• International journal of advanced smart convergence
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• v.5 no.3
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.75-86
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• 2022

This study aims to solve the problem of class imbalance in numerical data by using a deep learning-based Variational AutoEncoder and to improve the performance of the learning model by augmenting the learning data. We propose 'D-VAE' to artificially increase the number of records for a given table data. The main features of the proposed technique go through discretization and feature selection in the preprocessing process to optimize the data. In the discretization process, K-means are applied and grouped, and then converted into one-hot vectors by one-hot encoding technique. Subsequently, for memory efficiency, sample data are generated with Variational AutoEncoder using only features that help predict with RFECV among feature selection techniques. To verify the performance of the proposed model, we demonstrate its validity by conducting experiments by data augmentation ratio.

• Journal of Life Science
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• v.12 no.2
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• pp.53-56
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• 2002

The baculovirus expression vector system (BEVS) is one of the powerful heterologous protein expression systems using insect cells. As a result this has become a hot issue in the fleld of biotechnology. The advantage of the BEVS is that the large-scale production of heterologous proteins, which undergo posttranslational modification in the endoplasmic reticulum (ER), can be accomplished. Altrough posttranslational modification of heterologous proteins in insect cells is more similar to mammalian cells than yeast, it is not always identical. Therefore, aggregation and degradation can sometimes occur in the ER. To produce a high level of bioactive heterologous proteins using BEVS in insect cells, the prerequisite is to completely understand the posttranslational conditions that determine how newly synthesized polypeptides are folded and assembling with ER chaperones in the ER lumen. Here, we provide information on current BEVS problems and the possibility of successful heterologous protein production from mammalian cells.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.795-797
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• 2019

In this paper, we propose a method to generate adversarial examples for toxicity detection neural networks. Our dataset is represented by a one-hot vector and we constrain that only one character is allowed to be modified. The location to be changed is founded by the maximum area of input gradient, which represents the most affecting character the model to make decisions. Despite the fact that we have strong constraint compared to the image-based adversarial attack, we have achieved about 49% successful rate.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.590-593
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• 2011

Time goes on, Ad Hoc network is hot issues. So far, there are a lot of protocols have been proposed for Ad Hoc routing protocol to support the mobility. This paper presents an enhanced DSDV(Destination-Sequenced Distance Vector) routing protocol which nominates one node to take care of a specific area. Simply Area-Aware(AA) DSDV routing protocol has one nominee to take care of some area. It has two jobs. One is to take care of its neighbour and another is to transfer the routing table to its other node as it works. It is called as Area Nominee(AN). The new scheme extends the routing table to include the nominee in the area. The general node is the same as the previous DSDV routing protocol. In the other hands, the node which is nominated has two routing protocols. One is for Regional Routing(RR) table which is the same routing table in DSDV. Another is Global Routing(GR) table which is about the area round its area which it cares nearby. GR table is the table for the designated node like the nominee. Each area has one nominee to transfer between ANs. It has only nominee's information about every area. This concept decreases the topology size and makes the information of topology more accurate.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.35.2-35.2
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• 2011

We have investigated a coronal jet near the limb on 2010 June 27 by Hinode/X-Ray Telescope (XRT), EUV Imaging Spectrograph (EIS), SDO/Atmospheric Imaging Assembly (AIA), and STEREO. From EUV (AIA and EIS) and soft X-ray (XRT) images we identify the erupting jet feature in cool and hot temperatures. Using the high temporal and multi wavelength AIA images, we found that the hot jet preceded its associated cool jet and their structures are well consistent with the numerical simulation of the emerging flux-reconnection model. From the spectroscopic analysis, we found that the jet structure changes from blue shift to red one with time, which may indicate the helical structure of the jet. The STEREO observation, which enables us to observe this jet on the disk, shows that there was a dim loop associated with the jet. On the other hand, we found that the structure of its associated active region seen in STEREO is similar to that in AIA observed 5 days before. Based on this fact, we compared the jet morphology on the limb with the magnectic fields extrapolated from a HMI vector magnetogram of this active region observed on the disk. Interestingly, the comparison shows that the open and closed magnetic field configuration correspond to the jet and the dim loop, respectively, as the Shibata's jet model predicted.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.147-150
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• 2017

Deep learning의 개발에 따라 개체명 인식에도 neural network가 적용된 연구가 활발히 일어나고 있다. 영어권 개체명 인식에서는 F1 score 90%을 웃도는 성능을 내는 연구들이 나오고 있다. 하지만 한국어는 영어와 언어적 특질이 많이 달라 이를 그대로 적용시키는 데는 어려움이 있어 영어권 개체명 인식기에 비해 비교적 낮은 성능을 보인다. 본 논문에서는 "하다" 접사의 동사형이 보존된 워드 임베딩을 사용하고 한국어 개체명의 특징을 담은 one-hot 벡터를 추가하여 한국어의 특질에 보다 적합한 데이터를 deep learning 기술에 적용하였다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.147-150
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• 2017

Deep learning의 개발에 따라 개체명 인식에도 neural network가 적용된 연구가 활발히 일어나고 있다. 영어권 개체명 인식에서는 F1 score 90%을 웃도는 성능을 내는 연구들이 나오고 있다. 하지만 한국어는 영어와 언어적 특질이 많이 달라 이를 그대로 적용시키는 데는 어려움이 있어 영어권 개체명 인식기에 비해 비교적 낮은 성능을 보인다. 본 논문에서는 "하다" 접사의 동사형이 보존된 워드 임베딩을 사용하고 한국어 개체명의 특징을 담은 one-hot 벡터를 추가하여 한국어의 특질에 보다 적합한 데이터를 deep learning 기술에 적용하였다.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.148-152
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• 2018

Adversarial attacks on artificial intelligence (AI) systems use adversarial examples to achieve the attack objective. Adversarial examples consist of slightly changed test data, causing AI systems to make false decisions on these examples. When used as a tool for attacking AI systems, this can lead to disastrous results. In this paper, we propose an ensemble of degraded convolutional neural network (CNN) modules, which is more robust to adversarial attacks than conventional CNNs. Each module is trained on degraded images. During testing, images are degraded using various degradation methods, and a final decision is made utilizing a one-hot encoding vector that is obtained by summing up all the output vectors of the modules. Experimental results show that the proposed ensemble network is more resilient to adversarial attacks than conventional networks, while the accuracies for normal images are similar.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.725-732
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• 2020

This paper uses variables following as : to follow me well(0-9), it takes a lot of time to make a decision (0-9), lethargy(0-9) during physical activity in the exercise learning program of the children in the marginalized class. This paper classifies 'gender', 'physical education classroom', and 'upper, middle and lower' of age, and observe changes in ego-resiliency and self-control through sports rehabilitation therapy to find out changes in mental health. To achieve this, the data acquired was merged and the characteristics of large and small numbers were removed using the Label encoder and One-hot encoding. Then, to evaluate the performance by applying each algorithm of MLP, SVM, Dicesion tree, RNN, and LSTM, the train and test data were divided by 75% and 25%, and then the algorithm was learned with train data and the accuracy of the algorithm was measured with the Test data. As a result of the measurement, LSTM was the most effective in sex, MLP and LSTM in physical education classroom, and SVM was the most effective in age.