• Title/Summary/Keyword: recognition-rate

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Computer Interface for the Disabled Using Gyro-sensors and Artificial Neural Network (자이로 센서와 인공신경망을 이용한 장애인용 컴퓨터)

  • 안용식;엄광문;김철승;허지운;나유진
    • Journal of Biomedical Engineering Research
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    • v.24 no.5
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    • pp.411-419
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    • 2003
  • This paper aims at developing 'gyro-mouse' which provides decent and comfortable human-computer interface that supports the usage of such software as an internet-browser in PC for the people paralyzed in upper limbs. This interface operates on information collected from head movement to get the cursor control. The interface is composed of two modules. One is hardware module in which the head horizontal and vertical angular velocities are detected and transmitted into PC. The other is a PC software that translates the received data into movement and click signals of the mouse. The ANN (artificial neural network) learns the quick nodding pattern of each user as click input so that it can provide user-friendly interface. The performance of the system was evaluated by three indices that are click recognition rate. error in cursor position control. and click rate of the moving target box. The performance result of the gyro-mouse was compared with that of the optical-mouse to assess the efficiency of the gyro-mouse. The average click recognition rate was 93%, average error in cursor position control was 1.4∼5 times of optical mouse. and the click rate with 50 pixels target box was 40%(30 clicks/min) to that of optical mouse. The click rate increased monotonously with the number of trial from 35% to 44%. The suggested system is expected to provide a new possibility to communicate with the society.

Learning-based Detection of License Plate using SIFT and Neural Network (SIFT와 신경망을 이용한 학습 기반 차량 번호판 검출)

  • Hong, Won Ju;Kim, Min Woo;Oh, Il-Seok
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.8
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    • pp.187-195
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    • 2013
  • Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.

A study on characteristics of the autonomic nervous system in students with Keongke - Using Heart Rate Variability and Pupil Size Variability - (경계(驚悸) 증상을 지닌 학생 집단의 자율 신경 기능 특성에 대한 연구 - 심박변이도와 동공크기 변이도를 중심으로 -)

  • Lee, Seung-Gi;Lee, Jeong-Chan;Kim, Ji-Eun;Park, Kyung-Mo;Kang, Hee-Chul
    • Journal of Oriental Neuropsychiatry
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    • v.17 no.2
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    • pp.133-145
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    • 2006
  • Objective : The purpose of this experimental-controlled study was to investigate the characteristics of the autonomic nervous system in students with Keongke by using HRV(Heart rate variability) and PSV(Pupil size variability). Method : The study group was consisted of 11 students with self recognition as the experimental group, and 25 normal students as the control group. Informations on gender and age were obtained by medical charts and personal interviews. By using heart rate variability and pupil size variability, we measured the value of HRT(Heart rate), SDNN(Standard deviation of NN intervals), LFnorm(Low frequency normalization), HFnorm(High frequency normalization), LF/HF ratio, Pupil area, B.S.(Basic size), C.R.(Max Constriction Rate) and 1s.d.(1sec Dilation Rate). I compared the degrees of the sympathetic and parasympathetic activity. Result : 1. In the result of heart rate variability between experimental and control group, none of the parameters of experimental group were significantly different from control group. And even though there were no statistical significance, there were some numerical differences in SDNN, LF norm, HF norm. 2. In pupil size variability, C.R. and 1s.d. of the experimental group were increased compared to control group. Conclusion : The study results suggest that the group with Keongke has differences of autonomic nervous system as compared to those in normal state. Measurement value of PSV is a new technical approach to estimate the autonomic nervous system.

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Hangul Character Recognition Using Fuzzy Reasoning:Hangul Character Type Classification by Maximum Run Length Projenction (퍼지추론을 이용한 한글 문자 인식:최대 길이 투영에 의한 한글 문자 유형 분류)

  • 이근수;최형일
    • Korean Journal of Cognitive Science
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    • v.3 no.2
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    • pp.249-270
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    • 1992
  • The purpose of this paper is to classify the types of input characters,printed Hangul characters,using Maximum Run Length Projection(MRLP)that is used to extract features of input character.Because the number of Hangul characters is large and its structure is complex,there exists close similarities among characters.This paper,therefore,tried to increment the type classification rate using fuzzy resoning.The Maximum Run Length Projection is very immune to noise,and also useful to extracting the demanding information efficiently.In a test case with the most frequently use 917 printed Hangul characters,it achieved 98.58%correct classification rate.

The User Identification System Using Walking Pattern over the ubiFloor

  • Yun, Jae-Seok;Lee, Seung-Hun;Woo, Woon-Tack;Ryu, Je-Ha
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.1046-1050
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    • 2003
  • In general, conventional user identification systems require users to carry a TAG or badge or to remember ID and password. Though biometric identification systems may relieve these problems, they are susceptible to environmental noise to some degree. We propose a natural user identification system, ubiFloor, exploiting user's walking pattern to identify the user. The system identifies a user, while tracking the user's location, with a set of simple ON/OFF switch sensors or equipments. Experimental results show that the proposed system can recognize the registered users at the rate of 92%. Future improvement in recognition rate may be achieved by combining other sensors such as camera, microphone, etc.

