• Journal of Korea Game Society
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• v.7 no.2
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• pp.73-82
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• 2007

This study deals with how computer game affects spatial-temporal capability of game users. Focusing on college students, this study shows almost the same results as 'Mozart effect' by Gordon Shaw's previously famous study for music on spatial-temporal capability. Additionally, in comparison to music and animation, on-line racing game has more positive influence on a specific spatial-temporal capability.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.3
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• pp.175-196
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• 2002

The increase of meteorological uncertainty and unstable fluctuation of its behavior due to the global warming affect the temporal variation and spatial distribution of water resources and water use patterns in agriculture. There have been steady efforts to understand hydrological components and deal with such water related problems. This paper reviews firstly, the future effect of water resources due to climate changes, secondly, recent progress for precipitation, evapotranspiration, soil moisture, and thirdly, GIS/RS based integrated information systems conducted by both researchers and government ministries. There should be continuous studies and investments to cope with climate changes, and to accomplish sustainable development with the help of agricultural water resources. Some research topics were suggested to attempt with substantial contents considering our present capability and situation.

• Journal of KIISE
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• v.42 no.5
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• pp.601-608
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• 2015

In order to answer questions successfully on behalf of the human contestant in DeepQA environments such as 'Jeopardy!', the American quiz show, the computer needs to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a hybrid spatial reasoning algorithm, among various efficient spatial reasoning methods, for handling directional and topological relations. Our algorithm not only improves the query processing time while reducing unnecessary reasoning calculation, but also effectively deals with the change of spatial knowledge base, as it takes a hybrid method that combines forward and backward reasoning. Through experiments performed on the sample spatial knowledge base with the hybrid spatial reasoner of our algorithm, we demonstrated the high performance of our hybrid spatial reasoning algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1A
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• pp.53-64
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• 2004

As to medium data which is involved in the form of streaming for a multimedia application, it characterizes that spatial locality occurs strongly but temporal locality appears even weaker. In this paper, with regard to dynamic prefetching, we suggest a method to make the most of memory reference regularities which typically innate by nature in the multimedia data with strong spatial locality but with weak temporal locality. Especially, the suggested method has a remarkable capability such that it can reduce prefetching errors substantially compared to existing prefetching methods for an application Program which divides an way into small sub-blocks and, plus executes in the unit of sub-block. We carried out experiments to test the suggested method using various MediaBench benchmarks. From the results, we have confirmed that the occurrences of prefetching error decrease effectively than those of existing linear prefetching methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5782-5799
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• 2018

With the development of GPS and the popularity of mobile devices with positioning capability, collecting massive amounts of trajectory data is feasible and easy. The daily trajectories of moving objects convey a concise overview of their behaviors. Different social roles have different trajectory patterns. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit life patterns. However, most existing daily trajectories mining studies mainly focus on the spatial and temporal analysis of raw trajectory data but missing the essential semantic information or behaviors. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient approach for stay regions extraction from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on spatial and temporal similarity factor. Furthermore, a pruning strategy is proposed to lighten tedious calculations and comparisons. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.11
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• pp.673-679
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• 2005

This Paper presents the 3D wavelet transformation based video compression system, which possesses the capability of progressive transmission by increasing resolution and increasing rate for multimedia applications. The 3D wavelet packet based video compression system removes the temporal correlation of the input sequences using the motion compensation filter and decomposes the spatio-temporal subband using the spatial wavelet packet transformation. The proposed system allocates the higher bit rate to the low frequency image of the 3D wavelet sequences and improves the 0.49dB PSNR performance of the reconstructed image in comparison with that of H.263. In addition to the limitation on the propagation of the motion compensation error by the 3D wavelet transformation, the proposed system progressively transmits the input sequence according to the resolution and rate scalability.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.12
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• pp.2016-2024
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• 1989

