• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.219-226
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• 2010

Recently most parking control systems provide customers with various services, but most of the services are just the extension of parking spaces, automatic parking control system and so on. It is essential to use the satisfaction degree as the extent that customer are satisfied with parking control system to improve the quality of the system services and diversify the system services. The degree of satisfaction is different from customer to customer in same condition and can be represented as linguistic variables. In this paper, we present therefore a technique that quantify how much customer are satisfied with parking control system and fuzzy inference system architecture as a solution that can help us to make a efficient decision for these parking problems. In this architecture, inference engine using fuzzy logic compares context data with the rules in the fuzzy rule-based system, gets the sub-results, aggregates them and defuzzifies the aggregated result using MATLAB application programming to obtain crisp value. Fuzzy inference system architecture presented in this paper, can be used as a efficient method to analyze the satisfaction degree which is represented as fuzzy linguistic variables by human emotion. And it can be used to improve the satisfaction degree of not only parking system but also other service systems of various domains.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1809-1815
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• 2003

This paper presents a human friendly system using fuzzy inference as a Part of study to embody intelligent ship. We also build intelligent ship's steering system to take advantage of speech recognition that is a part of the human friendly interface. It can bring an effect such as labor decrement in ship. In order to design the voice instruction based ship's steering gear control system, we build of the voice instruction based learning(VIBL) system based on speech recognition and intelligent learning method at first. Next, we design an quartermaster's operation model by fuzzy inference and construct PC based remote control system. Finally, we applied the unposed control system to the miniature ship and verified its effectiveness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2289-2294
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• 2009

In this paper, we proposed a fuzzy controller for racing of a line trace vehicle. Sensor values are computed by statuses of line detecting sensors attached to the line trace vehicle and these sensor values are used for fuzzy inference rules of steering angle control to decide steering angle as output. The decided steering angle is also used for fuzzy inference rules of motor speed control to decide motor speed as output. We experimented and analyzed two proposed methods - one is fuzzy control of steering angle only and the other is fuzzy control of both steering angle and motor speed. In the experiment, we verified that the second proposed method was more efficient in racing speed.

• Journal of the Korean housing association
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• v.22 no.3
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• pp.113-122
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• 2011

This study aimed to develop AI- (Artificial Intelligence) based thermal control logics and test their performance for identifying the optimal thermal control method in buildings. For this objective, a conventional Two-Position On/Off logic and two AI-based variable logics, which applied ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System), have developed. Performance of each logic was tested in a typical two-story residential building in U.S.A. using the computer simulation incorporating MATLAB and IBPT (International Building Physics Toolbox). In the analysis of the test results, AI-based control logic presented the advanced thermal comfort with stability compared to the conventional logic while they did not show significant energy saving effects. In conclusion, the predictive and adaptive AI-based control logics have a potential to maintain interior air temperature more comfortably, and the findings in this study could be a solid foundation for identifying the optimal thermal control method in buildings.

• ETRI Journal
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• v.40 no.3
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• pp.318-329
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• 2018

Femtocell (FC) technology envisaged as a cost-effective approach to attain better indoor coverage of mobile voice and data service. Deployment of FCs over macrocell forms a heterogeneous network. In urban areas, the key factor limits the successful deployment of FCs is inter-cell interference (ICI), which severely affects the performance of victim users. Autonomous FC transmission power setting is one straightforward way for coordinating ICI in the downlink. Application of intelligent control using soft computing techniques has not yet explored well for wireless networks. In this work, autonomous FC transmission power setting strategy using Adaptive Neuro Fuzzy Inference System is proposed. The main advantage of the proposed method is zero signaling overhead, reduced computational complexity and bare minimum delay in performing power setting of FC base station because only the periodic channel measurement reports fed back by the user equipment are needed. System level simulation results validate the effectiveness of the proposed method by providing much better throughput, even under high interference activation scenario and cell edge users can be prevented from going outage.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.199-202
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• 2001

An elevator system, which is essential equipment for vertical movement of an object, as a property of building, has been driven by various expenditures and purposes. Since developing electrical control technology, control system are highly developed. The elevator system has expanded widely, but a data accuracy acquisition technique and safety predict technique for securing system safety is still at a basic level. So, objective verification for elevator confidence condition requires an absolute accuracy measurement technique. Therefore, this study is executed in order to acquire a method of depending on sense of a manager with simple numeric measurement data, and to construct a logical, analytical foresight system for more efficient elevator management system. As an artificial intelligence for diagnosis, the fuzzy inference algorithm is used for foreseeing the system in this thesis, because the fuzzy algorithm is the most useful method for resolving subjective ideas and a vague judgment of humans. The fuzzy inference algorithm is developed for each sensor signal(i.e. vibration, velocity, current).

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.939-948
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• 1997

In this paper, we propose the new rate-based transmission rates control algorithm that allocates the fair band-width for ABR service in ATM network. In the traditional ABR service, bandwidth is allocated with constant rate increment or decrement, but in the proposed algorithm, it is allocated fairly to the connected calls by the fuzzy inference of the available bandwidth. The fuzzy inference uses buffer state and the buffer variant rate as the input variables, and uses the total transmission rate as a output variable. This inference a bandwidth is fairly distributed over all ABR calls in service. By simmulation, we showed that the proposed method improved 0.17% in link effectiveness when RIF, RDF is 1/4, 38.6% when RIF, RDF 1/16, and 82.4% when RIF, RDF 1/32 than that of the traditional EFPCA.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.579-583
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• 1991

A fuzzy logic controller with rule modification capability is proposed to overcome the difficulty of obtaining control rules from the human operators. This new SOC algorithm modifies control rules by a fuzzy inference machine utilizing data base. Computer simulation results show good performances on both a linear system and a nonlinear system.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3010-3022
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• 1997

Expert system is one of AI area which was came out at the end of 19705s. It simulates the human's way of thinking to give solutions of Problem in many applications. Most expert system consists of many components such as inference engine, knowledge base, and so on. Especially the performance of expert system depends on the control of enfficiency of inference engine. Inference engine has to get features; tirst, if possible to minimize restrictions when the knowledge base is constructed second, it has to serve various kinds of inferencing methods. In this paper, we design and implement the inference engine which is able to support the general functions to knowledge domain and inferencing method. For the purpose, forward chaining, backward chaining, and direct chaining was employed as an inferencing method in order to be able to be used by user request selectively. Also we not on1y selected production system which makes one ease staradization and modulation to obtain knowledges in target domain, but also constructed knowledge base by means of Extended Clause Bit Metrics (ECBM). Finally, the performance evaluation of inference engine between Rete pattern matching and ECBM has been done.