• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.684-687
• /
• 2006

Airborne imagery must be precisely orthorectified to be used as geographical information data. GPS/INS (Global Positioning System/Inertial Navigation System) and LIDAR (LIght Detection And Ranging) data were employed to automatically orthorectify airborne images. In this study, 154 frame airborne images and LIDAR vector data were acquired. LIDAR vector data were converted to raster image for employing as reference data. To derive images with constant brightness, flat field correction was applied to the whole images. The airborne images were geometrically corrected by calculating internal orientation and external orientation using GPS/INS data and then orthorectified using LIDAR digital elevation model image. The precision of orthorectified images was validated using 50 ground control points collected in arbitrary selected five images and LIDAR intensity image. In validation results, RMSE (Root Mean Square Error) was 0.365 smaller then two times of pixel spatial resolution at the surface. It is possible that the derived mosaicked airborne image by this automatic orthorectification method is employed as geographical information data.

• Journal of Ocean Engineering and Technology
• /
• v.38 no.3
• /
• pp.103-114
• /
• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.91-94
• /
• 2005

A numerical formula that presents relationship between a point of a satellite image and its ground position is called a sensor model. For precise geolocation of satellite images, we need an error-free sensor model. However, the sensor model based on GOES ephemeris data has some error, in particular after Image Motion Compensation (IMC) mechanism has been turned off. To solve this problem, we investigate three sensor models: Collinearity model, Direct Linear Transform (DLT) model and Orbit-based model. We apply matching between GOES images and global coastline database and use successful results as control points. With control points we improve the initial image geolocation accuracy using the three models. We compare results from three sensor models that are applied to GOES-9 images. As a result, a suitable sensor model for precise geolocation of GOES-9 images is proposed.

• Smart Structures and Systems
• /
• v.1 no.1
• /
• pp.1-12
• /
• 2005

This paper describes the area of intelligent systems research as funded by the Civil and Mechanical Systems (CMS) Division of the National Science Foundation (NSF). With developments in computer science, information technology, sensing and control the design of typical machines and structures by civil and mechanical engineers is evolving toward intelligent systems that can sense, decide and act. This trend toward electro-mechanical design is well-established in modern machines (e.g. vehicles, robots, disk drives) and often referred to as mechatronics. More recently intelligent systems design is becoming an important aspect of structures, such as buildings and bridges. We briefly review recent developments in structural control, including the role that NSF has played in their development, and discuss on-going CMS activities in this area. In particular, we highlight the interdisciplinary initiative on Sensors and Sensor Networks and the Network for Earthquake Engineering Simulation (NEES). NEES is a distributed cyberinfrastructure to support earthquake engineering research, and provides the pioneering NEES grid computing environment for simulation, teleoperation, data collection and archiving, etc.

• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.249-252
• /
• 2004

The automatic determination of vehicle operation status as well as continuous tracking of vehicle location with intelligent management is one of major elements to achieve the goals. Especially, vehicle operation status can only be analyzed in terms of expert experiences with real-time location data with scheduling information. However the scheduling information of individual vehicle is very difficult to be interpreted immediately because there are hundreds of thousand vehicles are run at the same time in the national wide range workplace. In this paper, we propose the location-based knowledge management system(LKMs) using the active trajectory analysis method with routing and scheduling information to cope with the problems. This system uses an inference technology with dispatching and geographic information to generate the logistics knowledge that can be furnished to the manager in the central vehicle monitoring and controlling center.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.11
• /
• pp.1003-1007
• /
• 2015

Depth calibration is a procedure for finding the conversion function that maps disparity data from a depth-sensing camera to actual distance information. In this paper, we present an optimal depth calibration method for Kinect$^{TM}$ sensors based on an experimental design and convex optimization. The proposed method, which utilizes multiple measurements from only two points, suggests a simplified calibration procedure. The confidence ellipsoids obtained from a series of simulations confirm that a simpler procedure produces a more reliable calibration function.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.5
• /
• pp.420-427
• /
• 2010

This paper describes a localization method based on Monte Carlo Localization approach for a mobile robot. The method uses range data which are measured from ultrasound transmitting beacons whose locations are given a priori. The ultrasound receiver on-board a robot detects the range from the beacons. The method requires several beacons, theoretically over three. The method proposes a sensor model for the range sensing based on statistical analysis of the sensor output. The experiment uses commercialized beacons and detector which are used for trilateration localization. The performance of the proposed method is verified through real implementation. Especially, it is shown that the performance of the localization degrades as the sensor update rate decreases compared with the MCL algorithm update rate. Though the method requires exact location of the beacons, it doesn't require geometrical map information of the environment. Also, it is applicable to estimation of the location of both the beacons and robot simultaneously.

• Journal of Institute of Control, Robotics and Systems
• /
• v.15 no.2
• /
• pp.192-197
• /
• 2009

In this paper, we proposed the obstacle avoidance system using a single camera image and LM(Levenberg-Marquart) neural network fuzzy controller. According to a robot technology adapt to various fields of industry and public, the robot has to move using self-navigation and obstacle avoidance algorithms. When the robot moves to target point, obstacle avoidance is must-have technology. So in this paper, we present the algorithm that avoidance method based on fuzzy controller by sensing data and image information from a camera and using the LM neural network to minimize the moving error. And then to verify the system performance of the simulation test.

• Proceedings of the KAIS Fall Conference
• /
• 2007.11a
• /
• pp.274-276
• /
• 2007

미리 정해진 특정 기능을 수행하기 위해 하드웨어와 소프트웨어가 내장된 전자 제어 시스템을 임베디드 시스템이라 하며, 우리 일상생활에서 이용하는 여러 가전제품이나 전자기기에는 어떤 식으로든 임베디드 시스템이 탑재돼 있다. 본 연구에서는 센서 노드로부터 수집된 센싱 정보를 서버에서 관리하는 기존의 방식과 달리 임베디드 시스템 기반의 센싱 정보 수집방안을 제시한다.

• Proceedings of the KIPE Conference
• /
• 2019.07a
• /
• pp.543-545
• /
• 2019

Recently, the demand for smart farms is increasing due to the increase in the cultivation area such as horticulture, fruit trees and special crops. However, due to the irregular weather changes and the cultivation method of the crops due to the different cultivation environment, there are frequent occurrence of diseases and insect pests and infectious diseases due to system error or carelessness, and the cycle is also very short. In addition, the Smart Farm business has been built by combining various sensors (temperature, humidity, CO2, illumination) and LED lighting, but it is costly in terms of frequent errors, lack of power supply, And thus the management can not be efficiently managed. Therefore, this paper combines real time sensing technology based on IoT Platform and high performance control technology to control pests and equipment errors and monitor the growth status of crops in real time based on big data analysis and Artificial Intelligence System.