• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.113-121
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• 1990

Let ${X_n : n=1,2,\cdots}$ be iid random variables with distribution $P_{\theta}, \theta \in H$ where $H$ is some abstract parameter space. We consider a sequential confidence interval I for the mean $\mu = \mu(\theta)$ of $P_{\theta}$ satisfying $P_{\theta}(\mu \in I) \geq 1-\alpha$ and $P_{\theta}(\mu-\delta(\mu) \in I) \leq \beta$ for all $\theta \in H$ for any given an imprecision real valued function $\delta(\mu) > 0$ and error probabilities $0 < \alpha, \beta < 1$. A one-sided sequential confidence interval is constructed under some restriction of the family {P_{\theta} : \theta \in H}$ and the imprecision function $\delta$. This is extended to the two-sided cases.

• Journal of Veterinary Clinics
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• v.6 no.2
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• pp.291-305
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• 1989

Present experiment was performed to establish the optimal reaction conditions for measurement of urinary gamma-glutamyltranspeptidase(${\gamma}$-GTP), N-acetyl-${\beta}$-D-glucosaminidase (AGS) and alanine aminopeptidase(AAP) activities in bovine and to investigate in vitro stability of the enzymes, within-run imprecision of the methods, and normal ranges. 1. The optimal wavelength for measurement of ${\gamma}$-GTP activity was 545nm. 2. The optimal pH of Tris-HCI buffer containing glycylglycine for measurement of urinary ${\gamma}$-GTP activity was 7.6～7.8(37$^{\circ}C$). 3. Coefficient of variance for within-run imprecision of urinary ${\gamma}$-GTP activity ranged from 4.8 to 7.2% and there was no significant difference among replications, 4. The optimal wavelength for measurement of urinary AGS activity was 405nm. 5. The optimal pH of citrate buffer for measurement urinary of AGS activity was 4.0(37$^{\circ}C$). 6. Coefficient of variance for within-run imprecision of urinary AGS activity ranged from 3.9 to 6.1％ and there was no significant difference among replications. 7. The optimal wavelength for measurement of urinary AAP activity was 400nm. 8. The optimal pH of phosphate buffer for measurement of urinary AAP was 7.8. 9. Coefficient of variance for within-run imprecision of urinary AAP activity ranged from 2.5 to 4.8% and there was no significant difference among replications. 10. ${\gamma}$-GTP and AGS activities were increased significantly by gel-filtration. 11. Turbidity interfered with measurement of urinary AAP activity in bovine unless the specimen was gel-filterated. 12. Preservation of the specimen at 5$^{\circ}C$ or -20$^{\circ}C$ did not affect the AGS activity at least for 7 days after collection. 13. Preservation of the specimen at 5$^{\circ}C$ or 20$^{\circ}C$ did not affect the ${\gamma}$-GTP and AAP activities statistically, but some individual specimens revealed fluctuation during preservation. 14. ${\gamma}$-GTP, AGS and AAP activities revealed fluctuation by the tine of the day when the specimen was collected. 15. The normal ranges of urinary ${\gamma}$ -GTP, AGS and AAP activities were 6.60${\pm}$3.26(2.36-14.50), 1.31 ${\pm}$ 0.81(0.33-3.78), and 1.73 ${\pm}$ 0.55(0.77-3.03)U/l. respectively.

• Journal of Korean Society for Quality Management
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• v.20 no.1
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• pp.107-117
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• 1992

Conventional fault tree analysis has several problems as the estimations and tolerances of the failure probability values. To overcome these problems, fuzzy concepts with natural language can be applied to conventional fault tree analysis. And, we propose the evaluation method of the imprecision of top/basic events and possibility importances of basic events.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.217-231
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• 2015

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.2
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• pp.3-17
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• 1993

The fuzzy pattern matching technique has been developed in the framework of fuzzy set and possibility theory in order to take into account the imprecision and the uncertainty pervading values which have to be compared to requirements (which may be fuzzy) in a pattern matching process. This paper restates the basic principles and extends them to situations where (sub)patterns are only required to be satisfied up to a given tolerance (which may be fuzzy), or where the different subparts of a compound pattern may have various levels of importance. Both cases correspond to a weakening of elementary patterns. which can be expressed by a fuzzy relations modelling an approximate equality or an uncertain strict equality respectively. We also study the more sophisticated case where some elementary patterns have not to be satisfied with the highest priority provided that weaker requirements remain satisfied. The fuzzy pattern matching technique applies in a variety of problems including the evaluation of soft queries with respect to a fuzzy database, the evaluation of the fuzzy condition parts of rules in approximate reasoning, or the evaluation of the belonging of an ill-known object to a flexible class in classification problems.

