• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.502-511
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• 2002

컴퓨터의 기술적 발전은 사회 여러 분야에 막대한 영향을 끼쳤다. 그중 악보 인식분야에도 커다란 영향을 주었다 그러나, On-line 상에서 그린 악보를 실시간으로 정형화된 악보형태로 변환하는 처리에 대한 연구가 미흡하여 이에 대한 연구가 필요하다. 본 논문에서는 실시간으로 악보를 인식하고, 사용자의 편의를 도모하기 위해 DP(Dynamic Programming) 매칭법을 이용한 On-Line 악보인식에 관한 방법을 제안하였다. 본 연구에서는 실시간으로 입력되는 악상기호를 인식하기 위해, 가장 유효한 정보인 악상 기호내의 방향, x, y 좌표를 이용하여 벡터형태로 추출한 후 음표와 비음표(쉼표, 기타기호)의 두개의 그룹으로 나누어진 표준패턴과의 DP매칭을 통해 인식한다. 먼저 tablet을 통해 실시간으로 악상 기호를 입력할 때 생기는 x, y좌표를 이용하여, 펜의 움직임에 대한 16방향 부호화를 수행한다. 음표와 비음표를 구분하기 위한 시간을 줄이고자 16방향 부호화를 적용하치 않고 사사분면부호화를 적용한다. 음표를 약식으로 그릴 경우 음표 머리에 해당하는 부분의 좌표는 삼사분면에 분포하고, 폐곡선의 음표일 경우에는 좌표가 사사분면에 고르게 나타난다. 폐곡선을 제외한 음표의 머리는 폐곡선과 같은 조건이면서 입력받은 y좌표값들 중에서 최소값과 최대값을 구한 다음 2로 나눈 값을 지나는 y좌표의 개수가 임의의 임계값 이상이면 음표로 판단한다. 위 조건을 만족하지 않을 경우 비음표로 취급한다. 음표와 비음표를 결정한 다음, 입력패턴과 표준패턴과의 DP매칭을 통하여 벌점을 구한다. 그리고 경로탐색을 통해 벌점에 대한 각각의 합계를 구해 최소값을 악상기호로 인식 하였다. 실험결과, 표준패턴을 음표와 비음표의 두개의 그룹으로 나누어 인식함으로써 DP 매칭의 처리 속도를 개선시켰고, 국소적인 변형이 있는 패턴과 특징의 수가 다른 패턴의 경우에도 좋은 인식률을 얻었다.r interferon alfa concentrated solution can be established according to the monograph of EP suggesting the revision of Minimum requirements for biological productss of e-procurement, e-placement, e-payment are also investigated.. monocytogenes, E. coli 및 S. enteritidis에 대한 키토산의 최소저해농도는 각각 0.1461 mg/mL, 0.2419 mg/mL, 0.0980 mg/mL 및 0.0490 mg/mL로 측정되었다. 또한 2%(v/v) 초산 자체의 최소저해농도를 측정한 결과, B. cereus, L. mosocytogenes, E. eoli에 대해서는 control과 비교시 유의적인 항균효과는 나타나지 않았다. 반면에 S. enteritidis의 경우는 배양시간 4시간까지는 항균활성을 나타내었지만, 8시간 이후부터는 S. enteritidis의 성장이 control 보다 높아져 배양시간 20시간에서는 control 보다 약 2배 이상 균주의 성장을 촉진시켰다.차에 따른 개별화 학습을 가능하게 할 뿐만 아니라 능동적인 참여를 유도하여 학습효율을 높일 수 있을 것으로 기대된다.향은 패션마케팅의 정의와 적용범위를 축소시킬 수 있는 위험을 내재한 것으로 보여진다. 그런가 하면, 많이 다루어진 주제라 할지라도 개념이나 용어가 통일되지 않고 사용되며 검증되어 통용되는 측정도구의 부재로 인하여 연구결과의 축적이 미비한 상태이다. 따라서, 이에 대한 재고와 새로운 방향 모색이 필요하다고 사료된다.로 사료되며, 임신관련 cytokine에 대한 다양한 연구가 요구되고 있다.₂/Hf(Variable)/Si 계에서 HfO₂ 박막이 Si 기판위에 직접 증착되면, 순수 HfO₂ 박막의

