• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.1017-1028
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• 2007

In this paper we propose an effective method to represent, store, and retrieve video streams efficiently from a video database. We extract features from each video frame, normalize the feature values, and represent them as values in the range [0,1]. In this way a video frame with f features can be represented by a point in the f-dimensional space $[0,1]^f$, and thus the video stream is represented by a trail of points in the multidimensional space. The video stream is partitioned into video segments based on camera shots, each of which is represented by a trend vector which encapsulates the moving trend of points in a segment. The video stream query is processed depending on the comparison of those trend vectors. We examine our method using a collection of video streams that are composed of sports, news, documentary, and educational videos. Experimental results show that our trend vector representation reduces a reconstruction error remarkably (average 37%) and the retrieval using a trend vector achieves the high precision (average 2.1 times) while maintaining the similar response time and recall rate as existing methods.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.129-164
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• 2023

In a situation where the use and introduction of Large Language Models (LLMs) is expanding due to recent developments in generative AI technology, it is difficult to find actual application cases or implementation methods for the use of internal company data in existing studies. Accordingly, this study presents a method of implementing generative AI services using the LLM application architecture using the most widely used LangChain framework. To this end, we reviewed various ways to overcome the problem of lack of information, focusing on the use of LLM, and presented specific solutions. To this end, we analyze methods of fine-tuning or direct use of document information and look in detail at the main steps of information storage and retrieval methods using the retrieval augmented generation (RAG) model to solve these problems. In particular, similar context recommendation and Question-Answering (QA) systems were utilized as a method to store and search information in a vector store using the RAG model. In addition, the specific operation method, major implementation steps and cases, including implementation source and user interface were presented to enhance understanding of generative AI technology. This has meaning and value in enabling LLM to be actively utilized in implementing services within companies.

• Journal of Broadcast Engineering
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• v.2 no.2
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• pp.136-145
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• 1997

As a 3D mesh model consists of a lot of vertices and polygons and each vertex position is represented by three 32 bit floating-point numbers in a 3D coordinate, the amount of data needed for representing the model is very excessive. Thus, in order to store and/or transmit the 3D model efficiently, a 3D model compression is necessarily required. In this paper, a 3D model compression method using PRVQ (predictive residual vector quantization) is proposed. Its underlying idea is based on the characteristics such as high correlation between the neighboring vertex positions and the vectorial property inherent to a vertex position. Experimental results show that the proposed method obtains higher compression ratio than that of the existing methods and has the advantage of being capable of transmitting the vertex position data progressively.

• The Journal of the Korea Contents Association
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• v.14 no.1
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• pp.14-23
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• 2014

There have been many researches for XML as the international standard to store and exchange data on the internet. Among these research fields, we focus on the techniques labeling the nodes of the XML tree that is required for querying the structural information. A labeling scheme assigns the unique label to the nodes and supports the queries for the structural information such as Ancestor-Descendant and Parent-Child relationships. In this paper, we propose a labeling scheme using vector representation where the assigned labels are not altered although XML documents are changed dynamically. Our labeling scheme reduces the storage requirement for the labels of the XML tree and provides the efficient query by using the fixed-length labels with a short size. Result of performance evaluation shows that our labeling scheme is superior to the previous approaches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.738-745
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• 2014

Recently, mobile users continuously increase, and mobile applications also increase As mobile applications increase, the mobile users used to store sensitive and private information such as Bank information, location information, ID, password on their mobile devices. Therefore, recent malicious application targeted to mobile device instead of PC environment is increasing. In particular, since the Android is an open platform and includes security vulnerabilities, attackers prefer this environment. This paper analyzes the performance of malware detection system applying linear SVM machine learning classifier to detect Android malware application. This paper also performs feature selection in order to improve detection performance.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.343-359
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• 2020