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A STUDY ON THE SIMULATED ANNEALING OF SELF ORGANIZED MAP ALGORITHM FOR KOREAN PHONEME RECOGNITION

  • Kang, Myung-Kwang;Ann, Tae-Ock;Kim, Lee-Hyung;Kim, Soon-Hyob
    • Proceedings of the Acoustical Society of Korea Conference
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    • 1994.06c
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    • pp.407-410
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    • 1994
  • In this paper, we describe the new unsuperivised learning algorithm, SASOM. It can solve the defects of the conventional SOM that the state of network can't converge to the minimum point. The proposed algorithm uses the object function which can evaluate the state of network in learning and adjusts the learning rate flexibly according to the evaluation of the object function. We implement the simulated annealing which is applied to the conventional network using the object function and the learning rate. Finally, the proposed algorithm can make the state of network converged to the global minimum. Using the two-dimensional input vectors with uniform distribution, we graphically compared the ordering ability of SOM with that of SASOM. We carried out the recognitioin on the new algorithm for all Korean phonemes and some continuous speech.

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Performance improvement of Classification of Steam Generator Tube Defects in Nuclear Power Plant Using Neural Network (신경회로망을 이용한 원전SG 세관 결함패턴 분류성능 향상기법)

  • Jo, Nam-Hoon;Han, Ki-Won;Song, Sung-Jin;Lee, Hyang-Beom
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.56 no.7
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    • pp.1224-1230
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    • 2007
  • In this paper, we study the classification of defects at steam generator tube in nuclear power plant using eddy current testing (ECT). We consider 4 defect patterns of SG tube: I-In type, I-Out type, V-In type, and V-Out type. Through numerical analysis program based on finite element modeling, 400 ECT signals are generated by varying width and depth of each defect type. In order to improve the classification performance, we propose new feature extraction technique. After extracting new features from the generated ECT signals, multi-layer perceptron is used to classify the defect patterns. Through the computer simulation study, it is shown that the proposed method achieves 100% classification success rate while the previous method yields 91% success rate.

Performance Evaluation of Real-time Voice Traffic over IEEE 802.15.4 Beacon-enabled Mode (IEEE 802.15.4 비컨 가용 방식에 의한 실시간 음성 트래픽 성능 평가)

  • Hur, Yun-Kang;Kim, You-Jin;Huh, Jae-Doo
    • IEMEK Journal of Embedded Systems and Applications
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    • v.2 no.1
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    • pp.43-52
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    • 2007
  • IEEE 802.15.4 specification which defines low-rate wireless personal area network(LR-WPAN) has application to home or building automation, remote control and sensing, intelligent management, environmental monitoring, and so on. Recently, it has been considered as an alternative technology to provide multimedia services such as automation via voice recognition, wireless headset and wireless camera for surveillance. In order to evaluate capability of voice traffic on the IEEE 802.15.4 LR-WPAN, we supposed two scenarios, voice traffic only and coexistence of voice and sensing traffic. For both cases we examined delay and packet loss rate in case of with and without acknowledgement, and various beacon period varying with beacon and superframe order values. In LR-WPAN with voice devices only, total 5 voice devices could be applicable and in the other case, i.e., coexisted cases of voice and sensor devices, a voice device was able to coexist with about 60 sensor devices.

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A Matching Strategy to Recognize Occluded Number (폐색된 숫자를 인식하는 매칭 방법)

  • Pham, Thi Thuong;Choi, Hyung-Il;Kim, Gye-Young
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2011.01a
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    • pp.55-58
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    • 2011
  • This paper proposes a method of occluded number recognition by matching interest points. Interest points of input pattern are found via SURF features extracting and matched to interest points of clusters in database following three steps: SURF matching, coordinate matching and SURF matching on coordinate matched points. Then the satisfied interest points are counted to compute matching rate of each cluster. The input pattern will be assigned to cluster having highest matching rate. We have experimented our method to different numerical fonts and got encouraging results.

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A design of the processor dedicated to LPC-CEPSTRUM (LPC-CEPSTRUM 추출을 위한 전용 프로세서의 설계)

  • 황인철;김성남;김영우;김태근;김수원
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.34C no.8
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    • pp.71-78
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    • 1997
  • An LPC cepstrum processor for speech recognition is implemented on CMOS array process. The designed processor contains a 24-bit floating-point MAC unit to perform the correlation quickly, which occupies the majority of operations used in the algorithm, and has 22 register files to store temporary variables. For the purpose of fast operations, the floating-point MAC consists of a 3-stage pipeline and the new post-normalization shceme is proposed and applied to it. Experimental result shows that it takes approximately 266.mu.s to process 200 samples/frame at 15 MHz clock rate. This processor runs at the maximum rate of 16.6 MHz and the number of gates are 27,760.

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