• Journal of Radiation Protection and Research
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• v.27 no.3
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• pp.171-179
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• 2002

The three-dimensional long-range dispersion model has been developed to understand the characteristics of the transport and diffusion of radioactive materials released into atmosphere. The model is designed to compute air concentration and ground deposition at distances up to some thousands of kilometers from the source point in horizontal direction. The vertical turbulent motion is considered separately within the mixing layer and above the mixing layer. The test simulation was performed In the area of Northeast Asia. The release point was assumed in the east part of China. The calculated concentration distributions art mainly advected toward the southeast part of release point by the wind fields. The developed model will be used to estimate the radiological consequences against a nuclear accident. The model will be supplemented by the comparative study using the data of the long-range field experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3267-3274
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• 2011

Recently, speech recognition technology has dramatically developed, with the increase in the user environment of various mobile devices and influence of a variety of speech recognition software. However, for speech recognition for multi-language, lack of understanding of multi-language lexical model and limited capacity of systems interfere with the improvement of the recognition rate. It is not easy to embody speech expressed with multi-language into a single acoustic model and systems using several acoustic models lower speech recognition rate. In this regard, it is necessary to research and develop a multi-language speech recognition system in order to embody speech comprised of various languages into a single acoustic model. This paper studied a system that can recognize Korean and English as International Phonetic Language (IPA), based on the research for using a multi-language acoustic model in mobile devices. Focusing on finding an IPA model which satisfies both Korean and English phonemes, we get 94.8% of the voice recognition rate in Korean and 95.36% in English.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.37-44
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• 2019

With the advance of digital IT technology, the performance of the printing and scanning devices is improved and their price becomes cheaper. As a result, the public can easily access these devices for crimes such as forgery of official and private documents. Therefore, if we can identify which printing device is used to print the documents, it would help to narrow the investigation and identify suspects. In this paper, we propose a deep learning model for printer identification. A convolutional neural network model based on local features which is widely used for identification in recent is presented. Then, another model including a step to calculate global features and hence improving the convergence speed and accuracy is presented. Using 8 printer models, the performance of the presented models was compared with previous feature-based identification methods. Experimental results show that the presented model using local feature and global feature achieved 97.23% and 99.98% accuracy respectively, which is much better than other previous methods in accuracy.

• The Journal of Korean Institute of Information Technology
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• v.17 no.8
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• pp.11-19
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• 2019

Against advanced image-related crimes, a high level of digital forensic methods is required. However, feature-based methods are difficult to respond to new device features by utilizing human-designed features, and deep learning-based methods should improve accuracy. This paper proposes a deep learning model to identify camera models based on DenseNet, the recent technology in the deep learning model field. To extract camera sensor features, a HPF feature extraction filter was applied. For camera model identification, we modified the number of hierarchical iterations and eliminated the Bottleneck layer and compression processing used to reduce computation. The proposed model was analyzed using the Dresden database and achieved an accuracy of 99.65% for 14 camera models. We achieved higher accuracy than previous studies and overcome their disadvantages with low accuracy for the same manufacturer.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.179-187
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• 2009

Most real world systems contain a series of dynamic and complex phenomena. One of common methods to understand these systems is to build a model and analyze the behavior of them. A two-step methodology comprised of clustering and then model creation is proposed for the analysis on time series data. An interface is designed for CRM(Customer Relationship Management) that provides user with 1:1 customized information using system modeling. It was confirmed from experiments that better clustering would be derived from model based approach than similarity based one. Clustering is followed by model creation over the clustered groups, by which future direction of time series data movement could be predicted. The effectiveness of the method was validated by checking how similarly predicted values from the models move together with real data such as stock prices.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.13 no.1
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• pp.15-26
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• 2008

