• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.262-265
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• 2018

Deep learning, a sub-field of machine learning changing the prospects of artificial intelligence (AI) because of its recent advancements and application in various field. Deep learning deals with algorithms inspired by the structure and function of the brain called artificial neural networks. This works reviews basic architecture and recent advancement of deep structured learning. It also describes contemporary applications of deep structured learning and its advantages over the treditional learning in artificial interlligence. This study is useful for the general readers and students who are in the early stage of deep learning studies.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.233-246
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• 2022

In this study, the city of Osijek is used as a case study area for low to medium seismicity regions with deep soil over deep geological deposits to determine horizontal PGA values. For this reason, we propose new regional attenuation equations for PGA that can simultaneously capture the effects of deep geology and local soil conditions. A micro-zoning map for the city of Osijek is constructed using the derived empirical scaling equations and compared to all prior seismic hazard estimates for the same area. The findings suggest that the deep soil atop deep geological sediments results in PGA values that are only 6 percent larger than those reported at rock soil sites atop geological rocks. Given the rarity of ground motion records for deep soils atop deep geological layers around the world, we believe this case study is a start toward defining more reliable PGA estimates for similar areas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1738-1764
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• 2019

With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.129-133
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• 2023

Image compression is a fundamental technique in the field of digital image processing, which will help to decrease the storage space and to transmit the files efficiently. Recently many deep learning techniques have been proposed to promise results on image compression field. Since many image compression techniques have artifact problems, this paper has compared two deep learning approaches to verify their performance experimentally to solve the problems. One of the approaches is a deep autoencoder technique, and another is a deep convolutional neural network (CNN). For those results in the performance of peak signal-to-noise and root mean square error, this paper shows that deep autoencoder method has more advantages than deep CNN approach.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.97-97
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• 2021

Hydrometeorological variables contain highly complex system for temporal revolution and it is quite challenging to illustrate the system with a temporal linear and nonlinear models. In recent years, deep learning algorithms have been developed and a number of studies has focused to model the complex hydrometeorological system with deep learning models. In the current study, we investigated the temporal structure inside deep learning models for the hydrometeorological variables such as streamflow and climate indices. The results present a quite striking such that each hidden unit of the deep learning model presents different dependence structure and when the number of hidden units meet a proper boundary, it reaches the best model performance. This indicates that the deep dependence structure of deep learning models can be used to model selection or investigating whether the constructed model setup present efficient or not.

• Journal of the korean academy of Pediatric Dentistry
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• v.51 no.1
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• pp.1-10
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• 2024

Deep learning algorithms are becoming more prevalent in dental research because they are utilized in everyday activities. However, dental researchers and clinicians find it challenging to interpret deep learning studies. This review aimed to provide an overview of the general concept of deep learning and current deep learning research in dental radiographic image analysis. In addition, the process of implementing deep learning research is described. Deep-learning-based algorithmic models perform well in classification, object detection, and segmentation tasks, making it possible to automatically diagnose oral lesions and anatomical structures. The deep learning model can enhance the decision-making process for researchers and clinicians. This review may be useful to dental researchers who are currently evaluating and assessing deep learning studies in the field of dentistry.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1019-1029
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• 2020

In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.43 no.4
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• pp.373-379
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• 2010

The artificial upwelling of deep seawater increases primary production. This study conducted a lab-scale experiment to investigate the growth of phytoplankton with the mixing ratio of deep and surface seawater. The chlorophyll content in the sample of pure deep seawater was highest, regardless of the phytoplankton groups. Nutrients contained in the deep seawater positively influenced the growth of phytoplankton. The optimum mixture to apply in an artificial upwelling system was a 1:1 ratio of deep and surface seawater. An experiment considering other environmental conditions, such as luminance and specific gravity, should be performed.

• Journal of Food Hygiene and Safety
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• v.10 no.3
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• pp.155-161
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• 1995

In the study, attempts were made to investigate the safety of the deep-fried instant noodles. A total of 50 deep-fried instant noodles were puchased from a local supermarket. Acid value , peroxide value, preservatives, heavy metals and pesticide residues were determined. Acid value(AV) and peroxide value(POV) of deep-fried instant noodles were lower than the Food Law in force. Any preservatives were not detected in all deep-fried instant noodles. The level of all heavy metals and pesticide residues found in deep-fried instant noodles were fairly low, and pesticide residues in deep-fried instant noodles was almost removed after cooking. It was conclued from these results that deep-fried instant noodles may be no harmful in oxidative stability(AV, POV) and sanitary safety(preservatives, heavy metals and pesticides).