• Korean Journal of Optics and Photonics
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• v.34 no.5
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• pp.192-201
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• 2023

The band-limited angular spectrum method (BL-ASM) causes aliasing errors due to spatial frequency control problems. In this paper, a sampling interval adjustment technique for phase holograms and a technique for reducing speckle noise and improving image quality using a deep-learningbased U-net model are proposed. With the proposed technique, speckle noise is reduced by first calculating the sampling factor and controlling the spatial frequency by adjusting the sampling interval so that aliasing errors can be removed in a wide range of propagation. The next step is to improve the quality of the reconstructed image by learning the phase hologram to which the deep learning model is applied. In the S/W simulation of various sample images, it was confirmed that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were improved by 5% and 0.14% on average, compared with the existing BL-ASM.

• Research in Plant Disease
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• v.21 no.4
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• pp.268-272
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• 2015

Bacterial leaf blight caused by Pseudomonas syringae pv. porri is one of the major bacterial diseases of garlic (Allium sativum). In South Korea, the disease has only been observed in garlic-growing regions of Jeju island. The spatial distribution pattern of the disease was analyzed by binary power law, in which the natural logarithm of the observed variance is regressed on the natural logarithm of the binomial variance. The estimated slope (b=1.361) of the regression was greater than 1 which meant that the diseased plants were aggregated. The sequential sampling plans were developed for estimating the mean incidence rate ($p_m$) and classifying the mean incidence as being below or above the critical incidence rate ($p_t$). These results could be used on more efficient and higher precisive sampling for bacterial blight of garlic compared to fixed sample sized sampling.

• Journal of Biosystems Engineering
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• v.27 no.3
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• pp.259-266
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• 2002

It is crucial to know spatial soil variability for precision farming. However, it is time-consuming, and difficult to measure spatial soil properties. Therefore, there are needs fur sensing technology to estimate spatial soil variability, and for electronic mapping technology to store, manipulate and process the sampled data. This research was conducted to develop a real-time soil organic matter sensor and an electronic mapping system. A soil organic matter sensor was developed with a spectrophotometer in the 900∼1,700 nm range. It was designed in a penetrator type to measure reflectance of soil at 15cm depth. The signal was calibrated with organic matter content (OMC) of the soil which was sampled in the field. The OMC was measured by the Walkeley-Black method. The soil OMCs were ranged from 0.07 to 7.96%. Statistical partial least square and principle component regression analyses were used as calibration methods. Coefficient of determination, standard error prediction and bias were 0.85 0.72 and -0.13, respectively. The electronic mapping system was consisted of the soil OMC sensor, a DGPS, a database and a makeshift vehicle. An algorithm was developed to acquire data on sampling position and its OMC and to store the data in the database. Fifty samples in fields were taken to make an N-fertilizer dosage map. Mean absolute error of these data was 0.59. The Kring method was used to interpolate data between sampling nodes. The interpolated data was used to make a soil OMC map. Also an N-fertilizer dosage map was drawn using the soil OMC map. The N-fertilizer dosage was determined by the fertilizing equation recommended by National Institute of Agricultural Science and Technology in Korea. Use of the N-fertilizer dosage map would increase precision fertilization up to 91% compared with conventional fertilization. Therefore, the developed electronic mapping system was feasible to not only precision determination of N-fertilizer dosage, but also reduction of environmental pollution.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.117-124
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• 2004

Various multimedia systems have been developed and their application areas widely proliferate. Thus, the interoperability is getting important among various networks and devices. The video transcoding is a technology to solve this interoperability problem among various coding standards. Transcoding can be defined as the conversion of one compressed coded data to another. In this paper, MPEG-2 to MPEG-4 transcoder in the spatial domain is compared with that in the DCT domain. The transcoder is very useful when a video sequence that is originally encoded for digital TV, DVD or satellite broadcasting is served in mobile environment. In order to compare two transcoders, all modules except motion compensation and down sampling are implemented identically. In addition, both transcoders do not search for motion vector. Instead, the decoded information is reused to the encoder. The experimental results show that the transcoder in the spatial domain is usually better than that in the DCT domain with respect to PSNR (Peak Signal-to-Noise Ratio), bitrate and execution time.

