• The Korean Journal of Air & Space Law and Policy
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• v.29 no.2
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• pp.109-134
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• 2014

Regulation (EC) No 261/2004 of the European Parliament and of the Council of 11 February 2004 establishing common rules on compensation of assistance to passengers in the event of denied boarding and of cancellation or long delay of flights (Regulation No 261/2004) provides extra protection to air passengers in circumstances of denied boarding, cancellation and long-delay. The Regulation intends to provide a high level of protection to air passengers by imposing obligations on air carriers and, at the same time, offering extensive rights to air passengers. If denied boarding, cancellation and long-delay are caused by reasons other than extraordinary circumstances, passengers are entitled for compensation under Article 7 of Regulation No 261/2004. In Wallentin-Hermann v Alitalia-Linee Aeree Italiane SpA(Case C-549/07, [2008] ECR I-11061), the Court did, however, emphasize that this does not mean that it is never possible for technical problems to constitute extraordinary circumstances. It cited specific examples of where: an aircraft manufacturer or competent authority revealed that there was a hidden manufacturing defect on an aircraft which impacts on safety; or damage was caused to an aircraft as a result of an act of sabotage or terrorism. Such events are not inherent in the normal exercise of the activity of the air carrier concerned and is beyond the actual control of that carrier on account of its nature or origin. One further point arising out of the court's decision is worth mentioning. It is not just necessary to satisfy the extraordinary circumstances test for the airline to be excused from paying compensation. It must also show that the circumstances could not have been avoided even if all reasonable measures had been taken. It is clear from the language of the Court's decision that this is a tough test to meet: the airline will have to establish that, even if it had deployed all its resources in terms of staff or equipment and the financial means at its disposal, it would clearly not have been able - unless it had made intolerable sacrifices in the light of the capacities of its undertaking at the relevant time - to prevent the extraordinary circumstances with which it was confronted from leading to the cancellation of the flight.

• 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.