• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1708-1721
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• 2011

Recent outbreak of cattle diseases such as foot-and-mouth disease(FMD) requires constant monitoring of burial sites of mass cull of cattles. However, current monitoring system takes environmental samples from burial sites with period of between one and two weeks, which makes it impossible for non-stop management of hazardous bio-waste. Therefore, in this study, we suggest an improved real-time environmental monitoring system for such bio-hazardous sites based on wireless sensor networks, which makes constant surveillance of the FMD burial sites possible. The system consists mainly several wireless environmental monitoring sensors(i.e dust, Co2, VOC, NH3, H2S, temperature, humidity) nodes and GPS location tracking nodes. Through analysis of the relayed of the environmental monitoring data via gateway, the system makes it possible for constant monitoring and quick response for emergency situation of the burial sites. In order to test the effectiveness of the system, we have installed a set of sensor to gas outlets of the burial sites, then collected and analyzed measured bio-sensing data. We have conducted simulated emergency test runs and was able to detect and monitor the foul smell constantly. With our study, we confirm that the preventive measures and quick response of bio environmental accident are possible with the help of a real-time environmental monitoring system.

• Journal of the Korean Society for Computer Game
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• v.31 no.4
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• pp.197-203
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• 2018

As people's interest in Virtual Reality has recently increased, peripherals have also made many progress. There is a lot of research being done from VR environment to VR configuration through scanning at room level with various interface devices that can interact with objects in the environment. According to current VR research Home VR uses multiple haptic interfaces to interact with objects configured in the VR environment, the method uses room scanning to some extent is beyond the spatial constraints and may use tracking equipment to interact with real objects. And advances in 3D printers have enabled the distribution of commercial 3D printers and home 3D printers, and made it easy for 3D printers to create models of their choice at home or at home. Considering the above two factors, We think it is necessary to study the difference between a model's object that people feel when interacting directly with an easy-to-create model in a VR environment. Therefore, in this paper, we are going to implement objects produced by 3D printers in VR space and study the differences between using real objects and other general interaction equipment through user testing with those that are actually implemented.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.51-59
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• 2018

The Multi-Mission Unmanned Surface Vehicle(MMUSV), which is manufactured using glass Fiber Reinforced Plastic(FRP) material, is designed to perform a surveillance and reconnaissance on the sea. Various navigation sensors, such as RADAR, RIDAR, camera, are mounted on a mast to perform an autonomous navigation. And a dummy gun is mounted on the deck of the MMUSV for a target tracking and disposal. It is necessary to analyze a strength for structures mounted on the deck because the MMUSV performs missions under a severe sea state. In this paper, a strength analysis of the mast structure is performed on static loads and lateral external loads to verify an adequacy of the designed mast through a series of simulations. Based on the results of captive model tests, a strength analysis for a heave motion of the mast structure is conducted using a simulation tool. Also a simulation and fatigue test for a mounting part between the MMUSV and the dummy gun are performed using a specimen. The simulation and test results are represented that a structure of the mast and mounting part of the dummy gun are appropriately designed.he impact amount are performed through simulation and experiments.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.3
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• pp.219-237
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• 2021

Visual information is widely used to deliver health information more effectively on social media, but there is lack of research on how effectively visual information delivers health information on social media. This study reports Facebook users' reading patterns and cognitive tests (recall and recognition tests) results using health-related Facebook posts. For this study, 21 college students participated in online questionnaire, eye tracking experiment, and recall and recognition tests. First, users paid their attention to the area that contains information (i.e., users focused on the main text rather than photos that do not contain information). Second, in the case of Facebook posts containing infographics, users paid their attention on the infographics, but the recall and recognition test results of the posts with infographics were lower than the posts containing photos. Particularly, when the infographics are in a complex collage format, recall and recognition tests result lower scores. Third, regarding the length of the text, the Facebook posts with short text resulted in higher recall and recognition test scores than the posts with medium or long texts. This study suggested to Facebook health information providers and distributors how to design Facebook posts for delivering health information more effectively.

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.351-360
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• 2024

With the increasing use of drone technology, the Unmanned Aerial Vehicle (UAV) is now being utilized in various fields. However, this increased use of drones has resulted in various issues. Due to its small size, the drone is difficult to detect with radar or optical equipment, so acoustical tracking methods have been recently applied. In this paper, a method of localization of multiple drones using the acoustic characteristics of the quadcopter drone is suggested. Because the acoustic characteristics induced by each rotor are differentiated depending on the type of drone and its movement state, the sound source of the drone can be reconstructed by spatially clustering the results of the estimated positions of the blade passing frequency and its harmonic sound source. The reconstructed sound sources are utilized to finally determine the location of multiple-drone sound sources by applying the source localization algorithm. An experiment is conducted to analyze the acoustic characteristics of the test quadcopter drones, and the simulations for three different types of drones are conducted to localize the multiple drones based on the measured acoustic signals. The test result shows that the location of multiple drones can be estimated by utilizing the acoustic characteristics of the drone. Also, one can see that the clarity of the separated drone sound source and the source localization algorithm affect the accuracy of the localization for multiple-drone sound sources.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.45-58
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• 2014

