• The Journal of Society for e-Business Studies
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• v.15 no.3
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• pp.99-129
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• 2010

The objective of this research is to identify the factors which influence users' intention to use mobile Internet. There are lots of researches based on Technology Acceptance Model trying to identify the factors which have influence on accepting mobile Internet and its related services. These researches, however, have some limitation in the sense that they focus only on the features of mobile Internet itself while users make overall decision after the comparison of a new service channel with existing service channels in many directions. Therefore, we are going to analyze the impact of users' perception on the difference between mobile and stationary Internet toward their intention to use mobile Internet. We identified the features like ubiquitous availability, context awareness, compatibility, friendliness, and economic value from literature review and developed a structural model about the impact of users' perception on the differences of these features between mobile and stationary Internet toward their intention to use mobile Internet through mediate variables such as perceived ease of use, perceived playfulness, and perceived usefulness. After that, we conducted an experimental analysis for the model, and addressed a solution to rev up the usage of mobile Internet based on the results of the analysis.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.21-30
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• 2014

Waste landfill site which is the facility usually rejected by communities is generally perceived as one of the serious social problems. It causes serious conflicts between interested parties from the beginning of the site selection process and produces various difficulties throughout the installation process. This study suggests the systematization and standardization of the landfill site selection process to reduce those problems and to objectify the process. The study process and results are as follows. First, the landfill site selection process was divided into 4 general phases rather than more specific fragmented phases and the requirements for each phase were suggested accordingly. This can make the process clearly organized and bring the standardization of the process by increasing the applicability of each phase for various situations. Second, the utilization of GIS(Geographic Information System) and PAPRIKA (Potentially All Pairwise RanKings of all possible Alternatives) among the various MCDA(Multi-Criteria Decision Analysis) methods was suggested as the objective and scientific method. Third, the hypothetical case study on the landfill site selection process of Cheongju city was conducted based on the information above and the results show the practicability and objectivity of the newly defined landfill site selection process in this study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.157-172
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• 2024

Autonomous driving technology, when commercialized, has the potential to improve the safety, mobility, and environmental performance of transportation networks. However, safe autonomous driving may be hindered by poor sensor performance and limitations in long-distance detection. Therefore, cooperative autonomous driving that can supplement information collected from surrounding vehicles and infrastructure is essential. In addition, since HDVs, AVs, and CAVs have different ranges of perceivable information and different response protocols, countermeasures are needed for mixed traffic that occur during the transition period of autonomous driving technology. There is a lack of research on traffic flow optimization that considers the penetration rate of autonomous vehicles and the different characteristics of each road segment. The objective of this study is to develop weights based on safety, operational, and environmental factors for each infrastructure control use case and autonomous vehicle MPR. To develop an integrated evaluation index, infra-guidance AHP and hybrid AHP weights were combined. Based on the results of this study, it can be used to give right of way to each vehicle to optimize mixed traffic.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.