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

• Korean Journal of Remote Sensing
• /
• v.35 no.5_1
• /
• 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.

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

• Journal of Korea Spatial Information System Society
• /
• v.10 no.1
• /
• pp.29-40
• /
• 2008

With the spread of mobile devices and advances in communication techknowledges, the needs of application which uses the movement patterns of moving objects in history trajectory data of moving objects gets Increasing. Especially, to design public transportation route or road network of the new city, we can use the similar patterns in the trajectories of moving objects that move on the spatial network such as road and railway. In this paper, we propose a spatio-temporal similar trajectory search algorithm for moving objects on road network. For this, we define a spatio-temporal similarity measure based on the real road network distance and propose a grid-based index structure for similar trajectory search. Finally, we analyze the performance of the proposed similar trajectory search algorithm in order to show its efficiency.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.6
• /
• pp.49-60
• /
• 2021

BCSA (Bias-Correction and Stochastic Analog) is a statistical downscaling technique designed to effectively correct the systematic errors of GCM (General Circulation Model) output and reproduce basic statistics and spatial variability of the observed precipitation filed. In this study, the applicability of BCSA was evaluated using the ASOS observation data over South Korea, which belongs to the monsoon climatic zone with large spatial variability of rainfall and different rainfall characteristics. The results presented the reproducibility of temporal and spatial variability of daily precipitation in various manners. As a result of comparing the spatial correlation with the observation data, it was found that the reproducibility of various climate indices including the average spatial correlation (variability) of rainfall events in South Korea was superior to the raw GCM output. In addition, the needs of future related studies to improve BCSA, such as supplementing algorithms to reduce calculation time, enhancing reproducibility of temporal rainfall patterns, and evaluating applicability to other meteorological factors, were pointed out. The results of this study can be used as the logical background for applying BCSA for reproducing spatial details of the rainfall characteristic over the Korean Peninsula.

• Korean Journal of Veterinary Research
• /
• v.53 no.1
• /
• pp.49-54
• /
• 2013

To investigate the transmission pattern of geographical area and temporal trends of the 2010~2011 foot-and-mouth disease (FMD) outbreaks in Korea, and to explore temporal intervals at which spatial clustering of FMD cases space-time analysis based on georeferenced database of 3,575 burial sites, from 30 November 2010 to 23 February 2011, was performed. The cases represent approximately 98.1% of all infected farms (n = 3,644) during the same period. Descriptive maps of spatial patterns of the outbreaks were generated by ArcGIS. Spatial Scan Statistics, using SaTScan software, was applied to investigate geographical clusters of FMD cases across the country. Overall, spatial heterogeneity was identified, and the transmission pattern was different by province. Cattle have more clusters in number but smaller in size, as compared to the swine population. In addition, spatiotemporal analysis and the comparison of clustering patterns between the first 7 days and days 8 to 14 of the outbreak revealed that the strongest spatial clustering was identified at the 7-day interval, although clustering over longer intervals (8~14 days) was also observed. We further discussed the importance of time period elapsed between FMD-suspected notice and the date of confirmation, and emphasized the necessity of region-specific and species-specific control measures.

• Korean Journal of Soil Science and Fertilizer
• /
• v.48 no.6
• /
• pp.658-665
• /
• 2015

Temporal and spatial variation of soil moisture is important for understanding patterns of climate change, for developing and evaluating land surface models, for designing surface soil moisture observation networks, and for determining the appropriate resolution for satellite-based remote sensing instruments for soil moisture. In this study, we measured several soil moistures in upland soil using Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Content (SMC) during eight-month period in Chungbuk province. The upland soil moisture properties were expressed by simple statistical methods (average, standard deviation and coefficient of variation) from the monthly context. Supplementary studies were also performed about the effect of top soil texture on the soil moisture responses. If the results from this study were utilized well in specific cities and counties in Korea, it would be helpful to establish the countermeasures and action plans for preventing disasters because it was possible to compare with the relationship between soil moisture and top soil texture of each region. And it would be the fundamental data for estimating the effect of future agricultural plan.

• Animal cells and systems
• /
• v.1 no.4
• /
• pp.589-594
• /
• 1997

The neuronal voltage-sensitive calcium channels (VSCCs) are multisubunit complexes consisting of $\alpha_1,\;\alpha_2-\delta$ and $\beta$ subunits. Heterologous expression and biochemical studies have shown that the activity of VSCCs is regulated by their $\beta$ subunits in a $\beta$ subunit isoform-specific manner. To elucidate the $\beta$ subunit identity of the P/Q-type calcium channel encoded by an $\alpha_{1A}$ subunit, which is exclusively expressed in the Purkinje and granule cell of the cerebellum, we have examined the spatial and temporal expression patterns of $\beta$ subunits and compared them with those of $\alpha_{1A}$ subunit in the developing rat cerebellum. Reverse transcriptase- polymerase chain reaction (RT-PCR) and Northern blot analysis have shown that $\beta_4$ subunit mRNA was prominently expressed in the cerebellum and much more abundant than any other distinct $\beta$ subunits. RNase protection assay has further demonstrated that the expression of $\alpha_{1A}$ and $\beta_4$ subunits increased during cerebellar development, while the amount of $\beta_2$ and $\beta_3$ mRNAs did not significantly change. In addition, a $\beta_4$ transcript was present in cultured cerebellar granule cells, but not in astrocyte cells, and the level of $\beta_4$ mRNA expression increased gradually in vitro seen as in vivo. Based on the spatial and temporal expression patterns of $\beta_4$ subunit, we conclude that $\beta_4$ may predominantly associate, but probably not exclusively, with the $\alpha_{1A}$ subunit in rat cerebellar granule cells.

• Journal of Korean Society on Water Environment
• /
• v.31 no.6
• /
• pp.723-731
• /
• 2015

We investigated the spatial and temporal patterns of water quality in the Gangjung-Goryoung weir that is located in the middle area of the Nakdong river, Korea. The monitoring results indicated that there are discernible vertical differences in water quality during the pre- and post-monsoon periods (May to September). During this period, it was observed that the weak thermal stratification formed at the maximum level, and pH, Chl-a, and DO concentrations in the surface layer were higher than those in the bottom layer. This vertical difference was especially noticeable for DO concentrations: there were DO depletions at the bottom layer in late June to early August. During the summer monsoon period with heavy rainfall, there was a decline in vertical differences in water quality. From this study, it was suggested that continuous monitoring of vertical profiles could become a useful tool for identifying the spatial and temporal distributions of water quality and for developing the best management policy for water quality in the Nakdong river.

• Journal of KIISE:Computing Practices and Letters
• /
• v.11 no.3
• /
• pp.284-291
• /
• 2005

I propose a framework for detecting, recognizing and classifying facial features based on learned expression patterns. The framework recognizes facial expressions by using PCA and expression HMM(EHMM) which is Hidden Markov Model (HMM) approach to represent the spatial information and the temporal dynamics of the time varying visual expression patterns. Because the low level spatial feature extraction is fused with the temporal analysis, a unified spatio-temporal approach of HMM to common detection, tracking and classification problems is effective. The proposed recognition framework is accomplished by applying posterior probability between current visual observations and previous visual evidences. Consequently, the framework shows accurate and robust results of recognition on as well simple expressions as basic 6 facial feature patterns. The method allows us to perform a set of important tasks such as facial-expression recognition, HCI and key-frame extraction.