• Electronics and Telecommunications Trends
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• v.35 no.1
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• pp.60-70
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• 2020

Artificial intelligence (AI) is expected to bring about a wide range of changes in the industry, based on the assessment that it is the most innovative technology in the last three decades. The manufacturing field is an area in which various artificial intelligence technologies are being applied, and through accumulated data analysis, an optimal operation method can be presented to improve the productivity of manufacturing processes. In addition, AI technologies are being used throughout all areas of manufacturing, including product design, engineering, improvement of working environments, detection of anomalies in facilities, and quality control. This makes it possible to easily design and engineer products with a fast pace and provides an efficient working and training environment for workers. Also, abnormal situations related to quality deterioration can be identified, and autonomous operation of facilities without human intervention is made possible. In this paper, AI technologies used in smart factories, such as the trends in generative product design, smart workbench and real-sense interaction guide technology for work and training, anomaly detection technology for quality control, and intelligent manufacturing facility technology for autonomous production, are analyzed.

• Journal of Environmental Science International
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• v.13 no.7
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• pp.611-618
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• 2004

The Neural Network Models which mathematically interpret human thought processes were applied to resolve the uncertainty of model parameters and to increase the model's output for the streamflow forecast model. In order to test and verify the flood discharge forecast model eight flood events observed at Kumho station located on the midstream of Kumho river were chosen. Six events of them were used as test data and two events for verification. In order to make an analysis the Levengerg-Marquart method was used to estimate the best parameter for the Neural Network model. The structure of the model was composed of five types of models by varying the number of hidden layers and the number of nodes of hidden layers. Moreover, a logarithmic-sigmoid varying function was used in first and second hidden layers, and a linear function was used for the output. As a result of applying Neural Networks models for the five models, the N10-6model was considered suitable when there is one hidden layer, and the Nl0-9-5model when there are two hidden layers. In addition, when all the Neural Network models were reviewed, the Nl0-9-5model, which has two hidden layers, gave the most preferable results in an actual hydro-event.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

• Journal of Intelligence and Information Systems
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• v.10 no.1
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• pp.65-75
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• 2004

It is notorious that PIN(Personal Identification Number) is used widely for user verification and authentication in networked environment. But, when the user Identification and password are exposed by hacking, we can be damaged monetary damage as well as invasion of privacy. In this paper, we adopt face recognition-based authentication which have nothing to worry what the ID and password will be exposed. Also, we suggest the remote authentication and verification system by considering not only 2-Tier system but also 3-Tier system getting be distributed. In this research, we analyze the face feature data using the SVM(Support Vector Machine) and PCA(Principle Component Analysis), and implement artificial intelligence face recognition module in distributed environment which increase the authentication speed and heightens accuracy by utilizing artificial intelligence techniques.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1505-1509
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• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

• Journal of Auto-vehicle Safety Association
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• v.7 no.4
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• pp.26-30
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• 2015

Exceeding the speed limit during vehicle driving is a key factor in the severity of lots of road accidents, and SLIF(Speed Limit Information Function) application is in the initial phase in Korea. SLIF helps the drivers to observe a speed limit when they are driving by providing alert and informing the current limit speed information based on external data using camera and/or digital map, for that reason, environmental conditions could be causes of SLIF malfunctions. In this study, design adequacy analysis of SLIF in respect of false recognition as the Korea traffic environment has been performed. As tentative results, road conditions and structure of speed limit sign as well as system performance often caused misrecognition.

• Journal of the Korean Society of Costume
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• v.66 no.7
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• pp.154-170
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• 2016

The female leadership styles in the 21st century have been established as a major axis. Especially, the fashion of female politicians being exposed to the public during political activities has become a main element of a measure displaying visually female leadership styles in the 21st century and image making as well. Consequently, this study conducted qualitative research through the interview method to figure out regular voters' thoughts in depth about images being required for female political leaders and the fashion maximizing those images, and drew the detailed design elements. Suggesting the clothes design reflecting those elements for female political leaders by 3D virtual clothing works emerging as a new market creating profits related to fashion. The images which female political leaders have to have and were extracted through the interviews in this study, showed as feminine, strong leader, honest, and intelligent images, and also it was shown that female political leaders displaying proper images depending on the circumstances and using those images in politics rather than sticking to a fashion identity were favored by interviewees. The present study intends to contribute to being used as basic data of various research and fashion items of virtual reality and establishment of successful fashion strategy for female political leaders.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.4
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• pp.101-115
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• 2009

This research was based on Howard Gardner's multiple intelligences theory. The purpose of this study was to describe students' book selection preferences using the theory of multiple intelligences. We have conducted a survey of high schools in the Chung-Nam province, consisting of 100 students, 50 in the high academically achieving group and another 50 in the low achieving group, in an attempt to analyze the relationship between their book selection preferences and the types of students' multiple intelligences. We want to assist in the research data for the reading guide.

• Journal of Fashion Business
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• v.15 no.4
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• pp.155-166
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• 2011

The purposes of this study was to examine Chinese consumers's perception toward Korean fashion brands. Especially, this study aimed to compare the Chinese consumers in three local groups (Beijing, Shanghai, & Yangji). The subjects used for this study were one hundred ninety-six participants(male; 110, female; 86) in 20s age who live in China. For data analysis, descriptive statistics, Cronbach's alpha, and t-test were used. Cronbach's alpha test revealed that all instruments which were used for this study had over 0.85. As the results, first, 67.9% of Chinese consumers perceived Korean brands correctly as Korean brands. Also, 42.5% of Chinese participants had purchased Korean fashion brand products such as Teenie Weenie or E-land. Second, there were not significant differences in brand attitudes among three group participants. However, there was a significant difference in 'brand preference' factor, one of three brand attitudes, between two ethnic groups. Finally, there were not signifiant differences in brand image, while there was a significant difference in intelligent brand image, one of 4 brand image factors, between two ethnic groups. These results of this study would be very useful for Korean fashion brand marketers in order to understand Chinese fashion consumers more details, and provide more efficient fashion marketing strategies.