• 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 the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.93-101
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• 2019

The objective of this study is to develop a model which can predict the seismic performance of the slope relatively accurately and efficiently by using artificial neural network(ANN) technique. The quantification of such the seismic performance of the slope is not easy task due to the randomness and the uncertainty of the earthquake input and slope model. Under these circumstances, probabilistic seismic fragility analyses of slope have been carried out by several researchers, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, a traditional statistical linear regression analysis has shown a limit that cannot accurately represent the nonlinearistic relationship between the slope of various conditions and seismic performance. In order to overcome these problems, in this study, we attempted to apply the ANN to generate prediction models of the seismic performance of the slope. The validity of the derived model was verified by comparing this with the conventional multi-linear and multi-nonlinear regression models. As a result, the models obtained through the ANN basically showed excellent performance in predicting the seismic performance of the slope, compared to the models obtained by the statistical regression analyses of the previous study.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.45-50
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• 2021

In this paper, we propose the development of a smart space security device that can be opened and closed remotely using IoT. Existing space security devices can control opening and closing by breaking hardware or only using button devices or replicated keys. The recent COVID-19 crisis has created several applications for non-contact devices. In this study, we propose the development of a small space security device that has the function of unlocking through an app without touching the device. By transferring the control authority to a smartphone, device that cannot be opened or closed by only operating hardware at the user's option. It is convenient and hygienic because it can be opened and closed using an app without touching the locking device. Multiple security is possible because security can be released using an app after user authentication by fingerprint recognition and pattern input on a smartphone. If the user wishes, after using the app security, the security is released by directly touching a button installed in the safe or space or opening it with a key. In addition, by adding an inactive function to the app, it is designed so that the door of the safe cannot be opened when the key is lost or the small safe is lost. This study is expected to be able to effectively expand the security system by applying variously to objects that require security.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.338-347
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• 2021

In this paper, the beta-ray sensor circuit used in the true random number generator was designed using DB HiTek's 0.18㎛ CMOS process. The CSA circuit proposed a circuit having a function of selecting a PMOS feedback resistor and an NMOS feedback resistor, and a function of selecting a feedback capacitor of 50fF and 100fF. And for the pulse shaper circuit, a CR-RC2 pulse shaper circuit using a non-inverting amplifier was used. Since the OPAMP circuit used in this paper uses single power instead of dual power, we proposed a circuit in which the resistor of the CR circuit and one node of the capacitor of the RC circuit are connected to VCOM instead of GND. And since the output signal of the pulse shaper does not increase monotonically, even if the output signal of the comparator circuit generates multiple consecutive pulses, the monostable multivibrator circuit is used to prevent signal distortion. In addition, the CSA input terminal, VIN, and the beta-ray sensor output terminal are placed on the top and bottom of the silicon chip to reduce capacitive coupling noise between PCB traces.

• Journal of Korea Water Resources Association
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• v.55 no.5
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• pp.345-356
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• 2022

A severe flooding occured at a small urban catchment in Daejeon-si South Korea on July 30, 2020 causing significant loss of property (inundated 78 vehicles and two apartments) and life (one casualty and 56 victims). In this study, a retrospective analysis of the inundation event was implemented using a physically-based urban flood model, H12 with high-resolution data. H12 is an integrated 1-dimensional sewer network and 2-dimensional surface flow model supported by hybrid parallel techniques to efficiently deal with high-resolution data. In addition, we evaluated the impact of the flooding barriers which were installed after the flood disaster. As a result, it was found that the inundation was affected by a combination of multiple components including the shape of the basin, the low terrain of the inundation area located in the downstream part of the basin, and lack of pipe capacity to drain discharge from the upstream during heavy rain. The impact of the flooding barriers was analyzed by modeling with and without barriers on the high-resolution terrain input data. It was evaluated that the flood barriers effectively lower the water depth in the apartment complex. This study demonstrates capability of high-resolution physically-based urban modeling to quantitatively assess the past inundation event and the impact of the reduction measures.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.610-620
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• 2022

