• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.23-30
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• 2011

In the wireless sensor network(WSN), the detection of precise location of sensor nodes is essential for efficiently utilizing the sensing data acquired from sensor nodes. Among various location methods, the received signal strength (RSS) based localization scheme is mostly preferable in many applications since it can be easily implemented without any additional hardware cost. Since the RSS localization method is mainly effected by radio channel between two nodes, outlier data can be included in the received signal strength measurement specially when some obstacles move around the link between nodes. The outlier data can have bad effect on estimating the distance between two nodes such that it can cause location errors. In this paper, we propose a RSS-based localization method using Robust Statistic and Gaussian filter algorithm for enhancing the accuracy of RSS-based localization. In the proposed algorithm, the outlier data can be eliminated from samples by using the Robust Statistics as well as the Gaussian filter such that the accuracy of localization can be achieved. Through simulation, it is shown that the proposed algorithm can increase the accuracy of localization and is more robust to non gaussian noise channels.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.11
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• pp.463-468
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• 2017

To sense a wide area with mobile nodes, the uniformity of node deployment is a very important issue. In this paper, we consider the coverage problem to sense big data in sparse mobile ad hoc networks. In most existing works on the coverage problem, it has been assumed that the number of nodes is large enough to cover the area in the network. However, the coverage problem in sparse mobile ad hoc networks differs in the sense that a long-distance between nodes should be formed to avoid the overlapping coverage areas. We formulate the sensor coverage problem in sparse mobile ad hoc networks and provide the solution to the problem by a self-organized approach without a central authority. The experimental results show that our approach is more efficient than the existing ones, subject to both of coverage areas and energy consumption.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.217-222
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• 2015

Clustering is one of important data mining techniques known as exploratory data analysis and is being applied in various engineering and scientific fields such as pattern recognition, remote sensing, and so on. The method organizes data by abstracting underlying structure either as a grouping of individuals or as a hierarchy of groups. Weather radar observes atmospheric objects by utilizing reflected signals and stores observed data in corresponding coordinate. To analyze the radar data, it is needed to be separately organized precipitation and non-precipitation echo based on similarities. Thus, this paper studies to apply clustering method to radar data. In addition, in order to solve the problem when precipitation echo locates close to non-precipitation echo, fuzzy logic based clustering method which can consider both distance and other properties such as reflectivity and Doppler velocity is suggested in this paper. By using actual cases, the suggested clustering method derives better results than previous method in near-located precipitation and non-precipitation echo case.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.37-44
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• 2016

Optical fiber temperature sensing systems have incomparable advantages over traditional electrical-cable-based monitoring systems. However, the fiber optic interrogators and sensors have often been rejected as a temperature monitoring technology in real-world industrial applications because of high cost and over-specification. This study proposes a multiplexed fiber optic temperature monitoring sensor system using an economical Optical Time-Domain Reflectometer (OTDR) and Hard-Polymer-Clad Fiber (HPCF). HPCF is a special optical fiber in which a hard polymer cladding made of fluoroacrylate acts as a protective coating for an inner silica core. An OTDR is an optical loss measurement system that provides optical loss and event distance measurement in real time. A temperature sensor array with the five sensor nodes at 10-m interval was economically and quickly made by locally stripping HPCF clad through photo-thermal and photo-chemical processes using a continuous/pulse hybrid-mode laser. The exposed cores created backscattering signals in the OTDR attenuation trace. It was demonstrated that the backscattering peaks were independently sensitive to temperature variation. Since the 1.5-mm-long exposed core showed a 5-m-wide backscattering peak, the OTDR with a spatial resolution of 40 mm allows for making a sensor node at every 5 m for independent multiplexing. The performance of the sensor node included an operating range of up to $120^{\circ}C$, a resolution of $0.59^{\circ}C$, and a temperature sensitivity of $-0.00967dB/^{\circ}C$. Temperature monitoring errors in the environment tests stood at $0.76^{\circ}C$ and $0.36^{\circ}C$ under the temperature variation of the unstrapped fiber region and the vibration of the sensor node. The small sensitivities to the environment and the economic feasibility of the highly multiplexed HPCF temperature monitoring sensor system will be important advantages for use as system-integrated temperature sensors.

• Korean Journal of Materials Research
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• v.20 no.4
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• pp.223-227
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• 2010

Recently, one-dimensional semiconducting nanomaterials have attracted considerable interest for their potential as building blocks for fabricating various nanodevices. Among these semiconducting nanomaterials,, $SnO_2$ nanostructures including nanowires, nanorods, nanobelts, and nanotubes were successfully synthesized and their electrochemical properties were evaluated. Although $SnO_2$ nanowires and nanobelts exhibit fascinating gas sensing characteristics, there are still significant difficulties in using them for device applications. The crucial problem is the alignment of the nanowires. Each nanowire should be attached on each die using arduous e-beam or photolithography, which is quite an undesirable process in terms of mass production in the current semiconductor industry. In this study, a simple process for making sensitive $SnO_2$ nanowire-based gas sensors by using a standard semiconducting fabrication process was studied. The nanowires were aligned in-situ during nanowire synthesis by thermal CVD process and a nanowire network structure between the electrodes was obtained. The $SnO_2$ nanowire network was floated upon the Si substrate by separating an Au catalyst between the electrodes. As the electric current is transported along the networks of the nanowires, not along the surface layer on the substrate, the gas sensitivities could be maximized in this networked and floated structure. By varying the nanowire density and the distance between the electrodes, several types of nanowire network were fabricated. The $NO_2$ gas sensitivity was 30~200 when the $NO_2$ concentration was 5~20ppm. The response time was ca. 30~110 sec.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.375-380
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• 1999

