The Requirement and Effect of the Document of Carriage in Respect of the International Carriage of Cargo by Air (국제항공화물운송에 관한 운송증서의 요건 및 효력)
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- The Korean Journal of Air & Space Law and Policy
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- v.23 no.2
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- pp.67-92
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- 2008
The purpose of this paper is to research the requirements and effect of the document of carriage in respect of the carriage of cargo by air under the Montreal Convention of 1999, IATA Conditions of Carriage for Cargo, and the judicial precedents of Korea and foreign countries. Under the Article 4 of Montreal Convention, in respect of the carriage of cargo, an air waybill shall be delivered. If any other means which preserves a record of the carriage are used, the carrier shall, if so requested by the consignor, deliver to the consignor a cargo receipt. Under the Article 7 of Montreal convention, the air waybill shall be made out by the consignor. If, at the request of the consignor, the carrier makes it out, the carrier shall be deemed to have done so on behalf of the consignor. The air waybill shall be made out in three original parts. The first part shall be marked "for the carrier", and shall be signed by the consignor. The second part shall be marked "for the consignee", and shall be signed by the consignor and by the carrier. The third part shall be signed by the carrier who shall hand it to the consignor after the goods have been accepted. Under the Article 5 of Montreal Convention, the air waybill or the cargo receipt shall include (a) an indication of the places of departure and destination, (b) an indication of at least one agreed stopping place, (c) an indication of the weight of the consignment. Under the Article 10 of Montreal Convention, the consignor shall indemnify the carrier against all damages suffered by the carrier or any other person to whom the carrier is liable, by reason of the irregularity, incorrectness or incompleteness of the particulars and statement furnished by the consignor or on its behalf. Under the Article 9 of Montreal Convention, non-compliance with the Article 4 to 8 of Montreal Convention shall not affect the existence of the validity of the contract, which shall be subject to the rules of Montreal Convention including those relating to limitation of liability. The air waybill is not a document of title or negotiable instrument. Under the Article 11 of Montreal Convention, the air waybill or cargo receipt is prima facie evidence of the conclusion of the contract, of the acceptance of the cargo and of the conditions of carriage. Under the Article 12 of Montreal Convention, if the carrier carries out the instructions of the consignor for the disposition of the cargo without requiring the production of the part of the air waybill or the cargo receipt, the carrier will be liable, for any damage which may be accused thereby to any person who is lawfully in possession of that part of the air waybill or the cargo receipt. According to the precedent of Korea Supreme Court sentenced on 22 July 2004, the freight forwarder as carrier was not liable for the illegal delivery of cargo to the notify party (actual importer) on the air waybill by the operator of the bonded warehouse because the freighter did not designate the boned warehouse and did not hold the position of employer to the operator of the bonded warehouse. In conclusion, as the Korea Customs Authorities will drive the e-Freight project for the carriage of cargo by air, the carrier and freight forwarder should pay attention to the requirements and legal effect of the electronic documentation of the carriage of cargo by air.
A study was conducted to evaluate the effect of
The purpose of this study is that I should look for a desirous directions about home economics by studying the requirements and perception of the high school parents who have finished the course of home economics. It was about 600 parents whom I have searched Seoul-Pusan, Ganwon. Ghynggi province, Choongcheong-Gyungsang province, Cheonla and Jeju province of 600, I chose only 560 as apparently suitable research. The questions include 61 requirements about home economics and one which we never fail to keep among the contents, whenever possible and one about the perception of home economics aims 11 about the perception of home economics courses and management. The collections were analyzed frequency, percent, mean. standard deviation t-test by using SAS program. The followings is the summary result of studying of it. 1. All the boys and girls learning together about the Idea of healthy lives and desirous human formulation and knowledge together are higher. 2. Among the teaching purposes of home economics, the item of the scientific principle and knowledge for improvements of home life shows 15.7% below average value. 3. The recognition degree about the quality of home economics is highly related with the real life, and about the system. we recognize lacking in periods and contents of home economics field and about guiding content, accomplishment and application qualities are higher regardless of sex. 4. The important term which we should emphasize in the subject of home economics is family part. 5. Among the needs of home economic requirement in freshman, in the middle unit, their growth and development are higher than anything else, representing 4.11, and by contrast the basic principle and actuality is 3.70, which is lowest among them. 6. In the case of second grade requirement of home economics content for parents in the middle unit young man and consuming life is 4.09 highest. 7. In the case of 3rd grade requirement of economics contents in the middle unit the choice of coming direction and job ethics is highest 4.16, and preparing meals and evaluation is lowest 3.50.
.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used