The Korean Culture Contents spread out to Worldwide, because the Korean wave is sweeping in the world. The contents stand in the middle of the Korean wave that we are used it. Each country is ongoing to keep their Culture industry improve the national brand and High added value. Performing contents is important factor of arousal in the enterprise industry. To improve high arousal confidence of product and positive attitude by populace is one of important factor by advertiser. Culture contents is the same situation. If culture contents have trusted by everyone, they will give information their around to spread word-of-mouth. So, many researcher study to measure for person's arousal analysis by statistical survey, physiological response, body movement and facial expression. First, Statistical survey has a problem that it is not possible to measure each person's arousal real time and we cannot get good survey result after they watched contents. Second, physiological response should be checked with surround because experimenter sets sensors up their chair or space by each of them. Additionally it is difficult to handle provided amount of information with real time from their sensor. Third, body movement is easy to get their movement from camera but it difficult to set up experimental condition, to measure their body language and to get the meaning. Lastly, many researcher study facial expression. They measures facial expression, eye tracking and face posed. Most of previous studies about arousal and interest are mostly limited to reaction of just one person and they have problems with application multi audiences. They have a particular method, for example they need room light surround, but set limits only one person and special environment condition in the laboratory. Also, we need to measure arousal in the contents, but is difficult to define also it is not easy to collect reaction by audiences immediately. Many audience in the theater watch performance. We suggest the system to measure multi-audience's reaction with real-time during performance. We use difference image analysis method for multi-audience but it weaks a dark field. To overcome dark environment during recoding IR camera can get the photo from dark area. In addition we present Multi-Audience Engagement Index (MAEI) to calculate algorithm which sources from sound, audience' movement and eye tracking value. Algorithm calculates audience arousal from the mobile survey, sound value, audience' reaction and audience eye's tracking. It improves accuracy of Multi-Audience Engagement Index, we compare Multi-Audience Engagement Index with mobile survey. And then it send the result to reporting system and proposal an interested persons. Mobile surveys are easy, fast, and visitors' discomfort can be minimized. Also additional information can be provided mobile advantage. Mobile application to communicate with the database, real-time information on visitors' attitudes focused on the content stored. Database can provide different survey every time based on provided information. The example shown in the survey are as follows: Impressive scene, Satisfied, Touched, Interested, Didn't pay attention and so on. The suggested system is combine as 3 parts. The system consist of three parts, External Device, Server and Internal Device. External Device can record multi-Audience in the dark field with IR camera and sound signal. Also we use survey with mobile application and send the data to ERD Server DB. The Server part's contain contents' data, such as each scene's weights value, group audience weights index, camera control program, algorithm and calculate Multi-Audience Engagement Index. Internal Device presents Multi-Audience Engagement Index with Web UI, print and display field monitor. Our system is test-operated by the Mogencelab in the DMC display exhibition hall which is located in the Sangam Dong, Mapo Gu, Seoul. We have still gotten from visitor daily. If we find this system audience arousal factor with this will be very useful to create contents.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.623-634
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• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.133-137
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• 2010

Purpose: Breast cancer is known to be more vulnerable to bone metastasis and lymph node metastasis than other types of cancer, and nuclear examinations whole body bone scan and lymphoscintigraphy are performed commonly before and after breast cancer operation. In case whole body bone scan is performed on the day before lymphoscintigraphy, the radiopharmaceutical taken into and remaining in the bones provides anatomical information for tracking and locating sentinel lymph nodes. Thus, this study purposed to examine how much bone density affects in locating sentinel lymph nodes. Materials and Methods: The subjects of this study were 22 patients (average age $52{\pm}7.2$) who had whole body bone scan and lymphoscintigraphy over two days in our hospital during the period from January to December, 2009. In the blind test, 22 patients (average age $57{\pm}6.5$) who had lymphoscintigraphy using $^{57}Co$ flood phantom were used as a control group. In quantitative analysis, the relative ratio of the background to sentinel lymph nodes was measured by drawing ROIs on sentinel lymph nodes and the background, and in gross examination, each of a nuclear physician and a radiological technologist with five years' or longer field experience examined images through blind test in a five-point scale. Results: In the results of quantitative analysis, the relative ratio of the background to sentinel lymph nodes was 14.2:1 maximum and 8.5:1 ($SD{\pm}3.48$) on the average on the front, and 14.7:1 maximum and 8.5:1 ($SD{\pm}3.42$) on the average on the side. In the results of gross examination, when $^{57}Co$ flood phantom images were compared with images containing bones, the score was relative high as 3.86 ($SD{\pm}0.35$) point for $^{57}Co$ flood phantom images and 4.09 ($SD{\pm}0.42$) for bone images. Conclusion: When whole body bone scan was performed on the day before lymphoscintigraphy, the ratio of the background to sentinel lymph nodes was over 10:1, so there was no problem in locating lymph nodes. In addition, we expect to reduce examination procedures and improve the quality of images by indicating the location of sentinel lymph nodes using bone images as body contour without the use of a source.