• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2488-2511
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• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.

• Smart Structures and Systems
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• v.14 no.1
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• pp.1-16
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• 2014

In this work, we address the so-called sensor reachback problem for Wireless Sensor Networks, which consists in collecting the measurements acquired by a large number of sensor nodes into a sink node which has major computational and power capabilities. Focused on applications such as Structural Health Monitoring, we propose a cooperative communication protocol that exploits the spatio-temporal correlations of the sensor measurements in order to save energy when transmitting the information to the sink node in a non-stationary environment. In addition to cooperative communications, the protocol is based on two well-studied adaptive filtering techniques, Least Mean Squares and Recursive Least Squares, which trade off computational complexity and reduction in the number of transmissions to the sink node. Finally, experiments with real acceleration measurements, obtained from the Canton Tower in China, are included to show the effectiveness of the proposed method.

• Journal of Environmental Science International
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• v.25 no.11
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• pp.1563-1573
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• 2016

The aim of this study was to evaluate the occurrence of vanadium in Jeju Island groundwater, focusing on the spatio-temporal patterns and geochemical controlling factors of vanadium. For this, we collected two sets of groundwater data: 1) concentrations of major constituents of 2,595 groundwater samples between 2008 and 2014 and 2) 258 groundwater samples between December 2006 and June 2008. The concentrations of groundwater vanadium were in the range of $0.2{\sim}71.0{\mu}g/L$ (average, $12.0{\mu}g/L$) and showed local enrichments without temporal/seasonal variation. This indicated that vanadium distribution was controlled by 1) the geochemical/mineralogical composition and dissolution processes of original materials (i.e., volcanic rock) and 2) the flow and chemical properties of groundwater. Vanadium concentration was significantly positively correlated with that of major ions ($Cl^-$, $Na^+$, and $K^+$) and trace metals (As, Cr, and Al), and with pH, but was negatively correlated with $NO_3-N$ concentration. The high concentrations of vanadium (>$15{\mu}g/L$) occurred in typically alkaline groundwater with high pH (${\geq}8.0$), indicating that a higher degree of water-rock interaction resulted in vanadium enrichment. Thus, higher concentrations of vanadium occurred in groundwater of $Na-Ca-HCO_3$, $Na-Mg-HCO_3$ and $Na-HCO_3$ types and were remarkably lower in groundwater of $Na-Ca-NO_3$(Cl) type that represented the influences from anthropogenic pollution.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.47 no.6
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• pp.1055-1062
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• 2014

Hydroacoustic surveys were conducted for spatio-temporal distribution and size estimation of fish in the Yongdam reservoir, Korea, from April to July 2014. Acoustic data were collected along acoustic transects using a 120 kHz scientific echosounder. The received acoustic data were the in situ acoustic target strength (dB) and nautical area scattering coefficient ($m^2/mile^2$). Data on fish behavioral patterns and size were collected using a DIDSON acoustic camera at stationary stations. Fish were mainly distributed in the center channel and close to the outer Yongdam reservoir. Acoustic density of fish in the summer season were higher than in the spring season. The seasonal vertical distribution pattern of fish aggregations may be strongly related to the vertical temperature structure. The size distribution of fish obtained from an acoustic camera correlated well with the acoustic size of fish from an echosounder.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.361-380
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• 2023

Magnetoencephalography (MEG) is a technique to record oscillatory magnetic fields coming from ongoing neuronal activity. Functional brain activities performing cognitive or physiological tasks are performed on structural connections between neurons or brain regions. MEG data can be characterized as highly correlated, spatio-temporal, multidimensional, multilayered dynamic networks. Due to its complex structure, many studies on MEG network have not yet been conducted. In this study, we will explain the concept, necessity, and possible approaches of MEG network analysis. We reviewed the characteristics of MEG data. Network measures and potential network models in MEG and clinical studies are also reviewed.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.19-37
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• 2017

Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS-DNB) data provides a much higher capability for observing and quantifying nighttime light (NTL) brightness in comparison with Defense Meteorological Satellite-Operational Linescan System (DMSP-OLS) data. In South Korea, there is little research on the detection of NTL brightness change using VIIRS-DNB data. This study analyzed the spatial distribution and change of NTL brightness between 2013 and 2016 using VIIRS-DNB data, and detected its spatial relation with possible influencing factors using regression models. The intra-year seasonality of NTL brightness in 2016 was also studied by analyzing the deviation and change clusters, as well as the influencing factors. Results are as follows: 1) The higher value of NTL brightness in 2013 and 2016 is concentrated in Seoul and its surrounding cities, which positively correlated with population density and residential areas, economic land use, and other factors; 2) There is a decreasing trend of NTL brightness from 2013 to 2016, which is obvious in Seoul, with the change of population density and area of industrial buildings as the main influencing factors; 3) Areas in Seoul, and some surrounding areas have high deviation of the intra-year NTL brightness, and 71% of the total areas have their highest NTL brightness in January, February, October, November and December; and 4) Change of NTL brightness between summer and winter demonstrated a significantly positive relation with snow cover area change, and a slightly and significantly negative relation with albedo change.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.21 no.6
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• pp.351-360
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• 1988

Based on historic data of monthly means of sea surface temperatures (SST) for 24 years $(1921\~1944) $ at 23 Korean and Japanese coastal stations in the East Sea (the Japan Sea), we analyzed spatio-temporal characteristics of coastal SST and SST anomalies. The means of SST at Korean coast are higher than those at Japanese coast of the same latitudes, and the annual range of SST at Korean coast are larger than those at Japanese coast. Empirical orthogonal function analysis shows that almost all $(96\%)$ of the SST fluctuations are described by simultaneous seasonal variations. The flurtuations of SST anomalies are small in the Korea Strait and large at the boundaries between the warm and told currents in the basin. The fluctuations of SST anomalies along Korean coast are correlated each other The same is true for SST anomalies along Japanese coast. However, there is only weak correlation between the SST anomalies at Korean coast and those at Japanese coast. Empirical orthogonal function analysis shows that $27\%$ of the coastal SST anomalies in the East Sea are described by simultaneous fluctuations, and $12\%$ of them are described by alternating fluctuations between Korean and Japanese coasts.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.35-44
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• 2009

The emerging notion of data stream has brought many new challenges to the research communities as a consequence of its conceptual difference with conventional concepts of just data. One typical example is data stream processing in sensor networks. The range of data processing considerations in a sensor network is very wide, from physical resource restrictions such as bandwidth, energy, and memory to the peculiarities of query processing including continuous and specific types of queries. In this paper, as one of the physical constraints in data stream processing, we consider the problem of limited memory and propose a new scheme for data stream reduction based on the Principal Component Analysis (PCA) technique. PCA can transform a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables. We adapt PCA for the data stream of a sensor network assuming the cooperation of a query engine (or application) with a network base station. Our method exploits the spatio-temporal correlation among multiple measurements from different sensors. Finally, we present a new framework for data processing and describe a number of experiments under this framework. We compare our scheme with the wavelet transform and observe the effect of time stamps on the compression ratio. We report on some of the results.

• Phonetics and Speech Sciences
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• v.11 no.3
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• pp.31-38
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• 2019

The current electromagnetic articulography study analyzes several articulatory measures and examines whether, and if so, how they are interconnected, with a focus on cluster types and an additional consideration of speech rates and morphosyntactic contexts. Using articulatory data on non-assimilating contexts from three Seoul-Korean speakers, we examine how speaker-dependent gestural overlap between C1 and C2 in a low vowel context (/a/-to-/a/) and their resulting intergestural coordination are realized. Examining three C1C2 sequences (/k(#)t/, /k(#)p/, and /p(#)t/), we found that three normalized gestural overlap measures (movement onset lag, constriction onset lag, and constriction plateau lag) were correlated with one another for all speakers. Limiting the scope of analysis to C1 velar stop (/k(#)t/ and /k(#)p/), the results are recapitulated as follows. First, for two speakers (K1 and K3), i) longer normalized constriction plateau lags (i.e., less gestural overlap) were observed in the pre-/t/ context, compared to the pre-/p/ (/k(#)t/>/k(#)p/), ii) the tongue dorsum at the constriction offset of C1 in the pre-/t/ contexts was more anterior, and iii) these two variables are correlated. Second, the three speakers consistently showed greater horizontal distance between the vertical tongue dorsum and the vertical tongue tip position in /k(#)t/ sequences when it was measured at the time of constriction onset of C2 (/k(#)t/>/k(#)p/): the tongue tip completed its constriction onset by extending further forward in the pre-/t/ contexts than the uncontrolled tongue tip articulator in the pre-/p/ contexts (/k(#)t/>/k(#)p/). Finally, most speakers demonstrated less variability in the horizontal distance of the lingual-lingual sequences, which were taken as the active articulators (/k(#)t/=/k(#)p/ for K1; /k(#)t/