• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence 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 AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Progress in Medical Physics
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• v.29 no.4
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• pp.123-136
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• 2018

The complex dose distribution and dose transfer characteristics of intensity-modulated radiotherapy increase the importance of precise beam data measurement and review in the acceptance inspection and preparation stages. In this study, we propose a process map for the introduction and installation of high-precision radiotherapy devices and present items and guidelines for risk management at the acceptance test procedure (ATP) and commissioning stages. Based on the ATP of the Varian and Elekta linear accelerators, the ATP items were checked step by step and compared with the quality assurance (QA) test items of the AAPM TG-142 described for the medical accelerator QA. Based on the commissioning procedure, dose quality control protocol, and mechanical quality control protocol presented at international conferences, step-by-step check items and commissioning guidelines were derived. The risk management items at each stage were (1) 21 ionization chamber performance test items and 9 electrometer, cable, and connector inspection items related to the dosimetry system; (2) 34 mechanical and dose-checking items during ATP, 22 multileaf collimator (MLC) items, and 36 imaging system items; and (3) 28 items in the measurement preparation stage and 32 items in the measurement stage after commissioning. Because the items presented in these guidelines are limited in terms of special treatment, items and practitioners can be modified to reflect the clinical needs of the institution. During the system installation, it is recommended that at least two clinically qualified medical physicists (CQMP) perform a double check in compliance with the two-person rule. We expect that this result will be useful as a radiation safety management tool that can prevent radiation accidents at each stage during the introduction of radiotherapy and the system installation process.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.10C
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• pp.1015-1026
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• 2002

As per increasing research interest in the field of collaborative computing in recent year, the importance of security issues on that area is also incrementally growing. Generally, the persistency of collaborative system is facilitated with conventional authentication and cryptography schemes. It is however, hard to meet the access control requirements of distributed collaborative computing environments by means of merely apply the existing access control mechanisms. The distributed collaborative system must consider the network openness, and various type of subjects and objects while, the existing access control schemes consider only some of the access control elements such as identity, rule, and role. However, this may cause the state of security level alteration phenomenon. In order to handle proper access control in collaborative system, various types of access control elements such as identity, role, group, degree of security, degree of integrity, and permission should be taken into account. Futhermore, if we simply define all the necessary access control elements to implement access control algorithm, then collaborative system consequently should consider too many available objects which in consequence, may lead drastic degradation of system performance. In order to improve the state problems, we propose a novel access control framework that is suitable for the distributed collaborative computing environments. The proposed scheme defines several different types of object elements for the accessed objects and subjects, and use them to implement access control which allows us to guarantee more solid access control. Futhermore, the objects are distinguished by three categories based on the characteristics of the object elements, and the proposed algorithm is implemented by the classified objects which lead to improve the systems' performance. Also, the proposed method can support scalability compared to the conventional one. Our simulation study shows that the performance results are almost similar to the two cases; one for the collaborative system has the proposed access control scheme, and the other for it has not.

• Journal of Drive and Control
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• v.13 no.4
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• pp.59-67
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• 2016

This paper proposes a model-prediction-based collision-avoidance algorithm for excavators for which the recursive-least-squares (RLS) estimation of the excavator's rotational inertia is used. To estimate the rotational inertia of the excavator, the RLS estimation with multiple forgetting and two updating rules for the nominal parameter and the forgetting factors was conducted based on the excavator-swing dynamics. The average value of the estimated rotational inertia that is for the minimizing effects of the estimation error was computed using the recursive-average method with forgetting. Based on the swing dynamics, the computed average of the rotational inertia, the damping coefficient for braking, and the excavator's braking angle were predicted, and the predicted braking angle was compared with the detected-object angle for a safety evaluation. The safety level defined in this study consists of the three levels safe, warning, and emergency braking. The analytical rotational-inertia-based performance evaluation of the designed estimation algorithm was conducted using a typical working scenario. The results of the safety evaluation show that the predictive safety-evaluation algorithm of the proposed model can evaluate the safety level of the excavator during its operation.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.15-29
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• 2018

