• Journal of Internet of Things and Convergence
• /
• v.6 no.1
• /
• pp.83-95
• /
• 2020

Being a dense and complex task of precisely detecting the weeds in practical crop field environment, previous approaches lack in terms of speed of processing image frames with accuracy. Although much of the attention has been given to classify the plants diseases but detecting crop weed issue remained in limelight. Previous approaches report to use fast algorithms but inference time is not even closer to real time, making them impractical solutions to be used in uncontrolled conditions. Therefore, we propose a detection model for the complex rice weed detection task. Experimental results show that inference time in our approach is reduced with a significant margin in weed detection task, making it practically deployable application in real conditions. The samples are collected at two different growth stages of rice and annotated manually

• Journal of KIISE
• /
• v.43 no.11
• /
• pp.1307-1314
• /
• 2016

Virtual Hangul keyboards like Chun-Ji-In, Narat-Gul, and QWERTY are based on eyesight recognition, in which input letter positions are fixed in the smartphone environment. The input method of a fixed-position style is not very convenient for visually impaired individuals. In order to resolve the issue of inconvenience of the Hangul input system, we propose a new paradigm of the finger-touch based Hangul input system that does not need eyesight recognition of input buttons. For the convenience of learning the touch-motion based keyboard, finger touches are designed by considering the shape and frequencies of Hangul vowels and consonants together with the preference of fingers. The base position is decided by the first touch of the screen, and the finger-touch keyboard is used in the same way for all the other touch-style devices, regardless of the differences in size and operation system. In this input method, unique finger-touch motions are assigned for Hangul letters that significantly reduce the input errors.

• Journal of Platform Technology
• /
• v.6 no.2
• /
• pp.3-9
• /
• 2018

Recently VR environment is used in many areas including mobile learning, smart factory. However HMD(head-mounted display) is required to a dedicated and expensive system with high-end specification. When designing a VR system, it is needed to handle performance, mobility and usability. Many VR applications need to handle diverse sensors and user inputs continuously in a streaming manner. In this paper we design a VR mobile platform and implement a low-cost mobile VR HMD running on the platform. The VR HMD supports 3D contents delivery in a mobile manner. It is used to detect the motion detection based on angle value of a VR player from accelerator and gyro sensor. The MPU-6050, 6-axis sensor, is used to get a sensory value and the sensory value is taken as an input to a VR rendering server on a Unity game engine that is generated 3D images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.482-484
• /
• 2017

Over the past 70 years, movies and television have revolutionized the way people communicate. However, even with this development, TV has been used only as a means of communication targeting an unspecified number of people due to the restriction of media such as radio waves and movies. However, the development of the Internet and online video has come to a time when 100 million people watch YouTube videos uploaded from the other side of the world by eliminating these restrictions. The message that you want to deliver now can be delivered to anyone, but making the image with these messages remains the last obstacle to communication. To solve these problems, we implemented a web application and a video production program through AWS. This system basically provides the administrator with the video production through the easy interface, the information management and the program on the server on the internet through AWS, the assigned lecture including the computer and the smart phone, the learning materials, And implemented to increase the efficiency of educational video production service.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.12
• /
• pp.163-173
• /
• 2010

PKI(Public Key Infrastructure)-based information certification technology has some limitations to be universally applied to mobile banking services, using smart phones, since PKI is dependent on the specific kind of web browser, Internet Explorer. OTP(One Time Password) is considered to be a substitute or complementary service of PKI, but it still shows low acceptance rate. Therefore, in this research, we analyze why OTP has not been very popular, and provide useful implications of making OTP more extensively and frequently used in the mobile environment. Perceived security of OTP was set as a higher-order construct of integrity, confidentiality, authentication, and non-repudiation. Research findings show that security awareness and perceived security of OTP is positively associated, and the relationship between perceived security and trust on OTP is statistically significant. Also, trust is positively related to intention to use OTP continuously.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.10
• /
• pp.503-510
• /
• 2016

FsGr model, which has been proposed recently, is an approach of accelerometer-based gesture recognition by applying DTW algorithm in two steps, which improved recognition success rate. In FsGr model, sets of similar gestures will be produced through training phase, in order to define the notion of a set of similar gestures. At the 1st attempt of gesture recognition, if the result turns out to belong to a set of similar gestures, it makes the 2nd recognition attempt to feature-strengthened parts extracted from the set of similar gestures. However, since a same gesture show drastically different characteristics according to physical traits such as body size, age, and sex, FsGr model may not be good enough to apply to multi-user environments. In this paper, we propose FsGrM model that extends FsGr model for multi-user environment and present a program which controls channel and volume of smart TV using FsGrM model.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.1
• /
• pp.75-80
• /
• 2021

Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected. A neural network was introduced to create synthetic data from the collected actual data. Based on this neural network, the RSSI value versus distance was predicted. The real value of RSSI was obtained as a neural network for generating synthetic data, and based on this value, the coordinates of the object were estimated by learning a neural network that tracks the location of a terminal in a virtual environment.

• Journal of Broadcast Engineering
• /
• v.27 no.5
• /
• pp.738-750
• /
• 2022

In this study, we reviewed and discussed whether auditory stimuli with short length can evoke emotion-related neurological responses. The findings implicate that if personalized sound tracks are provided to XR users based on machine learning or probability network models, user experiences in XR environment can be enhanced. We also investigated that the arousal-relaxed factor evoked by short auditory sound can make distinct patterns in functional connectivity characterized from background EEG signals. We found that coherence in the right hemisphere increases in sound-evoked arousal state, and vice versa in relaxed state. Our findings can be practically utilized in developing XR sound bio-feedback system which can provide preference sound to users for highly immersive XR experiences.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.3
• /
• pp.63-73
• /
• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• Geomechanics and Engineering
• /
• v.28 no.6
• /
• pp.599-611
• /
• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.