• 정보보호학회논문지
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• 제34권2호
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• pp.167-177
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• 2024

본 논문은 isogeny 기반 전자 서명 알고리즘인 SeaSign의 최적화 방안을 제안한다. SeaSign은 CSIDH의 class group action에 Fiat-Shamir with abort를 결합한 전자서명 알고리즘이다. CSIDH 기반 암호는 SIDH 기반 암호가 다항시간안에 공격됨에 따라 다시 주목받고 있지만, 이를 기반한 전자서명인 SeaSign은 비효율적인 속도로 많은 최적화가 진행되지 않았다. 본 논문에서는 SeaSign에 대한 효율적인 서명 방법을 제안한다. 제안하는 서명 방법은 간단하지만 강력하며, 알고리즘 내에서 rejection sampling의 위치 변경을 통해 이루어진다. 추가로, 본 논문에서는 제안하는 알고리즘이 최적 성능을 제공할 수 있는 파라미터를 제시한다. 제시한 결과, 기존 SeaSign의 파라미터를 사용할 경우, 본 논문에서 제안한 서명방법은 기존 SeaSign 대비 3배 빠른 성능을 보인다. 추가로 신규 제시된 파라미터와 본 논문의 서명 방법을 결합한 경우, 기존 SeaSign 대비 290배 빠른 성능과 Decru 등이 제안한 방법 대비 7.47배 빠른 성능을 제공한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.141-144
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• 1997

This paper presents a method of navigation sign recognition in indoor environments using a fuzzy inference for an autonomous mobile robot. In order to adapt to image deformation of a navigation sign resulted from variations of view-points and distances, a multi-labeled template matching(MLTM) method and a dynamic area search method(DASM) are proposed. The DASM is proposed to detect correct feature points among incorrect feature points. Finally sugeno-style fuzzy inference are adopted for recognizing the navigation sign.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권1호
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• pp.82-91
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• 2009

수화 적출은 연속된 손 동작에서 의미 있는 수화 단어를 검출 및 인식하는 것을 말한다. 수화는 손의 움직임과 모양의 변화가 다양하기 때문에 수화 문장에서 수화를 적출하는 것은 쉬운 문제가 아니다. 특히, 자연스러운 수화 문장에는 의미 있는 수화, 수화가 아닌 손동작이 무작위로 발생한다. 본 논문에서는 CRF(Conditional Random Field)에 기반한 적응적 임계치 모델을 제안한다. 제한된 모델은 수화 어휘집에 정의된 수화 손동작과 수화가 아닌 손동작을 구별하기 위한 적응적 임계치 역할을 수행한다. 또한, 수화 적출 및 인식의 성능 향상을 위해 손 모양 기반 수화 인증기, 짧은 수화 적출기, 부사인(subsign) 추론기를 제안된 시스템에 적용하였다. 실험 결과, 제안된 방법은 연속된 수화 동작 데이타에서 88%의 적출률, 사전에 적출된 수화 동작 데이타에서 94%의 인식률을 보였으며, 적응적 임계치 모델, 짧은 수화 적출기, 손 모양 기반 수화 인증기, 부사인 추론기를 사용하지 않은 CRF 모델은 연속된 수화 동작 데이터에서 74%의 적출률, 사전에 적출된 수화 동작 데이타에서 90%의 인식률을 보였다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(A)
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• pp.491-493
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• 2011

Traffic sign detection is the domain of automatic driver assistant systems. There are literatures for traffic sign detection using color information, however, color-based method contains ill-posed condition and to extract the region of interest is difficult. In our work, we propose a method for traffic sign detection using k-means clustering method, back-propagation neural network, and projection histogram features that yields the robustness for ill-posed condition. Using the color information of traffic signs enables k-means algorithm to cluster the region of interest for the detection efficiently. In each step of clustering, a cluster is verified by the neural network so that the cluster exactly represents the location of a traffic sign. Proposed method is practical, and yields robustness for the unexpected region of interest or for multiple detections.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.175-185
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• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

• 한국해양학회지
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• 제28권3호
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• pp.192-201
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• 1993

