• Korean Journal of Human Ecology
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• v.12 no.1
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• pp.63-73
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• 2003

The purpose of this study was to quantify the hands of fabrics for the Korean folk clothes using both a KES-FB and an artificial neural network. In order to select the proper input parameters, we calculated the correlation using step-wise regression between mechanical properties and the hand value of fabrics. For the classification, the primary hand values and total hand value, five neural networks with three-layered structure were constructed using the error back propagation algorithm and, in order to reduce errors and to speed up learning, the momentum method was selected. From the analysis of the primary and total hands using a self-constructed artificial intelligence system, the error rates of sleekness, stiffness, silkiness, and roughness compared with the judgement of expert panels were found to be 3.3%, 3.3%, 1.6%, and 4.9%, respectively, while that of the total hand was 9.83%.

• The Journal of Society for e-Business Studies
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• v.15 no.3
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• pp.147-162
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• 2010

East Asian region, specifically China, Japan, and Korea, is considered as an area of advanced mobile handsets and mobile services. The well-established infrastructure of this region is well known due to rapid introduction of diverse feature-equipped handsets and advanced capabilities of mobile network operators. However, the status of mobile business has rarely been dealt with in previous studies. In this paper, we compare mobile value chains among these three countries. China has adopted open platform for mobile data services while Korea and Japan's mobile network operators control mobile portals for accessing diverse contents and services. We also discuss some possible reasons for the differences among the three countries in terms of value chain structures.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1419-1425
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• 2000

When we predict of software reliability, we can't know the testing stopping time and how many faults be residues in software the (the maximum value of data) during these software testing process, therefore we assume the maximum value and the training result can be inaccuracy. In this paper, we present neural network approach for software reliability prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data.

• Journal of Distribution Science
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• v.20 no.5
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• pp.97-104
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• 2022

Purpose: This study intends to analyze the effect of competitor pressure and customer pressure on social media technology use and the value of the business network and their implications for marketing performance. Research design, and methodology: A constructed questionnaire was conducted with 90 respondents of MSME's Business Actors in South Sulawesi Partial Least Square (PLS) analysis was applied to analyze and verify all the data. Results: Competitor pressure has a significant effect on social media technology, Competitor pressure has a positive and significant impact on business network accentuation. Customer pressure has a positive and significant impact on social media technology. Customer pressure has a positive and significant impact on business network accentuation. Social media technology utilization has a significant impact on Business Network Accentuation. Social media technology utilization has a significant effect on Marketing Performance. Business Network has a significant effect on Marketing Performance. Conclusions: It is an important thing for MSMEs to increase the use of social media technology to meet the demands of consumers and pressure from competitors. The use of social media technology must be implemented effectively and efficiently so that it can be utilized as an effective tool for distributing the value that own by a company to improve the company's marketing performance.

• The Journal of Economics, Marketing and Management
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• v.6 no.3
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• pp.10-13
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• 2018

Purpose - On the basis of discussing the network economy concept and the commentary of the marginal revenue decreasing of traditional economic theory, The concept of network economy has just been put forward in recent years. The reason why such a concept appears is that the information technology, marked by computer network, plays an increasingly important role in economic activities. Some people define network economy as an economic form based on network technology and human capital. this paper points out network economy existing the marginal revenue increasing and analyzes the reasons that influencing the marginal revenue increasing. Research design, data, methodology - The network economy has fundamentally changed the traditional economic laws. The economic basis of industrial society is the law of incremental marginal cost, which reflects the socialization of high cost in industrial society. Results - As the number of network members increases, the value of the network increases explosively, and the value increases attract more members to join, resulting in more returns. Conclusion - In conclusion, network economy has changed many aspects of traditional economy, resulting in decreasing marginal cost, decreasing transaction cost in and out of enterprise organizations, and making the effect of increasing scale compensation more prominent. This is of great significance to the information construction in China.

• Journal of IKEEE
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• v.23 no.4
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• pp.1187-1194
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• 2019

This paper proposes a CNN architecture as value function network of an artificial intelligence Othello game agent and its learning scheme using reinforcement learning algorithm. We propose an approach to construct the value function network by using CNN to learn the records of professional players' real game and an approach to enhance the network parameter by learning from self-play using reinforcement learning algorithm. The performance of value function network CNN was compared with existing ANN by letting two agents using each network to play games each other. As a result, the winning rate of the CNN agent was 69.7% and 72.1% as black and white, respectively. In addition, as a result of applying the reinforcement learning, the performance of the agent was improved by showing 100% and 78% winning rate, respectively, compared with the network-based agent without the reinforcement learning.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.255-264
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• 2002

The topography is recognized as an important factor in determining the streamflow response of watershed to precipitation. In watershed analysis, stream networks are very important parameters. Each DEM grid size and flowaccumulation threshold value of drainage accumulation matrix have influence on stream networks extracted by using grid DEM. Therefore, stream networks extracted from DEM varies with each DEM grid size and flowaccumulation threshold value. Generally, small threshold values will generate more detailed stream network with higher drainage density High threshold values will generate coarser stream networks. In this paper, total stream length in the study area was used to calculate the flowaccumulation threshold value by each DEM grid size. Stream network was derived by each DEM grid size, which is applied flowaccumulation threshold value. Regression equation was derived by correlation between flowaccumulation threshold value and each DEM grid size.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.642-646
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• 2006

The fuzzy TAM(Topographical Attentive Mapping) network is a supervised method of pattern analysis which is composed of input layer, category layer, and output layer. But if we don't know the target value of the pattern, the network can not be trained. In this case, the target value can be replaced by a result induced by using an unsupervised neural network as the SOM (Self-organizing Map). In this paper, we apply the results of SOM to fuzzy TAM network and show its usefulness through the case study.

• ETRI Journal
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• v.43 no.1
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• pp.152-162
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• 2021

In the last few years, detection has become a powerful methodology for network protection and security. This paper presents a new detection scheme for data recorded over a computer network. This approach is applicable to the broad scientific field of information security, including intrusion detection and prevention. The proposed method employs bidimensional (time-frequency) data representations of the forms of the short-time Fourier transform, as well as the Wigner distribution. Moreover, the method applies matrix factorization using singular value decomposition and principal component analysis of the two-dimensional data representation matrices to detect intrusions. The current scheme was evaluated using numerous tests on network activities, which were recorded and presented in the KDD-NSL and UNSW-NB15 datasets. The efficiency and robustness of the technique have been experimentally proved.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2E
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• pp.14-23
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• 1996

This paper is a on the neural network to recognize the phonemes, the weight initialization to reduce learning speed, and LR parser for continuous speech recognition. The neural network spots the phonemes in continuous speech and LR parser parses the output of neural network. The whole phonemes recognized in neural network are divided into several groups which are grouped by the similarity of phonemes, and then each group consists of neural network. Each group of neural network to recognize the phonemes consisits of that recognize the phonemes of their own group and VGNN(Verify Group Neural Network) which judges whether the inputs are their own group or not. The weights of neural network are not initialized with random values but initialized from learning data to reduce learning speed. The LR parsing method applied to this paper is not a method which traces a unique path, but one which traces several possible paths because the output of neural network is not accurate. The parser processes the continuous speech frame by frame as accumulating the output of neural network through several possible paths. If this accumulated path-value drops below the threshold value, this path is deleted in possible parsing paths. This paper applies the continuous speech recognition system to the threshold value, this path is deleted in possible parsing paths. This paper applies the continuous speech recognition system to the continuous Korea digits recognition. The recognition rate of isolated digits is 97% in speaker dependent, and 75% in speaker dependent. The recognition rate of continuous digits is 74% in spaker dependent.