• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.164-173
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• 2014

In underwater robotics, vision would be a key element for recognition in underwater environments. However, due to turbidity an underwater optical camera is rarely available. An underwater imaging sonar, as an alternative, delivers low quality sonar images which are not stable and accurate enough to find out natural objects by image processing. For this, artificial landmarks based on the characteristics of ultrasonic waves and their recognition method by a shape matrix transformation were proposed and were proven in Part 1. But, this is not working properly in undulating and dynamically noisy sea-bottom. To solve this, we propose a framework providing a selection phase of likelihood candidates, a selection phase for final candidates, recognition phase and tracking phase in sequence images, where a particle filter based selection mechanism to eliminate fake candidates and a mean shift based tracking algorithm are also proposed. All 4 steps are running in parallel and real-time processing. The proposed framework is flexible to add and to modify internal algorithms. A pool test and sea trial are carried out to prove the performance, and detail analysis of experimental results are done. Information is obtained from tracking phase such as relative distance, bearing will be expected to be used for control and navigation of underwater robots.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.31-38
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• 2011

A RAM-based Neural Net is a weightless neural network based on binary neural network. 3-D neural network using this paper is binary neural network with multiful information bits and store counts of training. Recognition method by MRD technique is based on the supervised learning. Therefore neural network by itself can not distinguish between the categories and well-separated categories of training data can achieve only through the performance. In this paper, unsupervised learning algorithm is proposed which is trained existing 3-D neural network without distinction of data, to distinguish between categories depending on the only input training patterns. The training data for proposed unsupervised learning provided by the NIST handwritten digits of MNIST which is consist of 0 to 9 multi-pattern, a randomly materials are used as training patterns. Through experiments, neural network is to determine the number of discriminator which each have an idea of the handwritten digits that can be interpreted.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.4
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• pp.55-61
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• 2010

This paper is concerned with an experimental research to random vibration control caused by external loads specially in bridges which tend to be structurally flexible. Experimenting on a reduced structure modelled on Seohae Grand Bridge, we inflicted a reduced form of El-centro wave on the model structure to a proper proportion. On the center of its middle span, we placed a shear type MR damper which was to control its vibration and also acquire its structural responses such as displacement and acceleration at the same site. The experiments concerning controlling vibration were performed according to a variety of theories including un-control, passive on/off control, and Lyapunov stability theory. Its control performance was evaluated in terms of the peak absolute displacements, the peak absolute accelerations and the total power required to control the bridge which differ from each different experiment method. Among all the methods applied in this paper, case of Lyapunov control method turned out to be the most effective to reduces of displacement and acceleration. Also, this method could to decrease consuming of external power for vibration control. Finally, it was noteworthy that Lyapunov control method was specially effective in the vibration control employing a semi-active damper such MR damper.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.1
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• pp.85-94
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• 2011

This paper is concerned to constitute a semi-active realtime feedback vibration control system and evaluate it through experiments in order to control in realtime the vibration externally generated, specially on the bridges which is structurally flexible. For the experiment of vibration control, we built a model bridge structure of Seohae Grand Bridge in a 1/200 reduced form and inflicted El-centro wave on the model structure also in a reduced force considering the lab condition. The externally excited vibration was to be controled by placing a shear type MR damper vertically on the center of bridge span, and the response (displacement and acceleration) of structure was to be acquired by placing LVDT and Accelerometer at the same time. As for the experiment concerning controlling vibration, a realtime feedback vibration control experiments were performed under each different condition largely such as un-control, passive on/off control, Lyapunov stability theory control, and Clipped-optimal control. Its control performance under different condition was quantitatively evaluated in terms of the peak absolute displacements, the peak absolute accelerations and the power required for control on the center of span. The results of experiments proved that the Lyapunov control and clipped-iptimal control were effective to decrease the displacement and acceleration of the structure, and also to decrease the power consumption to a great extent. Finally, the semi-active realtime feedback vibration control system constituted in this research was proven to be an effective way to control and manage the vibration generated on bridge structure.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.331-338
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• 2022

Successor representation (SR) is a model of human reinforcement learning (RL) mimicking the underlying mechanism of hippocampal cells constructing cognitive maps. SR utilizes these learned features to adaptively respond to the frequent reward changes. In this paper, we evaluated the performance of SR under the context where changes in latent variables of environments trigger the reward structure changes. For a benchmark test, we adopted SR-Dyna, an integration of SR into goal-driven Dyna RL algorithm in the 2-stage Markov Decision Task (MDT) in which we can intentionally manipulate the latent variables - state transition uncertainty and goal-condition. To precisely investigate the characteristics of SR, we conducted the experiments while controlling each latent variable that affects the changes in reward structure. Evaluation results showed that SR-Dyna could learn to respond to the reward changes in relation to the changes in latent variables, but could not learn rapidly in that situation. This brings about the necessity to build more robust RL models that can rapidly learn to respond to the frequent changes in the environment in which latent variables and reward structure change at the same time.

