• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.5
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• pp.39-53
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• 2007

The purpose of this paper is the development of a compensation algorithm by which the interoperability of fingerprint recognition can be improved among various different fingerprint sensor. In order to compensate for the different characteristics of fingerprint sensor, an initial evaluation of the sensors using both the ink-stamped method and the flat artificial finger pattern method was undertaken. This paper proposes Common resolution method and Relative resolution method for compensating different resolution of fingerprint images captured by disparate sensors. Both methods can be applied to image-level and minutia-level. In order to compensate the direction of minutiae in minutia-level, Unit vector method is proposed. The EER of the proposed method was improved by average 64.8% better than before compensation. This paper will make a significant contribution to interoperability in the system integration using different sensors.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.243-251
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• 2022

In this paper, we propose a phenomenon that analyze the impact of market sentiment on China's real estate market through the perspective of behavioral economics. Previously, real estate market analyzation basically focus on some fundamental principles which include market price, monetary policies and income, etc. However, little research has explored market sentiment and its influence. By using principal components analysis (PCA), this study first creates buyer's sentiment and seller's sentiment to measure the heat of China's real estate market. Different from using traditional estimation method, the vector autoregressive model (VAR) is used to analyze how both sentiments affect real estate return. The overall results show that from unit root test and impulse response analyzation, the impact of seller's sentiment is positive to real estate market while buyer's sentiment is negative. At the same time, the higher seller's sentiment will have different influence on the housing market compared with the higher buyer's sentiment.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• The Journal of Korean Society of Virology
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• v.30 no.3
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• pp.203-210
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• 2000

Hantaviruses are etiologic agents of hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS) in the world. Various hantaviruses were isolated from HFRS patients and several different rodent species in the world. Four hantavirus isolates from Indonesia and three isolates from Thailand among 89 Bandicotas captured in Yogyakarta, east region of Sumatra island, Indonesia and at Chiang Mai in Thailand during 1996 were made through several passages in Vero E6 cells. Viral genome M segment from two Indonesian isolates and three Thailand isolates were amplified using hantavirus generic primers of the M segment and cloned into pCRII vector. The genetic differences were analyzed by comparison of partial sequence of the M segment and antigenic differences were made by IFA. Nucleotide sequence homology of two isolates BC 8, BC 34 from Indonesia and two isolates thai 1322, thai 1330 to Seoul virus was 99% and 96%, respectively, but Thai 1164 was 80%Thai 1164 strain has shown 95% homology to Thai 749 virus. In conclusion it is indicated that two different serotype hantaviruses, Seoul and Thailand, are cocirculating among Bandicota in Thailand, in contrast Seoul serotype virus is circulating in Indonesia.

• Parasites, Hosts and Diseases
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• v.61 no.2
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• pp.154-162
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• 2023

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is caused by X-linked recessive disorderliness. It induces severe anemia when a patient with G6PD deficiency is exposed to oxidative stress that occurs with administration of an antimalarial drug, primaquine. The distribution of G6PD deficiency remains unknown while primaquine has been used for malaria treatment in Myanmar. This study aimed to investigate the prevalence of G6PD deficiency and its variants in Chin State, Myanmar. Among 322 participants, 18 (11 males and 7 females) demonstrated a G6PD deficiency. Orissa variant was dominant in the molecular analysis. This would be related to neighboring Indian and Bangladeshi population, in which Orissa variant was also reported as the main mutation type. The screening test for G6PD deficiency before primaquine treatment appears to be important in Myanmar.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.57-67
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• 2018

The ensemble is a unified approach used for getting better performance by using multiple algorithms in machine learning. In this paper, we introduce boosting and bagging, which have been widely used in ensemble techniques, and design a method using support vector regression, radial basis function network, Gaussian process, and multilayer perceptron. In addition, our experiment was performed by adding a recurrent neural network and MOHID numerical model. The drifter data used for our experimental verification consist of 683 observations in seven regions. The performance of our ensemble technique is verified by comparison with four algorithms each. As verification, mean absolute error was adapted. The presented methods are based on ensemble models using bagging, boosting, and machine learning. The error rate was calculated by assigning the equal weight value and different weight value to each unit model in ensemble. The ensemble model using machine learning showed 61.7% improvement compared to the average of four machine learning technique.

