• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.186-188
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• 2005

Because forest fire changes the direction according to the environmental elements, it is difficult to predict the direction of it. Currently, though some researchers have been studied to which predict the forest fire occurrence and the direction of it, using the remote detection technique, it is not enough and efficient. And recently because of the development of the sensor technique, a lot of In-Situ sensors are being developed. These kinds of In-Situ sensor data are used to collect the environmental elements such as temperature, humidity, and the velocity of the wind. Accordingly we need the prediction technique about the environmental elements analysis and the direction of the forest fire, using the In-Situ sensor data. In this paper, as a technique for predicting the direction of the forest fire, we propose the correlation analysis technique about In-Situ sensor data such as temperature, humidity, the velocity of the wind. The proposed technique is based on the clustering method and clusters the In-Situ sensor data. And then it analyzes the correlation of the multivariate correlations among clusters. These kinds of prediction information not only helps to predict the direction of the forest fire, but also finds the solution after predicting the environmental elements of the forest fire. Accordingly, this technique is expected to reduce the damage by the forest fire which occurs frequently these days.

• Journal of Korean Society of Water and Wastewater
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• v.33 no.2
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• pp.151-158
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• 2019

Objectives of this study were to identify the hotspot for displacement of the on-line water quality sensors, in order to detect illicit discharge of untreated wastewater. A total of twenty-six water quality parameters were measured in sewer networks of the industrial complex located in Daejeon city as a test-bed site of this study. For the water qualities measured on a daily basis by 2-hour interval, the self-organizing maps(SOMs), one of the artificial neural networks(ANNs), were applied to classify the catchments to the clusters in accordance with patterns of water qualities discharged, and to determine the hotspot for priority sensor allocation in the study. The results revealed that the catchments were classified into four clusters in terms of extent of water qualities, in which the grouping were validated by the Euclidean distance and Davies-Bouldin index. Of the on-line sensors, total organic carbon(TOC) sensor, selected to be suitable for organic pollutants monitoring, would be effective to be allocated in D and a part of E catchments. Pb sensor, of heavy metals, would be suitable to be displaced in A and a part of B catchments.

• Journal of the Korea Society of Computer and Information
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• v.24 no.4
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• pp.169-176
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• 2019

Wireless sensor networks are used to monitor and control areas in a variety of military and civilian areas such as battlefield surveillance, intrusion detection, disaster recovery, biological detection, and environmental monitoring. Since the sensor nodes are randomly placed in the area of interest, separation of the sensor network area may occur due to environmental obstacles or a sensor may not exist in some areas. Also, in the situation where the sensor node is placed in a non-relocatable place, some node may exhaust energy or physical hole of the sensor node may cause coverage hole. Coverage holes can affect the performance of the entire sensor network, such as reducing data reliability, changing network topologies, disconnecting data links, and degrading transmission load. It is possible to solve the problem that occurs in the coverage hole by finding a coverage hole in the sensor network and further arranging a new sensor node in the detected coverage hole. The existing coverage hole detection technique is based on the location of the sensor node, but it is inefficient to mount the GPS on the sensor node having limited resources, and performing other location information processing causes a lot of message transmission overhead. In this paper, we propose an Adjacent Matrix-based Hole Coverage Discovery(AMHCD) scheme based on connectivity of neighboring nodes. The method searches for whether the connectivity of the neighboring nodes constitutes a closed shape based on the adjacent matrix, and determines whether the node is an internal node or a boundary node. Therefore, the message overhead for the location information strokes does not occur and can be applied irrespective of the position information error.

• Smart Structures and Systems
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• v.12 no.1
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• pp.73-95
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• 2013

Nine deoxyribonucleic acid (DNA) sequences were used to functionalize single-walled carbon nanotube (SWNT) sensors to detect the trace amount of methanol, acetone, and HCl in vapor. DNA 24 Ma (24 randomly arranged nitrogenous bases with one amine at each end of it) decorated SWNT sensor and DNA 24 A (only adenine (A) base with a length of 24) decorated SWNT sensor have demonstrated the largest sensing responses towards acetone and HCl, respectively. On the other hand, for the DNA GT decorated SWNT sensors with different sequence lengths, the optimum DNA sequence length for acetone and HCl sensing is 32 and 8, separately. The detection of methanol, acetone, and HCl have identified that DNA functionalized SWNT sensors exhibit great selectivity, sensitivity, and repeatability with an accuracy of more than 90%. Further, a sensor array composed of SWNT functionalized with various DNA sequences was utilized to identify acetone and HCl through pattern recognition. The sensor array is a combination of four different DNA functionalized SWNT sensors and two bare SWNT sensors (work as reference). This wireless sensing system has enabled real-time gas monitoring and air quality assurance for safety and security.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.81-84
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• 2005

