• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2960-2976
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• 2018

The limited storage and computing capacity hinder the development of cognitive radio ad hoc networks (CRAHNs). To solve the problem, a new paradigm of cloud-based CRAHN has been proposed, in which a CRAHN will make use of the computation and storage resources of the cloud. This paper envisions an integrated CRAHN-cloud network architecture. In this architecture, some cognitive radio users (CUs) who satisfy the required metrics could perform as mobile gateway candidates to connect other ordinary CUs with the cloud. These mobile gateway candidates are dynamically clustered according to different related metrics. Cluster head and time-to-live value are determined in each cluster. In this paper, the gateway advertisement and discovery issues are first addressed to propose a hybrid gateway discovery mechanism. After that, a QoS-based gateway selection algorithm is proposed for each CU to select the optimal gateway. Simulations are carried out to evaluate the performance of the overall scheme, which incorporates the proposed clustering and gateway selection algorithms. The results show that the proposed scheme can achieve about 11% higher average throughput, 10% lower end-to-end delay, and 8% lower packet drop fractions compared with the existing scheme.

• Geotechnical Engineering
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• v.26 no.9
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• pp.33-47
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• 2010

Underground constructions continue to provide challenges to Geotechnical Engineers yet they pose the best opportunities for development and deployment of advance technologies for analysis, design and construction. The reason for this is that, by virtue of the nature of underground constructions, more data and information on ground characteristics and response become available as the construction progresses. However, due to several barriers, these data and information are rarely, if ever, utilized to modify and improve project design and construction during the construction stage. To enable the use of evolving realtime data and information, and adaptively modify and improve design and construction, the paper presents an analysis and design system, called AMADEUS, for underground projects. AMADEUS stands for Adaptive, real-time and geologic Mapping, Analysis and Design of Underground Space. AMADEUS relies on recent advances in IT (Information Technology), particularly in digital imaging, data management, visualization and computation to significantly improve analysis, design and construction of underground projects. Using IT and remote sensors, real-time data on geology and excavation response are gathered during the construction using non-intrusive techniques which do not require expensive and time-consuming monitoring. The real-time data are then used to update geological and geomechanical models of the excavation, and to determine the optimal, construction sequences and stages, and structural support. Virtual environment (VE) systems are employed to allow virtual walk-throughs inside an excavation, observe geologic conditions, perform virtual construction operations, and investigate stability of the excavation via computer simulation to steer the next stages of construction.

• Journal of Mechanical Science and Technology
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• v.15 no.3
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• pp.300-308
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• 2001

Tracking of moving objects within video streams is a complex and time-consuming process. Large number of moving objects increases the time for computation of tracking the moving objects. Because of large computations, there are real-time processing problems in tracking of moving objects. Also, the change of environment causes errors in estimation of tracking information. In this paper, we present a new method for tracking of moving objects using optical flow motion analysis. Optical flow represents an important family of visual information processing techniques in computer vision. Segmenting an optical flow field into coherent motion groups and estimating each underlying motion are very challenging tasks when the optical flow field is projected from a scene of several moving objects independently. The problem is further complicated if the optical flow data are noisy and partially incorrect. Optical flow estimation based on regulation method is an iterative method, which is very sensitive to the noisy data. So we used the Combinatorial Hough Transform (CHT) and Voting Accumulation for finding the optimal constraint lines. To decrease the operation time, we used logical operations. Optical flow vectors of moving objects are extracted, and the moving information of objects is computed from the extracted optical flow vectors. The simulation results on the noisy test images show that the proposed method finds better flow vectors and more correctly estimates the moving information of objects in the real time video streams.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.6
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• pp.701-713
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• 2003

The BEM is a highly efficient method in the sense of economical computation. However, boundary integration is not easy for the complex geometry and moving surface, e.g. a rotating blade. Thus, Kirchhoff surface is designed in an effort to overcome the difficulty resulting from complex boundary conditions. A Kirchhoff surface is a fictitious surface which envelopes acoustic sources of main concern. Acoustic sources may be distributed on each Kirchhoff surface element according to their acoustic characteristics. In this study, an axial fan is assumed to have unsteady loading noise as a dominant source. Dipole sources can be modeled to solve the FW-H equation. Acoustic field is then computed by determining Kirchhoff surface on which near-field is implemented, to analyze the effect of Kirchhoff surface on it. The optimal shape and the location of Kirchhoff surface are discussed by comparing with experimental data acquired in an anechoic chamber.

