• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.43 no.7
• /
• pp.1193-1205
• /
• 1994

This paper presents a fuzzy system that adaptively classifies subimages to four classes according to image activity distribution. In adaptive transform image coding, subimage classification improves the compression performance by assigning different bit maps to different classes. A conventional classification method sorts subimages by their AC energy and divides them to classes with equal number of subimages. The fuzzy system provides more flexible classification to natural images with various distribution of image details than does the conventional method. Clustering of training data in the input-output product space generated the fuzzy rules for subimage classification. The fuzzy system of small number of fuzzy rules successfully classified subimages to improve the compression performance of the transform image coding without sorting of AC energies.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.2 no.2
• /
• pp.42-60
• /
• 1992

An adaptive fuzzy system can efficiently classify subimages into four categories according to image activity level for image data compression. The system estimates fuzzy rules by clustering input-output data generated from a given adaptive transform image coding process. The system encodes different images without modification and reduces side information when encoding multiple images. In the second part, a fuzzy system estimates optimal bit maps for the four subimage classes in noisy channels assuming a Gauss-Markov image model. The fuzzy systems respectively estimate the sampled subimage classification and the bit-allocation processes without a mathematical model of how outputs depend on inputs and without rules articulated by experts.

• Journal of Korean Institute of Industrial Engineers
• /
• v.17 no.2
• /
• pp.17-26
• /
• 1991

This paper discusses an expert system to generate part codes and construct part families, ESPCC, for the group technology application. The ESPCC, that is developed by using VP-Expert rule-based expert system development tool, embodies the specific knowledge of human experts to determine part codes consistent with the OPITZ classification and coding system. The ESPCC is implemented on an IBM compatible personal computers running MS-DOS.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.7
• /
• pp.3286-3300
• /
• 2016

High Efficiency Video Coding (HEVC) Standard, as the latest coding standard, introduces satisfying compression structures with respect to its predecessor Advanced Video Coding (H.264/AVC). The new coding standard can offer improved encoding performance compared with H.264/AVC. However, it also leads to enormous computational complexity that makes it considerably difficult to be implemented in real time application. In this paper, based on machine learning, a fast partitioning method is proposed, which can search for the best splitting structures for Intra-Prediction. In view of the video texture characteristics, we choose the entropy of Gray-Scale Difference Statistics (GDS) and the minimum of Sum of Absolute Transformed Difference (SATD) as two important features, which can make a balance between the computation complexity and classification performance. According to the selected features, adaptive decision trees can be built for the Coding Units (CU) with different size by offline training. Furthermore, by this way, the partition of CUs can be resolved as a binary classification problem. Experimental results have shown that the proposed algorithm can save over 34% encoding time on average, with a negligible Bjontegaard Delta (BD)-rate increase.

• Communications for Statistical Applications and Methods
• /
• v.14 no.2
• /
• pp.377-388
• /
• 2007

As one of multi-class classification methods, ECOC (Error Correcting Output Coding) method is known to have low classification error rate. This paper aims at suggesting effective multi-class classification method (1) by comparing various encoding methods and decoding methods in ECOC method and (2) by comparing ECOC method and direct classification method. Both SVM (Support Vector Machine) and logistic regression model were used as binary classifiers in comparison.

• Journal of Engineering Education Research
• /
• v.17 no.4
• /
• pp.43-47
• /
• 2014

The rapid development of science & technology and the globalization of society have accelerated the fractionation and specialization of academic disciplines. Accordingly, Korean colleges and universities are continually dropping antiquated courses to make room for new courses that better meet societal demands. With emphasis placed on providing students with a broader range of choices in terms of course selection, compulsory courses have given way to elective courses. On average, 4 year institutions of higher learning in Korea currently offer somewhere in the neighborhood of 1,000 different courses yearly. The classification of an ever growing list of courses offered and the practical use of such data would not be possible without the aid of computers. For example, if we were able to show the pre/post requisite relationship among various courses as well as the commonalities in substance among courses, such data generated regarding the interrelationship of different courses would undoubtedly greatly benefit the students, as well as the professors, during course registration. Furthermore, the GT system's relatively simple approach to course classification and coding will obviate the need for the development of a more complicated keyword based search engine, and hopefully contribute to the standardization of the course coding scheme in the future..Therefore, as a sample case project, this study will use GT to classify and code all courses offered at the College of Engineering of K University, thereby developing a system that will facilitate the scanning of relevant courses.