• Geophysics and Geophysical Exploration
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• v.17 no.2
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• pp.88-94
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• 2014

Due to mechanical failure or geographical accessibility, the seismic data can be partially missed. In addition, it can be coarsely sampled such as crossline of the marine streamer data. This seismic data that irregular sampled and spatial aliased may cause problems during seismic data processing. Accurate and efficient interpolation method can solve this problem. Futhermore, interpolation can save the acquisition cost and time by reducing the number of shots and receivers. Among various interpolation methods, the Matching Pursuit method can be applied to any sampling type which is regular or irregular. However, in case of using sinusoidal basis function, this method has a limitation in spatial aliasing. Therefore, in this study, we have developed wavelet based Matching Pursuit method that uses wavelet instead of sinusoidal function for the improvement of dealiasing performance. In addition, we have improved interpolation speed by using inner product instead of L-2 norm.

• Korean Journal of Environmental Biology
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• v.35 no.4
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• pp.427-436
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• 2017

A fixed-precision-level sampling plan was developed to establish control of the two-spotted spider mite, Tetranychus urticae, in two strawberry greenhouses (conventional plot, natural enemy plot). T. urticae was sampled by taking a three-leaflet leaf (1 stalk) from each plant (3 three-leaflet leaves) from each sampling position. Each leaflet was divided into three different units (1-leaflet, 2-leaflet, and 3-leaflet units) to compare relative net precision (RNP) values for selection of the appropriate sampling unit. The relative net precision values indicated that a 1-leaflet unit was more precise and cost-efficient than other units. The spatial distribution analysis was performed using Taylor's power law (TPL). Homogeneity of the TPL parameters in each greenhouse was evaluated by using the analysis of covariance (ANCOVA). A fixed-precision-level sequential sampling plan was developed using the parameters of TPL generated from the combined data of the conventional plot and natural enemy plot in a 1-leaflet sampling unit. Sequential classification sampling plans were also developed using the action threshold of 3 and 10 mites for pooled data. Using the results obtained in the independent data, simulated validation of the developed sampling plan by Resampling validation for sampling plan (RVSP) indicated a reasonable level of precision.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.95-100
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• 2024

The public survey performance review conducted by the Spatial Information Quality Management Institute is conducted at the screening rate in accordance with the regulations, and the examiner directly judges the overall trend of the submitted performance based on the extracted sample. However, the evaluation of the Ministry of Land, Infrastructure and Transport, the evaluation trustee shall be specified by random extraction (Random Collection) is specified by the sample. In this study, it analyzed the details of the actual site and analyzed through securing actual performance review data. In addition, we analyzed considerations according to various field conditions and studied ways to apply the public survey performance review sampling algorithm. Therefore, detailed sampling criteria analysis by performance reviewers is necessary. A relative comparison was made feasible by comparing the data for which the real performance evaluation was performed with the outcomes of the Python automation program. This automation program is expected to be employed as a foundation program for the automated application of public survey performance evaluation sampling in the future.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.229-244
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• 2007

Among the small area estimation methods, it has been known that hierarchical Bayesian(HB) approach is the most reasonable and effective method. However any model based approaches need good explanatory variables and finding them is the key role in the model based approach. As the lacking of explanatory variables, adopting the spatial terms in the model was introduced. Here in this paper, we evaluate the model based methods with/without spatial terms using the diagnostic methods which were introduced by Brown et al. (2001). And Economic Active Population Survey(2005) is used for data analysis.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.89-96
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• 2016

By reviewing preceding studies of big data and spatial big data, spatial big data was defined as one part of big data, which spatialize location information and systematize time series data. Spatial big data, as one part of big data, should not be separated with big data and application methods within the system is to be examined. Therefore in this study, services that spatial big data is required to provide were suggested. Spatial big data must be available of various spatial analysis and is in need of services that considers present and future spatial information. Not only should spatial big data be able to detect time series changes in location, but also analyze various type of big data using attribute information of spatial data. To successfully provide the requirements of spatial big data and link various type of big data with spatial big data, methods of forming sample points and extracting attribute information were proposed in this study. The increasing application of spatial information related to big data is expected to attribute to the development of spatial data industry and technological advancement.