• Architectural research
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• v.21 no.4
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• pp.91-98
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• 2019

In modern times, numerous cities are competing to create the unique skyline adjacent to the water. Tall buildings located across the river have a great contribution to the skyline of a riverfront city and can be a precious asset for the city. Moreover, in several cities, tall buildings and their impact on the urban skyline are a matter that should be considered and regulated in urban design. Therefore, as a prominent element in a larger visual setting of the city, tall buildings should improve the visual quality of the skyline rather than diminish that quality. This research attempts to provide an objective method to analyze the visual quality of the skyline made by a group of tall buildings through their feature of heights and spatial arrangement from riverfront views. The analysis is determined by the design variables of building heights variation, heights transition, density, and spacing of a group of tall buildings. A comparative case study of tall buildings in Yeouido and Lujiazui was conducted to prove the effectiveness of the analysis. The proposed method can be used in a simple way in the quantitative approach to quantify the visual quality of the skyline. In conclusion, Yeuido's skyline is not quite interesting from the riverfront view in terms of height variation and continuity of the skyline view because they are dispersed. Conversely, Lujiazui's skyline from the riverfront vantage points has a good quality in all aspects of the feature of height and spatial arrangements of tall buildings cluster. These factors can be used for the urban designer on how proposed tall buildings within the cluster should appropriately respond to adding image on the skyline.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.453-456
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• 2003

Compared with other features of the image, color features are less sensitive to noise and background complication. Besides, this adding to object segmentation has more accuracy of image retrieval. This paper presents object segmentation and HAQ(Histogram Analysis and Quantization) algorithm approach to extract features(the object information and the characteristic colors) of an image. The empirical results shows that this method presents exactly spatial and color information of an image as image retrieval's feature.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.60-74
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• 2007

For quantitative analysis of spatio-temporal distribution pattern on marine environment, spatial autocorrelation statistics on the both global and local aspects was applied to the observed data obtained from Gwangyang Bay in South Sea of Korea. Global indexes such as Moran's I and General G were used for understanding environmental distribution pattern in the whole study area. LISAs (local indicators of spatial association) such as Moran's I ($I_i$) and $G_i{^*}$ were considered to find similarity between a target feature and its neighborhood features and to detect hot spot and/or cold spot. Additionally, the significance test on clustered patterns by Z-scores was carried out. Statistical results showed variations of spatial patterns quantitatively in the whole year. Then all of general water quality, nutrients, chlorophyll-a and phytoplankton had strong clustered pattern in summer. When global indexes showed strong clustered pattern, the front region with a negative $I_i$ which means a strong spatial variation was observed. Also, when global indexes showed random pattern, hot spot and/or cold spot were/was found in the small local region with a local index $G_i{^*}$. Therefore, global indexes were useful for observing the strength and time series variations of clustered patterns in the whole study area, and local indexes were useful for tracing the location of hot spot and/or cold spot. Quantification of both spatial distribution pattern and clustering characteristics may play an important role to understand marine environment in depth and to find the reasons for spatial pattern.

• The KIPS Transactions:PartB
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• v.17B no.6
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• pp.429-436
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• 2010

This paper presents a method to extract 3D outlines of objects in an image obtained from a monocular vision. After detecting the general outlines of the object by MOPS(Multi-Scale Oriented Patches) -algorithm and we obtain their spatial coordinates. Simultaneously, it obtains the space-coordinates with feature points to be immanent within the outlines of objects through SIFT(Scale Invariant Feature Transform)-algorithm. It grasps a form of objects to join the space-coordinates of outlines and SIFT feature points. The method which is proposed in this paper, it forms general outlines of objects, so that it enables a rapid calculation, and also it has the advantage capable of collecting a detailed data because it supplies the internal-data of outlines through SIFT feature points.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.2 s.29
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• pp.53-59
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• 2004

In this paper, we investigated a method for feature-based classification to develop a software which is suitable for the classification of high resolution satellite imagery. We developed algorithms for image segmentation and fuzzy-based classification required for feature-based classification and designed user interfaces to support interaction with user, considering various elements required for the feature-based classification. Evaluation of the software was accomplished using real image. Classification results were compared and analysed with eCognition software which is unique commercial software for feature-based classification. The classification results from both softwares showed essentially same results and the developed software showed better result in the processing speed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4395-4412
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• 2020

The multisource image fusion has become an active topic in the last few years owing to its higher segmentation rate. To enhance the accuracy of multimodal pig-body feature segmentation, a multisource image fusion method was employed. Nevertheless, the conventional multisource image fusion methods can not extract superior contrast and abundant details of fused image. To superior segment shape feature and detect temperature feature, a new multisource image fusion method was presented and entitled as NSST-GF-IPCNN. Firstly, the multisource images were resolved into a range of multiscale and multidirectional subbands by Nonsubsampled Shearlet Transform (NSST). Then, to superior describe fine-scale texture and edge information, even-symmetrical Gabor filter and Improved Pulse Coupled Neural Network (IPCNN) were used to fuse low and high-frequency subbands, respectively. Next, the fused coefficients were reconstructed into a fusion image using inverse NSST. Finally, the shape feature was extracted using automatic threshold algorithm and optimized using morphological operation. Nevertheless, the highest temperature of pig-body was gained in view of segmentation results. Experiments revealed that the presented fusion algorithm was able to realize 2.102-4.066% higher average accuracy rate than the traditional algorithms and also enhanced efficiency.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.642-645
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.

• Proceedings of the KSRS Conference
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• v.1
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• pp.294-296
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• 2006

Recently the development of GIS and spatial information technology is used to construct very detail forest information. In addition, in order to classify forest characteristic, the geographical characteristic information of forest could be very useful for the forest classification, In this study sampling points were arranged to clarify the difference between the orm area and the land forest area. Also, forest feature pattern could be discriminated by using satellite images and SML. This study result should be constructed to efficiency forest management in especially forest area in Jeju Island

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.515-520
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• 2012

In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).

• Journal of Korean Society for Quality Management
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• v.34 no.2
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• pp.122-128
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• 2006

One feature of a computer simulation experiment, different from a physical experiment, is that the output is often deterministic. Moreover the codes are computationally very expensive to run. This paper deals with the design and analysis of computer experiments(DACE) which is a relatively new statistical research area. We model the response of computer experiments as the realization of a stochastic process. This approach is basically the same as using a spatial linear model. Applications to the optimal mechanical designing and model calibration problems are illustrated. Algorithms for selecting the best spatial linear model are also proposed.