• Archives of design research
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• v.17 no.1
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• pp.153-162
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• 2004

This paper focused on the effect of Kansei design on the web in branding as well as its influence factors. As a key of this research, it classified web users' Kansei into five categories; 1) functional Kansei, 2) sensoryKansei, 3) psychological Kansei, 4) relational Kansei and 5) cultural Kansei, and organized relevant factors. Online surveys were conducted on seven websites of the fast food brands in Korea (Lotteria, Mcdonald, BurgerKing, Popeyes, KFC, Pizza Hut, Domino's) which are targeting 463 male and females in 20s. As a result, an average of 58% responded that they had a positive Kansei experience and could enhanceits brand preference. Of the sensory Kansei, visual design factors were the one that gave the greatest effect on brand preference enhancement. Regarding the functional Kansei satisfaction, such as user convenience and access speed were also one of the most crucial variables for the whole Kansei satisfaction. Moreover, the preference enhancement brought not only a positive effect on its reliability but also its brand image and consumers' purchasing desire. Based on the survey results, the additional FGI (Focus Group Interview) had been conducted and determined \circled1 what kind of major Kansei that users wanted to have satisfied, \circled2 what type of design can give strong Kansei appeal to its users, and \circled3what design factors gave an effect on sensory emotion. In the course of this research, Itried to renew the awareness of the web importance as a major channel in non-mass interactive marketing, and suggest the effect and its possibility of emotional branding through Kansei design in the web as well as design principles of strategic Kansei design.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.343-347
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• 2008

There are two kind of system in hotel for evaluating management. the first is infra system, for example Front management system - reservation system, C/I, C/O system, POS system , web system, call center management -, Back office system, interface system, and so on which manage direct control for hotel, and next is strategic system, for example BSC , CS . which is supporting hotel management. The purpose of this study was to find using the Balanced Scorecard to evaluate hotel management perfomance. Management performance is based on productivity, quality, time, effect, and responsiveness. Hotel economic value is not equal visual asset which is measured financial value. It is include hotel brand, image, customers who have good loyalty, employees who give customers special service, and systems which was affected by internal process. In conclusion, innovation, growth, internal system process, empioyee and customer have effect hotel performance. Therefore hotel has to know non-financial performance which has positively relate to financial performance.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.4
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• pp.309-318
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• 2004

This paper proposes the H.264-based selective fine granular scalable (FGS) coding scheme that selectively uses the temporal prediction data in the enhancement layer. The base layer of the proposed scheme is basically coded by the H.264 (MPEG-4 Part 10 AVC) visual coding scheme that is the state-of-art in codig efficiency. The enhancement layer is basically coded by the same bitplane-based algorithm of the MPEG-4 (Part 2) fine granular scalable coding scheme. In this paper, we introduce a new algorithm that uses the temproal prediction mechanism inside the enhancement layer and the effective selection mechanism to decide whether the temporally-predicted data would be sent to the decoder or not. Whenever applying the temporal prediction inside the enhancement layer, the temporal redundancies may be effectively reduced, however the drift problem would be severly occurred especially at the low bitrate transmission, due to the mismatch bewteen the encoder's and decoder's reference frame images. Proposed algorithm selectively uses the temporal-prediction data inside the enhancement layer only in case those data could siginificantly reduce the temporal redundancies, to minimize the drift error and thus to improve the overall coding efficiency. Simulation results, based on several test image sequences, show that the proposed scheme has 1∼3 dB higher coding efficiency than the H.264-based FGS coding scheme, even 3∼5 dB higher coding efficiency than the MPEG-4 FGS international standard.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.105-114
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• 2015

This paper proposes methods to remove background area and segment region of interest (ROI) in chest X-ray images. Conventional algorithms to improve detail or contrast of images normally utilize brightness and frequency information. If we apply such algorithms to the entire images, we cannot obtain reliable visual quality due to unnecessary information such as background area. So, we propose two effective algorithms to remove background and segment ROI from the input X-ray images. First, the background removal algorithm analyzes the histogram distribution of the input X-ray image. Next, the initial background is estimated by a proper thresholding on histogram domain, and it is removed. Finally, the body contour or background area is refined by using a popular guided filter. On the other hand, the ROI, i.e., lung segmentation algorithm first determines an initial bounding box using the lung's inherent location information. Next, the main intensity value of the lung is computed by vertical cumulative sum within the initial bounding box. Then, probable outliers are removed by using a specific labeling and the pre-determined background information. Finally, a bounding box including lung is obtained. Simulation results show that the proposed background removal and ROI segmentation algorithms outperform the previous works.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.156-163
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• 2015

