• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4A
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• pp.403-410
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• 2008

In this paper, we propose a variant of the QRM-MLD signal detection method that is used for spatially multiplexed multiple antenna system. The original QRM-MLD signal detection method combines the QR decomposition with the M-algorithm, thereby significantly reduces the prohibitive hardware complexity of the ML signal detection method, still achieving a near ML performance. When the number of transmitter antennas and/or constellation size are increased to achieve higher bit rate, however, its increased complexity makes the hardware implementation challenging. In an effort to overcome this drawback of the original QRM-MLD, a number of variants were proposed. A most strong variant among them, in our opinion, is the ranking method, in which the constellation points are ranked and computation is performed for only highly ranked constellation points, thereby reducing the required complexity. However, the variant using the ranking method experiences a significant performance degradation, when compared with the original QRM-MLD. In this paper, we point out the reasons of the performance degradation, and we propose a novel variant that overcomes the drawbacks. We perform a set of computer simulations to show that the proposed method achieves a near performance of the original QRM-MLD, while its computational complexity is near to that of the QRM-MLD with ranking method.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

• Journal of the military operations research society of Korea
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• v.15 no.1
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• pp.44-53
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• 1989

This paper concerns with the case of having a goal program with no preassigned deterministic ranking for the goals. The priority ranking in this case depends on the states of nature which are random variables. The Bayesian approach is performed to obtain the nondominated set of rankings.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.2
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• pp.35-43
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• 1992

In this paper fuzzy ranking method using dominance relation is suggested. This method is compared to the existing fuzzy number comparing methods. All ten methods with the suggested method are evaluated using six example based on four criteria.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.293-303
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• 2014

The mobile game market is fast growing after the Kakao game launched. Especially, the market is placed at the second highest by occupying 33.1% of the market. However, the rate of the total sales amount is only 6%, showing quite an imbalance between the market occupancy and the sales amount. This means that the profit-making models are not stabilized yet in the mobile game market. The absence of profit-making models can be ascertained by the relationship of DAU and sales ranking. There are several games which are ranked at DAU Top10 among Google free games, but not ranked at top 10 among sales amounts. On the other hand, there are several games which are low in DAU ranking but high in sales amount ranking. This result shows that there is no direct interrelation between the product attractiveness which users feel and the profit-making models in the market. This study compared the Google play free games which are ranked at top 10 in terms of DAU ranking and sales amount ranking to find out the factor which causes the imbalance between the DAU ranking and sales amount ranking. Based on this outcome, this study presents the reference point for the design of profit-making models on behalf of the manufacturers who wish to launch into the mobile game market in the future.

• Journal of Soil and Groundwater Environment
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• v.13 no.6
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• pp.62-71
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• 2008

Soil quality standards (SQS) are necessary to protect the human health and soil biota from the exposure to soil pollutants. The current SQS in Korea contain only sixteen substances, and it is scheduled to expand the number of substances. Chemical ranking and scoring (CRS) system is very effective to screen the priority chemicals for the future SQS in terms of their toxicity and exposure potential. In this study, several CRS systems were extensively compared to propose the assessment factors that required for the screening of soil pollutants The CRS systems considered in this study include the CHEMS-1 (Chemical Hazard Evaluation for Management Strategies), SCRAM (Scoring and Ranking Assessment Model), EURAM (European Union Risk Ranking Method), ARET (Accelerated Reduction/Elimination of Toxics), CRSKorea, and other systems. The additional assessment factors of CRS suitable for soil pollutants were suggested. We suggest soil adsorption factor as an appropriate factor of CRS system to consider chemical transport from soil to groundwater. Other factors such as soil emission rate and cases of accident of soil pollutants were included. These results were reflected to screen the priority chemicals in Korea, as a part of the project entitled ‘Setting the Priority of Soil Contaminants'.

