• Journal of Information Processing Systems
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• 제11권3호
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• pp.438-449
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• 2015

Recognition systems for scanned or printed music scores that have been implemented on personal computers have received attention from numerous scientists and have achieved significant results over many years. A modern trend with music scores being captured and played directly on mobile devices has become more interesting to researchers. The limitation of resources and the effects of illumination, distortion, and inclination on input images are still challenges to these recognition systems. In this paper, we introduce a novel approach for recognizing music scores captured by mobile cameras. To reduce the complexity, as well as the computational time of the system, we grouped all of the symbols extracted from music scores into ten main classes. We then applied each major class to SVM to classify the musical symbols separately. The experimental results showed that our proposed method could be applied to real time applications and that its performance is competitive with other methods.

• 대한지역사회영양학회지
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• 제8권1호
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• pp.71-82
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• 2003

In this study, hygiene knowledge and recognition on job performance levels of foodservice employees at different degrees of HACCP application had been compared. As for the comparison of hygiene knowledge for HACCP application, foodservice employees at "Appointed" showed significantly higher HACCP knowledge (p < 0.01) . As for recognition on job performance levels, this status also assured the significant difference between the comparison groups (p < 0.01) : "Appointed" showed the highest point of 4.24 while "Voluntary applying" and "Non-applying" showed 3.39 and 2.53 respectively. The "Appointed" group showed the well performance of most of the surveyed items. "Voluntary applying" group showed unsatisfactory performance in various log recordings and some part of surveyed items. As for "Non-applying", many items were performed under the average score as they do not apply HACCP.ems were performed under the average score as they do not apply HACCP.

• 한국멀티미디어학회논문지
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• 제22권4호
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• pp.455-462
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• 2019

This paper proposes a method of utilizing convolutional neural network to classify the images of Bender Gestalt Test (BGT), which is a tool to understand and analyze a person's characteristic. The proposed network is composed of 29 layers including 18 convolutional layers and 2 fully connected layers, where the network is to be trained with augmented images. To verify the proposed method, 10 fold validation was adopted. In results, the proposed method classified the images into 9 classes with the mean f1 score of 97.05%, which is 13.71%p higher than a previous method. The analysis of the results shows the classification accuracy of the proposed method is stable over all the patterns as the worst f1 score among all the patterns was 92.11%.

• 가족자원경영과 정책
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• 제2권2호
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• pp.21-37
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• 1998

The purpose of this study was to investigate the hypothesijed a causal model of sujective perception of contribution to the family economy utilijing system theory. The major findings of this study were as follows. 1) The mean score of respondent’s personal subsystems were 3.62 out of 5. And the family solidarity was affected by the following variables in order : housewives’ age, income, type of occupation, degree of resourcefulness recognition, job satisfaction, control orientation, and information utilization. 2) The mean score of managerial subsystems were 3.68 out of 5. And The financial management of housewives was affected by the following variables. in order: housewives’ type of occupation, degree of resourcefulness recognition, control orientation, information utilization. 3) The mean score of housewives’subjective perception of Economic Contribution to the Family was 3.36 out of 5. The sujective perception of contribution to the family economy of the employed housewives were greater than unemployed housewives. 4) It was found that managerial subsystems had mostly effected on sujective perception of economic contribution to the family.

• 한국전자통신학회논문지
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• 제19권4호
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• pp.701-708
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• 2024

딥러닝 분야에서 트랜스포머 아키텍쳐의 출현은 자연어 처리 연구가 획기적인 발전을 가져왔다. 개체명 인식은 자연어 처리의 한 분야로 정보 검색과 같은 태스크에 중요한 연구 분야이다. 생의학 분야에서도 그 중요성이 강조되나 학습용 한국어 생의학 말뭉치의 부족으로 AI를 활용한 한국어 임상 연구 발전에 제약이 되고 있다. 본 연구에서는 한국어 생의학 개체명 인식을 위해 새로운 생의학 말뭉치를 구축하고 대용량 한국어 말뭉치로 사전 학습된 언어 모델들을 선정하여 전이 학습시켰다. F1-score로 선정된 언어 모델의 개체명 인식 성능과 태그별 인식률을 비교하고 오류 분석을 하였다. 인식 성능에서는 KlueRoBERTa가 상대적인 좋은 성능을 보였다. 태깅 과정의 오류 분석 결과 Disease의 인식 성능은 우수하나 상대적으로 Body와 Treatment는 낮았다. 이는 문맥에 기반하여 제대로 개체명을 분류하지 못하는 과분할과 미분할로 인한 것으로, 잘못된 태깅들을 보완하기 위해서는 보다 정밀한 형태소 분석기와 풍부한 어휘사전 구축이 선행되어야 할 것이다.

