• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.3
• /
• pp.197-203
• /
• 2022

Urban space expansion is an important symbol of the urbanization process and has always been an important topic in urban studies. In addition, for sustainable city management, it is important to identify factors that can influence, such as the driving force and direction of urban space expansion, from the stage of establishing an urban development plan. To understand these factors, by observing the expansion process of a specific city, it is possible to sufficiently observe how the urban spatial dimension changes. Through a series of processes, the spatial growth characteristics of the city are analyzed, and the influence and results of important factors are analyzed. For this purpose, this paper examines the changes in the city's outer boundary and land use structure through monitoring data on urban areas of 14 cities in Hunan Province, China from 2000 to 2016. Temporal and spatial regularity according to the urban space expansion of these cities were analyzed, and a preliminary assessment was made on whether the urban space expansion is coordinated with the urban population growth. The assessment result showed: (1) The urban space of most cities has been extended rapidly in 2000-2015 however, the rate and the intensity of urban space expanding has been declining. (2) The construction of the industrial park is the core driving force of the urban space expanding, and the change of the urban space structure is manifested as enclave city expansion because that the industrial park is usually far away from the city center. (3) The population agglomeration is another driving force of the urban space expanding. At this time, the urban space expanding is like boundary extension. (4) Except Changsha city, all of the cities has a high urbanization-area-growth elastic coefficient. It means that most of the cities should enhance the land use degree.

• Journal of Astronomy and Space Sciences
• /
• v.16 no.2
• /
• pp.241-254
• /
• 1999

Differential time-series observations of BL Camelopardalis classified as a double mode SX Phoenicis type variable were secured with a charge coupled device. The observed photometric data was reduced using the IRAF Package and the differential magnitudes were obtained through aperture photometry. The periods of BL Cam were analyzed with the Generalized Least-Square Method by Vanicek (1971) and the Fourier Decomposition Method. It was found that the first and second period of BL Cam were 0.0391 day respectively which lead the period ratio of P1/P0=0.81. This period ratio is much different from 0.78 determined by other investigators and also much more larger than that of other double-mode SX Phe type variables. In addition, this period ratio is much different from the value expected from the relation between the metallicity and period ratio. From these results, it can be confirmed that BL Cam is the most extreme case among all double-mode SX Phe type variables.

• Journal of Korea Multimedia Society
• /
• v.3 no.2
• /
• pp.201-208
• /
• 2000

In this paper We describe an algorithm which is devised for 4he partition o# the input space and the generation of fuzzy rules by the fuzzy entropy and tested with the time series prediction problem using Mackey-Glass chaotic time series. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rules base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. The Proposed algorithm has been naturally derived by means of the synergistic combination of the approximative approach and the descriptive approach. Each output of the rule's consequences has expressed with its connection weights in order to minimize the system parameters and reduce its complexities.

• Journal of Ocean Engineering and Technology
• /
• v.18 no.2
• /
• pp.1-9
• /
• 2004

As the first step to investigate the nonlinear interactions between turbulence and marine structures inside a viscous NWT, a LES technique was applied to solve the turbulent channel flow for =150. The employed turbulence models included 4 types: the Smagorinsky model, the Dynamic SGS model, the Structure Function model, and the Generalized Normal Stress model. The simulated data in time-series for the LESs were averaged in both time and space, and statistical analyses were performed. The results of the LESs were compared with those of a DNS, developed in the present study and two spectral methods by Yoon et al.(2003) and Kim et a1.(1987). Based on this research, the accuracy of LESs has been found to be still related to the number of grids for fine grid size).

• Journal of Aerospace System Engineering
• /
• v.7 no.4
• /
• pp.1-11
• /
• 2013

In this study, probabilistic reliability analysis was conducted for hybrid rocket performance using Monte-Carlo Simulation. For the accuracy, reliability analysis was performed with experimental data. To simplify the analysis process, the oxidizer was supplied with constant pressure, so that pressure variation with time can be eliminated. And time-space averaged regression rate model was used. The regression rate is obtained with a series of experiments. For reliability analysis of thrust, constant exponent of regression rate is assumed that has probabilistic character. So, the efficiency of characteristic velocity has also probabilistic values. As a results, probability distribution of the thrust is obtained by Monte-Carlo simulation using random samples of the input parameter and validated under the 95% confidence level.

