• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.54-67
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• 2010

Applying the conventional machine-learning method which has been frequently used in health-care area has several fundamental problems for modern U-health service analysis. First of all, we are still lack of application examples of the traditional method for our modern U-health environment because of its short term history of U-health study. Second, it is difficult to apply the machine-learning method to our U-health service environment which requires real-time management of disease because the method spends a lot of time in the process of learning. Third, we cannot implement a personalized U-health diagnosis system using the conventional method because there is no way to assign weights on the disease-related variables although various kinds of machine-learning schemes have been proposed. In this paper, a novel diagnosis scheme PCADP is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method and it makes the bio-data analysis just a 'process' in the U-health service system. In addition, we offer a semantics modeling of the U-health ontology framework in order to describe U-health data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring of decision process. Upto the best of authors' knowledge, the PCADP scheme and ontology framework proposed in this paper reveals one of the best characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring among recently developed U-health schemes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.7
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• pp.791-799
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• 2016

The purpose of this work is to analyse the impact of new accident risks on maritime safety in Korea. The new accident risks have been induced from new/rare or unprecedented events in world maritime transportation, as identified by 46 experts in the previous study. To measure the impact of these new accident risks on maritime safety in Korea, the statistical accident data reported by the Korean Maritime Safety Tribunals (KMST) has been used for calculation, and the concept of Risk Index (RI) = Frequency Index (FI) + Severity Index (SI)established in a Formal Safety Assessment (FSA) by the IMO has also been introduced. After calculating two kinds of weight for FI and SI from the statistical accident data, high ranked scenarios were identified and their relationships between new risks and these scenarios were analysed. The results from this analysis showed, the root cause of the top-ranked scenario to be "developing high technology", which leads to "shorten cargo handling time". These results differed from optimum RCOs such as "business competition" and "crewing problems" which were identified in the previous study.

• The Journal of the Acoustical Society of Korea
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• v.21 no.5
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• pp.430-437
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• 2002

Having a variety of good characteristics against other pattern recognition techniques, the multilayer perceptron (MLP) has been widely used in speech recognition and speaker recognition. But, it is known that the error backpropagation (EBP) algorithm that MLP uses in learning has the defect that requires restricts long learning time, and it restricts severely the applications like speaker recognition and speaker adaptation requiring real time processing. Because the learning data for pattern recognition contain high redundancy, in order to increase the learning speed it is very effective to use the online-based learning methods, which update the weight vector of the MLP by the pattern. A typical online EBP algorithm applies the fixed learning rate for each update of the weight vector. Though a large amount of speedup with the online EBP can be obtained by choosing the appropriate fixed rate, firing the rate leads to the problem that the algorithm cannot respond effectively to different learning phases as the phases change and the number of patterns contributing to learning decreases. To solve this problem, this paper proposes a Changing rate and Omitting patterns in Instant Learning (COIL) method to apply the variable rate and the only patterns necessary to the learning phase when the phases come to change. In this paper, experimentations are conducted for speaker verification and speech recognition, and results are presented to verify the performance of the COIL.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.549-560
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• 2015

There is a wide variety of data quality attributes such as the ones proposed by the ISO/IEC organization and also by many other domestic and international institutions. However, it takes considerable time and costs to apply those criteria and guidelines to real environment. Therefore, it needs to define data quality evaluation attributes which are easily applicable and are not influenced by organizational environment limitations. The purpose of this paper is to derive data quality attributes and order of their priorities based on customer requirements for managing the process systematically and evaluating the data quantitatively. This study identifies the customer cognitive constructs of data quality attributes using the RGT(Repertory Grid Technique) based on a Korean quality standard model (DQC-M). Also the correlation analysis on the identified constructs is conducted, and the evaluation attributes is prioritized and ranked using the AHP. As the results of this paper, the consistent system, the accurate data, the efficient environment, the flexible management, and the continuous improvement are derived at the first level of the data quality evaluation attributes. Also, Control Compliance(13%), Regulatory Compliance(10%), Requirement Completeness(9.6%), Accuracy(8.4%), and Traceability(6.8%) are ranked on the top 5 of the 19 attributes in the second level.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.45-54
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• 2012

Location-based services with GPS positioning technology as a key technology, but recognizing the current location through satellite communication is not possible in an indoor location-aware technology, low-power short-range communication is primarily made of the study. Especially, as Chirp Spread Spectrum(CSS) based location-aware approach for low-power physical layer IEEE802.15.4a is selected as a standard, Ranging distance estimation techniques and data transfer speed enhancements have been more developed. It is known that the distance measured by CSS ranging has quite a lot of noise as well as its bias. However, the noise problem can be adjusted by modeling the non-zero mean noise value by a scaling factor which corresponds to the change of magnitude of a measured distance vector. In this paper, we propose a localization system using the CSS signal to measure distance for a mobile node taken a measurement of the exact coordinates. By applying the extended kalman filter and least mean squares method, the localization system is faster, more stable. Finally, we evaluate the reliability and accuracy of the proposed algorithm's performance by the experiment for the realization of localization system.

