• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.883-898
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• 1996

In this paper, we propose improved method to recognize the shape for enhancing the quality of the pattern recognition system by compressing the source images. In the proposed method, we reduced the data amount by skeletonizing the source images using mathematical morphology, and then matched patterns after accomplishing the translation and scale normalization, and rotation invariance on the transformed images. Through the scale normalization, it was possible for the shape recognition at minimum amount of the pixel by giving the weight to the skeleton pixel. As the source images was replaced by the skeleton images, it was possible to reduce the amount of data and computational loads dramatically, and so become much faster even with a smaller memory capacity. Through the experiment, we investigated the optimum scale factor and good result was proved when realizing the pattern recognition system.

• Knowledge Management Research
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• v.22 no.4
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• pp.27-40
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• 2021

Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.183-186
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• 2020

방대한 비디오 데이터의 지능형 분석을 수행하는 기계를 위한 비디오 부호화 기술의 필요성이 대두되면서 MPEG 에서는 VCM(Video Coding for Machine) 표준화를 시작하였다. VCM 은 지능형 머신(machine)의 임무 수행을 위한 비디오 또는 비디오 특징(feature)의 압축 표준 기술로 기술 탐색 단계의 표준화를 진행하고 있다. 본 논문에서는 머신비전(machine vision) 네트워크에서 추출되는 대용량의 특징 압축을 위한 전처리 단계로 보다 효과적인 특징 표현 방법을 제시한다. 제안하는 특징 표현 방법은 정규화, 양자화 과정을 거쳐 특징 데이터 크기를 감소시킨다. 실험에서 특징을 4 개의 값으로 양자화 했을 때, 원본 대비 16 배의 데이터 크기가 감소되지만 mAP 평가 성능은 35.4592 로 높은 수준으로 유지함을 확인하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.161-166
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• 2020

This paper suggests an extension of the unified Bayesian Tikhonov regularization method. The unified method establishes the relationship between Tikhonov regularization parameter and Bayesian hyper-parameters, and presents a formula for obtaining the regularization parameter using the maximum posterior probability and the evidence framework. When the dimension of the data matrix is m by n (m >= n), we derive that the total misfit has the range of m ± n instead of m. Thus the search range is extended from one to 2n + 1 integer points. Golden section search rather than linear one is applied to reduce the time. A new benchmark for optimizing relative error and new model selection criteria to target it are suggested. The experimental results show the effectiveness of the proposed method in the image restoration problem.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.31-36
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• 2011

In this paper, Mel-Frequency Cepstral Coefficient (MFCC), Decorrelated Filter Bank (DFB), Octave-based Spectral Contrast (OSC), Zero-Crossing Rate (ZCR), and Spectral Contract/Roll-Off are combined as a set of multiple feature-vectors for the music genre classification system based on the Support Vector Machine (SVM) classifier. In the conventional system, feature vectors for the entire genre classes are normalized for the SVM model training and classification. However, in this paper, selected feature vectors that are compared based on the One-Against-One (OAO) SVM classifier are only used for normalization. Using OSC as a single feature-vector and the multiple feature-vectors, we obtain the genre classification rates of 60.8% and 77.4%, respectively, with the conventional normalization method. Using the proposed normalization method, we obtain the increased classification rates by 8.2% and 3.3% for OSC and the multiple feature-vectors, respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.17-25
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• 2006

As information systems to be computerized get significantly scaled up, data modeling issues apparently considered to be crucial once again as the early 1980's under the terms of data governance, data architecture or data quality. Unfortuately, merely resorting to heuristics-based field approaches with more or less no firm theoretical foundation of knowledge with regard to criteria of data design lead quite often to major failures in efficacy of data modeling. In this paper, we have compared normalization-critical data modeling approach, well-known as the Non-Stop Data Modeling methodology in the literature, to the Information Engineering in which in many occasions the notion of do-normalization is supported and even recommended as a mandatory part in its modeling nature. Quantitative analyses have revealed that NS methodology ostensibly outperforms IE methodology in terms of efficiency indices like adequacy of entity judgement, degree of existence of data circulation path that confirms the balancedness of data design and ratio of unnecessary data attribute replication.