• International journal of advanced smart convergence
• /
• v.11 no.3
• /
• pp.79-84
• /
• 2022

Due to a series of online sex crimes cases and online class conversions caused by the spread of the coronavirus, alternatives to sex education in schools are urgently required. As a result of this study, the metaverse sex education platform was designed. Using this platform, learners are expected to cultivate correct adult awareness and digital citizenship. Within the metaverse platform, learners can participate more actively in learning. Instead of exposing one's name and face in a place dealing with sensitive gender issues, one can participate in education through his or her decorated avatar and participate in education much more actively than face-to-face education and express one's opinion through chat. In addition, education by level can be received regardless of time and place, which can have the effect of bridging the educational gap between urban and rural areas. In this paper, we propose a new sex education platform without time and space constraints by utilizing metaverse.

• Korean Journal of Remote Sensing
• /
• v.27 no.3
• /
• pp.329-338
• /
• 2011

The time-series normalized difference vegetation index (NDVI) product has proven to be a powerful tool to investigate the phenological information because it can monitor the change of the forests with very high time-resolution, This study described the application of the DFT analysis over the 9 year MODIS data for the identification of the two types of vegetation cover, Pinus densiflora(Pd) and Querqus mongolica(Qm) which are dominant species of evergreen and broadleaved deciduous forest, respectively, The total number of samples was 5148 reference cycles which consist of 2160 Pd and 2988 Qm. They were extracted from the pixel-based MODIS scenes over the 9 years from 2000 to 2008 of South Korea. The DFT analysis was mainly focused on the 0th and $1^{st}$ harmonic components, each of which represents the mean value and the variation amplitude of the NDVI over the years, respectively. The $0^{th}$ harmonic values of the vegetation Pd and Qm averaged over the 9 years were 0.74 and 0.65, respectively. This implies that Pd has a higher NDVI than Qm. Similarly obtained $1^{st}$ harmonic values of Pd and Qm were 0.19 and 0.27, respectively. This can be intuitively understood considering that the seasonal variation of Qm is much larger than Pd. This distinctive difference of the $1^{st}$ harmonic value has been used to identify evergreen and deciduous forests. Overall agreement between the Fourier analysis-based map and the actal vegetation map has been estimated to be as high as 75%. This study found that the DFT analysis can be a concise and repeatable method to separate and trace the changes of evergreen and deciduous forest using the annual NDVI cycles.

• Journal of KIISE:Databases
• /
• v.34 no.6
• /
• pp.483-492
• /
• 2007

Time series of conventional prediction techniques uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Journal of Korea Water Resources Association
• /
• v.45 no.3
• /
• pp.275-290
• /
• 2012

In this study, we have developed an optimal time distribution model through extraction of peaks over threshold (POT) series. The median values for annual maximum rainfall dataset, which are obtained from the magnetic recording (MMR) and the automatic weather system(AWS) data at Seoul meteorological observatory, were used as the POT criteria. We also suggested the improved methodology for the time distribution of extreme rainfall compared to Huff method, which is widely used for time distributions of design rainfall. The Huff method did not consider changing in the shape of time distribution for each rainfall durations and rainfall criteria as total amount of rainfall for each rainfall events. This study have suggested an extracting methodology for rainfall events in each quartile based on interquartile range (IQR) matrix and selection for the mode quartile storm to determine the ranking cosidering weighting factors on minutely observation data. Finally, the optimal time distribution model in each rainfall duration was derived considering both data size and characteristics of distribution using kernel density function in extracted dimensionless unit rainfall hyetograph.

• Proceedings of the KIEE Conference
• /
• 2000.11d
• /
• pp.803-805
• /
• 2000

In this paper, we propose fuzzy-neural Networks(FNN) which is useful for identification algorithms. The proposed FNN model consists of two steps: the first step, which determines premise and consequent parameters approximately using FCM_RI method, the second step, which adjusts the premise and consequent parameters more precisely by gradient descent algorithm. The FCM_RI algorithm consists FCM clustering algorithm and Recursive least squared(RLS) method, this divides the input space more efficiently than convention methods by taking into consideration correlations between components of sample data. To evaluate the performance of the proposed FNN model, we use the time series data for gas furnace.