• Smart Media Journal
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• v.12 no.3
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• pp.49-59
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• 2023

Optical Camera Communication (OCC), known as the next-generation wireless communication technology, is currently under extensive research. The performance of OCC technology is affected by the communication environment, and various strategies are being studied to improve it. Among them, the most prominent method is applying convolutional neural networks (CNN) to the receiver of OCC using deep learning technology. However, in most studies, CNN is simply used to detect the transmitter. In this paper, we experiment with applying the convolutional neural network not only for transmitter detection but also for the Rx demodulation system. We hypothesize that, since the data images of the OCC system are relatively simple to classify compared to other image datasets, high accuracy results will appear in most CNN models. To prove this hypothesis, we designed and implemented an OCC system to collect data and applied it to 12 different CNN models for experimentation. The experimental results showed that not only high-performance CNN models with many parameters but also lightweight CNN models achieved an accuracy of over 99%. Through this, we confirmed the feasibility of applying the OCC system in real-time on mobile devices such as smartphones.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.275-277
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• 2017

사물인터넷(Internet of things)의 발전과 함께 스마트 팩토리에 대한 관심이 증대되고 있다. 제조의 전 과정에서 발생하는 데이터를 실시간으로 수집하고 관리를 자동화하는 것이 스마트 팩토리의 목적이다. 그러나 공장에서는 현재까지도 많이 사용되는 아날로그 게이지를 관리하는 일은 사람의 노동력을 필요로 한다. 또한 아날로그 게이지는 쓰임새에 따라 모양과 형태가 매우 다양하다. 본 논문에서는 아날로그 게이지의 형태에 따라 분류하는 방법에 대해 제안한다. 제안하는 방법은 학습하기 위해 필요한 게이지 영상 데이터를 수집하고 나서 각 분류에 속하는 이미지 데이터를 CNN(Convolutional Neural Network) 딥러닝 기법으로 학습시킨 후, 각 분류에 해당하는 특징 정보를 추출하고 아날로그 게이지의 형태를 인식하는 방법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.795-798
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• 2017

근래에 들어, 웨어러블 기기의 발전으로 사람의 움직임에 대한 측정이 손쉬워 지면서, 워킹, 러닝, 사이클링 등의 인간의 신체 활동 상태를 감지하여 더 효율적인 운동을 할 수 있도록 정보를 획득, 제공하려는 연구가 계속되고 있다. 본 연구에서는 웨어러블 기기중 하나인 스마트 인솔을 통해서 수집되는 가속도 정보와 압력 정보를 사용하여 운동시에 사람의 운동 자세를 감지하고 측정하는 시스템을 구현하였다. 사람이 헬스센터에서 수행하는 각각의 자세는 운동의 특성에 따라 시계열 신호의 표현 패턴이 다르게 나타나며 이 패턴을 통한 정확한 자세의 감지를 위해서 본 연구에서는 다양한 신호처리 알고리즘을 사용하였으며 이 경우 더 정확한 자세를 측정할 수 있음을 알 수 있었다. 따라서 본 연구에서는 정확한 자세의 감지를 위해 운동의 특징에 따라 알고리즘을 선택하여 시계열 정보를 처리 분석 하는 시스템을 제안하였으며 이를 통해 보다 정확하게 사람의 신체활동을 분석할 수 있었다.

• Smart Media Journal
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• v.10 no.2
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• pp.76-83
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• 2021

With the development and use of smart devices such as smartphones and tablets increases, the mobile application market based on mobile devices is growing rapidly. Mobile application users write reviews to share their experience in using the application, which can identify consumers' various needs and application developers can receive useful feedback on improving the application through reviews written by consumers. However, there is a need to come up with measures to minimize the amount of time and expense that consumers have to pay to manually analyze the large amount of reviews they leave. In this work, we propose to collect delivery application user reviews from Google PlayStore and then use machine learning and deep learning techniques to classify them into four categories like application feature advantages, disadvantages, feature improvement requests and bug report. In the case of the performance of the Hugging Face's pretrained BERT-based Transformer model, the f1 score values for the above four categories were 0.93, 0.51, 0.76, and 0.83, respectively, showing superior performance than LSTM and GRU.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.195-200
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• 2021

