• Nuclear Engineering and Technology
• /
• v.50 no.5
• /
• pp.738-744
• /
• 2018

Fuel failure due to debris is a major cause of failure in pressurized water reactors. Fuel vendors have developed various filtering devices to reduce debris-induced failure and have evaluated filtering performance with their own test facilities and methods. Because of the different test facilities and methods, it is difficult to compare filtering performances objectively. This study presents an improved filtering test and an efficiency calculation method to fairly compare fuel-filtering efficiency regardless of the vendor's filtering features. To enhance the reliability of our evaluation, we established requirements for the test method and had a facility constructed according to the requirements. This article describes the debris specimens, the amount of debris, and the replicates for the proposed test method. A calculation method of comprehensive debris-filtering efficiency using a weighted mean is proposed. The test method was verified by repeated tests, and the tests were carried out using the PLUS7 and 17ACE7 test fuels to calculate the comprehensive debris-filtering efficiencies. The evaluation results revealed that the filtering performance of PLUS7 is better than that of 17ACE7. The proposed method can be used on any kind of debris-filtering devices and is appropriate for use as a standard.

• The Journal of Bigdata
• /
• v.7 no.1
• /
• pp.187-192
• /
• 2022

Many schools are trying to improve students' competencies through many subjects and non-curricular activities, each students has different goals and different activities to prepare for employment. Accordingly, it is difficult to determine whether the programs offered in a comprehensive and comprehensive manner in the existing subject and non-curricular subjects systems are actually suitable for students, so it is necessary to introduce a personalized system. In this study, a method was proposed to classify non-departmental subjects that are uniformly provided to all students of Chungbuk National University by grade level and department. In addition, three types of collaborative filtering models are implemented using the evaluation score of students who participated in the non-curricular program, and personalized recommendations are proposed with the most accurate model by comparing performance.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.4
• /
• pp.83-90
• /
• 2012

In e-commerce systems that deal with a large number of items, the function of personalized recommendation is essential. Collaborative filtering that is a successful recommendation algorithm, suffers from the sparsity, cold-start, and scalability restrictions. Additionally, this work raises a new flaw of the algorithm, inconsistent performance of recommendation. This is also not measurable by the current MAE-based evaluation that does not consider the deviation of prediction error, and furthermore is performed independently of precision and recall measurement. To evaluate the collaborative filtering comprehensively, this work proposes an extended evaluation model that includes the current criteria such as MAE, Precision, Recall, deviation, and applies it to cluster-based combined collaborative filtering.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.6
• /
• pp.2310-2332
• /
• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.5
• /
• pp.1260-1272
• /
• 2024

This paper tackles the prevalent challenges faced by existing tourism route recommendation methods, including data sparsity, cold start, and low accuracy. To address these issues, a novel intelligent tourism route recommendation method based on collaborative filtering is introduced. The proposed method incorporates a series of key steps. Firstly, it calculates the interest level of users by analyzing the item attribute rating values. By leveraging this information, the method can effectively capture the preferences and interests of users. Additionally, a user attribute rating matrix is constructed by extracting implicit user behavior preferences, providing a comprehensive understanding of user preferences. Recognizing that user interests can evolve over time, a weight function is introduced to account for the possibility of interest shifting during product use. This weight function enhances the accuracy of recommendations by adapting to the changing preferences of users, improving the overall quality of the suggested tourism routes. The results demonstrate the significant advantages of the approach. Specifically, the proposed method successfully alleviates the problem of data sparsity, enhances neighbor selection, and generates tourism route recommendations that exhibit higher accuracy compared to existing methods.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.41 no.2
• /
• pp.149-156
• /
• 2017

Nuclear fuel requires high reliability and safety and therefore contains debris filtering devices to prevent failure-inducing debris from entering it. The debris filtering performance of nuclear fuel is one of the most important factors for fuel integrity. Therefore, the performance must be evaluated and the measurement must be reasonable. In this study, a calculation method of the comprehensive filtering efficiency using the weighted mean was proposed to establish a standard filtering efficiency index. To confirm the suitability of the proposed method, representative debris specimens were selected and the filtering efficiency with the weighted mean was compared with the efficiency of the arithmetic mean. The weighting factor of the weighted mean was introduced to produce a fair evaluation. In addition, the analysis of the debris filtering mechanism was performed according to the size of debris specimens, and the main dimensions of the filtering feature for commercial nuclear fuel.