This paper proposes an enhanced object tracking algorithm to compensate the lack of temporal information in existing particle swarm optimization based object trackers using the trajectory of the target object. The proposed scheme also enables the tracking and documentation of the location of an online updated set of distractions. Based on the trajectories information and the distraction set, a rule based approach with adaptive parameters is utilized for occlusion detection and determination of the target position. Compare to existing algorithms, the proposed approach provides more comprehensive use of available information and does not require manual adjustment of threshold values. Moreover, an effective weight adjustment function is proposed to alleviate the diversity loss and pre-mature convergence problem in particle swarm optimization. The proposed weight function ensures particles to search thoroughly in the frame before convergence to an optimum solution. In the existence of multiple objects with similar feature composition, this algorithm is tested to significantly reduce convergence to nearby distractions compared to the other existing swarm intelligence based object trackers.

• Journal of Korea Water Resources Association
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• v.37 no.12
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• pp.1043-1054
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• 2004

An unsteady flow model for channel network including various internal boundaries if developed. It is a multiply-connected network model based on the Preissmann's four-point scheme and the Newton-Raphson method, where looped double-sweep algorithm is used. The model is capable of simulating flow through hydraulic structures such as dams and submerged weirs. It can also simulate automatic reservoir operation method (Auto ROM) for flood control, that is to maintain a target water level, by incorporating the strategy to the unsteady flow model as internal boundary condition. The model is applied to the Han River system that includes the downstream reaches of Choongju dam and Hwacheon dam as well as the downstream reach of the Paldang dam. Roughness coefficient for the downstream reach of Choongju dam is estimated. Automatic ROM is presumed for the Paldang, Chungpyung, Euiam, and Choonchun dams. The model is tested using historical flood records, and the flood control strategy is successfully simulated.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.3
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• pp.74-82
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• 2011

This paper describes a control system for positioning the real-time locations of the autonomous warfare vehicles and infantry, and for avoiding collisions between them. The control system utilizes the low-cost RSSI (Received Signal Strength Indication) for positioning the locations of the wireless devices. The mathematical mean filtering processes are applied to the calculation of the RSS matrix to improve the performance for positioning the wireless devices in the multi-path propagation environment. A fuzzy rule is proposed to recover and replace the broken packets occurring in the wireless communication. The gradient and geometric triangulation algorithms are proposed to trace the real-time locations of wireless devices, based on the distances between them. The estimated location results of the geometric triangulation algorithm are compared with the results of the GPS and the gradient algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.8
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• pp.39-47
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• 1999

This paper describes on-line retrainable RBF network in order to control the coiling temperature for a hot coil strip at Pohang Iron & Steel Company(POSCO). The proposed neural network can be used for improving conventional rule-based lookup table, which generates a heat transmission coefficient. To cope with time-varying characteristics of hot coil process, additional synaptic weights for on-line retraining purposes are introduced to hidden-to-output weights of conventional RBF network. Those weights are locally adjusted to newly incoming test data while preserving old information trained with off-line past data. Hence the effect of catastrophic interference can be greatly alleviated with the proposed network. In addition, rejection scheme is introduced for reliability concerns. From the experimental results applied to the actual process, it is noticed that overall control performance represents about 2.2% increase compared to the conventional one.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.637-646
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• 2013

In order to model a variety of natural trees that are appropriate to outdoor terrains consisting of multiple trees, this study proposes a modeling method of new growth rules(based on the convolution sums of divisor functions). Basically, this method uses an existing growth-volume based algorithm for efficient management of the branches and leaves that constitute a tree, as well as natural propagation of branches. The main features of this paper is to introduce the theory of convolution sums of divisor functions that is naturally expressed the growth or fate of branches and leaves at each growth step. Based on this, a method of modeling various tree is proposed to minimize user control through a number of divisor functions having generalized generation functions and modification of the growth rule. This modeling method is characterized by its consideration of both branches and leaves as well as its advantage of having a greater effect on the construction of an outdoor terrain composed of multiple trees. Natural and varied tree model creation through the proposed method was conducted, and using this, the possibility of constructing a wide nature terrain and the efficiency of the process for configuring multiple trees were evaluated experimentally.