• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.349-354
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• 2020

The preliminary investigation to expand the green space requires a lot of cost and time. In this paper, we solve the problem by measuring the ratio of green space in a specific region through a convolutional neural network based the green space classification using Google Earth images. First, the proposed method collects various region images in Google Earth and learns them by using the convolutional neural network. The proposed method divides the image recursively to measure the green space ratio of the specific region, and it determines whether the divided image is green space using a trained convolutional neural network model, and then the green space ratio is calculated using the regions determined as the green space. Experimental results show that the proposed method shows high performance in measuring green space ratios in various regions.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.943-950
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• 2019

Machine learning can greatly improve the efficiency of work by replacing people. In particular, the importance of machine learning is increasing according to the requests of fourth industrial revolution. This paper predicts monthly power transactions using MLP, RNN, LSTM, and ANFIS of neural network algorithms. Also, this paper used monthly electricity transactions for mount and money, final energy consumption, and diesel fuel prices for vehicle provided by the National Statistical Office, from 2001 to 2017. This paper learns each algorithm, and then shows predicted result by using time series. Moreover, this paper proposed most excellent algorithm among them by using RMSE.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.415-423
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• 2018

In recent years, Computer-based learning, such as machine learning and deep learning in the computer field, is attracting attention. They start learning from the lowest level and propagate the result to the highest level to calculate the final result. Research literature has shown that systematic learning and growth can yield good results. However, systematic models based on systematic models are hard to find, compared to various and extensive research attempts. To this end, this paper proposes the first TNT(Transitive Nested Triangle)model, which is a growth and fusion model that can be used in various aspects. This model can be said to be a recursive model in which each function formed through geometric forms an organic hierarchical relationship, and the result is used again as they grow and converge to the top. That is, it is an analytical method called 'Horizontal Sibling Merges and Upward Convergence'. This model is applicable to various aspects. In this study, we focus on explaining the TNT model.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.11-18
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• 2022

De novo drug design is the process of developing new drugs that can interact with biological targets such as protein receptors. Traditional process of de novo drug design consists of drug candidate discovery and drug development, but it requires a long time of more than 10 years to develop a new drug. Deep learning-based methods are being studied to shorten this period and efficiently find chemical compounds for new drug candidates. Many existing deep learning-based drug design models utilize recurrent neural networks to generate a chemical entity represented by SMILES strings, but due to the disadvantages of the recurrent networks, such as slow training speed and poor understanding of complex molecular formula rules, there is room for improvement. To overcome these shortcomings, we propose a deep learning model for SMILES string generation using variational autoencoders with self-attention mechanism. Our proposed model decreased the training time by 1/26 compared to the latest drug design model, as well as generated valid SMILES more effectively.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.109-112
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• 1999

본 연구에서는 신호원에 대한 사전 정보 없이 혼합된 신호로부터 잡음 신호만을 선택적으로 제거할 수 있는 새로운 형태의 능동 소음 제거기(Active noise canceller)를 제안한다. 음성신호와 같은 독특성을 갖는 신호의 분리에 효과적으로 사용되는 동적 재귀 신경망 (Dynamic recurrent neural network)을 원하는 신호원에 섞인 잡음신호를 분리하여 선택적으로 제거하기 위한 능동소음제거기의 전처리기로 미용한다. 능동 소음 제거기는 분리된 잡음 신호에 대한 역 위상 신호를 적응적으로 발생함으로써 특정 위치에서 원하는 신호만을 선택적으로 남길 수 있도록 한다. 컴퓨터를 이용한 시뮬레이션에서는 제안된 시스템이 선택적인 소음제거에 효과적임을 보인다.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.23-30
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• 1997

In this paper, a decision feedback recurrent neural equalization (DFRNE) scheme is proposed for adaptive equalization problems. The proposed equalizer models a nonlinear infinite impulse response (IIR) filter. The modified Real-Time recurrent Learning Algorithm (RTRL) is used to train the DFRNE. The DFRNE is applied to both linear channels with only intersymbol interference and nonlinear channels for digital video cassette recording (DVCR) system. And the performance of the DFRNE is compared to those of the conventional equalizaion schemes, such as a linear equalizer, a decision feedback equalizer, and neural equalizers based on multi-layer perceptron (MLP), in view of both bit error rate performance and mean squared error (MSE) convergence. It is shown that the DFRNE with a reasonable size not only gives improvement of compensating for the channel introduced distortions, but also makes the MSE converge fast and stable.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.3-8
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• 1999

In this paper, we propose a new active noise control system that cancels the only noise signal from the mixture selectively. A blind source separation realized by a dynamic recurrent neural network is used as a preprocessor of the active noise control system and separates the desired signal and the noise signal. The active noise control system adaptively generates an anti-noise signal to remove the only noise signal separated by the blind source separation. Computer simulation results show that the proposed scheme is effective to construct a selective attention system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.633-636
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• 2022

The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.475-480
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• 2011

In this paper, we propose a collision risk assesment system. First, using Kalman Filter, we estimate the information of pedestrian, and second, we compute the collision probability using Monte Carlo Simulations(MCS) and neural network(NN). And we update the collision risk using time history which is called belief. Belief update consider not only output of Kalman Filter of only current time step but also output of Kalman Filter up to the first time step to current time step. The computer simulations will be shown the validity of our proposed method.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.39-46
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• 2021

To analyze and defend malware codes, reverse engineering is used as identify function location information. However, the stripped binary is not easy to find information such as function location because function symbol information is removed. To solve this problem, there are various binary analysis tools such as BAP and BitBlaze IDA Pro, but they are based on heuristics method, so they do not perform well in general. In this paper, we propose a technique to extract function information using LSTM-based models by applying algorithms of N-byte method that is extracted binaries corresponding to reverse assembling instruments in a recursive descent method. Through experiments, the proposed techniques were superior to the existing techniques in terms of time and accuracy.