쯔나미파에 의한 피해를 줄이기 위해서는 쯔나미파를 재현할 수 있는 방법의 개발 이 필요하다. 본 논문에서는 Sign Method를 도입하여 검조기에 기록된 쯔나미파를 분 석하고 재현해 봄으로써, 쯔나미파 분석에서의 Sign Method의 사용가능성을 검토하였 다. 쯔나미파의 시계열자료 Y(t)는 각 쯔나미의 발생특징을 반영하는 함수인 source evolution function E(t')와 파가 전파되는 지역의 특징을 나타내는 함수인 wave propagation function K9t-t')의 convolution 적분에 의해 나타낼 수 있다. Y(t)=.int. E(t')K(t-t')dt' 일본의 6개 지점과 한국의 9개 지점에서 기록된 1940년, 1964년, 1983년의 쯔나미파를 분석하여 sourve function 들과 wave function들을 구하 고, 두 함수를 이용하여 기록자료도 재현해 보았다. 우리 나라 동해안처럼 쯔나미의 진원지로부터 멀리 위치한 지역에서는 Sign Metho가 효과적인 방법임을 알 수 있었다. 또한, 기록되지 않은 쯔나미파도 인접지점의 source function 과 다른 시기의 쯔나미 에 대한 wave function을 이용하여 추정할 수 있다.

• 한국자동차공학회논문집
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• 제22권3호
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• pp.50-59
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• 2014

This paper proposes a method of localization of vehicle especially the horizontal position for the purpose of recognizing the driving lane. Through tracking road signs, the relative position between the vehicle and the sign is calculated and the absolute position is obtained using the known information from the regulation for installation. The proposed method uses Kalman filter for road sign tracking and analyzes the motion using the pinhole camera model. In order to classify the road sign, ORB(Oriented fast and Rotated BRIEF) features from the input image and DB are matched. From the absolute position of the vehicle, the driving lane is recognized. The Experiments are performed on videos from the highway driving and the results shows that the proposed method is able to compensate the common GPS localization errors.

• ETRI Journal
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• 제40권4호
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• pp.435-445
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• 2018

This paper proposes a human-like sign-language learning method that uses a deep-learning technique. Inspired by the fact that humans can learn sign language from just a set of pictures in a book, in the proposed method, the input data are pre-processed into an image. In addition, the network is partially pre-trained to imitate the preliminarily obtained knowledge of humans. The learning process is implemented with a well-known network, that is, a convolutional neural network. Twelve sign actions are learned in 10 situations, and can be recognized with an accuracy of 99% in scenarios with low-cost equipment and limited data. The results show that the system is highly practical, as well as accurate and robust.

• 한국컴퓨터정보학회논문지
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• 제24권2호
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• pp.193-200
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• 2019

Hearing impaired people are living in a voice culture area, but due to the difficulty of communicating with normal people using sign language, many people experience discomfort in daily life and social life and various disadvantages unlike their desires. Therefore, in this paper, we study a sign language translation system for communication between a normal person and a hearing impaired person using sign language and implement a prototype system for this. Previous studies on sign language translation systems for communication between normal people and hearing impaired people using sign language are classified into two types using video image system and shape input device. However, existing sign language translation systems have some problems that they do not recognize various sign language expressions of sign language users and require special devices. In this paper, we use machine learning method of artificial neural network to recognize various sign language expressions of sign language users. By using generalized smart phone and various video equipment for sign language image recognition, we intend to improve the usability of sign language translation system.

• 한국정보처리학회논문지
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• 제7권5호
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• pp.1317-1329
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• 2000

The advanced techniques for video processing and computer graphics enables a sign language education system to appear. the system is capable of showing a sign language motion for an arbitrary sentence using the captured video clips of sign language words. In this paper, a method is suggested which generates the frames between the last frame of a word and the first frame of its following word in order to animate hand motion. In our method, we find hand locations and angles which are required for in between frame generation, capture and store the hand images at those locations and angles. The inbetween frames generation is simply a task of finding a sequence of hand angles and locations. Our method is computationally simple and requires a relatively small amount of disk space. However, our experiments show that inbetween frames for the presentation at about 15fps (frame per second) are achieved so tat the smooth animation of hand motion is possible. Our method improves on previous works in which computation cost is relativey high or unnecessary images are generated.