• 여성건강간호학회지
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• 제26권1호
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• pp.5-9
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• 2020

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

• 정보과학회 논문지
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• 제44권6호
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• pp.607-612
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• 2017

도시에서 홍수 피해를 방지하기 위한 침수를 예측하기 위해 본 논문에서는 딥러닝(Deep Learning) 기법을 적용한다. 딥러닝 기법 중 시계열 데이터 분석에 적합한 Recurrent Neural Networks (RNNs)을 활용하여 강의 수위 관측 데이터를 학습하고 침수 가능성을 예측하였다. 예측 정확도 검증을 위해 사용한 데이터는 미국의 트리니티강의 데이터로, 학습을 위해 2013 년부터 2015 년까지 데이터를 사용하였고 평가 데이터로는 2016 년 데이터를 사용하였다. 입력은 16개의 레코드로 구성된 15분단위의 시계열 데이터를 사용하였고, 출력으로는 30분과 60분 후의 강의 수위 예측 정보이다. 실험에 사용한 딥러닝 모델들은 표준 RNN, RNN-BPTT(Back Propagation Through Time), LSTM(Long Short-Term Memory)을 사용했는데, 그 중 LSTM의 NE(Nash Efficiency)가 0.98을 넘는 정확도로 기존 연구에 비해 매우 높은 성능 향상을 보였고, 표준 RNN과 RNN-BPTT에 비해서도 좋은 성능을 보였다.

• 한국인터넷방송통신학회논문지
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• 제17권5호
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• pp.97-102
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• 2017

최근 고해상도 원격탐사 자료의 분류방안으로 컨볼루션 신경망(Convolutional Neural Networks)을 비롯한 딥 러닝 기법들이 소개되고 있다. 본 논문에서는 드론으로 촬영된 농경지 영상의 작물 분류를 위해 컨볼루션 신경망을 적용하여 가능성을 검토하였다. 농경지를 논, 고구마, 고추, 옥수수, 깻잎, 과수, 비닐하우스로 총 7가지 클래스로 나누고 수동으로 라벨링 작업을 완료했다. 컨볼루션 신경망 적용을 위해 영상 전처리와 정규화 작업을 수행하였으며 영상분류 결과 98%이상 높은 정확도를 확인할 수 있었다. 본 논문을 통해 기존 영상분류 방법들에서 딥 러닝 기반 영상분류 방법으로의 전환이 빠르게 진행될 것으로 예상되며, 그 성공 가능성을 확신할 수 있었다.

• 한국정보시스템학회지:정보시스템연구
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• 제27권4호
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• pp.115-128
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• 2018

Purpose The purpose of this study is to predict future behaviors of business process. Specifically, this study tried to predict the last activities of process instances. It contributes to overcoming the limitations of existing approaches that they do not accurately reflect the actual behavior of business process and it requires a lot of effort and time every time they are applied to specific processes. Design/methodology/approach This study proposed a novel approach based using deep learning in the form of dynamic recurrent neural networks. To improve the accuracy of our prediction model based on the approach, we tried to adopt the latest techniques including new initialization functions(Xavier and He initializations). The proposed approach has been verified using real-life data of a domestic small and medium-sized business. Findings According to the experiment result, our approach achieves better prediction accuracy than the latest approach based on the static recurrent neural networks. It is also proved that much less effort and time are required to predict the behavior of business processes.

• International Journal of Internet, Broadcasting and Communication
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• 제14권1호
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• pp.136-141
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• 2022

