• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.80-84
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• 2001

When the fuzzy controller apply to a real plant, We have not excepted result of a satisfactory control by modeling error and lacking information about an plant. In this case, we have to adjust the control factors for improvement of the control performance and this method need a lot of time and cost for perform a trial and error. In this paper, we proposed the fuzzy regulator with the automatic adjustment of scaling factors. It was improve upon the control performance using a adequate scale factor by fuzzy inference. We implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.17-24
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• 1996

In this paper, we present a robust performance improvement method for the NLCF(normalized left coprime factor) uncertain structure using loop shaping and the structure singular value. For this, we select weighting functions for a loop shaping considering condition numer, and transform the NLCF uncertain structure into the 4-block structure. However, we can't get a good performance on account of the restriction of weighting functions. To cope with this, we motivate the use of structured singular vlaue in the robust performance improvement procedure. After all, the robust performance improvement can be obtained by a factor W$_{a}$ and a scaling factor of D-K iteration.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.2 no.1
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• pp.35-40
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• 2004

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed information about the characteristics and the quantities of radionuclides in waste package. Most of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentration of the Difficult-to-Measure (DTM) nuclide is estimated using the correlations of concentration - it is called the scaling factor - between Easy-to-Measure (Key) nuclides and DTM nuclides with the measured concentration of the Key nuclide. In general, the scaling factor is determined by the log mean average (LMA) method and the regression method. However, these methods are inadequate to apply to fission product nuclides and some activation product nuclides such as 14$^{C}$ and 90$^{Sr}$ . In this study, the artificial neural network (ANN) method is suggested to improve the conventional SF determination methods - the LMA method and the regression method. The root mean squared errors (RMSE) of the ANN models are compared with those of the conventional SF determination models for 14$^{C}$ and 90$^{Sr}$ in two parts divided by a training part and a validation part. The SF determination models are arranged in the order of RMSEs as the following order: ANN model

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.73-82
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• 2022

Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.991-996
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• 2001

The long-term durability characteristics of high-early-strength concrete were assessed. The effect of long-term durability characteristics of high-early-strength concrete were investigated. In experiment, two different types of fiber were adopted for improvement of durability. High-early-strength fiber reinforced concretes using regulated-set cements are compared with high-early-strength concrete without fiber. The durability performance of the laboratory-cured high-early-strength concrete specimens was determined by conducting an accelerated chloride permeability, abrasion resistance, freeze-thaw, surface deicer salt scaling and wet-dry repetition test. The results indicated that incorporation of fibers enhance durability performance.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.123-126
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• 1998

기존의 DTW를 이용한 화자 인식 시스템은 DTW의 단점이라 할 수 있는 과다한 계산량을 갖는다는 문제점을 갖고 있다. 따라서 본 논문은 텍스트 종속 화자 인식 시스템에서 피치 분포도를 갖는 개별 화자의 lDTW를 수행하기 전에 시간축 스케일링을 이용한 전처리로 인식시의 계산량을 감소시키는 과정을 미리 수행할 후 감소된 기준패턴들의 입력신호에 대해서만 DTW를 수행하는 방법을 제안하고자 한다. 제안한 방법을 실험하였을 경우 87.5%의 평균 처리 시간이 감소하였고, 더불어 인식률 감소는 거의 없었다.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.117-126
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• 2018

