• The Korean Journal of Air & Space Law and Policy
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• v.34 no.1
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• pp.203-234
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• 2019

The concept of denied boarding is defined in Article 2(j) of Regulation 261/2004 thus: "denied boarding means a refusal to carry passengers on a flight, although they have presented themselves for boarding under the conditions laid down in Article 3(2), except where there are reasonable grounds to deny them boarding, such as reasons of health, safety or security, or inadequate travel documentation." So far as relevant to this case, to be entitled to compensation, if denied boarding, Article 3(2) provides a passenger must first come within the scope of the protection of the Regulation, which applies under the following conditions: "${\cdots}$.that passengers (a) have a confirmed reservation on the flight concerned and, except in the case of cancellation referred to in Article 5, present themselves for check-in, as stipulated and at the time indicated in advance and in writing (including by electronic means) by the air carrier, the tour operator or an authorised travel agent, or, if no time is indicated, not later than 45 minutes before the published departure time." This paper reviews the EU Cases such as Rodríguez Cachafeiro v. Iberia [2012] Case C-321/11; Finnair Oyj v. Timy Lassooy [2012] Case C-22/11; Caldwell v. easyJet Airline Co. Ltd. [2015] ScotSC 64. ECJ and Sheriff court of Scotland held that the concept of denied boarding, within the meaning of Articles 2(j) and 4 of Regulation No 261/2004 establishing common rules on compensation and assistance to passengers in the event of denied boarding and of cancellation or long delay of flights, and repealing Regulation No 295/91, must be interpreted as relating not only to cases where boarding is denied because of overbooking but also to those where boarding is denied on other grounds, such as operational reasons. Also, ECJ ruled that Articles 2(j) and 4(3) must be interpreted as meaning that the occurrence of extraordinary circumstances resulting in an air carrier rescheduling flights after those circumstances arose cannot give grounds for denying boarding on those later flights or for exempting that carrier from its obligation, under Article 4(3) of that regulation, to compensate a passenger to whom it denies boarding on such a flight.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.287-310
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• 2022

Companies' interest in developing AI-based intelligent new products is increasing. Recently, the main concern of companies is to innovate customer experience and create new values by developing new products through the effective use of Artificial intelligence technology. However, due to the nature of products based on radical technologies such as artificial intelligence, intelligent products differ from existing products and development methods, so it is clear that there is a limitation to applying the existing development methodology as it is. This study proposes a new research method based on KANO-TOPSIS for the successful development of AI-based intelligent new products by using car voice assistants as an example. Using the KANO model, select and evaluate functions that customers think are necessary for new products, and use the TOPSIS method to derives priorities by finding the importance of functions that customers need. For the analysis, major categories such as vehicle condition check and function control elements, driving-related elements, characteristics of voice assistant itself, infotainment elements, and daily life support elements were selected and customer demand attributes were subdivided. As a result of the analysis, high recognition accuracy should be considered as a top priority in the development of car voice assistants. Infotainment elements that provide customized content based on driver's biometric information and usage habits showed lower priorities than expected, while functions related to driver safety such as vehicle condition notification, driving assistance, and security, also showed as the functions that should be developed preferentially. This study is meaningful in that it presented a new product development methodology suitable for the characteristics of AI-based intelligent new products with innovative characteristics through an excellent model combining KANO and TOPSIS.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.2
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• pp.205-212
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• 2012

This study was performed to investigate the prevalence of metabolic syndrome (MS) and assess nutrient intake levels for the purpose of improving MS risk factors. The participants in this study were 512 adults consisting of 271 men and 241 women aged 30 and over, who visited a public health center for a medical check up. The diagnosis of MS subjects was adapted from the NCEP-ATPIII guidelines and the WHO Asia-Pacific Area criteria for obesity. The MS group was defined as subjects displaying three or more risk factors, and the non MS group was defined as those displaying two or less risk factors. A dietary survey was conducted using the 24-hour recall method. The number of subjects displaying MS syndrome factors was 158 (30.9%), broken down into, 89 men and 69 women. Regarding risk factors in the MS group, the prevalence of waist circumference was 40.5%, hypertension 34.2%, hyperglycemia 31.0%, low HDL-cholesterol 24.7%, and hypertriglycemia 19.6%. BMI, sistolic blood pressure, blood glocose, blood triglyceride, and blood HCL-cholesterol of the MS group were significantly higher compared to the non MS group. Male subjects in the MS group reported high intakes of cereals, sugar, fruits, meat and poultry, oil and fats, and beverages and total food intake was significantly higher compared to the non MS group. Women in the MS group reported high intakes of meat and poultry, milk and dairy products, beverages, and seasonings, and total food intake was higher compared to the non MS group. Dietary diversity score (DDS) was 3.82~4.04, which was not significant among the groups. In men, dietary variety score (DVS) was 16.3 in the MS group and 19.4 in the non MS group, whereas in women, the DVS was 15.2 in the non MS group and 17.0 in the MS group. In GMVDF pattern, 11111 pattern was 30.7%, followed by 01111 for men and 11101 for women. Calorie, fat, and cholesterol intakes in men as well as, calorie, fat, and folate intakes in women in the MS group were higher compared to the non MS group. Intakes of protein, P, Fe, Na, vitamin $B_1$, vitamin $B_2$, niacin, vitamin E, and Zn were higher than the KDRIs. On the other hand, intakes of Ca, K, fiber, vitamin $B_2$, and vitamin C were below the KDRIs. Intakes of lipids, animal food, Na, and cholesterol in the MS group were higher compared to the non MS group, whereas intake of dietary fiber was lower. Our results indicate that continuous, systematic nutritional education program must implemented to reduce the risk factors associated with MS.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.