• The Pure and Applied Mathematics
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• v.2 no.2
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• pp.157-162
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• 1995

Let I be the interval, $S^1$ the circle and let X be a compact metric space. And let $C^{circ}(X,\;X)$ denote the set of continuous maps from X into itself. For any f$f\in\;C\circ(X,\;X),\;let\;P(f),\;R(f),\;\Gamma(f),\;\Lambda(f)\;and\;\Omega(f)$ denote the collection of the periodic points, recurrent points, ${\gamma}-limit{\;}points,{\;}{\omega}-limit$ points and nonwandering points, respectively.(omitted)

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.212-214
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• 2000

The distance relay technique used for transmission line protection operates overreach and underreach to the self protection region because the power system becomes complex and fault conditions are different. To solve these problems, this paper describes new technique to set the reliable self protection lesion. The trip region of the quadrilateral distance relay is set by training of multi layer recurrent elman network. The proposed network is able to reach the trip zone for the fault impedance, fault initial angle and source impedance variance correctly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.174-176
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• 2022

In this paper, we present a mobile application for 'personalized customized learning' for children with articulation disorders using an artificial intelligence (AI) algorithm. A dataset (Data Set) to analyze, judge, and predict the learner's articulation situation and degree. In particular, we designed a prototype model by looking at how AI can be improved and advanced compared to existing applications from the UX/UI (GUI) aspect. So far, the focus has been on visual experience, but now it is an important time to process data and provide a UX/UI (GUI) experience to users. The UX/UI (GUI) of the proposed mobile application was to be provided according to the learner's articulation level and situation by using CRNN (Convolution Recurrent Neural Network) of DeepLearning and Auto Encoder GPT-3 (Generative Pretrained Transformer). The use of artificial intelligence algorithms will provide a learning environment with a high degree of perfection to children with articulation disorders, thereby enhancing the learning effect. I hope that you do not have any fear or discomfort in conversation by improving the perfection of articulation with 'personalized and customized learning'.

• Radiation Oncology Journal
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• v.35 no.2
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• pp.144-152
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• 2017

Purpose: The role of radiotherapy (RT) was largely deserted after the introduction of platinum-based chemotherapy, but still survival rates are disappointingly low. This study focuses on assessing the clinical efficacy of RT in relation to chemotherapy resistance. Materials and Methods: From October 2002 to January 2015, 44 patients were diagnosed with epithelial ovarian cancer (EOC) and treated with palliative RT for persistent or recurrent EOC. All patients received initial treatment with optimal debulking surgery and adjuvant platinum-based chemotherapy. The biologically effective dose (BED) was calculated with ${\alpha}/{\beta}$ set at 10. Ninety-four sites were treated with RT with a median BED of 50.7 Gy (range 28.0 to 79.2 Gy). The primary end-point was the in-field local control (LC) interval, defined as the time interval from the date RT was completed to the date any progressive or newly recurring disease within the RT field was detected on radiographic imaging. Results: The median follow-up duration was 52.3 months (range 7.7 to 179.0 months). The 1-year and 2-year in-field LC rates were 66.0% and 55.0%, respectively. Comparisons of percent change of in-field tumor response showed similar distribution of responses among chemoresistant and chemosensitive tumors. On multivariate analysis of predictive factors for in-field LC analyzed by sites treated, $BED{\geq}50Gy$ (hazard ratio, 0.4; confidence interval, 0.2-0.9; p = 0.025) showed better outcomes. Conclusion: Regardless of resistance to platinum-based chemotherapy, RT can be a feasible treatment modality for patients with persistent of recurrent EOC. The specific role of RT using updated approaches needs to be reassessed.

• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.57-64
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• 2002

Few studies have been systematically pursued on a multimedia data mining in despite of the overwhelming amounts of multimedia data by the development of computer capacity, storage technology and Internet. Based on the preliminary image processing and content-based image retrieval technology, this paper presents the methods for discovering association rules from recurrent items with spatial relationships in huge data repositories. Furthermore, multimedia mining algorithm is proposed to find implicit association rules among objects of which content-based descriptors such as color, texture, shape and etc. are recurrent and of which descriptors have spatial relationships. The algorithm with recurrent items in images shows high efficiency to find set of frequent items as compared to the Apriori algorithm. The multimedia association-rules algorithm is specially effective when the collection of images is homogeneous and it can be applied to many multimedia-related application fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3399-3419
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• 2020

In the Vehicular Ad-hoc Network (VANET), the data transmission of time-sensitive applications requires low latency, such as accident warnings, driving guidance, etc. However, frequent changes of topology in VANET will result in data transmission failures. In order to improve the efficiency of VANETs data transmission and increase the timeliness of data, this paper proposes a relay scheme based on Recurrent Neural Network (RNN) trajectory prediction, which can be used to select the optimal relay vehicle to transmit data. The proposed scheme learns vehicle trajectory in a distributed manner and calculates the predicted trajectory, and then the optimal vehicle can be selected to complete the data transmission, which ensures the timeliness of the data. Finally, we carry out a set of simulations to demonstrate the performance of the algorithm. Simulation results show that the proposed scheme enhances the timeliness of the data and the accuracy of the predicted driving trajectory.

• Korean Journal of Agricultural Science
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• v.45 no.3
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• pp.385-400
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• 2018

Marker-assisted backcrossing (MABC) is useful for selecting offspring with a highly recovered genetic background for a recurrent parent at early generation unlike rice and other field crops. Molecular marker sets applicable to practical MABC are scarce in vegetable crops including tomatoes. In this study, we used the National Center for Biotechnology Information- short read archive (NCBI-SRA) database that provided the whole genome sequences of 234 tomato accessions and selected 27,680 tag-single nucleotide polymorphisms (tag-SNPs) that can identify haplotypes in the tomato genome. From this SNP dataset, a total of 143 tag-SNPs that have a high polymorphism information content (PIC) value (> 0.3) and are physically evenly distributed on each chromosome were selected as a MABC marker set. This marker set was tested for its polymorphism in each pairwise cross combination constructed with 124 of the 234 tomato accessions, and a relatively high number of SNP markers polymorphic for the cross combination was observed. The reliability of the MABC SNP set was assessed by converting 18 SNPs into Luna probe-based high-resolution melting (HRM) markers and genotyping nine tomato accessions. The results show that the SNP information and HRM marker genotype matched in 98.6% of the experiment data points, indicating that our sequence analysis pipeline for SNP mining worked successfully. The tag-SNP set for the MABC developed in this study can be useful for not only a practical backcrossing program but also for cultivar identification and F1 seed purity test in tomatoes.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.91-98
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• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.156-156
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• 2019

In this study, a gated recurrent unit (GRU) network is constructed based on a deep neural network (DNN) with the aim of restoring the missing daily flow data in river basins. Lai Chau hydrological station is located upstream of the Da river basin (Vietnam) is selected as the target station for this study. Input data of the model are data on observed daily flow for 24 years from 1961 to 1984 (before Hoa Binh dam was built) at 5 hydrological stations, in which 4 gauge stations in the basin downstream and restoring - target station (Lai Chau). The total available data is divided into sections for different purposes. The data set of 23 years (1961-1983) was employed for training and validation purposes, with corresponding rates of 80% for training and 20% for validation respectively. Another data set of one year (1984) was used for the testing purpose to objectively verify the performance and accuracy of the model. Though only a modest amount of input data is required and furthermore the Lai Chau hydrological station is located upstream of the Da River, the calculated results based on the suggested model are in satisfactory agreement with observed data, the Nash - Sutcliffe efficiency (NSE) is higher than 95%. The finding of this study illustrated the outstanding performance of the GRU network model in recovering the missing flow data at Lai Chau station. As a result, DNN models, as well as GRU network models, have great potential for application within the field of hydrology and hydraulics.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.153-162
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• 2010

The finite buffer GI/G/1/K system is set up by using an unconventional arrangement of the state space, in which the remaining interarrival time or service time is chosen as the level. The stationary distributions of resulting Markov chain can be explicitly determined, and the chain is positive recurrent without any restriction. This is an advantage of this method, compared with that using the elapsed time approach [2].