• Journal of Korean Society for Quality Management
• /
• v.30 no.4
• /
• pp.15-25
• /
• 2002

This paper considers supplier selection process for e-business & ISO 9001 quality management system environments. Determining suitable suppliers in the electronic commerce has become a key strategic consideration. However, the nature of these decisions is usually complex and unstructured. In this paper, a Quality Estimated Supplier Selection (QESS) model is proposed to deal with the supplier selection problems in the e-business(Business to Business: B to B). In the supplier selection, quality management factors will be considered for the first time, and then price, and delivery etc. In the first level, we deal with the quality management factors such as quality management audit, product test, engineering man-power, capability index and training time etc., based on the five point scale. In the second level, a QESS model determines the final solution by considering factors such as price, production lead-time and delivery time.

• Journal of Convergence for Information Technology
• /
• v.12 no.4
• /
• pp.260-269
• /
• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.

• Journal of the Korean Society of Earth Science Education
• /
• v.12 no.1
• /
• pp.82-93
• /
• 2019

The purpose of this study is to analyze the results of satisfaction for learning eco-system on the teacher training program conducted at the IS-Geo (International School for Geoscience Resources) which is KIGAM (Korea Institute of Geoscience and Mineral Resources), and to determine the satisfaction and educational effects of the teacher training programs on elementary and secondary teachers. And then, to suggest improvement points in the future operation of the teacher training program at the IS-Geo. Therefore, we conducted questionnaire of satisfaction for learning eco-system based on the data collected by a survey of 98 elementary and secondary teachers who participated in the teacher training program at the IS-Geo, from July 2017 to August 2018. The research results are as follows. First, the results of satisfaction for learning eco-system showed high values of 4.58 or higher in both the elementary and secondary programs, and the teacher training program conducted by the IS-Geo had a positive effect on the training participants. Second, internal factors indicating learning motivation and learning development were elementary teacher training 4.70 and secondary teacher training 4.64, and it is necessary to develop training contents and programs by classifying them into majors other than the earth science department. Third, intermediate factors indicating contents of education and learning curriculum were 4.67 for an elementary teacher training program and 4.72 for secondary teacher training program. In addition, in order to operate the teacher training program according to the purpose of science and technology culture, it is necessary to develop a teaching-learning model and to improve the quality of teaching. Fourth, external factors indicating learner support and quality of instructors were 4.83 for an elementary teacher training program and 4.72 for a secondary teacher training program. In particular, it is necessary to develop teaching materials that can be used immediately in school classes and can generate interest.

• Electronics and Telecommunications Trends
• /
• v.35 no.1
• /
• pp.60-70
• /
• 2020

Artificial intelligence (AI) is expected to bring about a wide range of changes in the industry, based on the assessment that it is the most innovative technology in the last three decades. The manufacturing field is an area in which various artificial intelligence technologies are being applied, and through accumulated data analysis, an optimal operation method can be presented to improve the productivity of manufacturing processes. In addition, AI technologies are being used throughout all areas of manufacturing, including product design, engineering, improvement of working environments, detection of anomalies in facilities, and quality control. This makes it possible to easily design and engineer products with a fast pace and provides an efficient working and training environment for workers. Also, abnormal situations related to quality deterioration can be identified, and autonomous operation of facilities without human intervention is made possible. In this paper, AI technologies used in smart factories, such as the trends in generative product design, smart workbench and real-sense interaction guide technology for work and training, anomaly detection technology for quality control, and intelligent manufacturing facility technology for autonomous production, are analyzed.

• International Journal of Industrial Entomology and Biomaterials
• /
• v.8 no.1
• /
• pp.1-15
• /
• 2004

Due to recent scientific innovations in mulberry cultivation, leaf yield has been increased manifold. However, with successive leaf harvest, a quantum drop in leaf yield and quality has been noted. This inturn has affected the silkworm rearing and farmers suffered by the frequent crop failures. This is mainly due to nutrient deficiencies in mulberry leaf. Moreover, no complete information is available about hunger signs of nutritional disorders, analytical diagnosis and critical levels of nutrients required. The present paper, thus, may serve as an important nutrient guide for identification of hunger signs, leaf nutrients status under deficiency and critical levels of the elements namely N, P, K, Ca, Mg, S, B, Cu, Fe, Mn and Zn requirements for higher leaf yield and quality. The leaf nutrient status provided may help chemist for correcting the soil status. Besides, an integration of mulberry intercropping with legumes and applications of neem and castor oil cakes, VA-mycorrhizal inoculation, biofertilizer and vermicompost are suggested as integrated nutrient management for sustainable sericulture industry. Based on the information described in this paper, a model needs to be framed for maintaining continuous supply of nutrients to obtain desired quantity and quality of mulberry leaf for successful silkworm cocoon crop and increasing overall silk productivity.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.5
• /
• pp.2539-2554
• /
• 2017