• Computers and Concrete
• /
• v.23 no.6
• /
• pp.445-457
• /
• 2019

Cracks are an important distress of concrete bridges, and may reduce the life and safety of bridges. However, the traditional manual crack detection means highly depend on the experience of inspectors. Furthermore, it is time-consuming, expensive, and often unsafe when inaccessible position of bridge is to be assessed, such as viaduct pier. To solve this question, the real-time automatic crack detecting system with unmanned aerial vehicle (UAV) become a choice. This paper designs a new automatic detection system based on real-time comprehensive image processing for bridge crack. It has small size, light weight, low power consumption and can be carried on a small UAV for real-time data acquisition and processing. The real-time comprehensive image processing algorithm used in this detection system combines the advantage of connected domain area, shape extremum, morphology and support vector data description (SVDD). The performance and validity of the proposed algorithm and system are verified. Compared with other detection method, the proposed system can effectively detect cracks with high detection accuracy and high speed. The designed system in this paper is suitable for practical engineering applications.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.1
• /
• pp.39-48
• /
• 2012

Filtering process that separates ground and non-ground points from LIDAR data is important in order to create the digital elevation model (DEM) or extract objects on the ground. The purpose of this research is to select the most effective filtering algorithm through qualitative and quantitative analysis for the existing filtering method used to extract ground points from LIDAR data. For this, four filtering methods including Adaptive TIN(ATIN), Perspective Center-based filtering method(PC), Elevation Threshold with Expand Window(ETEW) and Progressive Morphology(PM) were applied to mountain area, urban area and the area where building and mountains exist together. Then the characteristics for each method were analyzed. For the qualitative comparison of four filtering methods used for the research, visual method was applied after creating shaded relief image. For the quantitative comparison, an absolute comparison was conducted by using control points observed by GPS and a relative comparison was conducted by the digital elevation model of the National Geographic Information Institute. Through the filtering experiment of the LIDAR data, the Adaptive TIN algorithm extracted the ground points in mountain area and urban area most effectively. In the area where buildings and mountains coexist, progressive morphology algorithm generated the best result. In addition, as a result of qualitative and quantitative comparisons, the applicable filtering algorithm regardless of topographic characteristics appeared to be ATIN algorithm.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.18 no.2
• /
• pp.143-156
• /
• 2014

This paper aims to give a quick view on denoising without comprehensive details. Denoising can be understood as removing unwanted parts in signals and images. Noise incorporates intrinsic random fluctuations in the data. Since noise is ubiquitous, denoising methods and models are diverse. Starting from what noise means, we briefly discuss a denoising model as maximum a posteriori estimation and relate it with a variational form or energy model. After that we present a few major branches in image and signal processing; filtering, shrinkage or thresholding, regularization and data adapted methods, although it may not be a general way of classifying denoising methods.

• BMB Reports
• /
• v.45 no.2
• /
• pp.120-125
• /
• 2012

We have developed a biologist-friendly, Java GUI application (GoBean) for GO term enrichment analysis. It was designed to be a comprehensive and flexible GUI tool for GO term enrichment analysis, combining the merits of other programs and incorporating extensive graphic exploration of enrichment results. An intuitive user interface with multiple panels allows for extensive visual scrutiny of analysis results. The program includes many essential and useful features, such as enrichment analysis algorithms, multiple test correction methods, and versatile filtering of enriched GO terms for more focused analyses. A unique graphic interface reflecting the GO tree structure was devised to facilitate comparisons of multiple GO analysis results, which can provide valuable insights for biological interpretation. Additional features to enhance user convenience include built in ID conversion, evidence code-based gene-GO association filtering, set operations of gene lists and enriched GO terms, and user -provided data files. It is available at http://neon.gachon.ac.kr/GoBean/.