• Korean Journal of Remote Sensing
• /
• v.25 no.2
• /
• pp.95-105
• /
• 2009

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series including bad or missing observation that result from mechanical problems or sensing environmental condition. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. An adaptive feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. In this study, the Normalized Difference Vegetation Index (NDVI) image was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula, and the adaptive reconstruction of harmonic model was then applied to the NDVI time series from 1996 to 2000 for tracking changes on the ground vegetation. The results show that the adaptive approach is potentially very effective for continuously monitoring changes on near-real time.

• Korean Journal of Remote Sensing
• /
• v.15 no.2
• /
• 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.28 no.8
• /
• pp.90-97
• /
• 2014

This paper presents a compensation method of unbalanced phase currents in interleaved bi-directional converters. Phase currents in interleaved bi-directional converter are apt to be unbalanced due to circuit parameter error and switch operation difference. This problem causes the switch element failure and the reduced efficiency of the converter. Therefore, it is necessary that a certain balance control algorithm is provided in interleaved bi-directional converter system. In this paper, a balance control algorithm based on the circular chain control method is proposed. Further, in order to reduce the number of phase current sensors, this paper shows a simple method in which phase currents can be extracted indirectly through a DC-link current sensor in both charging and discharging modes. The validity and the effectiveness of the proposed phase currents balance control algorithm are illustrated through the simulation results.

• Korean Journal of Remote Sensing
• /
• v.31 no.1
• /
• pp.1-9
• /
• 2015

Mobility and cloud platform have become the dominant paradigm to develop web services dealing with huge and diverse digital contents for scientific solution or engineering application. These two trends are technically combined into mobile cloud computing environment taking beneficial points from each. The intention of this study is to design and implement a mobile cloud application for remotely sensed image fusion for the further practical geo-based mobile services. In this implementation, the system architecture consists of two parts: mobile web client and cloud application server. Mobile web client is for user interface regarding image fusion application processing and image visualization and for mobile web service of data listing and browsing. Cloud application server works on OpenStack, open source cloud platform. In this part, three server instances are generated as web server instance, tiling server instance, and fusion server instance. With metadata browsing of the processing data, image fusion by Bayesian approach is performed using functions within Orfeo Toolbox (OTB), open source remote sensing library. In addition, similarity of fused images with respect to input image set is estimated by histogram distance metrics. This result can be used as the reference criterion for user parameter choice on Bayesian image fusion. It is thought that the implementation strategy for mobile cloud application based on full open sources provides good points for a mobile service supporting specific remote sensing functions, besides image fusion schemes, by user demands to expand remote sensing application fields.

• Journal of KIISE:Databases
• /
• v.36 no.2
• /
• pp.99-111
• /
• 2009

In wireless sensor networks, various schemes have been proposed to store and process sensed data efficiently. A Data-Centric Storage(DCS) scheme assigns distributed data regions to sensors and stores sensed data to the sensor which is responsible for the data region overlapping the data. The DCS schemes have been proposed to reduce the communication cost for transmitting data and process exact queries and range queries efficiently. Recently, KDDCS that readjusts the distributed data regions dynamically to sensors based on K-D tree was proposed to overcome the storage hot-spots. However, the existing DCS schemes including KDDCS suffer from Query Hot-Spots that are formed if the query regions are not uniformly distributed. As a result, it causes reducing the life time of the sensor network. In this paper, we propose a new DCS scheme, called TPDCS(Time-Parameterized DCS), that avoids the problems of storage hot-spots and query hot-spots. To decentralize the skewed. data and queries, the data regions are assigned by a time dimension as well as data dimensions in our proposed scheme. Therefore, TPDCS extends the life time of sensor networks. It is shown through various experiments that our scheme outperform the existing schemes.

• Journal of Mechanical Science and Technology
• /
• v.15 no.3
• /
• pp.291-299
• /
• 2001

Recently, severe wear on the shutdown rod cladding of Ulchin Nuclear Power Plant #1, #2 were observed by the Eddy Current Test(E.C.T.). In particular, the wear at the sixth card location was up to 75%. The test results indicated that the Flow Induced Vibration(F.I.V.) might be the cause of the fretting wear resulting from the contact between Rod Cluster Control Assemblies(RCCAs) and their spacing cards(guide plates) arranged in the guide tube. From reviewing RCCAs fretting wear repots and analyzing the general characteristics of F.I.V. mechanism in the reactor, geometric layout and flow conditions around the control rod, it is concluded that the turbulence excitation is the most probable vibration mechanism of RCCA. To identify the governing mechanism of RCCA vibration, an experiment was performed for a representative rod position in which the most serious fretting wear experienced among the six rod positions. The experimental rig was designed and set up to satisfy the governing nondimensional numbers which are Reynolds number and mass damping parameter. The vibration amplitude measurement by the non-contact laser displacement sensor showed good agreements in the frequency and the maximum wearing(vibration) location with Ulchin E.C.T. results and Framatome report, respectively. The sudden increase in the vibration amplitude was sensed around the 6th guide plate with mass flow rate variation. Comparing the similitude rod behaviour with the idealized response of a cylinder in flow induced vibration, it was found that he dominant mechanism of vibration was transferred from turbulence excitation to periodic shedding at the mass flow ate 90ι/min. Also the critical velocity of the vibration in RCCAs was determined and the vibration can be prevented by reducing the bypass flow rate below the critical velocity.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.16 no.1
• /
• pp.59-66
• /
• 2013

When the typhoons are passing over the ocean, the ocean environment has both physical and biological impacts on the East, South sea and Yellow sea of Korea. As a result of typhoon path, vertical mixing and upwelling injured colder subsurface water, and leaded to phytoplankton blooming along the typhoons. The ocean environment before and after a typhoon played an important role in the biological effect of sea surface. Although the magnitude of sea surface temperature (SST) gets cooler because of typhoon path, other physical and biophysical responses are quite different such as chlorophyll, K490 and SST. The purpose of this study is to compare with the typhoon path that influenced the Korean Peninsula and ocean environment parameters which were observed by ocean color remotely-sensed data. The MODIS data were used to assess the parameters of ocean environments such as K490 and chlorophyll data from 2002 to 2005. Mean chlorophyll from MODIS data increased by about 1-4% in the East sea after the typhoon. Mean concentration of MODIS chlorophyll in the post-typhoon period increased along the typhoon passage. However, Jeju coastal area has different patterns from those of the East sea.

