• Korean Journal of Acupuncture
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• 제25권1호
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• pp.61-71
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• 2008

Objectives : The aim of this study was to show the rationale of point-selection on the methods of acupuncture and moxibustion in the Rhinological section(in the Oehyeong chapter) of the Dong Ui BO Gam. Methods : First, We summarized the cause of each disease in the Rhinological section(in the Oehyeong chapter) of the Dong Ui BO Gam. Then, We explained the rationale of acupoint-selection referring to the cause of disease, physiology of the Oriental medicine, other uses of each acupoints in the Dong Ui BO Gam, character of each acupoints, flow of meridian pathways and specific acupoints etc. Results and Conclusions : Total 15 acupoints were used in the Rhinological section(in the Oehyeong chapter) of the Dong Ui BO Gam. Most of acupoints were specific acupoints. But, some rationale of acupoint-selection were explained by the cause of disease, physiology of the Oriental medicine, other uses of each acupoints in the Dong Ui BO Gam, flow of meridian pathways etc.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.3972-3988
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• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• 플랜트 저널
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• 제8권2호
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• pp.59-69
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• 2012

This study focuses on avoiding the failures from the wrong selections by experiences as simulation programs is not available, and suggests the methods which effectively select the alternatives when the selected model is not appropriate for the original plan. First, CC8800-1K of DEMAG has the longest boom whose length is 216 m at the maximum. The combination of the boom is feasible to second level except for MANITIWOC M 2250 (M-1200 RINGER) which is possible to third level. Second, the angle of boom is from 20 degrees to 82 degrees. Suitable angle to work is in the 55-78 degrees. The working load of crawler type and hydraulic one to be applied is 75-85% in the critical loads capacity. As increasing operating radius, crawler type is a favorable position over hydraulic one. Lastly, related problems were verified through examination by suggestions for the design of the selection methods for the case analysis. The major problems are stemming from the selection based on its experiences, unreasonable demand for the existing facility and repeated selections by the designer who accumulates his experiences via same or similar projects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2162-2179
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• 2019

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier's performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.

• 기술혁신학회지
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• 제1권1호
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• pp.117-124
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• 1998

Corporations are pursuing maximum returns from their R&D investment. They are also interested in sound measures to quantify returns. In fact, they use various measures and criteria for measuring returns from the R&D investment. But the fundamental problem is that there is no generic and widely acceptable measures and criteria. To make things more complicated, measures are very powerful and influential to the people in the corporations. Herbert Simon already indicated that people do many things but people usually do their best for the only tasks which are measured. Many researchers, like Chester(1995), are interested in R&D productivity measures and risks because what the company measures really influence R&D people and output. This article present design concepts of the R&D project selection and evaluation system in POSCO(Pohang Iron & Steel Company). This is an output extract from the 6-month joint activities with POSRI(POSCO Research Institute) researchers and POSCO R&D personnel. Process changes, new organizations and new selection and evaluation criteria are developed to improve R&D performance and to enhance technology management of the POSCO. This article covers new selection and evaluation criteria only. We would like to share our experience about how we redesign the selection and evaluation of R&D projects. We also bring insights how we seamlessly integrate 4 different project selection and evaluation steps as a whole. We hope that this case will give you a clue to improve your R&D management.

• Asian-Australasian Journal of Animal Sciences
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• 제27권12호
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• pp.1678-1683
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• 2014

This study considers a problem of genomic selection (GS) for adjacent genetic markers of Yorkshire pigs which are typically correlated. The GS has been widely used to efficiently estimate target variables such as molecular breeding values using markers across the entire genome. Recently, GS has been applied to animals as well as plants, especially to pigs. For efficient selection of variables with specific traits in pig breeding, it is required that any such variable selection retains some properties: i) it produces a simple model by identifying insignificant variables; ii) it improves the accuracy of the prediction of future data; and iii) it is feasible to handle high-dimensional data in which the number of variables is larger than the number of observations. In this paper, we applied several variable selection methods including least absolute shrinkage and selection operator (LASSO), fused LASSO and elastic net to data with 47K single nucleotide polymorphisms and litter size for 519 observed sows. Based on experiments, we observed that the fused LASSO outperforms other approaches.

• ETRI Journal
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• 제33권6호
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• pp.904-913
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• 2011

Three-dimensional (3D) memories using through-silicon vias (TSVs) as vertical buses across memory layers will likely be the first commercial application of 3D integrated circuit technology. The memory dies to stack together in a 3D memory are selected by a die-selection method. The conventional die-selection methods do not result in a high-enough yields of 3D memories because 3D memories are typically composed of known-good-dies (KGDs), which are repaired using self-contained redundancies. In 3D memory, redundancy sharing between neighboring vertical memory dies using TSVs is an effective strategy for yield enhancement. With the redundancy sharing strategy, a known-bad-die (KBD) possibly becomes a KGD after bonding. In this paper, we propose a novel die-selection method using KBDs as well as KGDs for yield enhancement in 3D memory. The proposed die-selection method uses three search-space conditions, which can reduce the search space for selecting memory dies to manufacture 3D memories. Simulation results show that the proposed die-selection method can significantly improve the yield of 3D memories in various fault distributions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.38-59
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• 2022

Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services.

• 전기학회논문지
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• 제64권1호
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• pp.90-98
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• 2015

In this paper, an advanced teaching-learning based optimization(TLBO) method for the magnetic levitation controller of Maglev transportation system is proposed to optimize the control performances. An attraction-type levitation system is intrinsically unstable and requires a delicate control. It is difficult to completely satisfy the desired performance through the methods using conventional methods and intelligent optimizations. In the paper, we use TLBO and clonal selection algorithm to choose the optimal control parameters for the magnetic levitation controller. To verify the proposed algorithm, we compare control performances of the proposed method with the genetic algorithm and the particle swarm optimization. The simulation results show that the proposed method is more effective than conventional methods.

• 디지털콘텐츠학회 논문지
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• 제19권4호
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• pp.739-747
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• 2018

고차원 데이터에서는 데이터마이닝 기법 중에서 특징 선택은 매우 중요한 과정이 되었다. 그러나 전통적인 단일 특징 선택방법은 더 이상 효율적인 특징선택 기법으로 적합하지 않을 수 있다. 본 논문에서 우리는 고차원 데이터에 대한 효율적인 특징선택을 위하여 혼합형 특징선택 기법을 제안하였다. 본 논문에서는 KNHANES 데이터에 제안한 혼합형 특징선택기법을 적용하여 분류한 결과 기존의 분류기법을 적용한 모델보다 5% 이상의 정확도가 향상되었다.