• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1248-1248
• /
• 2001

Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and considerable compositional changes in milk, reducing milk quality. The potential of near infrared (NIR) spectroscopy in the region from 1100 to 2500nm and chemometric method for classification to detect milk from mastitic cows was investigated. A total of 189 milk samples from 7 Holstein cows were collected for 27 days, consecutively, and analyzed for somatic cells (SCC). Three of the cows were healthy, and the rest had mastitis periods during the experiment. NIR transflectance milk spectra were obtained by the InfraAlyzer 500 spectrophotometer in the spectral range from 1100 to 2500nm. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. The classification of the samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two concentration of SCC - 200 000 cells/ml and 300 000 cells/ml, respectively, were used as thresholds fer separation of healthy and mastitis cows. The best detection accuracy was found for models, obtained using 200 000 cells/ml as threshold and smoothed absorbance data - 98.41% from samples in the calibration set and 87.30% from the samples in the independent test set were correctly classified. SIMCA results for classes, based on 300 000 cells/ml threshold, showed a little lower accuracy of classification. The analysis of changes in the loading of first PC factor for group of healthy milk and group of mastitic milk showed, that separation between classes was indirect and based on influence of mastitis on the milk components. The accuracy of mastitis detection by SIMCA method, based on NIR spectra of milk would allow health screening of cows and differentiation between healthy and mastitic milk samples. Having SIMCA models, mastitis detection would be possible by using only DIR spectra of milk, without any other analyses.

• Journal of Biomedical Engineering Research
• /
• v.31 no.6
• /
• pp.456-463
• /
• 2010

In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.

• Structural Monitoring and Maintenance
• /
• v.5 no.1
• /
• pp.67-78
• /
• 2018

The stability of cutting slope influences the safety of railway operation, and how to identify the stability of the slope quickly and determine the rational monitoring plan is a pressing problem at present. In this study, the attribute recognition model of risk assessment for high cutting slope stability in the heavy haul railway is established based on attribute mathematics theory, followed by the consequent monitoring scheme design. Firstly, based on comprehensive analysis on the risk factors of heavy haul railway loess slope, collapsibility, tectonic feature, slope shape, rainfall, vegetation conditions, train speed are selected as the indexes of the risk assessment, and the grading criteria of each index is established. Meanwhile, the weights of the assessment indexes are determined by AHP judgment matrix. Secondly, The attribute measurement functions are given to compute attribute measurement of single index and synthetic attribute, and the attribute recognition model was used to assess the risk of a typical heavy haul railway loess slope, Finally, according to the risk assessment results, the monitoring content and method of this loess slope were determined to avoid geological disasters and ensure the security of the railway infrastructure. This attribute identification- risk assessment- monitoring design mode could provide an effective way for the risk assessment and control of heavy haul railway in the loess plateau.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.1
• /
• pp.11-20
• /
• 1999

Because conventional spatial databases process the spatial information that is valid at current time, it is difficult to manage historical information efficiently which has been changed from the past to current. Recently, there are rapid increasing of interest to solve this problem so that makes databases to support historical information as well as spatial management at the same time. It can be eventually used in a various application areas. The formal semantics in a database is used to represent database structures and operations in order to prove the correctiveness of them in terms or mathematics. It also plays an important role in database to design a database and database management system. So in this paper, we suggest spatiotemporal domain, object, data, and spatiotemporal geometric/topological operations. And we not only formalize relational algebra/calculus using formal semantics for a spatiotemporal data model, but also show the example of real orld with them.

• KIISE Transactions on Computing Practices
• /
• v.23 no.11
• /
• pp.625-631
• /
• 2017

The stock price prediction for stock markets remains an unsolved problem. Although there have been various overtures and studies to predict the price of stocks scientifically, it is impossible to predict the future precisely. However, stock price predictions have been a subject of interest in a variety of related fields such as economics, mathematics, physics, and computer science. In this paper, we will study fluctuation patterns of stock prices and predict future trends using the Deep learning. Therefore, this study presents the three deep learning models using Tensorflow, an open source framework in which each learning model accepts different input features. We expand the previous study that used simple price data. We measured the performance of three predictive models increasing the number of priced-based input features. Through this experiment, we measured the performance change of the predictive model depending on the price-based input features. Finally, we compared and analyzed the experiment result to evaluate the impact of the price-based input features in stock price prediction.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.1
• /
• pp.595-598
• /
• 2013

