• Journal of Biosystems Engineering
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• v.23 no.4
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• pp.365-372
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• 1998

This study was carried out to develop a nutrient-solution mixing-and-supplying system, which used a low-cost metering device instead of expensive metering pumps and a fuzzy logic controller. A low cost and precise overflow-type metering device was developed and evaluated by testing the flow discharge for the automatic nutrient-solution mixing-and-supplying system for snail-scale hydroponic sewers. The fuzzy logic controllers, which could predict and meet the desired values of EC and supply rate of nutrient solution were developed and verified by simulation and experiment. this fuzzy logic controller, whose algorithm consists of four crisp inputs, two crisp outputs and nine rules, was developed to predict the desired value of EC and supply rate of nutrient solution and two crisp inputs, one crisp output and nine rules used to control EC to the desired values. The nutrient-solution mixing-and-supplying system showed satisfactory EC control performance with the maximum overshooting of 0.035 mS/cm and the maximum settling time of 15 minutes in case of increasing 0.7 mS/cm. also, the accuracy of the overflow-type metering device in terms of the full-scale error was 2.29% when using solenoid valve only and 0.2% when using solenoid valve and flow control valve together.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.44-51
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• 1999

This paper presents a massively parallel computational model for the efficient integration of speech and natural language understanding. The phoneme model is based on continuous Hidden Markov Model with context dependent phonemes, and the language model is based on a knowledge base approach. To construct the knowledge base, we adopt a hierarchically-structured semantic network and a memory-based parsing technique that employs parallel marker-passing as an inference mechanism. Our parallel speech recognition algorithm is implemented in a multi-Transputer system using distributed-memory MIMD multiprocessors. Experimental results show that the parallel speech recognition system performs better in recognition accuracy than a word network-based speech recognition system. The recognition accuracy is further improved by applying code-phoneme statistics. Besides, speedup experiments demonstrate the possibility of constructing a realtime parallel speech recognition system.

• Knowledge Management Research
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• v.11 no.2
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• pp.75-93
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• 2010

Even though there have been many efforts on driver's route recommendation, driver still should get involved to choose the driving path in a manual manner. Uncertain traffic information provided to the driver delays his arrival time and hence may cause diminished economic values. One of the solutions of reducing the uncertainty is to provide various kinds of traffic information, rather than send real-time information. Therefore, as the wireless communication technology improves and at the same time volume of utilizable traffic contents increases in geometrical progression, selecting traffic information based on driver's context in a timely and individual manner will be needed. Hence, the purpose of this paper is to propose a methodology that efficiently sends the rich traffic contents to the personal in-vehicle navigation. To do so, driver preference is modeled and then the recommendation algorithm of traffic information contents was developed using the preference model. Secondly, ontology based traffic situation analyzation method is suggested to automatically inference the noticeable information from the traffic context on driver's route. To show the feasibility of the idea proposed in this paper, an open API service is implemented in consideration of ease of use.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.255-260
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• 2008

In the recently years, there has been a big interest in ad hoc wireless network as they have tremendous military and commercial potential. An Ad hoc wireless network is composed of mobile computing devices that use having no fixed infrastructure of a multi-hop wireless network formed. So, the fact that limited resource could support the network of robust, simple framework and energy conserving etc. In this paper, we propose a new ad hoc multicast routing protocol for based on the ontology scheme called inference network. Ontology knowledge-based is one of the structure of context-aware. And the ontology clustering adopts a tree structure to enhance resilient against mobility and routing complexity. This proposed multicast routing protocol utilizes node locality to be improve the flexible connectivity and stable mobility on local discovery routing and flooding discovery routing. Also attempts to improve route recovery efficiency and reduce data transmissions of context-awareness. We also provide simulation results to validate the model complexity. We have developed that proposed an algorithm have design multi-hierarchy layered networks to simulate a desired system.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.697-709
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• 2001

In this paper we study two vector-valued process capability indices $C_{p}$=($C_{px}$, $C_{py}$ ) and C/aub pm/=( $C_{pmx}$, $C_{pmy}$) considering process capability indices $C_{p}$ and $C_{pm}$ . First, two asymptotic distributions of plug-in estimators $C_{p}$=($C_{px}$, $C_{py}$ ) and $C_{pm}$ =) $C_{pmx}$, $C_{pmy}$) are derived.. With the asymptotic distributions, we propose asymptotic confidence regions for our indices. Next, obtaining the asymptotic distributions of two bootstrap estimators $C_{p}$=($C_{px}$, $C_{py}$ )and $C_{pm}$ =( $C_{pmx}$, $C_{pmy}$) with our bootstrap algorithm, we will provide the consistency of our bootstrap for statistical inference. Also, with the consistency of our bootstrap, we propose bootstrap asymptotic confidence regions for our indices. (no abstract, see full-text)see full-text)e full-text)

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1159-1166
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• 2010

Today, detecting the location of moving object has been traced as various methods in our world. In this paper, we preset the system to improve the estimation accuracy utilizing detail localization using radar sensor based on WSN and situational awareness for a calibration (context aware) database, Rail concept. A variety of existing location tracking method has a problem with receiving of data and accuracy as tracking methodology, and since these located data are the only data to be collected for location tracing, the context aware or monitering as the surrounding environment is limited. So, in this paper, we enhanced the distance aware accuracy using radar sensor utilizing the Doppler effect among the distance measuring method, estimated the location using the Triangulation algorithm. Also, since we composed the environment data(temperature, illuminancem, humidity, noise) to entry of the database, it can be utilized in location-based service according to the later action information inference and positive context decision. In order to verify the validity of the suggested method, we give a few random situation and built test bed of designed node, and over the various test we proved the utilizing the context information through route tracking of moving and data processing.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.539-544
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• 2004

We investigate a visual object tracking algorithm based upon particle filters, namely CONDENSATION, in order to combine multiple observation models such as active contours of digitally subtracted image and the particle measurement of object color. The former is applied to matching the contour of the moving target and the latter is used to independently enhance the likelihood of tracking a particular color of the object. Particle filters are more efficient than any other tracking algorithms because the tracking mechanism follows Bayesian inference rule of conditional probability propagation. In the experimental results, it is demonstrated that the suggested contour tracking particle filters prove to be robust in the cluttered environment of robot vision.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.1-6
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• 2012

In this paper, we present an obstacle avoidance control algorithm for mobile robots based on SFLC (single-input fuzzy logic controller) with an efficient fuzzy logic look-up table to replace the traditional complicated operation. This method achieves better performance than traditional methods in terms of efficiency. The output of a SFLC leads the robot to the target automatically although many obstacles on the path. Our experiments show that the robot has good performance in the view of path tracking and other efficiency.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.225-230
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• 1992

In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.