• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
• /
• 2008.10a
• /
• pp.270-271
• /
• 2008

The positioning technology for a moving object is an important and essential component of ubiquitous communication computing environment and applications, for which Radio Frequency IDentification Identification(RFID) is has been considered as also a core technology for ubiquitous wireless communication. RFID-based positioning system calculates the position of moving object based on k-nearest neighbor(k-nn) algorithm using detected k-tags which have known coordinates and k can be determined according to the detection range of RFID system. In this paper, RFID-based positioning system determines the position of moving object not using weight factor which depends on received signal strength but assuming that tags within the detection range always operate and have same weight value. Because the latter system is much more economical than the former one. The geometries of tags were determined with considerations in huge buildings like office buildings, shopping malls and warehouses, so they were determined as the line in 1-Dimensional space, the square in 2-Dimensional space and the cubic in 3-Dimensional space. In 1-Dimensional space, the optimal detection range is determined as 125% of the tag spacing distance through the analytical and numerical approach. Here, the analytical approach means a mathematical proof and the numerical approach means a simulation using matlab. But the analytical approach is very difficult in 2- and 3-Dimensional space, so through the numerical approach, the optimal detection range is determined as 134% of the tag spacing distance in 2-Dimensional space and 143% of the tag spacing distance in 3-Dimensional space. This result can be used as a fundamental study for designing RFID-based positioning system.

• Fisheries and Aquatic Sciences
• /
• v.10 no.4
• /
• pp.205-214
• /
• 2007

Surface water and sediment samples collected from the Mokpo coast of Korea were analyzed for molecular markers of organic municipal wastewaters, i.e., 11 fecal sterols including coprostanol (Cop) and nonylphenolic compounds (NPs), to characterize the main routes of these wastewaters to the coast and to assess contamination levels. Concentrations of Cop ranged from 94 to 7,568 ng/L in surface water and from 43 to 38,108 ng/g dry weight in sediments. Concentrations of NPs [nonylphenol (NP) and nonylphenol mono- and di-ethoxylates ($NP_{1-2}EOs$)] ranged from 123 to 4,729 ng/L in surface water and from 4 to 2,119 ng/ng dry weight in sediments. The levels of these compounds were much higher at stations near the rivers that pass through the urban center of Mokpo and the outfall of the wastewater treatment plant (WWTP). The spatial distribution of Cop levels was statistically similar to that of NPs (r=0.809 and 0.982 in surface water and sediments, respectively), indicating that these compounds may have similar discharge points, transport, mixing, and deposition in the study area. These results suggest that considerable amounts of organic wastewater compounds are discharged through rivers and WWTP effluent to the Mokpo coast.

• Atmosphere
• /
• v.22 no.2
• /
• pp.233-243
• /
• 2012

The Barnes scheme is used in Digital Forecast System (DFS) of the Korea Meteorological Administration (KMA) for real-time analysis. This scheme is an objective analysis scheme with a distance-dependent weighted average. It has been widely used for mesoscale analyses in limited geographic areas. The isotropic Gaussian weight function with a constant effective radius might not be suitable for certain conditions. In particular, the analysis error can be increased for stations located near mountains. The terrain of South Korea is covered with mountains and wide plains that are between successive mountain ranges. Thus, it is needed to consider the terrain effect with the information of elevations for each station. In order to improve the accuracy of the temperature objective analysis, we modified the weight function which is dependent on a distance and elevation in the Barnes scheme. We compared the results from the Barnes scheme used in the DFS (referred to CTL) with the new scheme (referred to EXP) during a year of 2009 in this study. The analysis error of the temperature field was verified by the root-mean-square-error (RMSE), mean error (ME), and Priestley skill score (PSS) at the DFS observation stations which is not used in objective analysis. The verification result shows that the RMSE and ME values are 1.68 and -0.41 in CTL and 1.42 and -0.16 in EXP, respectively. In aspect of spatial verification, we found that the RSME and ME values of EXP decreased in the vicinity of Jirisan (Mt. Jiri) and Taebaek Mountains. This indicates that the new scheme performed better in temperature verification during the year 2009 than the previous scheme.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.12
• /
• pp.11-21
• /
• 2011

An embedded sensor is significantly influenced by its spatial environment, such as barriers or distance, through low power and signal strength. Due to these causes, noise data frequently occur in an embedded sensor. Because the information acquired from the embedded sensor exists in a time series, it is hard to detect an error which continuously takes place in the time series information on a realtime basis. In this paper, we proposes an error detection method based on time-series prediction that detects error signals of embedded sensors in real time in consideration of the physical characteristics of embedded devices. The error detection method based on time-series prediction proposed in this paper determines errors in generated embedded device signals using a stable distance function. When detecting errors by monitoring signals from an embedded device, the stable distance function can detect error signals effectively by applying error weight to the latest signals. When detecting errors by monitoring signals from an embedded device, the stable distance function can detect error signals effectively by applying error weight to the latest signals.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.7
• /
• pp.81-89
• /
• 1999

