• Journal of Information Processing Systems
• /
• 제14권5호
• /
• pp.1075-1086
• /
• 2018

In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small and medium sized databases but they suffer from serious problems when it comes to using them with large databases like those encountered in biometrics applications. To address this issue, we proposed in this work a new training algorithm for PNNs to reduce the hidden layer's size and avoid over-fitting at the same time. The proposed training algorithm generates networks with a smaller hidden layer which contains only representative examples in the training data set. Moreover, adding new classes or samples after training does not require retraining, which is one of the main characteristics of this solution. Results presented in this work show a great improvement both in the processing speed and generalization of the proposed classifier. This improvement is mainly caused by reducing significantly the size of the hidden layer.

• Environmental Engineering Research
• /
• 제14권2호
• /
• pp.102-110
• /
• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• 대한소화기학회지
• /
• 제73권1호
• /
• pp.3-6
• /
• 2019

There is a growing interest in gastroenterology and hepatology fellowship training in Korea and other countries. The Korean Society of Gastroenterology held an international symposium for gastroenterology and hepatology fellowship training, titled, "GI Fellow Training in Asia-Pacific Countries" on April 14, 2018. The Japanese education system was different for each hospital. The American societies for gastroenterology set up their education system together and have continued with frequent modification. The Taiwan and Singapore education systems are well organized and localized. We need a well-organized and sustainable education system for gastroenterology and hepatology fellowship training suitable for Korea.

• 창의정보문화연구
• /
• 제7권3호
• /
• pp.145-155
• /
• 2021

본 연구에서는 SW 교육을 학교에 적용하는 교사들의 SW 역량을 함양하기 위해 초등 교원 SW 쌍방향 교육 연수 프로그램을 개발하고, 학교 연수 현장에 적용하여 그 효과를 분석하였다. 연수 프로그램 개발을 위해 현재 진행되는 SW 교원연수 프로그램을 바탕으로 연수 개발 방향을 설정하고, 코로나-19 상황에서 비대면으로 연수를 진행할 수 있도록 쌍방향 연수 프로그램을 개설하였다. 개발된 연수 프로그램을 경기도 초등 교원 104명을 대상으로 적용하였다. 쌍방향 연수 프로그램의 효과성을 분석하기 위해 교수 효능감과 만족도 조사를 실시하였으며, 교수 효능감과 프로그램 만족도 부분에서 긍정적인 결과를 확인하였다. 앞으로 교원을 대상으로 하는 다양한 SW·AI 교육 연수들이 쌍방향 연수로 진행될 것으로 예상하는 만큼, SW·AI 교육 연수의 효과에 대한 분석 연구의 시행이 필요할 것으로 판단된다.

• Asia pacific journal of information systems
• /
• 제17권4호
• /
• pp.187-206
• /
• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

• 정보처리학회논문지D
• /
• 제8D권2호
• /
• pp.175-180
• /
• 2001

본 논문은 미래의 소프크웨어 공장 수나 고장시간 예측 정확성을 얻기 위해, 뉴로-피지 시스템을 이용할 경우 최적의 검증 데이터 할당 비율에 대한 연구이다. 훈련 데이터가 주어졌을 때, 과소 적합과 과잉 적합을 회피하면서 최적의 일반화 능력을 얻기 취해 Early Stopping 방법이 일반적으로 사용되고 있다. 그러나 훈련과 검증 데이터로 얼마나 많은 데이터를 할당갈 것인가는 시행착오법을 이용해 경험적으로 해를 구해야만 하며, 과다한 시간이 소요된다. 최적의 검증 데이터 양을 구하기 위해 규칙 수를 증가시키면서 다양한 검증 데이터 양을 할당하였다. 실험결과 최소의 검증 데이터로도 좋은 예측 능력을 보였다. 이 결과는 뉴로-퍼지 시스템을 소프트웨어 신뢰성 분야에 적용시 실질직언 지침을 제공할 수 있는 것이다.