• Journal of Communications and Networks
• /
• v.14 no.2
• /
• pp.188-194
• /
• 2012

Cognitive networks (CNs) are capable of enabling dynamic spectrum allocation, and thus constitute a promising technology for future wireless communication. Whereas, the implementation of CN will lead to the requirement of an increased energy-arrival rate, which is a significant parameter in energy harvesting design of a cognitive user (CU) device. A well-designed spectrum-sensing scheme will lower the energy-arrival rate that is required and enable CNs to self-sustain, which will also help alleviate global warming. In this paper, spectrum sensing in a multi-user cognitive ad hoc network with a wide-band spectrum is considered. Based on the prospective spectrum sensing, we classify CN operation into two modes: Distributed and centralized. In a distributed network, each CU conducts spectrum sensing for its own data transmission, while in a centralized network, there is only one cognitive cluster header which performs spectrum sensing and broadcasts its sensing results to other CUs. Thus, a wide-band spectrum that is divided into multiple sub-channels can be sensed simultaneously in a distributed manner or sequentially in a centralized manner. We consider the energy consumption for spectrum sensing only of an analog-to-digital convertor (ADC). By formulating energy consumption for spectrum sensing in terms of the sub-channel sampling rate and whole-band sensing time, the sampling rate and whole-band sensing time that are optimal for minimizing the total energy consumption within sensing reliability constraints are obtained. A power dissipation model of an ADC, which plays an important role in formulating the energy efficiency problem, is presented. Using AD9051 as an ADC example, our numerical results show that the optimal sensing parameters will achieve a reduction in the energy-arrival rate of up to 97.7% and 50% in a distributed and a centralized network, respectively, when comparing the optimal and worst-case energy consumption for given system settings.

• Korean Journal of Remote Sensing
• /
• v.14 no.2
• /
• pp.175-195
• /
• 1998

Automatic lineament extraction algorithms had been developed by various researches for geological purpose using remotely sensed data. However, most of them are designed for a certain topographic model, for instance rugged mountainous region or flat basin. Most of common topographic characteristic in Korea is a mountainous region along with alluvial plain, and consequently it is difficult to apply previous algorithms directly to this area. A new algorithm of automatic lineament extraction from remotely sensed images is developed in this study specifically for geological applications. An algorithm, named as DSTA(Dynamic Segment Tracing Algorithm), is developed to produce binary image composed of linear component and non-linear component. The proposed algorithm effectively reduces the look direction bias associated with sun's azimuth angle and the noise in the low contrast region by utilizing a dynamic sub window. This algorithm can successfully accomodate lineaments in the alluvial plain as well as mountainous region. Two additional algorithms for estimating the individual lineament vector, named as ALEHHT(Automatic Lineament Extraction by Hierarchical Hough Transform) and ALEGHT(Automatic Lineament Extraction by Generalized Hough Transform) which are merging operation steps through the Hierarchical Hough transform and Generalized Hough transform respectively, are also developed to generate geological lineaments. The merging operation proposed in this study is consisted of three parameters: the angle between two lines($\delta$$\beta$), the perpendicular distance($(d_ij)$), and the distance between midpoints of lines(dn). The test result of the developed algorithm using Landsat TM image demonstrates that lineaments in alluvial plain as well as in rugged mountain is extremely well extracted. Even the lineaments parallel to sun's azimuth angle are also well detected by this approach. Further study is, however, required to accommodate the effect of quantization interval(droh) parameter in ALEGHT for optimization.

• Journal of Korea Water Resources Association
• /
• v.55 no.6
• /
• pp.447-459
• /
• 2022

This study estimated the grid-type precipitation quantile for the Korean Peninsula using PERSIANN-CCS-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record), a satellite based re-analysis precipitation data. The period considered is a total of 38 years from 1983 to 2020. The spatial resolution of the data is 0.04° and the temporal resolution is 3 hours. For the probability distribution, the Gumbel distribution which is generally used for frequency analysis was used, and the probability weighted moment method was applied to estimate parameters. The duration ranged from 3 hours to 144 hours, and the return period from 2 years to 500 years was considered. The results were compared and reviewed with the estimated precipitation quantile using precipitation data from the Automated Synoptic Observing System (ASOS) weather station. As a result, the parameter estimates of the Gumbel distribution from the PERSIANN-CCS-CDR showed a similar pattern to the results of the ASOS as the duration increased, and the estimates of precipitation quantiles showed a rather large difference when the duration was short. However, when the duration was 18 h or longer, the difference decreased to less than about 20%. In addition, the difference between results of the South and North Korea was examined, it was confirmed that the location parameters among parameters of the Gumbel distribution was markedly different. As the duration increased, the precipitation quantile in North Korea was relatively smaller than those in South Korea, and it was 84% of that of South Korea for a duration of 3 h, and 70-75% of that of South Korea for a duration of 144 h.