• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.273-280
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• 2022

Along with the advancement of deep learning technology, securing high-quality dataset for verification of developed technology is emerging as an important issue, and developing robust deep learning models to the domestic road environment is focused by many research groups. Especially, unlike expressways and automobile-only roads, in the complex city driving environment, various dynamic objects such as motorbikes, electric kickboards, large buses/truck, freight cars, pedestrians, and traffic lights are mixed in city road. In this paper, we built our dataset through multi camera-based processing (collection, refinement, and annotation) including the various objects in the city road and estimated quality and validity of our dataset by using YOLO-based model in object detection. Then, quantitative evaluation of our dataset is performed by comparing with the public dataset and qualitative evaluation of it is performed by comparing with experiment results using open platform. We generated our 2D dataset based on annotation rules of KITTI/COCO dataset, and compared the performance with the public dataset using the evaluation rules of KITTI/COCO dataset. As a result of comparison with public dataset, our dataset shows about 3 to 53% higher performance and thus the effectiveness of our dataset was validated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.344-349
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• 2012

This paper describes experimental results and their analysis on the new test images, called as the McMaster image dataset, to develop demosaicking techniques. The well-known image dataset for demosaicking is so far the Kodak image dataset. However, different results have been reported, as the new image dataset is engaged in developing demosaicking techniques. Thus, we conduct a series of experiments on both the McMaster dataset and the Kodak dataset; we analyze and compare those experimental results; and we provide the peculiar features of the new dataset. Also, the experimental results and their analysis indicate that the McMaster dataset deserves to be a test image dataset for future demosaicking techniques; thus, we expect they can be utilized as basic data for demosaicking.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1006-1018
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• 2020

Dataset is getting more import to develop a learning based algorithm. Quality of the algorithm definitely depends on dataset. So we introduce new dataset over 200 thousands images which are fully labeled multi modal sensor data. Proposed dataset was designed and constructed for researchers who want to develop detection, tracking, and action classification in outdoor environment for surveillance scenarios. The dataset includes various images and multi modal sensor data under different weather and lighting condition. Therefor, we hope it will be very helpful to develop more robust algorithm for systems equipped with difference kinds of sensors in outdoor application. Case studies with the proposed dataset are also discussed in this paper.

• Journal of Korean Society of Archives and Records Management
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• v.5 no.2
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• pp.103-124
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• 2005

The purpose of the study is to propose the necessity of management and long-term preservation of dataset as records. Although government and corporate bodies produce various dataset in the regular course of the business, dataset have been stored and managed in the information system. Dataset as records should be captured into the record management system and managed in the overall system. They can provide a evidence of the decision-making process of the government and fundamental information of the process. If agents do not perform the right management, dataset records will disappear in the future.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.245-254
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• 2022

Sensor dataset for autonomous driving is one of the essential components as the deep learning approaches are widely used. However, most driving datasets are focused on typical environments such as sunny or cloudy. In addition, most datasets deal with color images and lidar. In this paper, we propose a driving dataset with multi-spectral images and lidar in adverse weather conditions such as snowy, rainy, smoky, and dusty. The proposed data acquisition system has 4 types of cameras (color, near-infrared, shortwave, thermal), 1 lidar, 2 radars, and a navigation sensor. Our dataset is the first dataset that handles multi-spectral cameras in adverse weather conditions. The Proposed dataset is annotated as 2D semantic labels, 3D semantic labels, and 2D/3D bounding boxes. Many tasks are available on our dataset, for example, object detection and driveable region detection. We also present some experimental results on the adverse weather dataset.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.80-87
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• 2024

The Manchu language holds historical significance, but a complete dataset of Manchu script letters for training optical character recognition machine-learning models is currently unavailable. Therefore, this paper describes the process of creating a robust dataset of extracted Manchu script letters. Rather than performing automatic letter segmentation based on whitespace or the thickness of the central word stem, an image of the Manchu script was manually inspected, and one copy of the desired letter was selected as a region of interest. This selected region of interest was used as a template to match all other occurrences of the same letter within the Manchu script image. Although the dataset in this study contained only 4,000 images of five Manchu script letters, these letters were collected from twenty-eight writing styles. A full dataset of Manchu letters is expected to be obtained through this process. The collected dataset was normalized and trained using a simple convolutional neural network to verify its effectiveness.

• Journal of Sasang Constitutional Medicine
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• v.29 no.3
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• pp.242-247
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• 2017

Objective Korean medicine Data Center (KDC) has established large-scale biological and clinical data based on Korean medicine to demonstrate and validate its theory. The aim of this study was to develop KDC teaching dataset and user guideline to improve utilization of the KDC. Method KDC teaching dataset were selected using stratified random sampling according to the Sasang constitution (SC). This dataset included 72 variables of 500 sample subjects. The user guideline described how to conducted eight statistical analysis methods using the teaching dataset. Results The KDC teaching dataset was sampled from 200(40%) Taeeumin, 125(25%) Soeumin, and 175(35%) Soyanain. It was consisted of questionnaire (basic, habit, disease, symptom), physical exam (body measurement, blood pressure), blood exam, and expert' SC diagnosis. The usage guidelines provided instruction for users to perform several statistical analysis step by step with KDC teaching dataset. Conclusion We hope that our results will contribute to enhancing KDC utilization and understanding.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.3
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• pp.23-47
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• 2017

According to a recent survey, over 18,000 government information systems have numerous different functions and characteristics. Although every dataset that is created and maintained in government information systems is declared as a collection of records according to the Public Records Management Act, current electronic records management policies cannot cover dataset records management. This study suggests the policy directions for dataset records management at the national level. It emphasizes the necessity to preserve the appearance and behavior (function) of database systems to ensure the authenticity of dataset records. In addition, this study investigates "emulation" as a representation and long-term preservation methodology for dataset-type records. It also suggests a dataset records model.

• Journal of the Korean Society for information Management
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• v.38 no.3
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• pp.263-285
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• 2021

It is necessary to recognize the urgency and importance of administrative information dataset management and study effective management measures and specific procedures applicable in practice. Particularly, identify dataset records and developing records schedule for records management needs to be presented in detail and specific. This study designed and verified an identification method and appraisal procedure of dataset records in public administrative information systems dataset operating in public institutions. In addition, this study presented the role of the participants including the records center in the appraisal process. Through this, useful implications are derived for the development of specific and practical processes and tools for dataset management in the records center.