• Structural Engineering and Mechanics
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• v.44 no.5
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• pp.633-647
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• 2012

Applying Information Technology (IT) in practical engineering has become one of the most important issues in the past few decades, especially on internal solitary wave, intelligent robot interaction, artificial intelligence, fuzzy Lyapunov, tension leg platform (TLP), consumer and service quality. Other than affecting the traditional teaching mode or increasing the inter-relation with users, IT can also be connected with the current society by collecting the latest information from the internet. It is apparently a fashion-catching-up technology. Therefore, the learning of how to use IT facilities is becoming one of engineers' skills nowadays. In addition to studying how well engineers learn to operate IT facilities and apply them into teaching, how engineers' general capacity of information effects the results of learning IT are also discussed. This research introduces the "Combined TAM and TPB mode," to understand the situation of engineers using IT facilities.

• Proceedings of the KIPE Conference
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• 2004.07a
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• pp.38-41
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• 2004

Artificial intelligence control that use Fuzzy, Neural network, genetic algorithm etc. in the speed control of induction motor recently is studied much. Also, sensors such as Encoder and Resolver are used to receive the speed of induction motor and information of position. However, this control method or sensor use receives much effects in surroundings environment change and react sensitively to parameter change of electric motor and control Performance drops. Presume the speed and position of induction motor by ANN in this treatise, and because using FMRLC that is consisted of two Fuzzy Logic, can correct Fuzzy Rule Base through teaming and save good response special quality in change of condition such as change of parameter.

• Korean Journal of Computational Design and Engineering
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• v.5 no.2
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• pp.127-135
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• 2000

Emergent computing paradigms such as genetic algorithms have found increased use in problems in engineering design. These computational tools have been shown to be applicable in the solution of generically difficult design optimization problems characterized by nonconvexities in the design space and the presence of discrete and integer design variables. Another aspect of these computational paradigms that have been lumped under the bread subject category of soft computing, is the domain of artificial intelligence, knowledge-based expert system, and machine learning. The paper explores a machine learning paradigm referred to as teaming classifier systems to construct the high-quality global function approximations between the design variables and a response function for subsequent use in design optimization. A classifier system is a machine teaming system which learns syntactically simple string rules, called classifiers for guiding the system's performance in an arbitrary environment. The capability of a learning classifier system facilitates the adaptive selection of the optimal number of training data according to the noise and multimodality in the design space of interest. The present study used the polynomial based response surface as global function approximation tools and showed its effectiveness in the improvement on the approximation performance.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.313-326
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• 2020

Deep Learning is the most important key to the development of Artificial Intelligence (AI). There are several distinguishable architectures of neural networks such as MLP, CNN, and RNN. Among them, we try to understand one of the main architectures called Recurrent Neural Network (RNN) that differs from other networks in handling sequential data, including time series and texts. As one of the main tasks recently in Natural Language Processing (NLP), we consider Neural Machine Translation (NMT) using RNNs. We also summarize fundamental structures of the recurrent networks, and some topics of representing natural words to reasonable numeric vectors. We organize topics to understand estimation procedures from representing input source sequences to predict target translated sequences. In addition, we apply multiple translation models with Gated Recurrent Unites (GRUs) in Keras on English-Korean sentences that contain about 26,000 pairwise sequences in total from two different corpora, colloquialism and news. We verified some crucial factors that influence the quality of training. We found that loss decreases with more recurrent dimensions and using bidirectional RNN in the encoder when dealing with short sequences. We also computed BLEU scores which are the main measures of the translation performance, and compared them with the score from Google Translate using the same test sentences. We sum up some difficulties when training a proper translation model as well as dealing with Korean language. The use of Keras in Python for overall tasks from processing raw texts to evaluating the translation model also allows us to include some useful functions and vocabulary libraries as well.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.225-232
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• 2004

Recently, there has been a rapid development in computer technology, which has in turn led to develop the automated welding system using Artificial Intelligence (AI). However, the automated welding system has not been achieved duo to difficulties of the control and sensor technologies. In this paper, the classification of the optimized bead geometry such as bead width, height penetration and bead area in the Gas Metal Arc (GMA) welding with fuzzy logic is presented. The fuzzy C-Means algorithm (FCM), which is best known an unsupervised fuzzy clustering algorithm is employed here to analysis the specimen of the bead geometry. Then the quality of the GMA welding can be classified by this fuzzy clustering technique and the choice for obtaining the optimal bead geometry can also be determined.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.8
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• pp.235-242
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• 2020

In order to promote digital transformation of our agricultural and rural communities in the wake of the fourth industrial revolution and prepare for the upcoming artificial intelligence era, it is necessary to establish a system and system that can collect, analyze and utilize necessary quality data. To this end, we will investigate and analyze problems and issues felt by various stakeholders such as farmers and agricultural officials, and present strategic measures to revitalize big data, which must be decided in order to promote digital transformation of our agricultural and rural communities, such as expanding big data platforms for joint utilization, establishing sustainable big data governance, and revitalizing the foundation for big data utilization based on demand.

• Journal of Welding and Joining
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• v.17 no.5
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• pp.40-46
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• 1999

The sensors employed in the robotic are welding system must detect the changes in weld characteristics and produce the output that is in some way related to the change being detected. Such adaptive systems, which synchronise the robot arm and eyes using a primitive brain will form the basis for the development of robotic GMA(Gas Metal Arc) welding which increasingly higher levels of artificial intelligence. The objective of this paper is to realize the mapping characteristics of bead height through learning. After learning, the neural estimation can estimate the bead height desired from the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) are chosen from an estimation error analysis. A series of bead of bead-on-plate GMA welding experiments was carried out in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the bead height with reasonable accuracy and guarantee the uniform weld quality.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.5
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• pp.383-388
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• 2017

The $4^{th}$ Industry Revolution was advocated by Klaud Schwab who is founder of World Economic Forum at the Davos Forum in 2016, and there are big differences on ICT based $4^{th}$ Industry revolution in the aspects of speed, scope and impact compared with the 3rd Industry revolution. Creating new industries and values through technology such as internet of things, cloud, big data, and artificial intelligence are included in the meaning of The $4^{th}$ industry revolution. In this article, the direction of change to water technology in response to the $4^{th}$ Industry revolution is surveyed. 4G Water Infra should minimize environmental impact under the consideration of sustainable development and advanced technologies. To solve the existing water infra problems, it is common and fundamental that the intake water from nature can be regarded as borrowed from nature and it should be returned to natural state with improved water quality. Government, academic organizations and industries should prepare and collaborate together in order to help our country with outstanding capabilities in infrastructure construction and ICT to lead the 4G water technology development.

• Electronics and Telecommunications Trends
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• v.33 no.5
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• pp.103-110
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• 2018

Studies on artificial intelligence have been developed for the past couple of decades. After a few periods of prosperity and recession, a new machine learning method, so-called Deep Learning, has been introduced. This is the result of high-quality big- data, an increase in computing power, and the development of new algorithms. The main targets for deep learning are 1D audio and 2D images. The application domain is being extended from a discriminative model, such as classification/segmentation, to a generative model. Currently, deep learning is used for processing 3D data. However, unlike 2D, it is not easy to acquire 3D learning data. Although low-cost 3D data acquisition sensors have become more popular owing to advances in 3D vision technology, the generation/acquisition of 3D data remains a very difficult problem. Moreover, it is not easy to directly apply an existing network model, such as a convolution network, owing to the variety of 3D data representations. In this paper, we summarize the 3D deep learning technology that have started to be developed within the last 2 years.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.