In this paper, we propose an active water level and distance measurement algorithm using a light beam pattern. On behalf of conventional water level gauge types of pressure, float-well, ultrasonic, radar, and others, recently, extensive research for video analysis based water level measurement methods is gradually increasing as an importance of accurate measurement, monitoring convenience, and much more has been emphasized. By turning a reference light beam pattern on bridge or embankment actively, we suggest a new approach that analyzes and processes the projected light beam pattern image obtained from camera device, measures automatically water level and distance between a camera and a bridge or a levee. As contrasted with conventional methods that passively have to analyze captured video information for recognition of a watermark attached on a bridge or specific marker, we actively use the reference light beam pattern suited to the installed bridge environment. So, our method offers a robust water level measurement. The reasons are as follows. At first, our algorithm is effective against unfavorable visual field, pollution or damage of watermark, and so on, and in the next, this is possible to monitor in real-time the portable-based local situation by day and night. Furthermore, our method is not need additional floodlight. Tests are simulated under indoor environment conditions from distance measurement over 0.4-1.4m and height measurement over 13.5-32.5cm.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.111-121
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• 2006

The classification using spectral angle is a new approach based on the fact that the spectra of the same type of surface objects in RS data are approximately linearly scaled variations of one another due to atmospheric and topographic effects. There are many researches on the unsupervised classification using spectral angle recently. Nevertheless, there are only a few which consider the characteristics of Hyperspectral data. On this study, we propose the ISOMUSAC(Iterative Self-Organizing Modified Unsupervised Spectral Angle Classification) which can supplement the defects of previous unsupervised spectral angle classification. ISOMUSAC uses the Angle Division for the selection of seed points and calculates the center of clusters using spectral angle. In addition, ISOMUSAC perform the iterative merging and splitting clusters. As a result, the proposed algorithm can reduce the time of processing and generate better classification result than previous unsupervised classification algorithms by visual and quantitative analysis. For the comparison with previous unsupervised spectral angle classification by quantitative analysis, we propose Validity Index using spectral angle.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.293-302
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• 2019

This study systematically analyzed computer-based driving intervention programs for seniors, to provide the academic background for driving intervention for seniors. Articles published from January 2009 till December 2018 were researched and analyzed. 'PubMed, Google Scholar, and Science Direct' were used to search articles published overseas, and 'RISS, KERIS, and KISS' searched for articles published in Korea. Based on the inclusion and exclusion criteria, totally 359 papers were retrieved, and 10 articles were finally analyzed; 8 articles (80%) were evidence level I, and 2 articles (20%) were evidence level III. Amongst the computer-based interventions, driving simulators (70%) were the most common, followed by two video image training (20%) and one Nintendo Wii program (10%). In most studies, driving simulators trained the cognitive and visual abilities of seniors and enhanced their abilities to cope with risk situations under various simulated circumstances. Other interventions were also reported to have a positive effect. For evaluating elderly drivers, the driving performance evaluation using a driving simulator was the most common; in addition, evaluations of attention, space-time ability, cognitive function, risk perception, depression and anxiety were also commonly used. We believe that it is appropriate to employ computer-based driving intervention programs for seniors to train and evaluate various domains. We expect that these interventions can be used as an effective tool for safe driving.

• 재활복지
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• v.21 no.3
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• pp.129-146
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• 2017

In an event of an emergency, such as fire in a building, visually impaired and blind people are prone to exposed to a level of danger that is greater than that of normal people, for they cannot be aware of it quickly. Current fire detection methods such as smoke detector is very slow and unreliable because it usually uses chemical sensor based technology to detect fire particles. But by using vision sensor instead, fire can be proven to be detected much faster as we show in our experiments. Previous studies have applied various image processing and machine learning techniques to detect fire, but they usually don't work very well because these techniques require hand-crafted features that do not generalize well to various scenarios. But with the help of recent advancement in the field of deep learning, this research can be conducted to help solve this problem by using deep learning-based object detector that can detect fire using images from security camera. Deep learning based approach can learn features automatically so they can usually generalize well to various scenes. In order to ensure maximum capacity, we applied the latest technologies in the field of computer vision such as YOLO detector in order to solve this task. Considering the trade-off between recall vs. complexity, we introduced two convolutional neural networks with slightly different model's complexity to detect fire at different recall rate. Both models can detect fire at 99% average precision, but one model has 76% recall at 30 FPS while another has 61% recall at 50 FPS. We also compare our model memory consumption with each other and show our models robustness by testing on various real-world scenarios.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.855-865
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• 2018

Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.197-202
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• 2019

In the era of the fourth revolution, interest in content production using proven engines in the broadcasting sector, such as Vizrt, is growing. The new visual effects required in the 5G era are critical to content production training. Vizrt has a good production time utility and affordability for broadcast and media content. In this paper, we are going to use this to present a practical case of the theorem and application of the basic training course in the production of virtual content, and to present the basic training direction. In the introduction, the graphic algorithm analyzed and studied the characteristics and environmental factors of the Vizrt engine. In this paper, the production process was studied separately, and the work carried out through engine implementation was presented. The VS Studio Foundation was provided as a practical production case at each stage. The Vizrt engine operator process is important in graphic approach and application, and through the results of the lecture, the method of understanding and implementing algorithms for virtual reality perspective suitable for basic learning was studied. Based on practice, the research method of main theory was to create Vizrt contents specialized in 5G contents work in each sector and to implement graphic production in new areas from contents image. Through this study, we came to the conclusion of the basic training method through virtual reality content work based on Vizrt by practicing content creation according to the subject. It also proposes the effect of creating Vizrt content and the direction of building Vizrt basic training courses.