• Journal of muscle and joint health
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• v.28 no.1
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• pp.10-17
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• 2021

Purpose: This was integrative literature review for analyzing interventions of musculoskeletal symptom and pain on nurses. Methods: Data were searched by using databases, namely SAGE journals, EBSCOhost, PubMed, RISS, NDSL and KCI Search terminology included nurse and musculoskeletal and the language was Korean or Engli.sh. Data analysis was classified by risk of bias, inconsistency, indirectness and imprecision. Total six studies, one Korean and five English articles analyzed. Results: Risk of bias was serious in three of six studies. Regarding inconsistency, one study was moderate effect size and two studies were small effect size. There was no serious indirectness except one study. Regarding imprecision, three studies were not serious. Conclusion: Evidence from six studies was insufficient to support intervention's effect in means of no repeated studies and no high effect size of intervention. Evidence-based interventions must be applied on nurses for reducing musculoskeletal symptoms.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.1
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• pp.57-71
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• 1988

Checklist method is rapid and comprehensive to evaluate in practice. Check items are commonly rated by subjective utility function; i.e., evaluator's significant judgment. Since human judgment includes fuzziness (vagueness) inherently in spite of its significance, fuzzy set theory is useful in this case. The paper illustrates a evaluation method using fuzzy checklist where check items are rated as fuzzy numbers. Pairwise comparison data is used to determine the weights of check items, since it has comparative advantage for human's fuzzy judgment. Sample of BASIC program is provided for microcomputer. When uncertainty is due to subjectivity or imprecision of data, this method can be applied to practical problems widely.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.1
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• pp.41-52
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• 1994

Imprecision of evaluation or lack of prior information about preference can be an obstacle for decision maker in representing his strict preference. Therefore, fuzziness of preference can take place, and in addition, intransitivity or incomparability of preference becomes the critical difficulty in making complete preorder of alternatives. In order to get better solution and to improve practical usufulness, MCDM should be established as a pseudo-criterion model that include fuzzy preference. In this paper, we suggest a pseudo-criterion model that can make complete preorder of alternatives by metric distance measure.

• Journal of the Chungcheong Mathematical Society
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• v.5 no.1
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• pp.87-97
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• 1992

Sequential procedure with ${\beta}$-protection for the mean vector ${\mu}(\theta)$ of a p(> 1)-variate multivariate distribution $P_{\theta}$, ${\theta}{\in}{\Theta}$, with covariance matrix ${\sum}(\theta)$ is considered when the only nuisance parameters is ${\sum}(\theta)$. We obtain a confidence set for ${\mu}(\theta)$ with coverage probability condition and ${\beta}$-protection at ${\mu}-{\delta}(\mu)$ for some imprecision function ${\delta}:\mathbb{R}^p{\rightarrow}\mathbb{R}^p$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.11-15
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• 1998

일반적으로 러프집합은 지식베이스 시스템에서 근사공간을 이용한 불확실한 데이터의 분류, 추론 및 의사결정 등에 사용된다. 지식베이스 시스템의 데이터 중에서 연속적인 구간 특성을 갖는 정량적 속성값이 불연속적일 때 중복 또는 불일치 등의 불확실성이 발생된다. 본 논문은 러프집합의 정량적 속성값들의 정성적 속성으로 변환시킬 때 식별 불가능 영역에 있는 정량적 속성값들을 명확한 경계를 갖는 보조구간으로 분리하여 불확실성을 제거함으로써 러프집합의 분류능력을 향상시키는 방법을 제안한다.