A very complex task in deep learning such as image classification must be solved with the help of neural networks and activation functions. The backpropagation algorithm advances backward from the output layer towards the input layer, the gradients often get smaller and smaller and approach zero which eventually leaves the weights of the initial or lower layers nearly unchanged, as a result, the gradient descent never converges to the optimum. We propose a two-factor non-saturating activation functions known as Bea-Mish for machine learning applications in deep neural networks. Our method uses two factors, beta (𝛽) and alpha (𝛼), to normalize the area below the boundary in the Mish activation function and we regard these elements as Bea. Bea-Mish provide a clear understanding of the behaviors and conditions governing this regularization term can lead to a more principled approach for constructing better performing activation functions. We evaluate Bea-Mish results against Mish and Swish activation functions in various models and data sets. Empirical results show that our approach (Bea-Mish) outperforms native Mish using SqueezeNet backbone with an average precision (AP50val) of 2.51% in CIFAR-10 and top-1accuracy in ResNet-50 on ImageNet-1k. shows an improvement of 1.20%.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제7권8호
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• pp.307-324
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• 2018

최근 순환 신경 망(Recurrent Neural Networks)은 시간에 대한 의존성을 고려한 구조를 통해 순차 데이터(Sequential data)의 예측 문제 해결에서 각광받고 있다. 하지만 순차 데이터의 시간 스텝이 늘어남에 따라 발생하는 그라디언트 소실(Gradients vanishing)이 문제로 대두되었다. 이를 해결하기 위해 장단기 기억 모델(Long Short-Term Memory)이 제안되었지만, 많은 데이터를 저장하고 장기간 보존하는 데에 한계가 있다. 따라서 순환 신경망과 메모리 요소(Memory component)를 활용한 학습 모델인 메모리-증대 신경망(Memory-Augmented Neural Networks)에 대한 연구가 최근 활발히 진행되고 있다. 본 논문에서는 딥 러닝(Deep Learning) 분야의 화두로 떠오른 메모리-증대 신경망 주요 모델들의 구조와 특징을 열거하고, 이를 활용한 최신 기법들과 향후 연구 방향을 제시한다.

• 반도체디스플레이기술학회지
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• 제22권2호
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• pp.128-132
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• 2023

Recently, with the development of deep learning technology, research on pedestrian attribute recognition technology using deep neural networks has been actively conducted. Existing pedestrian attribute recognition techniques can be obtained in such a way as global-based, regional-area-based, visual attention-based, sequential prediction-based, and newly designed loss function-based, depending on how pedestrian attributes are detected. It is known that the performance of these pedestrian attribute recognition technologies varies greatly depending on the type of backbone network that constitutes the deep neural networks model. Therefore, in this paper, several backbone networks are applied to the baseline pedestrian attribute recognition model and the performance changes of the model are analyzed. In this paper, the analysis is conducted using Resnet34, Resnet50, Resnet101, Swin-tiny, and Swinv2-tiny, which are representative backbone networks used in the fields of image classification, object detection, etc. Furthermore, this paper analyzes the change in time complexity when inferencing each backbone network using a CPU and a GPU.

• 스마트미디어저널
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• 제11권3호
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• pp.26-30
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• 2022

스파이킹 신경망은 실제 두뇌 뉴런의 작동원리를 적용한 신경망으로, 뉴런의 생물학적 메커니즘으로 인해 기존 신경망보다 학습과 추론에 소모되는 전력이 적다. 최근 딥러닝 모델이 거대해지며 운용에 소모되는 비용 또한 기하급수적으로 증가함에 따라 스파이킹 신경망은 합성곱, 순환 신경망을 잇는 3세대 신경망으로 주목받으며 관련 연구가 활발히 진행되고 있다. 그러나 스파이킹 신경망 모델을 산업에 적용하기 위해서는 아직 선행되어야 할 연구가 많이 남아있고, 새로운 모델을 적용하기 위한 모델 재학습 문제 역시 해결해야 한다. 본 논문에서는 기존의 학습된 딥러닝 모델의 가중치를 추출하여 스파이킹 신경망 모델의 가중치로 변환하는 것으로 모델 재학습 비용을 최소화하는 방법을 제안한다. 또한, 변환된 가중치를 사용한 추론 결과와 기존 모델의 결과를 비교해 가중치 변환이 올바르게 작동함을 보인다.

• ETRI Journal
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• 제39권6호
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• pp.832-840
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• 2017

Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception-based spatial and spectral-domain noise-reduced harmonic features are extracted from multichannel audio and used as high-resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short-term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.