• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.17-22
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• 2023

In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

• Bulletin of the Korean Chemical Society
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• v.34 no.11
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• pp.3191-3192
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• 2013

• Polymer Science and Technology
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• v.24 no.3
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• pp.254-260
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• 2013

• Proceedings of the Korean Society of Crop Science Conference
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• 2021.10a
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• pp.115-115
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• 2021

• Journal of Platform Technology
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• v.6 no.2
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• pp.3-9
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• 2018

Recently VR environment is used in many areas including mobile learning, smart factory. However HMD(head-mounted display) is required to a dedicated and expensive system with high-end specification. When designing a VR system, it is needed to handle performance, mobility and usability. Many VR applications need to handle diverse sensors and user inputs continuously in a streaming manner. In this paper we design a VR mobile platform and implement a low-cost mobile VR HMD running on the platform. The VR HMD supports 3D contents delivery in a mobile manner. It is used to detect the motion detection based on angle value of a VR player from accelerator and gyro sensor. The MPU-6050, 6-axis sensor, is used to get a sensory value and the sensory value is taken as an input to a VR rendering server on a Unity game engine that is generated 3D images.

• Genomics & Informatics
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• v.18 no.1
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• pp.4.1-4.6
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• 2020

Transposable elements (TEs) constitute approximately half of Bovine genome. They can be a powerful species-specific marker without regression mutations by the structure variation (SV) at the time of genomic evolution. In a previous study, we identified the Hanwoo-specific SV that was generated by a TE-association deletion event using traditional PCR method and Sanger sequencing validation. It could be used as a molecular marker to distinguish different cattle breeds (i.e., Hanwoo vs. Holstein). However, PCR is defective with various final copy quantifications from every sample. Thus, we applied to the droplet digital PCR (ddPCR) platform for accurate quantitative detection of the Hanwoo-specific SV. Although samples have low allele frequency variation within Hanwoo population, ddPCR could perform high sensitive detection with absolute quantification. We aimed to use ddPCR for more accurate quantification than PCR. We suggest that the ddPCR platform is applicable for the quantitative evaluation of molecular markers.

• Journal of Sensor Science and Technology
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• v.28 no.6
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• pp.377-384
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• 2019

People currently spend more than 80% of their time indoors; therefore, the management of indoor air quality has become an important issue. The contamination of indoor air can cause sick house syndrome and various environmental diseases such as atopy and nephropathy. Formaldehyde gas, which is the main contaminant of indoor air, is lethal even with microscopic exposure; however, it is commonly used as an adhesive and waterproofing agent for indoor building materials. Therefore, there is a need for a gas sensor capable of detecting trace amounts of formaldehyde gas. In this review, we summarize recent studies on metal oxide-based semiconductor gas sensors for formaldehyde gas detection, methods to improve the gas-sensing properties of metal oxides of various dimensions, and the effects of catalysts for the detection of parts-per-billion level gases. Through this, we discuss the necessary characteristics of the metal oxidebased semiconductors for gas sensors for the development of next-generation sensors.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.15-27
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• 2000

The paper proposes a hybrid fire fighting control intelligent system(H-FFIS) using rules and cases to detect fire in Integrated Platform Management System. By far most conventional systems have been based on rule-based system in which expert knowledges are expressed with production rules. It is hard to express the knowledges to detect fire with production rules only. The knowledges of fire detection are often based on previously encountered situations of fires. For improvement of system capability renewing and adding of rules is needed in an already build-up system and such adding and renewing procedures could hinder users from fluent utilization of the system. We design and implement H-FFIS. Compared with rule-based FFIS(Fire Fighting control Intelligent System), H-FFIS extended with case-based reasoning shows that the system proposed here can lead to an improvement in fire detection rate.

• Journal of information and communication convergence engineering
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• v.15 no.4
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• pp.244-249
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• 2017

Wearable sensor-based gait analysis has been widely conducted to analyze various aspects of human ambulation abilities under the free-living condition. However, there have been few research efforts on using wearable sensors to analyze human walking on an unstable surface such as on a ship during a sea voyage. Since the motion of a ship on the unstable sea surface imposes significant differences in walking strategies, investigation is suggested to find better performing wearable sensor-based gait analysis algorithms on this unstable environment. This study aimed to compare two representative gait event algorithms including time domain and frequency domain analyses for detecting heel strike on an unstable platform. As results, although two methods did not miss any heel strike, the frequency domain analysis method perform better when comparing heel strike timing. The finding suggests that the frequency analysis is recommended to efficiently detect gait event in the unstable walking environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1860-1875
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• 2013

In this paper, we present a comparative performance evaluation of the sampling frequency synchronization method that eliminates the initial sampling frequency offset (SFO) to reduce the overall synchronization time in Digital Radio Mondiale (DRM) receivers. To satisfy the advanced synchronization performance requirements of DRM receivers, we introduce the conventional DRM synchronization method (Method 1) and another method (Method 2), which does not perform the initial sampling frequency synchronization in the conventional synchronization method (both methods are mentioned later in this paper). To demonstrate the effectiveness of the performance evaluation, analytical expressions for frame detection are derived and simulations are provided. Based on the simulations and numerical analysis, our result shows that Method 2, with a negligible performance loss, is robust to the effects of the initial sampling frequency synchronization even if SFO is present in the DRM signal. In addition, we verify that the inter-cell differential correlation used between reference cells is robust to the effect of the initial SFO.