• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.348-352
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• 2022

Hydrogen gas (H₂) which is odorless, colorless is attracting attention as a renewable energy source in varions applications but its leakage can lead to disastrous disasters, such as inflammable, explosive, and narcotic disasters at high concentrations. Therefore, it is necessary to develop H₂ gas sensor with high performance. In this paper, we confirmed that H₂ gas detection ability of SnO₂ based H₂ gas sensor along with thermal treatment effect of SnO₂. Proposed SnO₂ based H₂ gas sensor is fabricated by MEMS technologies such as photolithgraphy, sputtering and lift-off process, etc. Deposited SnO₂ thin films are thermally treated in various thermal treatement temperature in range of 500-900 ℃ and their H₂ gas detection ability is estimatied by measuring output current of H₂ gas sensor. Based on experimental results, fabricated H₂ gas sensor with SnO₂ thin film which is thermally treated at 700 ℃ has a superior H₂ gas detection ability, and it can be expected to utilize at the practical applications.

• Journal of Plant Biotechnology
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• v.39 no.3
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• pp.169-174
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• 2012

Quantitative trait loci (QTLs), which were related to the ability of callus induction and plant regeneration in seed culture of rice, were analyzed using a mapping population from a cross between the rice cultivars 'Samgang' (tongil type) and 'Nagdong' (japonica). A tongil type rice cultivar, 'Samgang' showed lower frequency (20%) of plant regeneration than that (35%) of japonica rice, 'Nagdong'. Transgressive segregations were observed for the ability of callus induction and plant regeneration from the seed-derived calli of 58 doubled haploid (DH) lines. The ability of plant regeneration of 58 doubled haploid lines showed a continuous distribution with comparatively wide range (10.0 to 66.7%) of variation. Composite interval mapping analysis was used to identify the QTLs controlling callus induction and plant regeneration ability. Four significant QTLs, qCWS6, qCWS8, qCWS9 and qCWS11, associated with callus weight per seed were detected on chromosomes 6, 8, 9, and 11 with LOD values of 3.30, 2.60, 2.70 and 2.43, explaining 36% of the total phenotypic variation. Three significant QTLs, qPR1, qPR6, and qPR11, for the ability of plant regeneration were located on chromosome 1, 6, and 11 at LOD score of 2.25, 2.15 and 2.55, accounting for 24 % of the total phenotypic variation. The present study should be useful for improving the efficiency of plant regeneration in tissue culture of indica rice by means of marker-assisted selection.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.309-314
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• 1992

Many dynamic mechanical systems, such as parts-feeders, walking machines, and percussive power tools, are described by equations of motion which are discontinuous. The discontinuities result from kinematic constraint changes which are difficult to foresee, especially in presence of impact. A simulation algorithm for these types of systems must be able to algorithmically predict and detect the kinematic constraint changes without any prior knowledge of the system's motion. This paper presents a rule-based approach to the prediction and detection of kinematic constraint changes between bodies with arc and line boundaries. The developed algorithm's ability to accurately and automatically detect the unpredicted changes of kinematic constraints is demonstrated with a numerical example.

• Journal of Biomedical Engineering Research
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• v.30 no.5
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• pp.412-417
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• 2009

The purpose of this study was to assess the ability of this device during clinically important episodes of extravasation. The extravasation detection accessory (EDA) system was based of infrared ray with detection sensor, an amplifier, alarm device, receiver, cable and a computer based system. This study was a prospective, observational study in which the EDA system was used to monitor the automated mechanical injection of contrast media. Three hundred patients referred for contrast media enhanced body computed tomography studied in a prospective, observation study in which the EDA system was used to identify and interrupt any injection associated with clinically important extravasation. There were 8 true-positive cases, 276 true-negative cases, 15 false-positive cases and 1 false-negative cases. The EDA system had a sensitivity of 88.8% and a specificity of 94.8% for the detection of clinically important extravasation. The EDA system had good sensitivity for the detection of clinically important extravasation and the EDA system has the clinical potential for the early detection of extravasation of the contrast medium that is administered with power injectors.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.443-461
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• 2022

Target Detection in synthetic aperture radar(SAR) image is critical for military and national defense. In this paper, we propose YOLOv4-Attention architecture which adds attention modules to YOLOv4 backbone architecture to complement the feature extraction ability for SAR target detection with high accuracy. For training and testing our framework, we present new SAR embedding datasets based on MSTAR SAR public datasets which are about poor environments for target detection such as various clutter, crowded objects, various object size, close to buildings, and weakness of signal-to-clutter ratio. Experiments show that our Attention YOLOv4 architecture outperforms original YOLOv4 architecture in SAR image target detection tasks in poor environments for target detection.

• 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.

• Annual Conference of KIPS
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• 2022.11a
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• pp.625-627
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• 2022

In the construction and development of modern industrial production technology, the traditional technology management mode is faced with many problems such as low qualification rates and high application costs. In the research, an improved workpiece defect detection method based on deep learning is proposed, which can control the application cost and improve the detection efficiency of irregular defects. Based on the research of the current situation of deep learning applications, this paper uses the improved Faster R-CNN network structure model as the core detection algorithm to automatically locate and classify the defect areas of the workpiece. Firstly, the robustness of the model was improved by appropriately changing the depth and the number of channels of the backbone network, and the hyperparameters of the improved model were adjusted. Then the deformable convolution is added to improve the detection ability of irregular defects. The final experimental results show that this method's average detection accuracy (mAP) is 4.5% higher than that of other methods. The model with anchor size and aspect ratio (65,129,257,519) and (0.2,0.5,1,1) has the highest defect recognition rate, and the detection accuracy reaches 93.88%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.881-891
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• 2023

Recently, various studies on malicious URL detection using artificial intelligence have been conducted, and most of the research have shown great detection performance. However, not only does classical machine learning require a process of analyzing features, but the detection performance of a trained model also depends on the data analyst's ability. In this paper, we propose a DL-ML Fusion Hybrid Model for malicious web site URL detection based on URL lexical features. the propose model combines the automatic feature extraction layer of deep learning and classical machine learning to improve the feature engineering issue. 60,000 malicious and normal URLs were collected for the experiment and the results showed 23.98%p performance improvement in maximum. In addition, it was possible to train a model in an efficient way with the automation of feature engineering.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.152-155
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• 2002

In this paper, a new approach is proposed for the error detection dedicated to MPEG-4 video coding by using the digital watermarking. In the process of encoding, the proposed scheme abstracts the macroblock features of headers, motion vectors, and Discrete Cosine Transform (DCT) coefficients, which are embedded in the quantized DCT coefficients as the digital watermarks. The decoder performs the accurate error detection through the watermark extraction. Simulation results demonstrate that the error detection ability of the proposed approach can be significantly enhanced, and that digital watermark embedding incurs little degradation to the picture quality.

• Journal of Biosystems Engineering
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• v.23 no.1
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• pp.67-74
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• 1998

A nondestructive quality inspection technique using acoustic impulse response method was developed for eggshell inspection. An experimental system was built to generate the impact force, to measure the response signal and to analyze the frequency spectrum. This system includes an impulse generating unit, an egg holding seal a microphone with preamplifier, and a DSP board installed on Personal Computer. A simple algorithm .was developed for crack detection. Using the developed system with algorithm, crack detection ability was evaluated and the error rate to estimate the normal egg as cracked was found to be 4% and the error rate to estimate the cracked egg as normal was also found to be 4%. This system could be adopted in industry with some modification.