• The Journal of Information Technology and Database
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• v.7 no.1
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• pp.113-130
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• 2000

As software projects become larger and more complex, we need to take a more systematic approach to quality assurance. One plausible approach is the use of SQA (software quality assurance) methodology. Since this SQA methodology was not available, our study presents a SQA methodology. This methodology has a repository in which a set of quality assurance tasks with their related techniques and deliverables is defined and from which one can select only appropriate tasks based upon characteristics of project. This study further suggests a rule-based approach for supporting task selection process.

• Bulletin of the Korean Chemical Society
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• v.9 no.1
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• pp.27-29
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• 1988

The surface-enhanced Raman scattering(SERS) of benzylcyanide in a silver sol was investigated. It was concluded that the molecule adsorbed onto the silver surface via the ${\pi}$ system of the CN group. The molecule was assumed to coordinate with either a single atom or two silver atoms. According to the SERS selection rule, the benzene ring of the adsorbed species seemed to assume a flat stance with respect to the silver surface.

• Bulletin of the Korean Chemical Society
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• v.8 no.4
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• pp.251-254
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• 1987

The surface-enhanced Raman scattering(SERS) of benzonitrile in a silver sol was investigated. It was concluded that the molecule adsorbed onto the silver surface via the nitrogen lone pair electrons. Using the surface selection rule, the orientation of the benzene ring with respect to the surface plane could not be determined conclusively. However, it seemed likely that benzonitrile is adsorbed edge-on to the surface. It was demonstrated that the SERS technique provides a useful method for detailed characterization of the chemisorbed layer.

• BMB Reports
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• v.34 no.1
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• pp.51-58
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• 2001

Attempts using in vitro and in vivo selection procedures have been made to search for hammerhead ribozymes that have higher activities than the wild-type ribozyme and also to determine whether other sequences might be possible in the catalytic core of the hammerhead ribozyme. Active sequences selected in the past conformed broadly to the consensus core sequence except at A9, and no sequences were associated with higher activity than that of the hammerhead with the consensus core, an indication that the consensus sequence derived from viruses and virusoids is probably the optimal sequence [Vaish et al. (1997) Biochemistry 36, 6495-6501]. Recently, during construction of ribozyme expression vectors, we isolated a mutant hammerhead ribozyme, with an insertion of G between A9 and G10.1, that appeared to show significant activity [Kawasaki et al. (1996) Nucleic Acids Res. 24, 3010-3016; Kawasaki et al. (1998) Nature 393, 284-289]. We, therefore, characterized kinetic properties of the G-inserted mutant ribozymes in terms of the NUX rule. We demonstrate that the NUX rule is basically applicable to the G-inserted ribozymes and, more importantly, one type of G-inserted ribozyme was very active with $k_{cat}$, value of $6.4\;min^{-1}$ in 50 mM Tris-HCl (pH 8.0) and 10 mM $MgCl_2$ at $37^{\circ}C$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.29-40
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• 2000

Process planning (selection and ordering of processes) and material selection for product manufacturing are two key things determined before taking full-scale manufacturing. Knowledge on product design. material characteristics, processes, time and cost all-together are mutually related and should be considered concurrently. Due to the complexity of problem, human experts have got only one of the feasilbe solutions with their field knowledge and experiences. We propose a hierarchical expert system framework of knowledge representation and reasoning in order to overcome the complexity. Manufacturing processes have inherently hierarchical relationships, from top level processes to bottom level operation processes. Process plan of one level is posted in process blackboard and used for lower level process planning. Process information on blackboard is also used to adjust the process plan in order to resolve the dead-end or inconsistency situation during reasoning. Decision variables for process, material, tool, time and cost are represented as object frames, and their relationships are represented as constraints and rules. Constraints are for relationship among variables such as compatibility, numerical inequality etc. Rules are for causal relationships among variables to reflect human expert\`s knowledge such as process precedence. CRSP(Constraint and Rule Satisfaction Problem) approach is adopted in order to obtain solution to satisfy both constraints and rules. The trade-off procedure gives user chances to see the impact of change of important variables such as material, cost, time and helps to determine the preferred solution. We developed the prototype system using visual C++ MFC, UNIK, and UNlK-CRSP on PC.

• Smart Structures and Systems
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• v.20 no.2
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• pp.219-229
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• 2017

Outlier detection is an imperative task to identify the occurrence of abnormal events before the structures are suffered from sudden failure during their service lives. This paper proposes a two-phase method for the outlier detection of Global Positioning System (GPS) monitoring data. Prompt judgment of the occurrence of abnormal data is firstly carried out by use of the relational analysis as the relationship among the data obtained from the adjacent locations following a certain rule. Then, a negative selection algorithm (NSA) is adopted for further accurate localization of the abnormal data. To reduce the computation cost in the NSA, an improved scheme by integrating the adjustable radius into the training stage is designed and implemented. Numerical simulations and experimental verifications demonstrate that the proposed method is encouraging compared with the original method in the aspects of efficiency and reliability. This method is only based on the monitoring data without the requirement of the engineer expertise on the structural operational characteristics, which can be easily embedded in a software system for the continuous and reliable monitoring of civil infrastructure.

• Korean Journal of Acupuncture
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• v.31 no.3
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• pp.108-116
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• 2014

Objectives : The purpose of this study is to identify changes of texts by investigating similarities and differences of the third chapter of limb diseases section between Beijiqianjinyaofang(BJQJYF) and Sunzhenrenqianjinfang(SZRQJF). Methods : I reviewed the third chapter of limb diseases section both of BJQJYF and SZRQJF and analysed the changes of texts. Results : 1. Hand, shoulder and low back pains mentioned in the second chapter of glossopathy from SZRQJF were moved to the third chapter of limb diseases in BJQJYF. 2. Inappropriate indications were changed reasonably. 3. Contents related with treatment were revised, by addition or deletion of contents. 4. There were some contents which were worth clinically in SZRQJF. 5. The rule of choosing acupoints for hand, arm, leg, knee and limb disease was selection of local points, and for shoulder and low back disease was selection of distant points. Conclusions : Classification and contents of the third chapter of limb diseases were re-organized systematically through proofreading by medical printing authority. However, some contents deleted from SZRQJF were worth clinically, and more studies are necessary to identify the reason why the indication and selection of acupoints were changed by proofreading.

• Journal of The Korean Astronomical Society
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• v.27 no.2
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• pp.103-117
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• 1994

To extend the work of Gott, Park, and Lee (1989), statistical properties of gravitational lensing in a wide variety of cosmological models involving non-zero cosmological constant is investigated, using the redshifts of both lens and source and observed angular separation of images for gravitational lens systems. We assume singular isothermal sphere as lensing galaxy in homogenous and isotropic Friedmann­Lemaitre-Robertson- Walker universe, Schechter luminosity function, standard angular diameter distance formula and other galaxy parameters used in Fukugita and Turner (1991). To find the most adequate flat cosmological model and put a limit on the value of dimensionless cosmological constant $\lambda_0$, the mean value of the angular separation of images, probability distribution of angular separation and cumulative probability are calculated for given source and lens redshifts and compared with the observed values through several statistical methods. When there is no angular selection effect, models with highest value of $\lambda_0$ is preferred generally. When the angular selection effects are considered, the preferred model depends on the shape of the selection functions and statistical methods; yet, models with large $\lambda_0$ are preferred in general. However, the present data can not rule out any of the flat universe models with enough confidence. This approach can potentially select out best model. But at the moment, we need more data.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.177-184
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• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.