• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.217-228
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• 2018

The purpose of this study is to develop STEAM program for gifted education by combining educational contents of humanities, arts, engineering, technology, and design into various subjects, focusing on mathematics-science curriculum of elementary school. The achievement standards and curriculum contents of elementary mathematics-science curriculum were analyzed while considering 2015 revised national curriculum. And then, a 16 class-hour convergence education program consisting of 3-hour block time was developed by applying the STEAM model with 4 steps. The validity of the program developed through this process was verified, and four educational experts evaluate whether the program can be applied to the elementary school. Based on the evaluation results, the convergence education program was finalized. As a result of implementing the gifted education program for mathematics-science, students achieved the objectives and values of convergence education such as creative design, self-directed participation, cooperative learning, and interest in class activities (game, making). If this convergence education program is applied to regular class, creative experiential class, or class for gifted children, students can promote their scientific creativity, artistic sensitivity, design sence, and so on.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.221-228
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• 2017

With the recent development of IoT technology, medical services using IoT technology are increasing in many medical institutions providing health care services. However, as the number of IoT sensors attached to the user body increases, the healthcare information transmitted to the server becomes complicated, thereby increasing the time required for analyzing the user's healthcare information in the server. In this paper, we propose a deep learning based health care information management method to collect and process healthcare information in a server for a large amount of healthcare information delivered through a user - attached IoT device. The proposed scheme constructs a subnet according to the attribute value by assigning an attribute value to the healthcare information transmitted to the server, and extracts the association information between the subnets as a seed and groups them into a hierarchical structure. The server extracts optimized information that can improve the observation speed and accuracy of user's treatment and prescription by using deep running of grouped healthcare information. As a result of the performance evaluation, the proposed method shows that the processing speed of the medical service operated in the healthcare service model is improved by 14.1% on average and the server overhead is 6.7% lower than the conventional technique. The accuracy of healthcare information extraction was 10.1% higher than the conventional method.

• Journal of Korean Society of Steel Construction
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• v.19 no.1
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• pp.29-41
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• 2007

Structural design, like other complex decision problems, involves many trade-offs among competing criteria. Although mathematical programming models are becoming increasingly realistic, they often have design limitations, that is, there are often relevant issues that cannot be easily captured. From the understanding of these limitations, a decision-support system is developed that can generate some useful alternatives as well as a single optimum value in the optimization of steel frame structures. The alternatives produced using this system are "good" with respect to modeled objectives, and yet are "different," and are often better, with respect to interesting objectives not present in the model. In this study, we created a decision-support system for designing the most cost-effective moment-resisting steel frame structures for resisting lateral loads without compromising overall stability. The proposed approach considers the cost of steel products and the cost of connections within the design process. This system makes use of an optimization formulation, which was modified to generate alternatives of optimum value, which is the result of the trade-off between the number of moment connections and total cost. This trade-off was achieved by reducing the number of moment connections and rearranging them, using the combination of analysis based on the LRFD code and optimization scheme based on genetic algorithms. To evaluate the usefulness of this system, the alternatives were examined with respect to various design aspects.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.6
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• pp.349-357
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• 2019

Finite element method (FEM) is utilized in the development of products to realistically analyze and predict the mechanical behavior of materials in various fields. However, the approach based on the numerical analysis of glass fiber reinforced plastic (GFRP) composites, for which the fiber orientation and strain rate affect the mechanical properties, has proven to be challenging. The purpose of this study is to define and evaluate the mechanical properties of glass fiber reinforced plastic composites using the numerical analysis models of Digimat, a linear, nonlinear multi-scale modeling program for various composite materials such as polymers, rubber, metal, etc. In addition, the aim is to predict the behavior of realistic polymeric composites. In this regard, the tensile properties according to the fiber orientation and strain rate of polybutylene terephthalate (PBT) with short fiber weight fractions of 30wt% among various polymers were investigated using references. Information on the fiber orientation was calculated based on injection analysis using Moldflow software, and was utilized in the finite element model for tensile specimens via a mapping process. LS-Dyna, an explicit commercial finite element code, was used for coupled analysis using Digimat to study the tensile properties of composites according to the fiber orientation and strain rate of glass fibers. In addition, the drawbacks and advantages of LS-DYNA's various anisotropic material models were compared and evaluated for the analysis of glass fiber reinforced plastic composites.

• Journal of the Korean Society of Earth Science Education
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• v.10 no.1
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• pp.76-89
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• 2017

The purpose of this study is to develop a science field study area using Geumgang(Geum River), fossil origins and various geological resources in Youngdong area of Chungcheongbuk-do as educational resources; and utilize them to develop an education program to cultivate earth science and topophilia. The Youngdong sedimentary basin (Cretaceous period) has a well-developed outcrop along the Geumgang and it is therefore easy to find various geological structures, plant fossils, and dinosaur fossils. Also, it has a distinct sedimentary structure, such as mud cracks, ripple marks and cross-bedding. Science field study area(6 observation sites) were developed based on school curriculum, textbook analysis, and professional earth science education panel discussion to create a convergence education program. The result of validating the developed program showed that all the items were satisfactory ($CVR{\geq}0.88$) in the test categories. The science field study teaching-learning model was applied to actual classes. The evaluation result for class satisfaction was positive, scoring Rickert scale 4.18. The result of observation about the outdoor classroom process in the science field study area revealed that students were able to form a new image of the beautiful scenery of the Geumgang. Also, the students could gain a new understanding, concept and value of various geological objects (sandy beach, stepping-stones, dinosaur footprint fossils, sedimentary formation), which naturally allowed them to form topophilia.

• Asia pacific journal of information systems
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• v.23 no.4
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• pp.129-149
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• 2013

According to the development of new Information Technologies, firms consistently invest a significant amount of money in IT activities, such as establishing internal and external information systems. However, several anti-Information activities-such as hacking, leakage of information and system destruction-are also rapidly increasing, thus many firms are exposed to direct and indirect threats. Therefore, firms try to establish information security systems and manage these systems more effectively via an enterprise perspective. However, stakeholders or some managers have negative opinions about information security systems. Therefore, in this research, we study the relationship between multibusiness firms' performance and information security systems. Information security indicates physical and logical correspondence of information system department against threats and disaster. Studies on information security systems suggested frameworks such as IT Governance Cube and COBIT Framework to identify information security systems. Thus, this study define that information security systems is a controlled system on enterprise IT process and resource on IT Governance perspective rather than independent domain of IT. Thus, Information Security Systems should be understood as a subordinate concept of IT and business processes. In addition, this study incorporates information capability to information security system literature to show the positive relationship between Information Security Systems and Corporate Performance. The concept of information capability suggested that an interaction of human, information, technical and an effect on corporate performance using three types of capability (IT Practice, Information Management Practice, Information Behaviors and Values). Information capability is about firms' capability to manage IT infrastructure and information as well as individual employees who use IT infrastructure and information. Thus, this study uses information capability as a mediating variable for the relationship between information security systems and firms' performance. To investigate the relationship between Information Security Systems and multibusiness firms' performance, this study extends the IT relatedness concept into Information Security Systems. IT relatedness provides understanding of how corporations cope with conflicts between headquarters and business units to create a synergy effect and achieve high performance using IT resources. Based on the previous literature, this study develops the IT Security Relatedness model. IT Security Relatedness is our main independent variable, while Information Capability and Information Security Performance are mediating variables. To control for the common method bias, we collect each multibusiness firm's financial performance and use it as our dependent variable. We find that Information Security Systems influence Information Capability and Information Security Performance positively, and these two variables consequently influence Corporate Performance positively. In addition, this result indirectly shows that corporations under a multibusiness environment can obtain synergy effects using the integrated Information Security Systems. This positive impact of Information Security Systems on multibusiness firms' performance has an important implication to various stakeholders. Therefore, multibusiness firms need to establish Information Security Systems to achieve better financial performance.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.107-116
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• 2019

Korean construction engineering firms want to pave the way for expansion of overseas markets through the World Bank's Official Development Assistance (ODA) projects as a way to improve their overseas project performance. However, since the World Bank project competes with global companies for limited projects, building partnerships with suitable business partners is essential to gain an upper hand in bidding competition and meet the institutional conditions of the recipient country. In this regard, many network studies have been conducted in the past through Social Network Analysis (SNA), but few have been analyzed based on the process of changes in the network. So, This study collected winning data from the three Southeast Asian countries that ended after the World Bank's ODA project performed smoothly, and established a learning-based link prediction model that reflected the dynamic nature of the network. As a result, the 11 main variables acting on building a cooperative relationship between winning companies were derived and the effect of each variables on the probability value of cooperation between individual links was identified.

• Journal of the Korea Institute of Building Construction
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• v.19 no.4
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• pp.361-371
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• 2019

In 2017, the ratio of the number of victims and deaths in the construction industry was the highest with 25.2% and 29.6%, respectively. Especially, as safety accidents at construction sites continue to increase, the economic loss is greatly increased too. Therefore, in order to prevent safety accidents in the construction work, the safety risk assessment index by type of construction was developed, and the main results of this study are as follows. First, 17 factors related to safety accidents at construction sites were derived through survey and interview survey, and this study suggested 9 items(process, type of construction, progress rate, contract amount, number of floors, safety education, working days and weather) throughout the expert advisory meeting. Second, the risk assessment index for safety accidents was developed based on the ratio and intensity of safety accidents. Third, to verify the risk assessment model, the construction safety risk assessment index by type of construction was derived by surveying and analyzing the statistics of the construction accident. In addition, the risk strength was calculated by dividing human damage caused by construction safety accidents into those killed and injured. The risk assessment index based on the frequency and intensity of safety accidents by type of construction is expected to be utilized as basic data when assessing the risk of similar projects in the future.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.23-30
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• 2021

This paper proposes a noise-tolerant image classification system using multiple autoencoders. The development of deep learning technology has dramatically improved the performance of image classifiers. However, if the images are contaminated by noise, the performance degrades rapidly. Noise added to the image is inevitably generated in the process of obtaining and transmitting the image. Therefore, in order to use the classifier in a real environment, we have to deal with the noise. On the other hand, the autoencoder is an artificial neural network model that is trained to have similar input and output values. If the input data is similar to the training data, the error between the input data and output data of the autoencoder will be small. However, if the input data is not similar to the training data, the error will be large. The proposed system uses the relationship between the input data and the output data of the autoencoder, and it has two phases to classify the images. In the first phase, the classes with the highest likelihood of classification are selected and subject to the procedure again in the second phase. For the performance analysis of the proposed system, classification accuracy was tested on a Gaussian noise-contaminated MNIST dataset. As a result of the experiment, it was confirmed that the proposed system in the noisy environment has higher accuracy than the CNN-based classification technique.