The NEMURO model is aimed to efficiently understand the interaction among factors of lower trophic level of a marine ecosystem, using data on solar radiation and sea water temperature. In this study, we analyzed the seasonal pattern of nutrients and planktons, and estimated productivity and biomass of planktons from 2002 to 2005. Nutrients($NO_3$, $NH_4$, and $Si(OH)_4$) which were used by phytoplankton showed a high concentration before the bloom of phytoplankton. Nutrients (DON, PON, and Opal) which were a byproduct of phytoplankton showed a high concentration in the same period as the bloom of phytoplankton. Both phytoplankton and zooplankton had two peaks in March and August. Estimated phytoplankton biomass from the NEMURO model showed a similar pattern with observed chlorophyll a concentrations. Biomasses of phytoplankton were bigger than those of zooplankton. Annual mean biomasses of small and large phytoplankton were estimated at 30.961 and $14.070\;{\mu}g\;l^{-1}$ respectively. Annual mean biomass of predatory zooplankton was greater than those of small and large zooplankton.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.82-88
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• 2024

As the modern maritime industry rapidly progresses through technological advancements, data processing technology is emphasized as a key driver of this development. Natural language processing is a technology that enables machines to understand and process human language. Through this methodology, we aim to develop a model that predicts the proportions of outcomes when entering new written judgments by analyzing the rulings of the Marine Safety Tribunal and learning the cause-providing ratios of previously adjudicated ship collisions. The model calculated the cause-providing ratios of the accident using the navigation applied at the time of the accident and the weight of key keywords that affect the cause-providing ratios. Through this, the accuracy of the developed model could be analyzed, the practical applicability of the model could be reviewed, and it could be used to prevent the recurrence of collisions and resolve disputes between parties involved in marine accidents.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.721-732
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• 2024

Species identification of zooplankton is the most basic process in understanding the marine ecosystem and studying global warming. In this study, we propose an convolutional neural network model that can classify females and males of three zooplankton at the species level. First, training data including morphological features is constructed based on microscopic images acquired by researchers. In constructing training data, a data argumentation method that preserves morphological feature information of the target species is applied. Next, we propose a convolutional neural network model in which features can be learned from the constructed learning data. The proposed model minimized the information loss of training image in consideration of high resolution and minimized the number of learning parameters by using the global average polling layer instead of the fully connected layer. In addition, in order to present the generality of the proposed model, the performance was presented based on newly acquired data. Finally, through the visualization of the features extracted from the model, the key features of the classification model were presented.

• Convergence Security Journal
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• v.24 no.3
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• pp.41-49
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• 2024

A tokenizer, which is a key component of the Transformer model, lacks the ability to effectively comprehend numerical data. Therefore, to develop a Transformer-based intrusion detection model that can operate within a real-world network environment by training packet payloads as sentences, it is necessary to convert the hexadecimal packet payloads into a character-based format. In this study, we applied three character encoding methods to convert packet payloads into numeric or character format and analyzed how detection performance changes when training them on transformer architecture. The experimental dataset was generated by extracting packet payloads from PCAP files included in the UNSW-NB15 dataset, and the RoBERTa was used as the training model. The experimental results demonstrate that the ISO-8859-1 encoding scheme achieves the highest performance in both binary and multi-class classification. In addition, when the number of tokens is set to 512 and the maximum number of epochs is set to 15, the multi-class classification accuracy is improved to 88.77%.

• Korean Journal of Ecology and Environment
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• v.51 no.1
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• pp.86-95
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• 2018

The objective of this study was to describe the development and testing of an initial ecological health assessment model, based on the index of biological integrity (IBI) using fish assemblages, before establishing the final and currently used model for ecological health assessment, conservation and management of freshwater fish in Korea. The initial fish IBI model was developed during 2004~2006 and included 10 metrics, and in 2007 the final IBI 8-metric model was established for application to streams and rivers in four major Korean watersheds. In this paper, we describe how we developed fish sampling methods, determined metric attributes and categorized tolerance guilds and trophic guilds during the development of the multi-metric model. Two of the initial metrics were removed and the initial evaluation categories were reduced from six to four (excellent, good, fair, poor) before establishing the final national fish model. In the development phase, IBI values were compared with chemical parameters (BOD and COD as indicators of organic matter pollution) and physical habitat parameters to identify differences in IBI model values between chemical and physical habitat conditions. These processes undertaken during the development of the IBI model may be helpful in understanding the modifications made and contribute to creating efficient conservation and management strategies for stream environments to be used by limnologists and fish ecologists as well as stream/watershed managers.