• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10a
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• pp.1-54
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• 1995

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.105-129
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• 2004

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.131-138
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• 2022

Soft sensors are used to anticipate complicated model parameters using data from classifiers that are comparatively easy to gather. The goal of this study is to use artificial intelligence techniques to design and build soft sensors. The combination of a Long Short-Term Memory (LSTM) network and Grey Wolf Optimization (GWO) is used to create a unique soft sensor. LSTM is developed to tackle linear model with strong nonlinearity and unpredictability of manufacturing applications in the learning approach. GWO is used to accomplish input optimization technique for LSTM in order to reduce the model's inappropriate complication. The newly designed soft sensor originally brought LSTM's superior dynamic modeling with GWO's exact variable selection. The performance of our proposal is demonstrated using simulations on real-world datasets.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.69-76
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• 2022

Education systems have made efforts to prepare students by providing technical and nontechnical courses. With video games, however, there is the potential to develop dedicated metaverses that can help teach soft skills even during casual pastimes. The research conducted will propose a set of design practices for metaverse and game development to promote soft skills. While there are many soft skills people can acquire, this paper will focus on certain aspects based on specific games and studies. There will be some information collected from the information to support the design model and arguments. This paper will provide developers with a starting point for imaginative game creation and impart users with soft skills to assist in their professions and social life.

• Nonlinear Functional Analysis and Applications
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• v.28 no.3
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• pp.753-774
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• 2023

The fundamental aim of the proposed work is to introduce the concept of soft rectangular b-metric spaces, which involves generalizing the notions of rectangular metric spaces and b-metric spaces. Furthermore, an investigation into specific characteristics and topological aspects of the underlying generalization of metric spaces is conducted. Moreover, the research establishes fixed point theorems for mappings that satisfy essential criteria within soft rectangular b-metric spaces. These theorems offer a broader perspective on established results in fixed point theory. Additionally, several congruous examples are presented to enhance the understanding of the introduced spatial framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.325-328
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• 2022

A convolutional neural network(CNN) is widely used in the computer vision tasks, but its computing power requirement needs a design of a special circuit. Most of the computations in a CNN can be implemented efficiently in a digital circuit, but the SoftMax layer has operations unsuitable for circuit implementation, which are exponential and logarithmic functions. This paper proposes a new method to integrate the exponential and logarithmic tables of the conventional circuits into a single table. The proposed structure accesses a look-up table (LUT) only with a few maximum values, and the LUT has the result value directly. Our proposed method significantly reduces the space complexity of the SoftMax layer circuit implementation. But our resulting circuit is comparable to the original baseline with small degradation in precision.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.161-164
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• 2001

This paper partially presents a Hierachical neural network architecture for providing the intelligent control of complex Automatic Transmission(A/T) system which is usually nonlinear and hard to model mathematically. It consists of the module to apply or release an engine brake at the slope and that to judge the intention of the driver. The HNN architecture simplifies the structure of the overall system and is efficient for the learning time. This paper describes how the sub-neural networks of each module have been constructed and will compare the result of the intelligent A/T control to that of the conventional shift pattern.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.133-136
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• 2001

This paper partially presents a Hierachical neural network architecture for providing the intelligent control of complex Automatic Transmission(AJT) system which is usually nonlinear and hard to model mathematically. It consists of the module to apply or release an engine brake at the slope and that to judge the intention of the driver. The HNN architecture simplifies the structure of the overall system and is efficient for the learning time. This paper describes how the sub-neural networks of each module have been constructed and will compare the result of the intelligent hJT control to that of the conventional shift pattern.

• Computers and Concrete
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• v.13 no.3
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• pp.389-409
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• 2014

Precast hollow core slabs (HCS) are technically advanced products in the precast concrete industry, widely used in the last years due to their versatility, their multipurpose potential and their low cost. Using three dimensional FEM (Finite Element Method) elements, this study focuses on the stresses induced by the prestressing of steel. In particular the investigation of the spalling crack formation that takes place during prestressing is carried out, since it is important to assure the appropriate necessary margins concerning such stresses. In fact, spalling cracks may spread rapidly towards the web, leading to the detachment of the lower part of the slab. A parametric study takes place, capable of evaluating the influence of the tendon position and of the web width on the spalling stress. Consequently, after an extensive literature review on the topic of soft computing, an optimization of the HCS is performed by means of Genetic Algorithms coupled with 3-D FEM models.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.55-60
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• 2004

Most vehicles having automatic transmission system use fixed standard shift map to provide automatic transmission for driver. In this case, driver who operates vehicle may be complaint with the fixed transmission pattern being different from the driver's intention. In this paper, therefore, to infer the driver's intention module for learning the driver's intention with related input variables using soft computing method is proposed. After inference, the standard shift map will be shifted according to a certain parameter decided from the proposed module for providing proper shift pattern. The efficiency of the proposed module is evaluated by the data acquired from real time driving.