• The Research Journal of the Costume Culture
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• v.19 no.3
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• pp.612-621
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• 2011

The process of making or cultivating indigo dyes is very cumbersome and complex. The dye extraction and dyeing methods using general plant dye, moth repellent dye, fast acting natural dye, and other dyes are very different. This research investigates the extraction of indigo dye and liquid dye extraction of polygonum(indigo) plants using calcium oxide water. While extracting indigo dye the concentration of purified indigo dye may be controlled by adjusting the pH level. Due to the various uses of dyes the adjustment of surface color must be considered. In regard to the change according to different concentrations of reducing agents, it was found that cotton fabrics and ramie fabrics show the highest color difference at 0.4% and 0.3% respectively. As the reduction temperature increases, the color difference increases as well. The maximum color difference was found to appear at $90^{\circ}C$. Cotton fabrics and ramie fabrics showed 70.55 and 67.01 respectively. The color difference increases as the concentration of dyes increases, but at a concentration of 300%, cotton fabrics was found to show 6.22PB in H value using the Munsell color system, containing purple and blue color. The pH of the polygonum dyes extracted through this experiment were adjusted by adding calcium oxide to the experimental water, without directly adding calcium oxide to the liquid polygonum extract. In a refine state, it was mixed with polygonum extract to extract a more refine and highly concentrated indigo dye. When lye and reducing agents are added to extracted indigo dye and sealed for long-term storage, it can be effective and easily used for dyeing.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.504-511
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• 2010

In this paper, we propose the face region recognition system for City-Security CCTV(Closed Circuit Television) using hippocampal neural network which is modelling of human brain's hippocampus. This system is composed of feature extraction, learning and recognition part. The feature extraction part is constructed using PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis). In the learning part, it can label the features of the image-data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in a dentate gyrus and remove the noise through the auto-associative memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are shape change and light change. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.322-327
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• 2017

The classification problem in the field of Natural Language Processing has been studied for a long time. Continuing forward with our previous research, which classifies large-scale text using Convolutional Neural Networks (CNN), we implemented Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM) and Gated Recurrent Units (GRU). The experiment's result revealed that the performance of classification algorithms was Multinomial Naïve Bayesian Classifier < Support Vector Machine (SVM) < LSTM < CNN < GRU, in order. The result can be interpreted as follows: First, the result of CNN was better than LSTM. Therefore, the text classification problem might be related more to feature extraction problem than to natural language understanding problems. Second, judging from the results the GRU showed better performance in feature extraction than LSTM. Finally, the result that the GRU was better than CNN implies that text classification algorithms should consider feature extraction and sequential information. We presented the results of fine-tuning in deep neural networks to provide some intuition regard natural language processing to future researchers.

• Journal of Korean Library and Information Science Society
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• v.36 no.2
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• pp.327-348
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• 2005

The purpose of this study is to emphasize the importance of index definition and to prepare the basis of optimal index in bibliographic retrieval system. For the purpose, this research studied a index extraction theory on index tag definition and index normalization from the bibliographic marc database and analyzed a retrieval utilization rate of extracted index. In this experiment, we divided index between text-type and code-type about the generated 29,219,853 indexes from 2,200,488 bibliographic records and analyzed utilization rate by the comparison of index-type and index term of web logs. According to the result, the text-type indexes such as title, author, publication, subject are showed high utilization rate while the code-type indexes were showed low utilization rate. So this study suggests that the unused index is removed from index definition to optimize index.

• Journal of Korean Library and Information Science Society
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• v.47 no.4
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• pp.289-307
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• 2016

This paper introduces a software system and process model for constructing domain-specific relation extraction datasets semi-automatically. The system uses a set of terms such as genes, proteins diseases and so forth as inputs and then by exploiting massive biological interaction database, generates a set of term pairs which are utilized as queries for retrieving sentences containing the pairs from scientific databases. To assess the usefulness of the proposed system, this paper applies it into constructing a genic interaction dataset related to Alzheimer's disease domain, which extracts 3,510 interaction-related sentences by using 140 gene names in the area. In conclusion, the resulting outputs of the case study performed in this paper indicate the fact that the system and process could highly boost the efficiency of the dataset construction in various subfields of biomedical research.

• Restorative Dentistry and Endodontics
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• v.36 no.1
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• pp.12-18
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• 2011

Objectives: This retrospective study evaluated the therapeutic effects of the intentional replantation (IR) procedure performed on the maxillary and mandibular molars of 35 patients. Materials and Methods: For the subjects, IR was performed due to difficulties in anatomically accessing the lesions and/or close proximity to the thick cortical bone, inferior alveolar nerve, or maxillary sinus, which rendered the ordinary periradicular surgery impossible. The patients'progress was followed for a year and up to 2 years and 4 months. The success of the procedure was evaluated in terms of clinical and radiographic success (%). Results: The results revealed the following: (a) 1 case (3%) of failed tooth extraction during IR; (b) 2 cases (6%) of extraction due to periodontal diseases and inflammatory root resorption; (c) 3 cases (9%) of normally functioning teeth in the oral cavity with minor mobility and apical root resorption, and; (d) 29 cases (82%) of normally functioning teeth without obvious problems. Conclusions: IR was confirmed to be a reliably repeatable, predictable treatment option for those who cannot receive conventional periradicular surgery because of anatomic limitations or patient factors.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.636-649
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• 2005

