• Journal of the Korea Institute of Building Construction
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• v.11 no.3
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• pp.238-246
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• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.4
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• pp.386-402
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• 2012

In this paper, a method to estimate ice loads as a function of the buttock angle of an icebreaker is presented with respect to polycrystalline freshwater ice. Ice model tests for different buttock angles and impact velocities are carried out to investigate ice pressure loads and tendencies of ice pressure loads in terms of failure modes. Experimental devices were fabricated with an idealized icebreaker bow shape, and medium-scale ice specimens were used. A dry-drop machine with a freefall system was used, and four pressure sensors were installed at the bottom to estimate ice pressure loads. An estimation equation was suggested on the basis of the test results. We analyzed the estimation equation for design ice loads of the International Association of Classification Societies (IACS) classification rules. We suggest an estimation equation considering the relation between ice load, buttock angle, and velocity by modifying the equations given in the IACS classification rules.

• Journal of Korean Academy of Nursing
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• v.40 no.6
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• pp.903-912
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• 2010

Purpose: This study was conducted to examine whether the level of classification for long-term care service under longterm care insurance reflects resource utilization level for residents in nursing homes. Methods: From 2 long-term care facilities, the researchers selected 95 participants and identified description and time of care services provided by nurses, certified caregivers, physical therapists and social workers during a 24-hr-period. Results: Resource utilization level was: 281.04 for level 1, 301.05 for level 2 and 270.87 for level 3. Resource utilization was not correlated with level. Differences in resource utilization within the same level were similar with the coefficient of variance, 22.7-27.1%. Physical function was the most influential factor on long-term care scores (r=.88, p<.001). The level for long-term care service did not reflect differences in resource utilization level of residents on long-term care insurance. Conclusion: The results of this study indicate that present grading for long-term care service needs to be reconsidered. Further study is needed to adjust the long-term care classification system to reflect the level of resource utilization for care recipients on the long-term care insurance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1284-1288
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• 2008

Recently, there is increasing the development requirement of middleware and interface for GPS(Global Positioning System), RFID(Radio Frequency IDentification), sensor networks. GPS supports a useful location based service. RFID supports products logistic and distribution services through the identification. A sensor network collects a context information, such as humidity, temperature and atmospheric pressure. This paper implements and verifies a integrated service middleware for supporting efficiently process of sensing data collected from RFID, GPS and sensor network. This middleware have the temporary store function, the redundancy exclusion function, certification function, the classification function and the database storage function. Additionary, this middleware connects with low-level adaptor using socket interface and supports the high-level application services using database connection. Therefore, user can develop easily various many ubiquitous application system using proposed middleware instead of each RFID middleware, GPS middleware and, middleware based on sensor network.

• Journal of information and communication convergence engineering
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• v.1 no.3
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• pp.157-162
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• 2003

We propose a learning method for improving the performance of the backpropagation algorithm. The proposed method is using a fuzzy logic system for automatic tuning of the learning rate of each weight. Instead of choosing a fixed learning rate, the fuzzy logic system is used to dynamically adjust the learning rate. The inputs of fuzzy logic system are delta and delta bar, and the output of fuzzy logic system is the learning rate. In order to verify the effectiveness of the proposed method, we performed simulations on the XOR problem, character classification, and function approximation. The results show that the proposed method considerably improves the performance compared to the general backpropagation, the backpropagation with momentum, and the Jacobs'delta-bar-delta algorithm.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.118-130
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• 1996

Fuzzy inference is very useful in expressing ambiguous problems quantitatively and solving them. But like the most of the knowledge based inference systems. It has many difficulties in constructing rules and no learning capability is available. In this paper, we proposed a fuzzy inference system based on fuzy associative memory to solve such problems. The inference system proposed in this paper is mainly composed of learning phase and inference phase. In the learning phase, the system initializes it's basic structure by determining fuzzy membership functions, and constructs fuzzy rules in the form of weights using learning function of fuzzy associative memory. In the inference phase, the system conducts actual inference using the constructed fuzzy rules. We applied the fuzzy inference system proposed in this paper to a pattern classification problem and show the results in the experiment.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.168-175
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• 2021

Goal of this study is to perform the correlation about Gross motor function, eating-drinking function, and oral motor function, to identify necessity for invervention of feeding disorders on severity of the function of children with cerebral palsy. Subjects were 61 children diagnosed with cerebral palsy. The subject were evaluated for oral motor function, feeding function by GMFCS, EDACS, OMAS. The results of this study showed a significant correlation between gross motor function, eating and drinking functions, and oral motor functions. That is, the more severe the deterioration of the motor function, the lower the functional level of eating and drinking and oral motor function deterioration. In evaluating and treating the eating activity of children with cerebral palsy through this study, it seems necessary to check the eating and drinking function and oral motor function according to the gross motor function.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.483-490
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• 2019

In the Network Intrusion Detection System (NIDS), the function of classification is very important, and detection performance depends on various features. Recently, a lot of research has been carried out on deep learning, but network intrusion detection system experience slowing down problems due to the large volume of traffic and a high dimensional features. Therefore, we do not use deep learning as a classification, but as a preprocessing process for feature extraction and propose a research method from which classifications can be made based on extracted features. A stacked AutoEncoder, which is a representative unsupervised learning of deep learning, is used to extract features and classifications using the Random Forest classification algorithm. Using the data collected in the IOT environment, the performance was more than 99% when normal and attack traffic are classified into multiclass, and the performance and detection rate were superior even when compared with other models such as AE-RF and Single-RF.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.256-257
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• 2013

Roof and exterior wall of general formal buildings are designed and constructed through design focused exterior wall system and drainage and waterproof roof system. However, there are no classification of exterior wall and roof in freeform buildings and they are integrated as a surface of freeform buildings. Therefore it is necessary to develop the dry roof construction method using double skin roof system satisfying the design and function of the envelope. In this study, we have an effort to develop construction method of double-skin roof system using metal panel and PV.

• Journal of The Korean Association For Science Education
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• v.32 no.3
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• pp.524-531
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• 2012

Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI. Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed. Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.