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Shim, Jooeun,Ko, Jooyoung,Shim, Jaechang Korea Multimedia Society 2015 The journal of multimedia information system Vol.2 No.1
The aim of this study was to develop and analyze a short course educating App Inventor and Arduino that showed the importance of software for youth. The course consists of a total of 10 missions for a 4 hour course divided into 2 parts, each 2 hours respectively. We conducted a basic course of Arduino for hardware and software, Processing for server programming, and App Inventor for programming for smartphones. The final mission was to send a signal to a server with a smartphone and to control light connected to a relay which passes Arduino connected with a server and serial communication. Participants completed 95% of missions, and we found the course had an educational effect for improving creativity and realization of software importance.
심재창(Jaechang Shim),오미선(Miseon Oh),고주영(Jooyoung Ko) 한국정보기술학회 2012 한국정보기술학회논문지 Vol.10 No.4
In this paper, we implemented a fixed mobile convergence system for robot control and proposed a protocol. TCP, ZigBee, WiFi and 3G network are combined to extend robot control area and it is possible to control where Internet or phone service is available. Proposed system is composed of a robot, server and client computers and an android platform based smartphone. The communication between robot and the system is done by ZigBee, that of the system connected to Internet is done by TCP, between Internet and smartphone is by WiFi or 3G network, and the system is focused on practicality. Based on proposed system and protocol, we can confirm communication between server and client, robot and computer. And we can control the robot by a smartphone, monitor acquired information. In addition, we tested image transfer from the camera attached to the robot.
Deep Learning and Color Histogram based Fire and Smoke Detection Research
Yeunghak Lee,Jaechang Shim 한국인터넷방송통신학회 2019 Journal of Advanced Smart Convergence Vol.8 No.2
The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.
Contrast HOG and Feature Spatial Relocation based Two Wheeler Detection Research using Adaboost
Lee, Yeunghak,Shim, Jaechang Korea Multimedia Society 2017 The journal of multimedia information system Vol.4 No.1
This article suggests a new algorithm for detecting two-wheelers on the road that have various shapes according to viewpoints. Because of complicated shapes, it is more difficult than detecting a human. In general, the Histograms of Oriented Gradients(HOG) feature is well known as a useful method of detecting a standing human. We propose a method of detecting a human on a two-wheelers using the spatial relocation of HOG (Histogram of Oriented Gradients) features. And this paper adapted the contrast method which is generally using in the image process to improve the detection rate. Our experimental results show that a two-wheelers detection system based on proposed approach leads to higher detection accuracy, less computation, and similar detection time than traditional features.
Lee, Yeunghak,Shim, Jaechang Korea Multimedia Society 2016 The journal of multimedia information system Vol.3 No.4
This study aims to suggest a new algorithm for detecting two-wheelers on road that have various shapes according to the viewing angle for vision based intelligent vehicles. This article describes a new approach to two-wheelers detection algorithm riding on people based on modified Histogram of Oriented Gradients (HOG) using correlation coefficient (CC). The CC between two local area variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using HOG which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the CC between the area of each cell and one of two-wheelers, can be extracted as the weighting factor in process for normalizing the modified HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.
A Study on the Short Range Wireless Communication of Smart Clothing for Elderly and the Infirm
Jooyoung Ko,Jaechang Shim,Hyenki Kim 보안공학연구지원센터 2015 International Journal of Software Engineering and Vol.9 No.3
Through prolongation of life expectancy and development of medical technology in modern society, population of elderly is sharply rising. The elderly need prompt assistance when emergency situations occur in everyday life. Therefore there are studies being done to help health maintenance and everyday life for the elderly including emergency situation monitoring. Smart clothing is an electronic equipment system that can be worn on the body created through joint study of electronics and clothing. This study explores short range wireless communication that can be easily applied to smart clothing that the elderly use. The study was conducted through implementing a system using wireless communication and through implementing a system combining Bluetooth, ZigBee, and WiFi.
Deep Learning and Color Histogram based Fire and Smoke Detection Research
Lee, Yeunghak,Shim, Jaechang The Institute of Internet 2019 International journal of advanced smart convergenc Vol.8 No.2
The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.
A Comparison of the Construction for IoT System in Smart Clothing
Ko, Jooyoung,Shim, Jaechang Korea Multimedia Society 2015 The journal of multimedia information system Vol.2 No.4
Recently, as microcomputers and sensors have been miniaturized due to dropdown of their market rates, this lead to a favorable environment for implementing the Internet of Things. Smart clothing refers to a system which can be wearable or portable, and allows people to communicate or conduct sensing. Applying the Internet of things, the role of the server computer is to receive and process data obtained from the sensor. An ordinary PC can act as a server but during the implementation of IoT, a PC has limited application due to a large size and the inconvenient portability. This study proposes a model that allows a variety of functions while implementation with the server from the sensing using the Arduino and Raspberry Pi. If we apply this proposed model, everyone can easily and inexpensively experience mobile IoT system.