• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.6
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• pp.497-508
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• 2012

Occupants' visual perception, psychological responses and spatial satisfaction under various indoor lighting environments were analyzed in this study. Field measurements and surveys were conducted in four coffeeshop space where different lighting conditions were used. Results imply that worse visual comfort was reported under direct lighting conditions that exposed light sources to occupants. To improve spatial satisfaction in space, lighting environments should lessen visual thresholds and distraction. Also, necessary illuminance levels should be kept with appropriate color of light that occupants prefer. Worse spatial satisfaction was reported under direct lighting environments, and spatial satisfaction was strongly relevant to visual comfort. Psychological comfort for space was positive in space where visual thresholds were minimized and visual comfort was positively evaluated. Psychological and spatial satisfaction was relevant each other. Occupants preferred to stay longer in space where psychological and spatial satisfaction was positively achieved due to less visual thresholds and improved visual comfort. Better psychological and spatial satisfaction was achieved in space where temporary mood and visual perception were favorably evaluated under indirect lighting environments.

• Korean Journal of Remote Sensing
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• v.9 no.1
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• pp.1-19
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• 1993

A simple bispectral threshold technique which reflects the temporal and spatial characteristics of the analysis area has been developed to classify the cloud type and estimate the cloud cover from GMS/S-VISSR(Stretched Visible and Infrared Spin Scan Radiometer) imagery. In this research, we divided the analysis area into land and sea to consider their different optical properties and used the same time observation data to exclude the solar zenith angle effects included in the raw data. Statistical clear sky radiance(CSRs) was constructed using maximum brightness temperature and minimum albedo from the S-VISSR imagery data during consecutive two weeks. The CSR used in the cloud anaysis was updated on the daily basis by using CSRs, the standard deviation of CSRs and present raw data to reflect the daily variation of temperature. Thresholds were applied to classify the cloud type and estimate the cloud cover from GMS/S-VISST imagery. We used a different thresholds according to the earth surface type and the thresholds were enough to resolve the spatial variation of brightness temperature and the noise in raw data. To classify the ambiguous pixels, we used the time series of 2-D histogram and local standard deviation, and the results showed a little improvements. Visual comparisons among the present research results, KMA's manual analysis and observed sea level charts showed a good agreement in quality.

• The Journal of Engineering Geology
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• v.34 no.2
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• pp.173-191
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• 2024

Landslides pose significant threats to many countries globally, yet the development and implementation of effective landslide early warning systems (LEWS) remain challenging due to multifaceted complexities spanning scientific, technological, and political domains. Addressing these challenges demands a holistic approach. Technologically, integrating thresholds, such as rainfall thresholds, with real-time data within accessible, open-source software stands as a promising solution for LEWS. This article introduces LandScient_EWS, a PHP-based program tailored to address this need. The software facilitates the comparison of real-time measured data, such as rainfall, with predefined landslide thresholds, enabling precise calculations and graphical representation of real-time landslide advisory levels across diverse spatial scales, including regional, basin, and hillslope levels. To illustrate its efficacy, the program was applied to a case study in Medellin, Colombia, where a rainfall event on August 26, 2008, triggered a shallow landslide. Through pre-defined rainfall intensity and duration thresholds, the software simulated advisory levels during the recorded rainfall event, utilizing data from a rain gauge positioned within a small watershed and a single grid cell (representing a hillslope) within that watershed. By identifying critical conditions that may lead to landslides in real-time scenarios, LandScient_EWS offers a new paradigm for assessing and responding to landslide hazards, thereby improving the efficiency and effectiveness of LEWS. The findings underscore the software's potential to streamline the integration of rainfall thresholds into both existing and future landslide early warning systems.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1087-1089
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• 2003

The aim of this study is to evaluate the Pi-SAR data for land cover discrimination using a standard method. For this purpose, the original polarization and Pauli components of the Pi-SAR X-band and L-band data are used and the results are compared. As a method for the land cover discrimination, the traditional method of statistical maximum likelihood decision rule is selected. To increase the accuracy of the classification result, different spatial thresholds based on local knowledge are determined and used for the actual classification process. Moreover, to reduce the speckle noise and increase the spatial homogeneity of different classes of objects, a speckle suppression filter is applied to the original Pi-SAR data before applying the classification decision rule. Overall, the research indicated that the original Pi-SAR polarization components can be successfully used for separation of different land cover types without taking taking special polarization transformations.

