• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.759-765
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• 2006

In this paper, it is proposed an effective face expression recognition LDA mixed mode using a triangularity membership fuzzy function and wavelet basis. The proposal algorithm gets performs the optimal image, fuzzy wavelet algorithm and Expression recognition is consisted of face characteristic detection step and face Expression recognition step. This paper could applied to the PCA and LDA in using some simple strategies and also compares and analyzes the performance of the LDA mixed model which is combined and the facial expression recognition based on PCA and LDA. The LDA mixed model is represented by the PCA and the LDA approaches. And then we calculate the distance of vectors dPCA, dLDA from all fates in the database. Last, the two vectors are combined according to a given combination rule and the final decision is made by NNPC. In a result, we could showed the superior the LDA mixed model can be than the conventional algorithm.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.17-23
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• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1338-1343
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• 2003

Data is the expression of the language or numerical values that show some characteristics. And information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns and make decisions. Today, knowledge extraction and application of the knowledge are broadly accomplished to improve the comprehension and to elevate the performance of systems in several industrial fields. The knowledge extraction could be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge can be drawn by rules. Clustering (CU, input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for expression the knowledge by rules. In this paper, the various approaches of the knowledge extraction are examined by categories that separate the methods by the applied industrial fields. Also, the several test data and the experimental results are compared and analysed based upon the applied techniques that include CL, ISP, NF, NN, EM, and so on.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.210-216
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• 2006

In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.17-22
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• 2001

Because the quantitative relationships between welding parameters and welding result are not yet blown, optimal values of welding parameters for $CO_2$ robotic arc welding is a difficult task. Using the various artificial data processing methods may solve this difficulty. This research aims to develop an expert system for $CO_2$ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis and fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current voltage, speed, weaving width, and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule as teaming algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of rules. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic $CO_2$ fillet welding with 1.2, 1.4, and 1.6mm diameter the solid wires or flux-cored wires.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.144-156
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• 2023

Smart agriculture is a rapidly growing field that seeks to optimize crop yields and reduce risk through the use of advanced technology. A key challenge in this field is the need to create a comprehensive smart farm system that can effectively monitor and control the growth environment of crops, particularly when cultivating new varieties. This is where fuzzy theory comes in, enabling the collection and analysis of external environmental factors to generate a rule-based system that considers the specific needs of each crop variety. By doing so, the system can easily set the optimal growth environment, reducing trial and error and the user's risk burden. This is in contrast to existing systems where parameters need to be changed for each breed and various factors considered. Additionally, the type of house used affects the environmental control factors for crops, making it necessary to adapt the system accordingly. While developing such a framework requires a significant investment of labour and time, the benefits are numerous and can lead to increased productivity and profitability in the field of smart agriculture. We developed an AI platform for optimal control of facility houses by integrating data from mushroom crops and environmental factors, and analysing the correlation between optimal control conditions and yield. Our experiments demonstrated significant performance improvement compared to the existing system.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2000.09a
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• pp.171-181
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• 2000

Many Studies of tunnel and tunnelling safety have been developed continuously based on the increasing social interests in underground space since 1990's in Korea. Because the growth of population in metropolitan has been accelerated at a faster pace than the development of the cities, underground facilities have been created as a great extent in view of less land space available. In this study, a lot of types of tunnel failure were surveyed and the detail causes were studied after many cases of tunnel failure were collected. There were suggested brief countermeasure of tunnel failure through case study. An expert system was developed to predict the safety of tunnel and choose proper tunnel reinforcement system using fuzzy quantification theory and fuzzy inference rule based on tunnel information database. The comparison result between the predicted reinforcement system level and measured ones was very similar. In-situ data were obtained in three tunnel sites including subway tunnel under Han river. This system will be very helpful to make the most of in-situ data and suggest proper applicability of tunnel reinforcement system developing more resonable tunnel support method from dependance of some experienced experts for the absent of guide.

• Tunnel and Underground Space
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• v.10 no.3
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• pp.418-429
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• 2000

Many Studies of tunnel and tunnelling safety have been developed continuously based on the increasing social interests in underground space since 1990's in Korea. Because the growth of population in metropolitan has been accelerated at a faster pace than the development of the cities, underground facilities have been created as a great extent in view of less land space available. In this study, a lot of types of tunnel failure were surveyed and the detail causes were studied after many cases of tunnel failure were collected. There were suggested brief countermeasure of tunnel failure through case study. An expert system was developed to predict the safety of tunnel and choose proper tunnel reinforcement system using fuzzy quantification theory and fuzzy inference rule based on tunnel information database. The comparison result between the predicted reinforcement system level and measured ones was very similar. In-situ data were obtained in three tunnel sites including subway tunnel under Han river. This system will be very helpful to make the most of in-situ data and suggest proper applicability of tunnel reinforcement system developing more resonable tunnel support method from dependance of some experienced experts for the absent of guide.

• Geomechanics and Engineering
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• v.18 no.5
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• pp.515-525
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• 2019

A karst collapse, as a natural hazard, is totally different to a normal collapse. In recent years, karst collapses have caused substantial economic losses and even threatened human safety. A risk assessment model for karst collapse was developed based on the fuzzy analytic hierarchy process (FAHP) and grey relational analysis (GRA), which is a simple and effective mathematical algorithm. An evaluation index played an important role in the process of completing the risk assessment model. In this study, the proposed model was applied to Jiaobai village in southwest China. First, the main controlling factors were summarized as an evaluation index of the model based on an investigation and statistical analysis of the natural formation law of karst collapse. Second, the FAHP was used to determine the relative weights and GRA was used to calculate the grey relational coefficient among the indices. Finally, the relational sequence of evaluation objects was established by calculating the grey weighted relational degree. According to the maximum relational rule, the greater the relational degree the better the relational degree with the hierarchy set. The results showed that the model accurately simulated the field condition. It is also demonstrated the contribution of various control factors to the process of karst collapse and the degree of collapse in the study area.