• Atmosphere
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• v.16 no.2
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• pp.125-139
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• 2006

Precipitation forecasts from MM5 have been verified for the period 1989-2001 over Yeongdong region to show a tendency of model forecast. We select 57 events which are related with the heavy snowfall in Yeongdong region. They are classified into three precipitation types; mountain type, cold-coastal type, and warm type. The threat score (TS), the probability of detection (POD), and the false-alarm rate (FAR) are computed for categorical verification and the mean squared error (MSE) is also computed for scalar accuracy measures. In the case of POD, warm, mountain, and cold-coastal precipitation type are 0.71, 0.69, and 0.55 in turn, respectively. In aspect of quantitative verification, mountain and cold-coastal type are relatively well matched between forecasts and observations, while for warm type MM5 tends to overestimate precipitation. There are 12 events for the POD below 0.2, mountain, cold-coastal, warm type are 2, 7, 3 events, respectively. Most of their precipitation are distributed over the East Sea nearby Yeongdong region. These events are also shown when there are no or very weak easterlies in the lower troposphere. Even in the case that we use high resolution sea surface temperature (about 18 km) for the boundary condition, there are not much changes in the wind direction to compare that with low resolution sea surface temperature (about 100 km).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1310-1338
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• 2023

As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3- layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.

• Journal of English Language & Literature
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• v.57 no.3
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• pp.515-527
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• 2011

Miss Pettigrew Lives for a Day, Winifred Watson's novel of 1938, is a fairytale in novel form. Set in London of 1938, the story revolves around a one-day adventure of an ill-starred but truthful governess who is granted a second chance. This light-hearted comedy of manners was turned into a film by director Bharat Nalluri in 2008. An Anglo-American collaboration, co-scripted by Simon Beaufoy and David McGee, the film converts Watson's quaint novel into an edged heritage piece that encapsulates the 1930s, the problematic decade between the two World Wars. The film, while sustaining the narrative core of Watson's Cinderella story, attempts to place it firmly within a wider current of the novel's setting or London in 1938, tapping into the major concerns of the interwar years that engage with characters in one way or another. Stylistically, the film presents Art Deco as a main visual idiom to convey the prevailing mood of nihilism and decadence of the day. The setting here takes on significance in that it offers a telling counterpoint to the giddy superficial world of the novel. The 1930s was a highly charged decade under the threat of fascism and the Great Depression, fraught with economic and socio-political tensions and apprehensions. The film makes an explicit reference to the dismal context which is suppressed in the original text. The thirties is, therefore, portrayed as a decade of contradiction. It features gay buoyant festivity, rampant consumerism, and shifting morals and attitudes towards love, marriage and sexuality. Yet lurking beneath the surface glamour are the symptoms of crises and the deep-seated anxieties on the eve of World War II. In this way, Watson's novel of manners has been recreated into a defining film on the 1930s with its period feel propped by the atmospheric lighting, the exuberant Jazz score, and the splendid Art Deco costume and production design.

• Atmosphere
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• v.34 no.2
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• pp.217-231
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• 2024

The Korea Integrated Model (KIM)-based real-time volcanic ash dispersion prediction system, which employs the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, has been developed to quantitatively predict volcanic ash dispersion in East Asia and the Northwest Pacific airspace. This system, known as KIM-HYSPLIT, automatically generates forecasts for the vertical and horizontal spread of volcanic ash up to 72 hours. These forecasts are initiated upon the receipt of a Volcanic Ash Advisory (VAA) from the Tokyo Volcanic Ash Advisory Center by the server at the Korea Meteorological Administration (KMA). This system equips KMA forecasters with diverse volcanic ash prediction information, complemented by the Unified Model (UM)-based HYSPLIT (UM-HYSPLIT) system. Extensive experiments have been conducted using KIM-HYSPLIT across 128 different volcanic scenarios, along with qualitative comparisons with UM-HYSPLIT. The results indicate that the ash direction predictions from KIM-HYSPLIT are consistent with those from UM-HYSPLIT. However, there are slight differences in the horizontal extent and movement speed of the volcanic ash. Additionally, quantitative verifications of the KIM-HYSPLIT forecasts have been performed, including threat score evaluations, based on recent eruption cases. On average, the KIMHYSPLIT forecasts for 6 and 12 hours show better quantitative alignment with the VAA forecasts compared to UM-HYSPLIT. Nevertheless, both models tend to predict a broader horizontal spread of the ash cloud than indicated in the VAA forecasts, particularly noticeable in the 6-hour forecast period.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1551-1559
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• 2015

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.

• Journal of the Korean Applied Science and Technology
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• v.37 no.5
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• pp.1390-1401
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• 2020

This study is a descriptive research to identify the relationship among violence experience, resilience and job stress of nurses working in emergency department. The subjects of this study were 143 nurses with over one year working in emergency departments of 6 hospitals located in D city and C city and collected data through structured questionnaire. It was from November 6th to November 15th. The degree of violent experience of the subjects was 1.26 ± 1.31 out of 4. The average score of resillience was 2.50 ± 0.55 out of 4. The average score of job stress was 3.62 ± 0.49 out of 5. The result of correlation between violence experience, resilience and job stress, among the sub factors, in the correlation among violence experience and job stress sub factors, verbal violence experience was significantly positively correlated with nursing work(r=.194, p=.010), role conflict stress(r=.158, p=.030), and physical threat experience was positively correlated with nursing work(r=.200, p=.008), role conflict(r=.162, p=.027), and conflict with doctor(r=.145, p=.042). In the correlation between resilience and job stress sub factors, nursing work stress is hardness(r=-.189, p=.012), persistence(r=-.165, p=.025), and optimism (r=-.186, p=.013) and there was a negative correlation with the region. Expertise stress is hardness(r=-.230, p=.003), persistence(r=-.195, p=.010), optimistic(r=-.194, p=.010) and there was a negative correlation. Nurse-treated stress was positively correlated with spirituality(r=.154, p=.033). In the subcategory correlations of resilience and violent experience, the hardness had a negative correlation with the physical threat(r=-.150, p=.037) experience. The persistence was negatively correlated with the experience of physical threats(r=-.138, p=.050). The optimism was negatively correlated with the experience of physical violence(r=-.151, p=.036). As a result, it is necessary to create a safe working environment free from violence and to reinforce training on how to deal with violence in order to reduce the job stress of emergency department nurses. In addition, measures to cope with stress according to age and work experience and programs to increase resilience should be developed and mediated to reduce the job stress of emergency department nurses.

