• The Journal of Society for e-Business Studies
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• v.22 no.3
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• pp.87-103
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• 2017

In recent years, many small and medium-sized manufacturing companies are making process innovation and product innovation through the public knowledge services. K Research institute provides different types of knowledge services in combination and due to this complexity, it is difficult to analyze the performance of knowledge service programs precisely. In this study, we derived performance items from bottom-up viewpoints, rather than top-down approaches selecting those items as in previous performance analysis. As a result, 74 items were finded from 82 companies in the K Research Institute case book, and the final result was refined to 17 items. After that a case-performance matrix was constructed, and binary data was entered to analyze. As a result, three clusters were identified through K-means clustering as 'enhancement of core competitiveness (product and patent),' 'expansion of domestic and overseas market,' and 'improvement of operational efficiency.'

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.545-554
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• 2006

Although the current Web search engines provide tremendous information, it is hard to find right information among the huge information. Users need to spend extra time to filter out unnecessary information. In order to overcome the limit of current Web search engines, Semantic Web was developed to provide efficient search, integration, and reuse of information by structuring semantic information from Web resources. In this paper, an elementary education contents retrieval system using Semantic Web is proposed. The proposed system emphasizes history contents that have high relevancy among data. For construction of the proposed system, ontology is proposed first for elementary study contents and ontology for historical contents is proposed for easy access to those contents using semantic relation among them. Based on the ontology, the proposed system is designed and implemented. The proposed system has the following characteristics. First, the system provides various query formats in detail so that search results can be refined efficiently. Second, the system presents only semantically information connected with key words or including key words using study contents ontology. Finally, the proposed system can increase study effects by presenting various contents that are related with query by users.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1217-1227
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• 2018

This paper aims to evaluate the applicability of dam inflow prediction model using recurrent neural network theory. To achieve this goal, the Artificial Neural Network (ANN) model and the Elman Recurrent Neural Network(RNN) model were applied to hydro-meteorological data sets for the Soyanggang dam and the Chungju dam basin during dam operation period. For the model training, inflow, rainfall, temperature, sunshine duration, wind speed were used as input data and daily inflow of dam for 10 days were used for output data. The verification was carried out through dam inflow prediction between July, 2016 and June, 2018. The results showed that there was no significant difference in prediction performance between ANN model and the Elman RNN model in the Soyanggang dam basin but the prediction results of the Elman RNN model are comparatively superior to those of the ANN model in the Chungju dam basin. Consequently, the Elman RNN prediction performance is expected to be similar to or better than the ANN model. The prediction performance of Elman RNN was notable during the low dam inflow period. The performance of the multiple hidden layer structure of Elman RNN looks more effective in prediction than that of a single hidden layer structure.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.104-123
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• 2022

Style transfer based on neural network provides very high quality results by reflecting the high level structural characteristics of images, and thereby has recently attracted great attention. This paper deals with the problem of resolution limitation due to GPU memory in performing such neural style transfer. We can expect that the gradient operation for style transfer based on partial image, with the aid of the fixed size of receptive field, can produce the same result as the gradient operation using the entire image. Based on this idea, each component of the style transfer loss function is analyzed in this paper to obtain the necessary conditions for partitioning and padding, and to identify, among the information required for gradient calculation, the one that depends on the entire input. By structuring such information for using it as auxiliary constant input for partition-based gradient calculation, this paper develops a recursive algorithm for super high-resolution image style transfer. Since the proposed method performs style transfer by partitioning input image into the size that a GPU can handle, it can perform style transfer without the limit of the input image resolution accompanied by the GPU memory size. With the aid of such super high-resolution support, the proposed method can provide a unique style characteristics of detailed area which can only be appreciated in super high-resolution style transfer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• International Journal of Highway Engineering
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• v.8 no.3 s.29
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• pp.129-141
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• 2006

The vertical soil pressure developed in the granular layer of asphalt pavement system is influenced by various factors, including the wheel load magnitude, the loading speed, and asphalt pavement temperature. This research observed the distribution of vertical soil pressure in pavement supporting layer by investigating measured data from soil pressure gage in the KHC Test Road. The existing specification of subbase and subgrade compaction was also evaluated with measured vertical pressure. The finite element analysis was conducted to verify the accuracy of results with measured data because it can maximize research capacity without significant field test. The test data was collected from A5, A7, A14, and A15 test sections at August, September, and November 2004 and August 2005. Those test sections and test data were selected because they had best quality. The size of influence area was evaluated and the vertical pressure variation was investigated with respect to load level, load speed, and pavement temperature. The lower speed, higher load level, and higher pavement temperature increased the vertical pressure and reduced the area of influence. The finite element result showed the similar trend of vertical pressure variation in comparison with measured data. The specification of compaction quality for subbase and subgrade is higher than the level of vertical pressure measured with truck load so that it should be lurker investigated.