• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.328-337
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• 2021

Air pollution, which is a class 1 carcinogen, such as asbestos and benzene, is the cause of various diseases. The spread of ultra-air pollution is one of the important causes of the spread of the corona virus. This paper analyzes and predicts fine dust and ultra-air pollution from 2015 to 2019 based on weather data such as average temperature, precipitation, and average wind speed in Seoul and atmospheric environment data such as SO2, NO2, and O3. Linear regression, SVM, and ensemble models among machine learning models were compared and analyzed to predict fine dust by grasping and analyzing the status of air pollution and ultra-air pollution by season and month. In addition, important features(attributes) that affect the generation of fine dust and ultra-air pollution are identified. The highest ultra-air pollution was found in March, and the lowest ultra-air pollution was observed from August to September. In the case of meteorological data, the data that has the most influence on ultra-air pollution is average temperature, and in the case of meteorological data and atmospheric environment data, NO2 has the greatest effect on ultra-air pollution generation.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.43-50
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• 2022

Correlation Power Analysis(CPA) is a sub-channel attack method that measures the detailed power consumption of attack target equipment equipped with cryptographic algorithms and guesses the secret key used in cryptographic algorithms with more than 90% probability. Since CPA performs analysis based on statistics, a large amount of data is necessarily required. Therefore, the CPA must measure power consumption for at least about 15 minutes for each attack. In this paper proposes a method of using a Convolutional Neural Network(CNN) capable of accumulating input data and predicting results to solve the data collection problem of CPA. By collecting and learning the power consumption of the target equipment in advance, entering any power consumption can immediately estimate the secret key, improving the computational speed and 96.7% of the secret key estimation accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.367-373
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• 2022

As deep learning-based autonomous driving technology develops, artificial intelligence models for various purposes have been studied. Based on these studies, several models were used simultaneously to develop autonomous driving systems. It can occur by increasing hardware resource consumption. We propose a multi-tasks model using a shared backbone to solve this problem. This can solve the increase in the number of backbones for using AI models. As a result, in the proposed lightweight model, the model parameters could be reduced by more than 50% compared to the existing model, and the speed could be improved. In addition, each lane can be classified through lane detection using the instance segmentation method. However, further research is needed on the decrease in accuracy compared to the existing model.

• Smart Media Journal
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• v.11 no.5
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• pp.26-37
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• 2022

In this paper, images are automatically acquired to control the initial disease of strawberries among facility cultivation crops, and disease analysis is performed using the EfficientNet model to inform farmers of disease status, and disease diagnosis service is proposed by experts. It is possible to obtain an image of the strawberry growth stage and quickly receive expert feedback after transmitting the disease diagnosis analysis results to farmers applications using the learned EfficientNet model. As a data set, farmers who are actually operating facility cultivation were recruited and images were acquired using the system, and the problem of lack of data was solved by using the draft image taken with a cell phone. Experimental results show that the accuracy of EfficientNet B0 to B7 is similar, so we adopt B0 with the fastest inference speed. For performance improvement, Fine-tuning was performed using a pre-trained model with ImageNet, and rapid performance improvement was confirmed from 100 Epoch. The proposed service is expected to increase production by quickly detecting initial diseases.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.443-450
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• 2022

The method of studying 3D-technologies abroad and in Ukraine is considered. The analysis of educational resources and experience of use of the equipment in branch is carried out. The didactic principles of the educational process for 3D-printing specialists are determined. The use of FDM technology and the ability to minimize the occurrence of defects in the future have been studied. An analysis of the international experience of the educational process of relevant specialists in the field. The content of training for 3D printing specialists has been developed. The experience of using 3D-technologies is described and the list of recommendations for elimination of defects during production of products by means of additive technologies is made. The recommendations will be useful not only for beginners, but also for experienced professionals in additive technologies. The need to study such experience is the main condition for the development of enterprises in Ukraine that plan to automate their own production. A 3D printing engineer must know the basics of economics and marketing, because his responsibilities include optimizing workflows to reduce the cost and speed up printing. Therefore, the knowledge gained from practical experience presented and in building for learning 3D printing engineers by the authors will be important.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1248-1254
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• 2022

Efforts are being made to prevent traffic accidents in the school zone in advance. However, traffic accidents in school zones continue to occur. If the driver can know the situation information in the child protection area in advance, accidents can be reduced. In this paper, we design a camera that eliminates blind spots in school zones and a number recognition camera system that can collect pre-traffic information. It is designed by improving the LIDAR system that recognizes vehicle speed and pedestrians. It collects and processes pedestrian and vehicle image information recognized by cameras and LIDAR, and applies artificial intelligence time series analysis and artificial intelligence algorithms. The artificial intelligence traffic accident prevention system learned by deep learning proposed in this paper provides a forced push service that delivers school zone information to the driver to the mobile device in the vehicle before entering the school zone. In addition, school zone traffic information is provided as an alarm on the LED signboard.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.778-789
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• 2021

Recently, various lightweight methods for using Convolutional Neural Network(CNN) models in mobile devices have emerged. Weight quantization, which lowers bit precision of weights, is a lightweight method that enables a model to be used through integer calculation in a mobile environment where GPU acceleration is unable. Weight quantization has already been used in various models as a lightweight method to reduce computational complexity and model size with a small loss of accuracy. Considering the size of memory and computing speed as well as the storage size of the device and the limited network environment, this paper proposes a method of compressing integer weights after quantization using a video codec as a method. To verify the performance of the proposed method, experiments were conducted on VGG16, Resnet50, and Resnet18 models trained with ImageNet and Places365 datasets. As a result, loss of accuracy less than 2% and high compression efficiency were achieved in various models. In addition, as a result of comparison with similar compression methods, it was verified that the compression efficiency was more than doubled.

• Journal of Advanced Navigation Technology
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• v.26 no.2
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• pp.113-118
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• 2022

Motivated by people highlighting important phrases while reading or taking notes we propose a neural network training method by highlighting defective pixel areas to classify effectively defect types of images with complex background textures. To verify our proposed method we apply it to the problem of classifying the defect types of tire band fabric images that are too difficult to classify. In addition we propose a backlight highlighting technique which is tailored to the tire band fabric images. Backlight highlighting images can be generated by using both the GradCAM and simple image processing. In our experiment we demonstrated that the proposed highlighting method outperforms the traditional method in the view points of both classification accuracy and training speed. It achieved up to 13.4% accuracy improvement compared to the conventional method. We also showed that the backlight highlighting technique tailored for highlighting tire band fabric images is superior to a contour highlighting technique in terms of accuracy.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.47-53
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• 2021

In this paper, we propose a quadtree-based optimization technique that enables fast Super-resolution(SR) computation by efficiently classifying and dividing physics-based simulation data required to calculate SR. The proposed method reduces the time required for quadtree computation by downscaling the smoke simulation data used as input data. By binarizing the density of the smoke in this process, a quadtree is constructed while mitigating the problem of numerical loss of density in the downscaling process. The data used for training is the COCO 2017 Dataset, and the artificial neural network uses a VGG19-based network. In order to prevent data loss when passing through the convolutional layer, similar to the residual method, the output value of the previous layer is added and learned. In the case of smoke, the proposed method achieved a speed improvement of about 15 to 18 times compared to the previous approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4065-4083
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• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.