• Korean Chemical Engineering Research
• /
• v.47 no.6
• /
• pp.715-719
• /
• 2009

The phase change electrode board for the bio-information detection through electrical property response of phase change material was developed in this study. We manufactured the electrode board using Aluminum first that is widely used in conventional semiconductor device process. Without further treatment, these aluminum electrodes tend to contain voids in PETEOS(plasma enhanced tetraethyoxysilane) material that are easily detected by cross-sectional SEM(Scanning Electron Microscope). The voids can be easily attacked and transformed into holes in between PETEOS and electrodes after etch back and washing process. In order to resolve this issue of Al electrode board, we developed a electrode board manufacturing method using low resistivity TiN, which has advantages in terms of the step-coverage of phase change($Ge_2Sb_2Te_5$, GST) thin film as well as thermodynamic stability, without etch back and washing process. This TiN material serves as the top and bottom electrode in PRAM(Phase-change Random Access Memory). The good connection between the TiN electrode and GST thin film was confirmed by observing the cross-section of TiN electrode board using SEM. The resistances of amorphous and crystalline GST thin film on TiN electrodes were also measured, and 1000 times difference between the amorphous and crystalline resistance of GST thin film was obtained, which is well enough for the signal detection.

• Journal of radiological science and technology
• /
• v.32 no.2
• /
• pp.129-140
• /
• 2009

As CT has been increasingly used as an accurate screening tool for lung disease, radiation dose becomes an important issue for both radiographers and patients. Many researches have been done for a low-dose CT as a screening tool for early detection of asymptomatic lung diseases. From those studies, it has been reported that chest dose rate from the low-dose CT is considerably lower than from standard CT. The patient dose is determined by scanning parameters such as kVp, mAs, pitch, scan time and the radiation risk of lung in screening examination may not be negligible. Herein, we suggest that Low-dose CT is useful as a screening tool in routine clinical practice on the basis of published articles, but further study is necessary because Low-dose CT has poor sensitivity and specificity for screening early stage of lung cancer according to the results of the studies. This article is to provide a brief overview of the screening examinations by Low-dose CT.

• Journal of the Korea Computer Graphics Society
• /
• v.23 no.3
• /
• pp.65-75
• /
• 2017

As a 360-degree image carries information of all directions, it often has too much information. Moreover, in order to investigate a 360-degree image on a 2D display, a user has to either click and drag the image with a mouse, or project it to a 2D panorama image, which inevitably introduces severe distortions. In consequence, investigating a 360-degree image and finding an object of interest in such a 360-degree image could be a tedious task. To resolve this issue, this paper proposes a method to find a region of interest and produces a 2D naturally looking image from a given 360-degree image that best matches a description given by a user in a natural language sentence. Our method also considers photo composition so that the resulting image is aesthetically pleasing. Our method first converts a 360-degree image to a 2D cubemap. As objects in a 360-degree image may appear distorted or split into multiple pieces in a typical cubemap, leading to failure of detection of such objects, we introduce a modified cubemap. Then our method applies a Long Short Term Memory (LSTM) network based object detection method to find a region of interest with a given natural language sentence. Finally, our method produces an image that contains the detected region, and also has aesthetically pleasing composition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.10a
• /
• pp.331-339
• /
• 2008

Nowadays with advancement in technology and aging society, the number of disabled citizens is increasing. The disabled citizens always need a caretaker for daily life routines especially for mobility. In future, the need is considered to increase more. To reduce the burden from the disabled, various devices for healthcare are introduced using computer technology. The power wheelchair is an important and convenient mobility device. The demand of power wheelchair is increasing for assistance in mobility. In this paper we proposed a robotic wheelchair for mobility aid to reduce the burden from the disabled. The main issue in an autonomous wheelchair is the automatic detection and avoidance of obstacles and going to the pre-designated place. The proposed algorithm detects the obstacles and avoids them to drive the wheelchair to the desired place safely. By this way, the disabled will not always have to worry about paying deep attention to the surroundings and his path. User has a handheld bio-sensor monitoring system for get user's bio-signal. If user detects unusual signal, alarm send to protector.

