• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.125-130
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• 2011

This paper investigates the possibility of exploiting haptic force-feedback technology for interacting with Nanta music. We use VR technologies including touch processing technologies and haptic devices to offer touch of cylinder objects and cup object to users. Haptic device is used to implement touch model in VR space. Matlab/Simulink and proSENCE Virtual Touch Toolbox of Handshake Inc. for experiment, are used as programing tools. Function needed to describe the movement of x, y, and z axis respectively are applied to delineate the natural movement of water in cup object modeled with 3D. A certain amount of water in cup object has the difference of sounds. In experiment, to perceive the appearance of 3D object by touch and to feel the tactile by touch are conducted with the effect of sound on Haptic perception. We also verify that it is possible to develop games or contents in VR space by using point.

• Journal of the Korea Computer Graphics Society
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• v.6 no.4
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• pp.35-42
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• 2000

3D texture mapping generates highly natural visual effects in which objects appear carved from lumps of materials rather than laminated with thin sheets as in 2D texture mapping. Storing 3D texture images in a table for fast mapping computations, instead of evaluating procedures on the fly, however, has been considered impractical due to the extremely high memory requirement. Recently, a practical real-time 3D texture mapping technique was proposed in [11], where they attempt to resolve the potential texture memory problem by compressing 3D textures using a wavelet-based encoding method. In this paper, we consider two other encoding schemes that could also be applied to the compression-based 3D texture mapping. In particular, we extend the vector quantization and FXT1 for 3D texture compression, and compare their performance with the wavelet-based encoding scheme.

• Journal of the Korean Society for Library and Information Science
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• v.35 no.3
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• pp.173-189
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• 2001

This paper presents a framework for observable behavior that can be used as a basis for user modeling, and it reports the results of a pair of user studies that examine the joint utility of two specific behaviors. User models can be constructed by hand, or they can be teamed automatically based on feedback provided by the user about the relevance of documents that they have examined. By observing user behavior, it is possible to obtain implicit feedback without requiring explicit relevance judgments. Four broad categories of potentially observable behavior are identified : examine, retain, reference, and annotate, and examples of specific behaviors within a category are further subdivided based on the natural scope of information objects being manipulated . segment object, or class. Previous studies using Internet discussion groups (USENET news) have shown reading time to be a useful source of implicit feedback for predicting a user's preferences. The experiments reported in this paper extend that work to academic and professional journal articles and abstracts, and explore the relationship between printing behavior and reading time. Two user studies were conducted in which undergraduate students examined articles or abstracts from the telecommunications or pharmaceutical literature. The results showed that reading time can be used to predict the user's assessment of relevance, that the mean reading time for journal articles and technical abstracts is longer than has been reported for USENET news documents, and that printing events provide additional useful evidence about relevance beyond that which can be inferred from reading time. The paper concludes with a brief discussion of the implications of the reported results.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.324-333
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• 2001

In this paper we present a novel color image segmentation algorithm based on a Gaussian Mixture Model(GMM). It is introduced a Deterministic Annealing Expectation Maximization(DAEM) algorithm which is developed using the principle of maximum entropy to overcome the local maxima problem associated with the standard EM algorithm. In our approach, the GMM is used to represent the multi-colored objects statistically and its parameters are estimated by DAEM algorithm. We also develop the automatic determination method of the number of components in Gaussian mixtures models. The segmentation of image is based on the maximum posterior probability distribution which is calculated by using the GMM. The experimental results show that the proposed DAEM can estimate the parameters more accurately than the standard EM and the determination method of the number of mixture models is very efficient. When tested on two natural images, the proposed algorithm performs much better than the traditional algorithm in segmenting the image fields.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1191-1193
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• 2003

Water quantity and quality monitoring at Taipei Watershed Management Bureau (WRATB) is not only a daily business but also a long term job. WRATB is responsible for providing high quality drinking water to about four millions population in Taipei. The quality of drinking water provided by WRATB is among one of the best in Taiwan. The total area is 717 square kilometers. The water resource pollution is usually divided into two categories, point source pollution and nonpoint source pollution. Garbage disposal is the most important component of the point source pollution, especially those by tourist during holidays and weekends. Pesticide pollution, fertilizer pollution, and natural pollution are the major contributions for nonpoint source pollution. The objective of this paper is to implement remote sensing, geographic information systems, and global positioning systems to monitor water quantity and water quality at WRATB. There are 12 water quality monitoring stations and four water gauge stations at WRATB. The coordinates of the 16 stations were determined by GPS devices and created into the base maps. MapObjects and visual BASIC were implemented to create application modules for water quality and quantity monitoring. Water quality of the two major watersheds at WRATB was put on Internet for public review monthly. The GIS software, ArcIMS, can put location maps and attributes of all 16 stations on Internet for general public review and technical implementations at WRATB. Inquiry and statistic charts automatic manipulations for the past 18 years are also available. Garbage disposal by community and tourist were also managed by GIS and GPS. The storage, collection, and transportation of garbage were reviewed by ArcMap file format. All garbage cart and garbage can at WRATB can be displayed on the base maps. Garbage disposal by tourist during holidays and weekends can be managed by a PDA with a GPS device and a digital camera. Man power allocation for tourist garbage disposal management can be done in an integration of GIS and GPS. Monitoring of water quality and quantity at WRATB can be done on Internet and by a PDA.

• Proceedings of the KIEE Conference
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• 2006.07e
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• pp.55-56
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• 2006

In prevention and diagnostic system of GIS, pattern classification is focused on the detection of unnatural patterns in PD(Partial discharge) image data. Fractals have been used extensively to provide a description and to model mathematically many of the naturally occurring complex shapes, such as coastlines, mountain ranges, clouds, etc., and have also received increased attention in the field of image processing, for purposes of segmentation and recognition of regions and objects present in natural scenes. Among the numerous fractal features that could be defined and used for image data, fractal dimension and lacunarity have been found to be useful for recognition purposes Partial discharge(PD) occuring in GIS system is a very complex phenomenon, and more so are the shapes of the various 2-d patterns obtained during routine tests and measurements. It has been fairly well established that these pattern shapes and underlying defects causing PD have a 1:1 correspondence, and therefore methods to describe and qunatify these pattern shapes must be explored, before recognition systems based on them could be developed. The computed fractal features(fractal dimension and lacunarity) for standard library of PD data were analyzed and found to possess fairly reasonable pattern discriminating abilities. This new approach appears promising, and further research is essential before any long-term predictions can be made.

• The Mathematical Education
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• v.49 no.2
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• pp.175-198
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• 2010

To understand natural or social phenomena, we need various information, knowledge, and thought skills. In this context, mathematics and sciences provide us with excellent tools for that purpose. This explains the reasons why there is always significant emphasis on mathematics and sciences in school education; some of the general goals in school education today are to illustrate physical phenomena with mathematical tools based on scientific consideration, to encourage students understand the mathematical concepts implied in the phenomena, and provide them with ability to apply what they learned to the real world problems. For the mentioned goals, we extract six fundamental principles for the integrated mathematics and science education (IMSE) from literature review and suggest a instructional design model. This model forms a fundamental of a case study we performed to which the IMSE was applied and tested to collect insights for design and practice. The case study was done for 10 students (2 female students, 8 male ones) at a coeducational high school in Seoul, the first semester 2009. Educational tools including graphic calculator(Voyage200) and motion detector (CBR) were utilized in the class. The analysis result for the class show that the students have successfully developed various mathematical concepts including the rate of change, the instantaneous rate of change, and derivatives based on the physical concepts like velocity, accelerate, etc. In the class, they described the physical phenomena with mathematical expressions and understood the motion of objects based on the idea of derivatives. From this result, we conclude that the IMSE builds integrated knowledge for the students in a positive way.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.85-89
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• 2010

Most images are composed as union of the various objects which can describe meaning respectively. Unlike human perception, The general computer systems used for image processing analyze images based on low level features like color, texture and shape. The semantic gap between low level image features and the richness of user semantic knowledges can bring about unsatisfactory classification results from user expectation. In order to deal with this problem, we propose a semantic cue based image classification method using salient points from object of interest. Salient points are used to extract low level features from images and to link high level semantic concepts, and they represent distinct semantic information. The proposed algorithm can reduce semantic gap using salient points modeling which are used for image classification like human perception. and also it can improve classification accuracy of natural images according to their semantic concept relative to certain object information by using salient points. The experimental result shows both a high efficiency of the proposed methods and a good performance.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.379-390
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• 2019

In this paper, we propose a novel deep neural network model for Vision-and-Language Navigation (VLN) named LVLN (Landmark-based VLN). In addition to both visual features extracted from input images and linguistic features extracted from the natural language instructions, this model makes use of information about places and landmark objects detected from images. The model also applies a context-based attention mechanism in order to associate each entity mentioned in the instruction, the corresponding region of interest (ROI) in the image, and the corresponding place and landmark object detected from the image with each other. Moreover, in order to improve the success rate of arriving the target goal, the model adopts a progress monitor module for checking substantial approach to the target goal. Conducting experiments with the Matterport3D simulator and the Room-to-Room (R2R) benchmark dataset, we demonstrate high performance of the proposed model.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.303-313
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• 2021

Recently, quality control of the Liquefied Natural Gas Carrier (LNGC) cargo hold and block-erection interference areas using 3D scanners have been performed, focusing on large shipyards and the international association of classification societies. In this study, as a part of the research on LNGC cargo hold quality management advancement, a study on deep-learning-based scaffolding system 3D point cloud object detection and post-processing were conducted using a LNGC cargo hold 3D point cloud. The scaffolding system point cloud object detection is based on the PointNet deep learning architecture that detects objects using point clouds, achieving 70% prediction accuracy. In addition, the possibility of improving the accuracy of object detection through parameter adjustment is confirmed, and the standard of Intersection over Union (IoU), an index for determining whether the object is the same, is achieved. To avoid the manual post-processing work, the object detection architecture allows automatic task performance and can achieve stable prediction accuracy through supplementation and improvement of learning data. In the future, an improved study will be conducted on not only the flat surface of the LNGC cargo hold but also complex systems such as curved surfaces, and the results are expected to be applicable in process progress automation rate monitoring and ship quality control.