The purpose of this study was to examine the relationship between the working posture and symptoms of musculoskeletal disorders of dental hygienists during scaling. The subjects in this study were 264 selected dental hygienists. A convergence study was implemented using questionnaire that was prepared to cover general characteristics, health care, the work of scaling, working posture and subjective musculoskeletal symptoms. As for data analysis, R 2.15.1 was employed. As a result of analyzing the collected data, the rate of good working posture during scaling stood at 29.9 percent; roughly good posture, at 37.5 percent; incorrect posture, at 32.6 percent. Regarding the area of the body in which they complained of musculoskeletal symptoms, the shoulders were 3.32-fold more mentioned than any other area(OR,3.32;95%CI, 1.58~6.98); the foot, 2.97-fold more(OR,2.97;95%CI, 1.18~7.48); the hands, 2.84-fold more(OR,2.84:95%CI, 1.35~5.98); the neck, 2.82-fold more(OR, 2.82;95%CI, 1.35~5.91); the back, 2.41-fold more(OR,2.41;95%CI, 1.02~5.68). The findings of the study that demonstrate the importance of good working posture are expected to make a contribution to the improvement of work environments, the development of efficient preventive programs and the preparation of sustained educational plans, and it's necessary in the future to make a research study by including psychosocial factors.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.333-344
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• 2010

This paper addresses the way to compose paronamic images from images taken the same objects. With the spread of digital camera, the panoramic image has been studied to generate with its interest. In this paper, we propose a panoramic image generation method using scaling and rotation invariant features. First, feature points are extracted from input images and matched with a RANSAC algorithm. Then, after the perspective model is estimated, the input image is registered with this model. Since the SURF feature extraction algorithm is adapted, the proposed method is robust against geometric distortions such as scaling and rotation. Also, the improvement of computational cost is achieved. In the experiment, the SURF feature in the proposed method is compared with features from Harris corner detector or the SIFT algorithm. The proposed method is tested by generating panoramic images using $640{\times}480$ images. Results show that it takes 0.4 second in average for computation and is more efficient than other schemes.

• Journal of Periodontal and Implant Science
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• v.39 no.sup2
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• pp.239-251
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• 2009

Purpose: Full-mouth disinfection enables to reduce the probability of cross contamination from untreated pockets to treated ones, for completing the entire SRP under local anesthesia with chlorhexidine as a mouth wash in two visits within 24 hours. This study aimed to compare the clinical effects of modified full-mouth disinfection (Fdis) after 6 months with those of conventional SRP (cSRP). Methods: Thirty non-smoking chronic periodontitis subjects were randomly allocated two groups. The Fdis group underwent the entire SRP under local anesthesia in two visits within 24 hours, a week after receiving supragingival scaling. A chlorhexidine (0.1%) solution was used for rinsing and subgingival irrigation for Fdis. The cSRP group received SRP per quadrant under local anesthesia at one-week intervals, one week after they had received scaling. Clinical parameters were recorded at baseline, after 1, 3 and 6 months. Results: There are significant (P<0.05) decreases in the sulcus bleeding index, and plaque index, and the increases in gingival recession were significantly smaller with Fdis after six months compared with cSRP. There was significant improvement in the probing depth and clinical attachment level for initially medium-deep pockets (4-6mm) after Fdis compared with cSRP. Multi-rooted teeth showed significantly larger attachment gain up to six months after Fdis. Single-rooted teeth showed significantly more attachment gain, 1 and 6 months after Fdis. Conclusions: Fdis has more beneficial effects on reducing gingival inflammation, plaque level, probing depth, gingival recession and improving clinical attachment level over cSRP.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.648-653
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• 2010

The non-metric multidimensional scaling (nMDS) is a method for analyzing the relation among objects by mapping them onto the Euclidean space. The nMDS is useful when it is difficult to use the concept of distance between pairs of objects due to non-metric dissimilarities between objects. The nMDS can be regarded as an optimization problem in which there are many local optima. Since the conventional nMDS algorithm utilizes the steepest descent method, it has a drawback in that the method can hardly find a better solution once it falls into a local optimum. To remedy this problem, in this paper, we applied the simulated annealing to the nMDS and proposed a new optimization algorithm which could search for a global optimum more effectively. We examined the algorithm using benchmarking problems and found that improvement rate of the proposed algorithm against the conventional algorithm ranged from 0.7% to 3.2%. In addition, the statistical hypothesis test also showed that the proposed algorithm outperformed the conventional one.