• Journal of Korea Port Economic Association
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• v.29 no.3
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• pp.113-133
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• 2013

Since 1995, Korean enterprises have been rapidly expanding their business, especially to Eastern European countries such as Poland, Slovakia, Czech Republic, Hungary and so on. After the establishment of Korea-EU FTA in 2011, close relationship between the two through economic cooperation has been maintained. To efficiently connect the seaport regions to inland factories located in Eastern European countries, researches on mode choice in the intermodal sector are needed to perform. However, there is a scant of research for mode choice factors on intermodal transportation service in Europe. Therefore, the aim of this research is to understand the current situation of intermodal transportation sector in Europe, identify key factors of mode choice, and weigh the importance among factors influencing intermodal selection in the perspective of Korean exporters or forwarders with overseas cargo to Europe. A survey and in-depth interviews to CEOs and executives who have more than 20 to 30 years of career in logistics sector were carried out from April 01 to May 01, 2013. Using the fuzzy theory as the methodology, 'Reliability of arrival time', 'Transit time', and 'Freight Rate' are equally ranked as the most important factor in the selection of intermodal transportation.

• Journal of the Korean Society for Railway
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• v.19 no.2
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• pp.262-269
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• 2016

Subway station scales are determined by peak predictions. In this study, the purpose behind the installation of a subway is public transportation convenience and public interest, but economic validity is also important. By proving that the scale of the station is excessive with regard to the target station size for Seoul subway Line 5-8, a reasonable plan. can be sought. According to station installation standards, the area of the station under investigation here is out of the service levels by six stages (A~F), and it must be four or more levels (D). The Actual level for the B level is a two-step design. The Actual ratio for over- Peak predictions is only 17.8% on average. The results of measurements of the excess area and determination of the excessive costs were analyzed by subdividing the area and by calculating it based on the B level, finding that it is possible to provide benefits for customers only in the current design, with an area ratio of 16.3%. Given the weight, it was estimated that current conditions can meet the needs of only 18.6% of the current area. Simplifying the scale calculation method of the station, it is convenient, safe, and advantageous to move citizens only if the scale can be streamlined. Then, with a reduced initial investment, maintenance costs during the operation can be reduced.

• The KIPS Transactions:PartD
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• v.17D no.1
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• pp.1-8
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• 2010

Association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from voluminous transactional data. Certainly, one of the major purposes of association rule mining is utilizing the acquired knowledge to provide marketing strategies such as catalogue design, cross-selling and shop allocation. However, this requires too much time and high cost to only extract the actionable and profitable knowledge from tremendous numbers of discovered patterns. In currently available literature, a number of interest measures have been devised to accelerate and systematize the process of pattern evaluation. Unfortunately, most of such measures, including support and confidence, are prone to yielding impractical results because they are calculated only from the sales frequencies of items. For instance, traditional measures cannot differentiate between the purchases in a small basket and those in a large shopping cart. Therefore, some adjustment should be made to the size of market baskets because there is a strong possibility that mutually irrelevant items could appear together in a large shopping cart. Contrary to the previous approaches, we attempted to consider market basket's size in calculating interest measures. Because the devised measure assigns different weights to individual purchases according to their basket sizes, we expect that the measure can minimize distortion of results caused by accidental patterns. Additionally, we performed intensive computer simulations under various environments, and we performed real case analyses to analyze the correctness and consistency of the devised measure.

• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.1262-1287
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• 2017

Recently, forecasting for next-generation technologies have influenced the competitiveness of companies. However, in previous studies, only extract factors influencing the adoption of technology have been investigated. Also, there are few researches on the importance of each decision factors or the competition between technologies. In this research, Lotka-Volterra model is used to confirm the technological competition in the new technology choice timing when the competition is intensified due to the emergence of new technologies. For purpose of this study, estimate the LVC model based on the data of the past competition and then derived the factors affecting the technology of competition and substitution from the literature survey. After that, we confirmed the factor value between the past and the present technology competition. The difference between the factor values derived from the previous step is used to revise the model estimated from the past data base. At this stage, regression analysis is used to derive the importance of each factor and use it as the weight. Through the correction model, the competitiveness is identified through 1:1 comparison with competition candidate technology and existing dominant design technology. In this research, we quantitatively propose the possibility that a specific technology can become a dominant design in the next generation, based on the difference in factor values and importance. This results will help the company's R&D strategy and decision making.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.