• Journal of the Korean Society of Food Science and Nutrition
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• v.24 no.6
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• pp.892-900
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• 1995

Dietary data of 538 middle school students have been analysed to identify the contribution of specific foods to absolute intake and between-person-variance in nutrient consumption. The 24-hour-dietary-recall method had been used to collect the data required. Contribution of specific foods, in terms of ranking order for both absolute intake and between-person-variance have been observed. Ranking order of food for absolute intake was given based on the percen of contribution whereas the ranking order of foods for between-person-variance was given based on the percent of contribution whereas the ranking order of foods for between-person-variance was given based on a coefficient fo variation. As a result, for most of the nutrients(except cholesterol), the ranking order of foods for the between-person-variance was quite different from that of absolute intake. The results indicate that to identify between-person-variance of nutrient intake in an epidemiology study, foods with a high ranking in between-person-variance should be included in developing the food frequency questionnaires rather than foods which showed a high ranking in absolute intake.

• Communications of Mathematical Education
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• v.24 no.2
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• pp.475-502
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• 2010

In this paper, the ability to use mathematical representations in solving math problem was analyzed according to student assessment levels using 113 first-year high school students, and the characteristics of their representation usage according to student assessment levels were also examined. For this purpose, problems were presented that could be solved using various mathematical representations, and the students were asked to solve them using a maximum of three different methods. Also, based on the comparative analysis results of a paper evaluation, six students were selected and interviewed, and the reasons for their representation usage differences were analyzed according to their student assessment levels. The results of the analysis show that over 50% of high ranking students used two or more representations in all questions to solve problems, but with middle ranking students, there were deviations depending on the difficulty of the questions. Low ranking students failed to use representation in diverse ways when solving problems. As for characteristics of symbol usage, high ranking students preferred using formulas and used mathematical representations efficiently while solving problems. In contrast, middle and low ranking students mostly used tables or pictures. Even when using the same representations, high ranking students' representations were expressed in a more structurally refined manner than those by middle and low ranking students.

• Journal of the HCI Society of Korea
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• v.13 no.3
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• pp.5-15
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• 2018

The biggest characteristic of Social Network Game(SNG) is that games are played through competition and cooperation with the actual acquaintances based on SNS. Even though such competition and challenge spirit have been dealt importantly as preceding factors having influence on the flow in games in the existing game area, it is rare to find researches deeply considering the characteristics of ranking competition between acquaintances in SNG. Moreover, it was not considered that such acquaintances could be the targets of competition and also challenge at the same time in SNG. Therefore, this study examined the achievements(big differences in ranking, small differences in ranking) of the targets for comparison and closeness(strong ties, weak ties) with the targets for comparison as factors having influence on competition and challenge spirit, and also empirically analyzed the influence of such factors and interactions between factors on players' competition and challenge spirit in the ranking competitive society, by analyzing the characteristics of ranking competition between acquaintances in the mobile puzzle, SNG based on SNS through the analysis on the preceding research on the self-evaluation maintenance model of the social comparison theory. In the results, when preferentially exposing competitors with small difference in ranking and also exposing competitors with stronger ties, players' competition is stimulated, so that it can improve their challenge spirit. Such results of this study can be expected to a lot contribute to the actual design work of SNG ranking table contents.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.25-39
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• 2017

Over 5 million people participate in cycle racing betting and its revenue is more than 2 trillion won. This study predicts the ranking of cycle racing using various statistical analyses and identifies important variables which have influence on ranking. We propose competitive ranking prediction models using various classification and regression methods. Our model can predict rankings with low misclassification rates most of the time. We found that the ranking increases as the grade of a racer decreases and as overall scores increase. Inversely, we can observe that the ranking decreases when the grade of a racer increases, race number four is given, and the ranking of the last race of a racer decreases. We also found that prediction accuracy can be improved when we use centered data per race instead of raw data. However, the real profit from the future data was not high when we applied our prediction model because our model can predict only low-return events well.