• Atmosphere
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• v.17 no.2
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• pp.125-133
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• 2007

Cloud amount calculation algorithm was developed using MTSAT-1R satellite data. The cloud amount is retrieved at 5 km ${\times}$ 5 km over the Korean Peninsula and adjacent sea area. The algorithm consists of three steps that are cloud detection, cloud type classification, and cloud amount calculation. At the first step, dynamic thresholds method was applied for detecting cloud pixels. For using objective thresholds in the algorithm, sensitivity test was performed for TBB and Albedo variation with temporal and spatial change. Detected cloud cover was classified into 3 cloud types (low-level cloud, cirrus or uncertain cloud, and cumulonimbus type high-level cloud) in second step. Finally, cloud amount was calculated by the integration method of the steradian angle of each cloud pixel over $3^{\circ}$ elevation. Calculated cloud amount was compared with measured cloud amount with eye at surface observatory for the validation. Bias, RMSE, and correlation coefficient were 0.4, 1.8, and 0.8, respectively. Validation results indicated that calculated cloud amount was a little higher than measured cloud amount but correlation was considerably high. Since calculated cloud amount has 5km ${\times}$ 5km resolution over Korean Peninsula and adjacent sea area, the satellite-driven cloud amount could show the possibility which overcomes the temporal and spatial limitation of measured cloud amount with eye at surface observatory.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.16-22
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• 2009

In this paper, we propose an auto gain/offset which considers the visibility of human visual system (HVS) and the histogram of a target image jointly. In the proposed method, the lower and upper clipping thresholds are determined to maximize the averaged visibility of the contrast-stretched image. The target image is then contrast-stitched by the gain and offset derived from the clipping thresholds. We define the visibility as a quantity related to the spatial JND, which means the threshold below which any change of a pixel from its textured neighbors is not recognized by the HVS. Experimental results show that the contrast-stretched images by the proposed method have better global and local contrasts compared to the results by some conventional methods.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.688-698
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• 2020

Point clouds have ability to express the spatial entities, however, the point clouds redundancy always involves some uncertainties in computer recognition and model construction. Therefore, point clouds thinning is an indispensable step in point clouds model reconstruction and other applications. To overcome the shortcomings of complex classification index and long time consuming in existing point clouds thinning algorithms, this paper proposes a point clouds fast thinning algorithm. Specifically, the two-dimensional index is established in plane linear array (x, y) for the scanned point clouds, and the thresholds of adjacent point distance difference and height difference are employed to further delete or retain the selected sample point. Sequentially, the index of sample point is traversed forwardly and backwardly until the process of point clouds thinning is completed. The results suggest that the proposed new algorithm can be applied to different targets when the thresholds are built in advance. Besides, the new method also performs superiority in time consuming, modelling accuracy and feature retention by comparing with octree thinning algorithm.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1323-1325
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• 2003

As to many conventional segmentation approaches , spatial autocorrelation, perhaps being the first law of geography, is always overlooked. Thus, the corresponding segmentation results are always not so satisfying, which will further affect the subsequent image processing or analyses. In order to improve segmentation results, a geostatistic based segmentation approach with the consideration of spatial autocorrelation hidden in remote-sensing images is proposed in this article. First, by calculating the mean variance between each pair of pixels at given different lag distances, information like the size of typical targets in the scene can be obtained, and segmentation thresholds are calculated accordingly. Second, an initial region growing segmentation approach is implemented. Finally, based on the segmentation thresholds obtained at the first step and the initial segmentation results, the final segmentation results are obtained using the same region growing approach by taking the local mutual best fitting strategy. From the experiment results, we found the approach is rather promising. However, there still exists some problems to be settled, and further researches should be conducted in the future.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.857-860
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• 2000

This paper presents a new watermarking method based on wavelet transform. Embedding algorithm call define robust area by varying thresholds and gives a watermark image the priority of significance by analyzing spatial correlation of the watermark image. Detector can adjust thresholds to the distorted watermarked image. Also detector can extract the embedded data without an original image. A new measurement for detecting the correct watermark is suggested. Simulation results show that the embedded watermark is robust against various signal processing and compression attacks.

• Proceedings of the KSRS Conference
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• v.2
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• pp.574-577
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• 2006

In this paper, we developed a forest fire detection algorithm which uses a regression function between NDVI and land surface temperature. Previous detection algorithms use the land surface temperature as a main factor to discriminate fire pixels from non-fire pixels. These algorithms assume that the surface temperatures of non-fire pixels are intrinsically analogous and obey Gaussian normal distribution, regardless of land surface types and conditions. And the temperature thresholds for detecting fire pixels are derived from the statistical distribution of non-fire pixels’ temperature using heuristic methods. This assumption makes the temperature distribution of non-fire pixels very diverse and sometimes slightly overlapped with that of fire pixel. So, sometimes there occur omission errors in the cases of small fires. To ease such problem somewhat, we separated non-fire pixels into each land cover type by clustering algorithm and calculated the residuals between the temperature of a pixel under examination whether fire pixel or not and estimated temperature of the pixel using the linear regression between surface temperature and NDVI. As a result, this algorithm could modify the temperature threshold considering land types and conditions and showed improved detection accuracy.