• Smart Media Journal
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• v.11 no.7
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• pp.76-84
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• 2022

In Korea, where rice is the staple food, there are many cases in which the nutritional composition of food is different at the same weight, even though the same ingredients are used and the food or food name is the same. The cause is closely related to the moisture content of the food according to the cooking method and cooking process. In order to design a diet tailored to individual health and supply accurate calories and nutrients, a method of expressing food data that is not affected by the cooking process or cooking method is required. Usually, the same ingredients or foods show a lot of deviation from the nutritional components presented in the standard food database due to the difference in moisture content. For this reason, there are problems that increase the complexity of the food ingredient database and the difficulty in using it. As a method to improve these problems, we would like to propose a food data expression method based on dry weight. As an example of this, the characteristics of rice as a food material and changes in major nutritional components according to the change in moisture of various rice-processed foods made from rice were considered. In addition, as an example of how to normalize food data through this, the dry weight-based nutrition label of rice was presented.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.97-103
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• 2017

Many hardware-based regular expression matching architectures have been proposed for high-performance matching. In particular, regular expression processors, which perform pattern matching by treating the regular expressions as the instruction sequence like general purpose processors, have been proposed. After instruction sequence and data are provided in the instruction memory and data memory, respectively, a regular expression processor can perform pattern matching. To use a regular expression processor as a coprocessor, we need the host interface to transfer the instruction and data into the memory of a regular expression processor. In this paper, we design and implement the host interface between a host and a regular expression processor in the DE1-SoC board and the application program interface. We verify the operations of the host interface and a regular expression processor by executing the application programs which perform pattern matching using the application program interface.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.191-194
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• 2000

최근의 삼풍백화점 붕괴사고 및 성수대교사고 등 대형사고의 원인을 살펴보면 건물의 안전진단 미비와 구조물의 안전관리 진단판정미비로 건축물의 붕괴를 예측하지 못한 결과이다. 이는 모든 불확실 시스템의 상태를 적당한 항목으로 판정 정규화한 계수값으로 나타내어 예방하지 못한 결과이다. 비단건축물 시스템뿐 아니라 실존 가시물(可視物)과 비가시물(悲歌視物)에 대해서도 비결정 상황의 상태표시계수를 예측하여 정규값으로 나타낼 필요가 있다. 즉, 교통도로의 교통량 특정, 통신신호의 수신율 측정 등을 최적화 예측할 수가 있게된다. 본 논문에서는 환경 및 시스템 출력값에 표시하여 어떤 결과를 가져오는지 확인하기 위하여 평면 상의 임의의 데이터의 조합으로부터 특정 데이타를 선택 최적화하는 과정을 실험화 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.69-72
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• 2006

최근 생명 정보학 기술의 발달로 마이크로 단위의 실험조작이 가능해짐에 따라 하나의 chip상에서 전체 genome의 expression pattern을 관찰할 수 있게 되었고, 동시에 수 만개의 유전자들 간의 상호작용도 연구가능하게 되었다. 이처럼 DNA 마이크로어레이 기술은 복잡한 생물체를 이해하는 새로운 방향을 제시해주게 되었다. 따라서 이러한 기술을 통해 얻어진 대량의 유전자 정보들을 효과적으로 분석하는 방법이 시급하다. 본 논문에서는 마이크로어레이 실험에서 다양한 원인에 의해 발생하는 잡음(noise)을 줄이거나 제거하는 과정인 정규화과정을 거쳐 특징 추출방법인 SVM(Support Vector Machine) 방법을 이용하여 데이터를 2개의 클래스로 나누고, 표준화 방법들의 성능 비교를 위해 Adaptive Simulated Annealing 알고리즘으로 정확도를 평가하는 분류 시스템을 설계 구현하였다.