• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.11
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• pp.65-73
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• 2007

This paper introduces design-for-test (DFT) techniques for low-cost system-on-chip (SoC) test. We present a Scan-Test method that controls IEEE 1500 wrapper thorough IEEE 1149.1 SoC TAP (Test Access Port) and design an at-speed test clock generator for delay fault test. Test cost can be reduced by using small number of test interface pins and on-chip test clock generator because we can use low-price automated test equipments (ATE). Experimental results evaluate the efficiency of the proposed method and show that the delay fault test of different cores running at different clocks test can be simultaneously achieved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.1087-1094
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• 2008

Recently, XML has been widely used as a standard for a data exchange, and there has emerged the tendency that the size of XML document becomes larger. The data transfer can cause problems due to the increase in traffic, especially when a massive data such as Data Warehouse is being collected and analyzed. Therefore, an XMDR wrapper can solve this problem since it analyzes the tree structures of XML Schema, regenerates XML Schema using the analyzed tree structures, and sends it to each station with an XMDR Query. XML documents which are returned as an outcome encode XML tags according to XML Schema, and send standardized messages. As the formalized XML documents decrease network traffic and comprise XML class information, they are efficient for extraction, conversion, and alignment of data. In addition, they are efficient for the conversion process through XSLT, too, as they have standardized forms. In this paper we profuse a method in which XML Schema and XMDR_Query sent to each station are generated through XMDR(extended Meta-Data Registry) and the generation of products and XML conversion occur in each station wrapper.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.73-82
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• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

• Journal of the Korean Society of Costume
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• v.18
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• pp.97-110
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• 1992

West African's traditional looms, weaving and raw materials, structural patterning, dyeing and basic forms of dress were examines in the present study in order to deepen the appreciation of the cultural heritage of West Africa, and to make a contribution to the policy planning for export market developing The research method employed was the analysis f written materials. The study was limited to the traditional clothing habit which is preserved and practicing by them at the present day and the origin and the process of the historical development of those are not included in the scope of the present study. Followings are the results of the study: (1) They have vertical single-heddle loom horizontal or ground single-heddle loom, and double-heddle loom. The width of the cloth produced on the single-heddle loom varies about 38.5cm to 123cm and double-heddle looms all produced on the single-heddle looms all produced narrow strips of cloth varying in width from about 1.3cm to 75cm, although the average is about 10-20cm. (2) Despite the relative simplicity of the llom technology a remarkable variety of textiles are produced. (3) The most popular decorative technique in West African compound weaves is extra-weft patterning which is produced on both single-heddle and double-heddle loom by men and women weavers. Other forms of secondary patterning on textiles in West Africa are dyeing, applique, patchwork and embroidery. (4) Two basic forms of dress have spread throughout West Africa, the poncho (bpibpi) and the wrapper. Some versions of these basic forms are supplemented by western inspired trousers, shirts and blouses coupled with accessories usually complete their traditional outfits. They have a great variety of basic poncho, like as Khasa, Gandura, Tuareg-poncho, Babariga, Rigas (agba-da), Grand-boubou, Afteck, Tagua, buba, Danshike etc. Although West Africa has long been in contact with the peoples of the Nile region as well as the Maghreb and Sahara, both the boubou styles and the wrapper styles appear to have developed with a minimum of outside influence. African Islam was the principal agent for the diffusion of the boubou styles.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.289-294
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• 2020

Peptic ulcer disease is a gastrointestinal disorder caused by Helicobacter pylori infection and the use of nonsteroid anti-inflammatory drugs. While many studies have been conducted to find the risk factors of peptic ulcers, there are no studies on the suggestion of peptic ulcer prediction models for Koreans. Therefore, the purpose of this study is to implement peptic ulcer prediction model using machine learning based on demographic information, obesity information, blood information, and nutritional information for middle-aged and elderly people. For model building, wrapper-based variable selection method and naive Bayes algorithm were used. The classification accuracy of the female prediction model was the area under the receiver operating characteristics curve (AUC) of 0.712, and males showed an AUC of 0.674, which is lower than that of females. These results can be used for prediction and prevention of peptic ulcers in the middle and elderly people.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.339-345
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• 2022

Cardiovascular diseases is one of the leading causes of death in the world. The objectives of this study were to build various models using sociodemographic variables based on three variable selection methods and seven machine learning algorithms for the identification of hypertension and dyslipidemia and to evaluate predictive powers of the models. In experiments based on full variables and correlation-based feature subset selection methods, our results showed that performance of models using naive Bayes was better than those of models using other machine learning algorithms in both two diseases. In wrapper-based feature subset selection method, performance of models using logistic regression was higher than those of models using other algorithms. Our finding may provide basic data for public health and machine learning fields.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.6
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• pp.569-579
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• 2000

XML will be used in lots of areas in the Web environment as a method of information exchange, We have to use dat'abases to manipulate lots of XML documents efficiently, When we use database to manipulate XML, not only type of database but also its interface is important We develop a system using relational database, object-oriented database and wrapper to store XML data, of which interfaces are XML-View, ODMG C++ binding, OQL and DOM, We discuss pros and cons of each method by the implementation of the system, and propose an efficient manipulation method of XNIL documents.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.233-242
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• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.541-546
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• 2021

Recently, there is a trend of developing various identification and prediction models for hypertension using clinical information based on artificial intelligence and machine learning around the world. However, most previous studies on identification or prediction models of hypertension lack the consideration of the ideas of non-invasive and cost-effective variables, race, region, and countries. Therefore, the objective of this study is to present hypertension prediction model that is easily understood using only general and simple sociodemographic variables. Data used in this study was based on the Korea National Health and Nutrition Examination Survey (2018). In men, the model using the naive Bayes with the wrapper-based feature subset selection method showed the highest predictive performance (ROC = 0.790, kappa = 0.396). In women, the model using the naive Bayes with correlation-based feature subset selection method showed the strongest predictive performance (ROC = 0.850, kappa = 0.495). We found that the predictive performance of hypertension based on only sociodemographic variables was higher in women than in men. We think that our models based on machine leaning may be readily used in the field of public health and epidemiology in the future because of the use of simple sociodemographic characteristics.

• BMB Reports
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• v.46 no.1
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• pp.41-46
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• 2013

Identifying genes indispensable for an organism's life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, protein-protein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature's relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods.