• Spatial Information Research
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• v.14 no.3 s.38
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• pp.299-319
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• 2006

This research is primarily intended to investigate the potential of estimating green space threshold in terms of total volume control using degradation trends of green space derived from remote sensing and GIS. An empirical study for a case study site was conducted to demonstrate how a standard remote sensing and GIS technology can be used to assist in estimating the total control volume for green space in terms of area-wide information, spatial resolution and change detection etc. Guidelines for a replicable methodology are presented to provide a strong theoretical basis for the standardization of factors involved in the estimation of the green space threshold; the meaningful definition of land mosaic, redefinition of degradation trends for green space. It was demonstrated that the degradation trends of green space could be used effectively as an indicator to restrict further development of the sites since the visual maps generated from remote sensing and GIS can present area-wide visual evidences by permanent record. It is anticipated that this research output could be used as a valuable reference to support more scientific and objective decision-making in introducing aggregate control of green space.

• Journal of Bio-Environment Control
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• v.20 no.4
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• pp.277-282
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• 2011

We carried out shape recognition. We found out cucumber's feature shape by means of neural network and back propagation algorithm. We developed an algorithm which finds object position and shape in real image and we gained following conclusion as a result. It was processed for feature shape extraction of cucumber to detect automatic. The output pattern rates of the miss-detected objects was 0.1~4.2% in the output pattern which was recognized as cucumber. We were gained output pattern according to image resolution $445{\times}363$, $501{\times}391$, $450{\times}271$, $297{\times}421$. It was appeared that no change was detected. When learning pattern was increased to 25, miss-detection ratio was 16.02%, and when learning pattern had 2 pattern, it didn't detect 8 cucumber in 40 images.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.11
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• pp.1185-1190
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• 2011

The lower limb somatosensory deficit observed among peripheral neuropathy patients is partially related to the decline in their balance ability. In general, balance ability has been examined by measuring the postural response (i.e., action). However, body motion is induced by integrated multisensory cues (i.e., motion perception). In this study, we hypothesized that the reduced lower limb somatosensation might also lower motion perception. We induced lower limb sensory deficits through ischemia and then measured the cutaneous sensory sensitivity and directional motion perception. The sensory deficit was successfully induced, and it also lowered the motion perception. However, the center of pressure (COP) variation did not significantly change under the sensory deficit. This result implies that measuring motion perception could enable the detection of precursors of sensory deficits.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.15-22
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• 2020

The 4th industrial revolution and the rapid change in the data environment revealed technical limitations in the existing relational database(RDB). As a new analysis method for unstructured data in all fields such as IDC/finance/insurance, interest in graph database(GDB) technology is increasing. The graph database is an efficient technique for expressing interlocked data and analyzing associations in a wide range of networks. This study extended the existing RDB to the GDB model and applied machine learning algorithms (pattern recognition, clustering, path distance, core extraction) to detect new abnormal signs. As a result of the performance analysis, it was confirmed that the performance of abnormal behavior(about 180 times or more) was greatly improved, and that it was possible to extract an abnormal symptom pattern after 5 steps that could not be analyzed by RDB.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.5
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• pp.1-11
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• 2009

TCS Data imply characteristics of traffic conditions. However, there are outliers in TCS data, which can not represent the travel time of the pertinent section, if these outliers are not eliminated, travel time may be distorted owing to these outliers. Various travel time can be distributed under the same section and time because the variation of the travel time is increase as the section distance is increase, which make difficult to calculate the representative of travel time. Accordingly, it is important to grasp travel time characteristics in order to compute the representative of travel time using TCS Data. In this study, after analyzing the variation ratio of the travel time according to the link distance and the level of congestion, the outlier elimination model and the smoothing model for TCS data were proposed. The results show that the proposed model can be utilized for estimating a reliable travel time for a long-distance path in which there are a variation of travel times from the same departure time, the intervals are large and the change in the representative travel time is irregular for a short period.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.113-121
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• 2017

Due to the development of IT technology, harmful multimedia contents are spreading out. In addition, obscene and violent contents have a negative impact on children. Therefore, in this paper, we propose a multimodal approach for blocking obscene and violent video contents. Within this approach, there are two modules each detects obsceneness and violence. In the obsceneness module, there is a model that detects obsceneness based on adult and racy score. In the violence module, there are two models for detecting violence: one is the blood detection model using RGB region and the other is motion extraction model for observation that violent actions have larger magnitude and direction change. Through result of these three models, this approach judges whether or not the content is harmful. This can contribute to the blocking obscene and violent contents that are distributed indiscriminately.

• Journal of Korea Water Resources Association
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• v.36 no.4
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• pp.633-645
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• 2003

The techniques of GIS and remote sensing are being applied to hydrology, geomorphology and various field of studies are performed by many researcher, related those techniques. In this paper, curve number change detection is tested according to soil map and land cover in mountain area. Neural networks method is applied for land cover classification and GIS for curve number calculation. The first, sample area are selected and tested land cover classification, NN(84.1％) is superior to MLC(80.9％). So we selected NN with land cover classifier. The second, curve number from the land cover by neural network classifier(57) is compared with that(curve number) from the land cover by manual work(55). Two values are so similar. The third, curve number classified by NN in sample area was applied and tested to whole study area. As results of this study, it is shown that curve number is more exact and efficient by using NN and GIS technique than by (using) manual work.

• Convergence Security Journal
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• v.2 no.1
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• pp.77-86
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• 2002

In this paper, we propose a segmentation algorithm that can reliably separate moving objects from noisy background in the image sequence received from a camera at the fixed position. Image segmentation is one of the most difficult process in image processing and an adoption in the change of environment must be considered for the increase in the accuracy of the image. The proposed algorithm consists of four process : generation of the difference image between the input image and the reference image, removes the background noise using the background nois modeling to a difference image histogram, then selects the candidate initial region using local maxima to the difference image, and gradually expanding the connected regions, region by region, using the shape information. The test results show that the proposed algorithm can detect moving objects like intruders very effectively in the noisy environment.

• Journal of Korean Public Health Nursing
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• v.26 no.1
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• pp.101-112
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• 2012

Purpose: The purposes of this study were to identify differences of duties, tasks, and task elements of care helpers between long term care (LTC) facilities and client's home (CH), and to provide data for the development of educational programs and policies. Methods: This study was a descriptive investigation; the subjects of the study were 418 care helpers. Duties, tasks, and task elements were measured using the framework proposed by Shin et al. (2012). Data were analyzed by t-test using PASW 18.0. Results: All of the jobs were statistically significant differences between LTC and CH. Dietary assistance and Daily work assistance were more frequently in CH, and the frequency of other tasks was higher in LTC than CH. Tasks with higher-reported difficulty by those who worked in LTC were as follows: personal hygiene, position change and movement, exercise and activity assistance, safety care, communication assistance, dietary assistance, environment management, daily work assistance, emergency prevention, early detection and speedy reporting, and dementia patient care. Conclusion: These findings suggest that training for care helpers of each facility type will be differentiated. Tasks and task elements reported by care helpers were modified and added to the standard textbook.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.