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.10-16
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• 2007

In this paper, we propose a technique for retrieving images using spatial color correlation and texture characteristics based on local fourier transform. In order to retrieve images, two new descriptors are proposed. One is a color descriptor which represents spatial color correlation. The other is a descriptor combining the proposed color descriptor with texture descriptor. Since most of existing color descriptors including color correlogram which represent spatial color correlation considered just color distribution between neighborhood pixels, the structural information of neighborhood pixels is not considered. Therefore, a novel color descriptor which simultaneously represents spatial color distribution and structural information is proposed. The proposed color descriptor represents color distribution of Min-Max color pairs calculating color distance between center pixel and neighborhood pixels in a block with 3x3 size. Also, the structural information which indicates directional difference between minimum color and maximum color is simultaneously considered. Then new color descriptor(min-max color correlation descriptor, MMCCD) containing mean and variance values of each directional difference is generated. While the proposed color descriptor includes by far smaller feature vector over color correlogram, the proposed color descriptor improves 2.5 % ${\sim}$ 13.21% precision rate, compared with color correlogram. In addition, we propose a another descriptor which combines the proposed color descriptor and texture characteristics based on local fourier transform. The combined method reduces size of feature vector as well as shows improved results over existing methods.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.163-172
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• 2012

Collaborative filtering which is used explicit method in a existing recommedation system, can not only reflect exact attributes of item but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. This paper proposes the personalized recommendation system using RFM method and k-means clustering in u-commerce which is required by real time accessablity and agility. In this paper, using a implicit method which is is not used complicated query processing of the request and the response for rating, it is necessary for us to keep the analysis of RFM method and k-means clustering to be able to reflect attributes of the item in order to find the items with high purchasablity. The proposed makes the task of clustering to apply the variable of featured vector for the customer's information and calculating of the preference by each item category based on purchase history data, is able to recommend the items with efficiency. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.245-253
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• 2011

Due to the imprudent spending of the fossil fuels, the environment was contaminated seriously and the exhaustion problems of the fossil fuels loomed large. Therefore people become taking a great interest in alternative energy resources which can solve problems of fossil fuels. The wind power energy is one of the most interested energy in the new and renewable energy. However, the plants of wind power energy and the traditional power plants should be balanced between the power generation and the power consumption. Therefore, we need analysis and prediction to generate power efficiently using wind energy. In this paper, we have performed a research to predict power generation patterns using the wind power data. Prediction approaches of datamining area can be used for building a prediction model. The research steps are as follows: 1) we performed preprocessing to handle the missing values and anomalous data. And we extracted the characteristic vector data. 2) The representative patterns were found by the MIA(Mean Index Adequacy) measure and the SOM(Self-Organizing Feature Map) clustering approach using the normalized dataset. We assigned the class labels to each data. 3) We built a new predicting model about the wind power generation with classification approach. In this experiment, we built a forecasting model to predict wind power generation patterns using the decision tree.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.456-462
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• 2008

The classification is that a new data is classified into one of given classes and is one of the most generally used data mining techniques. Memory-Based Reasoning (MBR) is a reasoning method for classification problem. MBR simply keeps many patterns which are represented by original vector form of features in memory without rules for reasoning, and uses a distance function to classify a test pattern. If training patterns grows in MBR, as well as size of memory great the calculation amount for reasoning much have. NGE, FPA, and RPA methods are well-known MBR algorithms, which are proven to show satisfactory performance, but those have serious problems for memory usage and lengthy computation. In this paper, we propose DPA (Dynamic Partition Averaging) algorithm. it chooses partition points by calculating GINI-Index in the entire pattern space, and partitions the entire pattern space dynamically. If classes that are included to a partition are unique, it generates a representative pattern from partition, unless partitions relevant partitions repeatedly by same method. The proposed method has been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory and FPA, and RPA.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.114-125
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• 2002

In this paper, we are proposing a hierarchical segmentation method that first segments the video data into units of shots by detecting cut and dissolve, and then decides types of camera operations or object movements in each shot. In our previous work[1], each picture group is divided into one of the three detailed categories, Shot(in case of scene change), Move(in case of camera operation or object movement) and Static(in case of almost no change between images), by analysing DC(Direct Current) component of I(Intra) frame. In this process, we have designed two-stage hierarchical neural network with inputs of various multiple features combined. Then, the system detects the accurate shot position, types of camera operations or object movements by searching P(Predicted), B(Bi-directional) frames of the current picture group selectively and hierarchically. Also, the statistical distributions of macro block types in P or B frames are used for the accurate detection of cut position, and another neural network with inputs of macro block types and motion vectors method can reduce the processing time by using only DC coefficients of I frames without decoding and by searching P, B frames selectively and hierarchically. The proposed method classified the picture groups in the accuracy of 93.9-100.0% and the cuts in the accuracy of 96.1-100.0% with three different together is used to detect dissolve, types of camera operations and object movements. The proposed types of video data. Also, it classified the types of camera movements or object movements in the accuracy of 90.13% and 89.28% with two different types of video data.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.494-503
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• 2011

In this paper, we propose a recognition application of facial expression for laughter theraphy on smartphone. It detects face region by using AdaBoost face detection algorithm from the front camera image of a smartphone. After detecting the face image, it detects the lip region from the detected face image. From the next frame, it doesn't detect the face image but tracks the lip region which were detected in the previous frame by using the three step block matching algorithm. The size of the detected lip image varies according to the distance between camera and user. So, it scales the detected lip image with a fixed size. After that, it minimizes the effect of illumination variation by applying the bilateral symmetry and histogram matching illumination normalization. After that, it computes lip eigen vector by using PCA(Principal Component Analysis) and recognizes laughter expression by using a multilayer perceptron artificial network. The experiment results show that the proposed method could deal with 16.7 frame/s and the proposed illumination normalization method could reduce the variations of illumination better than the existing methods for better recognition performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.59-66
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• 2016

This paper presents an empirical evaluation on dimensionality reduction strategies by which dissimilarity-based classifications (DBC) can be implemented efficiently. In DBC, classification is not based on feature measurements of individual objects (a set of attributes), but rather on a suitable dissimilarity measure among the individual objects (pair-wise object comparisons). One problem of DBC is the high dimensionality of the dissimilarity space when a lots of objects are treated. To address this issue, two kinds of solutions have been proposed in the literature: prototype selection (PS)-based methods and dimension reduction (DR)-based methods. In this paper, instead of utilizing the PS-based or DR-based methods, a way of performing DBC in Eigen spaces (ES) is considered and empirically compared. In ES-based DBC, classifications are performed as follows: first, a set of principal eigenvectors is extracted from the training data set using a principal component analysis; second, an Eigen space is expanded using a subset of the extracted and selected Eigen vectors; third, after measuring distances among the projected objects in the Eigen space using $l_p$-norms as the dissimilarity, classification is performed. The experimental results, which are obtained using the nearest neighbor rule with artificial and real-life benchmark data sets, demonstrate that when the dimensionality of the Eigen spaces has been selected appropriately, compared to the PS-based and DR-based methods, the performance of the ES-based DBC can be improved in terms of the classification accuracy.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.63-72
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• 2018

The human voice is one of the easiest methods for the information transmission between human beings. The characteristics of voice can vary from person to person and include the speed of speech, the form and function of the vocal organ, the pitch tone, speech habits, and gender. The human voice is a key element of human communication. In the days of the Fourth Industrial Revolution, voices are also a major means of communication between humans and humans, between humans and machines, machines and machines. And for that reason, people are trying to communicate their intentions to others clearly. And in the process, it contains various additional information along with the linguistic information. The Information such as emotional status, health status, part of trust, presence of a lie, change due to drinking, etc. These linguistic and non-linguistic information can be used as a device for evaluating the individual's credit worthiness by appearing in various parameters through voice analysis. Especially, it can be obtained by analyzing the relationship between the characteristics of the fundamental frequency(basic tonality) of the vocal cords, and the characteristics of the resonance frequency of the vocal track.In the previous research, the necessity of various methods of credit evaluation and the characteristic change of the voice according to the change of credit status were studied. In this study, we propose a personal credit discriminator by machine learning through parameters extracted through voice.

• Therapeutic Science for Rehabilitation
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• v.11 no.4
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• pp.23-39
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• 2022

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.