• Geomechanics and Engineering
• /
• 제31권1호
• /
• pp.113-127
• /
• 2022

The undrained shear strength of soil is considered one of the engineering parameters of utmost significance in geotechnical design methods. In-situ experiments like cone penetration tests (CPT) have been used in the last several years to estimate the undrained shear strength depending on the characteristics of the soil. Nevertheless, the majority of these techniques rely on correlation presumptions, which may lead to uneven accuracy. This research's general aim is to extend a new united soft computing model, which is a combination of random forest (RF) with grasshopper optimization algorithm (GOA) to the pile set-up parameters' better approximation from CPT, based on two different types of data as inputs. Data type 1 contains pile parameters, and data type 2 consists of soil properties. The contribution of this article is that hybrid GOA - RF for the first time, was suggested to forecast the pile set-up parameter from CPT. In order to do this, CPT data and related bore log data were gathered from 70 various locations across Louisiana. With an R² greater than 0.9098, which denotes the permissible relationship between measured and anticipated values, the results demonstrated that both models perform well in forecasting the set-up parameter. It is comprehensible that, in the training and testing step, the model with data type 2 has finer capability than the model using data type 1, with R² and RMSE are 0.9272 and 0.0305 for the training step and 0.9182 and 0.0415 for the testing step. All in all, the models' results depict that the A parameter could be forecasted with adequate precision from the CPT data with the usage of hybrid GOA - RF models. However, the RF model with soil features as input parameters results in a finer commentary of pile set-up parameters.

• 한국콘텐츠학회논문지
• /
• 제16권2호
• /
• pp.750-761
• /
• 2016

본 연구는 보육실습을 마친 3급 예비보육교사를 대상으로 보육실습에 대한 경험과 경험이 남긴 의미를 알아보고, 이를 통하여 3급 보육교사들의 효율적인 보육실습지도 방향을 제시하고자 실시되었다. 이를 위하여 경기소재 P 보육교사교육원과 S 보육교사교육원에서 교육받고 있는 16명을 대상으로 개인 면담을 실시한 후 질적으로 분석하였다. 연구결과 보육실습은 실습기관 선정의 어려움, 실습준비 부족, 교육과정과 현장의 연계 불일치, 실습의 스트레스, 영유아 지도의 어려움, 현장의 현실을 경험한 것으로 나타났다. 그리고 보육실습의 경험이 남긴 의미는 보육교사로서 자신의 진로 결정 및 가치관을 정립하는데 영향을 끼쳤으며, 현장에서 적용할 수 있는 실천적 지식을 형성할 수 있도록 해주었다. 논의에서는 도출된 주제를 중심으로 보육실습을 체계적으로 운영하기 위한 방안을 제시하였다.

• PNF and Movement
• /
• 제16권1호
• /
• pp.7-17
• /
• 2018

Purpose: The aim of this study was to investigate the effects of coordinative locomotor training in a chronic stroke patient. Methods: A left hemiplegic patient diagnosed with a right middle cerebral artery stroke participated in this research. The patient's functional conditions were assessed, and a coordinative locomotor training program was initiated to resolve the problems identified. A set of movements deemed difficult based on the brief International Classification of Functioning, Disability and Health core set for stroke and d4501 (long-distance walking) were agreed as improvement targets. The program comprised warm up, main, cool-down, and home exercises. Repeated measurements were obtained, as follows: five times at baseline (A), 10 times during the intervention (B), and five times after the intervention (A). The study period was 7 weeks, and the intervention period was 1 h per day, twice a week for 5 weeks. Various tools, including the community walking test (CWT), 10-m walking test (10 MWT), 6-min walking test (6 MWT), and timed up and go (TUG) test, were conducted to assess the patient's walking ability. Changes in functional domains before and after the ICF Qualifier were compared. The mean values of the descriptive statistics were calculated, and a visual analysis using graphs was used to compare the rates of change. Results: The results showed that the CWT, 10 MWT, 6 MWT, and TUG test scores during the intervention period improved and that this improvement remained, even during the baseline period. In addition, the ICF Qualifier before and after the comparison decreased from moderate to mild. Conclusion: Based on the results, we propose that coordinative locomotor training can have positive effects on community ambulation of chronic stroke patients.

• Korean Journal of Acupuncture
• /
• 제29권1호
• /
• pp.57-70
• /
• 2012

Objectives : The purpose of this study is to assess Traditional Korean Medicine (TKM) doctors' awareness and utilization of the case report, based on the opinion that careful observation on the case sometimes provides us with the important information as clinical trial especially in the traditional medicine research field. Methods : A questionnaire study was conducted among TKM doctors who participated in the annual continuous maintenance education (CME) held at 5 regions of the Republic of Korea. Results : Almost 60% of the respondents had read case reports published in medical journals and 67% had openly shared their clinical cases with their colleagues. Of the respondents, 18.6% had been educated on reporting cases, and only 16% had the experience of reporting cases on one's own. However, 32.6% of the respondents had the intention to report cases in the future. These results show significant differences between general physicians who holds a license but no hospital training experience and board certified TKM doctors who have training experience. Conclusions : A majority of TKM doctors have read case reports but holds little experience of having been properly trained. Through this research, it has been found that awareness of case reports is rising in hospital training. Thus, the objective of case report education for TKM doctors who have hospital training experience should be set on encouraging them to do more whereas for doctors without hospital training experience, the objective should be set on making them more exposed to case reports to heighten one's awareness.