• The KIPS Transactions:PartB
• /
• v.14B no.2
• /
• pp.65-70
• /
• 2007

Red tide is becoming hot issue of environmental problem worldwide since the 1990. Advanced nations, are progressing study that detect red tide area on early time using satellite for sea. But, our country most seashores bends serious. Also because there are a lot of turbid method streams on coast, hard to detect small red tide area by satellite for sea that is low resolution. Also, method by sea color that use one feature of satellite image for sea of existent red tide area detection was most. In this way, have a few feature in image with sea color and it can cause false negative mistake that detect red tide area. Therefore, in this paper, acquired texture information to use GLCM(Gray Level Co occurrence Matrix)'s texture 6 information about high definition land satellite south Coast image. Removed needless component reducing dimension through principal component analysis from this information. And changed into 2 principal component accumulation images, Experiment result 2 principal component conversion accumulation image's eigenvalues were 94.6%. When component with red tide area that uses only sea color image and all principal component image. displayed more correct result. And divided as quantitative,, it compares with turbid stream and the sea that red tide does not exist using statistical feature analysis about texture.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.43 no.9
• /
• pp.830-837
• /
• 2015

Space debris has been a major issue recently for the space-active nations because its growing population is expected to increase the collision risk with operational satellites. Radar and electro-optical system has been used for space debris surveillance, which may cause unnecessary anti-collision manoeuvers due to their low tracking accuracy. So an additional tracking system is required to improve the predicted orbit accuracy and then to jude the anti-collision maneouvers more efficiently. The laser tracking system has been considered as an alternative to decrease these unnecessary manoeuvers. Korea Astronomy and Space Science Institute has been developing a space object laser tracking system which is capable of laser tracking for satellites with retro-reflectors and for space debris using high power laser, and satellite imaging using adaptive optics. In this study, the tracking capability is analyzed for space debris using high power laser based on link budget, false alarm probability and signal detection probability.

• Korean Journal of Remote Sensing
• /
• v.35 no.5_1
• /
• pp.637-647
• /
• 2019

The detection of crop stress is an important issue for the accurate assessment of yield decline. The photochemical reflectance index (PRI) was developed as a remotely sensed indicator of light use efficiency (LUE). The PRI has been tested in crop stress detection and a number of studies demonstrated the feasibility of using it. However, only few studies have focused on the use of PRI from remote sensing imagery. The monitoring of PRI using drone and satellite is made difficult by the low spectral resolution image captures. In order to estimate PRI from multispectral sensor, we propose a band fusion method using adjacent bands. The method is applied to the drone-based hyperspectral and multispectral imagery and estimated PRI explain 79% of the original PRI. And time series analyses showed that two PRI data (drone-based and SRS sensor) had very similar temporal variations. From these results, PRI from multispectral imagery using band fusion can be used as a new method for evaluation of crop stress.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.3
• /
• pp.585-600
• /
• 2012

Security issues in online games are increasing as the online game industry grows. Real money trading (RMT) by online game users has become a security issue in several countries including Korea because RMT is related to criminal activities such as money laundering or tax evasion. RMT-related activities are done by professional work forces, namely gold-farmers, and many of them employ the automated program, bot, to gain cyber asset in a quick and efficient way. Online game companies try to prevent the activities of gold-farmers using game bots detection algorithm and block their accounts or IP addresses. However, game bot detection algorithm can detect a part of gold-farmer's network and IP address blocking also can be detoured easily by using the virtual private server or IP spoofing. In this paper, we propose a method to detect gold-farmer groups by analyzing their connection patterns to the online game servers, particularly information on their routing and source locations. We verified that the proposed method can reveal gold-farmers' group effectively by analyzing real data from the famous MMORPG.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_3
• /
• pp.1749-1760
• /
• 2022

In modern society, human and social damages caused by natural disasters and frequent disaster accidents have been increased year by year. Prompt access to dangerous disaster sites that are inaccessible or inaccessible using state-of-the-art Earth observation equipment such as satellites, drones, and survey robots, and timely collection and analysis of meaningful disaster information. It can play an important role in protecting people's property and life throughout the entire disaster management cycle, such as responding to disaster sites and establishing mid-to long-term recovery plans. This special issue introduces the National Disaster Management Research Institute (NDMI)'s disaster management technology that utilizes various Earth observation platforms, such as mobile survey vehicles equipped with close-range disaster site survey sensors, drones, and survey robots, as well as satellite technology, which is a tool of remote earth observation. Major research achievements include detection of damage from water disasters using Google Earth Engine, mid- and long-term time series observation, detection of reservoir water bodies using Sentinel-1 Synthetic Aperture Radar (SAR) images and artificial intelligence, analysis of resident movement patterns in case of forest fire disasters, and data analysis of disaster safety research. Efficient integrated management and utilization plan research results are summarized. In addition, research results on scientific investigation activities on the causes of disasters using drones and survey robots during the investigation of inaccessible and dangerous disaster sites were described.

• Journal of IKEEE
• /
• v.28 no.2
• /
• pp.150-157
• /
• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.