• International Journal of Internet, Broadcasting and Communication
• /
• v.10 no.1
• /
• pp.16-22
• /
• 2018

This paper presents a solution for personal profiling system based on user-oriented tracking. Here, we introduce a new way to identify and track humans by using two types of cameras: dome and face camera. Dome camera has a wide view angle so that it is suitable for tracking human movement in large area. However, it is difficult to identify a person only by using dome camera because it only sees the target from above. Thus, face camera is employed to obtain facial information for identifying a person. In addition, we also propose a new mechanism to locate human on targeted location by using grid-cell system. These result in a system which has the capability of maintaining human identity and tracking human activity (movement) effectively.

• International Journal of Internet, Broadcasting and Communication
• /
• v.9 no.1
• /
• pp.42-49
• /
• 2017

Despite the government's various safety measures, the number of safety accidents continues to rise and the interior is more likely to occur indoors than outdoors. Safety accidents involving young children account for more than 70 % of the safety accidents in the safety accidents, and the ratio of safety accidents in the classroom or classroom is more than 50 %. In this thesis the author proposes the system managing the accidents notification service using LED lighting and access entry control. Utilizing IoT technology, remote control or access can be controlled remotely by controlling multiple lights and entrances from each room and by means of a number of lighting and entrances. By monitoring and analyzing real-time status via server PC and mobile interface, it can control the maximum control and incident prevention, automatic control, and automatic control.

• International Journal of Internet, Broadcasting and Communication
• /
• v.8 no.1
• /
• pp.1-6
• /
• 2016

Keeping a system in stable state is one of the important issues of control theory. The main goal of our basic research is stability of unmanned aerial vehicle (quadrotor). This type of system uses a variety of sensors to stabilize. In control theory and automatic control system to stabilize any system it is need to apply feedback control based on information from sensors. Our aim is to provide balance based on the 3D spatial information in real time. We used PID control method for stabilization of a seesaw balancing system and the article presents our experimental results. This paper presents the possibility of balancing of seesaw system based on feedback information from stereo vision system only.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.4
• /
• pp.153-159
• /
• 2009

In this paper, we deal with the coordinate estimate algorithm of the wired tablet device. In order to got the position of the electric pen on the tablet, electromagnetic induction effect is used. Most electric tablets have used the electromagnetic induction effect. Tracking the position of the pen on a tablet that is directly related with the performance of the device is very important. In this research, a new real time coordinate estimate algorithm is introduced. To estimate the position of the pen, the electromagnetic induced signals og the wired tablet are used.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2011.06a
• /
• pp.319-320
• /
• 2011

본 논문에서는 초중등학교의 조회 및 실내 집체 교육은 내용과 수준에 따라 학년별로 서로 다른 내용의 방송을 동시에 실시할 필요성이 요구되고 있다. 그러나 현재 사용 중인 방송 설비는 일방적으로 같은 내용의 방송을 전달하고 있고, 서로 다른 내용의 방송을 위하여 별도의 라인을 설비해야하는 번거로움과 비용부담을 안고 있다. 또한, 큰 건물의 재난 방송 시스템도 장소와 위치에 따라 서로 다른 내용의 방송을 동시에 실시해야 하는 필요성이 제기되고 있다. 즉, 위치에 따라 비상탈출 안내 방송을 실시하고 상황변화에 따라 장소에 맞는 방송을 전달하기 위한 시스템이 필요하다. 따라서 본 논문에서는 인터넷 망을 이용하여 미리 계획된 스케줄에 따라 동시에 청취자에 맞는 맞춤형 방송을 실시할 수 있는 인터넷 스케줄링 방송 시스템의 임베디드 시스템을 개발한다.

• International Journal of Internet, Broadcasting and Communication
• /
• v.11 no.3
• /
• pp.59-63
• /
• 2019

VoIP technology is a technology for exchanging voice or video data through IP network. Various protocols are used for this technique, in particular, RTP(Real-time Transport Protocol) protocol is used to exchange voice data. In recent years, with the development of communication technology, there has been an increasing tendency of services such as "Kakao Voice Talk" to exchange voice and video data through IP network. Most of these services provide a service with security guarantee by a user authentication process and an encryption process. However, RTP protocol does not require encryption when transmitting data. Therefore, there is an exposition risk in the voice data using RTP protocol. We will present the risk of the situation where packets are sniffed in VoIP(Voice over IP) communication using RTP protocol. To this end, we configured a VoIP telephone network, applied our own sniffing tool, and analyzed the sniffed packets to show the risk that users' data could be exposed unprotected.

• International Journal of Internet, Broadcasting and Communication
• /
• v.11 no.3
• /
• pp.71-76
• /
• 2019

In this paper, we propose a simple one-click marketing solution for mobile devices which can advertise a product which is embedded in a mobile video while watching the video on a smartphone. If a specific product of interest appears in the video to the user, one can simply click on the product in the video and a pop-up window with information about the product is proposed. The implementation of the system is expected to enable users to gain real-time information about the product while watching the video without having to search for the product again after watching the movie, and thereby facilitating more mobile commerce. We use a two-fold system to prevent the failure of tracking which often occurs on a single online tracking system, so that the user cannot always get the commercial product information.

• International Journal of Internet, Broadcasting and Communication
• /
• v.13 no.1
• /
• pp.210-218
• /
• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.2
• /
• pp.136-146
• /
• 2022

Some surface defects make the weak quality of steel materials. To limit these defects, we advocate a one-stage detector model RetinaNet among diverse detection algorithms in deep learning. There are several backbones in the RetinaNet model. We acknowledged two backbones, which are ResNet50 and VGG19. To validate our model, we compared and analyzed several traditional models, one-stage models like YOLO and SSD models and two-stage models like Faster-RCNN, EDDN, and Xception models, with simulations based on steel individual classes. We also performed the correlation of the time factor between one-stage and two-stage models. Comparative analysis shows that the proposed model achieves excellent results on the dataset of the Northeastern University surface defect detection dataset. We would like to work on different backbones to check the efficiency of the model for real world, increasing the datasets through augmentation and focus on improving our limitation.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.3
• /
• pp.166-175
• /
• 2023

Reinforcement learning can be applied to a wide variety of problems. However, the fundamental limitation of reinforcement learning is that it is difficult to derive an answer within a given time because the problems in the real world are too complex. Then, with the development of neural network technology, research on deep reinforcement learning that combines deep learning with reinforcement learning is receiving lots of attention. In this paper, two types of neural networks are combined with reinforcement learning and their characteristics were compared and analyzed with existing value-based reinforcement learning algorithms. Two types of neural networks are FNN and CNN, and existing reinforcement learning algorithms are SARSA and Q-learning.