• Archives of design research
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• v.16 no.2
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• pp.375-384
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• 2003

This study is on the strategy for establishing successful corporate brand image, by understanding the need for increasing brand value based on the level of brand recognition. In order to carry this out, the PICS (Product Image Concentration System) is suggested, which includes Brand Image Analysis on a high-level, Product Image Programming based on the result of the image analysis, and Product Image Coherency Assessment and Management, resulting in setting up a guideline for gaining competitive advantage and brand management. Brand Image Analysis is a method that utilizes image association to understand brand disposition by analyzing the association pattern among available visual materials to measure the corporate and brand image inclinations. As the next step, Product Image Programming establishes design philosophy and principles based on the analysis of brand image, and the Visual Programming is a process for visualizing the intended product image direction. Lastly, Product Image Coherency Assessment examines whether to incorporate design philosophy and principles or not to arrive at an agreed evaluation criteria for developing designs coherent with the brand image. The PICS (Product Image Concentration System) is a practical method for increasing a company' competitive advantage and managing brand. The expectation on this system is to provide a guideline for applying brand image in design process more objectively. For further study, diversification of image spectrum based on expressive keywords and comparative analysis on images as well as a product image interpretation program to understand the order of visual materials will be necessary.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.25 no.4
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• pp.465-471
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• 2015

Objectives: An objective of this study is to apply a thermal image camera which shows various color according to temperature of indoor surface for estimating concentration of airborne fungi. Materials and Methods: While wall temperature were monitored by applying the thermal image camera, airborne bacteria as well as air temperature and relative humidity have been measured in lecture room and toilet of university for seven months. Results: Based on the results obtained from this study, the ranges of temperature and airborne fungi concentration were $20{\sim}24^{\circ}C$ and $20{\sim}400cfu/m^3 $ for red image, $17.5{\sim}20^{\circ}C$ and $35{\sim}150cfu/m^3$ for orange image, $15.5{\sim}17.5^{\circ}C$ and $25{\sim}650cfu/m^3$ for sky-blue image, and $13.5{\sim}15.5^{\circ}C$ and $50{\sim}200cfu/m^3$ for blue image, respectively. The color of indoor surface taken shot by thermal image camera showed consistent trend with temperature of indoor surface. There is, however, little correlation between color of indoor surface and airborne fungi concentration(p>0.05). Among environmental factors, relative humidity in indoor air showed a significant relationship with airborne fungi concentration(p<0.05). Conclusions: The more measurement data for proving statistically an association between color of indoor surface and airborne fungi concentration should be provided to easily estimate indoor level of airborne fungi.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Korean Journal of Remote Sensing
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• v.18 no.3
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• pp.141-146
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• 2002

Chlorophyll-a concentration maps of Yongdam reservoir in September and October, 2001 were produced using Landsat ETM imagery and the in-situ water quality measurement data. In-situ water samples were collected on 16th September and 18th October during the satellite overpass. The correlations between the DN values of the imagery and the values of chlorophyll-a concentration were analyzed. The visible bands(band 1, 2, 3) and the near infrared band(band 4) data of September image showed the correlation coefficient values higher than 0.9. The October image showed correlation coefficient values of about 0.7 due to the low variations of chlorophyll-a concentration. Regression models between the DN values of the Landsat ETM image and the chlorophyll-a concentration have been developed for each image. The developed regression models were then applied to each image, and finally the chlorophyll-a distribution maps of Yongdam reservoir were produced. The produced maps showed the spatial distribution of the chlorophyll-a in Yongdam reservoir in a synoptic way so that the tropic state could be easily monitored and analysed in the spatial domain.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.59-65
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• 2012

Hot pepper can be easily faded or discolored in drying process, so we need to use the sorting technique to improve the quality for dried hot pepper. Independent Component Analysis (ICA) is one of the most widely used methods for blind source separation. In this paper we use this technique to get a concentration image of the most important component which plays a role in the dried pepper. This concentration image is different from the binary image and it reflects the characteristics of major components, so that we know the distribution and quality of the component and how to sort the dried pepper. Also, the size of the concentration image can tell the relation with capsaicinoids which make hot taste. We propose a sorting method of the dried hot pepper that is faded or discolored and lacked a major component likes capsaicin in drying process using ICA concentration image.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.3
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• pp.365-370
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• 2004

