• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1057-1062
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• 2014

This paper proposes a tip-over detection method for a mobile robot using a support inscribed circle defined as an inscribed circle of a support polygon. A support polygon defined by the contact points between the robot and the terrain is often used to analyze the tip-over. For a robot moving on uneven terrain, if the intersection between the extended line of gravity from the robot's COG and the terrain is inside the support polygon, tip-over will not occur. On the contrary, if the intersection is outside, tip-over will occur. The terrain is detected by using an RGB-D sensor. The terrain is locally modeled as a plane, and thus the normal vector can be obtained at each point on the terrain. The support polygon and the terrain's normal vector are used to detect tip-over. However, tip-over cannot be detected in advance since the support polygon is determined depending on the orientation of the robot. Thus, the support polygon is approximated as its inscribed circle to detect the tip-over regardless of the robot's orientation. To verify the effectiveness of the proposed method, the experiments are carried out using a 4-wheeled robot, ERP-42, with the Xtion RGB-D sensor.

• Journal of Communications and Networks
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• v.11 no.4
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• pp.337-349
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• 2009

In this paper, we introduce a novel mobility model for mobile sinks in which the sinks move towards randomly distributed destinations, where each destination is associated with a mission. The novel mobility model is termed the random mobility with destinations. There have been many studies on mobile sinks; however, they merely support two extreme cases of sink mobility. The first case features the most common and general mobility, with the sinks moving randomly, unpredictably, and inartificially. The other case takes into account mobility only along predefined or determined paths such that the sinks can gather data from sensor nodes with minimum overhead. Unfortunately, these studies for the common mobility and predefined path mobility might not suit for supporting the random mobility with destinations. In order to support random mobility with destination, we propose a new protocol, in which the source nodes send their data to the next movement path of a mobile sink. To implement the proposed protocol, we first present a mechanism for predicting the next movement path of a mobile sink based on its previous movement path. With the information about predicted movement path included in a query packet, we further present a mechanism that source nodes send energy-efficiently their data along the next movement path before arriving of the mobile sink. Last, we present mechanisms for compensating the difference between the predicted movement path and the real movement path and for relaying the delayed data after arriving of the mobile sink on the next movement path, respectively. Simulation results show that the proposed protocol achieves better performance than the existing protocols.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.454-456
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• 2010

The demands for (a) individual vehicle has been gradually increasing recently due to increase of personal income and spare time. In 2009, the quantities of registered vehicles exceeds over 17,325,210 millions pieces, and the risks of traffic accidents and traffic jam are increasing days by days. It has some limitations to solve the problem of traffic jam by transportation facilities and causes lots of time and costs. For a possible solution, ITS(Intelligent Transport System) has been introduced, but it is an insufficient way for abrupt incidents or risks on roads. The riskiest matter on driving a vehicle is unforeseen situation. In this paper, the most efficient and economical system that communicates with a driver about unexpected accident by sensor network and GPS information, is introduced rather than a traditional method associated with lots of time and costs.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.595-618
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• 2010

This paper describes the application of a wireless sensor network to a 31 meter-tall medieval tower located in the city of Trento, Italy. The effort is motivated by preservation of the integrity of a set of frescoes decorating the room on the second floor, representing one of most important International Gothic artworks in Europe. The specific application demanded development of customized hardware and software. The wireless module selected as the core platform allows reliable wireless communication at low cost with a long service life. Sensors include accelerometers, deformation gauges, and thermometers. A multi-hop data collection protocol was applied in the software to improve the system's flexibility and scalability. The system has been operating since September 2008, and in recent months the data loss ratio was estimated as less than 0.01%. The data acquired so far are in agreement with the prediction resulting a priori from the 3-dimensional FEM. Based on these data a Bayesian updating procedure is employed to real-time estimate the probability of abnormal condition states. This first period of operation demonstrated the stability and reliability of the system, and its ability to recognize any possible occurrence of abnormal conditions that could jeopardize the integrity of the frescos.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1421-1427
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• 2018

Due to the rapid development of Internet-of-Things technology, different types of smart sensors are now devised and deployed widely. These smart sensors are now used in animal husbandry which was traditionally managed by the experience of farmers, such that wearable sensors for livestock, and the smart farm which is equipped with multiple sensors are utilized to increase the efficiency of livestock management. Herein, we consider a scheme in which the body temperature and the level of activity are measured by smart sensor which is attached to the neck of dairy cattle and the health condition is monitored based on collected data. Especially, we find that the estrous of dairy cattle which is one of most important metric in milk production, can be predicted with high precision using various machine learning techniques. By utilizing the proposed prediction scheme, estrous of cattle can be detected immediately and this can improve the efficiency of cattle management.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.255-257
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• 2021

During the winter season, when electricity usage increases rapidly every year, fires are frequent due to short circuits in aging electrical facilities in multi-use facilities such as traditional markets and jjimjilbangs, apartments, and multi-family houses. Most of the causes of such fires are caused by excessive loads applied to aging wires, causing the wire covering to melt and being transferred to surrounding ignition materials. In this study, we implement a system that measures the overload and overheating of the wire through a composite sensor, detects the toxic gas generated there, and logs it to the server through the gateway. Based on this, we will develop a platform that can predict, alarm and block electric fires in real time through big data analysis, and a simulator that can simulate fire occurrence experiments.

• Korean Journal of Optics and Photonics
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• v.19 no.6
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• pp.416-421
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• 2008

A scheme for predicting the concentration of a glucose solution based on its relative optical absorbance at multiple probe wavelengths was proposed and verified. The relative absorbance at each of the probe wavelength was obtained with respect to the absorbance at a reference wavelength. The single reference wavelength (1310 nm) and a group of four different probe wavelengths (1064, 1550, 1685, 1798 nm) were selected to exhibit the glucose absorbance with opposite signs, thereby enhancing the accuracy of the prediction. The final glucose concentration was estimated by taking the average of the predicted values provided by the four probe wavelengths. The absorbance of the glucose solution for the path length of 5 mm was $-1.42{\times}10^{-6}\;AU$/(mg/dL) at the reference wavelength of 1310 nm and peaked at $+8.12{\times}10^{-6}\;AU$/(mg/dL) at 1685 nm. The concentration of the glucose solution was decently predicted by means of the proposed scheme with the standard error of prediction of ${\sim}28\;mg/dL$. In addition, the influence of the ambient temperature and the fat thickness upon the prediction of the glucose concentration was examined. The absorption change with the temperature was $-9.1{\times}10^{-5}\;AU/^{\circ}C$ in the temperature range of $26{\sim}40^{\circ}C$ at the reference wavelength, and $-2.08{\times}10^{-2}\;AU/^{\circ}C$ at 1550 nm. And the absorption change with respect to the fat thickness was +1.093 AU/mm at the probe wavelength of 1685 nm.

• Journal of the Korea Society for Simulation
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• v.33 no.3
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• pp.13-26
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• 2024

The CBRN(Chemical, Biological, Radiological, and Nuclear) hazard prediction model is a system that supports commanders in making better decisions by creating contamination distribution and damage prediction areas based on the weapons used, terrain, and weather information in the events of biochemical and radiological accidents. NBC_RAMS(Nuclear, Biological and Chemical Reporting And Modeling S/W System) developed by ADD (Agency for Defense Development) is used not only supporting for decision making plan for various military operations and exercises but also for post analyzing CBRN related events. With the NBC_RAMS's core engine, we introduced a CBR hazard assessment scenario analysis system that can generate contaminant distribution prediction results reflecting various CBR scenarios, and described how to apply it in specific purposes in terms of input information, meteorological data, land data with land coverage and DEM, and building data with pologon form. As a practical use case, a technology development case is addressed that tracks the origin location of contaminant source with artificial intelligence and a technology that selects the optimal location of a CBR detection sensor with score data by analyzing large amounts of data generated using the CBRN scenario analysis system. Through this system, it is possible to generate AI-specialized CBRN related to training and analysis data and support planning of operation and exercise by predicting battle field.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.106-116
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• 2011

USN(Ubiquitous Sensor Network) environment permits us to access whatever information we want, whenever we want. The technologies to provide a basement to these environments premise an accurate location establishment. Especially, ITS(Intelligent Transportation Systems) is easily constructed by applying USN technology. Localization can be categorized as either Range-based or Range-free. Range-based is known to be not suitable for the localization based on sensor network, because of the irregularity of radio propagation and the additional device requirement. The other side, Range-free is much appropriated for the resource constrained sensor network because it can actively locate by means of the communication radio. But, generally the location accuracy of Range-free is low. Especially, it is very low in a low-density environment. So, these two methods have both merits and demerits. Therefore, it requires a new method to be able to improve tracking accuracy by combining the two methods. This paper proposes the tracking scheme based on range-hybrid, which can markedly enhance tracking accuracy by effectively using the information of surrounding nodes and the RSSI(Received Signal Strength Indication) that does not require additional hardware. Additionally, we present a method, which can improve the accuracy of vehicle tracking by adopting the prediction mechanism. Simulation results show that our method outperforms other methods in the transportation simulation environment.