• Journal of the Society of Naval Architects of Korea
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• v.34 no.1
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• pp.122-129
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• 1997

Thermo-elastic-plastic analyses used in solving plate forming process are often computationally expensive. To obtain an optimal process of line heating typically requires numerous iterations between the simulation and a finite element analysis. This process often becomes prohibitive due to the amount of computer time required for numerical simulation of line heating process. Therefore, a new techniques that could significantly reduce the computer time required to solve a complex analysis problem would be beneficial. In this paper, we considered factors that influence the bending effect by line heating and developed inference engine by using the concept of artificial neural network. To verify the validity of the neural network, we used results obtained from numerical analysis. We trained the neural network with the data made from numerical analysis and experiments varying the structure of neural network, in other words varying the number of hidden layers and the number of neurons in each hidden layers. From that we concluded that if the number of neurons in each hidden layers is large enough neural network having two hidden layers can be trained easily and errors between exact value and results obtained from trained network are not so large. Consequently, if there are enough number of training pairs, artificial neural network can infer similar results. Based on the numerical results, we applied the artificial neural network technique to deal with mechanical behavior of line heating at simulation stage effectively.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.39-57
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• 2004

Many dairy producers periodically receive information about their bulk tank milk with reference to bulk tank somatic cell counts, standard plate counts, and preliminary incubation counts. This information, when collected over a period of time, in combination with bulk tank mastitis culture reports can become a significant knowledge base. Several guidelines have been proposed to interpret farm bulk tank milk bacterial counts. However many of the suggested interpretive criteria lack validation, and provide little insight to the interrelationship between different groups of bacteria found in bulk tank milk. Also the linguistic terms describing bulk tank milk quality or herd management status are rather vague or fuzzy such as excellent, good or unsatisfactory. The objective of this paper was to develop a set of fuzzy descriptors to evaluate bulk tank milk quality and herd's milking practice based on bulk tank milk microbiology test results. Thus, fuzzy logic based reasoning methodologies were developed based on fuzzy inference engine. Input parameters were bulk tank somatic cell counts, standard plate counts, preliminary incubation counts, laboratory pasteurization counts, non agalactiae-Streptococci and Streptococci like organisms, and Staphylococcus aureus. Based on the input data, bulk tank milk quality was classified as excellent, good, milk cooling problem, cleaning problem, environmental mastitis, or mixed with mastitis and cleaning problems. The results from fuzzy reasoning would provide a reference regarding a good management practice for milk producers, dairy health consultants, and veterinarians.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.35-45
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• 2008

In order to constructan ubiquitous computing environment, it is necessary to develop a technology that can recognize users and circumstances. In this regard, the question of recognizing and expressing user Context regardless of computer and language types has emerged as an important task under the heterogeneous distributed processing system. As a means to solve this task of representing user Context in the ubiquitous environment, this paper proposes to describe user Context as the most similar form of human thinking by using semantic web and fuzzy concept independentof language and computer types. Because the conventional method of representing Context using an usual collection has some limitations in expressing the environment of the real world, this paper has chosen to use Fuzzy OWL language, a fusion of fuzzy concept and standard web ontology language OWL. Accordingly, this paper suggests the following method. First we represent user contacted environmental information with a numerical value and states, and describe it with OWL. After that we transform the converted OWL Context into Fuzzy OWL. As a last step, we prove whether the automatic circumstances are possible in this procedure when we use fuzzy inference engine FiRE. With use the suggested method in this paper, we can describe Context which can be used in the ubiquitous computing environment. This method is more effective in expressing degree and status of the Context due to using fuzzy concept. Moreover, on the basis of the stated Context we can also infer the user contacted status of the environment. It is also possible to enable this system to function automatically in compliance with the inferred state.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.655-666
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• 2007

Reasoners using typical Tableaux algorithm such as RacerPro, Pellet have a problem in Tableaux algorithm large ABox reasoning. Researches to solve these Problems are dealt with Instance Store of University of Manchester which uses Tableaux algorithm based reasoner and DBMS and KAON2 of University of Karlsruhe using Disjunctive Datalog approach. An evaluation experiment for present reasoners is the experiment of TBox reasoning in most of Tableaux algorithm based one. The most of benchmarking tests in reasoning systems haven't done with ABox reasoning based Tableaux Algorithm but done with TBox reasoning based Tableaux Algorithm. Especially, rarely reported benchmarking tests in reasoners have been issued nowadays. Therefore, this thesis evaluates systems with theory of each reasoners for large ABox reasoning that becomes issues recently with typical reasoners. The large AoBx reasoning engine will be analyzed using Instance Store and KAON2 of Manchester University for large ABox processing. At the analysing method, LUBM(Lehigh University BenchMark), benchmarking test method, and it's test system will be introduced. In conclusion, I recommend appropriate reasoner in various environment with experiment result and characteristic of algorithm used for each reasoner.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.177-183
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• 2002

Current state-of-the-art of VMS control is based upon simple knowledge-based inference engine with message set and each message's priority. And R&Ds of the VMS control are focused on the accurate detection and estimation of traffic condition of the subject roadways. However VMS display itself cannot achieve a desirable traffic allocation among alternative routes in the network In this context, VMS display strategy is the most crucial part in the VMS control. VMS itself has several limitations in its nature. It is generally known that VMS causes overreaction and concentration problems, which may be more serious in urban network than highway network because diversion should be more easily made in urban network. A feedback control algorithm is proposed in this paper to address the above-mentioned issues. It is generally true that feedback control approach requires low computational effort and is less sensitive to models inaccuracy and disturbance uncertainties. Major features of the proposed algorithm are as follows: Firstly, a regulator is designed to attain system optimal traffic allocation among alternative routes for each VMS in the network. Secondly, strategic messages should be prepared to realize the desirable traffic allocation, that is, output of the above regulator. VMS display strategy module is designed in this context. To evaluate Probable control benefit and to detect logical errors of the Proposed feedback algorithm, a offline simulation test is performed using real network in Daejon, Korea.

• 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.