• 한국균학회소식:학술대회논문집
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• 2015.05a
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• pp.41-41
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• 2015

BSA-seq technologies, combined Bulked Segregant Analysis (BSA) and Next-Generation Sequencing (NGS), are making it faster and more efficient to establish the association of agronomic traits with molecular markers or candidate genes, which is the requirement for marker-assisted selection in molecular breeding. Clubroot disease, caused by Plasmodiophora brassicae, is a serious threat to Brassica crops. Even we have breed new clubroot resistant varieties of Chinese cabbage (B. rapa ssp. pekinesis), the underlying genetic mechanism is unclear. In this study, an $F_2$ population of 340 plants were inoculated with P. brassicae from Xinye (Pathotype 2 on the differentials of Williams). Resistance phenotype segregation ratio for the populations fit a 3:1 (R:S) segregation model, consistent with a single dominant gene model. Super-BSA, using re-sequencing the parents, extremely R and S DNA pools with each 50 plants, revealed 3 potential candidate regions on the chromosome A03, with the most significant region falling between 24.30 Mb and 24.75 Mb. A linkage map with 31 markers in this region was constructed with several closely linked markers identified. A Major QTL for clubroot resistance, CRq, which was identified with the peak LOD score at 169.3, explaining 89.9% of the phenotypic variation. And we developed a new co-segregated InDel marker BrQ-2. Joint BSA-seq and traditional QTL analysis delimited CRq to an 250 kb genomic region, where four TIR-NBS-LRR genes (Bra019409, Bra019410, Bra019412 and Bra019413) clustered. The CR gene CRq and closely linked markers will be highly useful for breeding new resistant Chinese cabbage cultivars.

• Journal of the Korean earth science society
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• v.35 no.4
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• pp.215-224
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• 2014

Data assimilation effect of mobile rawinsonde observation was evaluated using Unified Model (UM) with a Three-Dimensional Variational (3DVAR) data assimilation system during the intensive observation program of 2013 summer season (rainy season: 20 June-7 July 2013, heavy rain period: 8 July-30 July 2013). The analysis was performed by two sets of simulation experiments: (1) ConTroL experiment (CTL) with observation data provided by Korea Meteorological Administration (KMA) and (2) Observing System Experiment (OSE) including both KMA and mobile rawinsonde observation data. In the model verification during the rainy season, there were no distinctive differences for 500 hPa geopotential height, 850 hPa air temperature, and 300 hPa wind speed between CTL and OSE simulation due to data limitation (0000 and 1200 UTC only) at stationary rawinsonde stations. In contrast, precipitation verification using the hourly accumulated precipitation data of Automatic Synoptic Observation System (ASOS) showed that Equivalent Threat Score (ETS) of the OSE was improved by about 2% compared with that of the CTL. For cases having a positive effect of the OSE simulation, ETS of the OSE showed a significantly higher improvement (up to 41%) than that of the CTL. This estimation thus suggests that the use of mobile rawinsonde observation data using UM 3DVAR could be reasonable enough to assess the improvement of prediction accuracy.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.30 no.4
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• pp.331-341
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• 2020

Objectives: This study was conducted to evaluate perceptions of safety and risk among Korean industrial hygienists and the change between before and after the Sewol Ferry Disaster in 2014. Two surveys with questionnaires composed of 51 questions were completed by attendees of the Korea Industrial Hygiene Association(KIHA) conference. Methods: One was conducted at the 2013 KIHA Fall Conference(N=181) and the other was from the 2014 KIHA Summer Conference(N=123). Between these two surveys was the Sewol Ferry Disaster on April 14, 2014, which was believed to seriously affect safety and risk perceptions in Korea. Results: It was revealed that industrial hygienists' awareness of safety rules strengthened after the Sewol Ferry Disaster(p<0.05). It was apparent that people over the age of 30 were more sensitive to social safety. There was no significant difference in the evaluation and attitude regarding governmental safety policy between the years of 2013 and 2014. The credibility of public organizations responsible for the disaster management system decreased. The self-evaluation of respondents' safety level also decreased. This trend shows mainly in the younger generation. It was evaluated that the overall social safety level decreased and the anxiety level increased. The score on social safety on a ±5 Likert scale was 0.68 in the 2013 survey and -0.33 in the 2014 survey(p<0.05). It was reported that the most serious threat factors for accident or disaster were 'building collapse > illegalities and corruption > side effects of radiation therapy >accidents in normal activity > occupational disease,' in order. They picked 'safety ignorance > hurry-up habits and culture > focusing on short-term benefit > easy-going attitude > insufficient safety education' for the causes of low social safety levels in 2013. In 2014, they were 'safety ignorance > easy-going attitude > focusing on short-term benefit > insufficient safety education > hurry-up habits and culture'. Conclusions: This study has some limitations because it was originally not designed to survey attitudes prior to the Sewol Ferry disaster in 2013. In addition, the survey targets are industrial hygienists who are familiar with occupational disease and injury.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1623-1631
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• 2022

Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.