• International Journal of Advanced Culture Technology
• /
• v.8 no.1
• /
• pp.165-172
• /
• 2020

Industrial Revolution means the improvement of productivity through technological innovation and has been a driving force of the whole change of economic system and social structure as the characteristic of technology as the tool of this productivity has changed. Since the first industrial revolution of the 18th century, productivity efficiency has been advanced through three industrial revolutions so far, and this fourth industrial revolution is expected to bring about another revolution of production. In this study, the demand for the introduction of artificial intelligence(AI) technology has been increasing in various business fields due to the rapid development of ICT technology, and the classification of HS(harmonized commodity description and coding system) items has been decided using artificial intelligence technology, which is the core of the fourth industrial revolution. And it is enough to construct HS classification system based on AI technology using inference and deep learning. Performing the HS item classification is not an easy task. Implementation of item classification system using artificial intelligence technology to analyze information of HS item classification which is performed manually by the current person more accurately and without any mistake, And the customs administrations, customs offices, and customs agencies, it is expected to be highly utilized in the innovation of trade practice and the customs administration innovation FTA origin agent.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.8
• /
• pp.1722-1732
• /
• 1997

In this paper, we propose a coding method of remotely sensed satellite image data using region classification and interband correlation. This method classifies each pixel vector consider spectral characteristics. Then we perform the classified intraband VQ to remove spatial (intraband redundancy for a reference band image. To remove interband redundancy effectively, we perform the classified interband prediction for the band images that the high correlation spectrally and perform the classified interband VQ for the remaining band images. Experiments on LANDSAT TM image show that the coding efficiency of the proposed method is better than that of the conventional Gupta's method. Especially, this method removes redundancies effectively for satellite iamge including various geographical objects and for and images that have low interband correlation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.5
• /
• pp.239-246
• /
• 2023

In this paper, we propose an open-loop algorithm to classify speech and music signals using the spectral flux parameters and Mel Frequency Cepstral Coefficients(MFCC) parameters for the audio coder. To increase responsiveness, the MFCC was used as a short-term feature parameter and spectral fluxes were used as a long-term feature parameters to improve accuracy. The overall voice/music signal classification decision is made by combining the short-term classification method and the long-term classification method. The Gaussian Mixed Model (GMM) was used for pattern recognition and the optimal GMM parameters were extracted using the Expectation Maximization (EM) algorithm. The proposed long-term and short-term combined speech/music signal classification method showed an average classification error rate of 1.5% on various audio sound sources, and improved the classification error rate by 0.9% compared to the short-term single classification method and 0.6% compared to the long-term single classification method. The proposed speech/music signal classification method was able to improve the classification error rate performance by 9.1% in percussion music signals with attacks and 5.8% in voice signals compared to the Unified Speech Audio Coding (USAC) audio classification method.

• Journal of the Korea Convergence Society
• /
• v.12 no.4
• /
• pp.23-30
• /
• 2021

An Automated Industry and Occupation Coding System assigns statistical classification code to the enormous amount of natural language data collected from people who write about their industry and occupation. Unlike previous studies that applied information retrieval, we propose a system that does not need an index database and gives proper code regardless of the level of classification. Also, we show our model, which utilized KoBERT that achieves high performance in natural language downstream tasks with deep learning, outperforms baseline. Our method achieves 95.65%, 91.51%, and 97.66% in Occupation/Industry Code Classification of Population and Housing Census, and Industry Code Classification of Census on Basic Characteristics of Establishments. Moreover, we also demonstrate future improvements through error analysis in the respect of data and modeling.