• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.137-141
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• 1994

The purpose of this paper is to examine what is meant by a ralid measure of health. Guyatt, Kirshner and Jaeschke propose that health tests should be designed so as to have one of several kinds of validity: 'longitudinal construct validity' for those which are used for longitudinal research designs, and 'cross-sectional construct validity' for those which are used for cross-sectional designs. Williams and Naylor argue that this approach to test classification and validation confuses what a test purports to measure with the purpose for which it is used, and that some tests have multiple uses. A review of the meanings of validity in the psychologica test literature shows that both sets of authors use the term validity in an idiosyncratic way. Although the use of a test (evaluated by content validity) should not be conflated with whether the test actually measures a specified construct (evaluated by construct validity);' if health is actually made up of several constructs (as suggested in Hyland's interactional model) then there may be an association between types of construct and types of purpose. Evidence is reviewed that people make several, independent judgements about their health: cognitive perceptions of health problems are likely to be more sensitive to change in a longitudinal research design. whereas emotional evaluations of health provide less bias in cross-sectional designs. Thus. a classification of health measures in terms of the purpose of the test may parallel a classification in terms of what tests purport to measure.

• Journal of The Korean Association For Science Education
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• v.36 no.1
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• pp.87-101
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• 2016

This study analyzes the content validity of descriptive tests developed for elementary schools, in order to acquire basic data to improve them. Various descriptive tests were collected and tested for differences in proportions between two-dimensional classification of educational objectives and the level of behavioral objectives. Results show that the descriptive tests developed by elementary school teachers mainly focused on "knowledge" and "understanding," and that content validity for behavioral levels to be low. Nine elementary school teachers were interviewed to understand the result. From the interviews, we found both internal and external factors that cause low content validity. The main internal factors were teachers' ability to make two-dimensional classification of educational objectives, the teachers' consideration of students' level, item level of difficulty, the ease of scoring, and path dependence. The main external factors were curriculum, parents, and administration. Based on the results, we suggested the factors related to elementary school teachers' PCK of descriptive tests.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1261-1267
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• 2015

The problem of underwater target detection and classification has been attracted a substantial amount of attention and studied from many researchers for both military and non-military purposes. The difficulty is complicate due to various environmental conditions. In this paper, we study classifier ensemble methods for active sonar target classification to improve the classification performance. In general, classifier ensemble method is useful for classifiers whose variances relatively large such as decision trees and neural networks. Bagging, Random selection samples, Random subspace and Rotation forest are selected as classifier ensemble methods. Using the four ensemble methods based on 31 neural network classifiers, the classification tests were carried out and performances were compared.

• Journal of Radiation Protection and Research
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• v.32 no.1
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• pp.35-44
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• 2007

Sealed sources have to conducted the tests be done according to the classification requirements for their typical usages in accordance with the relevant domestic notice standard and ISO 2919. After each test, the source shall be examined visually for loss of integrity and pass an appropriate leakage test. Tests to class a sealed source are temperature, external pressure, impact, vibration and puncture test. The environmental test conditions for tests with class numbers are arranged in increasing order of severity. In this study, the apparatus of tests, except the vibration test, were developed and applied to three kinds of sealed source. The conditions of the tests to class a sealed source were stated and the difference between the domestic notice standard and ISO 2919 were considered. And apparatus of the tests were made. Using developed apparatus we conducted the tests for $^{192}Ir$ brachytherapy sealed source and two kinds of sealed source for industrial radiography. $^{192}Ir$ brachytherapy sealed source is classified by temperature class 5, external pressure class 3, impact class 2 and vibration and puncture class 1. Two kinds of sealed source for industrial radiography are classified by temperature class 4, external pressure class 2, impact and puncture class 5 and vibration class 1. After the tests, Liquid nitrogen bubble test and vacuum bubble test were done to evaluate the safety of the sealed sources.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• Spatial Information Research
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• v.12 no.1
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• pp.111-125
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• 2004

Improvements of classification accuracy are main issues in satellite image classification. Considering the facts that multiple images in the same area are available, there are needs on researches aiming improvements of classification accuracy using multiple data sets. In this study, orthogonal summation method of Dempster-Shafer theory (theory of evidence) is proposed as a multiple imagery classification method and posterior probabilities and classification uncertainty are used in calculation process. Accuracies of the proposed method are higher than conventional classification methods, maximum likelihood classification(MLC) of each data and MLC of merged data sets, which can be certified through statistical tests of mean difference.

• Land and Housing Review
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• v.3 no.4
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• pp.389-397
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• 2012

In this study, crosshole seismic test, donwhole seismic test, SPT uphole test, and suspension PS logging (SPS logging) were conducted and the shear wave velocities of these tests were compared. The test demonstrated the following result: Downhole tests showed similar results compared to those of crosshole tests, which is known to be relatively accurate. SPS logging showed reliable results in the case of no casing, i.e. in the rock mass, while, in the case of soil ground, its values were lower or higher than those of other tests. SPT-uphole tests showed similar results in the soil ground and upper area of rock mass compared to other methods. However, reliable results could not be obtained from these tests because SPT sampler could not penetrate into the rock mass for the tests.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.215-235
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• 2017

Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

• Steel and Composite Structures
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• v.7 no.3
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• pp.185-200
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• 2007

Tubular construction is widely used in a range of civil and structural engineering applications. To date, the principal product range has comprised square, rectangular and circular hollow sections. However, hot-rolled structural steel elliptical hollow sections have been recently introduced and offer further choice to engineers and architects. Currently though, a lack of fundamental structural performance data and verified structural design guidance is inhibiting uptake. Of fundamental importance to structural metallic design is the concept of cross-section classification. This paper proposes slenderness parameters and a system of cross-section classification limits for elliptical hollow sections, developed on the basis of laboratory tests and numerical simulations. Four classes of cross-sections, namely Class 1 to 4 have been defined with limiting slenderness values. For the special case of elliptical hollow sections with an aspect ratio of unity, consistency with the slenderness limits for circular hollow sections in Eurocode 3 has been achieved. The proposed system of cross-section classification underpins the development of further design guidance for elliptical hollow sections.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.9-23
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• 2019

In this paper, we propose a layer structure of a pest image classifier model using CNN (Convolutional Neural Network) and background removal image processing algorithm for improving classification accuracy in order to build a smart monitoring system for pine wilt pest control. In this study, we have constructed and trained a CNN classifier model by collecting image data of pine wilt pest mediators, and experimented to verify the classification accuracy of the model and the effect of the proposed classification algorithm. Experimental results showed that the proposed method successfully detected and preprocessed the region of the object accurately for all the test images, resulting in showing classification accuracy of about 98.91%. This study shows that the layer structure of the proposed CNN classifier model classified the targeted pest image effectively in various environments. In the field test using the Smart Trap for capturing the pine wilt pest mediators, the proposed classification algorithm is effective in the real environment, showing a classification accuracy of 88.25%, which is improved by about 8.12% according to whether the image cropping preprocessing is performed. Ultimately, we will proceed with procedures to apply the techniques and verify the functionality to field tests on various sites.