Various design schemes for network using wireless sensor nodes have been widely studied on the several application areas ranging from the real world information collection to environmental monitor. Currently, the schemes are focused on the design of sensor network for low power consumption, power-aware routing protocol, micro miniature operating system and sensor network middleware. The indoor localization system that identifies the location of the distributed nodes in a wireless sensor network requires features dealing with mobility, plurality and other environmental constraints of a sensor node. In this paper, we present an efficient location system to cope with mobility of multiple mobile nodes by designing a location handler that processes location information selectively depending on the nodes' density in a specific region. In order to resolve plurality of multiple mobile nodes, a routing method for the location system is also proposed to avoid the occurrence of overlapped location data.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.913-919
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• 2011

In this paper, we present a new efficient event detection algorithm for sensor networks with faults. We focus on multi-attributed events, which are sets of data points that correspond to interesting or unusual patterns in the underlying phenomenon that the network monitors. Conventional algorithms cannot detect some events because they treat only their own sensor readings which can be affected easily by environmental or physical problem. Our approach exploits spatio-temporal correlation of sensor readings. Sensor nodes exchange a fault-tolerant code encoded their own readings with neighbors, organize virtual sensor readings which have spatio-temporal correlation, and determine a result for multi-attributed events from them. In the result, our proposed algorithm provides improvement of detecting multi-attributed events and reduces the number of false-negatives due to negative environmental effects.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2138-2142
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• 2005

A wearable sensor system is proposed as a man-machine interface to control a device for walking assistance. The sensor system is composed of small sensors to detect the information about the user's body motion such as the activity level of skeletal muscles and the acceleration of each body parts. Each sensor includes a microcomputer and all the sensors are connected into a network by using the serial communication function of the microcomputer. The whole network is integrated into a belt made of soft fabric, thus, users can put on/off very easily. The sensor system is very reliable because of its decentralized network configuration. The body information obtained from the sensor system is used for controlling the assisting device to achieve a comfortable and an effective walking training.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.4 s.316
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• pp.74-78
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• 2007

Novel concept of sensor mat and its signal processing method is proposed for patient monitoring in medical application. Proposed sensor mat structure has sensing inner layer which has cross-linked arrangement using plastic optical fiber(POF). Large core diameter of plastic optical fiber behaved as band pass filter by averaging the noise component caused by unwanted environmental factors. Signal processor followed by sensor output added noise immune performance by filtering out unwanted component. Fail-proof patient breath monitoring scheme was realized by using intelligent decision algorithm. Unlike the conventional approach by using mechanical sensor, which have high sensitivity both to signal and to environmental noise, our approach provided reliable breath motion detection.

• Economic and Environmental Geology
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• v.31 no.1
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• pp.53-57
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• 1998

Transient electromagnetic (TEM) method is also affected by cultural and natural electromagnetic (EM) noises, since it uses part of the broadband ($10^{-2}$ to $10^5Hz$) spectrum. Especially, predominant EM noise which affects a moving transmitter-receiver TEM system is sensor motion-induced noise. This noise is caused by the sensor motion in the earth magnetic field. The technique for reducing the sensor motion-induced EM noise presented in this paper is based on Halverson stacking. This Halverson stacking is generally used in a time-domain induced polarisation (IP) system to reject DC offset and linear drift. According to spectrum analysis of the vertical component of sensor motion-induced noise, the frequency range affected by the motion of an EM sensor is less than about 700 Hz in this study. With the decrease of the frequency, the spectral power caused by the motion of a sensor increases. For example, at the frequency of 200 Hz, the spectral power of the sensor motion-induced noise is $-90dBVrms^2$ while the spectral power of the EM noise measured with a fixed sensor on the ground is $-105dBVrms^2$, and at the frequency of 100 Hz, the spectral power of the sensor motion-induced noise is $-70dBVrms^2$ while the spectral power of the EM noise measured with a fixed sensor on the ground is $-105dBVrms^2$. With applying Halverson stacking to an artificial noise transient generated by adding a noise-free transient to sensor motion-induced noise measured without pulsing, it is shown that the filtered transient is nearly consistent with the noise-free transient within a delay time of $0.5{{\mu}sec}$. The inversion obtained from this filtered transient is in accord with the true model with an error of 5%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.135-152
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• 2018

The purpose of this study is to propose an optimal fusion method of aerial multi - sensor data to improve the accuracy of land cover classification. Recently, in the fields of environmental impact assessment and land monitoring, high-resolution image data has been acquired for many regions for quantitative land management using aerial multi-sensor, but most of them are used only for the purpose of the project. Hyperspectral sensor data, which is mainly used for land cover classification, has the advantage of high classification accuracy, but it is difficult to classify the accurate land cover state because only the visible and near infrared wavelengths are acquired and of low spatial resolution. Therefore, there is a need for research that can improve the accuracy of land cover classification by fusing hyperspectral sensor data with multispectral sensor and aerial laser sensor data. As a fusion method of aerial multisensor, we proposed a pixel ratio adjustment method, a band accumulation method, and a spectral graph adjustment method. Fusion parameters such as fusion rate, band accumulation, spectral graph expansion ratio were selected according to the fusion method, and the fusion data generation and degree of land cover classification accuracy were calculated by applying incremental changes to the fusion variables. Optimal fusion variables for hyperspectral data, multispectral data and aerial laser data were derived by considering the correlation between land cover classification accuracy and fusion variables.