• Convergence Security Journal
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• v.21 no.1
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• pp.45-53
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• 2021

When the security monitoring system is performed in a separation network, there is little normal anomaly detection in internal networks or high-risk sections. Therefore, after the establishment of the security network, a model is needed to evaluate state-of-the-art cyber threat anomalies for internal network in separation network to complete the optimized security structure. In this study, We evaluate it by generating datasets of cyber vulnerabilities and malicious code arising from general and separation networks, It prepare for the latest cyber vulnerabilities in internal network cyber attacks to analyze threats, and established a cyber security test evaluation system that fits the characteristics. The study designed an evaluation model that can be applied to actual separation network institutions, and constructed a test data set for each situation and applied a real-time security assessment model.

• Journal of the Korean Institute of Gas
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• v.25 no.3
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• pp.9-15
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• 2021

In this paper, a study on the construction of a knowledge base based on natural language processing and the design of a chemical substance estimation system for the development of a knowledge service for a real-time sensor information fusion detection system and symptoms of contact with chemical substances in industrial sites. The information on 499 chemical substances contact symptoms from the Wireless Information System for Emergency Responders(WISER) program provided by the National Institutes of Health(NIH) in the United States was used as a reference. AllegroGraph 7.0.1 was used, input triples are Cas No., Synonyms, Symptom, SMILES, InChl, and Formula. As a result of establishing the knowledge base, it was confirmed that 39 symptoms based on ammonia (CAS No: 7664-41-7) were the same as those of the WISER program. Through this, a method of establishing was proposed knowledge base for the symptom extraction process of the chemical substance estimation system.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.22 no.1
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• pp.57-62
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• 2019

Purpose: This study was performed to assess the clinical usefulness of transabdominal ultrasonography (TUS) in detecting peptic ulcer disease (PUD) in children. Methods: Twenty-four patients (19 boys, 5 girls; mean age, $10.6{\pm}4.5years$ [range, 3.0-17.9 years]) who were admitted to the hospital for acute abdomen or gastrointestinal bleeding and diagnosed with PUD by endoscopy and who underwent TUS were included. Clinical data were retrospectively collected by reviewing patient medical records. Gastric ulcer (GU) was suspected when the gastric wall exceeded 8 mm in thickness and had lost its five-layer structure on TUS. Duodenal ulcer (DU) was suspected if the duodenal wall thickness exceeded 5 mm. Results: Sensitivity of TUS in diagnosing PUD was 66.7% for GU and 38.9% for DU. Mean age and body weight of the 11 patients suspected with PUD on TUS were $10.9{\pm}4.4years$ and $38.1{\pm}17.2kg$, respectively. For 13 patients without suspected PUD, they were $12.1{\pm}4.1years$ and $39.6{\pm}17.0kg$, respectively. There was a significant difference in height, weight, and body mass index between patients who were suspected to have PUD and those who were not suspected on TUS (p=0.014, 0.008, and 0.005, respectively). A significant difference in the sensitivity of TUS in diagnosing PUD was found between patients under 30 kg and those over 30 kg (88.9% and 20.0%, respectively; p=0.003). Conclusion: TUS investigation of the stomach and duodenum is an efficient method for PUD detection in children with low body weight. TUS can be used in preliminary diagnostic work-up before further invasive tests.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• Smart Media Journal
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• v.8 no.2
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• pp.66-73
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• 2019

Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. It is often difficult to commercialize sensor-based object recognition technology because such approach requires an expensive sensor. On the other hand, object recognition technology using computer vision may replace sensors with inexpensive cameras. Moreover, Real-time recognition is viable due to the growth of CNN, which is actively introduced into other fields such as IoT and autonomous vehicles. Because object recognition model applications demand expert knowledge on deep learning to select and learn the model, such method, however, is challenging for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user 's desired condition. We also present the reason we need to select statistics-based object recognition model through conducted experiments on different models.