Image obtained by incomplete communication always include noise, blur and distortion, etc. In this paper, we propose and apply the new spatial filter algorithm, called an optimal adaptive generalized stack filter(AGSF), which optimizes adaptive generalized stack filter(AGSF) using neural network weight learning algorithm of back-propagation learning algorithm for improving noise removal and edge preservation rate. AGSF divides into two parts: generalized stack filter(GSF) and adaptive multistage median filter(AMMF), GSF improves the ability of stack filter algorithm and AMMF proposes the improved algorithm for reserving the sharp edge. Applied to neural network theory, the proposed algorithm improves the performance of the AGSF using two weight learning algorithms, such as the least mean absolute(LAM) and least mean square (LMS) algorithms. Simulation results of the proposed filter algorithm are presented and discussed.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.6
• /
• pp.73-82
• /
• 2005

This paper proposes a multi-level thresholding method using weighted FCM(Fuzzy C-Means) algorithm in color image. FCM algerian determines a more optimal thresholding value than the existing methods and can extend to multi-level thresholding. But FCM algerian is sensitive to noise because it doesn't include spatial information. To solve the problem, we can remove noise by applying a weight based on entropy that is obtained from neighboring pixels to FCM algerian. And we determine the optimal cluster number by using within-class distance in code image based on the clustered pixels of each color component. In the experiments, we show that the proposed method is more tolerant to noise and is more superior than the existing methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.18 no.2
• /
• pp.179-189
• /
• 2000

This study is tried to organic combination in implementing the suitability analysis of urban landuse between GSIS and ANN(Artificial Neural Network). ANN has merit that can decide rationally connectivity weights among neural network nodes through procedure of learning. It is estimated to be possible that replacing the weight among factors needed in spatial analysis of the connectivity weight on neural network. This study is composed of two kinds of neural networks to be executed. First neural network was used in the suitability analysis of landuse and second one was oriented to analyze of optimum landuse pattern. These neural networks were learned with back-propagation algorithm using the steepest gradient which is embodied by C++ program and used sigmoid function as a active function. Analysis results show landuse suitability map and optimum landuse pattern of study area consisted of residental, commercial. industrial and green zone in present zoning system. Each result map was written by the Grid format of Arc/Info. Also, suitability area presented in the suitability map and optimum landuse pattern show distribution pattern consistent with theroretical concept or urban landuse plan in aspect of location and space structure.

• Journal of the Institute of Convergence Signal Processing
• /
• v.17 no.2
• /
• pp.71-76
• /
• 2016

Digital image processing is being utilized in various fields including medical industry, satellite photos, and factory automation image recognition. However, this kind of image data produces heat by an external cause in the course of being processed, transmitted, and stored. Most typical noises added in the images are AWGN and salt and pepper. MF, CWMF, and AWMF are methods used to restore images damaged by AWGN and the existing methods are likely to damage detailed information such as an edge. Therefore, this paper suggests an algorithm applying weight of average filter, average filter depending on pixel, and spatial weight filter based on edge size of local mask in an AWGN environment, in a different way. Also, this paper compares functions of existing methods by using PSNR to prove excellence of the suggested algorithm.

• Steel and Composite Structures
• /
• v.36 no.1
• /
• pp.47-62
• /
• 2020

In this study, free vibration behavior of trapezoidal sandwich plates with porous core and two graphene platelets (GPLs) reinforced nanocomposite outer layers are presented. The distribution of pores and GPLs are supposed to be functionally graded (FG) along the thickness of core and nanocomposite layers, respectively. The effective Young's modulus of the GPL-reinforced (GPLR) nanocomposite layers is determined using the modified Halpin-Tsai micromechanics model, while the Poisson's ratio and density are computed by the rule of mixtures. The FSDT plate theory is utilized to establish governing partial differential equations and boundary conditions (B.C.s) for trapezoidal plate. The governing equations together with related B.C.s are discretized using a mapping- generalized differential quadrature (GDQ) method in the spatial domain. Then natural frequencies of the trapezoidal sandwich plates are obtained by GDQ method. Validity of current study is evaluated by comparing its numerical results with those available in the literature. A special attention is drawn to the role of GPLs weight fraction, GPLs patterns of two faces through the thickness, porosity coefficient and distribution of porosity on natural frequencies characteristics. New results show the importance of this permeates on vibrational characteristics of porous/GPLR nanocomposite plates. Finally, the influences of B.C.s and dimension as well as the plate geometry such as face to core thickness ratio on the vibration behaviors of the trapezoidal plates are discussed.

• Korean Journal of Remote Sensing
• /
• v.22 no.6
• /
• pp.485-493
• /
• 2006

This study is to combine precipitation data with different spatial-temporal characteristics using an optimal weighting method. This optimal weighting method is designed for combination of AWS rain gage data and S-band RADAR-estimated rain data with weighting function in inverse proportion to own mean square error for the previous time step. To decide the optimal weight coefficient for optimized precipitation according to different training time, the method has been performed on Changma case with a long spell of rainy hour for the training time from 1 hour to 10 hours. Horizontal field of optimized precipitation tends to be smoothed after 2 hours training time, and then optimized precipitation has a good agreement with synoptic station rainfall assumed as true value. This result suggests that this optimal weighting method can be used for production of high-resolution quantitative precipitation rate using various data sets.