Field-associated Terms itself have field Information. So, they determine field of document just like when human being perceives field. In case of Korean, we organized and experimented them by collecting approximately IS,999 document banks that are classified into 180 fields. We obtained high precision of extraction that 88,782 single field-associated terms are contracted into 8,405 ones thus recording compression rate as approximately 9$\%$ and recall as above 0.77 (average 0.85), precision as above 0.90 (average 0.94). By applying established field-associated terms to initial determination for document classification and comparing it with filed determination by human being, we got correct answers above approximately 90$\%$. We can use results of research as fundamental research for initial stage and apply it document retrieval between multilingual environment thus utilizing it as fundamental research for multilingual information retrieval.

• Journal of Periodontal and Implant Science
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• v.51 no.2
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• pp.135-143
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• 2021

Purpose: To investigate factors affecting the antagonistic and adjacent teeth in patients after implant restoration and prosthetic rehabilitation. Methods: In total, 160 patients who visited Kyungpook National University Dental Hospital for implant surgery, prosthesis placement, and supportive periodontal therapy (SPT) were included in this study. The average follow-up period was 88.06 months, and the maximum was 175 months. Patients' history of smoking, diabetes, hypertension, and osteoporosis was investigated, and panoramic radiographs were taken after surgery and prosthetic treatment. During the follow-up period, extraction and prosthetic/endodontic treatments of the antagonistic and adjacent teeth were analyzed. The statistical analyses were performed using descriptive statistics, the chi-square test, the Fisher exact test, and multiple logistic regression analyses. Results: Treatment was performed on 29.4% of the studied antagonistic teeth with extraction performed in 20.0% and prosthetic treatment in 10.0%. Furthermore, 19.4% of the studied adjacent teeth underwent treatment, of which extraction was performed in 12.5% and prosthetic treatment in 7.5%. The treatment rate for adjacent teeth was 25.3% in smokers, which was higher than that of non-smokers (12.3%) (P=0.039). Patients who were non-adherent to SPT showed a significantly higher rate (19.6%) of antagonistic prosthetic treatment than did those who were adherent (5.5%) (P=0.006). Conclusions: Implant restoration can affect the adjacent and antagonistic teeth. Smoking, osteoporosis history, and absence of SPT may be risk factors for the treatment of the adjacent and antagonistic teeth.

• Nuclear Engineering and Technology
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• v.56 no.9
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• pp.3740-3749
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• 2024

To reduce the losses caused by aging failure of insulation gate bipolar transistor (IGBT), which is the core components of nuclear power plant rod position indicating and rod control (RPC) system. It is necessary to conduct studies on its life prediction. The selection of IGBT failure characteristic parameters in existing research relies heavily on failure principles and expert experience. Moreover, the analysis and learning of time-domain degradation data have not been fully conducted, resulting in low prediction efficiency as the monotonicity, time correlation, and poor anti-interference ability of extracted degradation features. This paper utilizes the advantages of the stacked denoising autoencoder(SDAE) network in adaptive feature extraction and denoising capabilities to perform adaptive feature extraction on IGBT time-domain degradation data; establishes a long-short-term memory (LSTM) prediction model, and optimizes the learning rate, number of nodes in the hidden layer, and number of hidden layers using the Gray Wolf Optimization (GWO) algorithm; conducts verification experiments on the IGBT accelerated aging dataset provided by NASA PCoE Research Center, and selects performance evaluation indicators to compare and analyze the prediction results of the SDAE-LSTM model, PSOLSTM model, and BP model. The results show that the SDAE-LSTM model can achieve more accurate and stable IGBT life prediction.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.1-8
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• 2008

The belief propagation method that has been studied recently yields good performance in disparity extraction. The method in which a target function is modeled as an energy function based on Markov random field(MRF), solves the stereo matching problem by finding the disparity to minimize the energy function. MRF models provide robust and unified framework for vision problem such as stereo and image restoration. the belief propagation method produces quite correct results, but it has difficulty in real time implementation because of higher computational complexity than other stereo methods. To relieve this problem, in this paper, we propose a fast algorithm of the belief propagation method. Energy function consists of a data term and a smoothness tern. The data term usually corresponds to the difference in brightness between correspondences, and smoothness term indicates the continuity of adjacent pixels. Smoothness information is created from messages, which are assigned using four different message arrays for the pixel positions adjacent in four directions. The processing time for four message arrays dominates 80 percent of the whole program execution time. In the proposed method, we propose an algorithm that dramatically reduces the processing time require in message calculation, since the message.; are not produced in four arrays but in a single array. Tn the last step of disparity extraction process, the messages are called in the single integrated array and this algorithm requires 1/4 computational complexity of the conventional method. Our method is evaluated by comparing the disparity error rates of our method and the conventional method. Experimental results show that the proposed method remarkably reduces the execution time while it rarely increases disparity error.