• 한국응급구조학회지
• /
• 제11권3호
• /
• pp.153-161
• /
• 2007

This study was attempted to provide basic data develop CPR training program for layperson by looking into layperson's recognition and attitude of execution of CPR and to prepare for underlying data in drawing up training policy and suggesting relevant legislation so that trained laypersons can positively perform rescue activities. The survey was done from August 20 to September 20, 2007. Total Subjects in this study were 78. The collected data were analyzed by SPSS Program. The summary of the research is as follows : First, to the question of whether or not the recognition or performance of CPR is universal within the country, 82.1% of respondents gave a negative answer as 'No', The most reason was found to be 'Poor training and P.R. of CPR' accounting for 50.1%, 94.8% of the whole respondents answered that CPR training is necessary. As plan for activatin of CPR training, they answered that top priority shall be given to compulsory school training(79.5%). Secondly, when respondents observed their family's cardic 84.6% of them answered that they would conduct CPR but when they observed other's cardiac arrest, just 41.7% of them answered it. As an effective CPR activation plan, the most answer was training and P.R. of CPR as 79.5%. This study suggests that laypersons high perception of the effectiveness of the CPR and that they wound be willing to provide CPR in a medical emergency.

• 정보처리학회논문지B
• /
• 제13B권6호
• /
• pp.615-624
• /
• 2006

이 논문은 이동 에이전트 시스템에 기반을 둔 가상의 병렬분산 컴퓨팅 환경에서 병렬로 수행되는 다층 인공신경망 시뮬레이터를 구현하는 것을 목적으로 한다. 다층 신경망은 학습세션, 학습데이터, 계층, 노드, 가중치 수준에서 병렬화가 이루어진다. 이 논문에서는 네트워크의 통신량이 상대적으로 적은 학습세션 및 학습데이터 수준의 병렬화가 가능한 신경망 시뮬레이터를 개발하고 평가하였다. 평가결과, 학습세션 병렬화와 학습데이터 병렬화 성능분석에서 약 3.3배의 학습 수행 성능 향상을 확인할 수 있었다. 가상의 병렬 컴퓨터에서 신경망을 병렬로 구현하여 기존의 전용병렬컴퓨터에서 수행한 신경망의 병렬처리와 비슷한 성능을 발휘한다는 점에서 이 논문의 의의가 크다고 할 수 있다. 따라서 가상의 병렬 컴퓨터를 이용하여 신경망을 개발하는데 있어서, 비교적 시간이 많이 소요되는 학습시간을 줄임으로서 신경망 개발에 상당한 도움을 줄 수 있다고 본다.

• 대한간호
• /
• 제37권1호
• /
• pp.107-115
• /
• 1998

There is an important concern regarding the First-line nurse manager's leadership because of the recognition that effectiveness of Leadership in this position results in benefits for the whole health care organization. So knowledge and practice of effective leadership behavior are now more essential to nursing than ever before. First-line Nurse Managers must be effective leaders to meet today's challenge because staff nurse, patient are affected by them. So the purpose of this study was to identify and to analyse the need for Leadership program of First-Line nurse managers in university hospitals. There were three major purposes of this study. First, identify First-line nurse managers general characteristic, second, identify their experience of leadership training, third, identify and analysis their demands for leadership training program. The subjects for this study was 167 First-line nurse manager randomly from 18 university hospitals in Korea. The data were collected through questionnaires from Oct. 13th to Nov. 20th, 1997, data was analysed using frequencies and percentages. Especially the steps of analysis of descriptions were as follows: Initial analysis centered on the identification of the demands of first-line nurse managers. Later analysis collapsed the demands into broad categories. From the collect data, 283 demands of first-line nurse managers were identified. These demands were then sorted into 3 broad categories that included : Self development as first-line nurse managers, relationship with others, and practice. The result of the study were as follows ; 1) Most of nurse managers(79.6%) had leadership training course and had good experience to improve self leadership. 2) Their demands of leadership training course are as follows First, for self as first-line nurse managers, they want to learn leadership theory, identify their leadership style and then develop their leadership skill. Second, for others as first-line nurse managers, they want to improve their communication skill, empowering others, relationship with others. Third, for patients as first-line nurse managers, improve their knowledge of practice. From the above finding, this study can be suggested the following; 1. Develope a leadership training course to improve first- line nurse manager's leadership skill according to their demands, so they will be better able to lead staff nurses for organization purposes. 2. When develope leadership training program, it must be contained the factors which first-line nurse managers want to learn.

• ETRI Journal
• /
• 제33권6호
• /
• pp.914-923
• /
• 2011

This paper concerns robust and reliable speaker model training for text-independent speaker verification. The baseline speaker modeling approach is the Gaussian mixture model (GMM). In text-independent speaker verification, the amount of speech data may be different for speakers. However, we still wish the modeling approach to perform equally well for all speakers. Besides, the modeling technique must be least vulnerable against unseen data. A traditional approach for GMM training is expectation maximization (EM) method, which is known for its overfitting problem and its weakness in handling insufficient training data. To tackle these problems, variational approximation is proposed. Variational approaches are known to be robust against overtraining and data insufficiency. We evaluated the proposed approach on two different databases, namely KING and TFarsdat. The experiments show that the proposed approach improves the performance on TFarsdat and KING databases by 0.56% and 4.81%, respectively. Also, the experiments show that the variationally optimized GMM is more robust against noise and the verification error rate in noisy environments for TFarsdat dataset decreases by 1.52%.

