• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.368-376
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• 2021

With the recent development of deep learning technology, autonomous things technology is attracting attention, and DNNs are widely used in embedded systems such as drones and autonomous vehicles. Embedded systems that can perform large-scale operations and process multiple DNNs for high recognition accuracy without relying on the cloud are being released. DNNs with various levels of priority exist within these systems. DNNs related to the safety-critical applications of autonomous vehicles have the highest priority, and they must be handled first. In this paper, we propose a priority-based scheduling framework for DNNs when multiple DNNs are executed simultaneously. Even if a low-priority DNN is being executed first, a high-priority DNN can preempt it, guaranteeing the fast response characteristics of safety-critical applications of autonomous vehicles. As a result of checking through extensive experiments, the performance improved by up to 76.6% in the actual commercial board.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.517-525
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• 2022

In the era of big data, various data mining techniques have been proposed as major analysis methodologies. As complex and diverse data is mass-produced, data mining techniques have attracted attention as a method that forms the foundation of data science. In this paper, we focused on the decision tree, which is frequently used in practice and easy to understand as one of representative data mining methods. Specifically, we analyzed the effect of the splitting method of decision trees on the model performance. We compared the prediction power and structures of decision tree models with different split methods based on various simulated data. The results show that the linear combination split method can improve the prediction accuracy of decision trees in the case of data simulated from nonlinear models with complex structure.

• Journal of the Korean Geotechnical Society
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• v.26 no.2
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• pp.33-43
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• 2010

Geothermal energy is gaining wide attention as a highly efficient renewable energy and being increasingly used for heating/cooling systems of buildings. The standing column well (SCW) is especially efficient, cost-effective, and suitable for Korean geological and hydrological conditions. However, a numerical model that simulates the SCW has not yet been developed and applied in Korea. This paper describes the development of the SCW numerical model using a finite-volume analysis program. The model, through hydro-thermal coupled analyses, simulates heat transfer through advection, convection, and conduction. The accuracy of the model was verified through comparisons with field data measured at SCWs in the U.S. and Korea. Comparisons indicated that the SCW numerical model can closely predict the performance of a SCW. The numerical model was used to perform a comprehensive parametric study in the companion paper.

• Journal of Urban Science
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• v.6 no.2
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• pp.17-27
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• 2017

Recently, civil complaints have increased due to water pollution and bad smell in rivers. Therefore, attention is focused on improving the river environment. The purpose of this study is to acquire RGB and thermal infrared images using UAV for sewage outlet and to monitor the status of stream pollution and the applicability UAV based images for river embankment maintenance plan was examined. The accuracy of the 3D model was examination by SfM(Structure from Motion) based images analysis on river embankment maintenance area. Especially, The wastewater discharged from the factory near the river was detected as an thermal infrared images and the flow of wastewater was monitored. As a result of the study, we could monitor the cause and flows of wastewater pollution by detecting temperature change caused by wastewater inflow using UAV images. In addition, UAV based a high precision 3D model (DTM, Digital Topographic Map, Orthophoto Mosaic) was produced to obtain precise DSM(Digital Surface Model) and vegetation cover information for river embankment maintenance.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.83-95
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• 2020

Being a dense and complex task of precisely detecting the weeds in practical crop field environment, previous approaches lack in terms of speed of processing image frames with accuracy. Although much of the attention has been given to classify the plants diseases but detecting crop weed issue remained in limelight. Previous approaches report to use fast algorithms but inference time is not even closer to real time, making them impractical solutions to be used in uncontrolled conditions. Therefore, we propose a detection model for the complex rice weed detection task. Experimental results show that inference time in our approach is reduced with a significant margin in weed detection task, making it practically deployable application in real conditions. The samples are collected at two different growth stages of rice and annotated manually

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.6
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• pp.523-530
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• 2017

Laser-Induced Breakdown Spectroscopy (LIBS) which a plasma is irradiated at a specific wavelength depending on the material when a high-energy laser is irradiated, and a Raman spectroscopy which measures rotation and vibration in molecules as light-scattering phenomenon occurs, are attracting attention as a space exploration technology because of the advantages of high accuracy and real-time analysis, and the ability to perform long-range detection. In this study, the tendency of the laser spectrum according to the change of the soil component was analyzed by laser spectroscopy and the two - dimensional chemical distribution was conducted based on the trend of laser spectrum. We have also established the environment of Mars (4-7 torr) and lunar atmosphere (<1 torr) in experimental setup, to prove that it is possible to measure by difference of soil chemical composition using LIBS and Raman spectroscopy even in artificial space environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.159-165
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• 2020

The demand for food safety has emerged as a major social issue due to changes in diet patterns and consumers' perceptions, along with the advancements in society and the development of the food industry. Consumers are demanding more information about the food they consume, and are sensitive to food scandals. With such interest in food safety, blockchain technology is attracting attention as a means of effectively responding to poor food management resulting in food fraud or unsafe distribution. By ensuring the accuracy of, and trust in, traceability in the food supply chain, it is possible to build trust between traders and to ensure safe food distribution. This paper proposes a next-generation agri-food distribution system that can provide and manage (for suppliers, consumers, and distribution officials) a variety of agri-food information, such as the history, distribution, safety, quality, and freshness of food. Information on product status and distribution status in all processes, including production, processing, distribution, sales, and consumption, can be monitored and controlled in real time (anytime, anywhere), and users can check the safety level of each type of food in real time through an app.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.23-32
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• 2021

Semantic image segmentation technology in the field of computer vision is a technology that classifies an image by dividing it into pixels. This technique is also rapidly improving performance using a machine learning method, and a high possibility of utilizing information in units of pixels is drawing attention. However, this technology has been raised from the early days until recently for 'lack of detailed segmentation' problem. Since this problem was caused by increasing the size of the label map, it was expected that the label map could be improved by using the edge map of the original image with detailed edge information. Therefore, in this paper, we propose a post-processing algorithm that maintains semantic image segmentation based on learning, but modifies the resulting label map based on the edge map of the original image. After applying the algorithm to the existing method, when comparing similar applications before and after, approximately 1.74% pixels and 1.35% IoU (Intersection of Union) were applied, and when analyzing the results, the precise targeting fine segmentation function was improved.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.187-194
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• 2022

LNG is known as the transitional energy source for the future eco-friendly, attracting enormous market attention due to global eco-friendly regulations, Covid-19 Pandemic, Russia-Ukraine War. In addition, since new LNG suppliers such as the U.S. and Australia are also diversifying, the LNG spot market is expected to grow. On the other hand, research on the LNG transportation market has been marginalized. Therefore, this study attempted to predict short-term LNG 160K spot rates and compared the prediction performance between artificial neural networks and the ARIMA model. As a result of this paper, while it was difficult to determine the superiority and superiority of ARIMA and artificial neural networks, considering the relative free of ANN's contraints, we confirmed the feasibility of ANN in LNG 160K spot rate prediction. This study has academic significance as the first attempt to apply an artificial neural network to forecasting LNG 160K spot rates and are expected to contribute significantly in practice in that they can improve the quality of short-term investment decisions by market participants by increasing the accuracy of short-term prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.