Regression-based image super resolution (SR) methods have shown great advantage in time consumption while maintaining similar or improved quality performance compared to other learning-based methods. In this paper, we propose a novel single image SR method based on hierarchical regression to further improve the quality performance. As an improvement to other regression-based methods, we introduce a hierarchical scheme into the process of learning multiple regressors. First, training samples are grouped into different clusters according to their geometry similarity, which generates the structure layer. Then in each cluster, a compact dictionary can be learned by Sparse Coding (SC) method and the training samples can be further grouped by dictionary atoms to form the detail layer. Last, a series of projection matrixes, which anchored to dictionary atoms, can be learned by linear regression. Experiment results show that hierarchical scheme can lead to regression that is more precise. Our method achieves superior high quality results compared with several state-of-the-art methods.

• Journal of Information Technology Services
• /
• v.11 no.1
• /
• pp.211-221
• /
• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• Journal of radiological science and technology
• /
• v.43 no.6
• /
• pp.461-467
• /
• 2020

In the field of nuclear medicine, errors are sometimes generated because the assessment of the uniformity of gamma cameras relies on the naked eye of the evaluator. To minimize these errors, we created an artificial intelligence model based on CNN algorithm and wanted to assess its usefulness. We produced 20,000 normal images and partial cold region images using Python, and conducted artificial intelligence training with Resnet18 models. The training results showed that accuracy, specificity and sensitivity were 95.01%, 92.30%, and 97.73%, respectively. According to the results of the evaluation of the confusion matrix of artificial intelligence and expert groups, artificial intelligence was accuracy, specificity and sensitivity of 94.00%, 91.50%, and 96.80%, respectively, and expert groups was accuracy, specificity and sensitivity of 69.00%, 64.00%, and 74.00%, respectively. The results showed that artificial intelligence was better than expert groups. In addition, by checking together with the radiological technologist and AI, errors that may occur during the quality control process can be reduced, providing a better examination environment for patients, providing convenience to radiologists, and improving work efficiency.

• Quality Improvement in Health Care
• /
• v.29 no.2
• /
• pp.64-84
• /
• 2023

Purpose:Plaster splints are routinely performed in the Emergency Department (ED) and avoidable complications such as skin ulcerations and fracture instability arise mainly due to improper techniques. Despite its frequent use, there is often no formal training on the fundamental principles of plaster splint application for a medical officer rotating through ED. We aim to use Quality Improvement (QI) methodology to reduce number of incorrect plaster splint application to improve overall patient care via a bundled educational solution. Methods: We initiated a QI program implementing concepts derived from the Institute for Healthcare Improvement models, including Plan-Do-Study-Act (PDSA) cycles, to decrease the rate of incorrect plaster splint application. A bundled education solution consisting of three sequential interventions (practical teaching session, online video lecture and quick reference cards) were formulated to specifically target critical factors that had been identified as the cause of incorrect plaster splints in ED. Results: With the QI intervention, our overall rate of incorrect plaster splints was reduced from 84.1% to 68.6% over a 6-month period. Conclusion: Following the QI project implementation of the bundled educational solution, there has been a sustained reduction in incorrect plaster splints application. The continuation of the training program also ensures the sustainability of our efforts in ED.

• Journal of Korea Water Resources Association
• /
• v.34 no.6
• /
• pp.701-711
• /
• 2001

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Gongju station in Geum River. This is done by forecasting monthly water qualities such as DO, BOD, and TN, and comparing with those obtained by ARIMA model. The neural network models of this study use BP(Back Propagation) algorithm for training. In order to improve the performance of the training, the models are tested in three different styles ; MANN model which uses the Moment-Adaptive learning rate method, LMNN model which uses the Levenberg-Marquardt method, and MNN model which separates the hidden layers for judgement factors from the hidden layers for water quality data. the results show that the forecasted water qualities are reasonably close to the observed data. And the MNN model shows the best results among the three models tested