• Transactions of Materials Processing
• /
• v.19 no.1
• /
• pp.11-16
• /
• 2010

The influence of pre-strain on low cycle fatigue behavior of Fe-18Mn-0.05Al-0.6C TWIP steel was studied by conducting axial strain-controlled tests. As-received plates were deformed by rolling with reduction ratios of 10 and 30%, respectively. A triangular waveform with a constant frequency of 1 Hz was employed for low cycle fatigue test at the total strain amplitudes in the range of ${\pm}0.4\;{\sim}\;{\pm}0.6$ pct. The results showed that low-cycle fatigue life was strongly dependent on the amount of pre-strain as well as the strain amplitude. Increasing the amount of prestrain, the number of reversals to failure was significantly decreased at high strain amplitudes, but the effect was negligible at low strain amplitudes. A new model for predicting fatigue life of pre-strained body has been suggested by adding ${\Delta}E_{pre-strain}$ to the energy-based fatigue damage parameter. Also, high-cycle fatigue lives predicted using the low-cycle fatigue data well agreed with the experimental ones.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.14 no.1
• /
• pp.26-33
• /
• 2001

The proposed system was composed of pre-processor which was executing binary/high-pass filtering and post-processor which ranged from statistic data to prediction. In post-processor work, step one was filter process of image, step two was image recognition, and step three was destruction degree/time prediction. After these processing, we could predict image of the last destruction timestamp. This research was produced variation value according to growth of tree pattern. This result showed improved correction, when this research was applied image Processing. Pre-processing step of original image had good result binary work after high pas- filter execution. In the case of using partial discharge of the image, our research could predict the last destruction timestamp. By means of experimental data, this prediction system was acquired $\pm$3.2% error range.

• The Korean Journal of Emergency Medical Services
• /
• v.26 no.2
• /
• pp.7-18
• /
• 2022

Purpose: This study aimed to develop a pre-hospital subarachnoid hemorrhage (SAH) prediction tool by analyzing the extant predictive factors of patients with non-traumatic SAH who visited the hospital through the 119 emergency medical services. Methods: We retrospectively reviewed pre-hospital care reports (PCRs) and electronic medical records (EMRs) of 103 patients with non-traumatic SAH who were transported to the emergency department of two national hospitals via the 119 emergency medical service from January 1, 2017 to December 31, 2020. Variables required to apply the Ottawa SAH Rule and EMERALD SAH Rule, which are early prediction tools for SAH, were extracted and applied. Results: The most common symptoms-which were found in 94.1% and 97.0% of all patients according to PCRs and EMRs, respectively-appeared in the following order: headache, altered state of consciousness, and nausea/vomiting. When the variables used for the EMERALD Rule, namely systolic blood pressure (SBP), diastolic blood pressure (DBP), and blood sugar test (BST), were applied, the sensitivities of EMR and PCRs were 99.9% and 92.2%, respectively. Conclusion: For the timely prediction of SAH at the pre-hospital phase, patient age and symptoms should be assessed, and SBP, DBP, and BST should be measured to transport the patient to an appropriate hospital.

• Asia pacific journal of information systems
• /
• v.17 no.4
• /
• 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.

• Journal of Korean Society of Water and Wastewater
• /
• v.21 no.5
• /
• pp.601-607
• /
• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2001.07a
• /
• pp.319-322
• /
• 2001

The proposed system was composed of pre-processor which was executing binary/high-pass filtering and post-processor which ranged from statistic data to prediction. In post-processor work, step one was filter process of image, step two was image recognition, and step three was destruction degree/time prediction. After these processing, we could predict image of the last destruction timestamp. This research was produced variation value according to growth of tree pattern. This result showed improved correction, when this research was applied image Processing. Pre-processing step of original image had good result binary work after high pass- filter execution. In the case of using partial discharge of the image, our research could predict the last destruction timestamp. By means of experimental data, this Prediction system was acquired ${\pm}$3.2% error range.

• Transactions of Materials Processing
• /
• v.19 no.6
• /
• pp.344-351
• /
• 2010

This paper presents a precision on-line model for the prediction of the roll force and tension distributions across the strip in hot strip rolling. The approach is based on an approximate 3-D theory of rolling, and in particular, considers the effect of pre-deformation of the strip, which occurs near the roll entrance before the strip enters the bite zone. The prediction accuracy of the proposed model is examined through comparison with the predictions from the 3-D finite element models.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2011.06a
• /
• pp.1309-1310
• /
• 2011

• Journal of Broadcast Engineering
• /
• v.23 no.1
• /
• pp.162-165
• /
• 2018

In this paper, we propose a pre-processing procedure to obtain scene change information using spatial down-scaled input image for efficient encoding of super-high resolution image and propose a reconstruction of reference picture list in inter-picture prediction using this information. The experimental results show that the proposed method improves the BD-Rate by 0.44% and reduces encoding time by 12.46% when compared to HM 16.12.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.177-184
• /
• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.