. Buddhism and Mind Healing Lectures improved stress.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.157-165
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• 2020

Aiming at the development of neural network and self-driving data set, it is also an idea to improve the performance of network model to detect moving objects by dividing the data set. In Darknet network framework, the YOLOv4 (You Only Look Once v4) network model was used to train and test Udacity data set. According to 7 proportions of the Udacity data set, it was divided into three subsets including training set, validation set and test set. K-means++ algorithm was used to conduct dimensional clustering of object boxes in 7 groups. By adjusting the super parameters of YOLOv4 network for training, Optimal model parameters for 7 groups were obtained respectively. These model parameters were used to detect and compare 7 test sets respectively. The experimental results showed that YOLOv4 can effectively detect the large, medium and small moving objects represented by Truck, Car and Pedestrian in the Udacity data set. When the ratio of training set, validation set and test set is 7:1.5:1.5, the optimal model parameters of the YOLOv4 have highest detection performance. The values show mAP50 reaching 80.89%, mAP75 reaching 47.08%, and the detection speed reaching 10.56 FPS.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.237-250
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• 2022

In this study, we propose a method to estimate the biomass of invertebrate grazers from the videos with underwater drones by using a multi-object tracking model based on deep learning. In order to detect invertebrate grazers by classes, we used YOLOv5 (You Only Look Once version 5). For biomass estimation we used DeepSORT (Deep Simple Online and real-time tracking). The performance of each model was evaluated on a workstation with a GPU accelerator. YOLOv5 averaged 0.9 or more mean Average Precision (mAP), and we confirmed it shows about 59 fps at 4 k resolution when using YOLOv5s model and DeepSORT algorithm. Applying the proposed method in the field, there was a tendency to be overestimated by about 28%, but it was confirmed that the level of error was low compared to the biomass estimation using object detection model only. A follow-up study is needed to improve the accuracy for the cases where frame images go out of focus continuously or underwater drones turn rapidly. However,should these issues be improved, it can be utilized in the production of decision support data in the field of invertebrate grazers control and monitoring in the future.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.33-42
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• 2022

In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time. In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.