• Korean Journal of Remote Sensing
• /
• 제15권2호
• /
• pp.147-158
• /
• 1999

Remote sensing technique has offered better understanding of our environment for the decades by providing useful level of information on the landcover. In many applications using the remotely sensed data, digital image processing methodology has been usefully employed to characterize the features in the data and develop the models. Random field models, especially Markov Random Field (MRF) models exploiting spatial relationships, are successfully utilized in many problems such as texture modeling, region labeling and so on. Usually, remotely sensed imagery are very large in nature and the data increase greatly in the problem requiring temporal data over time period. The time required to process increasing larger images is not linear. In this study, the methodology to reduce the computational cost is investigated in the utilization of the Markov Random Field. For this, multiresolution framework is explored which provides convenient and efficient structures for the transition between the local and global features. The computational requirements for parameter estimation of the MRF model also become excessive as image size increases. A Bayesian approach is investigated as an alternative estimation method to reduce the computational burden in estimation of the parameters of large images.

• Review of Culture and Economy
• /
• 제20권2호
• /
• pp.89-117
• /
• 2017

As the industrial paradigm shifts and the manufacturing industry declines, many changes also take place in the region as well. In this regard, interest in industrial heritage as a facet of cultural heritage is on the increase. In this paper, the meaning of regional 'cultural contents' as industrial heritage is investigated within the scope of specific region. It is meant to move beyond the viewpoint of considering industrial heritage as only relating to industrial machinery and relevant landmarks from the past. The concept of industrial heritage is established more clearly through the review policy and case study analysis of existing research; the analysis is conducted to investigate the characteristics associated with it, and then to explore how best to utilize it. In particular, this paper attempts to focus on how it operates within these parameters using a spatio-temporal context as much as possible, and concentrating on the recognition and experience of the subject of industrial heritage as being traceable through human story. This research is based on the case of 'Sapporo' which focuses on modern history based on historical importance, and the 'Cheongju' case study, which contrasts the former by focusing on urban regeneration using a spatial lens. This paper identifies the possibility of regional development through the examination of past identity and diversity in the present, and highlights the features that could be linked to future usability and development. In addition, it proposes the possibility that the cycle of regional development could change in the process of the different stages of territorialization, de-territorialization and re-territorialization.

• Korean Journal of Remote Sensing
• /
• 제35권5_1호
• /
• pp.681-692
• /
• 2019

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.

• Journal of the Korean Society of Radiology
• /
• 제13권5호
• /
• pp.729-737
• /
• 2019

Fatty pancreas is an abnormal process of lipid deposition in cells, resulting in increased fat tissue and obesity. The result is a risk factor for cardiovascular and metabolic diseases. The aim of this study was to evaluate the usefulness of pancreatic fat as a predictor of cardiovascular disease and metabolic syndrome in pancreatic ultrasonography. In 407 patients who underwent a comprehensive screening at the W Health Care Center in Busan from September 2. 2018 to December 31, 2018, the degree of fat deposition in the pancreas was evaluated as the degree of mild, moderate. Data on non-obstructive atherosclerosis, BMI, hyperlipidemia, hypertension, and diabetes were collected to assess the association of pancreatic fat deposition with cardiovascular disease and metabolic syndrome. In addition, we tried to analyze the correlation between liver dysfunction and thyroid dysfunction as the degree of fat pancreas increased. We examined the relationship between six parameters including atherosclerosis, BMI, hyperlipidemia, hypertension, diabetes, liver dysfunction, and thyroid dysfunction among patients with fatty pancreas. We concluded that the carotid intima-media thickness of atherosclerosis, which is a risk factor of cardiovascular disease, is most closely related to fatty pancreas.

• Journal of Soil and Groundwater Environment
• /
• 제14권4호
• /
• pp.15-22
• /
• 2009

The remediation efficiency for a large scale petroleum-contaminated site was evaluated by using the Engineered Land-farming system which was consists of the following parameters; moisture & nutrient injector data, blower system, HDPE sheet and sump system. To enhance the degradation ability in the early stage, main nutrients such as nitrogen (N) and phosphorus (P) were adjusted for the site condition. As a result of the periodic tilling process, the concentration of contaminated soil was decreased to 348 mg/kg, which was lower than 500 mg/kg (regal standards) while satisfying remediation Efficiency of 82% (the maximum concentration of 1,893 mg/kg). The appropriate temperature range for an active operation was investigated between $28.9{\sim}35.6^{\circ}C$. For the contaminated soils having different initial concentration, the TPH (Total Petroleum Hydrocarbons) concentration was decreased evenly along with the CFU (Colony Forming Unit), moisture content and contaminant concentration after 38days of gratifying the legal standards of under 500 mg/kg.