• 음성과학
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• 제13권4호
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• pp.177-186
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• 2006

In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate of the speaker-independent speech recognition, we employ the Mahalanobis distance in the calculation of the similarity measure between feature vectors. It is assumed that the metric matrix of the Mahalanobis distance be diagonal for the sake of cost reduction in memory and time of calculation. We propose that the diagonal elements be given in terms of the variations of the feature vector components. Geometrically, this prescription tends to redistribute the set of data in the shape of a hypersphere in the feature vector space. The idea is applied to the speech recognition by hidden Markov model with fuzzy vector quantization. The result shows that the recognition is improved by an appropriate choice of the relevant adjustable parameter. The Viterbi score difference of the two winners in the recognition test shows that the general behavior is in accord with that of the recognition error rate.

• 디지털융복합연구
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• 제11권11호
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• pp.479-490
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• 2013

본 연구는 환자 개인정보보호에 대한 간호대학생의 인식과 실천정도를 규명하기 위한 조사연구이다. 조사대상은 간호대학생 383명으로 측정도구는 Lee & Park (2005)이 개발한 설문지를 학생용으로 수정하여 사용하였다. 자료는 descriptive statistics, t-test, ANOVA, Pearson correlation coefficients로 분석하였다. 분석 결과 대상자의 96.1%이상이 학교나 병원에서 환자 개인정보보호에 대한 교육을 받았으나 10분이내의 교육이 48.0%이었다. 환자 개인정보보호에 대한 인식정도는 평균 4.13점, 실천정도는 평균 3.84점이었다. 영역별로는 의사소통 업무 영역에서 인식과 실천 정도가 가장 높았다. 학력과 나이에 따라 영역별로 인식정도에 차이가 있었고, 병원에서의 교육경험 유무, 병원교육시 정규프로그램 유무, 병원교육 시 일 회당 교육시간에 따라 영역별로 실천정도에 차이가 있었다. 인식과 실천의 총점과 영역별 점수간에 유의한 상관관계가 있었다. 본 연구의 결과는 임상실습을 하는 간호대학생을 대상으로 환자의 개인정보를 보호하기 위한 교육 프로그램을 개발하는데 도움이 될 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권5호
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• pp.1766-1784
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• 2014

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.73-75
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• 2022

글자 인식 시스템은 무인 로봇, 자율 주행 자동차 등 자동화를 필요로 하는 인공지능 분야에서 사용되는 기술로, 주변 환경에 여러 장애물이 있음에도 글자를 정확하게 인식하는 것을 말한다. 영어만 인식했던 기존의 연구와 달리, 본 논문은 영어, 한국어, 특수문자와 숫자를 포함한 다양한 문자가 혼재되어 있는 경우에도 강한 인식률을 보여준다. 가장 높은 확률 값을 갖는 클래스 하나 만을 선택하는 것이 아닌 차 순위의 확률도 함께 고려하여 후보 단어 리스트를 생성하고, 이로 인해 기존에 오인식되는 단어를 교정할 수 있는 방법을 제안한다.

• 대한의용생체공학회:의공학회지
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• 제30권1호
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• pp.94-102
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• 2009

In this paper, we propose a hearing impairment simulator considering reduced frequency selectivity of the hearing impaired, and verify it's performance through experiments. The reduced frequency selectivity was embodied by spectral smearing using linear prediction coding(LPC). The experiments are composed of 4 kinds of tests; pure tone test, speech reception threshold(SRT) test, and word recognition score(WRS) test without spectral smearing and with spectral smearing. The experiments of the hearing impairment simulator were performed with 9 subjects who have normal hearing. The amount of spectral smearing was controlled by LPC order. The percentile score of WRS test without smearing is $89.78{\pm}2.420%$. The scores of WRS with 24th LPC order and with 8th LPC order are $88.00{\pm}3.556%$ and $83.78{\pm}2.123%$ respectively. It is verified that WRS score is lowered by decreasing LPC order. This is a reasonable result considering that spectral smearing is getting heavier according to decreasing LPC order. It is confirmed that spectral smearing using LPC simulates the reduced frequency selectivity of the hearing impaired and affects the clearness of speech reception.