• Journal of Drive and Control
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• v.14 no.2
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• pp.9-15
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• 2017

The steering systems of all-terrain cranes have been developed with various control strategies for the stability and drivability. To optimally control the input steering angle, an accurate mathematical model that represents the actual crane dynamics is required. The derivation of an accurate mathematical model to optimally control the steering angle, however, is difficult since the steering-control strategy generally varies with the magnitude of the crane's longitudinal velocity, and the postures of the crane's working parts vary while it is being driven. To address this problem, this paper proposes an automatic steering-control algorithm that is based on the MPC (model predictive control) with a disturbance observer for all-terrain cranes. The designed disturbance observer of this study was used to estimate the error between the base steering model and the actual crane. A model predictive controller was used for the computation of the optimal steering angle, along with the use of the base steering model with an estimated uncertainty. Performance evaluations of the designed control algorithms were conducted based on a curved-path scenario in the Matlab/Simulink environment. The performance-evaluation results show a sound reference-path-tracking performance despite the large uncertainties.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.65-73
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• 2017

The fifth generation(5G) of wireless networks will connect not only smart phone but also unimaginable things. Therefore, 5G cellular network is facing the soaring traffic demand of numerous user devices. To solve this problem, a huge amount of 5G base stations will need to be installed. The base station positioning problem is an NP-hard problem that does not know how long it will take to solve the problem. Because, it can not find an answer other than to check the number of all cases. In this paper, to solve the NP hard problem, we compare the tabu search and the genetic algorithm using real maps for optimal cell planning. We also perform Monte Carlo simulations to study the performance of the Tabu search and Genetic algorithm for 5G cell planning. As a results, Tabu search required 2.95 times less computation time than Genetic algorithm and showed accuracy difference of 2dBm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2914-2935
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• 2016

With the increasing popularization of cloud computing, how to reduce physical energy consumption and increase resource utilization while maintaining system performance has become a research hotspot of virtual machine deployment in cloud platform. Although some related researches have been reported to solve this problem, most of them used the traditional heuristic algorithm based on greedy algorithm and only considered effect of single-dimensional resource (CPU or Memory) on energy consumption. With considerations to multi-dimensional resource utilization, this paper analyzed impact of multi-dimensional resources on energy consumption of cloud computation. A multi-dimensional resource constraint that could maintain normal system operation was proposed. Later, a novel virtual machine deployment method (NVMDM) based on improved particle swarm optimization (IPSO) and Euclidean distance was put forward. It deals with problems like how to generate the initial particle swarm through the improved first-fit algorithm based on resource constraint (IFFABRC), how to define measure standard of credibility of individual and global optimal solutions of particles by combining with Bayesian transform, and how to define fitness function of particle swarm according to the multi-dimensional resource constraint relationship. The proposed NVMDM was proved superior to existing heuristic algorithm in developing performances of physical machines. It could improve utilization of CPU, memory, disk and bandwidth effectively and control task execution time of users within the range of resource constraint.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.2
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• pp.205-212
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• 1989

The minimum weight design for the simply supported orthogonally stiffened cylindrical shell subjected to axial compression is studied by a mathematical programming. A smeared-out method is used for the computation of buckling load in the optimization process and optimization is accomplished by a gradient projection method. Maximum eight design variables and twenty-one inequality constraints considering the buckling, stress and geometric restraints are used. The three stringer types are considered as the optimization models : (1) rectangular stringer (2) I-stringer (3) T-stringer. Two design examples are compared with those in the other studies and the results demonstrate the validity of the present study. From the calculation the design with T-stringer can be more efficient than the one with rectangular or I-stringer.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.3
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• pp.29-36
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• 1998

In this paper, We are proposed a design method of the digital PID controller based on the model following method which minimized the error integral of the step response between the control system and the reference model. And we are applied it by a speed control of the current type inverter induction motor. The dynamic characteristic of the system was expressed by the step response, and then the optimal parameter of the PID controller can be easily obtained by the matrix computation. The derived algorithm can be implemented through a simple and systematic design procedure. Finally, We have shown the result with a computer simulation by the present method which proposed the speed control system and stable operation and fairly transient performance. And then tt was found results by experimental process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.608-627
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• 2016

Wireless mesh networks (WMNs) based on IEEE 802.11s have emerged as one of the prominent technologies in multi-hop communications. However, the deployment of WMNs suffers from serious interference problem which severely limits the system capacity. Using multiple radios for each mesh router over multiple channels, the interference can be reduced and improve system capacity. Nevertheless, interference cannot be completely eliminated due to the limited number of available channels. An effective approach to mitigate interference is to apply dynamic channel switching (DCS) in WMNs. Conventional DCS schemes trigger channel switching if interference is detected or exceeds a predefined threshold which might cause unnecessary channel switching and long protocol overheads. In this paper, a P-learning based dynamic switching algorithm known as learning automaton (LA)-based DCS algorithm is proposed. Initially, an optimal channel for communicating node pairs is determined through the learning process. Then, a novel switching metric is introduced in our LA-based DCS algorithm to avoid unnecessary initialization of channel switching. Hence, the proposed LA-based DCS algorithm enables each pair of communicating mesh nodes to communicate over the least loaded channels and consequently improve network performance.