In this paper, a new algorithm is proposed for three-dimensional (3D) shape recognition using local features of model views and its sparse representation. The algorithm starts with the normalization of 3D models and the extraction of 2D views from uniformly distributed viewpoints. Consequently, the 2D views are stacked over each other to from view cubes. The algorithm employs the descriptors of 3D local features in the view cubes after applying Gabor filters in various directions as the initial features for 3D shape recognition. In the training stage, we store some 3D local features to build the prototype dictionary of local features. To extract an intermediate feature vector, we measure the similarity between the local descriptors of a shape model and the local features of the prototype dictionary. We represent the intermediate feature vectors of 3D models in the sparse domain to obtain the final descriptors of the models. Finally, support vector machine classifiers are used to recognize the 3D models. Experimental results using the Princeton Shape Benchmark database showed the average recognition rate of 89.7% using 20 views. We compared the proposed approach with state-of-the-art approaches and the results showed the effectiveness of the proposed algorithm.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.305-312
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• 1998

A small PACS based on PC is designed for CR. To receive the digital image from CR, a DICOM Interface Unit (DIU) is designed that complied with the medical image standard, DICOM V3.0. The CR images acquired through the DIU are stored in a file-server; the patient information of the images is stored in a database. To improve the performance of PC and to use it easily, multiresolution images are constructed by wavelet transform and displayed progressively. Wavelet compression method is newly adopted to store the images hierarchically to storage units. In this compression method, the image is decomposed into subclasses of image by wavelet transform, and then the subclasses of the image are vector quantized using a multiresolution codebook. The storage units for CR images were divided into the short-term storage in file-server and the harddisk in viewing station. Image processing tools supported by general PACS is implemented based on PC.

• IE interfaces
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• v.20 no.4
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• pp.448-457
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• 2007

The small & micro business has the characteristics of both consumer credit risk and business credit risk. In predicting the bankruptcy for small-micro businesses, the problem is that in most cases, the financial data for evaluating business credit risks of small & micro businesses are not available. To alleviate such problem, we propose a bankruptcy prediction mechanism using the credit card sales information available, because most small businesses are member store of some credit card issuers, which is the main purpose of this study. In order to perform this study, we derive some variables and analyze the relationship between good and bad signs. We employ the new statistical learning technique, support vector machines (SVM) as a classifier. We use grid search technique to find out better parameter for SVM. The experimental result shows that credit card sales information could be a good substitute for the financial data for evaluating business credit risk in predicting the bankruptcy for small-micro businesses. In addition, we also find out that SVM performs best, when compared with other classifiers such as neural networks, CART, C5.0 multivariate discriminant analysis (MDA), and logistic regression.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.309-314
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• 2006

Recently, multimedia DBMS is appeared to be the core technology of the information society to store, manage and retrieve multimedia data efficiently. In this paper, we propose a new method for content based-retrieval system using wavelet transform, energy value to extract automatically feature vector from image data, and suggest an effective retrieval technique through this method. Wavelet transform is widely used in image compression and digital signal analysis, and its coefficient values reflect image feature very well. The correlation in wavelet domain between query image data and the stored data in database is used to calculate similarity. In order to assess the image retrieval performance, a set of hundreds images are run. The method using standard derivation and mean value used for feature vector extraction are compared with that of our method based on energy value. For the simulation results, our energy value method was more effective than the one using standard derivation and mean value.

• Journal of the Korean Society of Clothing and Textiles
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• v.16 no.2
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• pp.263-275
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• 1992

This study intends to provide strategic positioning of brand image analysed from the view point of perceptual dimensions of clothing consumers. Consumers are segmented on the basis of the attributes of brand image, and in each segment, perceptual map is composed according to multidimensional scaling. The results are as follows; 1. According to the Benefit Segmentation, it is statistically significant that the consumers are divided into 'product-factor oriented group 'and' image-factor oriented group'. 2. From the analysis of perceptual map upon the 'similarity of brand image,'image-factor oriented group 'perceives more differently than 'product-factor oriented group' 3. From the analysis of perceptual map with the evaluation of attributes of brand image, price, promotion and design are significant determinants in 'total consumer group'. In addition, store image is significant determinant in' image-factor oriented group' and quality is significant determinant in' product-factor oriented group'. According to the evaluation of consumers on 8 brands with determining attribute-vector, ranks of brands in each segment are similar in the vector of price and promotion but different in the vector of design between segment groups. 4. From the analysis of perceptual map upon the preference of brand image, the distribution of preference and position of ideal point are different between segment groups. 5. With evaluation of purchase habit, statistically significant differences are found between groups segmented in the degree of importance of attributes, purchasing motive, purchasing time, sources of information and expenses for clothes.