• Journal of the Korean School Mathematics Society
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• v.13 no.1
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• pp.23-43
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• 2010

This study is to investigate how much highschool students, who have learned functional concepts included in the Middle school math curriculum, understand chapters of the function, to analyze the types of errors which they made in solving the mathematical problems and to look for the proper instructional program to prevent or minimize those ones. On the basis of the result of the above examination, it suggests a classification model for teaching-learning methods and teaching material development The result of this study is as follows. First, Students didn't fully understand the fundamental concept of function and they had tendency to approach the mathematical problems relying on their memory. Second, students got accustomed to conventional math problems too much, so they couldn't distinguish new types of mathematical problems from them sometimes and did faulty reasoning in the problem solving process. Finally, it was very common for students to make errors on calculation and to make technical errors in recognizing mathematical symbols in the problem solving process. When students fully understood the mathematical concepts including a definition of function and learned procedural knowledge of them by themselves, they did not repeat the same errors. Also, explaining the functional concept with a graph related to the function did facilitate their understanding,

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.99-120
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• 2017

In order to develop a semiautomatic support system that allows researchers concerned to efficiently analyze the technical trends for the ever-growing industry and market. This paper introduces a couple of Korean sentence generation models that can automatically generate definitional statements as well as descriptions of technical terms and concepts. The proposed models are based on a deep learning model called LSTM (Long Sort-Term Memory) capable of effectively labeling textual sequences by taking into account the contextual relations of each item in the sequences. Our models take technical terms as inputs and can generate a broad range of heterogeneous textual descriptions that explain the concept of the terms. In the experiments using large-scale training collections, we confirmed that more accurate and reasonable sentences can be generated by CHAR-CNN-LSTM model that is a word-based LSTM exploiting character embeddings based on convolutional neural networks (CNN). The results of this study can be a force for developing an extension model that can generate a set of sentences covering the same subjects, and furthermore, we can implement an artificial intelligence model that automatically creates technical literature.

• Korean Journal of Veterinary Research
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• v.48 no.3
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• pp.251-258
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• 2008

The neurotoxicity of C-terminal fragments of amyloid precusor protein (CT) is known to play some roles in Alzheimer's disease progression. In this study, we investigated the effects of the recombinant C-terminal 105 amino acid fragment of amyloid precusor protein (CT105) on cholinergic function using CT105-injected rat. To study the effects of CT105 on septohippocampal pathway, choline acetyltransferase (ChAT) positive neurons were examined in the medial septum and in the diagonal band after an injection of CT105 peptide into the lateral ventricle. Immunohistological analysis revealed that the number of ChAT-immunopositive cells decreased significantly in both medial septum and diagonal band. In addition, CT105 decreased ChAT-immunopositive cells in the hippocampal area, particulary in the dentate gyros. To study the effect of amyloid beta peptide ($A{\beta}$) and CT105 on the cholinergic system, each peptide was injected into the left lateral ventricle, and acetylcholine (ACh) levels were monitored in hippocampus. ACh level in the hippocampal area was reduced to 60% of control level in $A{\beta}$-treated group, and the level was reduced to 15% of control level in CT105-treated group, at one week after the injection. ACh level was further reduced to 35% of control in $A{\beta}$-treated group, whereas the level was slightly increased to 30% of control in CT105-treated group at 4 weeks after the injection. Taken together, the results in the present study suggest that CT105 impairs the septohippocampal pathway by reducing acetylcholine synthesis and release, which results in damage of learning and memory.

• Archives of Pharmacal Research
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• v.28 no.9
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• pp.1073-1078
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• 2005

A presenilin 2 mutation is believed to be involved in the development of Alzheimer's disease. In addition, transgenic mice with a presenilin 2 mutation have been reported to have learning and memory impairments. In this study, exposing PC12 cells expressing mutant presenilin 2 to $50{\mu}M\;A{\beta}_{25-35},\;30mM$ L-glutamate and $50{\mu}M\;H_2O_2$ caused a significant increase in acetylcholine esterase activity. An in vivo study revealed high levels of this enzyme activity in the mutant presenilin 2 transgenic brains compared with the wild type presenilin 2 transgenic and non-transgenic samples. These results suggest that a mutant presenilin 2-induced neurodegeneration in Alzheimer's disease might be involved in the increase in acetylcholinesterase activity. These findings might help in the development of an appropriate therapeutic intervention targeting mutant presenilin 2-induced Alzheimer's disease.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.1-9
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• 2010

Generally, dynamic hand gesture recognition is formed through preprocessing step, hand tracking step and hand shape detection step. In this paper, we present advanced dynamic hand gesture recognizing method that improves performance in preprocessing step and hand shape detection step. In preprocessing step, we remove noise fast by using dynamic table and detect skin color exactly on complex background for controling skin color range in skin color detection method using YCbCr color space. Especially, we increase recognizing speed in hand shape detection step through detecting Start Image and Stop Image, that are elements of dynamic hand gesture recognizing, using Guideline. Guideline is edge of input hand image and hand shape for comparing. We perform various experiments with nine web-cam video clips that are separated to complex background and simple background for dynamic hand gesture recognition method in the paper. The result of experiment shows similar recognition ratio but high recognition speed, low cpu usage, low memory usage than recognition method using learning exercise.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.179-184
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• 2021

In this paper, we propose the deep learning-based neural network model to predict bunker price. In the shipping industry, since fuel oil accounts for the largest portion of ship operation costs and its price is highly volatile, so companies can secure market competitiveness by making fuel oil purchasing decisions based on rational and scientific method. In this paper, short-term predictive analysis of HSFO 380CST in Singapore is conducted by using three recurrent neural network models like RNN, LSTM, and GRU. As a result, first, the forecasting performance of RNN models is better than LSTM and GRUs using long-term memory, and thus the predictive contribution of long-term information is low. Second, since the predictive performance of recurrent neural network models is superior to the previous studies using econometric models, it is confirmed that the recurrent neural network models should consider nonlinear properties of bunker price. The result of this paper will be helpful to improve the decision quality of bunker purchasing.

• Phonetics and Speech Sciences
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• v.13 no.3
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• pp.71-78
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• 2021

Most neural-network-based speech synthesis models utilize neural vocoders to convert mel-scaled spectrograms into high-quality, human-like voices. However, neural vocoders combined with mel-scaled spectrogram prediction models demand considerable computer memory and time during the training phase and are subject to slow inference speeds in an environment where GPU is not used. This problem does not arise in linear spectrogram prediction models, as they do not use neural vocoders, but these models suffer from low voice quality. As a solution, this paper proposes a Tacotron 2 and Transformer-based linear spectrogram prediction model that produces high-quality speech and does not use neural vocoders. Experiments suggest that this model can serve as the foundation of a high-quality text-to-speech model with fast inference speed.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.161-166
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• 2022

In this paper, we analyze the problems that occur when performing the big data analysis using R as a data analysis tool, and present the usefulness of the data analysis with SparkR which connects R and Spark to support distributed processing of big data effectively. First, we study the memory allocation problem of R which occurs when loading large amounts of data and performing operations, and the characteristics and programming environment of SparkR. And then, we perform the comparison analysis of the execution performance when linear regression analysis is performed in each environment. As a result of the analysis, it was shown that R can be used for data analysis through SparkR without additional language learning, and the code written in R can be effectively processed distributedly according to the increase in the number of nodes in the cluster.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.778-789
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• 2021

Recently, various lightweight methods for using Convolutional Neural Network(CNN) models in mobile devices have emerged. Weight quantization, which lowers bit precision of weights, is a lightweight method that enables a model to be used through integer calculation in a mobile environment where GPU acceleration is unable. Weight quantization has already been used in various models as a lightweight method to reduce computational complexity and model size with a small loss of accuracy. Considering the size of memory and computing speed as well as the storage size of the device and the limited network environment, this paper proposes a method of compressing integer weights after quantization using a video codec as a method. To verify the performance of the proposed method, experiments were conducted on VGG16, Resnet50, and Resnet18 models trained with ImageNet and Places365 datasets. As a result, loss of accuracy less than 2% and high compression efficiency were achieved in various models. In addition, as a result of comparison with similar compression methods, it was verified that the compression efficiency was more than doubled.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.69-75
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• 2023

Small communication devices used in the Internet of Things are vulnerable to various hacking because they do not apply advanced encryption techniques due to their low memory capacity or slow computation speed. In order to increase the authentication reliability of small-sized transmitters operating in 433MHz band, we introduce an RF fingerprint and adopt a convolutional neural network (CNN) as a classification algorithm. The preamble signal transmitted by each transmitter are extracted and collected using software-defined-radio to constitute a training data set, which is used for training the CNN. We tested identification of 20 transmitters in four different scenarios and obtained high identification accuracy. In particular, the accuracy of 95.8% and 92.6% was obtained, respectively in the scenario where the test was performed at a location different from the transmitter's location at the time of collecting training data, and in the scenario where the transmitter moves at walking speed.