• Journal of the Semiconductor & Display Technology
• /
• v.21 no.2
• /
• pp.90-94
• /
• 2022

In order to minimize the picking time when the products are released from the warehouse, they should be located close to the exit when the products are released. Currently, the warehouse determines the loading location based on the order of the requirement of products, that is, the frequency of arrival and departure. Items with lower requirement ranks are loaded away from the exit, and items with higher requirement ranks are loaded closer from the exit. This is a case in which the delivery time is faster than the products located near the exit, even if the products are loaded far from the exit due to the low requirement ranking. In this case, there is a problem in that the transit time increases when the product is released. In order to solve the problem, we use the idle time of the stocker in the warehouse to rearrange the products according to the order of delivery time. Temporal difference learning method using Q_learning control, which is one of reinforcement learning types, was used when relocating items. The results of rearranging the products using the reinforcement learning method were compared and analyzed with the results of the existing method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.3
• /
• pp.894-907
• /
• 2022

The features extracted by convolutional neural networks are more descriptive of images than traditional features, and their convolutional layers are more suitable for retrieving images than are fully connected layers. The convolutional layer features will consume considerable time and memory if used directly to match an image. Therefore, this paper proposes a feature weighting and region integration method for convolutional layer features to form global feature vectors and subsequently use them for image matching. First, the 3D feature of the last convolutional layer is extracted, and the convolutional feature is subsequently weighted again to highlight the edge information and position information of the image. Next, we integrate several regional eigenvectors that are processed by sliding windows into a global eigenvector. Finally, the initial ranking of the retrieval is obtained by measuring the similarity of the query image and the test image using the cosine distance, and the final mean Average Precision (mAP) is obtained by using the extended query method for rearrangement. We conduct experiments using the Oxford5k and Paris6k datasets and their extended datasets, Paris106k and Oxford105k. These experimental results indicate that the global feature extracted by the new method can better describe an image.

• International Commerce and Information Review
• /
• v.16 no.4
• /
• pp.307-330
• /
• 2014

The purpose of this study is to estimation of Technical Barriers of Trade(TBT) between Korea and China. TBT is one of the key issues in which both governments are interested since the Korea-China FTA negotiations had launched in 2012. In this paper, we aggregate nine country HS codes from World Bank and AIO codes from JETRO. Our estimation model based on modified price wedge approach differentiate previous researches in the sense that it covers all manufacture industries and uses nine country data set. Estimation results confirm the importance of TBT showing that TBT high ranking items significantly overlap high ranking export items. We also find that the size of Chinese TBT are much larger than that of Korean TBT, implying that Korean government needs smart and well prepared strategy for key items in TBT/FTA negotiation with Chinese government.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.43 no.6 s.312
• /
• pp.52-59
• /
• 2006

Recently, the internet has seen tremendous growth with plenty of enriched information due to an increasing number of specialized personal interests and popularizations of private cyber space called, blog. Many of today's blog provide internet users, RSS, which is also hewn as the syndication technology. It enables blog users to receive update automatically by registering their RSS channel address with RSS aggregator. In other words, it keeps internet users wasting their time checking back the web site for update. This paper propose the ways to manage RSS Channel Searching Crawler and collected RSS Channels for internet users to search for a specific RSS channel of their want without any obstacles. At the same time. This paper proposes RSS channel ranking based on user popularity. So, we focus on an idea of adding index to information and web update for users to receive appropriate information according to user property.

• Current Research on Agriculture and Life Sciences
• /
• v.27
• /
• pp.59-67
• /
• 2009

