• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.113-117
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• 2018

Human emotions are revealed by various factors. Words, actions, facial expressions, attire and so on. But people know how to hide their feelings. So we can not easily guess its sensitivity using one factor. We decided to pay attention to behaviors and facial expressions in order to solve these problems. Behavior and facial expression can not be easily concealed without constant effort and training. In this paper, we propose an algorithm to estimate human emotion through combination of two results by gradually learning human behavior and facial expression with little data through the deep learning method. Through this algorithm, we can more comprehensively grasp human emotions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3444-3457
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• 2021

In the field of anti-jamming based on dynamic spectrum, most methods try to improve the ability to avoid jamming and seldom consider whether the jammer would perceive the user's signal. Although these existing methods work in some anti-jamming scenarios, their long-term performance may be depressed when intelligent jammers can learn user's waveform or decision information from user's historical activities. Hence, we proposed a hidden anti-jamming method to address this problem by reducing the jammer's sense probability. In the proposed method, the action correlation between the user and the jammer is used to evaluate the hiding effect of the user's actions. And a deep reinforcement learning framework, including specific action correlation calculation and iteration learning algorithm, is designed to maximize the hiding and communication performance of the user synchronously. The simulation result shows that the algorithm proposed reduces the jammer's sense probability significantly and improves the user's anti-jamming performance slightly compared to the existing algorithms based on jamming avoidance.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.167-177
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• 2024

With advancements in satellite technology, interest in target detection and identification is increasing quantitatively and qualitatively. Synthetic Aperture Radar(SAR) images, which can be acquired regardless of weather conditions, have been applied to various areas combined with machine learning based detection algorithms. However, conventional studies primarily focused on the detection of stationary targets. In this study, we proposed a method to identify moving targets using an algorithm that integrates sub-aperture SAR images and cosine similarity calculations. Utilizing a transformer-based deep learning target detection model, we extracted the bounding box of each target, designated the area as a region of interest (ROI), estimated the similarity between sub-aperture SAR images, and determined movement based on a predefined similarity threshold. Through the proposed algorithm, the quantitative evaluation of target identification capability enhanced its accuracy compared to when training with the targets with two different classes. It signified the effectiveness of our approach in maintaining accuracy while reliably discerning whether a target is in motion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1341-1347
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• 2020

Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1046-1052
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• 2021

Artificial intelligence is being applied in various industrial fields to the development of the fourth industry and the construction of high-performance computing environments. In the medical field, deep learning learning such as cancer, COVID-19, and bone age measurement was performed using medical images such as X-Ray, MRI, and PET and clinical data. In addition, ICT medical fusion technology is being researched by applying smart medical devices, IoT devices and deep learning algorithms. Among these techniques, medical image-based deep learning learning requires accurate finding of medical image biomarkers, minimal loss rate and high accuracy. Therefore, in this paper, we would like to compare and analyze the performance of the Cross-Entropy function used in the image classification algorithm of the loss function that derives the loss rate in the chest X-Ray image-based deep learning learning process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.165-171
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• 2017

It is difficult to recognize Human's facial expression in the real-world. For these reason, when database and test data have similar condition, we can accomplish high accuracy. Solving these problem, we need to many facial expression data. In this paper, we propose the algorithm for gathering many facial expression data within various environment and gaining high accuracy quickly. This algorithm is training initial model with the ASSL (Active Semi-Supervised Learning) using deep learning network, thereafter gathering unlabeled facial expression data and repeating this process. Through using the ASSL, we gain proper data and high accuracy with less labor force.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.145-152
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• 2019

In this paper, we proposed a shaking image stabilization algorithm using deep learning. The proposed algorithm utilizes deep learning, unlike some 2D, 2.5D and 3D based stabilization techniques. The proposed algorithm is an algorithm that extracts and compares features of shaky images through CNN network structure and LSTM network structure, and transforms images in reverse order of movement size and direction of feature points through the difference of feature point between previous frame and current frame. The algorithm for stabilizing the shake is implemented by using CNN network and LSTM structure using Tensorflow for feature extraction and comparison of each frame. Image stabilization is implemented by using OpenCV open source. Experimental results show that the proposed algorithm can be used to stabilize the camera shake stability in the up, down, left, and right shaking images.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.1-9
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• 2022

The goal of this paper is to create a deep learning model based on triplet loss for generating similar child drawing selection algorithms. To assess the similarity of children's drawings, the distance between feature vectors belonging to the same class should be close, and the distance between feature vectors belonging to different classes should be greater. Therefore, a similar child drawing selection algorithm was developed in this study by building a deep learning model combining Triplet Loss and residual network(ResNet), which has an advantage in measuring image similarity regardless of the number of classes. Finally, using this model's similar child drawing selection algorithm, the similarity between the target child drawing and the other drawings can be measured and drawings with a high similarity can be chosen.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.388-390
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• 2021

This paper introduces a system that can help visually impaired people to board a bus using an embedded board with keypad, dot matrix, lidar sensor, NFC reader, a public data portal Open API system, and deep learning algorithm (YOLOv5). The user inputs the desired bus number through the NFC reader and keypad, and then obtains the location and expected arrival time information of the bus through the Open API real-time data through the voice output entered into the system. In addition, by displaying the bus number as the dot matrix, it can help the bus driver to wait for the visually impaired, and at the same time, a deep learning algorithm (YOLOv5) recognizes the bus number that stops in real time and detects the distance to the bus with a distance detection sensor such as lidar sensor.