• 한국군사과학기술학회지
• /
• 제25권3호
• /
• pp.300-310
• /
• 2022

A vast amount of labeled data is required for deep neural network training. A typical strategy to improve the performance of a neural network given a training data set is to use data augmentation technique. The goal of this work is to offer a novel image augmentation method for improving object detection accuracy. An object in an image is removed, and a similar object from the training data set is placed in its area. An in-painting algorithm fills the space that is eliminated but not filled by a similar object. Our technique shows at most 2.32 percent improvements on mAP in our testing on a military vehicle dataset using the YOLOv4 object detector.

• ETRI Journal
• /
• 제45권4호
• /
• pp.557-569
• /
• 2023

Traditional deterministic channel modeling is accurate in prediction, but due to its complexity, improving computational efficiency remains a challenge. In an alternative approach, we investigated a multilayer artificial neural network (ANN) to predict large-scale and small-scale channel characteristics in metro tunnels. Simulated high-precision training datasets were obtained by combining measurement campaign with a ray tracing (RT) method in a metro tunnel. Performance on the training data was used to determine the number of hidden layers and neurons of the multilayer ANN. The proposed multilayer ANN performed efficiently (10 s for training; 0.19 ms for prediction), and accurately, with better approximation of the RT data than the single-layer ANN. The root mean square errors (RMSE) of path loss (2.82 dB), root mean square delay spread (0.61 ns), azimuth angle spread (3.06°), and elevation angle spread (1.22°) were impressive. These results demonstrate the superior computing efficiency and model complexity of ANNs.

• 대한전기학회논문지:전력기술부문A
• /
• 제54권7호
• /
• pp.323-327
• /
• 2005

This paper presents an operator training simulator for power system restoration against massive black-out. The system is designed especially focused on the generality and convenient setting up for initial condition of simulation. The former is accomplished by using power flow calculation methodology, and PSS/E data is used to define the initial situation. The proposed simulator consists of three major components - the power flow(PF) module, data conversion(COW) module and GU subsystem. PF module calculates power flow, and then checks overvoltage of buses and overflow of lines. COW module composes an Y-Bus array and a data base at each restoration action. The initial Y-Bus array is constructed from PSS/E data. The user friendly GUI subsystem is developed including graphic editor and built-in operation manual. As a result, the maximum processing time for one step operation is 15 seconds, which is adequate for training purpose. Comparison with PSS/E simulation proves the accuracy and reliability of the training system.

• ETRI Journal
• /
• 제36권3호
• /
• pp.429-438
• /
• 2014

Semantic role labeling (SRL) is a task in natural-language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high-performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state-of-the-art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F-score.

• The Journal of the Acoustical Society of Korea
• /
• 제20권2E호
• /
• pp.17-24
• /
• 2001

In this paper, we propose the bootstrap and aggregating (bagging) vector quantization (VQ) classifier to improve the performance of the text-independent speaker recognition system. This method generates multiple training data sets by resampling the original training data set, constructs the corresponding VQ classifiers, and then integrates the multiple VQ classifiers into a single classifier by voting. The bagging method has been proven to greatly improve the performance of unstable classifiers. Through two different experiments, this paper shows that the VQ classifier is unstable. In one of these experiments, the bias and variance of a VQ classifier are computed with a waveform database. The variance of the VQ classifier is compared with that of the classification and regression tree (CART) classifier[1]. The variance of the VQ classifier is shown to be as large as that of the CART classifier. The other experiment involves speaker recognition. The speaker recognition rates vary significantly by the minor changes in the training data set. The speaker recognition experiments involving a closed set, text-independent and speaker identification are performed with the TIMIT database to compare the performance of the bagging VQ classifier with that of the conventional VQ classifier. The bagging VQ classifier yields improved performance over the conventional VQ classifier. It also outperforms the conventional VQ classifier in small training data set problems.

• 대한음성학회:학술대회논문집
• /
• 대한음성학회 2006년도 춘계 학술대회 발표논문집
• /
• pp.37-40
• /
• 2006

In this paper, we propose a VQ codebook design of speech recognition feature parameters in order to improve the performance of a distributed speech recognition system. For the context-dependent HMMs, a VQ codebook should be correlated with phonetic distributions in the training data for HMMs. Thus, we focus on a selection method of training data based on phonetic distribution instead of using all the training data for an efficient VQ codebook design. From the speech recognition experiments using the Aurora 4 database, the distributed speech recognition system employing a VQ codebook designed by the proposed method reduced the word error rate (WER) by 10% when compared with that using a VQ codebook trained with the whole training data.