A versatile incoherent optical processing system is developed and analyzed in detail, in which an acousto-optic modulator is used to generate the temporal offset frequency for heterodyning and an optical scanner to process the input object in scanning mode. The operational characteristics of the systems are studied with respect to spatial filtering in terms of the spectral width change of the light source, the temporal offset frequency, and a scanning rate. To enhance the system's capability, two schemes for tuning the system's OTF, structural tuning and defocused object tuning, are also developed and verified with the MTF measurements and computer calculations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.203-210
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• 2019

Recently, a number of researchers have produced research and reports in order to forecast more exactly air quality such as particulate matter and odor. However, such research mainly focuses on the atmospheric diffusion models that have been used for the air quality prediction in environmental engineering area. Even though it has various merits, it has some limitation in that it uses very limited spatial attributes such as geographical attributes. Thus, we propose the new approach to forecast an air quality using a deep learning based ensemble model combining temporal and spatial predictor. The temporal predictor employs the RNN LSTM and the spatial predictor is based on the geographically weighted regression model. The ensemble model also uses the RNN LSTM that combines two models with stacking structure. The ensemble model is capable of inferring the air quality of the areas without air quality monitoring station, and even forecasting future air quality. We installed the IoT sensors measuring PM2.5, PM10, H2S, NH3, VOC at the 8 stations in Jeonju in order to gather air quality data. The numerical results showed that our new model has very exact prediction capability with comparison to the real measured data. It implies that the spatial attributes should be considered to more exact air quality prediction.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.47-60
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• 2017

The permafrost active layer plays an important role in permafrost dynamics. Ecological patterns, processes, and water and ice contents in the active layer are spatially and temporally complex depending on landscape heterogeneity and local-scale variations in hydrological processes. Although there has been emerging interest in the application of optical remote sensing techniques to permafrost environments, optical sensors are significantly limited in accessing information on near surface geo-cryological conditions. The primary objective of this study was to investigate capability of L-band SAR data for monitoring spatio-temporal variability of permafrost ecosystems and underlying soil conditions. This study exploits information from different polarimetric SAR observables in relation to permafrost environmental conditions. Experimental results show that each polarimetric radar observable conveys different information on permafrost environments. In the case of the dual-pol mode, the radar observables consist of two backscattering powers and one correlation coefficient between polarimetric channels. Among them, the dual-pol scattering powers are highly sensitive to freeze/thaw transition and can discriminate grasslands or ponds in thermokarst area from other permafrost ecosystems. However, it is difficult to identify the ground conditions with dual-pol observables. Additional backscattering powers and correlation coefficients obtained from quad-pol mode help understanding seasonal variations ofradar scattering and assessing geo-cryological information on soil layers. In particular, co-pol coherences atHV-basis and circular-basis were found to be very usefultools for mapping and monitoring near surface soil properties.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.6
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• pp.1-12
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• 2006

Remote sensing cannot provide a direct measurement of vegetation index (VI) but it can provide a reasonably good estimate of vegetation index, defined as the ratio of satellite bands. The monitoring of vegetation in nearby urban regions is made difficult by the low spatial resolution and temporal resolution image captures. In this study, enhancing spatial resolution method is adapted as to improve a low spatial resolution. Recent studies have successfully estimated normalized difference vegetation index (NDVI) using improved resolution method such as from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS Terra satellite. Image enhancing spatial resolution is an important tool in remote sensing, as many Earth observation satellites provide both high-resolution and low-resolution multi-spectral images. Examples of enhancement of a MODIS multi-spectral image and a MODIS NDVI image of Cheongju using a Landsat TM high-resolution multi-spectral image are presented. The results are compared with that of the IHS technique is presented for enhancing spatial resolution of multi-spectral bands using a higher resolution data set. To provide a continuous monitoring capability for NDVI, in situ measurements of NDVI from paddy field was carried out in 2004 for comparison with remotely sensed MODIS data. We compare and discuss NDVI estimates from MODIS sensors and in-situ spectroradiometer data over Ochang plain region. These results indicate that the MODIS NDVI is underestimated by approximately 50%.