Background: Lung cancer is the leading cause of cancer death worldwide Therefore, identification of genetic as well as environmental factors is very important in developing novel methods of lung cancer prevention. However, this is a multi-layered problem. Therefore a lung cancer risk prediction system is here proposed which is easy, cost effective and time saving. Materials and Methods: Initially 400 cancer and non-cancer patients' data were collected from different diagnostic centres, pre-processed and clustered using a K-means clustering algorithm for identifying relevant and non-relevant data. Next significant frequent patterns are discovered using AprioriTid and a decision tree algorithm. Results: Finally using the significant pattern prediction tools for a lung cancer prediction system were developed. This lung cancer risk prediction system should prove helpful in detection of a person's predisposition for lung cancer. Conclusions: Most of people of Bangladesh do not even know they have lung cancer and the majority of cases are diagnosed at late stages when cure is impossible. Therefore early prediction of lung cancer should play a pivotal role in the diagnosis process and for an effective preventive strategy.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.5
• /
• pp.1082-1092
• /
• 1996

The design of distrbuted systems is difficult to achieve as the execution patterns of distrbuted systems are typically more complex than those of non- distributed systems. Thus, research toward the development of design methods for distributed systems is quitely needed. As object-oriented systems and distrbuted systems share similar properties, the combination of these two is somehow natural. In this work, a design of distributed systems is introduced. The goal of the method in this paper is to provide assistance to the process of specifying a formal object- oriented specification from graphical representation specification inputs such as data flow diagrams, state transition diagrams and Petri nets. It addresses the extraction of objects, operations and reationshipsfrom the problem domain with emphasis on the specification of the characteristics of distributed systems. This object identification method is supported by a knowledge base that provides for the automated analysis and reasoning about objects and their relationsships. The final object model is represented in a format which provides a formal mechanism for reprsenting the object information.

• Communications of Mathematical Education
• /
• v.26 no.2
• /
• pp.205-220
• /
• 2012

In this article convergent sequences with natural number terms are investigated and the behaviors of tails and limits of these natural number sequences are explored. Firstly this study showed how the pre-service teachers response to the intuitive limit definition using "getting closer" for constant sequences. As a case of convergent natural sequences, the sequences in which the latter term is determined by the sum of digit squares of the former term are considered. To exploring these sequences the computational and charting capabilities of spreadsheets are utilized and some mathematical findings are obtained. Spreadsheet can be instrumentalized by teachers or students to provide a laboratory-like environment to explore a mathematical problem.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.12
• /
• pp.139-155
• /
• 2011

In recent years, parents of students and government have been taking a growing interest in education for the gifted students and there are many research reports about the gifted education. Most of the reports, however, focuses on the conceptional feature of the gifted education program such as organization, operation, management, evaluation, etc,. In other words, there are very few researches on instructional materials for gifted students even though the materials is a critical factor for successful education programs. So, this paper introduces a lecture notes used in a personal education for gifted students to contribute in developing education contents in computer science area. The instructional materials titled as "The Necessity and Application of Matrix in Computer Science" is based on linear equation to usher the students into creative problem recognition and groping for solutions. Also, the instructional materials is useful for students to understand the tight mathematics-computer science relationship and the basic concept of liner algebra.

• The Journal of Korean Association of Computer Education
• /
• v.4 no.1
• /
• pp.127-134
• /
• 2001

Many teachers at vocational high schools have had difficulties overcoming the gap between what students know and what students have to achieve in many topics. Mathematics is toughest of all, since most of textbooks in electronics are assuming student's basic knowledge in math. Considering that many students with very low achievements are entering vocational high schools, reality is far from such assumption. Inevitably, we have to face two difficult questions; do we have enough time to teach these kids all the math that they need in two years? If not, what alternatives we should adopt? We just do not have enough time and therefore find out a way to cope with harsh reality. According to our preliminary study, we suggest that multimedia-based CAI may be the best way to attack this problem. From hardware point of view, fortunately, many of vocational high schools are reasonably equipped for multimedia-based education. However there have been hardly any effort to develop courseware for vocational education in Korea. In this paper, a CAI title for learning basic characteristics of alternating current has been designed and implemented. The developed multimedia-based CAI title has been applied with respect to first grade students at local vocational high schools. A survey after classes shows that CAI could help student feel much comfortable with Basic Electricity course and grasp physical understanding much easily. Accordingly we conclude that classes adopting CAI would be of great help to put education in vocational high schools on the right track.