With the development of transportation and mobiles demand for tourism travel is increasing and related industries are also developing significantly. The combination of augmented reality and tourism contents one of the areas of digital media technology, is also actively being studied, and artificial intelligence is already combined with the tourism industry in various directions, enriching tourists' travel experiences. In this paper, we propose a system that scans miniature models produced by reducing tourist areas, finds the relevant tourist sites based on models learned using deep learning in advance, and provides relevant information and 3D models as AR services. Because model learning and object detection are carried out using YOLOv3 neural networks, one of various deep learning neural networks, object detection can be performed at a fast rate to provide real-time service.

• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.327-333
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• 2019

The Fuel-cut driving is started when the acceleration pedal released with transmission gear engaged. Fuel economy of the vehicle improves by active fuel-cut driving. A deep-learning technique is proposed to predict fuel-cut driving with vehicle speed, acceleration and road gradient data in the study. It's 3~10 of hidden layers and 10~20 of variables and is applied to the 9600 data obtained in the test driving of a vehicle in the road of 12km. Its accuracy is about 84.5% with 10 variables, 7 hidden layers and Relu as activation function. Its error is regarded from the fact that the change rate of input data is higher than the rate of fuel consumption data. Therefore the accuracy can be better by the normalizing process of input data. It's unnecessary to get the signal of vehicle injector or OBD, and a deep-learning technique applied to the data to be got easily, like GPS. It can contribute to eco-drive for the computing time small.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.359-364
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• 2021

Recently, research on predictive systems using deep learning and machine learning of artificial intelligence is being actively conducted. Due to the development of artificial intelligence, the role of the investment manager is being replaced by artificial intelligence, and due to the higher rate of return than the investment manager, algorithmic trading using artificial intelligence is becoming more common. Algorithmic trading excludes human emotions and trades mechanically according to conditions, so it comes out higher than human trading yields when approached in the long term. The deep learning technique of artificial intelligence learns past time series data and predicts the future, so it learns like a human and can respond to changing strategies. In particular, the LSTM technique is used to predict the future by increasing the weight of recent data by remembering or forgetting part of past data. fbprophet, an artificial intelligence algorithm recently developed by Facebook, boasts high prediction accuracy and is used to predict stock prices and cryptocurrency prices. Therefore, this study intends to establish a sound investment culture by providing a new algorithm for automatic cryptocurrency trading by analyzing the actual value and difference using fbprophet and presenting conditions for accurate prediction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4193-4199
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• 2012

In this study, the intelligent robot equipped with an Android-based smartphone to enable the implementation of the performance of smartphone applications and robot platform has been designed and implemented. Smart phone that have touch screen, sound input/output, network and various sensor functions to robot platform that have simplicity function of power and motor etc. graft together and embodied so that can achieve function of remote control, home automation, game machine, R-running race etc. Phone used in the study of the Bluetooth communication sending and receiving data between the robot and from a remote computer over the Internet via WI-FI is designed to perform communication.

• Smart Media Journal
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• v.12 no.2
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• pp.66-75
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• 2023

Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are difficult to identify using traditional security solutions that monitor network metrics. In this paper, we propose a new detection model for detecting HTTP DoS attacks on cloud web applications by collecting the application metrics of web servers and learning them using machine learning. We collected 18 types of application metrics from an Apache web server and used five machine learning and two deep learning models to train the collected data. Further, we confirmed the superiority of the application metrics-based machine learning model by collecting and training 6 additional network metrics and comparing their performance with the proposed models. Among HTTP DoS attacks, we injected the RUDY and HULK attacks, which are low- and high-bandwidth attacks, respectively. As a result of detecting these two attacks using the proposed model, we found out that the F1 scores of the application metrics-based machine learning model were about 0.3 and 0.1 higher than that of the network metrics-based model, respectively.