• Economic and Environmental Geology
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• v.34 no.1
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• pp.1-12
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• 2001

The crystal structure of biotite-1M from Bancroft, Ontario, was determined by Rietveld refinement method using high-resolution neutron powder diffraction data at -26.3$^{\circ}C$, 2$0^{\circ}C$, 30$0^{\circ}C$, $600^{\circ}C$, 90$0^{\circ}C$. The crystal structure has been refined to a R sub(B) of 5.06%-11.9% and S (Goodness of fitness) of 2.97-3.94. The expansion rate of a, b, c unit cell dimensions with elevated temperature linearly increase to $600^{\circ}C$. The expansivity of the c dimension is $1.61{\times}10^{40}C^{-1}$, while $2.73{\times}10^{50}C^{-1}$ and $5.71{\times}10^{-50}C^{-1}$ for the a and b dimensions, respectively. Thus, the volume increase of the unit cell is dominated by expansion of the c axis as increasing temperature. In contrast to the trend, the expansivity of the dimensions is decreased at 90$0^{\circ}C$. It may be attributed to a change in cation size caused by dehydroxylation-oxidation of $Fe^{2+}$ to $Fe^{3+}$ in vacuum condition at such high temperature. The position of H-proton was determined by the refinement of diffraction pattern at low temperature (-2.63$^{\circ}C$). The position is 0.9103${\AA}$ from the O sub(4) location and located at atomic coordinates (x/a=0.138, y/b=0.5, z/c=0.305) with the OH vector almost normal to plane (001). According to the increase of the temperature, $\alpha$* (tetrahedral rotation angle), $t_{oct}$ (octahedral sheet thickness), mean distance increase except 90$0^{\circ}C$ data. But the trend is less clearly relative to unit cell dimension expansion because the expansion is dominant to the interlayer. Also, ${\Psi}$ (octahedral flattening angle) shows no trends as increasing temperature and it may be because the octahedron (M1, M2) is substituted by Mg and Fe.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Korean Journal of Agricultural Science
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• v.45 no.1
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• pp.38-42
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• 2018

Demand for tomatoes has been increasing every year as people desire more healthy food. In Korea, tomatoes are mainly grown in the Chungnam, Chunnam and Kyungnam provinces. Recently, reports of whitefly-transmitted viral diseases have increased due to newly emerging whitefly pressures caused by climate change in Korea. Specifically, in 2017, the main tomato growing areas, Buyeo and Nonsan in Chungnam, showed damage typical of viral infection; therefore, we investigated viral diseases in these areas. We collected samples with virus-like symptoms and found that not only whitefly transmitted Tomato yellow leaf curl virus (TYLCV) and Tomato chlorosis virus (ToCV) were detected but also Tomato mosaic virus (ToMV, for which no specific vector is known) and Tomato spotted wilt virus (TSWV, transmitted by thrips). The ToMV-infected samples were mostly co-infected with either TYLCV or ToCV. Mixed infections of different combinations of TYLCV, ToCV and ToMV were detected with the mixed infection of two whitefly-transmitted viruses (TYLCV and ToCV) causing the most severe symptoms. According to the CP sequence of each virus, the 100% identities were shown to be Mexico/ABG73017.1 (TYLCV), Greece/CDG34553.1 (ToCV), China/AKN79752 (TSWV), and Australia/NP078449.1 (ToMV). Based on the sequence data, we presumed that these tomato infecting viruses were transmitted through insects and seeds introduced from neighboring countries.

• Parasites, Hosts and Diseases
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• v.58 no.3
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• pp.267-278
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• 2020

The heterogeneity and complexity of malaria involves political and natural environments, socioeconomic development, cross-border movement, and vector biology; factors that cannot be changed in a short time. This study aimed to assess the impact of economic growth and cross-border movement, toward elimination of malaria in Yunnan Province during its pre-elimination phase. Malaria data during 2011-2016 were extracted from 18 counties of Yunnan and from 7 villages, 11 displaced person camps of the Kachin Special Region II of Myanmar. Data of per-capita gross domestic product (GDP) were obtained from Yunnan Bureau of Statistics. Data were analyzed and mapped to determine spatiotemporal heterogeneity at county and village levels. There were a total 2,117 malaria cases with 85.2% imported cases; most imported cases came from Myanmar (78.5%). Along the demarcation line, malaria incidence rates in villages/camps in Myanmar were significantly higher than those of the neighboring villages in China. The spatial and temporal trends suggested that increasing per-capita GDP may have an indirect effect on the reduction of malaria cases when observed at macro level; however, malaria persists owing to complex, multi-faceted factors including poverty at individual level and cross-border movement of the workforce. In moving toward malaria elimination, despite economic growth, cooperative efforts with neighboring countries are critical to interrupt local transmission and prevent reintroduction of malaria via imported cases. Cross-border workers should be educated in preventive measures through effective behavior change communication, and investment is needed in active surveillance systems and novel diagnostic and treatment services during the elimination phase.