• Computers and Concrete
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• v.14 no.6
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• pp.727-743
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• 2014

The management of existing concrete bridges has become a major social concern in many developed countries due to the large number of bridges exhibiting signs of significant deterioration. This problem has increased the demand for effective maintenance and renewal planning. In order to implement an appropriate management procedure for a structure, a wide array of corrective strategies must be evaluated with respect to not only the condition state of each defect but also safety, economy and sustainability. This paper describes a new performance evaluation system for existing concrete bridges. The system evaluates performance based on load carrying capability and durability from the results of a visual inspection and specification data, and describes the necessity of maintenance. It categorizes all girders and slabs as either unsafe, severe deterioration, moderate deterioration, mild deterioration, or safe. The technique employs an expert system with an appropriate knowledge base in the evaluation. A characteristic feature of the system is the use of neural networks to evaluate the performance and facilitate refinement of the knowledge base. The neural network proposed in the present study has the capability to prevent an inference process and knowledge base from becoming a black box. It is very important that the system is capable of detailing how the performance is calculated since the road network represents a huge investment. The effectiveness of the neural network and machine learning method is verified by comparing diagnostic results by bridge experts.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.2882-2887
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• 2010

This research develops an evaluation framework of social network analysis, overlooked in traditional program evaluation, and applies to university funding research program. By setting degree increase, an indicator of level of research collaboration, as a goal from the viewpoint of network, we can conclude that researchers funded by university in a form of competition show statistically significant changes in degree increase via one-way ANOVA and permutation-based simulation. This research provides a tool for measuring and managing the degree of contribution of university funding to promoting research collaboration.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.23-27
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• 2000

In this paper, we propose an emotional evaluation model based on information fusion. This model can transform the physical features of a color pattern to the emotional features. Our proposed model consists of the fuzzy logic system and neural network model. The evaluation values produced by them were fused. The model shows comparable performances to the neural network and fuzzy logic system for the approximation of the nonlinear transforms. We believe the evaluated results of a color pattern can be used to the emotion-based color image retrievals.

• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.45-52
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• 2002

This paper proposes the fuzzy closed RCMP queueing network model using fuzzy set theory for the performance evaluation of centralized distributed processing system with ambiguous system factors in the network environments. This model can derive the measures for system performances such as the job spending time, the system throughput, average job number and server utilizations using fuzzy mean value analysis which can process the fuzzy factors. Computer simulation has been performed centralized distributed system with fuzzy service requirement time for verifying the effectiveness of derived equations of performance evaluation according to the numbers of clients, and the results were analyzed. The proposed model provides more and flexible realistic than performance evaluation of conventional method when we evaluated system performance with ambiguous factors.

• Human Ecology Research
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• v.60 no.4
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• pp.625-643
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• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.

• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.101-114
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• 2023

This research aimed to recognize the determinant characteristics affecting consumers' clothing fit evaluation by employing text mining and social network analysis. For this aim, we first extracted text data linked to clothing fit from 2,000 consumer reviews collected from social network services and conducted semantic network examination and CONCOR analysis. As a result, we reported that "pants" and "skirts" were the most commonly associated clothing items with consumers' clothing fit evaluation. And the length of clothing was most commonly investigated. Then, the "waist" and "hip" were the most critical body parts affecting consumers' perception of clothing fit. Further, the four keywords including "wide," "large," "short," and "long" were the most employed ones in consumer reviews when evaluating clothing fit. This study is meaningful in that it specifically recognized the structural relationship and semantic meanings of keywords relevant to consumers' evaluation of clothing fit, which could bring empirical reference information for advanced clothing fit.

• Korean Journal of Social Welfare
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• v.59 no.3
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• pp.281-304
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• 2007

The purpose of this study is to verify the applicability of concept mapping in the process of planning of network outcomes in social welfare. Planning and evaluation of network outcomes involve many stakeholders. Recognizing the value and range of individuals' perspectives in the creation of a common framework is one of the biggest methodological challenges for planning of network outcomes. Concept mapping is a kind of methodology that creates a stakeholder-authored visual geography of ideas from a group. It uses both a quantitative and qualitative approach, including brainstorming, structuring the statement, specific analysis and data interpretation methods to produce maps that can then be used to guide planning and evaluation efforts on the issues that matter to the group. 13 network managers who work in the social welfare centers in Busan are core participants. The 50 statements on network outcomes from brainstorming session fell into six distinct clusters. After the interpretation session these clusters were rated according to the seven rating scales. This paper explores applicability of concept mapping in the process of planning of network outcomes in social welfare. Concept mapping helps stakeholders with different value and ideas about network outcomes to consensus on common conceptual framework. In addition, a multidimensional conceptualization of network outcomes was made. It will assist in designing future outcomes evaluation and guide the evaluators through a selection of key activities and outcomes.

• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.738-748
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• 2002

In this paper, a performance evaluation model of the Multistage Interconnection Network(MIN) with the multiple-buffered crossbar switches is Proposed and examined. Buffered switch technique is well known to solve the data collision problem of the MIN. The proposed evaluation model is developed by investigating the transfer patterns of data packets in a switch with output-buffers. The performance of the multiple-buffered${\alpha}{\times}{\alpha}$ crossbar switch is analyzed. Steady state probability concept is used to simplify the analyzing processes, Two important parameters of the network performance, throughput and delay, are then evaluated, To validate the proposed analysis model, the simulation is carried out on a Baseline network that uses the multiple buffered crossbar switches. Less than 2% differences between analysis and simulation results are observed. It is also shown that the network performance is significantly improved when the small number of buffer spaces is given. However, the throughput elevation is getting reduced and network delay becomes increasing as more buffer spaces are added in a switch.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.1 s.173
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• pp.52-61
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• 2000

The role of quantitative nondestructive evaluation of defects is becoming more important to assure the reliability and the safety of structure, which can eventually be used for residual life evaluation of structure on the basis of fracture mechanics approach. Although ultrasonic technique is one of the most widely used techniques for application of practical field test among the various nondestructive evaluation technique, there are still some problems to be solved in effective extraction and classification of ultrasonic signal from their noisy ultrasonic waveforms. Therefore, crack size determination through a neural network based on the back-propagation algorithm using back-scattered ultrasonic signals is established in this study. For this purpose, aluminum plate containing vertical or inclined surface breaking crack with different crack length was used to receive the back-scattered ultrasonic signals by pulse echo method. Some features extracted from these signals and sizes of cracks were used to train neural network and the neural network's output of the crack size are compared with the true answer.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.6
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• pp.213-223
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• 2006

In this study, artificial neural network is applied to the evaluation of bearing capacity of the PHC auger-drilled piles at sites of domestic decomposed granite soils. For the verification of applicability of error back propagation neural network, a total of 168 data of in-situ test results for PHC auger-drilled plies are used. The results show that the estimation of error back propagation neural network provide a good matching with pile test results by training and these results show the confidence of utilizing the neural networks for evaluation of the bearing capacity of piles.