• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.141-145
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• 2009

In this paper, we propose an efficient lossless compression algorithm using spatial and temporal information. The proposed method obtains higher lossless compression of images than other lossless compression techniques. It is divided into two parts, a motion adaptation based predictor part and a residual error coding part. The proposed nonlinear predictor can reduce prediction error by learning from its past prediction errors. The predictor decides the proper selection of the spatial and temporal prediction values according to each past prediction error. The reduced error is coded by existing context coding method. Experimental results show that the proposed algorithm has better performance than those of existing context modeling methods.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1931-1942
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• 2021

As the utility of an optical satellite image with a high spatial resolution (i.e., fine-scale) has been emphasized, recently, various studies of the land surface monitoring using those have been widely carried out. However, the usefulness of fine-scale satellite images is limited because those are acquired at a low temporal resolution. To compensate for this limitation, the spatiotemporal data fusion can be applied to generate a synthetic image with a high spatio-temporal resolution by fusing multiple satellite images with different spatial and temporal resolutions. Since the spatio-temporal data fusion models have been developed for mid or low spatial resolution satellite images in the previous studies, it is necessary to evaluate the applicability of the developed models to the satellite images with a high spatial resolution. For this, this study evaluated the applicability of the developed spatio-temporal fusion models for KOMPSAT-3A and Sentinel-2 images. Here, an Enhanced Spatial and Temporal Adaptive Fusion Model (ESTARFM) and Spatial Time-series Geostatistical Deconvolution/Fusion Model (STGDFM), which use the different information for prediction, were applied. As a result of this study, it was found that the prediction performance of STGDFM, which combines temporally continuous reflectance values, was better than that of ESTARFM. Particularly, the prediction performance of STGDFM was significantly improved when it is difficult to simultaneously acquire KOMPSAT and Sentinel-2 images at a same date due to the low temporal resolution of KOMPSAT images. From the results of this study, it was confirmed that STGDFM, which has relatively better prediction performance by combining continuous temporal information, can compensate for the limitation to the low revisit time of fine-scale satellite images.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.213-219
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• 2005

In the Korean seas, Sea Surface Temperature (SST) and Thermal ronts (TF) were analyzed temporally and spatially during 8 years from 1993 to 2000 using NOAA/AVHRR MCSST. In the application of EOF analysis for SST, the variance of the 1st mode was 97.6%. Temporal components showed annual variations, and spatial components showed that where it is closer to continents, the SST variations are higher. Temporal components of the 2nd mode presented higher values of 1993, 94 and 95 than those of other years. Although these phenomena were not remarkable, they could be considered ELNI . NO effects to the Korean seas as the time was when ELNI . NO occurred. The Sobel Edge Detection Method (SEDM) delineated four fronts: the Subpolar Front (SPF) separating the northern and southern parts of the East Sea; the Kuroshio Front (KF) in the East China Sea, the South Sea Coastal Front (SSCF) in the South Sea, and the Tidal Front (TDF) in the West Sea. TF generally occurred over steep bathymetry slopes, and spatial components of the 1st mode in SST were bounded within these frontal areas. EOF analysis of SST gradient values revealed the temporal and spatial variations of the TF. The SPF and SSCF were most intense in March and October; the KF was most significant in March and May.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.114-130
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• 2021

With the drastic growth of Information and Communication Technology (ICT) industry, global energy consumption is exponentially increased by mobile communications. The huge energy consumption and increased environmental awareness have triggered great interests on the research of dynamic distribution of cell user and traffic, and then designing the energy efficient cellular network. In this paper, we explore the temporal and spatial characteristics of human mobility and traffic distribution using real data set. The analysis results of cell traffic illustrate the tidal effect in temporal and spatial dimensions and obvious periodic characteristics which can be used to design Base Station (BS) dynamic with sleeping or shut-down strategy. At the same time, we designed a new Cell Zooming and BS cooperation mode. Through simulation experiments, we found that running in this mode can save about 35% of energy consumption and guarantee the required quality of service.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.789-796
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• 2012

Fingerprinting is a widely used positioning technology for received signal strength (RSS) based wireless local area network (WLAN) positioning system. Though spatial RSS variation is the key factor of the positioning technology, temporal RSS variation needs to be considered for more accuracy. To deal with the spatial and temporal RSS characteristics within a unified framework, this paper proposes an extended signal propagation mode (ESPM) and a fingerprint generation method. The proposed spatiotemporal fingerprint generation method consists of two algorithms running in parallel; Kalman filtering at several measurement-sampling locations and Kriging to generate location fingerprints at dense reference locations. The two different algorithms are connected by the extended signal propagation model which describes the spatial and temporal measurement characteristics in one frame. An experiment demonstrates that the proposed method provides an improved positioning accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.186-198
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• 2016

HPAI(Highly Pathogenic Avian Influenza) is a contagious animal disease that spreads rapidly by diffusion after the first occurrence. The disease has brought tremendous social costs and economic losses. KAHIS (Korea Animal Health Information System) is the integrated system for supporting the task of preventing epidemics. They provide decision-support information, recording vehicle visiting times and facility location, etc., which is possible by enforcing registration of all livestock related facilities and vehicles. KAHIS has accumulated spatial and temporal information that enables effective tracing of potential disease trajectories and diffusion through vehicle movements. The contact network is created utilizing spatial and temporal information in KAHIS to inform facility connection via vehicle visitation. Based on the contact network, it is possible to infer spatial and temporal mechanism of disease spread and diffusion. The study objective is to empirically demonstrate how to utilize primary spatial and temporal information in KAHIS in the form of the contact network. Based on the contact network, facilities with the possibility of infection can be pinpointed within the potential spatial and temporal extent where the disease has spread and diffused. This aids the decision-making process in the task of preventing epidemics. By interpreting our demonstration results, policy implications were presented. Finally, some suggestions were made to comprehensively utilize the contact network to draw enhanced decision-support information.

• Journal of Information Technology Applications and Management
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• v.12 no.1
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• pp.111-123
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• 2005

Many indexing methods were proposed so that process moving object efficiently. Among them, indexing methods like the 3D R-tree treat temporal and spatial domain as the same. Actually, however. both domain had better process separately because of difference in character and unit. Especially in this paper we deal with limited region such as indoor environment since spatial domain is limited but temporal domain is grown. In this paper we present a novel indexing structure, namely STS-tree(Separation of Time and Space tree). based on limited region. STS-tree is a hybrid tree structure which consists of R-tree and one-dimensional TB-tree. The R-tree component indexes static object and spatial information such as topography of the space. The TB-tree component indexes moving object and temporal information.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1225-1239
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• 2016

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.235-244
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• 2022

Many new techniques have been adopted in HEVC (High efficiency video coding) standard, such as quadtree-structured coding unit (CU), prediction unit (PU) partition, 35 intra-mode, and so on. To reduce computational complexity, the paper proposes two optimization algorithms which include fast CU depth range decision and fast PU partition mode decision. Firstly, depth range of CU is predicted according to spatial-temporal correlation. Secondly, we utilize the depth difference between the current CU and CU corresponding to the same position of adjacent frame for PU mode range selection. The number of traversal candidate modes is reduced. The experiment result shows the proposed algorithm obtains a lot of time reducing, and the loss of coding efficiency is inappreciable.