The purpose of this study is to propose the methodology for introducing green infrastructure that can improve the health of citizens by promoting walkability. The methodology is composed of the following three phases: classification of the types of green spaces, selection of core green spaces with two separate analyses, and introduction of the framework of green infrastructure to promote walkability. In the first phase, the classification of the types of green spaces was carried out in order to understand existing distribution pattern of green spaces in study site. In the second phase, walkable blocks were selected by such methods as walkability value. Through these two analyses, all the blocks were divided into three groups according to the ranking figured up the second analyses' results. The blocks in the first group, the group involved in the top 30% and having the greatest ranking, were defined as walkable blocks. In the last phase, a basic frame of the green infrastructure in study site was introduced by connecting the walkable blocks with using other blocks and the green spaces over 1ha. In case study, 28 important green spaces and 35 walkable blocks were selected through the two analysis process. Then, the basic framework of green infrastructure based on the selected 28 important green spaces and 35 walkable blocks was introduced. The methodology applied to this study can be used to get the best selections of the proper green infrastructure in accordance with the purpose of the ecological and recreational local development. In particular, this study will suggest a specific analysis model to use for the ecological and walkable urban planning with green spaces existing in the city.

• The KIPS Transactions:PartC
• /
• v.13C no.1 s.104
• /
• pp.129-136
• /
• 2006

Information Retrieval System aims towards providing fast and accurate information to users. However, current search systems are based on plain svntactic analysis which makes it difficult for the user to find the exact required information. This paper proposes the SW-IRS (Semantic Web-based Information Retrieval System) using an Ontology Server. The proposed system is purposed to maximize efficiency and accuracy of information retrieval of unstructured and semi-structured documents by using an agent-based automatic classification technology and semantic web based information retrieval methods. For interoperability and easy integration, RDF based repository system is supported, and the newly developed ranking algorithm was applied to rank search results and provide more accurate and reliable information. Finally, a new ranking algorithm is suggested to be used to evaluate performance and verify the efficiency and accuracy of the proposed retrieval system.

• Journal of Information Management
• /
• v.36 no.3
• /
• pp.51-70
• /
• 2005

It is meaningful to develop the scholarly digital archives in a respect that it could preserve the research papers using digital media. The scholarly digital archives provide scientists with the information source with which scientists could actively accomplish research. This study measured based on the citation analysis to what extent PMC, which is life science scholarly digital archives, has an effect on the scholarly communication in the same subject field. The findings are as follows. First, the three citation indicators, impact factor, immediacy index, and half-life, have no remarkable difference in between pre and post-digital archives era. Second, there were more cases that the major citation indicators increased rather than ones decreased with the appearance of the scholarly digital archives, as a result of ranking the scholarly journals after classifying according to subjects.

• International Journal of Knowledge Content Development & Technology
• /
• v.2 no.2
• /
• pp.17-29
• /
• 2012

All academic information on the web or elsewhere has its creator, that is, a subject who has created the information. The subject can be an individual, a group, or an institution, and can be a nation depending on the nature of the relevant information. Most information is composed of a title, an author, and contents. An essay which is under the academic information category has metadata including a title, an author, keyword, abstract, data about publication, place of publication, ISSN, and the like. A patent has metadata including the title, an applicant, an inventor, an attorney, IPC, number of application, and claims of the invention. Most web-based academic information services enable users to search the information by processing the meta-information. An important element is to search information by using the author field which corresponds to a personal name. This study suggests a method of efficient indexing and using the adjacent operation result ranking algorithm to which phrase search-based boosting elements are applied, and thus improving the accuracy of the search results of personal names. It also describes a method for providing the results of searching co-authors and related researchers in searching personal names. This method can be effectively applied to providing accurate and additional search results in the academic information services.

• Journal of information and communication convergence engineering
• /
• v.15 no.4
• /
• pp.217-226
• /
• 2017

Korea has the tenth largest film industry in the world; however, detailed analyses using the factors contributing to successful film commercialization have not been approached. Using big data, this paper analyzed both internal and external factors (including genre, release date, rating, and number of screenings) that contributed to the commercial success of Korea's top 10 ranking films in 2011-2015. The authors developed a WebCrawler to collect text data about each movie, implemented a Hadoop system for data storage, and classified the data using Map Reduce method. The results showed that the characteristic of "release date," followed closely by "rating" and "genre" were the most influential factors of success in the Korean film industry. The analysis in this study is considered groundwork for the development of software that can predict box-office performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.1
• /
• pp.109-134
• /
• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.