• Journal of information and communication convergence engineering
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• 제21권2호
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• pp.159-166
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• 2023

The COI gene is a sequence of approximately 650 bp at the 5' terminal of the mitochondrial Cytochrome c Oxidase subunit I (COI) gene. As an effective DeoxyriboNucleic Acid (DNA) barcode, it is widely used for the taxonomic identification and evolutionary analysis of species. We created a CNN-LSTM hybrid model by combining the gene features partially extracted by the Long Short-Term Memory ( LSTM ) network with the feature maps obtained by the CNN. Compared to K-Means Clustering, Support Vector Machines (SVM), and a single CNN classification model, after training 278 samples in a training set that included 15 genera from two orders, the CNN-LSTM hybrid model achieved 94% accuracy in the test set, which contained 118 samples. We augmented the training set samples and four genera into four orders, and the classification accuracy of the test set reached 100%. This study also proposes calculating the cosine similarity between the training and test sets to initially assess the reliability of the predicted results and discover new species.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.297-301
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• 2023

The study identified the various causes of odor problems, the discomfort they cause, and the importance of the public health and environmental issues associated with them. To solve the odor problem, you must identify the cause and perform an accurate analysis. Therefore, we proposed a CNN-Transformer hybrid model (CTHM) that combines CNN and Transformer and evaluated its performance. It was evaluated using a dataset consisting of 120,000 odor samples, and experimental results showed that CTHM achieved an accuracy of 93.000%, a precision of 92.553%, a recall of 94.167%, an F1 score of 92.880%, and an RMSE of 0.276. Our results showed that CTHM was suitable for odor analysis and had excellent prediction performance. Utilization of this model is expected to help address odor problems and alleviate public health and environmental concerns.

• 항공우주시스템공학회지
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• 제16권4호
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• pp.45-52
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• 2022

최근 차세대 운송시스템으로 주목받고 있는 UAM 분야에서 무인항공기 활용을 위한 기술 개발이 활발히 진행되고 있다. 이러한 기술이 적용된 무인항공기는 주로 도심에서 운용되기 때문에 추락사고를 예방하는 것이 중요하다. 그러나 충돌이 발생되는 무인항공기는 비선형성이 강하기 때문에 비정상 비행상태를 예측하는 것은 쉽지 않은 일이다. 본 논문에서는 CNN-LSTM 혼합모델을 이용하여 무인항공기의 비행상태를 예측하는 방법을 제안한다. 제안 모델은 비행 데이터간의 시간적, 공간적 특징을 추출하는 CNN 모델과 추출된 특징의 장단기 시간 의존성을 추출하는 LSTM 모델을 결합하여 미래의 특정 시점에서 비행 상태변수를 예측한다. 모의 실험은 제안하는 방법이 기존 인공신경망 모델에 기반한 예측 방법보다 우수한 성능을 보인다.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• 대한원격탐사학회지
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• 제37권4호
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.159-172
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• 2024

The number of healthcare products available for pets has increased in recent times, which has prompted active research into wearable devices for pets. However, the data collected through such devices are limited by outliers and missing values owing to the anomalous and irregular characteristics of pets. Hence, we propose pet behavior recognition based on a hybrid one-dimensional convolutional neural network (CNN) and long short- term memory (LSTM) model using pet wearable devices. An Arduino-based pet wearable device was first fabricated to collect data for behavior recognition, where gyroscope and accelerometer values were collected using the device. Then, data augmentation was performed after replacing any missing values and outliers via preprocessing. At this time, the behaviors were classified into five types. To prevent bias from specific actions in the data augmentation, the number of datasets was compared and balanced, and CNN-LSTM-based deep learning was performed. The five subdivided behaviors and overall performance were then evaluated, and the overall accuracy of behavior recognition was found to be about 88.76%.

• 한국지반신소재학회논문집
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• 제21권2호
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• pp.11-19
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• 2022

본 연구에서는 하수관거 내부에서 촬영된 균열 데이터를 활용하여 균열검출에 대한 시계열 예측 성능을 개선하기 위해 GoogleNet의 전이학습과 CNN- LSTM(Long Short-Term Memory) 결합 방법을 제안하였다. LSTM은 합성곱방법(CNN)의 장기의존성 문제를 해결할 수 있으며 공간 및 시간적 특징을 동시에 모델링 할 수 있다. 제안 방법의 성능을 검증하기 위해 하수관거 내부 균열 데이터를 활용하여 학습데이터, 초기학습률 및 최대 Epochs를 변화하면서 RMSE를 비교한 결과 모든 시험 구간에서 제안 방법의 예측 성능이 우수함을 알 수 있다. 또한 데이터가 발생하는 시점에 대한 예측 성능을 살펴본 결과 역시 제안방법이 우수하게 나타나 균열검출의 예측에서 제안 방법이 효율적인 것을 검증하였다. 기존 합성곱방법(CNN) 단독 모델과 비교함으로써 본 연구를 통해 확보된 제안 방법과 실험 결과를 활용할 경우 콘크리트 구조물의 균열데이터뿐만 아니라 시계열 데이터가 많이 발생하는 환경, 인문과학 등 다양한 영역에서 응용이 가능하다.

• Journal of Information Processing Systems
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• 제19권6호
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.

• International Journal of Advanced Culture Technology
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• 제11권4호
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• pp.464-469
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• 2023

As modern society develops, the number of diseases caused by bad smells is increasing. As it can harm people's health, it is important to predict in advance the extent to which bad smells may occur, inform the public about this, and take preventive measures. In this paper, we propose a hybrid neural network structure of CNN and LSTM that can be used to detect or predict the occurrence of odors, which are most required in manufacturing or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data, including hydrogen sulfide, ammonia, benzene, and toluene, in real time, and applies this data to the inference model to detect and predict the odor state. The proposed model evaluated the prediction accuracy of the training model through performance indicators based on accuracy, and the evaluation results showed an average performance of more than 94%.

• 한국정보통신학회논문지
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• 제26권1호
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• pp.76-84
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• 2022

부정맥은 심장 박동이 비정상 혹은 불규칙하게 뛰고 있는 상태를 말하며, 실신이나 심장돌연사 등과 같은 위험한 상황을 유발할 수 있기 때문에 이의 조기 검출은 매우 중요하다. 하지만 심전도 신호의 개인차로 인해 분류 시 성능하락이 나타날 수밖에 없다. 본 연구에서는 CNN-LSTM 하이브리드 결합 모델을 이용한 부정맥 분류 방법을 제안한다. 이를 위해 먼저 잡음을 제거한 ECG 신호에서 R파를 검출하고 단일 비트 세그먼트를 추출하였다. 이후 부정맥 신호의 특징을 세밀하게 추출하도록 8개의 합성곱 계층으로 구성하고 이를 LSTM의 입력으로 사용한 후 가중치를 학습시키고 검증 데이터로 모델을 평가한 후 정상 및 부정맥 분류의 변화를 확인하였다. 제안한 방법의 타당성 검증을 위해 MIT-BIH 부정맥 데이터베이스를 사용하여 정확도(accuracy), 정밀도(precision), 재현율(recall), F1 스코어가 사용되었다. 성능평가 결과, 정확도, 정밀도, 재현율, F1 스코어는 각각 92.3%, 90.98%, 92.20%, 90.72%의 우수한 분류율을 나타내었다.