An acoustic vector sensor can simultaneously receive vector quantities, such as particle velocity and acceleration, as well as acoustic pressure at one location, and thus it can be used as a single input multiple output receiver in underwater acoustic communication systems. On the other hand, vector signals received by a single vector sensor have different channel characteristics due to the azimuth angle between the source and receiver and the difference in propagation angle of multipath in each component, producing different communication performances. In this paper, we propose a channel parameter-based weighting method to improve the performance of an acoustic communication system using a single vector sensor. To verify the proposed method, we used communication data collected from the experiment conducted during the KOREX-17 (Korea Reverberation Experiment). For communication demodulation, block-based time reversal technique which is robust against time-varying channels were utilized. Finally, the communication results showed that the effectiveness of the channel parameter-based weighting method for the underwater communication system using a single vector sensor was verified.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.89-112
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• 2024

Metadata is a crucial component of record management, playing a vital role in properly managing and understanding the record. In cases where automatic metadata assignment is not feasible, manual input by records professionals becomes necessary. This study aims to alleviate the challenges associated with manual entry by proposing a method that harnesses ChatGPT technology for extracting records management metadata elements. To employ ChatGPT technology, a Python program utilizing the LangChain library was developed. This program was designed to analyze PDF documents and extract metadata from records through questions, both with a locally installed instance of ChatGPT and the ChatGPT online service. Multiple PDF documents were subjected to this process to test the effectiveness of metadata extraction. The results revealed that while using LangChain with ChatGPT-3.5 turbo provided a secure environment, it exhibited some limitations in accurately retrieving metadata elements. Conversely, the ChatGPT-4 online service yielded relatively accurate results despite being unable to handle sensitive documents for security reasons. This exploration underscores the potential of utilizing ChatGPT technology to extract metadata in records management. With advancements in ChatGPT-related technologies, safer and more accurate results are expected to be achieved. Leveraging these advantages can significantly enhance the efficiency and productivity of tasks associated with managing records and metadata in archives.

• Journal of dental hygiene science
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• v.24 no.1
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• pp.62-70
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• 2024

Background: This study aimed to evaluate ChatGPT's performance accuracy in responding to questions from the national dental hygienist examination. Moreover, through an analysis of ChatGPT's incorrect responses, this research intended to pinpoint the predominant types of errors. Methods: To evaluate ChatGPT-3.5's performance according to the type of national examination questions, the researchers classified 200 questions of the 49th National Dental Hygienist Examination into recall, interpretation, and solving type questions. The researchers strategically modified the questions to counteract potential misunderstandings from implied meanings or technical terminology in Korea. To assess ChatGPT-3.5's problem-solving capabilities in applying previously acquired knowledge, the questions were first converted to subjective type. If ChatGPT-3.5 generated an incorrect response, an original multiple-choice framework was provided again. Two hundred questions were input into ChatGPT-3.5 and the generated responses were analyzed. After using ChatGPT, the accuracy of each response was evaluated by researchers according to the types of questions, and the types of incorrect responses were categorized (logical, information, and statistical errors). Finally, hallucination was evaluated when ChatGPT provided misleading information by answering something that was not true as if it were true. Results: ChatGPT's responses to the national examination were 45.5% accurate. Accuracy by question type was 60.3% for recall and 13.0% for problem-solving type questions. The accuracy rate for the subjective solving questions was 13.0%, while the accuracy for the objective questions increased to 43.5%. The most common types of incorrect responses were logical errors 65.1% of all. Of the total 102 incorrectly answered questions, 100 were categorized as hallucinations. Conclusion: ChatGPT-3.5 was found to be limited in its ability to provide evidence-based correct responses to the Korean national dental hygiene examination. Therefore, dental hygienists in the education or clinical fields should be careful to use artificial intelligence-generated materials with a critical view.