• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.6C
• /
• pp.349-354
• /
• 2011

Passive millimeter wave imaging has the capability of detecting concealed objects under clothing. Also, passive millimeter imaging can obtain interpretable images under low visibility conditions like rain, fog, smoke, and dust. However, the image quality is often degraded due to low spatial resolution, low signal level, and low temperature resolution. This paper addresses image registration and fusion between passive millimeter images and visual images. The goal of this study is to combine and visualize two different types of information together: human subject's identity and concealed objects. The image registration process is composed of body boundary detection and an affine transform maximizing cross-correlation coefficients of two edge images. The image fusion process comprises three stages: discrete wavelet transform for image decomposition, a fusion rule for merging the coefficients, and the inverse transform for image synthesis. In the experiments, various types of metallic and non-metallic objects such as a knife, gel or liquid type beauty aids and a phone are detected by passive millimeter wave imaging. The registration and fusion process can visualize the meaningful information from two different types of sensors.

• Journal of the Korea Society for Simulation
• /
• v.20 no.4
• /
• pp.105-114
• /
• 2011

The roadmap-based planning is a path planning method which is used widely for a goal-directed movement in Robotics and has been applied to the world of computer animation such as computer games. However it is unnatural for computer characters to follow the path planned by the roadmap method as it is performed in Robotics. Flocking which is used for realistic and natural movements in computer animation enables character's movement by using a few simple rules without planning unlike the roadmap method. However it is impossible to achieve a goal-directed movement with flocking only because it does not keep states. In this paper we propose a simulation method which combines planning based on the road map with reactive actions for natural movements along the path planned. We define and implement steering behaviors for a leader which are needed to follow the trajectory naturally by analysing characteristics of roadmap-based paths and for the rest of members which follow the leader in various manners by detecting obstacles. The simulations are performed and demonstrated by using the implemented steering behaviors on every possible combination of roadmap-based path planning methods and models of configuration spaces. We also show that the detection of obstacle-collisions can be done effectively because paths are planned in the configuration space in which a moving object is reduced to a point.

• Journal of the Korean Magnetics Society
• /
• v.15 no.6
• /
• pp.325-331
• /
• 2005

The magnetic Hut leakage (MFL) type nondestructive testing (NDT) method is widely used to detect corrosion and defects, mechanical deformation of the underground gas pipelines. The object pipeline is magnetically saturated by the magnetic system with permanent magnet and yokes. Because of the strong magnetic field enough to saturate the pipe, there could be distortion of the sensing signals because of the magnetization of the pipeline itself, To detect the defects precisely, the sensing signals are need to be compensated to eliminate the distortions coming from the media hysteresis. In this paper, the magnetizations of the pipeline in MFL type NDT are analyzed by Preisach model and 3D FEM. The distortions of the sensing signals are analyzed.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.2
• /
• pp.95-111
• /
• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.1
• /
• pp.323-328
• /
• 2019

In order to construct 3D geospatial information about the terrain, current measurement using a total station, remote sensing, GNSS(Global Navigation Satellite System) have been used. However, ground survey and GNSS survey have time and economic disadvantages because they have to be surveyed directly in the field. In case of using aerial photographs and satellite images, these methods have the disadvantage that it is difficult to obtain the three-dimensional shape of the terrain. The terrestrial LiDAR can acquire 3D information of X, Y, Z coordinate and shape obtained by scanning innumerable laser pulses at densely spaced intervals on the surface of the object to be observed at high density, and the processing can also be automated. In this study, terrestrial LiDAR was used to analyze slope displacement. Study area slopes were selected and data were acquired using LiDAR in 2016 and 2017. Data processing has been used to generate slope cross section and slope data, and the overlay analysis of the generated data identifies slope displacements within 0.1 m and suggests the possibility of using slope LiDAR on land to manage slopes. If periodic data acquisition and analysis is performed in the future, the method using the terrestrial lidar will contribute to effective risk slope management.

• Journal of Food Hygiene and Safety
• /
• v.34 no.3
• /
• pp.277-282
• /
• 2019

The object of this study is to compare the performance of the 3M Molecular Detection Assay 2 (3M MDA 2) and the Korea Standard Food Codex (KSFC) Method (i.e., isolation media and real-time PCR) in detecting Salmonella Typhimurium and Listeria monocytogenes in traditional Korean foods. Yuk-hwe and Yuk-sashimi (types of raw beef dishes) were artificially inoculated with $10^0-10^4CFU/25g$ of L. monocytogenes and S. Typhimurium. Citrobacter freundii and Listeria innocua were used as competitive microflora. After enrichment, the samples were analyzed using 3M MDA 2 and real-time PCR. All samples inoculated at concentrations of $10^0-10^4CFU/25g$ without competitive microflora were positive for S. Typhimurium and L. monocytogenes, as detected by 3M MDA 2 and Korea Standard Food Codex (KFSC) Method. In addition, part of the samples were positive for the presence of C. freundii and L. innocua. The 3M MDA 2 - Salmonella and Korea Standard Food Codex (KFSC) Method showed similar detection performances in Yuk-hwe and Yuk-sashimi. The 3M MDA 2 method for Salmonella and Listeria, which is a LAMP-based technology, can be used for rapid detection of S. Typhimurium and L. monocytogenes in raw beef. LAMP bioluminescence assays provide results on the subsequent day and are simple to use compared with the Korea Standard Food Codex (KFSC) Method, particularly in terms of DNA preparation.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.6
• /
• pp.77-83
• /
• 2020

The heat treatment facility is in a situation where the scope of application of the remote IOT system is expanding due to the harsh environment caused by high heat and long working hours among the root industries. In this heat treatment process environment, the IOT middleware is required to play a pivotal role in interpreting, managing and controlling data information of IoT devices (sensors, etc.). Until now, the system controlled by the heat treatment remotely was operated with the command of the operator's batch system without overall monitoring of the site situation. However, for the safety and precise control of the heat treatment facility, it is necessary to control various sensors and recognize the surrounding work environment. As a solution to this, the heat treatment safety support system presented in this paper proposes a support system that can detect the access of the work manpower to the heat treatment furnace through thermal image detection and operate safely when ordering work from a remote location. In addition, an OPEN CV-based deterioration analysis system using DNN deep learning network was constructed for faster and more accurate recognition than general fixed hot spot monitoring-based thermal image analysis. Through this, we would like to propose a system that can be used universally in the heat treatment environment and support the safety management specialized in the heat treatment industry.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.1
• /
• pp.45-55
• /
• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.2
• /
• pp.101-115
• /
• 2022

This study compared the performance of LiDAR in detecting objects in rough weather with that in clear weather. An experiment that reproduced rough weather divided the fog visibility into four stages from 200 m to 50 m and controlled the rainfall by dividing it into 20 mm/h and 50 mm/h. The number of points cloud and intensity were used as the performance indicators. The difference in performance was statistically investigated by a T-Test. The result of the study indicates that the performance of LiDAR decreased in the order in situations of 20 mm/h rainfall, fog visibility less than 200 m, 50 mm/h rainfall, fog visibility less than 150 m, fog visibility less than 100 m, and fog visibility less than 50 m. The decreased performance was greater when the measurement distance was greater and when the color was black rather than white. However, in the case of white, there was no difference in performance at a measurement distance of 10 m even at 50 m fog visibility, which is considered the worst situation in this experiment. This no difference in performance was also statistically significant. These performance verification results are expected to be utilized in the manufacture of road facilities in the future that improve the visibility of sensors.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.1
• /
• pp.51-58
• /
• 2022

Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.