• Structural Monitoring and Maintenance
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• v.5 no.4
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• pp.463-488
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• 2018

In the last decades, research efforts have been spent to investigate the effect of prestressing on the dynamic behaviour of prestressed concrete (PSC) beams. Whereas no agreement has been reached among the achievements obtained by different Researchers and among the theoretical and the experimental results for simply supported beams, very few researches have addressed this problem in continuous PSC beams. This topic is, indeed, worthy of consideration bearing in mind that many relevant bridges and viaducts in the road and railway networks have been designed and constructed with this structural scheme. In this paper the attention is, thus, focused on the dynamic features of continuous PSC bridges taking into account the effect of prestressing. This latter, in fact, contributes to the modification of the distribution of the bending stress along the beam, also by means of the secondary moments, and influences the flexural stiffness of the beam itself. The dynamic properties of a continuous, two spans bridge connected by a nonlinear spring have been extracted by solving an eigenvalue problem in different linearized configurations corresponding to different values of the prestress force. The stiffness of the nonlinear spring has been calculated considering the mechanical behaviour of the PSC beam in the uncracked and in the cracked stage. The application of the proposed methodology to several case studies indicates that the shift from the uncracked to the cracked stage due to an excessive prestress loss is clearly detectable looking at the variation of the dynamic properties of the beam. In service conditions, this shift happens for low values of the prestress losses (up to 20%) for structure with a high value of the ratio between the permanent load and the total load, as happens for instance in long span, continuous box bridges. In such conditions, the detection of the dynamic properties can provide meaningful information regarding the structural state of the PSC beam.

• Mycobiology
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• v.47 no.2
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• pp.200-206
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• 2019

Allelic differences in A and B mating-type loci are a prerequisite for the progression of mating in the genus Pleurotus eryngii; thus, the crossing is hampered by this biological barrier in inbreeding. Molecular markers linked to mating types of P. eryngii KNR2312 were investigated with randomly amplified polymorphic DNA to enhance crossing efficiency. An A4-linked sequence was identified and used to find the adjacent genomic region with the entire motif of the A locus from a contig sequenced by PacBio. The sequence-characterized amplified region marker $7-2_{299}$ distinguished A4 mating-type monokaryons from KNR2312 and other strains. A BLAST search of flanked sequences revealed that the A4 locus had a general feature consisting of the putative HD1 and HD2 genes. Both putative HD transcription factors contain a homeodomain sequence and a nuclear localization sequence; however, valid dimerization motifs were found only in the HD1 protein. The ACAAT motif, which was reported to have relevance to sex determination, was found in the intergenic region. The SCAR marker could be applicable in the classification of mating types in the P. eryngii breeding program, and the A4 locus could be the basis for a multi-allele detection marker.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.944-945
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• 2016

Gamma-ray Detector for gamma-ray imaging device is composed of a shielding body for shielding gamma-rays incident from the radiation source. Distribution of the gamma ray can be represented by the distribution information on the direction in which the detecting section and the signal through the incident hole of collimator. The role of the shield is important because all signals should be treated as noise except for the signal from the incident hole.In this paper In this paper, we have produced a compact, lightweight and Collimator shield by changing the structure and physical properties with respect to the collimator and shielding of lead-based gamma-ray detectors. And we analyzed the shielding effectiveness relative to the incident gamma ray sphere measured signal value through the gamma irradiation test facility. The results confirmed that the production and Collimator shielding the imaging device Implementing more efficient to implement.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.4
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• pp.1-8
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• 2019

Plant phenomics is a technique for observing and analyzing morphological features in order to select plant varieties of excellent traits. The conventional methods is difficult to apply to the phenomics system. because the color threshold value must be manually changed according to the detection target. In this paper, we propose the convolution neural network (CNN) structure that can automatically segment plants from the background for the phenomics system. The LeafNet consists of nine convolution layers and a sigmoid activation function for determining the presence of plants. As a result of the learning using the LeafNet, we obtained a precision of 98.0% and a recall rate of 90.3% for the plant seedlings images. This confirms the applicability of the phenomics system.