• The Journal of Information Systems
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• v.28 no.4
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• pp.359-375
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• 2019

Purpose The purpose of this study is to investigate the nature of temporal non-use, namely, the type of temporal non-use and the dynamic relationships between temporary non-use and use by expanding the model of dynamic state transition of non-use which is proposed by the previous study. Design/methodology/approach This study adopted grounded theory methodology as a research methodology. Fifteen university students were interviewed for open coding and for selected coding which leads the research model ten students were interviewed. Findings Based on the coding, the types of use were divided into interactive use and one-way use, and the types of temporal use were classified into temporal non-use in which use is intended and temporal non-use in which use is not intended. The dynamic transition model is presented for each type of use and temporalnon-use, and an integrated model that combines them is presented. This study found the following four things: First, all non-use can be temporal Second, there are different types of temporal non-use. Third, the pattern of use is not just one, but new patterns will continue to emerge. Fourth, we found a change in the function of SNS.

• Journal of the Korean Geographical Society
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• v.37 no.2
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• pp.191-202
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• 2002

Characteristics and patterns of geographic features and human activities can be interpreted in terms of spatiality and temporality. The necessity to record the historical changes and the ability to reason in the real world has lead to a new field of research so called Integrated Spatio-Temporal analysis. The objective of this study is to investigate temporal maps for Spatio-temporal analysis, which have the integration functionality for visualizing spatiality and temporality of the geographic appearances and human activities. Land information is composed of spatial, attribute and temporal data and requires spatio-temporal representations. It is possible to visualize spatio-temporal variations with spatio-temporal databases and temporal map produced by integrated data models. This study constructs spatio-temporal model for temporal maps of land price variation analysis. Taking advantage of the spatio-temporal model proposed here, it is possible to visualize spatio-temporal variations with spatio-temporal database and temporal map. On a practical level, this study would be extended and utilized to various geographic features.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.567-584
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• 2005

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.

• Journal of Ecology and Environment
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• v.44 no.2
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• pp.62-71
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• 2020

Understanding vegetation structure and the relationship with environmental factors has been crucial for restoration and conservation of riparian zones. In this study, we conducted field survey in a riparian zone of Namhan River in South Korea both before and after flooding in order to understand temporal and spatial variations of riparian vegetation. There were significant temporal and spatial variations in species composition, and distribution patterns of vegetation were different along a gradient of elevation above the water level. At low elevation, Zizania latifolia was dominant throughout the field survey periods, and Bidens frondosa began to grow late and dominated both in post-flooding 1 and 2. Prior to flooding, Scirpus radicans and Polygonum thunbergii were widely distributed at middle elevation, while Artemisia vulgaris, Phragmites australis, and Miscanthus sacchariflorus were dominant at high elevation. After flooding, P. thunbergii was dominant at middle elevation with most other species decreasing, and more invasive or pioneer plants, including Artemisia princeps, H. scandens, and Sicyos angulatus, were observed at high elevation. Species composition and distribution patterns were homogeneous at low elevation, whereas dynamic variations of vegetation were observed both temporally and spatially at higher elevations. Elevation and distance from the water front were the most principal factors governing vegetation structure. Furthermore, soil physicochemical properties were also found to determine species composition and distribution patterns. These results indicate that vegetation structure in the riparian zones is formed by the combined effects of hydrological regime and soil physicochemical properties, inherent characteristics of species, and interspecific competition. Understanding of temporal and spatial variations of riparian vegetation may provide useful insights into ecological restoration and conservation of the vegetation within the riparian zones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1076-1085
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• 2009

In this paper, a new method for efficient computation of CGH patterns for 3-D video images is proposed by combined use of temporal redundancy and look-up table techniques. In the conventional N-LT method, fringe patterns for other object points on that image plane can be obtained by simply shifting these pre-calculated PFP (Principle Fringe Patterns). But there have been many practical limitations in real-time generation of 3-D video holograms because the computation time required for the generation of 3-D video holograms must be massively increased compared to that of the static holograms. On the other hand, as ordinary 3-D moving pictures have numerous similarities between video frames, called by temporal redundancy, and this redundancy is used to compress the video image. Therefore, in this paper, we proposed the efficient hologram generation method using the temporal redundancy of 3-D video image and N-LT method. To confirm the feasibility of the proposed method, some experiments with test 3-D videos are carried out, and the results are comparatively discussed with the conventional methods in terms of the number of object points and computation time.