Simultaneous measurement with PLIF(Planar Laser-Induced Fluorescence) and Stereo-PIV(Stereo Particle Image Velocimetry) was performed to investigate the structural characteristics of flow field in Rushton Turbine Mixer. Instantaneous 3D velocity fields are measured by two 2K${\times}$2K CCD cameras focused on an object plane with the angular displacement methods while the concentration fields are obtained through the measurement of the fluorescence intensity of Rhodamine B tracer excited by the second pulse of Nd:Yag laser light. Image distortion due to the camera view-angle is compensated by a mapping function. Finally, the spatial structures of turbulent flow around Rushton turbine were identified by the calculation of synchronized data of the velocity field and concentration field.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.694-699
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• 2003

Simultaneous measurement with PLIF(Planar Laser-Induced Fluorescence) and Stereo-PIV(Stereoscopic Particle Image Velocimetry) was performed to investigate the structural characteristics of flow field in Rushton Turbine Mixer. Instantaneous 3D velocity fields are measured by two 2K ${\times}$ 2K CCD cameras focused on an object plane with the angular displacement methods while the concentration fields are obtained through the measurement of the fluorescence intensity of Rhodamine B tracer excited by the second pulse of Nd:Yag laser light. Image distortion due to the camera view-angle is compensated by a mapping function. Finally, the spatial structures of turbulent mixing around Rushton turbine were identified by the calculation of cross-correlation fields between the velocity and concentration field.

• Journal of the Korean Society of Visualization
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• v.19 no.2
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• pp.32-40
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• 2021

Vapor-driven solutal Marangoni flow is governed by the concentration distribution of solutes on a liquid-gas interface. Typically, the flow structure is investigated by particle image velocimetry (PIV). However, to develop a theoretical model or to explain the working mechanism, the concentration distribution of solutes at the interface should be known. However, it is difficult to achieve the concentration profile theoretically and experimentally. In this paper, to find the concentration distribution of solutes around 2D droplet, the reverse tracking method with an artificial neural network based on PIV data was performed. Using the method, the concentration distribution of solutes around a 2D droplet was estimated for actual flow data from PIV experiment.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.1
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• pp.49-56
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• 2017

Recently, many research works on the icebreaking vessels have been published as the possibility of passing Arctic routes has been increasing. The model ship test on the pack ice model in the ice basin is actively carried out as a way to investigate the performance of icebreaking vessels. In this test, the concentration of pack ice is important since it directly affects the performance. However, it is difficult to measure the concentration because not only the pack ice has uneven shape but also it keeps floating around in the basin. In this paper, an algorithm to identify the concentration of pack ice is introduced. From a digital image of pack ice obtained in the ice basin, the goal is to measure the area of pack ice using an image processing technique. Instead of the general global binarization that yields numerical errors in this problem, a local binarization technique, coupled with image subdivision based on the quadtree structure, is developed. The concentration results obtained by the developed algorithm are compared with the manually measured data to prove its accuracy.

• Journal of the Korean Society of Visualization
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• v.13 no.3
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• pp.15-19
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• 2015

We developed a high-throughput microscopy (HTM) method which enabled us to replace a conventional phase contrast microscopy (PCM) method that has been used as a standard analytical method for airborne asbestos. We could obtain the concentration of airborne asbestos fibers under detection limit by automated image processing and analysis using HTM method. Here we propose an improved image processing algorithm with variable parameters to enhance the accuracy of the HTM analysis. Since the variable parameters that compensate the difference of the brightness are applied to the individual images in our new image processing method, it is possible to enhance the accuracy of the automatic image analysis method for sample slides with low asbestos concentration that caused errors in binary image processing. We demonstrated that enumeration of fibers by improved image processing algorithm remarkably enhanced the accuracy of HTM analysis in comparison with PCM. The improved HTM method can be a potential alternative to conventional PCM.