• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.6
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• pp.1-11
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• 2021

To develop an efficient procedure related to the flamelet library, the machine learning process based on artificial neural network(ANN) is applied for the gaseous hydrogen/liquid oxygen combustor under a supercritical pressure condition. For hidden layers, 25 combinations based on Rectified Linear Unit(ReLU) and hyperbolic tangent are adopted to find an optimum architecture in terms of the computational efficiency and the training performance. For activation functions, the hyperbolic tangent is proper to get the high learning performance for accurate properties. A transformation learning data is proposed to improve the training performance. When the optimal node is arranged for the 4 hidden layers, it is found to be the most efficient in terms of training performance and computational cost. Compared to the interpolation procedure, the ANN procedure reduces computational time and system memory by 37% and 99.98%, respectively.

• Journal of the Korean Magnetics Society
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• v.16 no.3
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• pp.186-191
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• 2006

Ferromagnetic amorphous $Ni_{16}Fe_{62}Si_8B_{14}$ and $Co_{70.5}Fe_{4.5}Si_{15}B_{10}$ layers have been devised and incorporated as free layers of magnetic tunnel junctions (MTJs) to improve MRAM reading and writing performance. The NiFeSiB and CoFeSiB single-layer film exhibited a lower saturation magnetization ($Ms=800emu/cm^3,\;and\;560emu/cm^3$, respectively) compared to that of a $Co_{90}Fe_{10}(Ms=1400emu/cm^3)$. Because amorphous ferromagnetic materials have lower Ms than crystalline ones, the MTJs incorporating amorphous ferromagnetic materials offer lower switching field ($H_{sw}$) values than that of the traditional CoFe-based MTJ. The double-barrier MTJ with an amorphous NiFeSiB free layer offered smooth surface resulting in low bias voltage dependence, and high $V_h\;and\;V_{bd}$ compared with the values of the traditional CoFe-based MTJ.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.592-599
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• 2020

This paper proposes a Convolutional Recurrent Neural Net (CRNN) structure that can simultaneously reflect both static and dynamic characteristics of seismic waveforms for various earthquake events classification. Addressing various earthquake events, including not only micro-earthquakes and artificial-earthquakes but also macro-earthquakes, requires both effective feature extraction and a classifier that can discriminate seismic waveform under noisy environment. First, we extract the static characteristics of seismic waveform through an attention-based convolution layer. Then, the extracted feature-map is sequentially injected as input to a multi-input single-output Long Short-Term Memory (LSTM) network structure to extract the dynamic characteristic for various seismic event classifications. Subsequently, we perform earthquake events classification through two fully connected layers and softmax function. Representative experimental results using domestic and foreign earthquake database show that the proposed model provides an effective structure for various earthquake events classification.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.4
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• pp.44-52
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• 2003

Shape memory alloys (SMA) are often used in smart structures as active components. Their ability to provide large recovery forces and displacements has been useful in many applications, including devices for artificial muscles, active structural acoustic control, and shape control. Based on the 3-dimensional SMA constitutive equation in this paper, the radial displacement control of externally pressurized circular and semicircular composite cylinders under external pressure with a thin SMA layer bonded on its inner surface or inserted between composite layers in investigated using 3-dimensional finite element analysis. Upon actuation through resistive heating, SMAs start to transform from martensitic into austenitic state, simultaneously recover the prestrain, and thus cause the composite cylinders to go back to their original shapes of the cylinder cross-sections.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.123-128
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• 2019

This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.703-709
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• 2019

This paper proposes speech recognition systems employing Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) structures combined with Hidden Markov Moldel (HMM) to effectively recognize the speech of VeloPharyngeal Insufficiency (VPI) patients, and compares the recognition performance of the systems to the Gaussian Mixture Model (GMM-HMM) and fully-connected Deep Neural Network (DNNHMM) based speech recognition systems. In this paper, the initial model is trained using normal speakers' speech and simulated VPI speech is used for generating a prior model for speaker adaptation. For VPI speaker adaptation, selected layers are trained in the CNN-HMM based model, and dropout regulatory technique is applied in the LSTM-HMM based model, showing 3.68 % improvement in recognition accuracy. The experimental results demonstrate that the proposed LSTM-HMM-based speech recognition system is effective for VPI speech with small-sized speech data, compared to conventional GMM-HMM and fully-connected DNN-HMM system.

• Journal of Korean Vacuum Science & Technology
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• v.5 no.2
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• pp.47-51
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• 2001

To obtain high remnant polarization and good crystalinity of ferroelectric thin films in non-volatile memory devices, the high temperature treatment in oxygen ambient is inevitable. Severe problems that occur in this process are oxygen diffusion into substrate, oxidation of electrode and buffer layer, degradation of microstructure and so on. We made ruthenium dioxide thin film by reactive sputtering with oxygen and argon mixture atmosphere. Comparing quantitatively the core-level spectra of Ru and RuO$_2$ obtained by x-ray photoelectron spectroscopy(XPS), we found that chemical state of RuO$_2$ is very stable and of good resistance to oxygen diffusion and oxidation of adjacent layers. It opens the use of RuO$_2$ thin film as a multifunctional layer of good conducting electrode and resistive barrier for the diffusion and the oxidation. We also suggest a correct understanding of Ru 3d core-level spectrum for RuO$_2$ based on the scheme of final state screening and charge transfer satellites.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.17-22
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• 2021

Recently, image super-resolution techniques used in convolutional neural networks (CNN) have led to remarkable performance in the research area of digital image processing applications and computer vision tasks. Convolutional layers stacked on top of each other can design a more complex network architecture, but they also use more memory in terms of the number of parameters and introduce the vanishing gradient problem during training. Furthermore, earlier approaches of single image super-resolution used interpolation technique as a pre-processing stage to upscale the low-resolution image into HR image. The design of these approaches is simple, but not effective and insert the newer unwanted pixels (noises) in the reconstructed HR image. In this paper, authors are propose a novel single image super-resolution architecture based on synchronized depthwise separable convolution with Dense Skip Connection Block (DSCB). In addition, unlike existing SR methods that only rely on single path, but our proposed method used the synchronizes path for generating the SISR image. Extensive quantitative and qualitative experiments show that our method (SDCN) achieves promising improvements than other state-of-the-art methods.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.47-54
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• 2023

This article reviews recent advances and perspectives in neuroscience related to consciousness, cognition, and neural networks in the brain. The neural mechanisms underlying cognitive processes, such as perception, attention, memory, and decision-making, are explored. The article also examines how these processes give rise to our experience of consciousness. The implications of these findings for our understanding of the brain and its functions are presented, as well as potential applications of this knowledge in fields such as medicine, psychology, and artificial intelligence. Additionally, the article explores the concept of a quantum viewpoint concerning consciousness, cognition, and creativity and how incorporating DNA as a key element could reconcile classical and quantum perspectives on human behaviour, consciousness, and cognition, as explained by genomic psychological theory. Furthermore, the article explains how the human brain processes external stimuli through the sensory nervous system and how it can be simulated using an artificial neural network (ANN) consisting of one input layer, multiple hidden layers, and an output layer. The law of learning is also discussed, explaining how ANNs work and how the modification of weight values affects the output and input values. The article concludes with a discussion of future research directions in this field, highlighting the potential for further discoveries and advancements in our understanding of the brain and its functions.