• Spatial Information Research
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• v.14 no.2 s.37
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• pp.223-234
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• 2006

In order to examine spatio-temporal characteristics of coastal water quality, we applied GIS spatial analysis to the water quality data collected from observation points located on Korean coastal area during 1997$\sim$2004. The water quality parameters measured included: chlorophyll-a, pH, DO, COD, SS, dissolved inorganic nitrogen, dissolved inorganic phosphorous, salinity, temperature. The water quality data used in this paper was obtained only at selected sites even though they are potentially available at any location in a continuous surface. Thus, it is necessary to estimate the values at unsampled locations so as to analyze spatial distribution patterns of coastal water quality, Owing to this reason, we applied IDW(inverse distance weighted) interpolation method to water quality data and evaluated the usefulness of IDW method. After IDW interfolation method was applied, we divided the Korean coastal area into 46 sections and examined spatio-temporal patterns of each section using GIS visualization technique. As a result of evaluation, we can blow that IDW interpolation and GIS are useful for understanding spatial and temporal distribution characteristics of coastal water quality data which is collected from a wide area far many years.

• Proceedings of the KGS Conference
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• 2004.11a
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• pp.8-8
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• 2004

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.3
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• pp.20-30
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• 2000

Average annual NDVI values from the NOAA/NASA Pathfinder AVHRR Land Data Sets from 1982 to 1994 showed comprehensive systematic displacement patterns in Asia. Inter-annual growing season data, however, did not show such systematic patterns. The most likely cause for the abrupt displacements, which appear especially in 1982, 1989 and 1990, may be changes in satellite sensors, although global warming, El Ni$\tilde{n}$o-Southern Oscillation events, changes in processing algorithms, and changes in land-use patterns in various parts of Asia may also play some role. The results suggest that researchers must be extremely careful in their inter-annual global change research, since direct use of the raw data could cause unexpected results. Growing-season NDVI shows decreases throughout most of Southeast Asia and modest gains in northern China and some parts in India, which could be related to land-use and land-cover changes.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.39 no.spc1
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• pp.214-222
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• 2006

Dominant species of macrobenthos were analyzed based on differentiation of three distinct methods: the density based method, the biomass based method and LeBris method, by considering the frequency of occurrence using quantitative data collected over 5 years (1993-1998) at 21 stations in Chonsu Bay. Sedimentary environments as well as species composition and diversity showed the spatial and temporal distribution patterns. The ranks of dominant species as determined by the density based method were more similar to the results by the LeBris method than to those from the biomass based method. Considering the temporal variation, LeBris method were more efficient than any other methods for the determination of dominant species in Chonsu Bay. Lumbrineris longifolia, Theora fragilis, and Moerella jedoensis were recognized by all three methods. A one-way analysis of variance indicated spatial distributions patterns among most of the dominant species. These species showed positive correlations to sedimentary parameters such as mean grain size. However, T. fragilis and Paraprinospio pinnata showed the temporal patterns in their distribution, and were also correlated to the benthic environment, organic content and dissolved oxygen. Some dominant species, e.g., T. fragilis, S. scutata, G. gurjanovae proved to be useful benthic indicators based on the environmental variations determinated by long-term benthic ecological monitoring in Chonsu Bay.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.2
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• pp.534-540
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• 1996

This paper presents an Adaptive Time-Delay Recurrent Neural Network (ATRN) for learning and recognition of temporal correlations of temporal patterns. The ATRN employs adaptive time-delays and recurrent connections, which are inspired from neurobiology. In the ATRN, the adaptive time-delays make the ATRN choose the optimal values of time-delays for the temporal location of the important information in the input parrerns, and the recurrent connections enable the network to encode and integrate temporal information of sequences which have arbitrary interval time and arbitrary length of temporal context. The ATRN described in this paper, ATNN proposed by Lin, and TDNN introduced by Waibel were simulated and applied to the chaotic time series preditcion of Mackey-Glass delay-differential equation. The simulation results show that the normalized mean square error (NMSE) of ATRN is 0.0026, while the NMSE values of ATNN and TDNN are 0.014, 0.0117, respectively, and in temporal learning, employing recurrent links in the network is more effective than putting multiple time-delays into the neurons. The best performance is attained bythe ATRN. This ATRN will be sell applicable for temporally continuous domains, such as speech recognition, moving object recognition, motor control, and time-series prediction.