• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1531-1538
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• 2014

The causes of cancer are diverse, complex, and only partially understood. Many factors including health behaviors, socioeconomic environments and geographical locations can directly damage genes or combine with existing genetic faults within cells to cause cancerous mutations. Collecting the cancer data and reporting the statistics, therefore, are important to help identify health trends and establish normal health changes in geographical areas. In this article, we analyzed cancer data and demon-strated how spatial patterns of the age-standardized rate and health indicators can be examined visually and simultaneously using linked micromap plots. As a result of data analysis, the age-standardized rate has positive correlativity with thyroid and breast cancer, but the rate has negative correlativity with smoking and drinking. In addition, the regions with high age-standardized rate are located in southwest and the areas of high population density while the standardized mortality ratio is higher in southwest and northeast where there are lots of rural areas.

• IE interfaces
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• v.13 no.4
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• pp.646-657
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• 2000

We developed an efficient small sized weather condition forecasting system (WFS). A cheap NT-server was utilized for handling a large amount of data, while traditional WFS has conventionally relied on Unix based workstation server. The proposed WFS contains automatic weather observing system (AWS). AWS was designed for collecting weather conditions automatically, and it was linked to WFS in order to provide various weather condition information. The existing two phase scheme and chain code algorithm were used for transforming AWS's data into WFS's data. The WFS's data were mapped into geometric information system using various display techniques. Finally the transformed WFS's data was also converted into JPG (Joint Photographic Group) data type, and the final JPG data could be accessible by others though Internet. The developed system was implemented using WWW environment and has provided weather condition forecasting information. Real case is given to show the presented integrated WFS with detail information.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.793-806
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• 2022

As the IMO introduced MASS (Maritime Autonomous Surface Ships), ISO(International Organization for Standardization) announced ISO 19847 of a maritime data sharing standard for collecting and remotely managing data of ship systems. Previous literature evaluated the risk using HAZOP for ISO 19847 and proved that risk assessment is useful through experiments. However, redundancy of ISO 19847 ship data server which is one of the risk reduction method suggested in previous literature, was designed but couldn't tested due to the limitations of the conditions. So, in this study, to prove the usefulness of the ship data server redundancy of ISO 19847 which was not tested in previous literature. It based on the design of previous literature, and the network of ship data servers was modeled using the SES/DEVS format and simulated using the DEVS# open source library.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.565-570
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• 2015

Supervised machine learning has become popular in discovering context descriptions from sensor data. However, collecting a large amount of labeled training data in order to guarantee good performance requires a great deal of expense and time. For this reason, semi-supervised learning has recently been developed due to its superior performance despite using only a small number of labeled data. In the existing semi-supervised learning algorithms, unlabeled data are used to build a graph Laplacian in order to represent an intrinsic data geometry. In this paper, we represent the unlabeled data as the spatial-temporal dataset by considering smoothly moving objects over time and space. The developed algorithm is evaluated for position estimation of a smartphone-based robot. In comparison with other state-of-art semi-supervised learning, our algorithm performs more accurate location estimates.

• Korean Journal of Construction Engineering and Management
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• v.5 no.1 s.17
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• pp.114-123
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• 2004

Contract time determination for highway construction projects has never been easy despite considerable research efforts from academia as well as industry. High variations in crew production rates are considered one of the main barriers to accurate contract time determination. This paper presents a methodology for collecting field information on crew production rates which will help to enhance the accuracy of contract time determination for highway bridge construction. Based on a standard data collection tool developed, data on field crew production rates was collected from 14 on going projects in Texas, USA, over the past two years. The production rates based on the data collected were considered by industry practitioners to be more realistic and practical than those available to the current practices. As more data becomes available, key drivers influencing production rates could be identified and provide site personnel with a means to better plan and control production in a project specific context.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5321-5334
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• 2019

Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on "place" and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.814-817
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• 2002

High-dimensional data with two or more attributes are considered. A typical example of such data is face images of various individuals and expressions. In these cases, collecting a complete data set is often difficult since the number of combinations can be large. In the present study, we propose a method to interpolate data of missing combinations from other data. If this becomes possible, robust recognition of multiple attributes is expectable. The key of this subject is appropriate extraction of the similarity that the face images of same individual or same expression have. Bilinear model ［1］has been proposed as a solution of this subjcet. However, experiments on application of bilinear model to classification of face images resulted in low performance ［2］. In order to overcome the limit of bilinear model, in this research, a nonlinear model on a neural network is adopted and usefulness of this model is experimentally confirmed.

• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.2
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• pp.136-140
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• 2011

Monitoring of water surfaces through permanent measurement of hydrodynamic and meteorological data is one of the main requirements in safe and sustainable water management. The Caspian Sea, the major surface water body in Iran, significantly affects more than 600 km of urban and industrial coastline. In the present work, an integrated marine monitoring network for the entire southern coastline of the Caspian Sea was developed. The main design concerns centered on the network measuring components and data recording, checking, filtering, gap recognition, and transferring systems. Four coastal monitoring stations were assigned, along with two regional collecting stations and one central data station for gathering, checking and delivering recorded data at different access levels. Applicable guidelines on selection of measuring devices for both shallow and deep water zones are presented herein.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1201-1211
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• 2015

WSN acts as an effective tool for tracking the large scale environments. In such environment, the battery life of the sensor networks is limited due to collection of the data, usage of sensing, computation and communication. To resolve this, a mobile robot is presented to identify the data present in the partitioned sensor networks and passed onto the sink. In novel data collection algorithm, the performance of the data collecting operation is reduced because mobile robot can be used only within the limited range. To enhance the data collection in a changing environment, Non Linear Error Identifier (NLEI) algorithm has been developed and presented in this paper to configure the robot by means of error models which are non-linear. Experimental evaluation has been conducted to estimate the performance of the proposed NLEI and it has been observed that the proposed NLEI algorithm increases the error correction rate upto 42% and efficiency upto 60%.

• Journal of Information Technology Services
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• v.8 no.1
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• pp.73-97
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• 2009

The IT implementation literature suggests that various implementation factors play critical roles in the success of an information system; however, there is little empirical research about the implementation of data warehousing systems has unique characteristics that may impact the importance of factors that apply to it. In this study, a cross-sectional survey investigated a model of data warehousing success. Data warehousing managers and data suppliers from 51 organizations completed paired mail questionnaires on implementation factors and the success of the warehouse. The purpose of this study is to identify which factors have a positive impact on the successful implementation of DW among organizational implementation success, project implementation success, and technical implementation success which are selected as implementation factors of DW. To do so, this study comprehensively analyze the previous studies on the success factors of the information system and DW to develop the study model and set the hypothesized. Then, it verified the hypothesis via the empirical analysis after collecting the materials through the survey. Finally, the discussion of the study's results, offered the implications of the findings, and the conclusions of the findings followed.