• Korean Journal of Remote Sensing
• /
• 제37권6_3호
• /
• pp.1951-1963
• /
• 2021

Remote sensing data using earth observation satellites in agricultural environment monitoring has many advantages over other methods in terms of time, space, and efficiency. Since the sensor mounted on the satellite measures the energy that sunlight is reflected back to the ground, noise is generated in the process of being scattered, absorbed, and reflected by the Earth's atmosphere. Therefore, in order to accurately measure the energy reflected on the ground (radiance), atmospheric correction, which must remove noise caused by the effect of the atmosphere, should be preceded. In this study, atmospheric correction sensitivity analysis, inter-satellite cross-analysis, and comparative analysis with ground observation data were performed to evaluate the application of KOMPSAT-3 satellite's atmospheric correction for agricultural application. As a result, in all cases, the surface reflectance after atmospheric correction showed a higher mutual agreement than the TOA reflectance before atmospheric correction, and it is possible to produce the time series vegetation index of the same standard. However, additional research is needed for quantitative analysis of the sensitivity of atmospheric input parameters and the tilt angle.

• Journal of the Microelectronics and Packaging Society
• /
• 제28권3호
• /
• pp.25-32
• /
• 2021

In this paper, the Magneto-Electric(ME) dipole array antenna with dual-polarization in the X-Band is proposed and it is implemented and measured. The proposed array antenna is composed of 32 single ME dipole antenna and a Teflon PCB. 1 × 1 ME dipole antenna is implemented dual-polarization by radiating vertical polarization and horizontal polarization from two pairs of radiators. 2-port feeding structures are realized by lamination process using LTCC. And, each port independently feeds the radiator through a Γ-shaped feeding strip with isolation between ports. The Teflon PCB used in the antenna array has a 4-layer structure, and 2-port is fed through the top and bottom layers. The λ_g/4 transformer is applied to the transmission line of the Teflon PCB for impedance matching of the arrayed antenna and the Teflon PCB, and the optimal parameters are obtained through simulation. The measured maximum antenna gains of port 1 was 18.2 dBi, Cross-pol was 1.0 dBi. And the measured maximum antenna gains of port 1 was 18.1 dBi, Cross-pol was 3.2 dBi.

• Journal of The Korean Society of Agricultural Engineers
• /
• 제63권6호
• /
• pp.77-88
• /
• 2021

Duck industry had a rapid growth in recent years. Nevertheless, researches to improve duck house environment are still not sufficient enough. Moisture generation of duck house litter is an important factor because it may cause severe illness and low productivity. However, the measuring process is difficult because it could be disturbed with animal excrements and other factors. Therefore, it has to be calculated according to the environmental data around the duck house litter. To cut through all these procedures, we built several machine learning regression model forecasting moisture generation of litter by measured environment data (air temperature, relative humidity, wind velocity and water contents). 5 models (Multi Linear Regression, k-Nearest Neighbors, Support Vector Regression, Random Forest and Deep Neural Network). have been selected for regression. By using R-Square, RMSE and MAE as evaluation metrics, the best accurate model was estimated according to the variables for each machine learning model. In addition, to address the small amount of data acquired through lab experiments, bootstrapping method, a technique utilized in statistics, was used. As a result, the most accurate model selected was Random Forest, with parameters of n-estimator 200 by bootstrapping the original data nine times.

• Journal of Life Science
• /
• 제31권4호
• /
• pp.410-417
• /
• 2021

Next-generation sequencing (NGS) is a high-throughput technique for sequencing large numbers of DNA fragments that are prepared from a genome. This sequencing technique has been used to elucidate whole genome sequences of living organisms and to analyze complementary DNA (cDNA) or chromatin immunoprecipitated DNA (ChIPed DNA) at the genome level. After NGS, the use of proper tools is important for processing and analyzing data with reasonable parameters. However, handling large-scale sequencing data and programing for data analysis can be difficult. The Galaxy platform, a public web service system, provides many different tools for NGS data analysis, and it allows researchers to analyze their data on a web browser with no deep knowledge about bioinformatics and/or programing. In this study, we explain the procedure for preparing chromatin immunoprecipitation-sequencing (ChIP-seq) libraries and steps for analyzing ChIP-seq data using the Galaxy platform. The data analysis steps include the NGS data upload to Galaxy, quality check of the NGS data, premapping processes, read mapping, the post-mapping process, peak-calling and visualization by window view, heatmaps, average profile, and correlation analysis. Analysis of our histone H3K4me1 ChIP-seq data in K562 cells shows that it correlates with public data. Thus, NGS data analysis using the Galaxy platform can provide an easy approach to bioinformatics.

• Food Engineering Progress
• /
• 제15권2호
• /
• pp.155-161
• /
• 2011

The aim of this study was to determine the effects of different extrusion conditions on the saccharification characteristics( initial reaction velocity, reaction rate constant, yield) of extruded corn starch. Extruded corn starch-water slurries were mixed with alpha-amylase for the enzymatic saccharification. The saccharification yield of extruded corn starch was high at lower feed moisture content and higher barrel temperature. The solubility of extrudates increased with increase in the SME input which increased with increase in the feed moisture content. Starch hydrolysates having DE 63.8 was obtained after 2 hr reaction. The initial reaction velocity of the extrudate slurry with alpha-amylase was higher with decrease in the feed moisture content. The initial reaction velocity of extruded corn starch was the highest ($2.26{\times}10^{-3}mmol/mL{\cdot}min$) at 25% feed moisture content and $120^{\circ}C$ barrel temperature, 250 rpm screw speed. The pregelatinized starch was $1.83{\times}10^{-3}mmol/mL{\cdot}min$ as a control. Reaction rate constant was a similar trend to initial reaction velocity.