Ocean Scanning Multispectral Imager (OSMI) is a payload on the KOMPSAT satellite to perform worldwide ocean color monitoring for the study of biological oceanography. The instrument images the ocean surface using a wisk-broom motion with a swath width of 800 km and a ground sample distance (GSD) of<1km over the entire field of view (FOV). The instrument is designed to have an on-orbit operation duty cycle of 20% over the mission lifetime of 3 years with the functions of programmable gain/offset and on-board image data compression/storage. The instrument also performs sun and dark calibration for on-board instrument calibration. The OSMI instrument is a multi-spectral imager covering the spectral range from 400nm to 900nm using CCD Focal Plane Array (FPA). The ocean colors are monitored using 6 spectral channels that can be selected via ground commands. KOMPSAT satellite with OSMI was integrated and the satellite level environment tests and instrument aliveness/functional test as well, such as launch environment, on-orbit environment (Thermal/vacuum) and EMl/EMC test were performed at KARI. Test results met the requirements and the OSMI data were collected and analyzed during each test phase. The instrument is launched on the KOMPSAT satellite in the late 1999 and the image is scheduled to start collecting ocean color data in the early 2000 upon completion of on-orbit instrument checkout.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.443-449
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• 1999

The investigation of the amount of the ecosystem damage on the shoreline due to the NAHODKA oil-spill accident, which occurred in the Sea of Japan, was attempted by using geoinformatics. At first, it was assumed that symbolical vegetation's distribution could be specified in the coast in Ishikawa Pref. where the heavy oil was washed, and surveyed the regional distribution. Then, the presumption result of those environmental capacities was arranged by GIS. In addition, the amount of the ecosystem damage was presumed as cost necessary though a symbolical living thing for the retreat because of the base line by the heavy oil drifting ashore was recovered. By comparing the vegetation line and the surveying data which shows environmental capacity, the retreat areas of the vegetation were 1100-1200 $m^2$. When the amount of damage on the ecosystem of the NAHODKA oil-spill accident was presumed based on the retreat area of this vegetation and the restoration cost, the amount of damage within Shioya beach which 150m in the surveying range became 2 to 2.5 million Yen. Because the extension distance from the Shioya beach to the Katano beach was about 3,500m, the amount of damage became about 46 to 65 million Yen. As a result of calculation for the amount of damage on the ecosystem of the NAHODKA oil-spill accident, it was estimated approximately 1,400 to 2,000 million Yen in the shoreline of Ishikawa Pref., because the total extension of beaches in Ishikawa Pref. is about 110km.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.262-266
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• 1999

Several commercial companies that plan to provide improved panchromatic and/or multi-spectral remote sensor data in the near future are suggesting that merge datasets will be of significant value. This study evaluated the utility of one major merging process-process components analysis and its inverse. The 6 bands of 30$\times$30m Landsat TM data and the 10$\times$l0m SPOT panchromatic data were used to create a new 10$\times$10m merged data file. For the image classification, 6 bands that is 1st, 2nd, 3rd, 4th, 5th and 7th band may be used in conjunction with supervised classification algorithms except band 6. One of the 7 bands is Band 6 that records thermal IR energy and is rarely used because of its coarse spatial resolution (120m) except being employed in thermal mapping. Because SPOT panchromatic has high resolution it makes 10$\times$10m SPOT panchromatic data be used to classify for the detailed classification. SPOT as the Landsat has acquired hundreds of thousands of images in digital format that are commercially available and are used by scientists in different fields. After the merged, the classifications used supervised classification and neural network. The method of the supervised classification is what used parallelepiped and/or minimum distance and MLC(Maximum Likelihood Classification) The back-propagation in the multi-layer perception is one of the neural network. The used method in this paper is MLC(Maximum Likelihood Classification) of the supervised classification and the back-propagation of the neural network. Later in this research SPOT systems and images are compared with these classification. A comparative analysis of the classifications from the TM and merged SPOT/TM datasets will be resulted in some conclusions.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1089-1096
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• 2019

Recently, small-scale IoT services using a small amount of information through low-performance computing have been spread. It requires low cost, low-power, and long-distance communication technologies with wide communication radius, relatively low power consumption. This paper proposes a MAC layer and routing protocol that supports multi-hop transmission in small-scale IoT environment distributed over a large area based on LoRa communication and delivering a small amount of sensing data. The terminal node is mobile and the communication type provides bidirectional transmission between the terminal node and the network application server. By applying the proposed protocol, a production line monitoring system for smart factory was implemented. It was confirmed that the basic monitoring functions are normally performed.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.6
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• pp.745-756
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• 2013

The purpose of this study is to evaluate representativeness of fuel-based emission factors. Twelve light-duty diesel vehicles which meet Euro-3 to 5 legislative emission limits were selected for emission tests. Second-by-second modal emission rates of vehicles were measured on a standard laboratory chassis dynamometer system. An off-cycle driving cycle was developed as a representative Korean real-world on-road driving cycle. Fuel-based emission factors were developed for short trip segments that involved in the selected driving cycle. Each segment was defined to have unit travel distance, which is 1 km, and characterized by its average speed and Relative Positive Acceleration (RPA). Fuel-based $NO_x$ emission factors demonstrate relatively good representativeness in terms of vehicle operation conditions. $NO_x$ emission factors are estimated to be within ${\pm}20%$ of area-wide emission factor under more than 40% of total driving situations. This result implies that the fuel-based $NO_x$ emission factor could be practically implemented into the on-road emission management strategies, such as a remote sensing device (RSD). High emitting vehicles as well as high emitting operating conditions heavily affect on the mean values and distributions of CO and THC emission factors. Few high emitting conditions are pulling up the mean value and biasing the distributions, which weaken representativeness of fuel-based CO and THC emission factors.