• Land and Housing Review
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• v.4 no.1
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• pp.89-98
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• 2013

In recent years, it was recognized that aurban policy paradigm of sustainable urban growth management and transportation policies have been shifted : from the era of automobile-oriented policy to the era of public transport policy. Against this backdrop, the introduction of the multi-modal transfer center is very consequential. Therefore, it is necessary for the introduction of wide-area and local-centric transfer facilities, as well as the center of the country-led national backbone transfer center. This study was applicable at the multi-modal transfer center plans to introduce guidelines to provide transit facilities, considering the regional and functional characteristics to classify the types of multi-modal transfer center. The final types of multi-modal transfer center were classified into six, and by considering the combination of the criteria to be classified. The multi-modal transfer center type classification based on the case analysis of the types of facilities at domestic and abroad. If the data of multi-modal transfer are accumulated continuously, can expect a more reliable type classification.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• Journal of Biomedical Engineering Research
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• v.39 no.5
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• pp.213-219
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• 2018

Stomach cancer is the most diagnosed cancer in Korea. When gastric cancer is detected early, the 5-year survival rate is as high as 90%. Gastroscopy is a very useful method for early diagnosis. But the false negative rate of gastric cancer in the gastroscopy was 4.6~25.8% due to the subjective judgment of the physician. Recently, the image classification performance of the image recognition field has been advanced by the convolutional neural network. Convolutional neural networks perform well when diverse and sufficient amounts of data are supported. However, medical data is not easy to access and it is difficult to gather enough high-quality data that includes expert annotations. So This paper evaluates the efficacy of transfer learning in gastroscopy classification and diagnosis. We obtained 787 endoscopic images of gastric endoscopy at Gil Medical Center, Gachon University. The number of normal images was 200, and the number of abnormal images was 587. The image size was reconstructed and normalized. In the case of the ResNet50 structure, the classification accuracy before and after applying the transfer learning was improved from 0.9 to 0.947, and the AUC was also improved from 0.94 to 0.98. In the case of the InceptionV3 structure, the classification accuracy before and after applying the transfer learning was improved from 0.862 to 0.924, and the AUC was also improved from 0.89 to 0.97. In the case of the VGG16 structure, the classification accuracy before and after applying the transfer learning was improved from 0.87 to 0.938, and the AUC was also improved from 0.89 to 0.98. The difference in the performance of the CNN model before and after transfer learning was statistically significant when confirmed by T-test (p < 0.05). As a result, transfer learning is judged to be an effective method of medical data that is difficult to collect good quality data.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2026-2033
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• 2023

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.2
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• pp.134-146
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• 2009

Traffic classification seeks to assign packet flows to an appropriate quality of service(QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

• Journal of Korean Library and Information Science Society
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• v.18
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• pp.139-192
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• 1991

Though the computer has become deeply entrenched as the major tool in information processing(library works), it may be obvious that automatic book classification techniques ate still under experimentation, and the techniques have not yet been tested against the criterion of usefulness. The purpose of this study is to design of knowledge base for automatic book classification which can be put to use in library operation, and to present a methodology of application of the automatic classification into the library. Since the enumerative classification schemes which are existing are manual systems, it cannot be applied to the automatic classification, the principle of faceted classification based on concept analysis is brought in and studied. The result of this study are summarized as follows : 1. The design of knowledge base confined the field of agriculture and medicine. 2. If title is entered by the computer keyboard it will be searched in knowledge base, and then be classified by the principle of automatic classification. 3. Program flowcharts are designed as a bases of classification procedures for automatic subject recognition and classification. 4. 283 books in agriculture, 196 books in medicine were drawn at random from Taegu University Library and Young-Nal Medical Center Library respectively. 5. The experiment of automatic classification is performed 143 books in agriculture 166 books in medicine except for other subject books. 6. It was proved that automatic book classification is possible by design of knowledge base. In addition the expected values from design of knowledge base for automatic book classification are as follows : 1. The prompt and accurate process of classification is possible. 2. Though some title is classified in any library, it can be classified the some classification number by a program. 3. The user can retrieve the classification codes of books for which he or she wants to search through the computer. 4. Since the concept coordination method is employed the representing of a multisubject concept is make simple. 5. By performing automatic book classification the automation of total system can be achieved. 6. The efficient international information transfer will be advanced since all the institution maintain unified classification number.

• Journal of Korean Society of Archives and Records Management
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• v.12 no.3
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• pp.71-92
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• 2012

As records managers are assigned in accordance with act on public records management, the record management has settled down a bit, and also much effort is put to perform the record management in accordance with law. The record management includes all the works like production, classification, organization, transfer, collection, evaluation, disuse, preservation, opening to the public and application. Among them, the record transfer is an important work that performs the initial stage in which the main agent of management is changed from administration department to record center. Thus this study suggested the improvement measures for non-electronic record transfer after examining the current transfer state of 16 local governments in Busan region and also problems occurring in the process of transfer through interviews with institutional records managers.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.12
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• pp.1383-1389
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• 2011

The current study focuses on the consistent analysis of heat transfer in multichannel volumetric solar receivers used for concentrating solar power. Changes in the properties of the absorbing material and channel dimensions are considered in an optical model based on the Monte Carlo ray-tracing method and in a one-dimensional heat transfer model that includes conduction, convection, and radiation. The optical model results show that most of the solar radiation energy is absorbed within a very small channel length of around 15 mm because of the large length-to-radius ratio. Classification of radiation losses reveals that at low absorptivity, increased reflection losses cause reduction of the receiver efficiency, notwithstanding the decrease in the emission loss. As the average temperature increases because of the large channel radius or small mass flow rate, both emission and reflection losses increase but the effect of emission losses prevails.

• Journal of Korean Society for Quality Management
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• v.32 no.3
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• pp.198-215
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• 2004

Technology competitiveness evaluates environmental and engineered technology and process at both the scientific and market levels. There are increasing concerns to measure the effects of the technology variables on the potential market feasibility levels. However, there are very little empirical analysis studies on that issue. This study investigates the impacts of technology variables on the levels of market feasibility based on 230 data obtained from Korea Technology Transfer Center. As various statistical analysis, the canonical discriminant model, logit discriminant model and classification model were used and their results were compared. This study results showed that major technology variables had very significant relations to discriminate high and low categories of market feasibility. Finally, this study will help building management strategies to level up the potential market performance and also help financial Institutions to decide funds needed for small-sized technology firms.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.2
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• pp.332-342
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• 2012

The current research aims at classifying the bus stops as transfer center in order to establish the rational bus transfer systems. Existing research typically identifies characteristics of demands for bus stops and land use surrounding the bus stops and classifies and profiles the bus stops. A common problem with this type of research is that the results with cross-sectional characteristics of land use and bus stop usage do not capture the details of trip chain, the fundamental characteristics of the trips with transfer. This paper therefore examines bus stop classifications with such variables as transport mode chains, intermediate stop chains and timing chains. The analysis on the data collected on Monday 20 April 2009 for passengers of Gyeong-gi bus results in a clear classification among bus stops in terms of such trip chain variables. The research would provide useful information for the decision support of transfer stops location choice and infrastructure design.