• Journal of the Korean Society for information Management
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• v.22 no.3 s.57
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• pp.37-53
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• 2005

The terminology management is absolutely necessary for maintaining the efficiency of thesaurus. This is because the creating, differentiating, disappearing, and other processes of the descriptor become accomplished dynamically, making effective management of thesaurus a very difficult task. Therefore, a device is required for accomplishing methods to construct and maintain the thesaurus. This study proposes the methods to construct the thesaurus management using the basic elements of a topic map which are topic, occurrence, and association. Second, the study proposes the methods to represent the basic and specific instances using the systematic mapping algorithm and merging algorithm. Also, using a hub document as a standard, this study gives the methods to expand and subsitute the descriptors using the topic type. The new method applying fixed concept for double layer management on terms is developed, too. The purpose of this method is to fix the conceptual term which represents independent concept of time and space, and to select the descriptor freely by external information circumstance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7A
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• pp.547-556
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• 2003

This paper proposes a novel arithmetic unit over GF(2$^{m}$ ) for low-area elliptic curve cryptographic processor. The proposed arithmetic unit, which is linear feed back shift register (LFSR) architecture, is designed by using hardware sharing between the binary GCD algorithm and the most significant bit (MSB)-first multiplication scheme, and it can perform both division and multiplication in GF(2$^{m}$ ). In other word, the proposed architecture produce division results at a rate of one per 2m-1 clock cycles in division mode and multiplication results at a rate of one per m clock cycles in multiplication mode. Analysis shows that the computational delay time of the proposed architecture, for division, is less than previously proposed dividers with reduced transistor counts. In addition, since the proposed arithmetic unit does not restrict the choice of irreducible polynomials and has regularity and modularity, it provides a high flexibility and scalability with respect to the field size m. Therefore, the proposed novel architecture can be used for both division and multiplication circuit of elliptic curve cryptographic processor. Specially, it is well suited to low-area applications such as smart cards and hand held devices.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.11
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• pp.957-962
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• 2012

Fluid/structure interaction (FSI) analysis is necessary to predict the response of a system in which aerodynamic pressure causes deformation of the structure, and vice versa. In dealing with a nonlinear behavior of the structure, however, a simple iterative algorithm of aerodynamic analysis with structural analysis yields no accurate results since aerodynamic pressure need to be changed in accordance with the deformation of structures. In this study, we explore an efficient and accurate method for integrating FSI analysis into structural nonlinear systems. During the course of nonlinear structural analysis, loading conditions are periodically updated by aerodynamic analysis. The accuracy and efficiency of the method is demonstrated with a high-aspect-ratio flexible wing of Global Hawk.

• Spatial Information Research
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• v.9 no.1
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• pp.1-13
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• 2001

Nowadays, SAR Interferometry is actively being studied as a new technique in topographic mapping using satellite imagery. It extracts height values using phase information derived by two SAR imageries covering same areas. Unlike when using SPOT imagery, it is not affected by atmospheric conditions and time. So to speak, we can say that SAR Interferometry is flexible in imagery acquisitions and can get height data economically over wide area. So, it is expected that SAR Interferometry will be widely using in GIS applications. But, in some area occurring geometric distortion, height data are misjudged or not extracted depending on phase unwrapping algorithms. IN the case of ERS tandem data, the accuracy of height data was worst in mountain area. It is the because of the short incidence angle resulted in layover effect. Of the phase unwrapping algorithms, path-following was better in height accuracy but could not get data in layover area. In this area, we could get height data using Height Sensitivity. In concludion, we could get DEM that maintained the accuracy of path-following method and have overall data across imagery.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.45-53
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• 2020

Blockchain can control transactions in a decentralized way and is already being considered for manufacturing, finance, banking, logistics, and medical industries due to its advantages such as transparency, security, and flexibility. And it is predicted to have a great economic effect. However, Blockchain has a Trilemma that is difficult to simultaneously improve scalability, decentralization and security characteristics. Among them, the biggest limitation of blockchain is scalability, which is very difficult to cope with the constantly increasing number of transactions and nodes. To make the blockchain scalable, higher performance should be achieved by modifying existing consensus methods or by improving the characteristics and network efficiency that affect many ways of scaling. Therefore, in this paper, we propose a cluster-based scalable PBFT consensus algorithm called CBS-PBFT which reduces the message complexity O(n²) of PBFT to O(n), which is a representative consensus algorithm of blockchain, and the validity is verified through simulation experiments.