• Journal of Korean Physical Therapy Science
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• v.8 no.1
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• pp.727-734
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• 2001

Contracture is defined as the lack of full passive range of motion resulting from pint, muscle or soft tissue limitationprolonged Pint immobilization will result in stress and stretch deprivation and gradual development of contracture. the tissue changes caused by immobilization may be categorized as cellular modeling, ground substance and collagen response, and tissue response. contracture can be divided into three categories according to the anatomical location of pathological changes :arthrogenic, myogenic, soft tissue contractures Therapeutic approach of contracture is thermal or cold agents application, stretch or restoration of length, traction, manipulation, mobilization positioning and restoration of function. The purpose of this article is to review current concepts of mechanical properties and synthesis of collagen tissue and the underlying pathomechanics as it relates to evaluation and treatment of contracture.

• The Journal of the Korean dental association
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• v.22 no.12 s.187
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• pp.1043-1046
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• 1984

The clinical and statistical evaluation on the patients of maxillofacial deformities who were operated by author were analyzed after several month or years or se. Pre and post operative cephalometric radiographs of 45 orthognathic surgery patients were compared. The post operative radiographs had been taken at least 9 month to several years. Measurements were made between constructed hard tissue and soft tissue points located on each before and after film tracing. The items studied and evaluated are as follows: 1) Classification and divid of patients 2) Operation technic adopted by operator. 3) Motives of patients for operation and their untowards. 4) Self satisfication of patients after operation. 5) Post operative changes of soft and hard tissue. 6) Side action during and after operation.

• The korean journal of orthodontics
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• v.31 no.5 s.88
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• pp.535-548
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• 2001

The purpose of this study was to evaluate changes in soft tissue chin thickness and to investigate correlations between hard and soft tissues measurements after orthodontic treatment conducted by premolars extraction and incisor retraction. The sample consisted of 35 female adults with Angle classification class I or class II division 1 malocclusion. Using lateral cephalometric radiographs taken before and after treatment, hard and soft tissue structures were measured and reproducible six landmark on soft chin tissue were used to locate the various points of soft tissue contour of the chin. The res에ts were as follows : 1. There were signigicant correlations between pretreatment B-B', Pm-Pm' and pretreatment vortical skeletal measurements such as $MP{\perp}HP,\;MP{\perp}PP$, ALFH and between a-a', b-b', Me-Me' and measurements of sym-physeal morphology such as SL, SW, PL. 2. There were significant decreases at B-B', Pm-Pm' and significant increases at a-a', b-b' between pre-and posttreatment mea surements. 3. There were significant correlations among soft tissues changes and hard tissue changes except for changes at B-B' and the range of correlation coefficient was about 0.3-0.4. 4. There were significant differences at ${\Delta}UI-VP,\;LI{\perp}, and B-B' measurements between subgroups divided by posttreatment Pog-Pog' changes. 5. There were significant differences at ${\Delta}overbite,\;NPog{\perp}HP,\;and\;Me-Me'$ measurements between subgroups divided by posttreatment Me-Me' changes.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.40 no.2
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• pp.61-67
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• 2014

Objectives: The present study was conducted to determine the reproducibility of peri-implant tissue assessment using the new implant success index (ISI) in comparison with the Misch classification. Materials and Methods: In this descriptive study, 22 cases of peri-implant soft tissue with different conditions were selected, and color slides were prepared from them. The slides were shown to periodontists, maxillofacial surgeons, prosthodontists and general dentists, and these professionals were asked to score the images according to the Misch classification and ISI. The intra- and inter-observer reproducibility scores of the viewers were assessed and reported using kappa and weighted kappa (WK) tests. Results: Inter-observer reproducibility of the ISI technique between the prosthodontists-periodontists (WK=0.85), prosthodontists-maxillofacial surgeons (WK=0.86) and periodontists-maxillofacial surgeons (WK=0.9) was better than that between general dentists and other specialists. In the two groups of general dentists and maxillofacial surgeons, ISI was more reproducible than the Misch classification system (WK=0.99 versus WK non-calculable, WK=1 and WK=0.86). The intra-observer reproducibility of both methods was equally excellent among periodontists (WK=1). For prosthodontists, the WK was not calculable via any of the methods. Conclusion: The intra-observer reproducibility of both the ISI and Misch classification techniques depends on the specialty and expertise of the clinician. Although ISI has more classes, it also has higher reproducibility than simpler classifications due to its ability to provide more detail.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.199-202
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• 1996

Although numerous studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the prediction performance. This paper proposes the post-model integration method, which means integration is performed after individual techniques produce their own outputs, by finding the best combination of the results of each method. To get the optimal or near optimal combination of different prediction techniques. Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints. This study applied three individual classification techniques (Discriminant analysis, Logit and Neural Networks) as base models to the corporate failure prediction context. Results of composite prediction were compared to the individual models. Preliminary results suggests that the use of integrated methods will offer improved performance in business classification problems.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.163-173
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• 1997

Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.104-112
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• 2016

The Adaboost chooses a good set of features in rounds. On each round, it chooses the optimal feature and its threshold value by minimizing the weighted error of classification. The involved process of classification performs a hard decision. In this paper, we expand the process of classification to a soft fuzzy decision. We believe this expansion could allow some flexibility to the Adaboost algorithm as well as a good performance especially when the size of a training data set is not large enough. The typical Adaboost algorithm assigns a same weight to each training datum on the first round of a training process. We propose a new algorithm to assign different initial weights based on some statistical properties of involved features. In experimental results, we assess that the proposed method shows higher performance than the traditional one.

• Geotechnical Engineering
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• v.9 no.3
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• pp.61-66
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• 1993

In this paper, the incorporation of qualitative(or soft) data, such as outputs of geophysical tests or construction experience which has so far been cumulated, was discussed for rock classsification. Geostatistics wart used for this research since the parameters for the design of tunnels are spatially correlated. In particular, indicator kriging technique, which is one of non -parametric approaches, was used. As a selection criteria for an optimal classification, the cost of errors was adopted and the binary classes were only considered for rock classification. In future, incorporating an appreciable amount of available qualitative data will be necessary in tunnelling projects in which quantitative data are scarce. In this respect, this research is of great significance.

• Management & Information Systems Review
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• v.3
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• pp.15-45
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• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• Advances in nano research
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• v.12 no.5
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• pp.489-499
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• 2022

Nanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables.