• Information Systems Review
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• v.26 no.1
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• pp.337-353
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• 2024

The purpose of this study is to investigate the relationship between consumer reviews and managerial responses, aiming to explore the necessity of webcare for efficiently managing consumer reviews. We intend to propose a methodology for effective webcare and to construct a webcare model using machine learning techniques based on an audiobook platform. In this study, we selected four audiobook platforms and conducted data collection and preprocessing for consumer reviews and managerial responses. We utilized techniques such as topic modeling, topic inconsistency analysis, and DBSCAN, along with various machine learning methods for analysis. The experimental results yielded significant findings in clustering managerial responses and predicting responses to consumer reviews, proposing an efficient methodology considering resource constraints and costs. This research provides academic insights by constructing a webcare model through machine learning techniques and practical implications by suggesting an efficient methodology, considering the limited resources and personnel of companies. The proposed webcare model in this study can be utilized as strategic foundational data for consumer engagement and providing useful information, offering both personalized responses and standardized managerial responses.

• International Journal of Concrete Structures and Materials
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• v.6 no.2
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• pp.123-134
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• 2012

There is significant concern in the engineering community regarding the safety and effectiveness of fiber-reinforced polymer (FRP) strengthening of RC structures because of the potential for brittle debonding failures. In this paper, previous research programs conducted by other researchers were reviewed in terms of the debonding failure of FRP laminates externally attached to concrete. This review article also discusses the influences on bond strength and failure modes as well as the existing experimental research and developed equations. Based on the review, several important conclusions were re-emphasized, including the finding that the bond transfer strength is proportional to the concrete compressive strength; that there is a certain bond development length that has to be exceeded; and that thinner adhesive layers in fact lower the chances of a concrete-adhesive interface failure. It is also found that there exist uncertainty and inaccuracy in the available models when compared with the experimental data and inconsistency among the models. This demonstrates the need for continuing research and compilation of data on the topic of FRP's bond strength.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.

• Survey Research
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• v.6 no.1
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• pp.83-98
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• 2005

Large scale survey database may contain some erroneous data or missing data. Incomplete or erroneous data may be produced in the process of data collection or data capture. Since erroneous data can cause some bias and inconsistency, data editing, which is the procedure for detecting and adjusting individual errors in data records, is a very important work in statistical survey. In this paper, we introduce an editing process for the housing price survey to enhance discussions on that topic. We explain how to decide some appropriate edit rules and show some related data. Furthermore, we describe input editing procedures which is appropriate for on-line survey and how to find and eliminate erroneous data through output editing.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.2
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• pp.73-80
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• 2013

Due to the fact that possible risk associated with soil-crop-food chain transfer, metal contamination in croplands has become a major topic of wide concern. Accumulation of toxic metals in edible parts of crops grown in contaminated soils has been reported from number of crops including rice, soybean, wheat, maize, and vegetables. Therefore, in order to ensure food safety, measures are needed to be taken in mitigating metal pollution and subsequent uptake by crop plants. Present paper critically reviewed some of the cost effective remediation techniques used in minimizing metal uptake by crops grown in contaminated soils. Liming with different materials such as limestone ($CaCO_3$), burnt lime (CaO), slaked lime [$Ca(OH)_2$], dolomite [$CaMg(CO_3)_2$], and slag ($CaSiO_3$) has been widely used because they could elevate soil pH rendering metals less-bioavailable for plant uptake. Zn fertilization, use of organic amendments, crop rotation and water management are among the other techniques successfully employed in reducing metal uptake by crop plants. However, irrespectively the mitigating measure used, heterogeneous accumulation of metals in different crop species is often reported. The inconsistency might be attributed to the genetic makeup of the crops for selective uptake, their morphological characteristics, position of edible parts on the plants in respect of their distance from roots, crop management practices, the season and to the soil characteristics. However, a sound conclusion in this regard can only be made when more scientific evidence is available on case-specific researches, in particular from long-term field trials which included risks and benefits analysis also for various remediation practices.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.669-676
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• 2006

Hydrological factors, especially the spatial distribution of interpretation on precipitation is often topic of interest in studying of water resource. The popular methods such as Thiessen method, inverse distance method, and isohyetal method are limited in calculating the spatial continuity and geographical characteristics. This study was intended to overcome those limitations with improved method that will yield higher accuracy. The monthly and yearly precipitation data were produced and compared with the observed daily precipitation to find correlation between them. They were then used as secondary variables in Co-kriging method, and the result was compared with the outcome of existing methods like inverse distance method and kriging method. The comparison of the data showed that the daily precipitation had high correlation with corresponding year's average monthly amounts of precipitation and the observed average monthly amounts of precipitation. Then the result from the application of these data for a Co-kriging method confirmed increased accuracy in the modeling of spatial distribution of precipitation, thus indirectly reducing inconsistency of the spatial distribution of hydrological factors other than precipitation.